Search results for: index structural equation model

Authors: James Moir

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Recent evidence suggests that academic staff face difficulties in applying new technologies as a means of assessing higher order assessment outcomes such as critical thinking, problem solving and creativity. Although higher education institutional mission statements and course unit outlines purport the value of these higher order skills there is still some question about how well academics are equipped to design curricula and, in particular, assessment strategies accordingly. Despite a rhetoric avowing the benefits of these higher order skills, it has been suggested that academics set assessment tasks up in such a way as to inadvertently lead students on the path towards lower order outcomes. This is a controversial claim, and one that this papers seeks to explore and critique in terms of challenging the conceptual basis of assessing higher order skills through new technologies. It is argued that the use of digital media in higher education is leading to a focus on students’ ability to use and manipulate of these products as an index of their flexibility and adaptability to the demands of the knowledge economy. This focus mirrors market flexibility and encourages programmes and courses of study to be rhetorically packaged as such. Curricular content has become a means to procure more or less elaborate aggregates of attributes. Higher education is now charged with producing graduates who are entrepreneurial and creative in order to drive forward economic sustainability. It is argued that critical independent learning can take place through the democratisation afforded by cultural and knowledge digitization and that assessment needs to acknowledge the changing relations between audience and author, expert and amateur, creator and consumer.

Keywords: higher education, curriculum, new technologies, assessment, higher order skills

Procedia PDF Downloads 362

Authors: Behnam Zahednejad, Saeed Kosari

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Widespread deployment of Internet of Things (IoT) has raised security and privacy issues in this environment. Designing a secure two-factor authentication scheme between the user and server is still a challenging task. In this paper, we focus on Cancelable Biometric (CB) as an authentication factor in IoT. We show that previous CB-based scheme fail to provide real two-factor security, Perfect Forward Secrecy (PFS) and suffer database attacks and traceability of the user. Then we propose our improved scheme based on CB and Physically Unclonable Functions (PUF), which can provide real two-factor security, PFS, user’s unlinkability, and resistance to database attack. In addition, Key Compromise Impersonation (KCI) resilience is achieved in our scheme. We also prove the security of our proposed scheme formally using both Real-Or-Random (RoR) model and the ProVerif analysis tool. For the usability of our scheme, we conducted a performance analysis and showed that our scheme has the least communication cost compared to the previous CB-based scheme. The computational cost of our scheme is also acceptable for the IoT environment.

Keywords: IoT, two-factor security, cancelable biometric, key compromise impersonation resilience, perfect forward secrecy, database attack, real-or-random model, ProVerif

Procedia PDF Downloads 85

Authors: E. Mendoza, D. Jackson-Maldonado, G. Avecilla-Ramírez, A. Mondaca

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Assessing language proficiency is a major issue in psycholinguistic research. There are multiple tools that measure language dominance and language proficiency in hearing bilinguals, however, this is not the case for Deaf bilinguals. Specifically, there are few, if not none, assessment tools useful in the description of the multilingual abilities of Mexican Deaf signers. Because of this, the linguistic characteristics of Mexican Deaf population have been poorly described. This paper attempts to explain the necessary changes done in order to adapt the Bilingual Language Profile (BLP) to Mexican Sign Language (LSM) and written/oral Spanish. BLP is a Self-Evaluation tool that has been adapted and translated to several oral languages, but not to sign languages. Lexical, syntactic, cultural, and structural changes were applied to the BLP. 35 Mexican Deaf signers participated in a pilot study. All of them were enrolled in Higher Education programs. BLP was presented online in written Spanish via Google Forms. No additional information in LSM was provided. Results show great heterogeneity as it is expected of Deaf populations and BLP seems to be a useful tool to create a bilingual profile of the Mexican Deaf population. This is a first attempt to adapt a widely tested tool in bilingualism research to sign language. Further modifications need to be done.

Keywords: deaf bilinguals, assessment tools, bilingual language profile, mexican sign language

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Authors: Lida Moghaddam-Banaem, Mahmood Tavoosi, Soheila Khalili

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Objective: The main objective of this study was designing a breast cancer health literacy questionnaire and assess its psychometric properties. Methods: A comprehensive literature review was performed to develop a primary questionnaire consisting of five domains. Qualitative and quantitative content validity were assessed by relevant experts, and after some modifications, the content validity index (CVI) and content validity ratio (CVR) were calculated. Qualitative and quantitative face validity were evaluated by a number of patients, and the impact score for each item was calculated. 225 women with breast cancer were asked to fill out the questionnaire and construct validity was determined by using exploratory factor analysis. The reliability was tested by Cronbach's alpha coefficient. Results: A 36-item questionnaire with five domains of reading, having access, understanding, assessing/judgment, and decision making/behavior was designed. 2 items were omitted in the qualitative content validity process. All items achieved optimum values in CVI, CVR and impact scores. Content and face validity of the questionnaire were confirmed too. According to the exploratory factor analysis, the five-factor solution accounted for 64.98 percent of the observed variance. Conclusion: Due to the obtained satisfactory validity and reliability, this tool can be used to assess health literacy in women with breast cancer. Health policy makers can use these findings for improving health-related behaviors in breast cancer patients.

