Search results for: graphics processing units

Authors: Archana Rao R., Deepak P., Chayashree P. D., Darshan H. S.

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Cognitive abilities in bilinguals have been widely studied over the last few decades. Bilingualism has been found to extensively facilitate the ability to store and manipulate information in Working Memory (WM). The mechanism of WM includes primary memory, attentional control, and secondary memory, each of which makes a contribution to WM. Many researches have been done in an attempt to measure WM capabilities through both verbal (phonological) and nonverbal tasks (visuospatial). Since there is a lot of speculations regarding the relationship between WM and bilingualism, further investigation is required to understand the nature of WM in bilinguals, i.e., with respect to sequential and simultaneous bilinguals. Hence the present study aimed to highlight the verbal working memory abilities in sequential and simultaneous bilinguals with respect to the processing and recall abilities of nouns and verbs. Two groups of bilinguals aged between 18-30 years were considered for the study. Group 1 consisted of 20 (10 males and 10 females) sequential bilinguals who had acquired L1 (Kannada) before the age of 3 and had exposure to L2 (English) for a period of 8-10 years. Group 2 consisted of 20 (10 males and 10 females) simultaneous bilinguals who have acquired both L1 and L2 before the age of 3. Working memory abilities were assessed using two tasks, and a set of stimuli which was presented in gradation of complexity and the stimuli was inclusive of frequent and infrequent nouns and verbs. The tasks involved the participants to judge the correctness of the sentence and simultaneously remember the last word of each sentence and the participants are instructed to recall the words at the end of each set. The results indicated no significant difference between sequential and simultaneous bilinguals in processing the nouns and verbs, and this could be attributed to the proficiency level of the participants in L1 and the alike cognitive abilities between the groups. And recall of nouns was better compared to verbs, maybe because of the complex argument structure involved in verbs. Similarly, authors found a frequency of occurrence of nouns and verbs also had an effect on WM abilities. The difference was also found across gradation due to the load imposed on the central executive function and phonological loop.

Keywords: bilinguals, nouns, verbs, working memory

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Authors: Jae-Il Jeong, Hoon-Jae Park, Jung-Woo Cho, Yang-Gon Kim, Jin-Young Park, Joo-Young Oh, Si-Geun Choi, Seock-Sam Kim, Young Tae Cho, Chan Gyu Kim, Jong-Hyoung Kim

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Bearing component has strongly required to decrease vibration and wear to achieve high durability and life time. In the industry field, bearing durability is improved by surface treatment on the bearing surface by centrifugal casting or gravity casting production method. However, this manufacturing method has caused problems such as long processing time, defect rate, and health harmful effect. To solve this problem, there is a laser cladding deposition treatment, which provides fast processing and food adhesion. Therefore, optimum conditions of white metal laser deposition should be studied to minimize bearing contact axis wear using laser cladding techniques. In this study, we deposit a soft white metal layer on SCM440, which is mainly used for shaft and bolt. On laser deposition process, the laser power and powder feed rate and laser head speed factors are controlled to find out the optimal conditions. We also measure hardness using micro Vickers, analyze FE-SEM (Field Emission Scanning Electron Microscope) and EDS (Energy Dispersive Spectroscopy) to study the mechanical properties and surface characteristics with various parameters change. Furthermore, this paper suggests the optimum condition of laser cladding deposition to apply in industrial fields. This work was supported by the Industrial Innovation Project of the Korea Evaluation Institute of Industrial Technology (KEIT) granted financial resource from the Ministry of Trade, Industry & Energy, Republic of Korea (Research no. 10051653).

Keywords: laser deposition, bearing, white metal, mechanical properties

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Authors: Natalie Cutler, Lorna Moxham, Moira Stephens

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Safety is a priority in mental health services, and no more so than in the acute inpatient setting. Mental health service policies and accreditation frameworks commonly approach safety from a risk reduction or elimination perspective leading to service approaches that are arguably more focused on risk than on safety. An exploration what safety means for people who have experienced admission to an acute mental health inpatient unit is currently under way in Sydney, Australia. Using a phenomenographic research approach, this study is seeking to understand the meaning of safety from the perspective of people who use, rather than those who deliver mental health services. Preliminary findings suggest that the meanings of safety for users of mental health services vary from the meanings inherent in the policies and frameworks that inform how mental health services and mental health practice are delivered. This variance has implications for the physical and environmental design of acute mental health inpatient facilities, the policies and practices, and the education and training of mental health staff in particular nurses, who comprise the majority of the mental health workforce. These variances will be presented, along with their implications for the way quality and safety in mental health services are evaluated.

Keywords: acute inpatient, mental health, nursing, phenomenography, recovery, safety

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Authors: Aradhna Pandarum

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South Africa is experiencing an exponential growth of distributed solar PV installations. This is due to various factors with the predominant one being increasing electricity tariffs along with decreasing installation costs, resulting in attractive business cases to some end-users. Despite there being a variety of economic and environmental advantages associated with the installation of PV, their potential impact on distribution grids has yet to be thoroughly investigated. This is especially true since the locations of these units cannot be controlled by Network Service Providers (NSPs) and their output power is stochastic and non-dispatchable. This report details two case studies that were completed to determine the possible voltage and technical losses impact of increasing PV penetration in the Northern Cape of South Africa. Some major impacts considered for the simulations were ramping of PV generation due to intermittency caused by moving clouds, the size and overall hosting capacity and the location of the systems. The main finding is that the technical impact is different on a constrained feeder vs a non-constrained feeder. The acceptable PV penetration level is much lower for a constrained feeder than a non-constrained feeder, depending on where the systems are located.

