Search results for: gauge capability (Cg and Cgk)

Authors: Lia Marchi, Ernesto Antonini

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The impact of industrial sites on people’s living environment both involves detrimental effects on the ecosystem and perceptual-aesthetic interferences with the scenery. These, in turn, affect the economic and social value of the landscape, as well as the wellbeing of workers and local communities. Given the diffusion of the phenomenon and the relevance of its effects, it emerges the need for a joint approach to assess and thus mitigate the impact of factories on the landscape –being this latest assumed as the result of the action and interaction of natural and human factors. However, the impact assessment tools suitable for the purpose are quite heterogeneous and mostly monodisciplinary. On the one hand, green building rating systems (GBRSs) are increasingly used to evaluate the performance of manufacturing sites, mainly by quantitative indicators focused on environmental issues. On the other hand, methods to detect the visual and social impact of factories on the landscape are gradually emerging in the literature, but they generally adopt only qualitative gauges. The research addresses the integration of the environmental impact assessment and the perceptual-aesthetic interferences of factories on the landscape. The GBRSs model is assumed as a reference since it is adequate to simultaneously investigate different topics which affect sustainability, returning a global score. A critical analysis of GBRSs relevant to industrial facilities has led to select the U.S. GBC LEED protocol as the most suitable to the scope. A revision of LEED v4 Building Design+Construction has then been provided by including specific indicators to measure the interferences of manufacturing sites with the perceptual-aesthetic and social aspects of the territory. To this end, a new impact category was defined, namely ‘PA - Perceptual-aesthetic aspects’, comprising eight new credits which are specifically designed to assess how much the buildings are in harmony with their surroundings: these investigate, for example the morphological and chromatic harmonization of the facility with the scenery or the site receptiveness and attractiveness. The credits weighting table was consequently revised, according to the LEED points allocation system. As all LEED credits, each new PA credit is thoroughly described in a sheet setting its aim, requirements, and the available options to gauge the interference and get a score. Lastly, each credit is related to mitigation tactics, which are drawn from a catalogue of exemplary case studies, it also developed by the research. The result is a modified LEED scheme which includes compatibility with the landscape within the sustainability assessment of the industrial sites. The whole system consists of 10 evaluation categories, which contain in total 62 credits. Lastly, a test of the tool on an Italian factory was performed, allowing the comparison of three mitigation scenarios with increasing compatibility level. The study proposes a holistic and viable approach to the environmental impact assessment of factories by a tool which integrates the multiple involved aspects within a worldwide recognized rating protocol.

Keywords: environmental impact, GBRS, landscape, LEED, sustainable factory

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Authors: R. O. Osaseri, A. R. Usiobaifo

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The power transformer which is responsible for the voltage transformation is of great relevance in the power system and oil-immerse transformer is widely used all over the world. A prompt and proper maintenance of the transformer is of utmost importance. The dissolved gasses content in power transformer, oil is of enormous importance in detecting incipient fault of the transformer. There is a need for accurate prediction of the incipient fault in transformer oil in order to facilitate the prompt maintenance and reducing the cost and error minimization. Study on fault prediction and diagnostic has been the center of many researchers and many previous works have been reported on the use of artificial intelligence to predict incipient failure of transformer faults. In this study machine learning technique was employed by using gradient decent algorithms and Support Vector Machine (SVM) in predicting incipient fault diagnosis of transformer. The method focuses on creating a system that improves its performance on previous result and historical data. The system design approach is basically in two phases; training and testing phase. The gradient decent algorithm is trained with a training dataset while the learned algorithm is applied to a set of new data. This two dataset is used to prove the accuracy of the proposed model. In this study a transformer fault diagnostic model based on Support Vector Machine (SVM) and gradient decent algorithms has been presented with a satisfactory diagnostic capability with high percentage in predicting incipient failure of transformer faults than existing diagnostic methods.

Keywords: diagnostic model, gradient decent, machine learning, support vector machine (SVM), transformer fault

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Authors: Jinge Liu, Shuyuan Min, Bingchuan Liu, Bangzhao Yin, Bo Peng, Peng Wen, Yun Tian

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WE43 magnesium alloy can be additively manufactured via laser powder bed fusion (LPBF) for biodegradable applications, but the as-built WE43 exhibits an excessively rapid corrosion rate. High-temperature oxidation (HTO) was performed on the as-built WE43 to improve its biodegradation behavior. A sandwich structure including an oxide layer at the surface, a transition layer in the middle, and the matrix was generated influenced by the oxidation reaction and diffusion of RE atoms when heated at 525 ℃for 8 hours. The oxide layer consisted of Y₂O₃ and Nd₂O₃ oxides with a thickness of 2-3 μm. The transition layer is composed of α-Mg and Y₂O₃ with a thickness of 60-70 μm, while Mg24RE5 could be observed except α-Mg and Y₂O₃. The oxide layer and transition layer appeared to have an effective passivation effect. The as-built WE43 lost 40% weight after the in vitro immersion test for three days and finally broke into debris after seven days of immersion. The high-temperature oxidation samples kept the structural integrity and lost only 6.88 % weight after 28-day immersion. The corrosion rate of HTO samples was significantly controlled, which improved the biocompatibility of the as-built WE43 at the same time. The samples after HTO had better osteogenic capability according to ALP activity. Moreover, as built WE43 performed unqualified in cell adhesion and hemolytic test due to its excessively rapid corrosion rate. While as for HTO samples, cells adhered well, and the hemolysis ratio was only 1.59%.

