Search results for: smart hybrid powerpack (SHP)

Authors: Svetlana Gudkova

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The Internet of Things experiences a rapid growth bringing inevitable changes into many spheres of human activities. As the Internet has changed the social and business landscape, IoT as its extension, can bring much more profound changes in economic value creation and competitiveness of the economies. It has been already recognized as the next industrial revolution. However, the development of IoT is in a great extent stimulated by the entrepreneurial activity. To expand and reach its full potential it requires proactive entrepreneurs, who explore the potential and create innovative ideas pushing the boundaries of IoT technologies' application further. The goal of the research is to analyze, how entrepreneurs utilize the opportunities created by IoT and how do they stimulate the development of IoT through discovering of new ways of generating economic value and creating opportunities, which attract other entrepreneurs. The qualitative research methods have been applied to prepare the case studies. Entrepreneurs are recognized as an engine of economic growth. They introduce innovative products and services into the market through the creation of a new combination of the existing resources and utilizing new knowledge. Entrepreneurs not only create economic value but what is more important, they challenge the existing business models and invent new ways of value creation. Through identification and exploitation of entrepreneurial opportunities, they create new opportunities for other entrepreneurs. It makes the industry more attractive to other profit/innovation-driven start-ups. IoT creates numerous opportunities for entrepreneurs in the different industries. Smart cities, healthcare, manufacturing, retail, agriculture, smart vehicles and smart buildings benefit a lot from IoT-based breakthrough innovations introduced by entrepreneurs. They reinvented successfully the business models and created new entrepreneurial opportunities for other start-ups to introduce next innovations.

Keywords: entrepreneurship, internet of things, breakthrough innovations, start-ups

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Authors: Rajesh Kumar G

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A challenging aspect of any clinical trial is to carefully plan the study design to meet the study objective in optimum way and to validate the assumptions made during protocol designing. And when it is a pediatric study, there is the added challenge of stringent guidelines and difficulty in recruiting the necessary subjects. Unlike adult trials, there is not much historical data available for pediatrics, which is required to validate assumptions for planning pediatric trials. Typically, pediatric studies are initiated as soon as approval is obtained for a drug to be marketed for adults, so with the adult study historical information and with the available pediatric pilot study data or simulated pediatric data, the pediatric study can be well planned. Generalizing the historical adult study for new pediatric study is a tedious task; however, it is possible by integrating various statistical techniques and utilizing the advantage of hybrid study design, which will help to achieve the study objective in a smoother way even with the presence of many constraints. This research paper will explain how well the hybrid study design can be planned along with integrated technique (SEV) to plan the pediatric study; In brief the SEV technique (Simulation, Estimation (using borrowed adult data and applying Bayesian methods)) incorporates the use of simulating the planned study data and getting the desired estimates to Validate the assumptions.This method of validation can be used to improve the accuracy of data analysis, ensuring that results are as valid and reliable as possible, which allow us to make informed decisions well ahead of study initiation. With professional precision, this technique based on the collected data allows to gain insight into best practices when using data from historical study and simulated data alike.

Keywords: adaptive design, simulation, borrowing data, bayesian model

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Authors: Skyler Kim

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An early diagnosis of leukemia has always been a challenge to doctors and hematologists. On a worldwide basis, it was reported that there were approximately 350,000 new cases in 2012, and diagnosing leukemia was time-consuming and inefficient because of an endemic shortage of flow cytometry equipment in current clinical practice. As the number of medical diagnosis tools increased and a large volume of high-quality data was produced, there was an urgent need for more advanced data analysis methods. One of these methods was the AI approach. This approach has become a major trend in recent years, and several research groups have been working on developing these diagnostic models. However, designing and implementing a leukemia diagnostic system in real clinical environments based on a deep learning approach with larger sets remains complex. Leukemia is a major hematological malignancy that results in mortality and morbidity throughout different ages. We decided to select acute lymphocytic leukemia to develop our diagnostic system since acute lymphocytic leukemia is the most common type of leukemia, accounting for 74% of all children diagnosed with leukemia. The results from this development work can be applied to all other types of leukemia. To develop our model, the Kaggle dataset was used, which consists of 15135 total images, 8491 of these are images of abnormal cells, and 5398 images are normal. In this paper, we design and implement a leukemia diagnostic system in a real clinical environment based on deep learning approaches with larger sets. The proposed diagnostic system has the function of detecting and classifying leukemia. Different from other AI approaches, we explore hybrid architectures to improve the current performance. First, we developed two independent convolutional neural network models: VGG19 and ResNet50. Then, using both VGG19 and ResNet50, we developed a hybrid deep learning architecture employing transfer learning techniques to extract features from each input image. In our approach, fusing the features from specific abstraction layers can be deemed as auxiliary features and lead to further improvement of the classification accuracy. In this approach, features extracted from the lower levels are combined into higher dimension feature maps to help improve the discriminative capability of intermediate features and also overcome the problem of network gradient vanishing or exploding. By comparing VGG19 and ResNet50 and the proposed hybrid model, we concluded that the hybrid model had a significant advantage in accuracy. The detailed results of each model’s performance and their pros and cons will be presented in the conference.