Keywords: health literacy, breast cancer, questionnaire, psychometric properties

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Authors: Salma Krafessi

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The financial markets have a multifaceted, intricate environment, and enormous volumes of data are produced every day. To find investment possibilities, possible fraudulent activity, and market oddities, accurate anomaly identification in this data is essential. Conventional methods for detecting anomalies frequently fail to capture the complex organization of financial data. In order to improve the identification of abnormalities in financial time series data, this study presents Tucker Decomposition as a reliable multi-way analysis approach. We start by gathering closing prices for the S&P 500 index across a number of decades. The information is converted to a three-dimensional tensor format, which contains internal characteristics and temporal sequences in a sliding window structure. The tensor is then broken down using Tucker Decomposition into a core tensor and matching factor matrices, allowing latent patterns and relationships in the data to be captured. A possible sign of abnormalities is the reconstruction error from Tucker's Decomposition. We are able to identify large deviations that indicate unusual behavior by setting a statistical threshold. A thorough examination that contrasts the Tucker-based method with traditional anomaly detection approaches validates our methodology. The outcomes demonstrate the superiority of Tucker's Decomposition in identifying intricate and subtle abnormalities that are otherwise missed. This work opens the door for more research into multi-way data analysis approaches across a range of disciplines and emphasizes the value of tensor-based methods in financial analysis.

Keywords: tucker decomposition, financial markets, financial engineering, artificial intelligence, decomposition models

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Authors: R. M. Chandima Ratnayake, Daniel Dyakov

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Super steel materials play vital role in construction and fabrication of structural, piping and pipeline components. They enable to minimize the life cycle costs in assuring the integrity of onshore and offshore operating systems. In this context, Duplex stainless steel (DSS) material related welding on constructions and fabrications play a significant role in maintaining and assuring integrity at an optimal expenditure over the life cycle of production and process systems as well as associated structures. In DSS welding, the factors such as gap geometry, shielding gas supply rate, welding current, and type of the welding process play a vital role on the final joint performance. Hence, an experimental investigation has been performed using engineering robust design approach (ERDA) to investigate the optimal settings that generate optimal super DSS (i.e. UNS S32750) joint performance. This manuscript illustrates the mathematical approach and experimental design, optimal parameter settings and results of verification experiment.

Keywords: duplex stainless steel welding, engineering robust design, mathematical framework, optimal parameter settings

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Authors: Delphine Ribes Lemay, Valentine Bernasconi, André Andrade, Lara DéFayes, Mathieu Salzmann, FréDéRic Kaplan, Nicolas Henchoz

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Digitization and preservation of large heritage induce high maintenance costs to keep up with the technical standards and ensure sustainable access. Creating impactful usage is instrumental to justify the resources for long-term preservation. The Museum für Gestaltung of Zurich holds one of the biggest poster collections of the world from which 52’000 were digitised. In the process of building a digital installation to valorize the collection, one objective was to develop an algorithm capable of predicting the next poster to show according to the ones already displayed. The work presented here describes the steps to build an algorithm able to automatically create sequences of posters reflecting associations performed by curator and professional designers. The exposed challenge finds similarities with the domain of song playlist algorithms. Recently, artificial intelligence techniques and more specifically, deep-learning algorithms have been used to facilitate their generations. Promising results were found thanks to Recurrent Neural Networks (RNN) trained on manually generated playlist and paired with clusters of extracted features from songs. We used the same principles to create the proposed algorithm but applied to a challenging medium, posters. First, a convolutional autoencoder was trained to extract features of the posters. The 52’000 digital posters were used as a training set. Poster features were then clustered. Next, an RNN learned to predict the next cluster according to the previous ones. RNN training set was composed of poster sequences extracted from a collection of books from the Gestaltung Museum of Zurich dedicated to displaying posters. Finally, within the predicted cluster, the poster with the best proximity compared to the previous poster is selected. The mean square distance between features of posters was used to compute the proximity. To validate the predictive model, we compared sequences of 15 posters produced by our model to randomly and manually generated sequences. Manual sequences were created by a professional graphic designer. We asked 21 participants working as professional graphic designers to sort the sequences from the one with the strongest graphic line to the one with the weakest and to motivate their answer with a short description. The sequences produced by the designer were ranked first 60%, second 25% and third 15% of the time. The sequences produced by our predictive model were ranked first 25%, second 45% and third 30% of the time. The sequences produced randomly were ranked first 15%, second 29%, and third 55% of the time. Compared to designer sequences, and as reported by participants, model and random sequences lacked thematic continuity. According to the results, the proposed model is able to generate better poster sequencing compared to random sampling. Eventually, our algorithm is sometimes able to outperform a professional designer. As a next step, the proposed algorithm should include a possibility to create sequences according to a selected theme. To conclude, this work shows the potentiality of artificial intelligence techniques to learn from existing content and provide a tool to curate large sets of data, with a permanent renewal of the presented content.

Keywords: Artificial Intelligence, Digital Humanities, serendipity, design research

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Authors: Lorena Antonovici, Maria Nicoleta Turliuc

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The process of becoming a vegetarian involves changes in several life aspects, including health. Despite its relevance, however, little research has been carried out to analyze vegetarians' self-perceived health, and even less empirical attention has received in the Romanian population. This study aimed to assess health-related beliefs and practices among vegetarian adults in a Romanian sample. We have undertaken 20 semi-structured interviews (10 males, 10 females) based on a snowball sample with a mean age of 31 years. The interview guide was divided into three sections: causes of adopting the diet, general aspects (beliefs, practices, tensions, and conflicts) and consequences of adopting the diet (significant changes, positive aspects, and difficulties, physical and mental health). Additional anamnestic data were reported by means of a questionnaire. Data analyses were performed using Tropes text analysis software (v. 8.2) and SPSS software (v. 24.0.) Findings showed that most of the participants considered a vegetarian diet as a natural and healthy choice as opposed to meat-eating, which is not healthy, and its consumption should be moderated among omnivores. A higher proportion of participants (65%) had an average body mass index (BMI), and several women even assumed having certain affections that no longer occur after following a vegetarian diet. Moreover, participants admitted having better moods and mental health status, given their self-contentment with the dietary choice. Relatives were perceived as more skeptical about their practices than others, and especially women had this view. This study provides a valuable insight into health-related beliefs and practices and how a vegetarian diet might interact.