Keywords: laser powder bed fusion, biodegradable metal, high temperature oxidation, biodegradation behavior, WE43

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Authors: Raymond Ezenweani, Jeffrey Ogbebor

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The menace of aquatic pollution has become increasingly of great concern and the effects of this pollution as a result of anthropogenic activities cannot be over emphasized. Phycoremediation is the application of algal remediation technology in the removal of harmful products from the environment. Harmful products also known as pollutants are usually introduced into the environment through variety of processes such as industrial discharge, agricultural runoff, flooding, and acid rain. This work has to do with the capability of algae in the efficient removal of different pollutants, ranging from hydrocarbons, eutrophication, agricultural chemicals and wastes, heavy metals, foul smell from septic tanks or dumps through different processes such as bioconversion, biosorption, bioabsorption and biodecomposition. Algae are capable of bioconversion of environmentally persistent compounds to degradable compounds and also capable of putting harmful bacteria growth into check in waste water remediation. Numerous algal organisms such as Nannochloropsis spp, Chlorella spp, Tetraselmis spp, Shpaerocystics spp, cyanobacteria and different macroalgae have been tested by different researchers in laboratory scale and shown to have 100% efficiency in environmental remediation. Algae as a result of their photosynthetic capacity are also efficient in air cleansing and management of global warming by sequestering carbon iv oxide in air and converting it into organic carbon, thereby making food available for the other organisms in the higher trophic level of the aquatic food chain. Algae play major role in the sustenance of the aquatic ecosystem by their virtue of being photosynthetic. They are the primary producers and their role in environmental sustainability is remarkable.

Keywords: Algae , Pollutant, ., Phycoremediation, Aquatic, Sustainability

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Authors: Daniel Adjei, Mesfin Getachew Ayele, Przemyslaw Wachulak, Andrzej Bartnik, Luděk Vyšín, Henryk Fiedorowicz, Inam Ul Ahad, Lukasz Wegrzynski, Anna Wiechecka, Janusz Lekki, Wojciech M. Kwiatek

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Laser produced plasma light sources, emitting high intensity pulses of X-rays, delivering high doses are useful to understand the mechanisms of high dose effects on biological samples. In this study, a desk-top laser plasma soft X-ray source, developed for radio biology research, is presented. The source is based on a double-stream gas puff target, irradiated with a commercial Nd:YAG laser (EKSPLA), which generates laser pulses of 4 ns time duration and energy up to 800 mJ at 10 Hz repetition rate. The source has been optimized for maximum emission in the “water window” wavelength range from 2.3 nm to 4.4 nm by using pure gas (argon, nitrogen and krypton) and spectral filtering. Results of the source characterization measurements and dosimetry of the produced soft X-ray radiation are shown and discussed. The high brightness of the laser produced plasma soft X-ray source and the low penetration depth of the produced X-ray radiation in biological specimen allows a high dose rate to be delivered to the specimen of over 28 Gy/shot; and 280 Gy/s at the maximum repetition rate of the laser system. The source has a unique capability for irradiation of cells with high pulse dose both in vacuum and He-environment. Demonstration of the source to induce DNA double- and single strand breaks will be discussed.

Keywords: laser produced plasma, soft X-rays, radio biology experiments, dosimetry

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Authors: Adrian Kong, William Chang, Rolando Valdes, Alec Rodriguez, Roberto Miki

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The Pneumask consists of a custom snorkel-specific adapter that attaches a snorkel-port of the mask to a 3D-printed filter. This full-face snorkel mask was designed for use as personal protective equipment (PPE) during the COVID-19 pandemic when there was a widespread shortage of PPE for medical personnel. Various clinical validation tests have been conducted, including the sealing capability of the mask, filter performance, CO2 buildup, and clinical usability. However, data regarding the filter efficiencies of Pneumask and multiple filter types have not been determined. Using an experimental system, we evaluated the filtration efficiency across various masks and filters during inhalation. Eighteen combinations of respirator models (5 P100 FFRs, 4 Dolfino Masks) and filters (2091, 7093, 7093CN, BB50T) were evaluated for their exposure to airborne particles sized 0.3 - 10.0 microns using an electronic airborne particle counter. All respirator model combinations provided similar performance levels for 1.0-micron, 3.0-micron, 5.0-micron, 10.0-microns, with the greatest differences in the 0.3-micron and 0.5-micron range. All models provided expected performances against all particle sizes, with Class P100 respirators providing the highest performance levels across all particle size ranges. In conclusion, the modified snorkel mask has the potential to protect providers who care for patients with COVID-19 from increased airborne particle exposure.