Keywords: acute lymphoblastic leukemia, hybrid model, leukemia diagnostic system, machine learning

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Authors: Andreas Helwig, John Bell, Wangmo

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Hybrid energy storage systems (HESS) are gaining popularity for grid energy storage (ESS) driven by the increasingly dynamic nature of energy demands, requiring both high energy and high power density. Particularly the ability of energy storage systems via inverters to respond to increasing fluctuation in energy demands, the combination of lithium Iron Phosphate (LFP) battery and supercapacitor (SC) is a particular example of complex electro-chemical devices that may provide benefit to each other for pulse width modulated DC to AC inverter application. This is due to SC’s ability to respond to instantaneous, high-current demands and batteries' long-term energy delivery. However, there is a knowledge gap on the current sharing mechanism within a HESS supplying a load powered by high-frequency pulse-width modulation (PWM) switching to understand the mechanism of aging in such HESS. This paper investigates the prediction of current utilizing various equivalent circuits for SC to investigate sharing between battery and SC in MATLAB/Simulink simulation environment. The findings predict a significant reduction of battery current when the battery is used in a hybrid combination with a supercapacitor as compared to a battery-only model. The impact of PWM inverter carrier switching frequency on current requirements was analyzed between 500Hz and 31kHz. While no clear trend emerged, models predicted optimal frequencies for minimized current needs.

Keywords: hybrid energy storage, carrier frequency, PWM switching, equivalent circuit models

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Authors: Basem Kamal Abasakhiroun Farag

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Fashion, like many other areas of design, has undergone numerous developments over the centuries. The aim of the article is to recognize and evaluate the importance of advanced technologies in fashion design and to examine how they are transforming the role of contemporary fashion designers by transforming the creative process. It also discusses how contemporary culture is involved in such developments and how it influences fashion design in terms of conceptualization and production. The methodology used is based on examining various examples of the use of technology in fashion design and drawing parallels between what was feasible then and what is feasible today. Comparison of case studies, examples of existing fashion designs and experiences with craft methods; We therefore observe patterns that help us predict the direction of future developments in this area. Discussing the technological elements in fashion design helps us understand the driving force behind the trend. The research presented in the article shows that there is a trend towards significantly increasing interest and progress in the field of fashion technology, leading to the emergence of hybrid artisanal methods. In summary, as fashion technologies advance, their role in clothing production is becoming increasingly important, extending far beyond the humble sewing machine.

Keywords: fashion, identity, such, textiles ambient intelligence, proximity sensors, shape memory materials, sound sensing garments, wearable technology bio textiles, fashion trends, nano textiles, new materials, smart textiles, techno textiles fashion design, functional aesthetics, 3D printing.

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Authors: Hiba S. Ahmed, Abbas A. Allawi, Riyadh A. Hindi

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Pultruded materials made of fiber-reinforced polymer (FRP) come in a broad range of shapes, such as bars, I-sections, C-sections, and other structural sections. These FRP materials are starting to compete with steel as structural materials because of their great resistance, low self-weight, and cheap maintenance costs-especially in corrosive conditions. This study aimed to evaluate the effectiveness of Glass Fiber Reinforced Polymer (GFRP) of the hybrid columns built by combining (GFRP) profiles with concrete columns because of their low cost and high structural efficiency. To achieve the aims of this study, nine circular columns with a diameter of (150 mm) and a height of (1000mm) were cast using normal concrete with compression strength equal to (35 MPa). The research involved three different types of reinforcement: hybrid circular columns type (IG) with GFRP I-section and 1% of the reinforcement ratio of steel bars, hybrid circular columns type (IS) with steel I-section and 1% of the reinforcement ratio of steel bars, (where the cross-section area of I-section for GFRP and steel was the same), compared with reference column (R) without I-section. To investigate the ultimate capacity, axial and lateral deformation, strain in longitudinal and transverse reinforcement, and failure mode of the circular column under different loading conditions (concentric and eccentric) with eccentricities of 25 mm and 50 mm, respectively. In the second part, an analytical finite element model will be performed using ABAQUS software to validate the experimental results.

Keywords: composite, columns, reinforced concrete, GFRP, axial load

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Authors: Dechrit Maneetham

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The aim of this paper was to design a smart electric power wheelchair (SEPW) with a novel control system for quadriplegics with head and neck mobility. Head movement has been used as a control interface for people with motor impairments in a range of applications. Acquiring measurements from the module is simplified through a synchronous a motor. Axis measures the two directions namely X ,Y and Z. The model of a DC motor is considered as a speed control by selection of a PID parameters using genetic algorithm. An experimental set-up constructed, which consists of micro controller Arduino ATmega32u4 as controllers, a DC motor driven SEPW and feedback elements. And this paper is tuning methods of parameter for a pulse width modulation (PWM) control system. A speed controller has been designed successfully for closed loop of the DC motor so that the motor runs very closed to the reference speed and angle. SEPW controller can be used to ensure the person’s head is attending the direction of travel asserted by a conventional, direction and speed control.