Keywords: medium voltage networks, power system losses, power system voltage, solar photovoltaic

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Authors: Yonggu Jang, Jisong Ryu, Woosik Lee

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The study aims to address the limitations of existing underground facility exploration equipment in terms of exploration depth range, relative depth measurement, data processing time, and human-centered ground penetrating radar image interpretation. The study proposed the use of 3D absolute positioning technology to develop a precision underground facility exploration system. The aim of this study is to establish a precise exploration system for underground facilities based on 3D absolute positioning technology, which can accurately survey up to a depth of 5m and measure the 3D absolute location of precise underground facilities. The study developed software and hardware technologies to build the precision exploration system. The software technologies developed include absolute positioning technology, ground surface location synchronization technology of GPR exploration equipment, GPR exploration image AI interpretation technology, and integrated underground space map-based composite data processing technology. The hardware systems developed include a vehicle-type exploration system and a cart-type exploration system. The data was collected using the developed exploration system, which employs 3D absolute positioning technology. The GPR exploration images were analyzed using AI technology, and the three-dimensional location information of the explored precise underground facilities was compared to the integrated underground space map. The study successfully developed a precision underground facility exploration system based on 3D absolute positioning technology. The developed exploration system can accurately survey up to a depth of 5m and measure the 3D absolute location of precise underground facilities. The system comprises software technologies that build a 3D precise DEM, synchronize the GPR sensor's ground surface 3D location coordinates, automatically analyze and detect underground facility information in GPR exploration images and improve accuracy through comparative analysis of the three-dimensional location information, and hardware systems, including a vehicle-type exploration system and a cart-type exploration system. The study's findings and technological advancements are essential for underground safety management in Korea. The proposed precision exploration system significantly contributes to establishing precise location information of underground facility information, which is crucial for underground safety management and improves the accuracy and efficiency of exploration. The study addressed the limitations of existing equipment in exploring underground facilities, proposed 3D absolute positioning technology-based precision exploration system, developed software and hardware systems for the exploration system, and contributed to underground safety management by providing precise location information. The developed precision underground facility exploration system based on 3D absolute positioning technology has the potential to provide accurate and efficient exploration of underground facilities up to a depth of 5m. The system's technological advancements contribute to the establishment of precise location information of underground facility information, which is essential for underground safety management in Korea.

Keywords: 3D absolute positioning, AI interpretation of GPR exploration images, complex data processing, integrated underground space maps, precision exploration system for underground facilities

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Authors: Robert Caulk

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A generalized framework for adaptive machine learning deployments in algorithmic trading is introduced, tested, and released as open-source code. The presented software aims to test the hypothesis that recent data contains enough information to form a probabilistically favorable short-term price prediction. Further, the framework contains various adaptive machine learning techniques that are geared toward generating profit during strong trends and minimizing losses during trend changes. Results demonstrate that this adaptive machine learning approach is capable of capturing trends and generating profit. The presentation also discusses the importance of defining the parameter space associated with the dynamic training data-set and using the parameter space to identify and remove outliers from prediction data points. Meanwhile, the generalized architecture enables common users to exploit the powerful machinery while focusing on high-level feature engineering and model testing. The presentation also highlights common strengths and weaknesses associated with the presented technique and presents a broad range of well-tested starting points for feature set construction, target setting, and statistical methods for enforcing risk management and maintaining probabilistically favorable entry and exit points. The presentation also describes the end-to-end data processing tools associated with FreqAI, including automatic data fetching, data aggregation, feature engineering, safe and robust data pre-processing, outlier detection, custom machine learning and statistical tools, data post-processing, and adaptive training backtest emulation, and deployment of adaptive training in live environments. Finally, the generalized user interface is also discussed in the presentation. Feature engineering is simplified so that users can seed their feature sets with common indicator libraries (e.g. TA-lib, pandas-ta). The user also feeds data expansion parameters to fill out a large feature set for the model, which can contain as many as 10,000+ features. The presentation describes the various object-oriented programming techniques employed to make FreqAI agnostic to third-party libraries and external data sources. In other words, the back-end is constructed in such a way that users can leverage a broad range of common regression libraries (Catboost, LightGBM, Sklearn, etc) as well as common Neural Network libraries (TensorFlow, PyTorch) without worrying about the logistical complexities associated with data handling and API interactions. The presentation finishes by drawing conclusions about the most important parameters associated with a live deployment of the adaptive learning framework and provides the road map for future development in FreqAI.

Keywords: machine learning, market trend detection, open-source, adaptive learning, parameter space exploration

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Authors: C. S. Caires, A. Barbosa, W. Hanyou

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In this paper, we present the main results achieved during a five-week international workshop on Interactive Furniture for the Classroom, with 22 Chinese design students, in Jiangmen city (Guangdong province, China), and five teachers from Portugal, France, Iran, Macao SAR, and China. The main goal was to engage design students from China with new skills and practice methodologies towards interactive design research for furniture and product design for the classroom. The final results demonstrate students' concerns on improving Chinese furniture design for the classrooms, including solutions related to collaborative learning and human-interaction design for interactive furniture products. The findings of the research led students to the fabrication of five original prototypes: two for kindergartens ('Candy' and 'Tilt-tilt'), two for primary schools ('Closer' and 'Eks(x)'), and one for art/creative schools ('Wave'). From the findings, it was also clear that collaboration, personalization, and project-based teaching are still neglected when designing furniture products for the classroom in China. Students focused on these issues and came up with creative solutions that could transform this educational field in China.

Keywords: product design, collaborative education, interactive design, design research and prototyping

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Authors: Mohammed Abu Shquier

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This paper introduces a word segmentation method using the novel BP-LSTM-CRF architecture for processing semantic output training. The objective of web morphological analysis tools is to link a formal morpho-syntactic description to a lemma, along with morpho-syntactic information, a vocalized form, a vocalized analysis with morpho-syntactic information, and a list of paradigms. A key objective is to continuously enhance the proposed system through an inductive learning approach that considers semantic influences. The system is currently under construction and development based on data-driven learning. To evaluate the tool, an experiment on homograph analysis was conducted. The tool also encompasses the assumption of deep binary segmentation hypotheses, the arbitrary choice of trigram or n-gram continuation probabilities, language limitations, and morphology for both Modern Standard Arabic (MSA) and Dialectal Arabic (DA), which provide justification for updating this system. Most Arabic word analysis systems are based on the phonotactic morpho-syntactic analysis of a word transmitted using lexical rules, which are mainly used in MENA language technology tools, without taking into account contextual or semantic morphological implications. Therefore, it is necessary to have an automatic analysis tool taking into account the word sense and not only the morpho-syntactic category. Moreover, they are also based on statistical/stochastic models. These stochastic models, such as HMMs, have shown their effectiveness in different NLP applications: part-of-speech tagging, machine translation, speech recognition, etc. As an extension, we focus on language modeling using Recurrent Neural Network (RNN); given that morphological analysis coverage was very low in dialectal Arabic, it is significantly important to investigate deeply how the dialect data influence the accuracy of these approaches by developing dialectal morphological processing tools to show that dialectal variability can support to improve analysis.

