Search results for: artificial conduit

Authors: Eric Pla Erra, Mariana Jimenez Martinez

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While aggregated loads at a community level tend to be easier to predict, individual household load forecasting present more challenges with higher volatility and uncertainty. Furthermore, the drastic changes that our behavior patterns have suffered due to the COVID-19 pandemic have modified our daily electrical consumption curves and, therefore, further complicated the forecasting methods used to predict short-term electric load. Load forecasting is vital for the smooth and optimized planning and operation of our electric grids, but it also plays a crucial role for individual domestic consumers that rely on a HEMS (Home Energy Management Systems) to optimize their energy usage through self-generation, storage, or smart appliances management. An accurate forecasting leads to higher energy savings and overall energy efficiency of the household when paired with a proper HEMS. In order to study how COVID-19 has affected the accuracy of forecasting methods, an evaluation of the performance of a state-of-the-art LGBM (Light Gradient Boosting Model) will be conducted during the transition between pre-pandemic and lockdowns periods, considering day-ahead electric load forecasting. LGBM improves the capabilities of standard Decision Tree models in both speed and reduction of memory consumption, but it still offers a high accuracy. Even though LGBM has complex non-linear modelling capabilities, it has proven to be a competitive method under challenging forecasting scenarios such as short series, heterogeneous series, or data patterns with minimal prior knowledge. An adaptation of the LGBM model – called “resilient LGBM” – will be also tested, incorporating a concept drift detection technique for time series analysis, with the purpose to evaluate its capabilities to improve the model’s accuracy during extreme events such as COVID-19 lockdowns. The results for the LGBM and resilient LGBM will be compared using standard RMSE (Root Mean Squared Error) as the main performance metric. The models’ performance will be evaluated over a set of real households’ hourly electricity consumption data measured before and during the COVID-19 pandemic. All households are located in the city of Barcelona, Spain, and present different consumption profiles. This study is carried out under the ComMit-20 project, financed by AGAUR (Agència de Gestiód’AjutsUniversitaris), which aims to determine the short and long-term impacts of the COVID-19 pandemic on building energy consumption, incrementing the resilience of electrical systems through the use of tools such as HEMS and artificial intelligence.

Keywords: concept drift, forecasting, home energy management system (HEMS), light gradient boosting model (LGBM)

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Authors: Onada Olawale Ahmed

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As the intensification of aquaculture production increases on global scale, the growing concern of fish farmers around the world is related to cost of fish production, where cost of feeding takes substantial percentage. Fishmeal (FM) is one of the most expensive ingredients, and its high dependence in aqua-feed production translates to high cost of feeding of stocked fish. However, to reach a sustainable aquaculture, new alternative protein sources including cheaper plant or animal origin proteins are needed to be introduced for stable aqua-feed production. Spirulina is a cyanobacterium that has good nutrient profile that could be useful in aquaculture. This review therefore emphasizes on the nutritional value of Spirulina as a potential replacement of FM in aqua-feed. Spirulina is a planktonic photosynthetic filamentous cyanobacterium that forms massive populations in tropical and subtropical bodies of water with high levels of carbonate and bicarbonate. Spirulina grows naturally in nutrient rich alkaline lake with water salinity ( > 30 g/l) and high pH (8.5–11.0). Its artificial production requires luminosity (photo-period 12/12, 4 luxes), temperature (30 °C), inoculum, water stirring device, dissolved solids (10–60 g/litre), pH (8.5– 10.5), good water quality, and macro and micronutrient presence (C, N, P, K, S, Mg, Na, Cl, Ca and Fe, Zn, Cu, Ni, Co, Se). Spirulina has also been reported to grow on agro-industrial waste such as sugar mill waste effluent, poultry industry waste, fertilizer factory waste, and urban waste and organic matter. Chemical composition of Spirulina indicates that it has high nutritional value due to its content of 55-70% protein, 14-19% soluble carbohydrate, high amount of polyunsaturated fatty acids (PUFAs), 1.5–2.0 percent of 5–6 percent total lipid, all the essential minerals are available in spirulina which contributes about 7 percent (average range 2.76–3.00 percent of total weight) under laboratory conditions, β-carotene, B-group vitamin, vitamin E, iron, potassium and chlorophyll are also available in spirulina. Spirulina protein has a balanced composition of amino acids with concentration of methionine, tryptophan and other amino acids almost similar to those of casein, although, this depends upon the culture media used. Positive effects of spirulina on growth, feed utilization and stress and disease resistance of cultured fish have been reported in earlier studies. Spirulina was reported to replace up to 40% of fishmeal protein in tilapia (Oreochromis mossambicus) diet and even higher replacement of fishmeal was possible in common carp (Cyprinus carpio), partial replacement of fish meal with spirulina in diets for parrot fish (Oplegnathus fasciatus) and Tilapia (Orechromis niloticus) has also been conducted. Spirulina have considerable potential for development, especially as a small-scale crop for nutritional enhancement and health improvement of fish. It is important therefore that more research needs to be conducted on its production, inclusion level in aqua-feed and its possible potential use of aquaculture.

Keywords: aquaculture, spirulina, fish nutrition, fish feed

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Authors: Alexander Guzman Urbina, Atsushi Aoyama

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The sustainability of traditional technologies employed in energy and chemical infrastructure brings a big challenge for our society. Making decisions related with safety of industrial infrastructure, the values of accidental risk are becoming relevant points for discussion. However, the challenge is the reliability of the models employed to get the risk data. Such models usually involve large number of variables and with large amounts of uncertainty. The most efficient techniques to overcome those problems are built using Artificial Intelligence (AI), and more specifically using hybrid systems such as Neuro-Fuzzy algorithms. Therefore, this paper aims to introduce a hybrid algorithm for risk assessment trained using near-miss accident data. As mentioned above the sustainability of traditional technologies related with energy and chemical infrastructure constitutes one of the major challenges that today’s societies and firms are facing. Besides that, the adaptation of those technologies to the effects of the climate change in sensible environments represents a critical concern for safety and risk management. Regarding this issue argue that social consequences of catastrophic risks are increasing rapidly, due mainly to the concentration of people and energy infrastructure in hazard-prone areas, aggravated by the lack of knowledge about the risks. Additional to the social consequences described above, and considering the industrial sector as critical infrastructure due to its large impact to the economy in case of a failure the relevance of industrial safety has become a critical issue for the current society. Then, regarding the safety concern, pipeline operators and regulators have been performing risk assessments in attempts to evaluate accurately probabilities of failure of the infrastructure, and consequences associated with those failures. However, estimating accidental risks in critical infrastructure involves a substantial effort and costs due to number of variables involved, complexity and lack of information. Therefore, this paper aims to introduce a well trained algorithm for risk assessment using deep learning, which could be capable to deal efficiently with the complexity and uncertainty. The advantage point of the deep learning using near-miss accidents data is that it could be employed in risk assessment as an efficient engineering tool to treat the uncertainty of the risk values in complex environments. The basic idea of using a Near-Miss Deep Learning Approach for Neuro-Fuzzy Risk Assessment in Pipelines is focused in the objective of improve the validity of the risk values learning from near-miss accidents and imitating the human expertise scoring risks and setting tolerance levels. In summary, the method of Deep Learning for Neuro-Fuzzy Risk Assessment involves a regression analysis called group method of data handling (GMDH), which consists in the determination of the optimal configuration of the risk assessment model and its parameters employing polynomial theory.

Keywords: deep learning, risk assessment, neuro fuzzy, pipelines

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Authors: Nirit Gavish, Rotem Halutz, Liad Neta

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Nowadays, chatbots are gaining more and more attention due to the advent of large language models. One of the important considerations in chatbot design is how to create an interface to achieve high user satisfaction, trust, and acceptance. Since WhatsApp conversations sometimes substitute for face-to-face communication, we studied whether WhatsAppization of the chatbot -making the conversation resemble a WhatsApp conversation more- will improve user satisfaction, trust, and acceptance, or whether the opposite will occur due to the Uncanny Valley (UV) effect. The task was a drive-sharing task, in which participants communicated with a textual chatbot via WhatsApp and could decide whether to participate in a ride to college with a driver suggested by the chatbot. WhatsAppization of the chatbot was done in two ways: By a dialog-style conversation (Dialog versus No Dialog), and by adding WhatsApp indicators – “Last Seen”, “Connected”, “Read Receipts”, and “Typing…” (Indicators versus No Indicators). Our 120 participants were randomly assigned to one of the four 2 by 2 design groups, with 30 participants in each. They interacted with the WhatsApp chatbot and then filled out a questionnaire. The results demonstrated that, as expected from the manipulation, the interaction with the chatbot was longer for the dialog condition compared to the no dialog. This extra interaction, however, did not lead to higher acceptance -quite the opposite, since participants in the dialog condition were less willing to implement the decision made at the end of the conversation with the chatbot and continue the interaction with the driver they chose. The results are even more striking when considering the Indicators condition. Both for the satisfaction measures and the trust measures, participants’ ratings were lower in the Indicators condition compared to the No Indicators. Participants in the Indicators condition felt that the ride search process was harder to operate, and slower (even though the actual interaction time was similar). They were less convinced that the chatbot suggested real trips and they trusted the person offering the ride and referred to them by the chatbot less. These effects were more evident for participants who preferred to share their rides using WhatsApp compared to participants who preferred chatbots for that purpose. Considering our findings, we can say that the WhatsAppization of the chatbot was detrimental. This is true for the both chatbot WhatsAppization methods – by making the conversation more a dialog and adding WhatsApp indicators. For the chosen drive-sharing task, the results were, in addition to lower satisfaction, less trust in the chatbot’s suggestion and even in the driver suggested by the chatbot, and lower willingness to actually undertake the suggested ride. In addition, it seems that the most problematic WhatsAppization method was using WhatsApp’s indicators during the interaction with the chatbot. The current study suggests that a conversation with an artificial agent should also not imitate a WhatsApp conversation very closely. With the proliferation of WhatsApp use, the emotional and social aspect of face-to face commination are moving to WhatsApp communication. Based on the current study’s findings, it is possible that the UV effect also occurs in WhatsAppization, and not only in humanization, of the chatbot, with a similar feeling of eeriness, and is more pronounced for people who prefer to use WhatsApp over chatbots. The current research can serve as a starting point to study the very interesting and important topic of chatbots WhatsAppization. More methods of WhatsAppization and other tasks could be the focus of further studies.