Keywords: wheelchair, quadriplegia, rehabilitation, medical devices, speed control

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Authors: Muhammad Bilal Qadir, Danish Umer, Amir Shahzad

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The use of conductive materials in smart and interactive textiles is gaining significant importance for creating value addition, innovation, and functional product development. These products find their potential applications in health monitoring, military, protection, communication, sensing, monitoring, actuation, fashion, and lifestyles. The materials which are most commonly employed in such type of interactive textile include intrinsically conducting polymers, conductive inks, and metallic coating on textile fabrics and inherently conducting metallic fibre yarns. In this study, silver coated polyester filament yarn is explored for the development of multifunctional interactive gloves. The composite yarn was developed by covering the silver coated polyester filament around the polyester spun yarn using hollow spindle technique. The electrical and tensile properties of the yarn were studied. This novel yarn was used to manufacture a smart glove to explore the antibacterial, functional, and interactive properties of the yarn. The change in electrical resistance due to finger movement at different bending positions and antimicrobial properties were studied. This glove was also found useful as an interactive tool to operate the commonly used touch screen devices due to its conductive nature. The yarn can also be used to develop the sensing elements like stretch, strain, and piezoresistive sensors. Such sensor can be effectively used in medical and sports textile for performance monitoring, vital signs monitoring and development of antibacterial textile for healthcare and hygiene.

Keywords: conductive yarn, interactive textiles, piezoresistive sensors, smart gloves

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Authors: Hilal Naimi, Amelbahahouda Adamou-Mitiche, Lahcene Mitiche

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The main problem in the area of medical imaging has been image denoising. The most defying for image denoising is to secure data carrying structures like surfaces and edges in order to achieve good visual quality. Different algorithms with different denoising performances have been proposed in previous decades. More recently, models focused on deep learning have shown a great promise to outperform all traditional approaches. However, these techniques are limited to the necessity of large sample size training and high computational costs. This research proposes a denoising approach basing on LDTCWT (Lifting Dual Tree Complex Wavelet Transform) using Hybrid Thresholding with Wiener filter to enhance the quality image. This research describes the LDTCWT as a type of lifting wavelets remodeling that produce complex coefficients by employing a dual tree of lifting wavelets filters to get its real part and imaginary part. Permits the remodel to produce approximate shift invariance, directionally selective filters and reduces the computation time (properties lacking within the classical wavelets transform). To develop this approach, a hybrid thresholding function is modeled by integrating the Wiener filter into the thresholding function.

Keywords: lifting wavelet transform, image denoising, dual tree complex wavelet transform, wavelet shrinkage, wiener filter

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Authors: Y. Kourd, D. Lefebvre

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The monitoring of industrial processes is required to ensure operating conditions of industrial systems through automatic detection and isolation of faults. This paper proposes a method of fault diagnosis based on a neuro-fuzzy hybrid structure. This hybrid structure combines the selection of threshold and decision tree. The validation of this method is obtained with the DAMADICS benchmark. In the first phase of the method, a model will be constructed that represents the normal state of the system to fault detection. Signatures of the faults are obtained with residuals analysis and selection of appropriate thresholds. These signatures provide groups of non-separable faults. In the second phase, we build faulty models to see the flaws in the system that cannot be isolated in the first phase. In the latest phase we construct the tree that isolates these faults.

Keywords: decision tree, residuals analysis, ANFIS, fault diagnosis

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Authors: Eric A. Morris, Xia Liu, Yee Ka Wong, Greg J. Evans, Jeff R. Brook

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Air quality monitoring stations tend to be widely distributed and are often located far from major roadways, thus, determining where, when, and which traffic-related air pollutants (TRAPs) have the greatest impact on public health becomes a matter of extrapolation. Compact, multipollutant sensor systems are an effective solution as they enable several TRAPs to be monitored in a geospatially dense network, thus filling in the gaps between conventional monitoring stations. This work describes two applications of one such system named AirSENCE for gathering actionable air quality data relevant to smart city infrastructures. In the first application, four AirSENCE devices were co-located with traffic monitors around the perimeter of a city block in Oshawa, Ontario. This study, which coincided with the COVID-19 outbreak of 2020 and subsequent lockdown measures, demonstrated a direct relationship between decreased traffic volumes and TRAP concentrations. Conversely, road construction was observed to cause elevated TRAP levels while reducing traffic volumes, illustrating that conventional smart city sensors such as traffic counters provide inadequate data for inferring air quality conditions. The second application used two AirSENCE sensors on opposite sides of a major 2-way commuter road in Toronto. Clear correlations of TRAP concentrations with wind direction were observed, which shows that impacted areas are not necessarily static and may exhibit high day-to-day variability in air quality conditions despite consistent traffic volumes. Both of these applications provide compelling evidence favouring the inclusion of air quality sensors in current and future smart city infrastructure planning. Such sensors provide direct measurements that are useful for public health alerting as well as decision-making for projects involving traffic mitigation, heavy construction, and urban renewal efforts.

Keywords: distributed sensor network, continuous ambient air quality monitoring, Smart city sensors, Internet of Things, traffic-related air pollutants

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Authors: W. Hasan, H. Farhat, A. Alhilo, L. Tamimi

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Oil in water (O/W) emulsions are utilized extensively for cooling and lubricating cutting tools during parts machining. A robust Lattice Boltzmann (LBM) thermal-surfactants model, which provides a useful platform for exploring complex emulsions’ characteristics under variety of flow conditions, is used here for the study of the fluid behavior during conventional tools cooling. The transient thermal capabilities of the model are employed for simulating the effects of the flow conditions of O/W emulsions on the cooling of cutting tools. The model results show that the temperature outcome is slightly affected by reversing the direction of upper plate (workpiece). On the other hand, an important increase in effective viscosity is seen which supports better lubrication during the work.