Keywords: NLP, DL, ML, analyser, MSA, RNN, CNN

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Authors: Won Jung, Azianti Ismail

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Functional safety is about electrical, electronics, and programmable electronic safety-related system focuses on the potential risk of malfunction which may have a significant impact on the safety of humans and/or the environment based on IEC 61508. In November 2011, the automotive industry has been introduced to automotive functional safety ISO 26262 which addresses the complete safety installation from sensor to actuator with its technical as well as management issues. Nowadays, most of the modern automobiles are equipped with embedded electronic systems which include many Electronic Controller Units (ECUs), electronic sensors, signals, bus systems and coding. Due to upcoming more sophisticated systems installed in automobiles, the need to carry out detailed safety is very crucial. Assimilation of existing practices with this new standard is a major challenge for the automotive industry in reducing redundancy, time and resources. Therefore, this paper will analyze the research trends on pre and post introduction of ISO 26262 through publications as well as to take a glimpse in the activities for implementing this standard by the automotive manufacturers around the world. It is going to highlight issues and challenges which have been discussed among the experts in this field. Even though it will take some time for this standard to be fully implemented, the benefits from this implementation will raise the competitiveness in the global automotive market.

Keywords: ISO 26262, automotive, functional safety, implementation, standard, challenges

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Authors: Z. Asadgol, A. Nadali, H. Arfaeinia, M. Khalifeh Gholi, R. Fateh, M. Fahiminia

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The present investigation was conducted to detect the type and concentrations of bacterial and fungal bioaerosols in one room (bedroom) of each selected residential building located in different regions of Qom during February 2015 (n=9) to July 2016 (n=11). Moreover, we evaluated the efficiency of negative air ions (NAIs) in bioaerosol reduction in indoor air in residential buildings. In the first step, the mean concentrations of bacterial and fungal in nine sampling sites evaluated in winter were 744 and 579 colony forming units (CFU)/m³, while these values were 1628.6 and 231 CFU/m³ in the 11 sampling sites evaluated in summer, respectively. The most predominant genera between bacterial and fungal in all sampling sites were detected as Micrococcus spp. and Staphylococcus spp. and also, Aspergillus spp. and Penicillium spp., respectively. The 95% and 45% of sampling sites have bacterial and fungal concentrations over the recommended levels, respectively. In the removal step, we achieved a reduction with a range of 38% to 93% for bacterial genera and 25% to 100% for fungal genera by using NAIs. The results suggested that NAI is a highly effective, simple and efficient technique in reducing the bacterial and fungal concentration in the indoor air of residential buildings.

Keywords: bacterial, fungal, negative air ions (NAIs), indoor air, Iran

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Authors: Mafawez Alharbi, Mahdi Jemmali

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E-learning is a developing area especially in education. E-learning can provide several benefits to learners. An intelligent system to collect all components satisfying user preferences is so important. This research presents an approach that it capable to personalize e-information and give the user their needs following their preferences. This proposal can make some knowledge after more evaluations made by the user. In addition, it can learn from the habit from the user. Finally, we show a walk-through to prove how intelligent process work.

Keywords: artificial intelligence, architecture, e-learning, software engineering, processing

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Authors: M. Aruna, Uma Gunasilan

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Hospitals play an ample role in securing the health of a country. Nevertheless, they also have an unhealthy side. Ecological issues strengthen ill-health throughout the domain which subsequently puts pressure on hospital supply chains. Medical waste indeed is hazardous for environment and subsequently for human. The hospital waste management is of immense prominence due to its infectious and hazardous nature that can source many effects on human health and the environment. Government regulations and public cognizance regarding hospital waste issues have imposed hospital units to admit these strategies. The innovative technologies and instruments have been developed to handle hospital wastes. Green supply chain management practices are common in the United States. In India, Green Supply Chain management (GSCM) has just started to be recognized and practiced. GSCM are green, integrated and ecologically optimized. In Green supply chain management environmental sustainability is found to be an important driver. Eleven barriers are identified in this work. Interpretive Structural Modeling (ISM) technique is used for ranking the obstructions.

Keywords: green supply chain management (GSCM), hospital waste management (HWM), interpretive structural modeling (ISM), medical waste (MW)

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Authors: Ch. Sandeep Kumar Subudhi, S. Sivanandam

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Our system is to monitor the human body temperature, blood pressure (BP), Pulse Rate and ECG and tracking the patient location. In our system the body temperature is detected by using LM35 temperature sensor, blood pressure is detected by the BP sensor, pulse rate is detected by the ear plug pulse sensor and the ECG is detected by the three lead ECG sensor in the working environment of the patient. The sensed information is sent to the PIC16F877 microcontroller through signal conditioning circuit. A desired amount of sensor value is set and if it is exceeded preliminary steps should be taken by indication by buzzer. The sensor information will be transmitted from the patient unit to the main controller unit with the help of Zigbee communication medium which is connected with the microcontrollers in the both units. The main controller unit will send those sensor data as well as the location of that patient by the help of GPS module to the observer/doctor. The observer/doctor can receive the SMS sent by GSM module and further decision can be taken. The message is sent to a cell phone using global system mobile (GSM) Modem. MAX232 acts as a driver between microcontroller and modem.