Keywords: chatbot, WhatsApp, humanization, Uncanny Valley, drive sharing

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Authors: Ibram Khalafalla Roshdy Shokry

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This paper gives service feel multiple get entry to (CSMA) verbal exchange model based totally totally on SoC format method. Such model can be used to guide the modelling of the complex c084d04ddacadd4b971ae3d98fecfb2a communique systems, consequently use of such communication version is an crucial method in the creation of excessive general overall performance conversation. SystemC has been selected as it gives a homogeneous format drift for complicated designs (i.e. SoC and IP based format). We use a swarm device to validate CSMA designed version and to expose how advantages of incorporating communication early within the layout process. The wireless conversation created via the modeling of CSMA protocol that may be used to attain conversation among all of the retailers and to coordinate get proper of entry to to the shared medium (channel).The device of automobiles with wi-fiwireless communique abilities is expected to be the important thing to the evolution to next era intelligent transportation systems (ITS). The IEEE network has been continuously operating at the development of an wireless vehicular communication protocol for the enhancement of wi-fi get admission to in Vehicular surroundings (WAVE). Vehicular verbal exchange systems, known as V2X, help car to car (V2V) and automobile to infrastructure (V2I) communications. The wi-ficiencywireless of such communication systems relies upon on several elements, amongst which the encircling surroundings and mobility are prominent. as a result, this observe makes a speciality of the evaluation of the actual performance of vehicular verbal exchange with unique cognizance on the effects of the actual surroundings and mobility on V2X verbal exchange. It begins by wi-fi the actual most range that such conversation can guide and then evaluates V2I and V2V performances. The Arada LocoMate OBU transmission device changed into used to check and evaluate the effect of the transmission range in V2X verbal exchange. The evaluation of V2I and V2V communique takes the real effects of low and excessive mobility on transmission under consideration.Multiagent systems have received sizeable attention in numerous wi-fields, which include robotics, independent automobiles, and allotted computing, where a couple of retailers cooperate and speak to reap complicated duties. wi-figreen communication among retailers is a critical thing of these systems, because it directly influences their usual performance and scalability. This scholarly work gives an exploration of essential communication factors and conducts a comparative assessment of diverse protocols utilized in multiagent systems. The emphasis lies in scrutinizing the strengths, weaknesses, and applicability of those protocols across diverse situations. The studies additionally sheds light on rising tendencies within verbal exchange protocols for multiagent systems, together with the incorporation of device mastering strategies and the adoption of blockchain-based totally solutions to make sure comfy communique. those developments offer valuable insights into the evolving landscape of multiagent structures and their verbal exchange protocols.

Keywords: communication, multi-agent systems, protocols, consensussystemC, modelling, simulation, CSMA

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Authors: María-Luisa Pinto-Salamanca, Wilson-Javier Pérez-Holguín

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Reconstruction of contact forces is a fundamental technique for analyzing the properties of a touched object and is essential for regulating the grip force in slip control loops. This is based on the processing of the distribution, intensity, and direction of the forces during the capture of the sensors. Currently, efficient hardware alternatives have been used more frequently in different fields of application, allowing the implementation of computationally complex algorithms, as is the case with tactile signal processing. The use of hardware for smart tactile sensing systems is a research area that promises to improve the processing time and portability requirements of applications such as artificial skin and robotics, among others. The literature review shows that hardware implementations are present today in almost all stages of smart tactile detection systems except in the force reconstruction process, a stage in which they have been less applied. This work presents a hardware implementation of a model-driven reported in the literature for the contact force reconstruction of flat and rigid tactile sensor arrays from normal stress data. From the analysis of a software implementation of such a model, this implementation proposes the parallelization of tasks that facilitate the execution of matrix operations and a two-dimensional optimization function to obtain a vector force by each taxel in the array. This work seeks to take advantage of the parallel hardware characteristics of Field Programmable Gate Arrays, FPGAs, and the possibility of applying appropriate techniques for algorithms parallelization using as a guide the rules of generalization, efficiency, and scalability in the tactile decoding process and considering the low latency, low power consumption, and real-time execution as the main parameters of design. The results show a maximum estimation error of 32% in the tangential forces and 22% in the normal forces with respect to the simulation by the Finite Element Modeling (FEM) technique of Hertzian and non-Hertzian contact events, over sensor arrays of 10×10 taxels of different sizes. The hardware implementation was carried out on an MPSoC XCZU9EG-2FFVB1156 platform of Xilinx® that allows the reconstruction of force vectors following a scalable approach, from the information captured by means of tactile sensor arrays composed of up to 48 × 48 taxels that use various transduction technologies. The proposed implementation demonstrates a reduction in estimation time of x / 180 compared to software implementations. Despite the relatively high values of the estimation errors, the information provided by this implementation on the tangential and normal tractions and the triaxial reconstruction of forces allows to adequately reconstruct the tactile properties of the touched object, which are similar to those obtained in the software implementation and in the two FEM simulations taken as reference. Although errors could be reduced, the proposed implementation is useful for decoding contact forces for portable tactile sensing systems, thus helping to expand electronic skin applications in robotic and biomedical contexts.

Keywords: contact forces reconstruction, forces estimation, tactile sensor array, hardware implementation

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Authors: Vivek Mahadev, Manoj Kumar, Neelu Mathur, Brahm Dutt Pandey

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Perimeter guarding and protection of critical installations require prompt intrusion detection and assessment to take effective countermeasures. Currently, visual and electronic surveillance are the primary methods used for perimeter guarding. These methods can be costly and complicated, requiring careful planning according to the location and terrain. Moreover, these methods often struggle to detect stealthy and camouflaged insurgents. The object of the present work is to devise a surveillance technique using seismic sensors that overcomes the limitations of existing systems. The aim is to improve intrusion detection, assessment, and characterization by utilizing seismic sensors. Most of the similar systems have only two types of intrusion detection capability viz., human or vehicle. In our work we could even categorize further to identify types of intrusion activity such as walking, running, group walking, fence jumping, tunnel digging and vehicular movements. A virtual fence of 60 meters at GCNEP, Bahadurgarh, Haryana, India, was created by installing four underground geophones at a distance of 15 meters each. The signals received from these geophones are then processed to find unique seismic signatures called features. Various feature optimization and selection methodologies, such as LightGBM, Boruta, Random Forest, Logistics, Recursive Feature Elimination, Chi-2 and Pearson Ratio were used to identify the best features for training the machine learning models. The trained models were developed using algorithms such as supervised support vector machine (SVM) classifier, kNN, Decision Tree, Logistic Regression, Naïve Bayes, and Artificial Neural Networks. These models were then used to predict the category of events, employing weighted ensemble voting to analyze and combine their results. The models were trained with 1940 training events and results were evaluated with 831 test events. It was observed that using the weighted ensemble voting increased the efficiency of predictions. In this study we successfully developed and deployed the virtual fence using geophones. Since these sensors are passive, do not radiate any energy and are installed underground, it is impossible for intruders to locate and nullify them. Their flexibility, quick and easy installation, low costs, hidden deployment and unattended surveillance make such systems especially suitable for critical installations and remote facilities with difficult terrain. This work demonstrates the potential of utilizing seismic sensors for creating better perimeter guarding and protection systems using multiple machine learning models in weighted ensemble voting. In this study the virtual fence achieved an intruder detection efficiency of over 97%.

Keywords: geophone, seismic perimeter surveillance, machine learning, weighted ensemble method

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Authors: Chrysthia Papacleovoulou

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The era of de-regulation, off-shore and tax haven jurisdictions, and shelf companies has come to an end. The usage of complex corporate structures involving trust instruments, special purpose vehicles, holding-subsidiaries in offshore haven jurisdictions, and taking advantage of tax treaties is soaring. States which raced to introduce corporate friendly legislation, tax incentives, and creative international trust law in order to attract greater FDI are now faced with regulatory challenges and are forced to revisit the corporate form and its tax treatment. The fiduciary services industry, which dominated over the last 3 decades, is now striving to keep up with the new regulatory framework as a result of a number of European and international legislative measures. This article considers the challenges to the company and the corporate form as a result of the legislative measures on tax planning and tax avoidance, CRS reporting, FATCA, CFC rules, OECD’s BEPS, the EU Commission's new transparency rules for intermediaries that extends to tax advisors, accountants, banks & lawyers who design and promote tax planning schemes for their clients, new EU rules to block artificial tax arrangements and new transparency requirements for financial accounts, tax rulings and multinationals activities (DAC 6), G20's decision for a global 15% minimum corporate tax and banking regulation. As a result, states are found in a race of over-regulation and compliance. These legislative measures constitute a global up-side down tax-harmonisation. Through the adoption of the OECD’s BEPS, states agreed to an international collaboration to end tax avoidance and reform international taxation rules. Whilst the idea was to ensure that multinationals would pay their fair share of tax everywhere they operate, an indirect result of the aforementioned regulatory measures was to attack private clients-individuals who -over the past 3 decades- used the international tax system and jurisdictions such as Marshal Islands, Cayman Islands, British Virgin Islands, Bermuda, Seychelles, St. Vincent, Jersey, Guernsey, Liechtenstein, Monaco, Cyprus, and Malta, to name but a few, to engage in legitimate tax planning and tax avoidance. Companies can no longer maintain bank accounts without satisfying the real substance test. States override the incorporation doctrine theory and apply a real seat or real substance test in taxing companies and their activities, targeting even the beneficial owners personally with tax liability. Tax authorities in civil law jurisdictions lift the corporate veil through the public registries of UBO Registries and Trust Registries. As a result, the corporate form and the doctrine of limited liability are challenged in their core. Lastly, this article identifies the development of new instruments, such as funds and private placement insurance policies, and the trend of digital nomad workers. The baffling question is whether industry and states can meet somewhere in the middle and exit this over-regulation frenzy.