Keywords: beliefs, health, practices, vegetarians

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Authors: Diogo Ferreira Lima Filho, José Roberto Amazonas

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Nanotechnology opens the door to new paradigms that introduces a variety of novel tools enabling a plethora of potential applications in the biomedical, industrial, environmental, and military fields. This work proposes an integrated node model by applying the same concepts of TCNet to networks of nanodevices where the nodes are cooperatively interconnected with a low-complexity Mealy Machine (MM) topology integrating in the same electronic system the modules necessary for independent operation in wireless sensor networks (WSNs), consisting of Rectennas (RF to DC power converters), Code Generators based on Finite State Machine (FSM) & Trellis Decoder and On-chip Transmit/Receive with autonomy in terms of energy sources applying the Energy Harvesting technique. This approach considers the use of a Graphene Composite Substrate (GCS) for the integrated electronic circuits meeting the following characteristics: mechanical flexibility, miniaturization, and optical transparency, besides being ecological. In addition, graphene consists of a layer of carbon atoms with the configuration of a honeycomb crystal lattice, which has attracted the attention of the scientific community due to its unique Electrical Characteristics.

Keywords: composite substrate, energy harvesting, finite state machine, graphene, nanotechnology, rectennas, wireless sensor networks

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Authors: Ibrahim H. Kilic, A. B. Tugrul

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Energy is a fundamental component in economic development and energy consumption is an index of prosperity and the standard of living. The consumption of energy per capita has increased significantly over the last decades, as the standard of living has improved. Turkey’s geographical location has several advantages for extensive use of wind power. Among the renewable sources, Turkey has very high wind energy potential. Information such as installation capacity of wind power plants in installation, under construction and license stages in the country are reported in detail. Some suggestions are presented in order to increase the wind power installation capacity of Turkey. Turkey’s economic and social development has led to a massive increase in demand for electricity over the last decades. Since the Turkey has no major oil or gas reserves, it is highly dependent on energy imports and is exposed to energy insecurity in the future. But Turkey does have huge potential for renewable energy utilization. There has been a huge growth in the construction of wind power plants and small hydropower plants in recent years. To meet the growing energy demand, the Turkish Government has adopted incentives for investments in renewable energy production. Wind energy investments evaluated the impact of feed-in tariffs (FIT) based on three scenarios that are optimistic, realistic and pessimistic with APLUS software that is developed for rational evaluation for energy market. Results of the three scenarios are evaluated in the view of electricity market for Turkey.

Keywords: APLUS, energy policy, renewable energy, wind power, Turkey

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Authors: Mathias B. Michael, Esther T. Akinlabi, Tien-Chien Jen

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Thermal energy consumption in palm oil production plant comprises mainly of steam, hot water and hot air. In most efficient plants, hot water and air are generated from the steam supply system. Research has shown that thermal energy utilize in palm oil production plants is about 70 percent of the total energy consumption of the plant. In order to manage the plants’ energy efficiently, the energy systems are modelled and optimized. This paper aimed to present the model of steam supply systems of a typical palm oil production plant in Ghana. The models include exergy and energy models of steam boiler, steam turbine and the palm oil mill. The paper further simulates the virtual plant model to obtain the thermal energy performance of the plant under study. The simulation results show that, under normal operating condition, the boiler energy performance is considerably below the expected level as a result of several factors including intermittent biomass fuel supply, significant moisture content of the biomass fuel and significant heat losses. The total thermal energy performance of the virtual plant is set as a baseline. The study finally recommends number of energy efficiency measures to improve the plant’s energy performance.

Keywords: palm biomass, steam supply, exergy and energy models, energy performance benchmark

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Authors: Aragorn Joaquin Pineda Dela Cruz

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Road traffic congestion is continually deteriorating in Hong Kong. The largest contributing factor is the increase in vehicle fleet size, resulting in higher competition over the utilisation of road space. This study proposes a project that can process closed-circuit television images and videos to provide real-time traffic detection and prediction capabilities. Specifically, a deep-learning model involving computer vision techniques for video and image-based vehicle counting, then a separate model to detect and predict traffic congestion levels based on said data. State-of-the-art object detection models such as You Only Look Once and Faster Region-based Convolutional Neural Networks are tested and compared on closed-circuit television data from various major roads in Hong Kong. It is then used for training in long short-term memory networks to be able to predict traffic conditions in the near future, in an effort to provide more precise and quicker overviews of current and future traffic conditions relative to current solutions such as navigation apps.