Keywords: LM35, heart beat sensor, ECG Sensor, BP Sensor, Zigbee module, GSM module, GPS module, PIC16F877A microcontroller

Procedia PDF Downloads 382

Authors: Mehrnaz Mostafavi

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The assessment and categorization of incidental lung nodules present a considerable challenge in healthcare, often necessitating resource-intensive multiple computed tomography (CT) scans for growth confirmation. This research addresses this issue by introducing a distinct computational approach leveraging radiomics and deep-learning methods. However, understanding local services is essential before implementing these advancements. With diverse tracking methods in place, there is a need for efficient and accurate identification approaches, especially in the context of managing lung nodules alongside pre-existing cancer scenarios. This study explores the integration of text-based algorithms in medical data curation, indicating their efficacy in conjunction with machine learning and deep-learning models for identifying lung nodules. Combining medical images with text data has demonstrated superior data retrieval compared to using each modality independently. While deep learning and text analysis show potential in detecting previously missed nodules, challenges persist, such as increased false positives. The presented research introduces a Structured-Query-Language (SQL) algorithm designed for identifying pulmonary nodules in a tertiary cancer center, externally validated at another hospital. Leveraging natural language processing (NLP) and machine learning, the algorithm categorizes lung nodule reports based on sentence features, aiming to facilitate research and assess clinical pathways. The hypothesis posits that the algorithm can accurately identify lung nodule CT scans and predict concerning nodule features using machine-learning classifiers. Through a retrospective observational study spanning a decade, CT scan reports were collected, and an algorithm was developed to extract and classify data. Results underscore the complexity of lung nodule cohorts in cancer centers, emphasizing the importance of careful evaluation before assuming a metastatic origin. The SQL and NLP algorithms demonstrated high accuracy in identifying lung nodule sentences, indicating potential for local service evaluation and research dataset creation. Machine-learning models exhibited strong accuracy in predicting concerning changes in lung nodule scan reports. While limitations include variability in disease group attribution, the potential for correlation rather than causality in clinical findings, and the need for further external validation, the algorithm's accuracy and potential to support clinical decision-making and healthcare automation represent a significant stride in lung nodule management and research.

Keywords: lung cancer diagnosis, structured-query-language (SQL), natural language processing (NLP), machine learning, CT scans

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Authors: Marianna Maiaru, Gregory M. Odegard

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During the processing of high-performance thermoset polymer matrix composites, chemical reactions occur during elevated pressure and temperature cycles, causing the constituent monomers to crosslink and form a molecular network that gradually can sustain stress. As the crosslinking process progresses, the material naturally experiences a gradual shrinkage due to the increase in covalent bonds in the network. Once the cured composite completes the cure cycle and is brought to room temperature, the thermal expansion mismatch of the fibers and matrix cause additional residual stresses to form. These compounded residual stresses can compromise the reliability of the composite material and affect the composite strength. Composite process modeling is greatly complicated by the multiscale nature of the composite architecture. At the molecular level, the degree of cure controls the local shrinkage and thermal-mechanical properties of the thermoset. At the microscopic level, the local fiber architecture and packing affect the magnitudes and locations of residual stress concentrations. At the macroscopic level, the layup sequence controls the nature of crack initiation and propagation due to residual stresses. The goal of this research is use molecular dynamics (MD) and finite element analysis (FEA) to predict the residual stresses in composite laminates and the corresponding effect on composite failure. MD is used to predict the polymer shrinkage and thermomechanical properties as a function of degree of cure. This information is used as input into FEA to predict the residual stresses on the microscopic level resulting from the complete cure process. Virtual testing is subsequently conducted to predict strength allowables. Experimental characterization is used to validate the modeling.

Keywords: molecular dynamics, finite element analysis, processing modeling, multiscale modeling

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Authors: Evgenia Dimova-Aleksandrova, Elitsa Deianova

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Dynamic changes in modern life and habitation due to demographic, urban, technology, and ecological factors affect the size of modern homes leading to a trend of decreasing their area. The current paper aims to investigate the differences between “micro” homes and “small” homes. In Bulgaria, these two types are not included in legal regulations, and therefore, a precise definition and special requirements are needed and sought in order to include their characteristic features in contemporary individual habitation. The purpose of the current study is to determine limits in built-up volume for the two types, to create a definition of the terms “micro” and “small” home, and to find methods to distinguish them. A comparative analysis will differentiate these types of habitation units, thus determining the boundaries for the built-up area for both concepts. The analysis is based on a case study from European practices and is focused on defining minimal requirements for “micro” and “small” home in the context of contemporary demands for high quality habitation in limited areas.

Keywords: Bulgaria, differentiation, micro home, requirements, small home

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Authors: M. Shahin Alam, Satoru Takahashi, Mariko Itoh, Miyuki Komura, Mayuko Suzuki, Natthanan Sangsriratanakul, Kazuaki Takehara

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Cleaning and disinfection are key components of routine biosecurity in livestock farming and food processing industry. The usage of suitable disinfectants and their proper concentration are important factors for a successful biosecurity program. Disinfectants have optimum bactericidal and virucidal efficacies at temperatures above 20°C, but very few studies on application and effectiveness of disinfectants at low temperatures have been done. In the present study, the bactericidal efficacies of food additive grade calcium hydroxide (FdCa(OH)), quaternary ammonium compound (QAC) and their mixture, were investigated under different conditions, including time, organic materials (fetal bovine serum: FBS) and temperature, either in suspension or in carrier test. Salmonella Infantis and Escherichia coli, which are the most prevalent gram negative bacteria in commercial poultry housing and food processing industry, were used in this study. Initially, we evaluated these disinfectants at two different temperatures (4°C and room temperature (RT) (25°C ± 2°C)) and 7 contact times (0, 5 and 30 sec, 1, 3, 20 and 30 min), with suspension tests either in the presence or absence of 5% FBS. Secondly, we investigated the bactericidal efficacies of these disinfectants by carrier tests (rubber, stainless steel and plastic) at same temperatures and 4 contact times (30 sec, 1, 3, and 5 min). Then, we compared the bactericidal efficacies of each disinfectant within their mixtures, as follows. When QAC was diluted with redistilled water (dW2) at 1: 500 (QACx500) to obtain the final concentration of didecyl-dimethylammonium chloride (DDAC) of 200 ppm, it could inactivate Salmonella Infantis within 5 sec at RT either with or without 5% FBS in suspension test; however, at 4°C it required 30 min in presence of 5% FBS. FdCa(OH)2 solution alone could inactivate bacteria within 1 min both at RT and 4°C even with 5% FBS. While FdCa(OH)2 powder was added at final concentration 0.2% to QACx500 (Mix500), the mixture could inactivate bacteria within 30 sec and 5 sec, respectively, with or without 5% FBS at 4°C. The findings from the suspension test indicated that low temperature inhibited the bactericidal efficacy of QAC, whereas Mix500 was effective, regardless of short contact time and low temperature, even with 5% FBS. In the carrier test, single disinfectant required bit more time to inactivate bacteria on rubber and plastic surfaces than on stainless steel. However, Mix500 could inactivate S. Infantis on rubber, stainless steel and plastic surfaces within 30 sec and 1 min, respectively, at RT and 4°C; but, for E. coli, it required only 30 sec at both temperatures. So, synergistic effects were observed on different carriers at both temperatures. For a successful enhancement of biosecurity during winter, the disinfectants should be selected that could have short contact times with optimum efficacy against the target pathogen. The present study findings help farmers to make proper strategies for application of disinfectants in their livestock farming and food processing industry.