Keywords: company, regulation, TAX, corporate structure, trust vehicles, real seat

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Authors: Mbonigaba Swale

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Malaria is an infection or disease caused by parasites (Plasmodium Falciparum — causes severe Malaria, plasmodium Vivax, Plasmodium Ovale, and Plasmodium Malariae), transmitted by bites of infected anopheles (female) mosquitoes to humans. These vectors comprise of two types in Africa, particularly in Uganda, i.e. anopheles fenestus and Anopheles gambaie (‘example Anopheles arabiensis,,); feeds on man inside the house mainly at dusk, mid-night and dawn and rests indoors and makes them effective transmitters (vectors) of the disease. People in both urban and rural areas have consistently become prone to repetitive attacks of malaria, causing a lot of deaths and significantly increasing the poverty levels of the rural poor. Malaria is a national problem; it causes a lot of maternal pre-natal and antenatal disorders, anemia in pregnant mothers, low birth weights for the newly born, convulsions and epilepsy among the infants. Cumulatively, it kills about one million children every year in sub-Saharan Africa. It has been estimated to account for 25-35% of all outpatient visits, 20-45% of acute hospital admissions and 15-35% of hospital deaths. Uganda is the leading victim country, for which Rakai and Masaka districts are the most affected. So, it is not clear whether these abhorrent situations and episodes of recurrences and failure to cure from the disease are a result of poor diagnosis, prescription and dosing, treatment habits and compliance of the patients to the drugs or the ethical domain of the stake holders in relation to the main stream methodology of malaria management. The research is aimed at offering an alternative approach to manage and deal absolutely with problem by using a knowledge based software model of Artificial Intelligence (Al) that is capable of performing common-sense and cognitive reasoning so as to take decisions like the human brain would do to provide instantaneous expert solutions so as to avoid speculative simulation of the problem during differential diagnosis in the most accurate and literal inferential aspect. This system will assist physicians in many kinds of medical diagnosis, prescribing treatments and doses, and in monitoring patient responses, basing on the body weight and age group of the patient, it will be able to provide instantaneous and timely information options, alternative ways and approaches to influence decision making during case analysis. The computerized system approach, a new model in Uganda termed as “Software Aided Treatment” (SAT) will try to change the moral and ethical approach and influence conduct so as to improve the skills, experience and values (social and ethical) in the administration and management of the disease and drugs (combination therapy and generics) by both the patient and the health worker.

Keywords: knowledge based software, management, treatment, diagnosis

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Authors: S. Sabatino, V. Calderaro, V. Galdi, G. Graber, L. Ippolito

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Climate change is a rapidly growing global threat caused mainly by increased emissions of carbon dioxide (CO₂) into the atmosphere. These emissions come from multiple sources, including industry, power generation, and the transport sector. The need to tackle climate change and reduce CO₂ emissions is indisputable. A crucial solution to achieving decarbonization in the transport sector is the adoption of electric vehicles (EVs). These vehicles use lithium (Li-Ion) batteries as an energy source, making them extremely efficient and with low direct emissions. However, Li-Ion batteries are not without problems, including the risk of overheating and performance degradation. To ensure its safety and longevity, it is essential to use a battery management system (BMS). The BMS constantly monitors battery status, adjusts temperature and cell balance, ensuring optimal performance and preventing dangerous situations. From the monitoring carried out, it is also able to optimally manage the battery to increase its life. Among the parameters monitored by the BMS, the main ones are State of Charge (SoC), State of Health (SoH), and State of Power (SoP). The evaluation of these parameters can be carried out in two ways: offline, using benchtop batteries tested in the laboratory, or online, using batteries installed in moving vehicles. Online estimation is the preferred approach, as it relies on capturing real-time data from batteries while operating in real-life situations, such as in everyday EV use. Actual battery usage conditions are highly variable. Moving vehicles are exposed to a wide range of factors, including temperature variations, different driving styles, and complex charge/discharge cycles. This variability is difficult to replicate in a controlled laboratory environment and can greatly affect performance and battery life. Online estimation captures this variety of conditions, providing a more accurate assessment of battery behavior in real-world situations. In this article, a hybrid approach based on a neural network and a statistical method for real-time estimation of SoC, SoH, and SoP parameters of interest is proposed. These parameters are estimated from the analysis of a one-day driving profile of an electric vehicle, assumed to be divided into the following four phases: (i) Partial discharge (SoC 100% - SoC 50%), (ii) Partial discharge (SoC 50% - SoC 80%), (iii) Deep Discharge (SoC 80% - SoC 30%) (iv) Full charge (SoC 30% - SoC 100%). The neural network predicts the values of ohmic resistance and incremental capacity, while the statistical method is used to estimate the parameters of interest. This reduces the complexity of the model and improves its prediction accuracy. The effectiveness of the proposed model is evaluated by analyzing its performance in terms of square mean error (RMSE) and percentage error (MAPE) and comparing it with the reference method found in the literature.

Keywords: electric vehicle, Li-Ion battery, BMS, state-of-charge, state-of-health, state-of-power, artificial neural networks

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Authors: Thomas James Bell III

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Pedagogy is thought of as one's philosophy, theory, or teaching method. This study examines the science of learning, considering the forced reconsideration of effective pedagogy brought on by the aftermath of the 2020 coronavirus pandemic. With the aid of various technologies, online education holds challenges and promises to enhance the learning environment if implemented to facilitate student learning. Behaviorism centers around the belief that the instructor is the sage on the classroom stage using repetition techniques as the primary learning instrument. This approach to pedagogy ascribes complete control of the learning environment and works best for students to learn by allowing students to answer questions with immediate feedback. Such structured learning reinforcement tends to guide students' learning without considering learners' independence and individual reasoning. And such activities may inadvertently stifle the student's ability to develop critical thinking and self-expression skills. Fundamentally liberationism pedagogy dismisses the concept that education is merely about students learning things and more about the way students learn. Alternatively, the liberationist approach democratizes the classroom by redefining the role of the teacher and student. The teacher is no longer viewed as the sage on the stage but as a guide on the side. Instead, this approach views students as creators of knowledge and not empty vessels to be filled with knowledge. Moreover, students are well suited to decide how best to learn and which areas improvements are needed. This study will explore the classroom instructor as a servant leader in the twenty-first century, which allows students to integrate technology that encapsulates more individual learning styles. The researcher will examine the Professional Scrum Master (PSM I) exam pass rate results of 124 students in six sections of an Agile scrum course. The students will be separated into two groups; the first group will follow a structured instructor-led course outlined by a course syllabus. The second group will consist of several small teams (ten or fewer) of self-led and self-empowered students. The teams will conduct several event meetings that include sprint planning meetings, daily scrums, sprint reviews, and retrospective meetings throughout the semester will the instructor facilitating the teams' activities as needed. The methodology for this study will use the compare means t-test to compare the mean of an exam pass rate in one group to the mean of the second group. A one-tailed test (i.e., less than or greater than) will be used with the null hypothesis, for the difference between the groups in the population will be set to zero. The major findings will expand the pedagogical approach that suggests pedagogy primarily exist in support of teacher-led learning, which has formed the pillars of traditional classroom teaching. But in light of the fourth industrial revolution, there is a fusion of learning platforms across the digital, physical, and biological worlds with disruptive technological advancements in areas such as the Internet of Things (IoT), artificial intelligence (AI), 3D printing, robotics, and others.