Keywords: intelligent transportation system, vehicle detection, traffic analysis, deep learning, machine learning, computer vision, traffic prediction

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Authors: Yi Shan Cheng, Ya Hui Yeh, Hsiao Wen Hsu

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Delirium is an acute state of confusion, which is mainly the result of the interaction of many factors, including: age>65 years, comorbidity, cognitive function and visual/auditory impairment, dehydration, pain, sleep disorder, pipeline retention, general anesthesia and major surgery… etc. Researches show the prevalence of delirium in hospitalized elderly patients over 50%. If it doesn't improve in time, may cause cognitive decline or impairment, not only prolong the length of hospital stay but also increase mortality. Some studies have shown that multiple nonpharmacological interventions are the most effective and common strategies, which are reorientation, early mobility, promoting sleep and nutritional support (including water intake), could improve or prevent delirium in the hospitalized elderly. In Taiwan, only one research to compare the delirium incidence of the older patients who have received orthopedic surgery between multi-nonpharmacological interventions and general routine care. Therefore, the purpose of this study is to address the prevention or improvement of delirium incidence density in medical hospitalized elderly, provide clinical nurses as a reference for clinical implementation, and develop follow-up related research. This study is a quasi-experimental design using purposive sampling. Samples are from two wards: the geriatric ward and the general medicine ward at a medical center in central Taiwan. The sample size estimated at least 100, and then the data will be collected through a self-administered structured questionnaire, including: demographic and professional evaluation items. Case recruiting from 5/13/2023. The research results will be analyzed by SPSS for Windows 22.0 software, including descriptive statistics and inferential statistics: logistic regression、Generalized Estimating Equation(GEE)、multivariate analysis of variance(MANOVA).

Keywords: multiple nonpharmacological interventions, hospitalized elderly, delirium incidence, delirium

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Authors: Jongtae Rhee, Sangkwon Han, Seungbin Ji, Junhyeong Park, Byeonghun Kim, Taekyung Kim, Byeonghyeon Jeon, Jiwoo Yang

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The shared kitchen is a concept that transfers the value of the sharing economy to the kitchen. It is a type of kitchen equipped with cooking facilities that allows multiple companies or chefs to share time and space and use it jointly. These shared kitchens provide economic benefits and convenience, such as reduced investment costs and rent, but also increase the risk of safety management, such as cross-contamination of food ingredients. Therefore, to manage the safety of food ingredients and finished products in a shared kitchen where several entities jointly use the kitchen and handle various types of food ingredients, it is critical to manage followings: the freshness of food ingredients, user hygiene and safety and cross-contamination of cooking equipment and facilities. In this study, it propose a machine learning-based system for hygiene safety and cross-contamination management, which are highly difficult to manage. User clothing management and user access management, which are most relevant to the hygiene and safety of shared kitchens, are solved through machine learning-based methodology, and cutting board usage management, which is most relevant to cross-contamination management, is implemented as an integrated safety management system based on artificial intelligence. First, to prevent cross-contamination of food ingredients, we use images collected through a real-time camera to determine whether the food ingredients match a given cutting board based on a real-time object detection model, YOLO v7. To manage the hygiene of user clothing, we use a camera-based facial recognition model to recognize the user, and real-time object detection model to determine whether a sanitary hat and mask are worn. In addition, to manage access for users qualified to enter the shared kitchen, we utilize machine learning based signature recognition module. By comparing the pairwise distance between the contract signature and the signature at the time of entrance to the shared kitchen, access permission is determined through a pre-trained signature verification model. These machine learning-based safety management tasks are integrated into a single information system, and each result is managed in an integrated database. Through this, users are warned of safety dangers through the tablet PC installed in the shared kitchen, and managers can track the cause of the sanitary and safety accidents. As a result of system integration analysis, real-time safety management services can be continuously provided by artificial intelligence, and machine learning-based methodologies are used for integrated safety management of shared kitchens that allows dynamic contracts among various users. By solving this problem, we were able to secure the feasibility and safety of the shared kitchen business.

Keywords: artificial intelligence, food safety, information system, safety management, shared kitchen

Procedia PDF Downloads 52

Authors: Mahmoud Ahmed Farrag, Ali Zain Elabdeen Heikal, Mohamed Shawky Ahmed, Ahmed Osama Amer

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Crowdsourced data refers to data that is collected and shared by a large number of individuals or organizations, often through the use of digital technologies such as mobile devices and social media. The shortage in crash data collection in developing countries makes it difficult to fully understand and address road safety issues in these regions. In developing countries, crowdsourced data can be a valuable tool for improving road safety, particularly in urban areas where the majority of road crashes occur. This study is the first to develop safety performance functions using crowdsourced data by adopting a negative binomial structure model and Full Bayes model to investigate traffic safety for urban road networks and provide insights into the impact of roadway characteristics. Furthermore, as a part of the safety management process, network screening has been undergone through applying two different methods to rank the most hazardous road segments: PCR method (adopted in the Highway Capacity Manual HCM) as well as a graphical method using GIS tools to compare and validate. Lastly, recommendations were suggested for policymakers to ensure safer roads.

Keywords: crowdsourced data, road crashes, safety performance functions, Full Bayes models, network screening

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Authors: Taju Mohammednur, Tesfaye Megersa, Karta Kaske, Tekeste Kifle

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Coffee is economically important in Ethiopia. However, the conditions under which coffee seeds are dried and processed significantly affect the seedling quality and productivity. The objective of this study was to evaluate the effect of pre-sowing treatments and drying methods on the physiological quality of coffee seeds and seedlings. The study was conducted at the Jimma Agricultural Research Center seed laboratory and nursery site. Two coffee varieties, 74110 and 75227, were used. Fully ripe red cherries were harvested, and the seeds were dried under two conditions: a shade drying room and direct open sun. Pre-sowing seed soaking treatments of 6, 8, 18, and 24 hours, including a control, were applied. A factorial design in a Complete Randomized Design was used. The highest germination percentage (91.48%), emergence rate (90.19%), and seedling vigor index (2236.30) were recorded for seeds dried in the shade drying room. In contrast, the lowest values (70.47%, 68.36%, and 1378.22, respectively) were observed for seeds dried in direct sunlight. There was a significant difference in seed germination based on soaking time, with the highest germination percentage (83.25%) recorded for seeds soaked for 6 hours, followed by 24 hours (83.13%), while the lowest (76.88%) was recorded for un-soaked seeds. The results indicated that drying seeds under shade is better for seed quality. Moreover, further investigation into seed treatment methods and preservation techniques is necessary to improve coffee seed quality.