Keywords: carrier, food additive grade calcium hydroxide (FdCa(OH)₂), quaternary ammonium compound, synergistic effects

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Authors: Sweta Iyer, Nemeshwaree Behary, Jinping Guan, Guoqiang Chen, Vincent Nierstrasz

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Flavin mononucleotide widely known as 'FMN' is a biobased resource derived from riboflavin. The isoalloxazine ring present in the FMN molecule attributes the photoluminescence phenomenon, whereas FMN molecule in the presence of bacterial luciferase enzyme and co-factors such as NADH, long chain aldehyde leads to bioluminescence reaction. In this study, the FMN molecule was treated on cellulosic textile using chromojet technique and the photoluminescence property was characterized using spectroscopy technique. Further, the FMN was used as a substrate along with enzymes and co-factors to treat the non-woven textile, and the bioluminescence property was explored using luminometer equipment. The investigation revealed photoluminescence property on cellulosic textile, and the emission peak was observed at a wavelength around 530 nm with an average corrected spectral intensity of 10×106 CPS/Microamps. In addition, the measurement of nonwoven textile using bioluminescence reaction system exhibited light intensity measured in the form of relative light units (RLU). The study enabled to explore the use of FMN as both photoluminescent and bioluminescent textile. Further investigation would require for stability study of the same to provide an eco-efficient approach to obtain luminescent textile.

Keywords: flavin mononucleotide, photoluminescence, bioluminescence, luminescent textile

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Authors: Ibrahim Mohammed, Suleiman Taofiq, Muhammad Naziru Yahya

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Geoelectrical measurements using Schlumberger Vertical Electrical Sounding (VES) method were carried out in Waziri Umaru Federal Polytechnic, Birnin Kebbi, Nigeria, with the aim of determining the groundwater potential in the area. Twelve (12) Vertical Electric Sounding (VES) data were collected using Terrameter (ABEM SAS 300c) and analyzed using computer software (IPI2win), which gives an automatic interpretation of the apparent resistivity. The results of the interpretation of VES data were used in the characterization of three to five geo-electric layers from which the aquifer units were delineated. Data analysis indicated that water bearing formation exists in the third and fourth layers having resistivity range of 312 to 767 Ωm and 9.51 to 681 Ωm, respectively. The thickness of the formation ranges from 14.7 to 41.8 m, while the depth is from 8.22 to 53.7 m. Based on the result obtained from the interpretation of the data, five (5) VES stations were recommended as the most viable locations for groundwater exploration in the study area. The VES stations include VES A4, A5, A6, B1, and B2. The VES results of the entire area indicated that the water bearing formation occurs at maximum depth of 53.7 m at the time of this survey.

Keywords: aquifer, depth, groundwater, resistivity, Schlumberger

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Authors: Mahshid Hatamzad, Geanette Polanco

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Improving the performance of Winter Road Maintenance (WRM) can increase the traffic safety and reduce the cost as well as environmental impacts. This study evaluates the efficiency of WRM technique, named salting, in the Arctic area by using Data Envelopment Analysis (DEA), which is a non-parametric linear method to measure the efficiencies of decision-making units (DMUs) based on handling multiple inputs and multiple outputs at the same time that their associated weights are not known. Here, roads are considered as DMUs for which the efficiency must be determined. The three input variables considered are traffic flow, road area and WRM cost. In addition, the two output variables included are level of safety in the roads and environment impacts resulted from WRM, which is also considered as an uncontrollable factor in the second scenario. The results show the performance of DMUs from the most efficient WRM to the inefficient/least efficient one and this information provides decision makers with technical support and the required suggested improvements for inefficient WRM, in order to achieve a cost-effective WRM and a safe road transportation during wintertime in the Arctic areas.