Keywords: COVID-19, PPE, mask, filtration, efficiency

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Authors: Rafael Alvarenga, Hubert Herbaux, Laurent Linguet

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The uncertainty concerning the power production of intermittent renewable energy is one of the main barriers to the integration of such assets into the power grid. Efforts have thus been made to develop methods to quantify this uncertainty, allowing producers to ensure more reliable and profitable engagements related to their future power delivery. Even though a diversity of probabilistic approaches was proposed in the literature giving promising results, the added value of adopting such methods for scheduling intermittent power plants is still unclear. In this study, the profits obtained by a decision-making model used to optimally schedule an existing PV power plant connected to batteries are compared when the model is fed with deterministic and probabilistic forecasts generated with two of the most recent methods proposed in the literature. Moreover, deterministic forecasts with different accuracy levels were used in the experiments, testing the utility and the capability of probabilistic methods of modeling the progressively increasing uncertainty. Even though probabilistic approaches are unquestionably developed in the recent literature, the results obtained through a study case show that deterministic forecasts still provide the best performance if accurate, ensuring a gain of 14% on final profits compared to the average performance of probabilistic models conditioned to the same forecasts. When the accuracy of deterministic forecasts progressively decreases, probabilistic approaches start to become competitive options until they completely outperform deterministic forecasts when these are very inaccurate, generating 73% more profits in the case considered compared to the deterministic approach.

Keywords: PV power forecasting, uncertainty quantification, optimal scheduling, power systems

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Authors: Lian Yang

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Object-oriented programming (OOP) is the dominant programming paradigm in today’s software industry and it has literally enabled average software developers to develop millions of commercial strength software applications in the era of INTERNET revolution over the past three decades. On the other hand, the lack of strict mathematical model and domain constraint features at the language level has long perplexed the computer science academia and OOP engineering community. This situation resulted in inconsistent system qualities and hard-to-understand designs in some OOP projects. The difficulties with regards to fix the current situation are also well known. Although the power of OOP lies in its unbridled flexibility and enormously rich data modeling capability, we argue that the ambiguity and the implicit facade surrounding the conceptual model of a class and an object should be eliminated as much as possible. We listed the five major usage of class and propose to separate them by proposing new language constructs. By using well-established theories of set and FSM, we propose to apply certain simple, generic, and yet effective constraints at OOP language level in an attempt to find a possible solution to the above-mentioned issues regarding OOP. The goal is to make OOP more theoretically sound as well as to aid programmers uncover warning signs of irregularities and domain-specific issues in applications early on the development stage and catch semantic mistakes at runtime, improving correctness verifiability of software programs. On the other hand, the aim of this paper is more practical than theoretical.

Keywords: new language constructs, set theory, FSM theory, user defined value type, function groups, membership qualification attribute (MQA), check-constraint (CC)

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Authors: S. Karmokar

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Over the last 20 years, there has been increasing interest in the topic of entrepreneurship education as it is seen in many countries as a way of enhancing the enterprise culture and promote capability building among community. There is also rapid growth of emerging technologies across the globe and forced entrepreneurs to searching for a new model of economic growth. There are two movements that are dominating and creating waves, Technology Entrepreneurship and Social Entrepreneurship. An increasing number of entrepreneurs are awakening to the possibility of combining the scalable tools and methodology of Technology Entrepreneurship with the value system of Social Entrepreneurship–‘Transformational Entrepreneurship’. To do this transitional educational institute’s need to figure out how to unite the scalable tools of Technology Entrepreneurship with the moral ethos of Social Entrepreneurship. The traditional entrepreneurship education model is wedded to top-down instructive approaches, that is widely used in management education have led to passive educational model. Despite the effort, disruptive’ pedagogies are rare in higher education; they remain underused and often marginalized. High impact and transformational entrepreneurship education and training require universities to adopt new practices and revise current, traditional ways of working. This is a conceptual research paper exploring the potential and growth of transformational entrepreneurship, investigating links between social entrepreneurship. Based on empirical studies and theoretical approaches, this paper outlines some educational approach for both academics and educational institutes to deliver emerging transformational entrepreneurship in tertiary education. The paper presents recommendations for tertiary educators to inform the designing of teaching practices, revise current delivery methods and encourage students to fulfill their potential as entrepreneurs.