Keywords: hybrid lattice Boltzmann method, Gunstensen model, thermal, surfactant-covered droplet, Marangoni stress

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Authors: Abobaker Mohammed Qasem Farhan, Shangming Yang, Mohammed Al-Nehari

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Pneumonia is a kind of lung disease that creates congestion in the chest. Such pneumonic conditions lead to loss of life of the severity of high congestion. Pneumonic lung disease is caused by viral pneumonia, bacterial pneumonia, or Covidi-19 induced pneumonia. The early prediction and classification of such lung diseases help to reduce the mortality rate. We propose the automatic Computer-Aided Diagnosis (CAD) system in this paper using the deep learning approach. The proposed CAD system takes input from raw computerized tomography (CT) scans of the patient's chest and automatically predicts disease classification. We designed the Hybrid Deep Learning Algorithm (HDLA) to improve accuracy and reduce processing requirements. The raw CT scans have pre-processed first to enhance their quality for further analysis. We then applied a hybrid model that consists of automatic feature extraction and classification. We propose the robust 2D Convolutional Neural Network (CNN) model to extract the automatic features from the pre-processed CT image. This CNN model assures feature learning with extremely effective 1D feature extraction for each input CT image. The outcome of the 2D CNN model is then normalized using the Min-Max technique. The second step of the proposed hybrid model is related to training and classification using different classifiers. The simulation outcomes using the publically available dataset prove the robustness and efficiency of the proposed model compared to state-of-art algorithms.

Keywords: CT scan, Covid-19, deep learning, image processing, lung disease classification

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Authors: Aydin Teymourifar, Gurkan Ozturk

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In this paper, a distributed hybrid algorithm is proposed for solving the job shop scheduling problem. The suggested method executes different artificial neural networks, heuristics and meta-heuristics simultaneously on more than one machine. The neural networks are used to control the constraints of the problem while the meta-heuristics search the global space and the heuristics are used to prevent the premature convergence. To attain an efficient distributed intelligent method for solving big and distributed job shop scheduling problems, Apache Spark and Hadoop frameworks are used. In the algorithm implementation and design steps, new approaches are applied. Comparison between the proposed algorithm and other efficient algorithms from the literature shows its efficiency, which is able to solve large size problems in short time.

Keywords: distributed algorithms, Apache Spark, Hadoop, job shop scheduling, neural network

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Authors: Jalal Maqbool, Gyu Myoung Lee

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Using smart devices for context aware mobile learning is becoming increasingly popular. This has led to mobile learning technology becoming an indispensable part of today’s learning environment and platforms. However, some fundamental issues remain - namely, mobile learning still lacks the ability to truly understand human reaction and user behaviour. This is due to the fact that current mobile learning systems are passive and not aware of learners’ changing contextual situations. They rely on static information about mobile learners. In addition, current mobile learning platforms lack the capability to incorporate dynamic contextual situations into learners’ preferences. Thus, this thesis aims to address these issues highlighted by designing a context aware framework which is able to sense learner’s contextual situations, handle data dynamically, and which can use contextual information to suggest bespoke learning content according to a learner’s preferences. This is to be underpinned by a robust recommendation system, which has the capability to perform these functions, thus providing learners with a truly context-aware mobile learning experience, delivering learning contents using smart devices and adapting to learning preferences as and when it is required. In addition, part of designing an algorithm for the recommendation engine has to be based on learner and application needs, personal characteristics and circumstances, as well as being able to comprehend human cognitive processes which would enable the technology to interact effectively and deliver mobile learning content which is relevant, according to the learner’s contextual situations. The concept of this proposed project is to provide a new method of smart learning, based on a capable recommendation engine for providing an intuitive mobile learning model based on learner actions.

Keywords: aware, context, learning, mobile

Procedia PDF Downloads 241

Authors: A. Q. Sobia, M. S. Hamidah, I. Azmi, S. F. A. Rafeeqi

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Fibre-reinforced polymer (FRP) strengthened reinforced concrete (RC) structures are susceptible to intense deterioration when exposed to elevated temperatures, particularly in the incident of fire. FRP has the tendency to lose bond with the substrate due to the low glass transition temperature of epoxy; the key component of FRP matrix.  In the past few decades, various types of high performance cementitious composites (HPCC) were explored for the protection of RC structural members against elevated temperature. However, there is an inadequate information on the influence of elevated temperature on the ultra high performance fibre-reinforced cementitious composites (UHPFRCC) containing ground granulated blast furnace slag (GGBS) as a replacement of high alumina cement (HAC) in conjunction with hybrid fibres (basalt and polypropylene fibres), which could be a prospective fire resisting material for the structural components. The influence of elevated temperatures on the compressive as well as flexural strength of UHPFRCC, made of HAC-GGBS and hybrid fibres, were examined in this study. Besides control sample (without fibres), three other samples, containing 0.5%, 1% and 1.5% of basalt fibres by total weight of mix and 1 kg/m³ of polypropylene fibres, were prepared and tested. Another mix was also prepared with only 1 kg/m³ of polypropylene fibres. Each of the samples were retained at ambient temperature as well as exposed to 400, 700 and 1000 °C followed by testing after 28 and 56 days of conventional curing. Investigation of results disclosed that the use of hybrid fibres significantly helped to improve the ambient temperature compressive and flexural strength of UHPFRCC, which was found to be 80 and 14.3 MPa respectively. However, the optimum residual compressive strength was marked by UHPFRCC-CP (with polypropylene fibres only), equally after both curing days (28 and 56 days), i.e. 41%. In addition, the utmost residual flexural strength, after 28 and 56 days of curing, was marked by UHPFRCC– CP and UHPFRCC– CB2 (1 kg/m³ of PP fibres + 1% of basalt fibres) i.e. 39% and 48.5% respectively.