Keywords: environmental impacts, DEA, risk and safety, WRM

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Authors: Xiaochen Mu

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Data rights confirmation, as a key legal issue in the development of the digital economy, is undergoing a transition from a traditional rights paradigm to a more complex private-economic paradigm. In this process, data rights confirmation has evolved from a simple claim of rights to a complex structure encompassing multiple dimensions of personality rights and property rights. Current data rights confirmation practices are primarily reflected in two models: holistic rights confirmation and process rights confirmation. The holistic rights confirmation model continues the traditional "one object, one right" theory, while the process rights confirmation model, through contractual relationships in the data processing process, recognizes rights that are more adaptable to the needs of data circulation and value release. In the design of the data property rights system, there is a hierarchical characteristic aimed at decoupling from raw data to data applications through horizontal stratification and vertical staging. This design not only respects the ownership rights of data originators but also, based on the usufructuary rights of enterprises, constructs a corresponding rights system for different stages of data processing activities. The subjects of data property rights include both data originators, such as users, and data producers, such as enterprises, who enjoy different rights at different stages of data processing. The intellectual property rights system, with the mission of incentivizing innovation and promoting the advancement of science, culture, and the arts, provides a complete set of mechanisms for protecting innovative results. However, unlike traditional private property rights, the granting of intellectual property rights is not an end in itself; the purpose of the intellectual property system is to balance the exclusive rights of the rights holders with the prosperity and long-term development of society's public learning and the entire field of science, culture, and the arts. Therefore, the intellectual property granting mechanism provides both protection and limitations for the rights holder. This perfectly aligns with the dual attributes of data. In terms of achieving the protection of data property rights, the granting of intellectual property rights is an important institutional choice that can enhance the effectiveness of the data property exchange mechanism. Although this is not the only path, the granting of data property rights within the framework of the intellectual property rights system helps to establish fundamental legal relationships and rights confirmation mechanisms and is more compatible with the classification and grading system of data. The modernity of the intellectual property rights system allows it to adapt to the needs of big data technology development through special clauses or industry guidelines, thus promoting the comprehensive advancement of data intellectual property rights legislation. This paper analyzes data protection under the virtual property layer theory and two-fold virtual property rights system. Based on the “bundle of right” theory, this paper establishes specific three-level data rights. This paper analyzes the cases: Google v. Vidal-Hall, Halliday v Creation Consumer Finance, Douglas v Hello Limited, Campbell v MGN and Imerman v Tchenquiz. This paper concluded that recognizing property rights over personal data and protecting data under the framework of intellectual property will be beneficial to establish the tort of misuse of personal information.

Keywords: data protection, property rights, intellectual property, Big data

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Authors: Paula Lopez-Arce, Hector Altamirano, Dimitrios Rovas, James Berry, Bryan Hindle, Steven Hodgson

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Efficient mould remediation and accurate moisture diagnostics leading to condensation and mould growth in dwellings are largely untapped. Number of factors are contributing to the rising trend of excessive moisture in homes mainly linked with modern living, increased levels of occupation and rising fuel costs, as well as making homes more energy efficient. Environmental monitoring by means of data collection though loggers sensors and survey forms has been performed in a range of buildings from different UK regions. Air and surface temperature and relative humidity values of residential areas affected by condensation and/or mould issues were recorded. Additional measurements were taken through different trials changing type, location, and position of loggers. In some instances, IR thermal images and ventilation rates have also been acquired. Results have been interpreted together with environmental key parameters by processing and connecting data from loggers and survey questionnaires, both in buildings with and without moisture issues. Monitoring exercises carried out during Winter and Spring time show the importance of developing and following accurate protocols for guidance to obtain consistent, repeatable and comparable results and to improve the performance of environmental monitoring. A model and a protocol are being developed to build a diagnostic tool with the goal of performing a simple but precise residential atmospheric moisture diagnostics to distinguish the cause entailing condensation and mould generation, i.e., ventilation, insulation or heating systems issue. This research shows the relevance of monitoring and processing environmental data to assign moisture risk levels and determine the origin of condensation or mould when dealing with a building atmospheric moisture excess.

Keywords: environmental monitoring, atmospheric moisture, protocols, mould

Procedia PDF Downloads 139

Authors: A. M. John-Otumu, M. M. Rahman, O. C. Nwokonkwo, M. C. Onuoha

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Online social media networks have long served as a primary arena for group conversations, gossip, text-based information sharing and distribution. The use of natural language processing techniques for text classification and unbiased decision-making has not been far-fetched. Proper classification of this textual information in a given context has also been very difficult. As a result, we decided to conduct a systematic review of previous literature on sentiment classification and AI-based techniques that have been used in order to gain a better understanding of the process of designing and developing a robust and more accurate sentiment classifier that can correctly classify social media textual information of a given context between hate speech and inverted compliments with a high level of accuracy by assessing different artificial intelligence techniques. We evaluated over 250 articles from digital sources like ScienceDirect, ACM, Google Scholar, and IEEE Xplore and whittled down the number of research to 31. Findings revealed that Deep learning approaches such as CNN, RNN, BERT, and LSTM outperformed various machine learning techniques in terms of performance accuracy. A large dataset is also necessary for developing a robust sentiment classifier and can be obtained from places like Twitter, movie reviews, Kaggle, SST, and SemEval Task4. Hybrid Deep Learning techniques like CNN+LSTM, CNN+GRU, CNN+BERT outperformed single Deep Learning techniques and machine learning techniques. Python programming language outperformed Java programming language in terms of sentiment analyzer development due to its simplicity and AI-based library functionalities. Based on some of the important findings from this study, we made a recommendation for future research.

Keywords: artificial intelligence, natural language processing, sentiment analysis, social network, text

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Authors: Sudeshna Chandra, Christian Gäbler, Christian Schliebe, Heinrich Lang

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Highly branched dendrimers provide structural homogeneity, controlled composition, comparable size to biomolecules, internal porosity and multiple functional groups for conjugating reactions. Electro-active dendrimers containing multiple redox units have generated great interest in their use as electrode modifiers for development of biosensors. The electron transfer between the redox-active dendrimers and the biomolecules play a key role in developing a biosensor. Ferrocenes have multiple and electrochemically equivalent redox units that can act as electron “pool” in a system. The ferrocenyl-terminated polyamidoamine dendrimer is capable of transferring multiple numbers of electrons under the same applied potential. Therefore, they can be used for dual purposes: one in building a film over the electrode for immunosensors and the other for immobilizing biomolecules for sensing. Electrochemical immunosensor, thus developed, exhibit fast and sensitive analysis, inexpensive and involve no prior sample pre-treatment. Electrochemical amperometric immunosensors are even more promising because they can achieve a very low detection limit with high sensitivity. Detection of the cancer biomarkers at an early stage can provide crucial information for foundational research of life science, clinical diagnosis and prevention of disease. Elevated concentration of biomarkers in body fluid is an early indication of some type of cancerous disease and among all the biomarkers, IgG is the most common and extensively used clinical cancer biomarkers. We present an IgG (=immunoglobulin) electrochemical immunosensor using a newly synthesized redox-active ferrocenyl dendrimer of generation 2 (G2Fc) as glassy carbon electrode material for immobilizing the antibody. The electrochemical performance of the modified electrodes was assessed in both aqueous and non-aqueous media using varying scan rates to elucidate the reaction mechanism. The potential shift was found to be higher in an aqueous electrolyte due to presence of more H-bond which reduced the electrostatic attraction within the amido groups of the dendrimers. The cyclic voltammetric studies of the G2Fc-modified GCE in 0.1 M PBS solution of pH 7.2 showed a pair of well-defined redox peaks. The peak current decreased significantly with the immobilization of the anti-goat IgG. After the immunosensor is blocked with BSA, a further decrease in the peak current was observed due to the attachment of the protein BSA to the immunosensor. A significant decrease in the current signal of the BSA/anti-IgG/G2Fc/GCE was observed upon immobilizing IgG which may be due to the formation of immune-conjugates that blocks the tunneling of mass and electron transfer. The current signal was found to be directly related to the amount of IgG captured on the electrode surface. With increase in the concentration of IgG, there is a formation of an increasing amount of immune-conjugates that decreased the peak current. The incubation time and concentration of the antibody was optimized for better analytical performance of the immunosensor. The developed amperometric immunosensor is sensitive to IgG concentration as low as 2 ng/mL. Tailoring of redox-active dendrimers provides enhanced electroactivity to the system and enlarges the sensor surface for binding the antibodies. It may be assumed that both electron transfer and diffusion contribute to the signal transformation between the dendrimers and the antibody.