Keywords: educational pedagogies, emerging technologies, social entrepreneurship, transformational entrepreneurship

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Authors: Cam Nguyen

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Rapid, accurate, and safe sensing and imaging of physical quantities or structures finds many applications and is of significant interest to society. Sensing and imaging using radio-frequency (RF) techniques, particularly, has gone through significant development and subsequently established itself as a unique territory in the sensing world. RF sensing and imaging has played a critical role in providing us many sensing and imaging abilities beyond our human capabilities, benefiting both civilian and military applications - for example, from sensing abnormal conditions underneath some structures’ surfaces to detection and classification of concealed items, hidden activities, and buried objects. We present the developments of several sensing and imaging systems implementing RF technologies like ultra-wide band (UWB), synthetic-pulse, and interferometry. These systems are fabricated completely using RF integrated circuits. The UWB impulse system operates over multiple pulse durations from 450 to 1170 ps with 5.5-GHz RF bandwidth. It performs well through tests of various samples, demonstrating its usefulness for subsurface sensing. The synthetic-pulse system operating from 0.6 to 5.6 GHz can assess accurately subsurface structures. The synthetic-pulse system operating from 29.72-37.7 GHz demonstrates abilities for various surface and near-surface sensing such as profile mapping, liquid-level monitoring, and anti-personnel mine locating. The interferometric system operating at 35.6 GHz demonstrates its multi-functional capability for measurement of displacements and slow velocities. These RF sensors are attractive and useful for various surface and subsurface sensing applications. This paper was made possible by NPRP grant # 6-241-2-102 from the Qatar National Research Fund (a member of Qatar Foundation). The statements made herein are solely the responsibility of the authors.

Keywords: RF sensors, radars, surface sensing, subsurface sensing

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Authors: Pratap Chandra Pati, Prabina Rajib, Parama Barai

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The traditional finance theory neglects the role of sentiment factor in asset pricing. However, the behavioral approach to asset-pricing based on noise trader model and limit to arbitrage includes investor sentiment as a priced risk factor in the assist pricing model. Investor sentiment affects stock more that are vulnerable to speculation, hard to value and risky to arbitrage. It includes small stocks, high volatility stocks, growth stocks, distressed stocks, young stocks and non-dividend-paying stocks. Since the introduction of Chicago Board Options Exchange (CBOE) volatility index (VIX) in 1993, it is used as a measure of future volatility in the stock market and also as a measure of investor sentiment. CBOE VIX index, in particular, is often referred to as the ‘investors’ fear gauge’ by public media and prior literature. The upward spikes in the volatility index are associated with bouts of market turmoil and uncertainty. High levels of the volatility index indicate fear, anxiety and pessimistic expectations of investors about the stock market. On the contrary, low levels of the volatility index reflect confident and optimistic attitude of investors. Based on the above discussions, we investigate whether market-wide fear levels measured volatility index is priced factor in the standard asset pricing model for the Indian stock market. First, we investigate the performance and validity of Fama and French three-factor model and Carhart four-factor model in the Indian stock market. Second, we explore whether India volatility index as a proxy for fearful market-based sentiment indicators affect the cross section of stock returns after controlling for well-established risk factors such as market excess return, size, book-to-market, and momentum. Asset pricing tests are performed using monthly data on CNX 500 index constituent stocks listed on the National stock exchange of India Limited (NSE) over the sample period that extends from January 2008 to March 2017. To examine whether India volatility index, as an indicator of fear sentiment, is a priced risk factor, changes in India VIX is included as an explanatory variable in the Fama-French three-factor model as well as Carhart four-factor model. For the empirical testing, we use three different sets of test portfolios used as the dependent variable in the in asset pricing regressions. The first portfolio set is the 4x4 sorts on the size and B/M ratio. The second portfolio set is the 4x4 sort on the size and sensitivity beta of change in IVIX. The third portfolio set is the 2x3x2 independent triple-sorting on size, B/M and sensitivity beta of change in IVIX. We find evidence that size, value and momentum factors continue to exist in Indian stock market. However, VIX index does not constitute a priced risk factor in the cross-section of returns. The inseparability of volatility and jump risk in the VIX is a possible explanation of the current findings in the study.

Keywords: India VIX, Fama-French model, Carhart four-factor model, asset pricing

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Authors: Nuruol S. Mohd, Safia Ahmed, Rumana Riffat, Baoqiang Li

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Anaerobic digestion at mesophilic and thermophilic temperatures have been widely studied and evaluated by numerous researchers. Limited extensive research has been conducted on anaerobic digestion in the intermediate zone of 45°C, mainly due to the notion that limited microbial activity occurs within this zone. The objectives of this research were to evaluate the performance and the capability of anaerobic digestion at 45°C in producing class A biosolids, in comparison to a mesophilic and thermophilic anaerobic digestion system operated at 35°C and 55°C, respectively. In addition to that, the investigation on the possible inhibition factors affecting the performance of the digestion system at this temperature will be conducted as well. The 45°C anaerobic digestion systems were not able to achieve comparable methane yield and high-quality effluent compared to the mesophilic system, even though the systems produced biogas with about 62-67% methane. The 45°C digesters suffered from high acetate accumulation, but sufficient buffering capacity was observed as the pH, alkalinity and volatile fatty acids (VFA)-to-alkalinity ratio were within recommended values. The accumulation of acetate observed in 45°C systems were presumably due to the high temperature which contributed to high hydrolysis rate. Consequently, it produced a large amount of toxic salts that combined with the substrate making them not readily available to be consumed by methanogens. Acetate accumulation, even though contributed to 52 to 71% reduction in acetate degradation process, could not be considered as completely inhibitory. Additionally, at 45°C, no ammonia inhibition was observed and the digesters were able to achieve volatile solids (VS) reduction of 47.94±4.17%. The pathogen counts were less than 1,000 MPN/g total solids, thus, producing Class A biosolids.