Keywords: fibre reinforced polymer materials (FRP), ground granulated blast furnace slag (GGBS), high-alumina cement, hybrid, fibres

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Authors: Punit Gandhi, J. C. Brezet, Tim Gorter, Uchechi Obinna

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This paper introduces how weather forecasting could help in more efficient energy management for smart homes with the use of Home Energy Management Systems (HEMS). The paper also focuses on educating consumers and helping them make more informed decisions while using the HEMS. A combined approach of technical and user perspective has been selected to develop a novel HEMS-product-service combination in a more comprehensive manner. The current HEMS switches on/off the energy intensive appliances based on the fluctuating electricity tariffs, but with weather forecasting, it is possible to shift the time of use of energy intensive appliances to maximum electricity production from the renewable energy system installed in the house. Also, it is possible to estimate the heating/cooling load of the house for the day ahead demand. Hence, relevant insight is gained in the expected energy production and consumption load for the next day, facilitating better (more efficient, peak shaved, cheaper, etc.) energy management practices for smart homes. In literature, on the user perspective, it has been observed that consumers lose interest in using HEMS after three to four months. Therefore, to further help in better energy management practices, the new system had to be designed in a way that consumers would sustain their interaction with the system on a structural basis. It is hypothesized that, if consumers feel more comfortable with using such system, it would lead to a prolonged usage, including more energy savings and hence financial savings. To test the hypothesis, a survey for the HEMS is conducted, to which 59 valid responses were recorded. Analysis of the survey helped in designing a system which imparts better information about the energy production and consumption to the consumers. It is also found from the survey that, consumers like a variety of options and they do not like a constant reminder of what they should do. Hence, the final system is designed to encourage consumers to make an informed decision about their energy usage with a wide variety of behavioral options available. It is envisaged that the new system will be tested in several pioneering smart energy grid projects in both the Netherlands and India, with a continued ‘design thinking’ approach, combining the technical and user perspective, as the basis for further improvements.

Keywords: weather forecasting, smart grid, renewable energy forecasting, user defined HEMS

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Authors: M. R. Vijayakumar, K. Priyanka, Ramoji Kosuru, Lakshmi, Sanjay Singh

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Trans resveratrol (RES) is a non-flavonoid poly-phenolic compound proved for its therapeutic and preventive effect against various types of cancer. However, the practical application of RES in cancer treatment is limited because of its higher dose (up to 7.5 g/day in humans), low biological half life, rapid metabolism and faster elimination in mammals. PEGylated core-shell type lipid polymer hybrid nanoparticles are the novel drug delivery systems for long circulation and improved anti cancer effect of its therapeutic payloads. Therefore, the main objective of this study is to extend the biological half life (long circulation) and improve the therapeutic efficacy of RES through core shell type of nanoparticles. D-α-tocopheryl polyethylene glycol 1000 succinate (vitamin E TPGS), a novel surfactant is applied for the preparation of PEGylated lipid polymer hybrid nanoparticles. The prepared nanoparticles were evaluated by various state of the art techniques such as dynamic light scattering (DLS) technique for particle size and zeta potential, TEM for shape, differential scanning calorimetry (DSC) for interaction analysis and XRD for crystalline changes of drug. Entrapment efficiency and invitro drug release were determined by ultracentrifugation method and dialysis bag method, respectively. Cancer cell viability studies were performed by MTT assay, respectively. Pharmacokinetic studies after i.v administration were performed in sprague dawley rats. The prepared NPs were found to be spherical in shape with smooth surfaces. Particle size and zeta potential of prepared NPs were found to be in the range of 179.2±7.45 to 266.8±9.61 nm and -0.63 to -48.35 mV, respectively. DSC revealed absence of potential interaction. XRD study revealed presence of amorphous form in nanoparticles. Entrapment efficiency was found to be 83.7 % and drug release was found to be in controlled manner. MTT assay showed low MEC and pharmacokinetic studies showed higher AUC of nanoformulaition than its pristine drug. All these studies revealed that the RES loaded PEG modified core-shell type lipid polymer hybrid nanoparticles can be an alternative tool for chemopreventive and therapeutic application of RES in cancer.

Keywords: trans resveratrol, cancer nanotechnology, long circulating nanoparticles, bioavailability enhancement, core shell nanoparticles, lipid polymer hybrid nanoparticles

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Authors: Gautam, Gurcharan Singh, Mandeep Kaur, Yogesh Aggarwal, Sanjeev Naval