Keywords: ferrocenyl dendrimers, electrochemical immunosensors, immunoglobulin, amperometry

Procedia PDF Downloads 337

Authors: Burak Oksuzoglu, Savas Yildirim, Ferhat Kutlu

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Opinion mining (OM) is one of the natural language processing (NLP) problems to determine the polarity of opinions, mostly represented on a positive-neutral-negative axis. The data for OM is usually collected from various social media platforms. In an era where social media has considerable control over companies’ futures, it’s worth understanding social media and taking actions accordingly. OM comes to the fore here as the scale of the discussion about companies increases, and it becomes unfeasible to gauge opinion on individual levels. Thus, the companies opt to automize this process by applying machine learning (ML) approaches to their data. For the last two decades, OM or sentiment analysis (SA) has been mainly performed by applying ML classification algorithms such as support vector machines (SVM) and Naïve Bayes to a bag of n-gram representations of textual data. With the advent of deep learning and its apparent success in NLP, traditional methods have become obsolete. Transfer learning paradigm that has been commonly used in computer vision (CV) problems started to shape NLP approaches and language models (LM) lately. This gave a sudden rise to the usage of the pretrained language model (PTM), which contains language representations that are obtained by training it on the large datasets using self-supervised learning objectives. The PTMs are further fine-tuned by a specialized downstream task dataset to produce efficient models for various NLP tasks such as OM, NER (Named-Entity Recognition), Question Answering (QA), and so forth. In this study, the traditional and modern NLP approaches have been evaluated for OM by using a sizable corpus belonging to a large private company containing about 76,000 comments in Turkish: SVM with a bag of n-grams, and two chosen pre-trained models, multilingual universal sentence encoder (MUSE) and bidirectional encoder representations from transformers (BERT). The MUSE model is a multilingual model that supports 16 languages, including Turkish, and it is based on convolutional neural networks. The BERT is a monolingual model in our case and transformers-based neural networks. It uses a masked language model and next sentence prediction tasks that allow the bidirectional training of the transformers. During the training phase of the architecture, pre-processing operations such as morphological parsing, stemming, and spelling correction was not used since the experiments showed that their contribution to the model performance was found insignificant even though Turkish is a highly agglutinative and inflective language. The results show that usage of deep learning methods with pre-trained models and fine-tuning achieve about 11% improvement over SVM for OM. The BERT model achieved around 94% prediction accuracy while the MUSE model achieved around 88% and SVM did around 83%. The MUSE multilingual model shows better results than SVM, but it still performs worse than the monolingual BERT model.

Keywords: BERT, MUSE, opinion mining, pretrained language model, SVM, Turkish

Procedia PDF Downloads 146

Authors: Ozan Kahraman, Hao Feng

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Several models such as Weibull, Modified Gompertz, Biphasic linear, and Log-logistic models have been proposed in order to describe non-linear inactivation kinetics and used to fit non-linear inactivation data of several microorganisms for inactivation by heat, high pressure processing or pulsed electric field. First-order kinetic parameters (D-values and z-values) have often been used in order to identify microbial inactivation by non-thermal processing methods such as ultrasound. Most ultrasonic inactivation studies employed first-order kinetic parameters (D-values and z-values) in order to describe the reduction on microbial survival count. This study was conducted to analyze the E. coli O157:H7 inactivation data by using five microbial survival models (First-order, Weibull, Modified Gompertz, Biphasic linear and Log-logistic). First-order, Weibull, Modified Gompertz, Biphasic linear and Log-logistic kinetic models were used for fitting inactivation curves of Escherichia coli O157:H7. The residual sum of squares and the total sum of squares criteria were used to evaluate the models. The statistical indices of the kinetic models were used to fit inactivation data for E. coli O157:H7 by MTS at three temperatures (40, 50, and 60 0C) and three pressures (100, 200, and 300 kPa). Based on the statistical indices and visual observations, the Weibull and Biphasic models were best fitting of the data for MTS treatment as shown by high R2 values. The non-linear kinetic models, including the Modified Gompertz, First-order, and Log-logistic models did not provide any better fit to data from MTS compared the Weibull and Biphasic models. It was observed that the data found in this study did not follow the first-order kinetics. It is possibly because of the cells which are sensitive to ultrasound treatment were inactivated first, resulting in a fast inactivation period, while those resistant to ultrasound were killed slowly. The Weibull and biphasic models were found as more flexible in order to determine the survival curves of E. coli O157:H7 treated by MTS on apple-carrot juice.