Keywords: pedagogy, behaviorism, liberationism, flipping the classroom, servant leader instructor, agile scrum in education

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Authors: Ammar Alali, Mahmoud Abughaban, William Contreras Otalvora

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Optimizing the drilling process for cost and efficiency requires the optimization of the rate of penetration (ROP). ROP is the measurement of the speed at which the wellbore is created, in units of feet per hour. It is the primary indicator of measuring drilling efficiency. Maximization of the ROP can indicate fast and cost-efficient drilling operations; however, high ROPs may induce unintended events, which may lead to nonproductive time (NPT) and higher net costs. The proposed ROP optimization solution is a hybrid, data-driven system that aims to improve the drilling process, maximize the ROP, and minimize NPT. The system consists of two phases: (1) utilizing existing geological and drilling data to train the model prior, and (2) real-time adjustments of the controllable dynamic drilling parameters [weight on bit (WOB), rotary speed (RPM), and pump flow rate (GPM)] that direct influence on the ROP. During the first phase of the system, geological and historical drilling data are aggregated. After, the top-rated wells, as a function of high instance ROP, are distinguished. Those wells are filtered based on NPT incidents, and a cross-plot is generated for the controllable dynamic drilling parameters per ROP value. Subsequently, the parameter values (WOB, GPM, RPM) are calculated as a conditioned mean based on physical distance, following Inverse Distance Weighting (IDW) interpolation methodology. The first phase is concluded by producing a model of drilling best practices from the offset wells, prioritizing the optimum ROP value. This phase is performed before the commencing of drilling. Starting with the model produced in phase one, the second phase runs an automated drill-off test, delivering live adjustments in real-time. Those adjustments are made by directing the driller to deviate two of the controllable parameters (WOB and RPM) by a small percentage (0-5%), following the Constrained Random Search (CRS) methodology. These minor incremental variations will reveal new drilling conditions, not explored before through offset wells. The data is then consolidated into a heat-map, as a function of ROP. A more optimum ROP performance is identified through the heat-map and amended in the model. The validation process involved the selection of a planned well in an onshore oil field with hundreds of offset wells. The first phase model was built by utilizing the data points from the top-performing historical wells (20 wells). The model allows drillers to enhance decision-making by leveraging existing data and blending it with live data in real-time. An empirical relationship between controllable dynamic parameters and ROP was derived using Artificial Neural Networks (ANN). The adjustments resulted in improved ROP efficiency by over 20%, translating to at least 10% saving in drilling costs. The novelty of the proposed system lays is its ability to integrate historical data, calibrate based geological formations, and run real-time global optimization through CRS. Those factors position the system to work for any newly drilled well in a developing field event.

Keywords: drilling optimization, geological formations, machine learning, rate of penetration

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Authors: Yi Jen Wang, Yu Ju Chen

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Background: Heart rate variability (HRV) biofeedback can promote autonomic nervous function, sleep quality and reduce psychological stress. In HRV biofeedback training, it is hoped that through the guidance of machine video or audio, the patient can breathe slowly according to his own heart rate changes so that the heart and lungs can achieve resonance, thereby promoting the related effects of autonomic nerve function; while, it is also pointed out that if slow breathing of 6 times per minute can also guide the case to achieve the effect of cardiopulmonary resonance. However, there is no relevant research to explore the comparison of the effectiveness of cardiopulmonary resonance by using video or audio HRV biofeedback training and metronome-guided slow breathing. Purpose: To compare the promotion of autonomic nervous function performance between using HRV biofeedback and slow breathing guided by a metronome. Method: This research is a kind of experimental design with convenient sampling; the cases are randomly divided into the heart rate variability biofeedback training group and the slow breathing training group. The HRV biofeedback training group will conduct HRV biofeedback training in a four-week laboratory and use the home training device for autonomous training; while the slow breathing training group will conduct slow breathing training in the four-week laboratory using the mobile phone APP breathing metronome to guide the slow breathing training, and use the mobile phone APP for autonomous training at home. After two groups were enrolled and four weeks after the intervention, the autonomic nervous function-related performance was repeatedly measured. Using the chi-square test, student’s t-test and other statistical methods to analyze the results, and use p <0.05 as the basis for statistical significance. Results: A total of 27 subjects were included in the analysis. After four weeks of training, the HRV biofeedback training group showed significant improvement in the HRV indexes (SDNN, RMSSD, HF, TP) and sleep quality. Although the stress index also decreased, it did not reach statistical significance; the slow breathing training group was not statistically significant after four weeks of training, only sleep quality improved significantly, while the HRV indexes (SDNN, RMSSD, TP) all increased. Although HF and stress indexes decreased, they were not statistically significant. Comparing the difference between the two groups after training, it was found that the HF index improved significantly and reached statistical significance in the HRV biofeedback training group. Although the sleep quality of the two groups improved, it did not reach that level in a statistically significant difference. Conclusion: HRV biofeedback training is more effective in promoting autonomic nervous function than slow breathing training, but the effects of reducing stress and promoting sleep quality need to be explored after increasing the number of samples. The results of this study can provide a reference for clinical or community health promotion. In the future, it can also be further designed to integrate heart rate variability biological feedback training into the development of AI artificial intelligence wearable devices, which can make it more convenient for people to train independently and get effective feedback in time.

Keywords: autonomic nervous function, HRV biofeedback, heart rate variability, slow breathing

Procedia PDF Downloads 174

Authors: Laura M. F. Bertens

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There is a long tradition of visually representing the art historical canon, in schematic overviews and diagrams. This is indicative of the desire for scientific, ‘objective’ knowledge of the kind (seemingly) produced in the natural sciences. These diagrams will, however, always retain an element of subjectivity and the modelling methods colour our perception of the represented information. In recent decades visualisations of art historical data, such as hand-drawn diagrams in textbooks, have been extended to include digital, computational tools. These tools significantly increase modelling strength and functionality. As such, they might be used to deconstruct and amend the very problem caused by traditional visualisations of the canon. In this paper, the use of digital tools for modelling the art historical canon is studied, in order to draw attention to the artificial nature of the static models that art historians are presented with in textbooks and lectures, as well as to explore the potential of digital, dynamic tools in creating new models. To study the way diagrams of the canon mediate the represented information, two modelling methods have been used on two case studies of existing diagrams. The tree diagram Stammbaum der neudeutschen Kunst (1823) by Ferdinand Olivier has been translated to a social network using the program Visone, and the famous flow chart Cubism and Abstract Art (1936) by Alfred Barr has been translated to an ontological model using Protégé Ontology Editor. The implications of the modelling decisions have been analysed in an art historical context. The aim of this project has been twofold. On the one hand the translation process makes explicit the design choices in the original diagrams, which reflect hidden assumptions about the Western canon. Ways of organizing data (for instance ordering art according to artist) have come to feel natural and neutral and implicit biases and the historically uneven distribution of power have resulted in underrepresentation of groups of artists. Over the last decades, scholars from fields such as Feminist Studies, Postcolonial Studies and Gender Studies have considered this problem and tried to remedy it. The translation presented here adds to this deconstruction by defamiliarizing the traditional models and analysing the process of reconstructing new models, step by step, taking into account theoretical critiques of the canon, such as the feminist perspective discussed by Griselda Pollock, amongst others. On the other hand, the project has served as a pilot study for the use of digital modelling tools in creating dynamic visualisations of the canon for education and museum purposes. Dynamic computer models introduce functionalities that allow new ways of ordering and visualising the artworks in the canon. As such, they could form a powerful tool in the training of new art historians, introducing a broader and more diverse view on the traditional canon. Although modelling will always imply a simplification and therefore a distortion of reality, new modelling techniques can help us get a better sense of the limitations of earlier models and can provide new perspectives on already established knowledge.

Keywords: canon, ontological modelling, Protege Ontology Editor, social network modelling, Visone

Procedia PDF Downloads 126

Authors: Dmitriy Yu. Korulkin, Raissa A. Muzychkina

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The last decade the growth of severe and disseminated forms of inflammatory diseases is observed in Kazakhstan, in particular, septic shock, which progresses on 3-15% of patients with infectious complications of postnatal period. In terms of the rate of occurrence septic shock takes third place after hemorrhagic and cardiovascular shock, in terms of lethality it takes first place. The structure of obstetric sepsis has significantly changed. Currently the first place is taken by postabortive sepsis (40%) that is connected with usage of imperfect methods of artificial termination of pregnancy in late periods (intraamnial injection of sodium chloride, glucose). The second place is taken by postnatal sepsis (32%); the last place is taken by septic complications of caesarean section (28%). In this connection, search for and assessment of effectiveness of new medicines for treatment of postoperative infectious complications, having biostimulating effect and speeding up regeneration processes, is very promising and topical. Essential oil was obtained by the method hydrodistillation air-dry aerial part of Sedum L. plants using Clevenger apparatus. Pilot batch of plant medicinal product based on Sedum essential oils was produced by Chimpharm JSC, Santo Member of Polpharma Group (Kazakhstan). During clinical test of the plant medicinal product based on Sedum L. essential oils 37 female patients at the age from 35 to 57 with clinical signs of complicated postoperative processes and 12 new mothers with clinical signs of inflammatory process on sutures on anterior abdominal wall after caesarean section and partial disruption of surgical suture line on perineum were examined. Medicine usage methods - surgical wound treatment 2 times a day, treatment with other medicines of local action was not performed. Before and after treatment general clinical test, determination of immune status, bacterioscopic test of wound fluid was performed to all women, medical history data was taken into account, wound cleansing and healing time, full granulations, side effects and complications, satisfaction with the used medicine was assessed. On female patients with inflammatory infiltration and partial disruption of surgical suture line anesthetic wound healing effect of plant medicinal product based on Sedum L. essential oils was observed as early as on the second day after beginning of using it, wound cleansing took place, as a rule, within the first row days. Hyperemia in the area of suture line also was not observed for 2-3-d day of usage of medicine, good constant course was observed. The absence of clinical effect on this group of patients was not registered. The represented data give evidence of that clinical effect was accompanied with normalization of changed laboratory findings. No allergic responses or side effects were observed during usage of the plant medicinal products based on Sedum L. essential oils.