Keywords: coffee, seed drying, germination, seed, seed longevity, seed priming

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Authors: V. S. Manivasagam, R. Nagarajan

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Modeling the soil, water and crop growth interactions are attaining major importance, considering the future climate change and water availability for agriculture to meet the growing food demand. Progress in understanding the crop growth response during water stress period through crop modeling approach provides an opportunity for improving and sustaining the future agriculture water use efficiency. An attempt has been made to evaluate the potential use of crop modeling approach for assessing the minimal supplementary irrigation requirement for crop growth during water limited condition and its practical significance in sustainable improvement of crop yield and water productivity. Among the numerous crop models, water driven-AquaCrop model has been chosen for the present study considering the modeling approach and water stress impact on yield simulation. The study has been evaluated in rainfed maize grown area of semi-arid Shanmuganadi watershed (a tributary of the Cauvery river system) located in southern India during the rabi cropping season (October-February). In addition to actual rainfed maize growth simulation, irrigated maize scenarios were simulated for assessing the supplementary irrigation requirement during water shortage condition for the period 2012-2015. The simulation results for rainfed maize have shown that the average maize yield of 0.5-2 t ha-1 was observed during deficit monsoon season (<350 mm) whereas 5.3 t ha-1 was noticed during sufficient monsoonal period (>350 mm). Scenario results for irrigated maize simulation during deficit monsoonal period has revealed that 150-200 mm of supplementary irrigation has ensured the 5.8 t ha-1 of irrigated maize yield. Thus, study results clearly portrayed that minimal application of supplementary irrigation during the critical growth period along with the deficit rainfall has increased the crop water productivity from 1.07 to 2.59 kg m-3 for major soil types. Overall, AquaCrop is found to be very effective for the sustainable irrigation assessment considering the model simplicity and minimal inputs requirement.

Keywords: AquaCrop, crop modeling, rainfed maize, water stress

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Authors: Muhammad Zainuddin Arriafdi, Hamudah Hakimah Abdullah, Mohd Helmi Sani, Wan Azlina Ahmad, Muhd Nazrul Hisham Zainal Alam

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The biotechnology process development demands numerous experimental works. In laboratory environment, this is typically carried out using a shake flask platform. This paper presents the design and fabrication of a miniature bioreactor system as an alternative research tool for bioprocessing. The working volume of the reactor is 100 ml, and it is made of plastic. The main features of the reactor included stirring control, temperature control via the electrical heater, aeration strategy through a miniature air compressor, and online optical cell density (OD) sensing. All sensors and actuators integrated into the reactor was controlled using an Arduino microcontroller platform. In order to demonstrate the functionality of such miniature bioreactor concept, series of batch Saccharomyces cerevisiae fermentation experiments were performed under various glucose concentrations. Results attained from the fermentation experiments were utilized to solve the Monod equation constants, namely the saturation constant, Ks, and cells maximum growth rate, μmax as to further highlight the usefulness of the device. The mixing capacity of the reactor was also evaluated. It was found that the results attained from the miniature bioreactor prototype were comparable to results achieved using a shake flask. The unique features of the device as compared to shake flask platform is that the reactor mixing condition is much more comparable to a lab-scale bioreactor setup. The prototype is also integrated with an online OD sensor, and as such, no sampling was needed to monitor the progress of the reaction performed. Operating cost and medium consumption are also low and thus, making it much more economical to be utilized for biotechnology process development compared to lab-scale bioreactors.

Keywords: biotechnology, miniature bioreactor, research tools, Saccharomyces cerevisiae

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Authors: C. Bolakis, I. S. Karanasiou, D. Grbovic, G. Karunasiri, N. Uzunoglu

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A new approach for efficient detection of THz radiation in biomedical imaging applications is proposed. A double-layered absorber consisting of a 32 nm thick aluminum (Al) metallic layer, located on a glass medium (SiO2) of 1 mm thickness, was fabricated and used to design a fine-tuned absorber through a theoretical and finite element modeling process. The results indicate that the proposed low-cost, double-layered absorber can be tuned based on the metal layer sheet resistance and the thickness of various glass media taking advantage of the diversity of the absorption of the metal films in the desired THz domain (6 to 10 THz). It was found that the composite absorber could absorb up to 86% (a percentage exceeding the 50%, previously shown to be the highest achievable when using single thin metal layer) and reflect less than 1% of the incident THz power. This approach will enable monitoring of the transmission coefficient (THz transmission ‘’fingerprint’’) of the biosample with high accuracy, while also making the proposed double-layered absorber a good candidate for a microbolometer pixel’s active element. Based on the aforementioned promising results, a more sophisticated and effective double-layered absorber is under development. The glass medium has been substituted by diluted poly-si and the results were twofold: An absorption factor of 96% was reached and high TCR properties acquired. In addition, a generalization of these results and properties over the active frequency spectrum was achieved. Specifically, through the development of a theoretical equation having as input any arbitrary frequency in the IR spectrum (0.3 to 405.4 THz) and as output the appropriate thickness of the poly-si medium, the double-layered absorber retains the ability to absorb the 96% and reflects less than 1% of the incident power. As a result, through that post-optimization process and the spread spectrum frequency adjustment, the microbolometer detector efficiency could be further improved.