Keywords: Weibull, Biphasic, MTS, kinetic models, E.coli O157:H7

Procedia PDF Downloads 366

Authors: Amaladhithyan Krishnamoorthy, Ruvaitha Banu

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Vision is considered as the most important sense in humans, without which leading a normal can be often difficult. There are many existing smart canes for visually impaired with obstacle detection using ultrasonic transducer to help them navigate. Though the basic smart cane increases the safety of the users, it does not help in filling the void of visual loss. This paper introduces the concept of Digi-Buddy which is an evolved smart cane for visually impaired. The cane consists for several modules, apart from the basic obstacle detection features; the Digi-Buddy assists the user by capturing video/images and streams them to the server using a wide-angled camera, which then detects the objects using Deep Convolutional Neural Network. In addition to determining what the particular image/object is, the distance of the object is assessed by the ultrasonic transducer. The sound generation application, modelled with the help of Natural Language Processing is used to convert the processed images/object into audio. The object detected is signified by its name which is transmitted to the user with the help of Bluetooth hear phones. The object detection is extended to facial recognition which maps the faces of the person the user meets in the database of face images and alerts the user about the person. One of other crucial function consists of an automatic-intimation-alarm which is triggered when the user is in an emergency. If the user recovers within a set time, a button is provisioned in the cane to stop the alarm. Else an automatic intimation is sent to friends and family about the whereabouts of the user using GPS. In addition to safety and security by the existing smart canes, the proposed concept devices to be implemented as a prototype helping visually-impaired visualize their surroundings through audio more in an amicable way.

Keywords: artificial intelligence, facial recognition, natural language processing, internet of things

Procedia PDF Downloads 355

Authors: Mirjana Đ. Laban, Dragan N. Đurica, Nemanja M. Erceg

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Volunteer fire departments (VFD) in Serbia were established as civic associations in XIX Century. The founders and members of the first VFDs were prominent members of local communities. Today, those are volunteer organizations for preventing and extinguishing fires and rescuing people and property in various accidents. The paper presents the results of research about the number and resources of active VFDs done in Autonomous Province of Vojvodina, Serbia and about activities they perform today. The survey was done based on data provided by all registered VFDs in Vojvodina. Firefighters Association of Vojvodina includes 35 municipal firefighting associations, 230 volunteer fire departments with 5,300 active members in qualified fire units and more than 15,000 supporting members. Volunteer involvement is primarily an expression of high moral values and as such it has to be respected and stimulated. Better position of the volunteers would have a major impact on the formation of safety culture concept and general public awareness of fire safety and risk reduction, and therefore the security of the society as a whole. Volunteer fire departments make a significant contribution to educate young people and prevent catastrophic consequences of fires and natural disasters.

Keywords: education, prevention, rescue, volunteer fire departments

Procedia PDF Downloads 201

Authors: Galit Shiff, Yael Gilad

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The need to retrofit old buildings lies in the fact that buildings are responsible for the main energy use and CO₂ emission. Existing old structures are more dominant in their effect than new energy-efficient buildings. Nevertheless not every case of urban renewal that aims to replace old buildings with new neighbourhoods necessarily has a financial or sustainable justification. Façade design plays a vital role in the building's energy performance and the unit's comfort conditions. A retrofit façade residential methodology and feasibility applicative study has been carried out for the past four years, with two projects already fully renovated. The intention of this study is to serve as a case study for limited budget façade retrofit in Mediterranean climate urban areas. The two case study buildings are set in Israel. However, they are set in different local climatic conditions. One is in 'Sderot' in the south of the country, and one is in' Migdal Hahemek' in the north of the country. The building typology is similar. The budget of the projects is around $14,000 per unit and includes interventions at the buildings' envelope while tenants are living in. Extensive research and analysis of the existing conditions have been done. The building's components, materials and envelope sections were mapped, examined and compared to relevant updated standards. Solar radiation simulations for the buildings in their surroundings during winter and summer days were done. The energy rate of each unit, as well as the building as a whole, was calculated according to the Israeli Energy Code. The buildings’ facades were documented with the use of a thermal camera during different hours of the day. This information was superimposed with data about the electricity use and the thermal comfort that was collected from the residential units. Later in the process, similar tools were further used in order to compare the effectiveness of different design options and to evaluate the chosen solutions. Both projects showed that the most problematic units were the ones below the roof and the ones on top of the elevated entrance floor (pilotis). Old buildings tend to have poor insulation on those two horizontal surfaces which require treatment. Different radiation levels and wall sections in the two projects influenced the design strategies: In the southern project, there was an extreme difference in solar radiations levels between the main façade and the back elevation. Eventually, it was decided to invest in insulating the main south-west façade and the side façades, leaving the back north-east façade almost untouched. Lower levels of radiation in the northern project led to a different tactic: a combination of basic insulation on all façades, together with intense treatment on areas with problematic thermal behavior. While poor execution of construction details and bad installation of windows in the northern project required replacing them all, in the southern project it was found that it is more essential to shade the windows than replace them. Although the buildings and the construction typology was chosen for this study are similar, the research shows that there are large differences due to the location in different climatic zones and variation in local conditions. Therefore, in order to reach a systematic and cost-effective method of work, a more extensive catalogue database is needed. Such a catalogue will enable public housing companies in the Mediterranean climate to promote massive projects of renovating existing old buildings, drawing on minimal analysis and planning processes.

Keywords: facade, low budget, residential, retrofit

Procedia PDF Downloads 208

Authors: Chen Xi, Liu Xuebing, Lao Xuerui, Kuan Sinman, Jiang Yike, Wang Hanwei, Yang Xiaolang, Zhou Junjie, Xie Jinpeng

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Climate change is a global problem facing humanity and this is now the consensus of the mainstream scientific community. In accordance with the carbon peak and carbon neutral targets and visions set out in the United Nations Framework Convention on Climate Change, the Kyoto Protocol and the Paris Agreement, this project uses the City Intelligent Model (CIM) and Artificial Intelligence Machine Vision (ICR) as the core technologies to accurately quantify low carbon behaviour into green corn, which is a means of guiding ecologically sustainable living patterns. Using individual communities as management units and blockchain as a guarantee of fairness in the whole cycle of green currency circulation, the project will form a modern resident carbon track management system based on the principle of enhancing the ecological resilience of communities and the cohesiveness of community residents, ultimately forming an ecologically sustainable smart village that can be self-organised and managed.

Keywords: urban planning, urban governance, CIM, artificial Intelligence, sustainable development

Procedia PDF Downloads 83