Keywords: antiinflammatory, bioactive substances, essential oils, isolation, sedum L., wound healing

Procedia PDF Downloads 266

Authors: Wei Xu, Abdo S. Haidar, Jianxin Gao

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Prosthetic socket is a component that connects the residual limb of an amputee with an artificial prosthesis. It is widely recognized as the most critical component that determines the comfort of a patient when wearing the prosthesis in his/her daily activities. Through the socket, the body weight and its associated dynamic load are distributed and transmitted to the prosthesis during walking, running or climbing. In order to achieve a good-fit socket for an individual amputee, it is essential to obtain the biomechanical properties of the residual limb. In current clinical practices, this is achieved by a touch-and-feel approach which is highly subjective. Although there have been significant advancements in prosthetic technologies such as microprocessor controlled knee and ankle joints in the last decade, the progress in designing a comfortable socket has been rather limited. This means that the current process of socket design is still very time-consuming, and highly dependent on the expertise of the prosthetist. Supported by the state-of-the-art sensor technologies and numerical simulations, a new socket design system is being developed to help prosthetists achieve rapid design of comfortable sockets for above knee amputees. This paper reports the research work related to establishing biomechanical models for socket design. Through numerical simulation using finite element method, comprehensive relationships between pressure on residual limb and socket geometry were established. This allowed local topological adjustment for the socket so as to optimize the pressure distributions across the residual limb. When the full body weight of a patient is exerted on the residual limb, high pressures and shear forces between the residual limb and the socket occur. During numerical simulations, various hyperplastic models, namely Ogden, Yeoh and Mooney-Rivlin, were used, and their effectiveness in representing the biomechanical properties of soft tissues of the residual limb was evaluated. This also involved reverse engineering, which resulted in an optimal representative model under compression test. To validate the simulation results, a range of silicone models were fabricated. They were tested by an indentation device which yielded the force-displacement relationships. Comparisons of results obtained from FEA simulations and experimental tests showed that the Ogden model did not fit well the soft tissue material indentation data, while the Yeoh model gave the best representation of the soft tissue mechanical behavior under indentation. Compared with hyperplastic model, the result showed that elastic model also had significant errors. In addition, normal and shear stress distributions on the surface of the soft tissue model were obtained. The effect of friction in compression testing and the influence of soft tissue stiffness and testing boundary conditions were also analyzed. All these have contributed to the overall goal of designing a good-fit socket for individual above knee amputees.

Keywords: above knee amputee, finite element simulation, hyperplastic model, prosthetic socket

Procedia PDF Downloads 203

Authors: Cristobal García

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The visual inspection of bolted joints in wind turbines is dangerous, expensive, and impractical due to the non-possibility to access the platform by workboat in certain sea state conditions, as well as the high costs derived from the transportation of maintenance technicians to offshore platforms located far away from the coast, especially if helicopters are involved. Consequently, the wind turbine operators have the need for simpler and less demanding techniques for the analysis of the bolts tightening. Vibration-based structural health monitoring is one of the oldest and most widely-used means for monitoring the health of onshore and offshore wind turbines. The core of this work is to find out if the modal parameters can be efficiently used as a key performance indicator (KPIs) for the assessment of joint bolts in a 1:50 scale tower of a floating offshore wind turbine (12 MW). A non-destructive vibration test is used to extract the vibration signals of the towers with different damage statuses. The procedure can be summarized in three consecutive steps. First, an artificial excitation is introduced by means of a commercial shaker mounted on the top of the tower. Second, the vibration signals of the towers are recorded for 8 s at a sampling rate of 20 kHz using an array of commercial accelerometers (Endevco, 44A16-1032). Third, the natural frequencies, damping, and overall vibration mode shapes are calculated using the software Siemens LMS 16A. Experiments show that the natural frequencies, damping, and mode shapes of the tower are directly dependent on the fixing conditions of the towers, and therefore, the variations of both parameters are a good indicator for the estimation of the static axial force acting in the bolt. Thus, this vibration-based structural method proposed can be potentially used as a diagnostic tool to evaluate the tightening torques of the bolted joints with the advantages of being an economical, straightforward, and multidisciplinary approach that can be applied for different typologies of connections by operation and maintenance technicians. In conclusion, TSI, in collaboration with the consortium of the FIBREGY project, is conducting innovative research where vibrations are utilized for the estimation of the tightening torque of a 1:50 scale steel-based tower prototype. The findings of this research carried out in the context of FIBREGY possess multiple implications for the assessment of the bolted joint integrity in multiple types of connections such as tower-to-nacelle, modular, tower-to-column, tube-to-tube, etc. This research is contextualized in the framework of the FIBREGY project. The EU-funded FIBREGY project (H2020, grant number 952966) will evaluate the feasibility of the design and construction of a new generation of marine renewable energy platforms using lightweight FRP materials in certain structural elements (e.g., tower, floating platform). The FIBREGY consortium is composed of 11 partners specialized in the offshore renewable energy sector and funded partially by the H2020 program of the European Commission with an overall budget of 8 million Euros.

Keywords: SHM, vibrations, connections, floating offshore platform

Procedia PDF Downloads 124

Authors: M. Abu Hanif, Anika Tabassum, Fuad Hasan Ovi, Ishrat Islam

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Water is essential for life. Enormous quantities of water are cycled each year through hydrologic cycle but only a fraction of circulated water is available each year for human use. Dhaka, the capital of Bangladesh is the 19th mega city in the world with a population of over 14 million (World City Information, 2011). As a result the growth of urban population is increasing rapidly; the city is not able to manage with altering situations due to resource limitations and management capacity. Water crisis has become an acute problem faced by the inhabitants of Dhaka city. It is found that total water demand in Dhaka city is 2,240 million liter per day (MLD) whereas supply is 2,150 (MLD). According to Dhaka Water Supply and Sewerage Authority about 87 percent of this supply comes from groundwater resources and rest 13 percent from surface water. According to Dhaka Water Supply and Sewerage Authority it has been found that the current groundwater depletion rate is 3.52 meter per year. Such a fast depletion of the water table will result in intrusion of southern saline water into the groundwater reservoir, depriving this mega city of pure drinking water. This study mainly focus on the potential of Rainwater Harvesting System(RWHS) in Kazipara area of Dhaka city, determine the perception level of local people in installation of rainwater harvesting system in their building and identify the factors regarding willingness of owner in installing rainwater harvesting system. As most of the residential area of Dhaka city is unplanned with small plots, Kazipara area has been chosen as study area which depicts similar characteristics. In this study only roof top area is considered as catchment area and potential of rainwater harvesting has been calculated. From the calculation it is found that harvested rainwater can serve the 66% of demand of water for toilet flushing and cleaning purposes for the people of Kazipara. It is also observed that if only rooftop rainwater harvesting applied to all the structures of the study area then two third of surface runoff would be reduced than present surface runoff. In determining the perception of local people only owners of the buildings were. surveyed. From the questionnaire survey it is found that around 75% people have no idea about the rainwater harvesting system. About 83% people are not willing to install rainwater harvesting system in their dwelling. The reasons behind the unwillingness are high cost of installation, inadequate space, ignorance about the system, etc. Among 16% of the willing respondents who are interested in installing RWHS system, it was found that higher income, bigger size of buildings are important factors in willingness of installing rainwater harvesting system. Majority of the respondents demanded for both technical and economical support to install the system in their buildings. Government of Bangladesh has taken some initiatives to promote rainwater harvesting in urban areas. It is very much necessary to incorporate rainwater harvesting device and artificial recharge system in every building of Dhaka city to make Dhaka city self sufficient in water supply management and to solve water crisis problem of megacity like as Dhaka city.

Keywords: rainwater harvesting, water table, willingness, storm water

Procedia PDF Downloads 242

Authors: Gyu-Dong Kim, Wuk-Jae Yoo, Sang-Youl Lee

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Composite structures offer numerous advantages over conventional structural systems in the form of higher specific stiffness and strength, lower life-cycle costs, and benefits such as easy installation and improved safety. Recently, there has been a considerable increase in the use of composites in engineering applications and as wraps for seismic upgrading and repairs. However, these composites deteriorate with time because of outdated materials, excessive use, repetitive loading, climatic conditions, manufacturing errors, and deficiencies in inspection methods. In particular, damaged fibers in a composite result in significant degradation of structural performance. In order to reduce the failure probability of composites in service, techniques to assess the condition of the composites to prevent continual growth of fiber damage are required. Condition assessment technology and nondestructive evaluation (NDE) techniques have provided various solutions for the safety of structures by means of detecting damage or defects from static or dynamic responses induced by external loading. A variety of techniques based on detecting the changes in static or dynamic behavior of isotropic structures has been developed in the last two decades. These methods, based on analytical approaches, are limited in their capabilities in dealing with complex systems, primarily because of their limitations in handling different loading and boundary conditions. Recently, investigators have introduced direct search methods based on metaheuristics techniques and artificial intelligence, such as genetic algorithms (GA), simulated annealing (SA) methods, and neural networks (NN), and have promisingly applied these methods to the field of structural identification. Among them, GAs attract our attention because they do not require a considerable amount of data in advance in dealing with complex problems and can make a global solution search possible as opposed to classical gradient-based optimization techniques. In this study, we propose an alternative damage-detection technique that can determine the degraded stiffness distribution of vibrating laminated composites made of Glass Fiber-reinforced Polymer (GFRP). The proposed method uses a modified form of the bivariate Gaussian distribution function to detect degraded stiffness characteristics. In addition, this study presents a method to detect the fiber property variation of laminated composite plates from the micromechanical point of view. The finite element model is used to study free vibrations of laminated composite plates for fiber stiffness degradation. In order to solve the inverse problem using the combined method, this study uses only first mode shapes in a structure for the measured frequency data. In particular, this study focuses on the effect of the interaction among various parameters, such as fiber angles, layup sequences, and damage distributions, on fiber-stiffness damage detection.