Keywords: 45°C anaerobic digestion, acetate accumulation, class A biosolids, salt toxicity

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Authors: Noraziah Nordin, Najihah Mohd Hashim, Nazia Abdul Majid, Mashitoh Abdul Rahman, Hamed Karimian, Hapipah Mohd Ali

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Enicosanthellum pulchrum belongs to family Annonaceae is also known as family of 'mempisang' in Malaysia. Liriodenine was isolated by prep-HPLC method. This method was first technique used for the isolation of this compound. The structure of the liriodenine was elucidated by 1D and 2D spectroscopy techniques. Liriodenine was tested on ovarian cancer cells line (CAOV-3) for MTT, AO/PI and cytotoxicity 3 assays. The MTT assay was performed to determine the cytotoxicity effect of lirodenine on CAOV-3 cells. The morphological changes on CAOV-3 cells were observed by AO/PI assay for the early and late stage of apoptosis, as well as necrosis. Meanwhile, the measurement of cell loss, nuclear morphology, DNA content, cell membrane permeability, mitochondrial membrane potential changes and cytochrome c release from mitochondria were detected through cytotoxicity 3 assay. The IC50 results showed liriodenine inhibits the growth of CAOV-3 cells after 24 h of treatment at 10.25 ± 1.06 µg/mL. After 48 and 72 h of treatments, the IC50 values were decreased to 7.65 ± 0:07 and 6.35 ± 1.62 µg/mL, respectively. The morphology changes can be seen on CAOV-3 with a production of cell membrane blebbing, cromatin condensation and apoptotic bodies with increasing time of treatment from 24 to 72 h. Evaluation of cytotoxicity 3 on CAOV-3 cells after treated with liriodenine, resulting loss of mitochondrial membrane potential and release of cytochrome c from mitochondria. The results demonstrated the capability of liriodenine as a promising anticancer agent, particularly on human ovarian cancer.

Keywords: Enicosanthellum pulchrum, ovarian cancer, apoptosis, cytotoxicity

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Authors: Natcha Ruamyat, Nichakorn Khondee

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The potential of an alkaliphilic bacteria isolated from soil in Thailand to utilized agro-industrial and agricultural wastes for the production of biosurfactants was evaluated in this study. Among five isolates, Pseudomonas mendocina NK41 used soapstock as substrate showing a high biosurfactant concentration of 7.10 g/L, oil displacement of 97.8 %, and surface tension reduction to 29.45 mN/m. Various agricultural residues were applied as mixed substrates with soapstock to enhance the synthesis of biosurfactants. The production of biosurfactant and bacterial growth was found to be the highest with coconut oil cake as compared to Sacha inchi shell, coconut kernel cake, and durian shell. The biodegradability of agro-industrial wastes was better than agricultural wastes, which allowed higher bacterial growth. The pretreatment of coconut oil cake by combined alkaline and hydrothermal method increased the production of biosurfactant from 12.69 g/L to 13.82 g/L. The higher microbial accessibility was improved by the swelling of the alkali-hydrothermal pretreated coconut oil cake, which enhanced its porosity and surface area. The pretreated coconut oil cake was reused twice in the repeated batch production, showing higher biosurfactant concentration up to 16.94 g/L from the second cycle. These results demonstrated the capability of using lignocellulosic wastes from agricultural and agro-industrial activities to produce a highly valuable biosurfactant. High biosurfactant yield with low-cost substrate reveals its potential towards further commercialization of biosurfactant on large-scale production.

Keywords: alkaliphilic bacteria, agricultural/agro-industrial wastes, biosurfactant, combined alkaline-hydrothermal pretreatment

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Authors: Taha Anjamrooz, Sareh Rajabi, Salwa Bheiry

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Selection between the available projects in bidding processes for the contractor is one of the essential areas to concentrate on. It is important for the contractor to choose the right projects within its portfolio during the tendering stage based on certain criteria. It should align the bidding process with its origination strategies and goals as a screening process to have the right portfolio pool to start with. Secondly, it should set the proper framework and use a suitable technique in order to optimize its selection process for concertation purpose and higher efforts during the tender stage with goals of success and winning. In this research paper, a two steps framework proposed to increase the efficiency of the contractor’s bidding process and the winning chance of getting the new projects awarded. In this framework, initially, all the projects pass through the first stage screening process, in which the portfolio basket will be evaluated and adjusted in accordance with the organization strategies to the reduced version of the portfolio pool, which is in line with organization activities. In the second stage, the contractor uses linear programming to optimize the portfolio pool based on available resources such as manpower, light equipment, heavy equipment, financial capability, return on investment, and success rate of winning the bid. Therefore, this optimization model will assist the contractor in utilizing its internal resource to its maximum and increase its winning chance for the new project considering past experience with clients, built-relation between two parties, and complexity in the exertion of the projects. The objective of this research will be to increase the contractor's winning chance in the bidding process based on the success rate and expected return on investment.