Keywords: bio-imaging, fine-tuned absorber, fingerprint, microbolometer

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Authors: Hannah Rosenqvist, Fanny Guay

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Critical infrastructure protection has been an important topic for several years. Programmes such as the European Programme for Critical Infrastructure Protection (EPCIP), Critical Infrastructure Warning Information Network (CIWIN) and the European Reference Network for Critical Infrastructure Protection (ENR-CIP) have been the pillars to the work done since 2006. However, measuring critical infrastructure resilience has not been an easy task. This has to do with the fact that the concept of resilience has several definitions and is applied in different domains such as engineering and social sciences. Since June 2015, the EU project IMPROVER has been focusing on developing a methodology for implementing a combination of societal, organizational and technological resilience concepts, in the hope to increase critical infrastructure resilience. For this paper, we performed research on how to include societal resilience as a form of measurement of the context of critical infrastructure resilience. Because one of the main purposes of critical infrastructure (CI) is to deliver services to the society, we believe that societal resilience is an important factor that should be considered when assessing the overall CI resilience. We found that existing methods for CI resilience assessment focus mainly on technical aspects and therefore that is was necessary to develop a resilience model that take social factors into account. The model developed within the project IMPROVER aims to include the community’s expectations of infrastructure operators as well as information sharing with the public and planning processes. By considering such aspects, the IMPROVER framework not only helps operators to increase the resilience of their infrastructures on the technical or organizational side, but aims to strengthen community resilience as a whole. This will further be achieved by taking interdependencies between critical infrastructures into consideration. The knowledge gained during this project will enrich current European policies and practices for improved disaster risk management. The framework for societal resilience analysis is based on three dimensions for societal resilience; coping capacity, adaptive capacity and transformative capacity which are capacities that have been recognized throughout a widespread literature review in the field. A set of indicators have been defined that describe a community’s maturity within these resilience dimensions. Further, the indicators are categorized into six community assets that need to be accessible and utilized in such a way that they allow responding to changes and unforeseen circumstances. We conclude that the societal resilience model developed within the project IMPROVER can give a good indication of the level of societal resilience to critical infrastructure operators.

Keywords: community resilience, critical infrastructure protection, critical infrastructure resilience, societal resilience

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Authors: Anukriti Kumar, Tanmay Singh, Dinesh Kumar Vishwakarma

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Traffic control has been one of the most common and irritating problems since the time automobiles have hit the roads. Problems like traffic congestion have led to a significant time burden around the world and one significant solution to these problems can be the proper implementation of the Intelligent Transport System (ITS). It involves the integration of various tools like smart sensors, artificial intelligence, position technologies and mobile data services to manage traffic flow, reduce congestion and enhance driver's ability to avoid accidents during adverse weather. Road and traffic signs’ recognition is an emerging field of research in ITS. Classification problem of traffic signs needs to be solved as it is a major step in our journey towards building semi-autonomous/autonomous driving systems. The purpose of this work focuses on implementing an approach to solve the problem of traffic sign classification by developing a Convolutional Neural Network (CNN) classifier using the GTSRB (German Traffic Sign Recognition Benchmark) dataset. Rather than using hand-crafted features, our model addresses the concern of exploding huge parameters and data method augmentations. Our model achieved an accuracy of around 97.6% which is comparable to various state-of-the-art architectures.

Keywords: multiclass classification, convolution neural network, OpenCV

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Authors: S. Chockalingam, U. Natarajan, D. M. Santhoshsarang

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Particle damping is a promising vibration attenuating technique in boring tool than other type of damping with minimal effect on the strength, rigidity and stiffness ratio of the machine tool structure. Due to the cantilever nature of boring tool holder in operations, it suffers chatter when the slenderness ratio of the tool gets increased. In this study, Copper-Stainless steel (SS) particles were packed inside the boring tool which acts as a damper. Damper suppresses chatter generated during machining and also improves the machining efficiency of the tool with better slenderness ratio. In the first approach of particle damping, combined Cu-SS particles were packed inside the vibrating tool, whereas Copper and Stainless steel particles were selected separately and packed inside another tool and their effectiveness was analysed in this simulation. This study reveals that the efficiency of finite element simulation of the boring tools when equipped with particles such as copper, stainless steel and a combination of both. In this study, the newly modified boring tool holder with particle damping was simulated using ANSYS12.0 with and without particles. The aim of this study is to enhance the structural rigidity through particle damping thus avoiding the occurrence of resonance in the boring tool during machining.

Keywords: boring bar, copper-stainless steel, chatter, particle damping

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Authors: Ghita Benayad

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Asset allocation, which affects the long-term profitability of portfolios by distributing assets to fulfill a range of investment objectives, is the cornerstone of investment management in the dynamic and complicated world of financial markets. This paper offers a technique for optimizing strategic asset allocation with the goal of improving portfolio performance by addressing the inherent complexity and uncertainty of the market through the use of Principal Component Analysis (PCA) in a multi-objective modeling framework. The study's first section starts with a critical evaluation of conventional asset allocation techniques, highlighting how poorly they are able to capture the intricate relationships between assets and the volatile nature of the market. In order to overcome these challenges, the project suggests a PCA-driven methodology that isolates important characteristics influencing asset returns by decreasing the dimensionality of the investment universe. This decrease provides a stronger basis for asset allocation decisions by facilitating a clearer understanding of market structures and behaviors. Using a multi-objective optimization model, the project builds on this foundation by taking into account a number of performance metrics at once, including risk minimization, return maximization, and the accomplishment of predetermined investment goals like regulatory compliance or sustainability standards. This model provides a more comprehensive understanding of investor preferences and portfolio performance in comparison to conventional single-objective optimization techniques. While applying the PCA-driven multi-objective optimization model to historical market data, aiming to construct portfolios better under different market situations. As compared to portfolios produced from conventional asset allocation methodologies, the results show that portfolios optimized using the proposed method display improved risk-adjusted returns, more resilience to market downturns, and better alignment with specified investment objectives. The study also looks at the implications of this PCA technique for portfolio management, including the prospect that it might give investors a more advanced framework for navigating financial markets. The findings suggest that by combining PCA with multi-objective optimization, investors may obtain a more strategic and informed asset allocation that is responsive to both market conditions and individual investment preferences. In conclusion, this capstone project improves the field of financial engineering by creating a sophisticated asset allocation optimization model that integrates PCA with multi-objective optimization. In addition to raising concerns about the condition of asset allocation today, the proposed method of portfolio management opens up new avenues for research and application in the area of investment techniques.