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The present ground reality scenario of suffering of humanity shows the evidence of failure to take wrong decisions to shape the civilization with Irresponsibilities in the history. A strong positive will of right responsibilities make the right civilization structure which affects itself and the whole world. Present suffering of humanity shows and reflect the failure of past decisions taken to shape the true culture with right social structure of society, due to unplanned system of Indian civilization and its rapid disaster of population make the failure to face all kind of problems which make the society sufferer. Our India is still suffering from disaster like earthquake, floods, droughts, tsunamis etc. and we face the uncountable disaster of deaths from the beginning of humanity at the present time. In this research paper our focus is to make a Disaster Resistance Structure having the solution of dense populated urban cities area by high vertical expansion HYBRID STRUCTURE. Our efforts are to analyse the Reinforced Concrete Hybrid Structure at different seismic zones, these concrete frames were analyzed using the response spectrum method to calculate and compare the different seismic displacement and drift. Seismic analysis by this method generally is based on dynamic analysis of building. Analysis results shows that the Reinforced Concrete Building at seismic Zone V having maximum peak story shear, base shear, drift and node displacement as compare to the analytical results of Reinforced Concrete Building at seismic Zone III and Zone IV. This analysis results indicating to focus on structural drawings strictly at construction site to make a HYBRID STRUCTURE. The study case is deal with the 10 story height of a vertical expansion Hybrid frame structure at different zones i.e. zone III, zone IV and zone V having the column 0.45x0.36mt and beam 0.6x0.36mt. with total height of 30mt, to make the structure more stable bracing techniques shell be applied like mage bracing and V shape bracing. If this kind of efforts or structure drawings are followed by the builders and contractors then we save the lives during earthquake disaster at Bhuj (Gujarat State, India) on 26th January, 2001 which resulted in more than 19,000 deaths. This kind of Disaster Resistance Structure having the capabilities to solve the problems of densely populated area of cities by the utilization of area in vertical expansion hybrid structure. We request to Government of India to make new plans and implementing it to save the lives from future disasters instead of unnecessary wants of development plans like Bullet Trains.

Keywords: history, irresponsibilities, unplanned social structure, humanity, hybrid structure, response spectrum analysis, DRIFT, and NODE displacement

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Authors: Bambang Heliyanto, Rully Dyah Purwati, Hasnam, Fadjry Djufry

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Jatropha curcas L., a drought tolerant and monoecious perennial shrub, has received attention worldwide during the past decade. Realizing the facts, the Indonesian government has decided to option for Jatropha and palm oil for in country biofuel production. To support the program development of high yielding jatropha varieties is necessary. This paper reviews Jatropha improvement program in Indonesia using mass selection and hybrid development. To start with, at the end of 2005, in-country germplasm collection was mobilized to Lampung and Nusa Tenggara Barat (NTB) provinces and successfully collected 15 provenances/sub-provenances which serves as a base population for selection. A significant improvement has been achieved through a simple recurrent breeding selection during 2006 to 2007. Seed yield productivity increased more than double, from 0.36 to 0.97 ton dry seed per hectare during the first selection cycle (IP-1), and then increased to 2.2 ton per hectare during the second cycles (IP-2) in Lampung provenance. Similar result was also observed in NTB provenance. Seed yield productivity increased from 0.43 ton to 1 ton dry seed per hectare in the first cycle (IP-1), and then 1.9 ton in the second cycle (IP-2). In 2008, the population IP-3 resulted from the third cycle of selection have been identified which were capable of producing 2.2 to 2.4 ton seed yield per hectare. To improve the seed yield per hectare, jatropha hybrid varieties was developed involving superior provenances. As a result a Jatropha Energy Terbarukan (JET) variety-2 was released in 2017 with seed yield potential of 2.6 ton per hectare. The use of this high yielding genotypes for biofuel is discussed.

Keywords: Jatropha curcas, provenance, biofuel, improve population, hybrid

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Authors: Patrick Santos, João Casal, João Santos Luis Varandas, Tiago Alves, Carlos Romeiro, Sérgio Lourenço

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Nowadays, technology makes it possible to, in one hand, communicate with various objects of the daily life through the Internet, and in the other, put these objects interacting with each other through this channel. Simultaneously, with the raise of smartphones as the most ubiquitous technology on persons lives, emerge new agents for these devices - Intelligent Personal Assistants. These agents have the goal of helping the user manage and organize his information as well as supporting the user in his/her day-to-day tasks. Moreover, other emergent concept is the Cloud Computing, which allows computation and storage to get out of the users devices, bringing benefits in terms of performance, security, interoperability and others. Connecting these three paradigms, in this work we propose an architecture for an intelligent system which provides an interface that assists the user on smart environments, informing, suggesting actions and allowing to manage the objects of his/her daily life.

Keywords: internet of things, cloud, intelligent personal assistant, architecture

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Authors: Anum Ali, Kashaf ad Dooja, Asif Saleem

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The future smart cities trend will be towards Internet of Things (IoT); IoT creates dynamic connections in a ubiquitous manner. Smart cities offer ease and flexibility for daily life matters. By using small devices that are connected to cloud servers based on IoT, network traffic between these devices is growing exponentially, whose security is a concerned issue, since ratio of cyber attack may make the network traffic vulnerable. This paper discusses the latest machine learning approaches in related work further to tackle the increasing rate of cyber attacks, machine learning algorithm is applied to IoT-based network traffic data. The proposed algorithm train itself on data and identify different sections of devices interaction by using supervised learning which is considered as a classifier related to a specific IoT device class. The simulation results clearly identify the attacks and produce fewer false detections.