Keywords: bidding process, internal resources, optimization, contracting portfolio management

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Authors: Xi Liu, Fan Yuan

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In the evolving landscape of physics-based machine learning (PBML), particularly within fluid dynamics and its applications in electromechanical engineering, robot vision, and robot learning, achieving precision and alignment with researchers' specific needs presents a formidable challenge. In response, this work proposes a methodology that integrates neural transformation with a modified smoothed particle hydrodynamics model for generating transformed 3D fluid simulations. This approach is useful for nanoscale science, where the unique and complex behaviors of viscoelastic medium demand accurate neurally-transformed simulations for materials understanding and manipulation. In electromechanical engineering, the method enhances the design and functionality of fluid-operated systems, particularly microfluidic devices, contributing to advancements in nanomaterial design, drug delivery systems, and more. The proposed approach also aligns with the principles of PBML, offering advantages such as multi-fluid stylization and consistent particle attribute transfer. This capability is valuable in various fields where the interaction of multiple fluid components is significant. Moreover, the application of neurally-transformed hydrodynamical models extends to manufacturing processes, such as the production of microelectromechanical systems, enhancing efficiency and cost-effectiveness. The system's ability to perform neural transfer on 3D fluid scenes using a deep learning algorithm alongside physical models further adds a layer of flexibility, allowing researchers to tailor simulations to specific needs across scientific and engineering disciplines.

Keywords: physics-based machine learning, robot vision, robot learning, hydrodynamics

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Authors: Sanjeev Kumar, Vikas Kumar

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Inconel 718 (IN718) is a high strength nickel-based superalloy designed for high-temperature applications up to 650 °C. It is widely used in gas turbines of jet engines and related aerospace applications because of its good mechanical properties and structural stability at elevated temperatures. Because of good performance ratio and excellent process capability, this alloy has been used predominantly for aeronautic engine components like compressor disc and compressor blade. The main precipitates that contribute to high-temperature strength of IN718 are γʹ Ni₃(Al, Ti) and mainly γʹʹ (Ni₃ Nb). Various processes have been used for modification of the surface of components, such as Laser Shock Peening (LSP), Conventional Shot Peening (SP) and Ultrasonic Shot Peening (USP) to induce compressive residual stress (CRS) and development of fine-grained structure in the surface region. Surface nanostructure by ultrasonic shot peening is a novel methodology of surface modification to improve the overall performance of structural components. Surface nanostructure was developed on the peak aged IN718 superalloy using USP and its effect was studied on low cycle fatigue (LCF) life. Nanostructure of ~ 49 to 73 nm was developed in the surface region of the alloy by USP. The gage section of LCF samples was USPed for 5 minutes at a constant frequency of 20 kHz using StressVoyager to modify the surface. Strain controlled cyclic tests were performed for non-USPed and USPed samples at ±Δεt/2 from ±0.50% to ±1.0% at strain rate (ė) 1×10⁻³ s⁻¹ under reversal loading (R=‒1) at room temperature. The fatigue life of the USPed specimens was found to be more than that of the non-USPed ones. LCF life of the USPed specimen at Δεt/2=±0.50% was enhanced by more than twice of the non-USPed specimen.

Keywords: IN718 superalloy, nanostructure, USP, LCF life

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Authors: Mehrnoosh Omati, Mahmod Reza Sahebi

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The information on land use/land cover changing plays an essential role for environmental assessment, planning and management in regional development. Remotely sensed imagery is widely used for providing information in many change detection applications. Polarimetric Synthetic aperture radar (PolSAR) image, with the discrimination capability between different scattering mechanisms, is a powerful tool for environmental monitoring applications. This paper proposes a new boundary-based segmentation algorithm as a fundamental step for land cover change detection. In this method, first, two PolSAR images are segmented using integration of marker-controlled watershed algorithm and coupled Markov random field (MRF). Then, object-based classification is performed to determine changed/no changed image objects. Compared with pixel-based support vector machine (SVM) classifier, this novel segmentation algorithm significantly reduces the speckle effect in PolSAR images and improves the accuracy of binary classification in object-based level. The experimental results on Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR) polarimetric images show a 3% and 6% improvement in overall accuracy and kappa coefficient, respectively. Also, the proposed method can correctly distinguish homogeneous image parcels.