Keywords: stiffness detection, fiber damage, genetic algorithm, layup sequences

Procedia PDF Downloads 272

Authors: Eiji Nakamachi, Tsuyoshi Eguchi, Sayo Yamamoto, Yusuke Morita, H. Sakamoto

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In this study, we aim to reveal the relationship between the meniscal tearing and the articular cartilage injury of knee joint by using the dynamic explicit finite element (FE) method. Meniscal injuries reduce its functional ability and consequently increase the load on the articular cartilage of knee joint. In order to prevent the induction of osteoarthritis (OA) caused by meniscal injuries, many medical treatment techniques, such as artificial meniscus replacement and meniscal regeneration, have been developed. However, it is reported that these treatments are not the comprehensive methods. In order to reveal the fundamental mechanism of OA induction, the mechanical characterization of meniscus under the condition of normal and injured states is carried out by using FE analyses. At first, a FE model of the human knee joint in the case of normal state – ‘intact’ - was constructed by using the magnetron resonance (MR) tomography images and the image construction code, Materialize Mimics. Next, two types of meniscal injury models with the radial tears of medial and lateral menisci were constructed. In FE analyses, the linear elastic constitutive law was adopted for the femur and tibia bones, the visco-hyperelastic constitutive law for the articular cartilage, and the visco-anisotropic hyperelastic constitutive law for the meniscus, respectively. Material properties of articular cartilage and meniscus were identified using the stress-strain curves obtained by our compressive and the tensile tests. The numerical results under the normal walking condition revealed how and where the maximum compressive stress occurred on the articular cartilage. The maximum compressive stress and its occurrence point were varied in the intact and two meniscal tear models. These compressive stress values can be used to establish the threshold value to cause the pathological change for the diagnosis. In this study, FE analyses of knee joint were carried out to reveal the influence of meniscal injuries on the cartilage injury. The following conclusions are obtained. 1. 3D FE model, which consists femur, tibia, articular cartilage and meniscus was constructed based on MR images of human knee joint. The image processing code, Materialize Mimics was used by using the tetrahedral FE elements. 2. Visco-anisotropic hyperelastic constitutive equation was formulated by adopting the generalized Kelvin model. The material properties of meniscus and articular cartilage were determined by curve fitting with experimental results. 3. Stresses on the articular cartilage and menisci were obtained in cases of the intact and two radial tears of medial and lateral menisci. Through comparison with the case of intact knee joint, two tear models show almost same stress value and higher value than the intact one. It was shown that both meniscal tears induce the stress localization in both medial and lateral regions. It is confirmed that our newly developed FE analysis code has a potential to be a new diagnostic system to evaluate the meniscal damage on the articular cartilage through the mechanical functional assessment.

Keywords: finite element analysis, hyperelastic constitutive law, knee joint injury, meniscal tear, stress concentration

Procedia PDF Downloads 243

Authors: Ja-Keoung Koo, Kensuke Nakamura, Hyohun Kim, Dongwha Shin, Yeonseok Kim, Ji-Su Ahn, Byung-Woo Hong

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The machine learning techniques based on a convolutional neural network (CNN) have been actively developed and successfully applied to a variety of image analysis tasks including reconstruction, noise reduction, resolution enhancement, segmentation, motion estimation, object recognition. The classical visual information processing that ranges from low level tasks to high level ones has been widely developed in the deep learning framework. It is generally considered as a challenging problem to derive visual interpretation from high dimensional imagery data. A CNN is a class of feed-forward artificial neural network that usually consists of deep layers the connections of which are established by a series of non-linear operations. The CNN architecture is known to be shift invariant due to its shared weights and translation invariance characteristics. However, it is often computationally intractable to optimize the network in particular with a large number of convolution layers due to a large number of unknowns to be optimized with respect to the training set that is generally required to be large enough to effectively generalize the model under consideration. It is also necessary to limit the size of convolution kernels due to the computational expense despite of the recent development of effective parallel processing machinery, which leads to the use of the constantly small size of the convolution kernels throughout the deep CNN architecture. However, it is often desired to consider different scales in the analysis of visual features at different layers in the network. Thus, we propose a CNN model where different sizes of the convolution kernels are applied at each layer based on the random projection. We apply random filters with varying sizes and associate the filter responses with scalar weights that correspond to the standard deviation of the random filters. We are allowed to use large number of random filters with the cost of one scalar unknown for each filter. The computational cost in the back-propagation procedure does not increase with the larger size of the filters even though the additional computational cost is required in the computation of convolution in the feed-forward procedure. The use of random kernels with varying sizes allows to effectively analyze image features at multiple scales leading to a better generalization. The robustness and effectiveness of the proposed CNN based on random kernels are demonstrated by numerical experiments where the quantitative comparison of the well-known CNN architectures and our models that simply replace the convolution kernels with the random filters is performed. The experimental results indicate that our model achieves better performance with less number of unknown weights. The proposed algorithm has a high potential in the application of a variety of visual tasks based on the CNN framework. Acknowledgement—This work was supported by the MISP (Ministry of Science and ICT), Korea, under the National Program for Excellence in SW (20170001000011001) supervised by IITP, and NRF-2014R1A2A1A11051941, NRF2017R1A2B4006023.

Keywords: deep learning, convolutional neural network, random kernel, random projection, dimensionality reduction, object recognition

Procedia PDF Downloads 285

Authors: Duncan Wallace, M-Tahar Kechadi

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In recent years, Machine Learning (ML) approaches have been successfully applied to an analysis of patient symptom data in the context of disease diagnosis, at least where such data is well codified. However, much of the data present in Electronic Health Records (EHR) are unlikely to prove suitable for classic ML approaches. Furthermore, as scores of data are widely spread across both hospitals and individuals, a decentralized, computationally scalable methodology is a priority. The focus of this paper is to develop a method to predict outliers in an out-of-hours healthcare provision center (OOHC). In particular, our research is based upon the early identification of patients who have underlying conditions which will cause them to repeatedly require medical attention. OOHC act as an ad-hoc delivery of triage and treatment, where interactions occur without recourse to a full medical history of the patient in question. Medical histories, relating to patients contacting an OOHC, may reside in several distinct EHR systems in multiple hospitals or surgeries, which are unavailable to the OOHC in question. As such, although a local solution is optimal for this problem, it follows that the data under investigation is incomplete, heterogeneous, and comprised mostly of noisy textual notes compiled during routine OOHC activities. Through the use of Deep Learning methodologies, the aim of this paper is to provide the means to identify patient cases, upon initial contact, which are likely to relate to such outliers. To this end, we compare the performance of Long Short-Term Memory, Gated Recurrent Units, and combinations of both with Convolutional Neural Networks. A further aim of this paper is to elucidate the discovery of such outliers by examining the exact terms which provide a strong indication of positive and negative case entries. While free-text is the principal data extracted from EHRs for classification, EHRs also contain normalized features. Although the specific demographical features treated within our corpus are relatively limited in scope, we examine whether it is beneficial to include such features among the inputs to our neural network, or whether these features are more successfully exploited in conjunction with a different form of a classifier. In this section, we compare the performance of randomly generated regression trees and support vector machines and determine the extent to which our classification program can be improved upon by using either of these machine learning approaches in conjunction with the output of our Recurrent Neural Network application. The output of our neural network is also used to help determine the most significant lexemes present within the corpus for determining high-risk patients. By combining the confidence of our classification program in relation to lexemes within true positive and true negative cases, with an inverse document frequency of the lexemes related to these cases, we can determine what features act as the primary indicators of frequent-attender and non-frequent-attender cases, providing a human interpretable appreciation of how our program classifies cases.

Keywords: artificial neural networks, data-mining, machine learning, medical informatics

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Authors: A. Chávez, A. Rodríguez, F. Pinzón

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Society is concerned about measures of environmental, economic and social impacts generated in the solid waste disposal. These places of confinement, also known as landfills, are locations where problems of pollution and damage to human health are reduced. They are technically designed and operated, using engineering principles, storing the residue in a small area, compact it to reduce volume and covering them with soil layers. Problems preventing liquid (leachate) and gases produced by the decomposition of organic matter. Despite planning and site selection for disposal, monitoring and control of selected processes, remains the dilemma of the leachate as extreme concentration of pollutants, devastating soil, flora and fauna; aggressive processes requiring priority attention. A biological technology is the activated sludge system, used for tributaries with high pollutant loads. Since transforms biodegradable dissolved and particulate matter into CO2, H2O and sludge; transform suspended and no Settleable solids; change nutrients as nitrogen and phosphorous; and degrades heavy metals. The microorganisms that remove organic matter in the processes are in generally facultative heterotrophic bacteria, forming heterogeneous populations. Is possible to find unicellular fungi, algae, protozoa and rotifers, that process the organic carbon source and oxygen, as well as the nitrogen and phosphorus because are vital for cell synthesis. The mixture of the substrate, in this case sludge leachate, molasses and wastewater is maintained ventilated by mechanical aeration diffusers. Considering as the biological processes work to remove dissolved material (< 45 microns), generating biomass, easily obtained by decantation processes. The design consists of an artificial support and aeration pumps, favoring develop microorganisms (denitrifying) using oxygen (O) with nitrate, resulting in nitrogen (N) in the gas phase. Thus, avoiding negative effects of the presence of ammonia or phosphorus. Overall the activated sludge system includes about 8 hours of hydraulic retention time, which does not prevent the demand for nitrification, which occurs on average in a value of MLSS 3,000 mg/L. The extended aeration works with times greater than 24 hours detention; with ratio of organic load/biomass inventory under 0.1; and average stay time (sludge age) more than 8 days. This project developed a pilot system with sludge leachate from Doña Juana landfill - RSDJ –, located in Bogota, Colombia, where they will be subjected to a process of activated sludge and extended aeration through a sequential Bach reactor - SBR, to be dump in hydric sources, avoiding ecological collapse. The system worked with a dwell time of 8 days, 30 L capacity, mainly by removing values of BOD and COD above 90%, with initial data of 1720 mg/L and 6500 mg/L respectively. Motivating the deliberate nitrification is expected to be possible commercial use diffused aeration systems for sludge leachate from landfills.