Keywords: IoT, traffic security, deep learning, classification

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Authors: L. Krpalkova, N. O' Mahony, A. Carvalho, S. Campbell, G. Corkery, E. Broderick, J. Walsh

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Farm management and operations will drastically change due to access to real-time data, real-time forecasting, and tracking of physical items in combination with Internet of Things developments to further automate farm operations. Dairy farms have embraced technological innovations and procured vast amounts of permanent data streams during the past decade; however, the integration of this information to improve the whole farm-based management and decision-making does not exist. It is now imperative to develop a system that can collect, integrate, manage, and analyse on-farm and off-farm data in real-time for practical and relevant environmental and economic actions. The developed systems, based on machine learning and artificial intelligence, need to be connected for useful output, a better understanding of the whole farming issue, and environmental impact. Evolutionary computing can be very effective in finding the optimal combination of sets of some objects and, finally, in strategy determination. The system of the future should be able to manage the dairy farm as well as an experienced dairy farm manager with a team of the best agricultural advisors. All these changes should bring resilience and sustainability to dairy farming as well as improving and maintaining good animal welfare and the quality of dairy products. This review aims to provide an insight into the state-of-the-art of big data applications and evolutionary computing in relation to smart dairy farming and identify the most important research and development challenges to be addressed in the future. Smart dairy farming influences every area of management, and its uptake has become a continuing trend.

Keywords: big data, evolutionary computing, cloud, precision technologies

Procedia PDF Downloads 188

Authors: Muhammad Hassan, Kemal Celebi

Abstract:

Developing a highly efficient electrochromic energy storage device with sufficient color fluctuation and significant electrochemical performance is highly desirable for practical energy-saving applications. Here, to achieve a highly stable material with a large electrochemical storage capacity, a W₁₈O₄₉ NW/Ti₃C₂Tₓ composite has been fabricated and deposited on a pre-assembled Ag and W₁₈O₄₉ NW conductive network by Langmuir-Blodgett technique. The resulting hybrid electrode composed of 15 layers of W₁₈O₄₉ NW/Ti₃C₂Tₓ exhibits an areal capacitance of 125 mF/cm², with a fast and reversible switching response. An optical modulation of 98.2% can be maintained at a current density of 5 mAcm⁻². Using this electrode, we fabricated a bifunctional symmetric electrochromic supercapacitor device having an energy density of 10.26 μWh/cm² and a power density of 0.605 mW/cm², with high capacity retention and full columbic efficiency over 4000 charge-discharge cycles. Meanwhile, the device displays remarkable electrochromic characteristics, including fast switching time (5 s for coloring and 7 s for bleaching) and a significant coloration efficiency of 116 cm²/C with good optical modulation stability. In addition, the device exhibits remarkable mechanical flexibility and fast switching while being stable over 100 bending cycles, which is promising for real-world applications.

Keywords: MXene, nanowires, supercapacitor, ion diffusion, electrochromic, coloration efficiency

Procedia PDF Downloads 67

Authors: J. Tamosaitiene

Abstract:

The article focuses on the identification and classification of key risk management criteria that represent the most important sustainability aspects of the construction industry. The construction sector is one of the most important sectors in Lithuania. Nowadays, the assessment of the risk level of a construction project is especially important for the quality of construction projects, the growth of enterprises and the sector. To establish the most important criteria for successful growth of the sector, a questionnaire for experts was developed. The analytic hierarchy process (AHP), the expert judgement method and other multicriteria decision making (MCDM) methods were used to develop the hybrid model. The results were used to develop an integrated knowledge system for the measurement of a risk level particular to construction projects. The article presents a practical case that details the developed system, sustainable aspects, and risk assessment.

Keywords: risk, system, model, construction

Procedia PDF Downloads 163

Authors: C. Shalini Devi

Abstract:

This work describes the stress concentration in a rectangular specimen with a circular hole made up of hybrid composite material with the combination of glass/carbon with epoxy. The arrangements of cross ply lamina in the sequence of alternative carbon and glass, using carbon fiber in panel, gives more strength to the structure as the carbon properties are higher when compared to glass. Typical aircraft and automobile components are with cut-outs, and such cut-outs reduce the weight of the aircraft according to the weight reduction law and also they reduce the bulking load carrying capacity. Experimental investigations were carried out using three specimens as per ASTM D5766 and three specimens as per ASTM D3039 in the Universal Testing Machine. Stress concentration in the rectangular specimen with a hole is also analysed using FEA and comparing the results.

Keywords: composite, stress concentration, finite element analysis, tensile strength

Procedia PDF Downloads 444

Authors: Malika Ourabia

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A new and efficient method is presented for the analysis of arbitrarily shaped discontinuities. The discontinuities is characterized using a hybrid spectral/numerical technique. This structure presents an arbitrary number of ports, each one with different orientation and dimensions. This article presents a hybrid method based on multimode contour integral and mode matching techniques. The process is based on segmentation and dividing the structure into key building blocks. We use the multimode contour integral method to analyze the blocks including irregular shape discontinuities. Finally, the multimode scattering matrix of the whole structure can be found by cascading the blocks. Therefore, the new method is suitable for analysis of a wide range of waveguide problems. Therefore, the present approach can be applied easily to the analysis of any multiport junctions and cascade blocks. The accuracy of the method is validated comparing with results for several complex problems found in the literature. CPU times are also included to show the efficiency of the new method proposed.