Keywords: coupled Markov random field (MRF), environment, object-based analysis, polarimetric SAR (PolSAR) images

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Authors: Chandan Deep Singh

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In present days, companies are facing the rapid competition in terms of customer requirements to be satisfied, new technologies to be integrated into future products, new safety regulations to be followed, new computer-based tools to be introduced into design activities that becomes more scientific. In today’s highly competitive market, survival focuses on various factors such as quality, innovation, adherence to standards, and rapid response as the basis for competitive advantage. For competitive advantage, companies have to produce various competencies: for improving the capability of suppliers and for strengthening the process of integrating technology. For more competitiveness, organizations should operate in a strategy driven way and have a strategic architecture for developing core competencies. Traditional ways to take such experience and develop competencies tend to take a lot of time and they are expensive. A new learning environment, which is built around a gaming engine, supports the development of competences in specific subject areas. Technology competencies have a significant role in firm innovation and competitiveness; they interact with the competitive environment. Technological competencies vary according to the type of competitive environment, thus enhancing firm innovativeness. Technological competency is gained through extensive experimentation and learning in its research, development and employment in manufacturing. This is a review paper based on competency and strategic success of automobile industry. The aim here is to study strategy formulation and competency tools in the industry. This work is a review of literature related to competency and strategy in automobile industry. This study involves review of 34 papers related to competency and strategy.

Keywords: manufacturing competency, strategic success, competitiveness, strategy formulation

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Authors: Yen Chia-Ju, Cheng Ding-Ruei

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This study took case office building as subject and explored the responsive work order repair request of facilities and equipment in offices and public areas by gray theory, with the purpose of providing for future related office building owners, executive managers, property management companies, mechanical and electrical companies as reference for deciding and assessing forecast model. Important conclusions of this study are summarized as follows according to the study findings: 1. Grey Relational Analysis discusses the importance of facilities repair number of six categories, namely, power systems, building systems, water systems, air conditioning systems, fire systems and manpower dispatch in order. In terms of facilities maintenance importance are power systems, building systems, water systems, air conditioning systems, manpower dispatch and fire systems in order. 2. GM (1,N) and regression method took maintenance hours as dependent variables and repair number, leased area and tenants number as independent variables and conducted single month forecast based on 12 data from January to December 2011. The mean absolute error and average accuracy of GM (1,N) from verification results were 6.41% and 93.59%; the mean absolute error and average accuracy of regression model were 4.66% and 95.34%, indicating that they have highly accurate forecast capability.

Keywords: rey theory, forecast model, Taipei 101, office buildings, property management, facilities, equipment

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Authors: Harold Mauricio Díaz-Vargas, Cristian Alfonso Jimenez-Castaño, David Augusto Cárdenas-Peña, Guillermo Alberto Ortiz-Gómez, Alvaro Angel Orozco-Gutierrez

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Precise identification of the nerves is a crucial task performed by anesthesiologists for an effective Peripheral Nerve Blocking (PNB). Now, anesthesiologists use ultrasound imaging equipment to guide the PNB and detect nervous structures. However, visual identification of the nerves from ultrasound images is difficult, even for trained specialists, due to artifacts and low contrast. The recent advances in deep learning make neural networks a potential tool for accurate nerve segmentation systems, so addressing the above issues from raw data. The most widely spread U-Net network yields pixel-by-pixel segmentation by encoding the input image and decoding the attained feature vector into a semantic image. This work proposes a conditioning approach and encoder pre-training to enhance the nerve segmentation of traditional U-Nets. Conditioning is achieved by the one-hot encoding of the kind of target nerve a the network input, while the pre-training considers five well-known deep networks for image classification. The proposed approach is tested in a collection of 619 US images, where the best C-UNet architecture yields an 81% Dice coefficient, outperforming the 74% of the best traditional U-Net. Results prove that pre-trained models with the conditional approach outperform their equivalent baseline by supporting learning new features and enriching the discriminant capability of the tested networks.

Keywords: nerve segmentation, U-Net, deep learning, ultrasound imaging, peripheral nerve blocking

Procedia PDF Downloads 86

Authors: Chanchai Wimon, Polin Muangngam, Thannapat Nimsumram, Chanin Prombutra, Prasert Aengchuan, Perawat Boonpuek

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Cleaning water containers is essential for drinking water production to prevent contamination and residual chemicals from washing liquid. Water distribution divisions in Thailand are facing a critical problem with residual contamination in 15-liter drinking water containers due to dust and residual chemicals (TDS value > 200) after normal washing. A thorough washing process is required before filling the purified water into each container. Unfortunately, the washing procedure and frequency do not align with the work instructions provided by the health department. The measured Total Dissolved Solids (TDS) value of the remaining water was found to range between 195–202, exceeding the standard TDS for excellent drinking water (50-190 ppm). This research uses the design of experiment technique in statistics to improve the washing process and reduce such contamination. Statistical data from our survey of the cleaning process is collected to identify affecting factors. Washing time and water volume are varied to test the efficiency of the washing process in reducing residual sediment in the water. The result indicates that cleaning the 15-liter container with 2 liters of tap water mixed with 15 milliliters of dishwashing liquid for 3.12 minutes per container is optimal, as the resulting TDS reduces to 189.75, falling within the standard value for good drinking water. This study result would benefit the drinking water industry in implementing a statistically evaluated cleaning procedure without conducting multiple trials, thus saving takt time and production costs.