Keywords: sludge, landfill, leachate, SBR

Procedia PDF Downloads 266

Authors: D. Malouch, M. Berchel, C. Dreanno, S. Stachowski-Haberkorn, P-A. Jaffres

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Biofouling is a complex natural phenomenon that involves biological, physical and chemical properties related to the environment, the submerged surface and the living organisms involved. Bio-colonization of artificial structures can cause various economic and environmental impacts. The increase in costs associated with the over-consumption of fuel from biocolonized vessels has been widely studied. Measurement drifts from submerged sensors, as well as obstructions in heat exchangers, and deterioration of offshore structures are major difficulties that industries are dealing with. Therefore, surfaces that inhibit biocolonization are required in different areas (water treatment, marine paints, etc.) and many efforts have been devoted to produce efficient and eco-compatible antifouling agents. The different steps of surface fouling are widely described in literature. Studying the biofilm and its stages provides a better understanding of how to elaborate more efficient antifouling strategies. Several approaches are currently applied, such as the use of biocide anti-fouling paint (mainly with copper derivatives) and super-hydrophobic coatings. While these two processes are proving to be the most effective, they are not entirely satisfactory, especially in a context of a changing legislation. Nowadays, the challenge is to prevent biofouling with non-biocide compounds, offering a cost effective solution, but with no toxic effects on marine organisms. Since the micro-fouling phase plays an important role in the regulation of the following steps of biofilm formation, it is desired to reduce or delate biofouling of a given surface by inhibiting the micro-fouling at its early stages. In our recent works, we reported that some amphiphilic compounds exhibited bacteriostatic or bactericidal properties at a concentration that did not affect mammalian eukaryotic cells. These remarkable properties invited us to assess this type of bio-inspired phospholipids to prevent the colonization of surfaces by marine bacteria. Of note, other studies reported that amphiphilic compounds interacted with bacteria leading to a reduction of their development. An amphiphilic compound is a molecule consisting of a hydrophobic domain and a polar head (ionic or non-ionic). These compounds appear to have interesting antifouling properties: some ionic compounds have shown antimicrobial activity, and zwitterions can reduce nonspecific adsorption of proteins. Herein, we investigate the potential of amphiphilic compounds as inhibitors of bacterial growth and marine biofilm formation. The aim of this study is to compare the efficacy of four synthetic phospholipids that features a cationic charge or a zwitterionic polar-head group to prevent microfouling with marine bacteria. Toxicity of these compounds was also studied in order to identify the most promising compounds that inhibit biofilm development and show low cytotoxicity on two links representative of coastal marine food webs: phytoplankton and oyster larvae.

Keywords: amphiphilic phospholipids, biofilm, marine fouling, non-toxique assays

Procedia PDF Downloads 131

Authors: Till Gramberg, Erwin Gross, Christoph Birenbaum

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This paper presents the approach of the Easy2IoT research project. Easy2IoT aims to enable companies in the prefabrication sheet metal and sheet metal processing industry to enter the Industrial Internet of Things (IIoT) with a low-threshold and cost-effective approach. It focuses on the development of physical hardware and software to easily capture machine activities from on a sawing machine, benefiting various stakeholders in the SME value chain, including machine operators, tool manufacturers and service providers. The methodological approach of Easy2IoT includes an in-depth requirements analysis and customer interviews with stakeholders along the value chain. Based on these insights, actions, requirements and potential solutions for smart services are derived. The focus is on providing actionable recommendations, competencies and easy integration through no-/low-code applications to facilitate implementation and connectivity within production networks. At the core of the project is a novel, non-invasive measurement and analysis system that can be easily deployed and made IIoT-ready. This system collects machine data without interfering with the machines themselves. It does this by non-invasively measuring the tension on a sawing machine. The collected data is then connected and analyzed using artificial intelligence (AI) to provide smart services through a platform-based application. Three Smart Services are being developed within Easy2IoT to provide immediate benefits to users: Wear part and product material condition monitoring and predictive maintenance for sawing processes. The non-invasive measurement system enables the monitoring of tool wear, such as saw blades, and the quality of consumables and materials. Service providers and machine operators can use this data to optimize maintenance and reduce downtime and material waste. Optimize Overall Equipment Effectiveness (OEE) by monitoring machine activity. The non-invasive system tracks machining times, setup times and downtime to identify opportunities for OEE improvement and reduce unplanned machine downtime. Estimate CO2 emissions for connected machines. CO2 emissions are calculated for the entire life of the machine and for individual production steps based on captured power consumption data. This information supports energy management and product development decisions. The key to Easy2IoT is its modular and easy-to-use design. The non-invasive measurement system is universally applicable and does not require specialized knowledge to install. The platform application allows easy integration of various smart services and provides a self-service portal for activation and management. Innovative business models will also be developed to promote the sustainable use of the collected machine activity data. The project addresses the digitalization gap between large enterprises and SME. Easy2IoT provides SME with a concrete toolkit for IIoT adoption, facilitating the digital transformation of smaller companies, e.g. through retrofitting of existing machines.

Keywords: smart services, IIoT, IIoT-platform, industrie 4.0, big data

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Authors: Orit Wolf

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Classical music performance is undergoing a profound transformation, marked by a confluence of technological advancements and evolving cultural dynamics. This academic paper explores the multifaceted changes and challenges faced by classical music performance, considering the impact of artificial intelligence (AI) along with other vital factors shaping this evolution. In the contemporary era, classical music is experiencing shifts in performance practices. This paper delves into these changes, emphasizing the need for adaptability within the classical music world. From repertoire selection and concert formats to artistic expression, performers and institutions navigate a delicate balance between tradition and innovation. We explore how these changes impact the authenticity and vitality of classical music performances. Furthermore, the influence of AI in the classical music concert world cannot be underestimated. AI technologies are making inroads into various aspects, from composition assistance to rehearsal and live performances. This paper examines the transformative effects of AI, considering how it enhances precision, adaptability, and creative exploration for musicians. We explore the implications for composers, performers, and the overall concert experience while addressing ethical concerns and creative opportunities. In addition to AI, there is the importance of cross-genre interactions within the classical music sphere. Mash-ups and collaborations with artists from diverse musical backgrounds are redefining the boundaries of classical music and creating works that resonate with a wider and more diverse audience. The benefits of cross-pollination in classical music seem crucial, offering a fresh perspective to listeners. As an active concert artist, Orit Wolf will share how the expectations of classical music audiences are evolving. Modern concertgoers seek not only exceptional musical performances but also immersive experiences that may involve technology, multimedia, and interactive elements. This paper examines how classical musicians and institutions are adapting to these changing expectations, using technology and innovative concert formats to deliver a unique and enriched experience to their audiences. As these changes and challenges reshape the classical music world, the need for a harmonious coexistence of tradition, technology, and innovation becomes evident. Musicians, composers, and institutions are striving to find a balance that ensures classical music remains relevant in a rapidly changing cultural landscape while maintaining the value it brings to compositions and audiences. This paper, therefore, aims to explore the evolving trends in classical music performance. It considers the influence of AI as one element within the broader context of change, highlighting the necessity of adaptability, cross-genre interactions, and a response to evolving audience expectations. By doing so, the classical music world can navigate this transformative period while preserving its timeless traditions and adding value to both performers and listeners. Orit Wolf, an international concert pianist, fulfils her vision to bring this music in new ways to mass audiences and will share her personal and professional experience as an artist who goes on stage and makes disruptive concerts.

Keywords: cross culture collaboration, music performance and ai, classical music in the digital age, classical concerts, innovation and technology, performance innovation, audience engagement in classical concerts

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Authors: Catherine McWhorter

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Robotics is a fast-evolving field lacking comprehensive and harm-mitigating regulation; it also lacks critical data on how human-robot interaction (HRI) may affect human psychology. As most anthropomorphic robots are intended as substitutes for humans, this paper asserts that the commercial robotics industry should be preemptively regulated at the federal level such that robots capable of embodying a victim role in criminal scenarios (“vicbots”) are prohibited until clinical studies determine their effects on the user and society. The results of these studies should then inform more permanent legislation that strives to mitigate risks of harm without infringing upon fundamental rights or stifling innovation. This paper explores these concepts through the lens of the sex robot industry. The sexbot industry offers some of the most realistic, interactive, and customizable robots for sale today. From approximately 2010 until 2017, some sex robot producers, such as True Companion, actively promoted ‘vicbot’ culture with personalities like “Frigid Farrah” and “Young Yoko” but received significant public backlash for fetishizing rape and pedophilia. Today, “Frigid Farrah” and “Young Yoko” appear to have vanished. Sexbot producers have replaced preprogrammed vicbot personalities in favor of one generic, customizable personality. According to the manufacturer ainidoll.com, when asked, there is only one thing the user won’t be able to program the sexbot to do – “…give you drama”. The ability to customize vicbot personas is possible with today’s generic personality sexbots and may undermine the intent of some current legislative efforts. Current debate on the effects of vicbots indicates a lack of consensus. Some scholars suggest vicbots may reduce the rate of actual sex crimes, and some suggest that vicbots will, in fact, create sex criminals, while others cite their potential for rehabilitation. Vicbots may have value in some instances when prescribed by medical professionals, but the overall uncertainty and lack of data further underscore the need for preemptive regulation and clinical research. Existing literature on exposure to media violence and its effects on prosocial behavior, human aggression, and addiction may serve as launch points for specific studies into the hyperrealism of vicbots. Of course, the customization, anthropomorphism and artificial intelligence of sexbots, and therefore more mainstream robots, will continue to evolve. The existing sexbot industry offers an opportunity to preemptively regulate and to research answers to these and many more questions before this type of technology becomes even more advanced and mainstream. Robots pose complicated moral, ethical, and legal challenges, most of which are beyond the scope of this paper. By examining the possibility for custom vicbots via the sexbots industry, reviewing existing literature on regulation, media violence, and vicbot user effects, this paper strives to underscore the need for preemptive federal regulation prohibiting vicbot capabilities in robots while advocating for further research into the potential for the user and societal harm by the same.