Keywords: asset allocation, portfolio optimization, principle component analysis, multi-objective modelling, financial market

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Authors: Hakan Gungunes, Ismail A. Auwal, Abdulhadi Baykal, Sagar E. Shirsath

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Barium hexaferrite, BaFe₁₂O₁₉, substituted by Bi³⁺ and La³⁺ (BaBiₓLaₓFe₁₂₋₂ₓO₁₉ where 0.0 ≤ x ≤ 0.5) were prepared by solid state synthesis route. The effect of substituted Bi³⁺ and La³⁺ ions on the structure, morphology, magnetic and cation distributions of barium hexaferrite were investigated by X-ray powder diffractometry (XRD), scanning electron microscopy (SEM), energy dispersive X-ray spectroscopy (EDX), Fourier transform infrared spectroscopy (FT-IR) and Mössbauer spectroscopy. XRD powder patterns were refined by the Rietveld analysis method which confirmed the formation of single phase magneto-plumbite structure and the substitution of La³⁺ and Bi³⁺ ions into the lattice of barium ferrite. These results show that both La³⁺ and Bi³⁺ ions completely enter into barium hexaferrite lattice without disturbing the hexagonal ferrite structure. The EDX spectra confirmed the presence of all the constituents in expected elemental percentage. From 57Fe Mössbauer spectroscopy data, the variation in line width, isomer shift, quadrupole splitting and hyperfine magnetic field values on Bi and La substitutions have been determined. Cation distribution in the presently investigated hexaferrite system was estimated using the relative area of Mössbauer spectroscopy.

Keywords: hexaferrite, mössbauer, cation distribution, solid state synthesis

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Authors: Ebrahim Solgi, Zahra Hamedani, Behrouz Mohammad Kari, Ruwan Fernando, Henry Skates

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The use of thermal energy storage (TES) as part of a passive design strategy can reduce a building’s energy demand. TES materials do this by increasing the lag between energy consumption and energy supply by absorbing, storing and releasing energy in a controlled manner. The increase of lightweight construction in the building industry has made it harder to utilize thermal mass. Consequently, Phase Change Materials (PCMs) are a promising alternative as they can be manufactured in thin layers and used with lightweight construction to store latent heat. This research investigates utilizing PCMs, with the first step being measuring their performance under experimental conditions. To do this requires three components. The first is a calorimeter for measuring indoor thermal conditions, the second is a pyranometer for recording the solar conditions: global, diffuse and direct radiation and the third is a data-logger for recording temperature and humidity for the studied period. This paper reports on the design and implementation of an experimental setup used to measure the thermal characteristics of PCMs as part of a wall construction. The experimental model has been simulated with the software EnergyPlus to create a reliable simulation model that warrants further investigation.

Keywords: phase change materials, EnergyPlus, experimental evaluation, night ventilation

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Authors: Kayla Marie Sarte

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This study sought to describe mother tongue-based classes in the light of classroom interactional discourse using the Sinclair and Coulthard model. It specifically identified the exchanges, grouped into Teaching and Boundary types; moves, coded as Opening, Answering and Feedback; and the occurrence of the 13 acts (Bid, Cue, Nominate, Reply, React, Acknowledge, Clue, Accept, Evaluate, Loop, Comment, Starter, Conclusion, Aside and Silent Stress) in the classroom, and determined what these reveal about the teaching and learning processes in the MTB classroom. Being a qualitative study, using the Single Collective Case Within-Site (embedded) design, varied data collection procedures such as non-participant observations, audio-recordings and transcription of MTB classes, and semi-structured interviews were utilized. The results revealed the presence of all the codes in the model (except for the silent stress) which also implied that the Hiligaynon mother tongue-based class was eclectic, cultural and communicative, and had a healthy, analytical and focused environment which aligned with the aims of MTB-MLE, and affirmed the purported benefits of mother tongue teaching. Through the study, gaps in the mother tongue teaching and learning were also identified which involved the difficulty of children in memorizing Hiligaynon terms expressed in English in their homes and in the communities.

Keywords: discourse analysis, language teaching and learning, mother tongue-based education, multilingualism

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Authors: Dipti Yadav, Latha Rangan, Pinakeswar Mahanta

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Recent fuel-development strategies to reduce oil dependency, mitigate greenhouse gas emissions, and utilize domestic resources have generated interest in the search for alternative sources of fuel supplies. Bioenergy production from lignocellulosic biomass has a great potential. Cellulose, hemicellulose and Lignin are main constituent of woods or agrowaste. In all the industries there are always left over or waste products mainly lignin, due to the heterogeneous nature of wood and pulp fibers and the heterogeneity that exists between individual fibers, no method is currently available for the quantitative isolation of native or residual lignin without the risk of structural changes during the isolation. The potential benefits from finding alternative uses of lignin are extensive, and with a double effect. Lignin can be used to replace fossil-based raw materials in a wide range of products, from plastics to individual chemical products, activated carbon, motor fuels and carbon fibers. Furthermore, if there is a market for lignin for such value-added products, the mills will also have an additional economic incentive to take measures for higher energy efficiency. In this study residual lignin were isolated from areca nut shells by acid hydrolysis and were analyzed and characterized by Fourier Transform Infrared (FTIR), LCMS and complexity of its structure investigated by NMR.