Keywords: segmentation, s parameters, simulation, optimization

Procedia PDF Downloads 523

Authors: Maria Ledinskaya

Abstract:

This paper examines the Morris Collection Conservation Project at the Sainsbury Centre for Visual Arts in the context of Agile, Lean, and Hybrid project management. It is part case study and part literature review. To date, relatively little has been written about non-traditional project management approaches in heritage conservation. This paper seeks to introduce Agile, Lean, and Hybrid project management concepts from business, software development, and manufacturing fields to museum conservation, by referencing their practical application on a recent museum-based conservation project. The Morris Collection Conservation Project was carried out in 2019-2021 in Norwich, UK, and concerned the remedial conservation of around 150 Abstract Constructivist artworks bequeathed to the Sainsbury Centre for Visual Arts by private collectors Michael and Joyce Morris. The first part introduces the chronological timeline and key elements of the project. It describes a medium-size conservation project of moderate complexity, which was planned and delivered in an environment with multiple known unknowns – unresearched collection, unknown condition and materials, unconfirmed budget. The project was also impacted by the unknown unknowns of the COVID-19 pandemic, such as indeterminate lockdowns, and the need to accommodate social distancing and remote communications. The author, a staff conservator at the Sainsbury Centre who acted as project manager on the Morris Collection Conservation Project, presents an incremental, iterative, and value-based approach to managing a conservation project in an uncertain environment. Subsequent sections examine the project from the point of view of Traditional, Agile, Lean, and Hybrid project management. The author argues that most academic writing on project management in conservation has focussed on a Traditional plan-driven approach – also known as Waterfall project management – which has significant drawbacks in today’s museum environment, due to its over-reliance on prediction-based planning and its low tolerance to change. In the last 20 years, alternative Agile, Lean and Hybrid approaches to project management have been widely adopted in software development, manufacturing, and other industries, although their recognition in the museum sector has been slow. Using examples from the Morris Collection Conservation Project, the author introduces key principles and tools of Agile, Lean, and Hybrid project management and presents a series of arguments on the effectiveness of these alternative methodologies in museum conservation, as well as the ethical and practical challenges to their implementation. These project management approaches are discussed in the context of consequentialist, relativist, and utilitarian developments in contemporary conservation ethics, particularly with respect to change management, bespoke ethics, shared decision-making, and value-based cost-benefit conservation strategy. The author concludes that the Morris Collection Conservation Project had multiple Agile and Lean features which were instrumental to the successful delivery of the project. These key features are identified as distributed decision making, a co-located cross-disciplinary team, servant leadership, focus on value-added work, flexible planning done in shorter sprint cycles, light documentation, and emphasis on reducing procedural, financial, and logistical waste. Overall, the author’s findings point largely in favour of a Hybrid model which combines traditional and alternative project processes and tools to suit the specific needs of the project.

Keywords: project management, conservation, waterfall, agile, lean, hybrid

Procedia PDF Downloads 92

Authors: Fatma Keskin Arabul, Ibrahim Senol, Ahmet Yigit Arabul, Ali Rifat Boynuegri

Abstract:

On account of the concern of the fossil fuel is depleting and its negative effects on the environment, interest in alternative energy sources is increasing day by day. However, considering the importance of transportation in human life, instead of oil and its derivatives fueled vehicles with internal combustion engines, electric vehicles which are sensitive to the environment and working with electrical energy has begun to develop. In this study, simulation was carried out for providing energy management and recovering regenerative braking in fuel cell-battery hybrid electric vehicle. The main power supply of the vehicle is fuel cell on the other hand not only instantaneous power is supplied by the battery but also the energy generated due to regenerative breaking is stored in the battery. Obtained results of the simulation is analyzed and discussed.

Keywords: electric vehicles, fuel cell, battery, regenerative braking, energy management

Procedia PDF Downloads 709

Authors: Carmen M. Gonzalez-Henriquez, Mauricio A. Sarabia-Vallejos, Juan Rodriguez-Hernandez

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DMAEMA, as a monomer, has been widely studied and used in several application fields due to their pH-sensitive capacity (tertiary amine protonation), being relevant in the biomedical area as a potential carrier for drugs focused on the treatment of genetic or acquired diseases (efficient gene transfection), among others. Additionally, the inhibition of bacterial growth and, therefore, their antimicrobial activity, can be used as dual-functional antifogging/antimicrobial polymer coatings. According to their interesting physicochemical characteristics and biocompatible properties, DMAEMA was used as a monomer to synthesize a smart pH-sensitive hydrogel, namely poly(HEMA-co-PEGDA575-co-DMAEMA). Thus, different mole ratios (ranging from 5:1:0 to 0:1:5, according to the mole ratio between HEMA, PEGDA, and DEAEMA, respectively) were used in this research. The surface patterns formed via a two-step polymerization (redox- and photo-polymerization) were first chemically studied via 1H-NMR and elemental analysis. Secondly, the samples were morphologically analyzed by using Field-Emission Scanning Electron Microscopy (FE-SEM) and Atomic Force Microscopy (AFM) techniques. Then, a particular relation between HEMA, PEGDA, and DEAEMA (0:1:5) was also characterized at three different pH (5.4, 7.4 and 8.3). The hydrodynamic radius and zeta potential of the micro-hydrogel particles (emulsion) were carried out as a possible control for morphology, exploring the effect that produces hydrogel micelle dimensions in the wavelength, height, and roughness of the wrinkled patterns. Finally, contact angle and cross-hatch adhesion test was carried out for the hydrogels supported on glass using TSM-silanized surfaces in order to measure their mechanical properties.

Keywords: wrinkled patterns, smart pH-sensitive hydrogels, hydrogel micelle diameter, adhesion tests

Procedia PDF Downloads 199