Keywords: human-robot interaction effects, regulation, research, robots

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Authors: Maryam Fallahpoor, Biswajeet Pradhan

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Background: Artificial intelligence (AI) serves as a valuable tool in mitigating the scarcity of human resources required for the evaluation and categorization of vast quantities of medical imaging data. When AI operates with optimal precision, it minimizes the demand for human interpretations and, thereby, reduces the burden on radiologists. Among various AI approaches, deep learning (DL) stands out as it obviates the need for feature extraction, a process that can impede classification, especially with intricate datasets. The advent of DL models has ushered in a new era in medical imaging, particularly in the context of COVID-19 detection. Traditional 2D imaging techniques exhibit limitations when applied to volumetric data, such as Computed Tomography (CT) scans. Medical images predominantly exist in one of two formats: neuroimaging informatics technology initiative (NIfTI) and digital imaging and communications in medicine (DICOM). Purpose: This study aims to employ DL for the classification of COVID-19-infected pulmonary patients and normal cases based on 3D CT scans while investigating the impact of image format. Material and Methods: The dataset used for model training and testing consisted of 1245 patients from IranMehr Hospital. All scans shared a matrix size of 512 × 512, although they exhibited varying slice numbers. Consequently, after loading the DICOM CT scans, image resampling and interpolation were performed to standardize the slice count. All images underwent cropping and resampling, resulting in uniform dimensions of 128 × 128 × 60. Resolution uniformity was achieved through resampling to 1 mm × 1 mm × 1 mm, and image intensities were confined to the range of (−1000, 400) Hounsfield units (HU). For classification purposes, positive pulmonary COVID-19 involvement was designated as 1, while normal images were assigned a value of 0. Subsequently, a U-net-based lung segmentation module was applied to obtain 3D segmented lung regions. The pre-processing stage included normalization, zero-centering, and shuffling. Four distinct 3D CNN models (ResNet152, ResNet50, DensNet169, and DensNet201) were employed in this study. Results: The findings revealed that the segmentation technique yielded superior results for DICOM images, which could be attributed to the potential loss of information during the conversion of original DICOM images to NIFTI format. Notably, ResNet152 and ResNet50 exhibited the highest accuracy at 90.0%, and the same models achieved the best F1 score at 87%. ResNet152 also secured the highest Area under the Curve (AUC) at 0.932. Regarding sensitivity and specificity, DensNet201 achieved the highest values at 93% and 96%, respectively. Conclusion: This study underscores the capacity of deep learning to classify COVID-19 pulmonary involvement using real 3D hospital data. The results underscore the significance of employing DICOM format 3D CT images alongside appropriate pre-processing techniques when training DL models for COVID-19 detection. This approach enhances the accuracy and reliability of diagnostic systems for COVID-19 detection.

Keywords: deep learning, COVID-19 detection, NIFTI format, DICOM format

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Authors: Ganesh V., Asit Kumar Khanra

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An in-situ powder metallurgy technique was employed to create Ni-Al3Ni/Al3Ni2 core-shell-shaped aluminum-based intermetallic reinforced composites. The impact of Ni addition on the phase composition, microstructure, and mechanical characteristics of the Al-4Cu-xNi (x = 0, 2, 4, 6, 8, 10 wt.%) in relation to various sintering temperatures was investigated. Microstructure evolution was extensively examined using X-ray diffraction (XRD), scanning electron microscopy with energy-dispersive X-ray spectroscopy (SEM-EDX), and transmission electron microscopy (TEM) techniques. Initially, under sintering conditions, the formation of "Single Core-Shell" structures was observed, consisting of Ni as the core with Al3Ni2 intermetallic, whereas samples sintered at 620°C exhibited both "Single Core-Shell" and "Double Core-Shell" structures containing Al3Ni2 and Al3Ni intermetallics formed between the Al matrix and Ni reinforcements. The composite achieved a high compressive yield strength of 198.13 MPa and ultimate strength of 410.68 MPa, with 24% total elongation for the sample containing 10 wt.% Ni. Additionally, there was a substantial increase in hardness, reaching 124.21 HV, which is 2.4 times higher than that of the base aluminum. Nanoindentation studies showed hardness values of 1.54, 4.65, 21.01, 13.16, 5.52, 6.27, and 8.39GPa corresponding to α-Al matrix, Ni, Al3Ni2, Ni and Al3Ni2 interface, Al3Ni, and their respective interfaces. Even at 200°C, it retained 54% of its room temperature strength (90.51 MPa). To investigate the deformation behavior of the composite material, experiments were conducted at deformation temperatures ranging from 300°C to 500°C, with strain rates varying from 0.0001s-1 to 0.1s-1. A sine-hyperbolic constitutive equation was developed to characterize the flow stress of the composite, which exhibited a significantly higher hot deformation activation energy of 231.44 kJ/mol compared to the self-diffusion of pure aluminum. The formation of Al2Cu intermetallics at grain boundaries and Al3Ni2/Al3Ni within the matrix hindered dislocation movement, leading to an increase in activation energy, which might have an adverse effect on high-temperature applications. Two models, the Strain-compensated Arrhenius model and the Artificial Neural Network (ANN) model, were developed to predict the composite's flow behavior. The ANN model outperformed the Strain-compensated Arrhenius model with a lower average absolute relative error of 2.266%, a smaller root means square error of 1.2488 MPa, and a higher correlation coefficient of 0.9997. Processing maps revealed that the optimal hot working conditions for the composite were in the temperature range of 420-500°C and strain rates between 0.0001s-1 and 0.001s-1. The changes in the composite microstructure were successfully correlated with the theory of processing maps, considering temperature and strain rate conditions. The uneven distribution in the shape and size of Core-shell/Al3Ni intermetallic compounds influenced the flow stress curves, leading to Dynamic Recrystallization (DRX), followed by partial Dynamic Recovery (DRV), and ultimately strain hardening. This composite material shows promise for applications in the automobile and aerospace industries.

Keywords: core-shell structure, hot deformation, intermetallic compounds, powder metallurgy

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Authors: Iman Farasat, Howard M. Salis

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Engineered genetic circuits reprogram cellular behavior to act as living computers with applications in detecting cancer, creating self-controlling artificial tissues, and dynamically regulating metabolic pathways. Phenemenological models are often used to simulate and design genetic circuit behavior towards a desired behavior. While such models assume that each circuit component’s function is modular and independent, even small changes in a circuit (e.g. a new promoter, a change in transcription factor expression level, or even a new media) can have significant effects on the circuit’s function. Here, we use statistical thermodynamics to account for the several factors that control transcriptional regulation in bacteria, and experimentally demonstrate the model’s accuracy across 825 measurements in several genetic contexts and hosts. We then employ our first principles model to design, experimentally construct, and characterize a family of signal amplifying genetic circuits (genetic OpAmps) that expand the dynamic range of cell sensors. To develop these models, we needed a new approach to measuring the in vivo binding free energies of transcription factors (TFs), a key ingredient of statistical thermodynamic models of gene regulation. We developed a new high-throughput assay to measure RNA polymerase and TF binding free energies, requiring the construction and characterization of only a few constructs and data analysis (Figure 1A). We experimentally verified the assay on 6 TetR-homolog repressors and a CRISPR/dCas9 guide RNA. We found that our binding free energy measurements quantitatively explains why changing TF expression levels alters circuit function. Altogether, by combining these measurements with our biophysical model of translation (the RBS Calculator) as well as other measurements (Figure 1B), our model can account for changes in TF binding sites, TF expression levels, circuit copy number, host genome size, and host growth rate (Figure 1C). Model predictions correctly accounted for how these 8 factors control a promoter’s transcription rate (Figure 1D). Using the model, we developed a design framework for engineering multi-promoter genetic circuits that greatly reduces the number of degrees of freedom (8 factors per promoter) to a single dimensionless unit. We propose the Ptashne (Pt) number to encapsulate the 8 co-dependent factors that control transcriptional regulation into a single number. Therefore, a single number controls a promoter’s output rather than these 8 co-dependent factors, and designing a genetic circuit with N promoters requires specification of only N Pt numbers. We demonstrate how to design genetic circuits in Pt number space by constructing and characterizing 15 2-repressor OpAmp circuits that act as signal amplifiers when within an optimal Pt region. We experimentally show that OpAmp circuits using different TFs and TF expression levels will only amplify the dynamic range of input signals when their corresponding Pt numbers are within the optimal region. Thus, the use of the Pt number greatly simplifies the genetic circuit design, particularly important as circuits employ more TFs to perform increasingly complex functions.

Keywords: transcription factor, synthetic biology, genetic circuit, biophysical model, binding energy measurement

Procedia PDF Downloads 472