Keywords: Areca nut, Lignin, wood, bioenergy

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Authors: Alireza Bahramian

Abstract:

High-purity TiO₂ nanoparticles (NPs) with size ranging between 50 nm and 100 nm are synthesized from titanium slag through sulphate route under sonochemical procedure. The effect of dissolution parameters such as the sulfuric acid/slag weight ratio, caustic soda concentration, digestion temperature and time, and initial particle size of the dried slag on the extraction efficiency of TiO₂ and removal of iron are examined. By optimizing the digestion conditions, a rutile TiO₂ powder with surface area of 42 m²/g and mean pore diameter of 22.4 nm were prepared. A thermo-kinetic analysis showed that the digestion temperature has an important effect, while the acid/slag weight ratio and initial size of the slag has a moderate effect on the dissolution rate. The shrinking-core model including both chemical surface reaction and surface diffusion is used to describe the leaching process. A low value of activation energy, 38.12 kJ/mol, indicates the surface chemical reaction model is a rate-controlling step. The kinetic analysis suggested a first order reaction mechanism with respect to the acid concentrations.

Keywords: TiO₂ nanoparticles, titanium slag, dissolution rate, sonochemical method, thermo-kinetic study

Procedia PDF Downloads 245

Authors: Jollen Dai, Truman You, Xinyun Du, Katrina Liu

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Transportation is one of the leading sources of greenhouse gas emissions (GHG). Thus, to meet the Paris Agreement 2015, all countries must adopt a different and more sustainable transportation system. From bikes to Maglev, the world is slowly shifting to sustainable transportation. To develop a utility public transit system, a sustainable web of buses must be implemented. As of now, only a handful of cities have adopted a detailed plan to implement a full fleet of e-buses by the 2030s, with Shenzhen in the lead. Every change requires a detailed plan and a focused analysis of the impacts of the change. In this report, the economic implications and financial implications have been taken into consideration to develop a well-rounded 10-year plan for New York City. We also apply the same financial model to the other cities, LA and Chicago. We picked NYC, Chicago, and LA to conduct the comparative NPB analysis since they are all big metropolitan cities and have complex transportation systems. All three cities have started an action plan to achieve a full fleet of e-bus in the decades. Plus, their energy carbon footprint and their energy price are very different, which are the key factors to the benefits of electric buses. Using TCO (Total Cost Ownership) financial analysis, we developed a model to calculate NPB (Net Present Benefit) /and compare EBS (electric buses) to DBS (diesel buses). We have considered all essential aspects in our model: initial investment, including the cost of a bus, charger, and installation, government fund (federal, state, local), labor cost, energy (electricity or diesel) cost, maintenance cost, insurance cost, health and environment benefit, and V2G (vehicle to grid) benefit. We see about $1,400,000 in benefits for a 12-year lifetime of an EBS compared to DBS provided the government fund to offset 50% of EBS purchase cost. With the government subsidy, an EBS starts to make positive cash flow in 5th year and can pay back its investment in 5 years. Please remember that in our model, we consider environmental and health benefits, and every year, $50,000 is counted as health benefits per bus. Besides health benefits, the significant benefits come from the energy cost savings and maintenance savings, which are about $600,000 and $200,000 in 12-year life cycle. Using linear regression, given certain budget limitations, we then designed an optimal three-phase process to replace all NYC electric buses in 10 years, i.e., by 2033. The linear regression process is to minimize the total cost over the years and have the lowest environmental cost. The overall benefits to replace all DBS with EBS for NYC is over $2.1 billion by the year of 2033. For LA, and Chicago, the benefits for electrification of the current bus fleet are $1.04 billion and $634 million by 2033. All NPB analyses and the algorithm to optimize the electrification phase process are implemented in Python code and can be shared.

Keywords: financial modeling, total cost ownership, net present benefits, electric bus, diesel bus, NYC, LA, Chicago

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Authors: Wang Haining, Zhang Hong

Abstract:

In China, rapid urbanization needs more and more buildings constructed for the growing population in cities. With the help of the methodology which contains investigation, contrastive analysis, design based on component with BIM and experiment before real construction, this research based on mobile light structure system, trying to the sustainable problems partly in present China by systematic study. The system cannot replace the permanent heavy structure completely. So the goal is the improvement of the whole building system by the addition of light structure. This house system uses modularized envelopes and standardized connections, which are pre-fabricated and assembled in factories and transported like containers. Aluminum is used as the structural material in this system, and inorganic thermal insulation material used in the envelope, which have high fireproof properties. The relationship between manufactory and construction of the system is progressive hierarchy. They exist as First Industrial, Second Industrial, Third Industrial and Site Assembly Stage. It could maximize the land usage capacity by fully exploit the area where normal permanent architecture can't take advantage of. Not only the building system itself especially the thermal isolated materials used and active solar photovoltaic system equipped can save energy, but also the way of product development is sustainable.

Keywords: aluminum house, light Structure, rapid assembly, repeat construction

Procedia PDF Downloads 476