Keywords: design of experiment, drinking water purification, quality management, production process reliability

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Authors: Sriram Venkatesh, V. Murali Mohan, T. V. Karthikeyan

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The main advantage of multi-directionally reinforced composites is the freedom to orient selected fibre types and hence derives the benefits of varying fibre volume fractions and there by accommodate the design loads of the final structure of composites. This technology provides the means to produce tailored composites with desired properties. Due to the high level of fibre integrity with through thickness reinforcement those composites are expected to exhibit superior load bearing characteristics with capability to carry load even after noticeable and apparent fracture. However a survey of published literature indicates inadequacy in the design and test data base for the complete characterization of the multidirectional composites. In this paper the research objective is focused on the development and testing of 4-D orthogonal composites with different preform configurations and resin systems. A preform is the skeleton 4D reinforced composite other than the matrix. In 4-D preforms fibre bundles are oriented in three directions at 1200 with respect to each other and they are on orthogonal plane with the fibre in 4th direction. This paper addresses the various types of 4-D composite manufacturing processes and the mechanical test methods followed for the material characterization. A composite analysis is also made, experiments on course and fine woven preforms are conducted and the findings of test results are discussed in this paper. The interpretations of the test results reveal several useful and interesting features. This should pave the way for more widespread use of the perform configurations for allied applications.

Keywords: multi-directionally reinforced composites, 4-D orthogonal preform, course weave, fine weave, fibre bundle spools, unit cell, fibre architecture, fibre volume fraction, fibre distribution

Procedia PDF Downloads 221

Authors: Sikander Nawaz Khan

Abstract:

Natural disasters like flood, earthquake, cyclone, volcanic eruption and others are causing immense losses to the property and lives every year. Current status and actual loss information of natural hazards can be determined and also prediction for next probable disasters can be made using different remote sensing and mapping technologies. Global Positioning System (GPS) calculates the exact position of damage. It can also communicate with wireless sensor nodes embedded in potentially dangerous places. GPS provide precise and accurate locations and other related information like speed, track, direction and distance of target object to emergency responders. Remote Sensing facilitates to map damages without having physical contact with target area. Now with the addition of more remote sensing satellites and other advancements, early warning system is used very efficiently. Remote sensing is being used both at local and global scale. High Resolution Satellite Imagery (HRSI), airborne remote sensing and space-borne remote sensing is playing vital role in disaster management. Early on Geographic Information System (GIS) was used to collect, arrange, and map the spatial information but now it has capability to analyze spatial data. This analytical ability of GIS is the main cause of its adaption by different emergency services providers like police and ambulance service. Full potential of these so called 3S technologies cannot be used in alone. Integration of GPS and other remote sensing techniques with GIS has pointed new horizons in modeling of earth science activities. Many remote sensing cases including Asian Ocean Tsunami in 2004, Mount Mangart landslides and Pakistan-India earthquake in 2005 are described in this paper.

Keywords: disaster mitigation, GIS, GPS, remote sensing

Procedia PDF Downloads 455

Authors: Imam Taufiqurrahman, Anugrah Adiwilaga, Egi Hidayat, Bambang Riyanto Trilaksono

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Autonomous Underwater Vehicle glider is one of the renewal of underwater vehicles. This vehicle is one of the autonomous underwater vehicles that are being developed in Indonesia. Glide ability is obtained by controlling the buoyancy and attitude of the vehicle using the movers within the vehicle. The glider motion mechanism is expected to provide energy resistance from autonomous underwater vehicles so as to increase the cruising range of rides while performing missions. The control system on the vehicle consists of three parts: controlling the attitude of the pitch, the buoyancy engine controller and the yaw controller. The buoyancy and pitch controls on the vehicle are sequentially referring to the finite state machine with pitch angle and depth of diving inputs to obtain a gliding cycle. While the yaw control is done through the rudder for the needs of the guide system. This research is focused on design and implementation of control system of Autonomous Underwater Vehicle glider based on PID anti-windup. The control system is implemented on an ARM TS-7250-V2 device along with a mathematical model of the vehicle in MATLAB using the hardware-in-the-loop simulation (HILS) method. The TS-7250-V2 is chosen because it complies industry standards, has high computing capability, minimal power consumption. The results show that the control system in HILS process can form glide cycle with depth and angle of operation as desired. In the implementation using half control and full control mode, from the experiment can be concluded in full control mode more precision when tracking the reference. While half control mode is considered more efficient in carrying out the mission.

Keywords: control system, PID, underwater glider, marine robotics

Procedia PDF Downloads 357

Authors: S. J. Matlan, M. Mukhlisin, M. R. Taha

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Characterization of the engineering behavior of unsaturated soil is dependent on the soil-water characteristic curve (SWCC), a graphical representation of the relationship between water content or degree of saturation and soil suction. A reasonable description of the SWCC is thus important for the accurate prediction of unsaturated soil parameters. The measurement procedures for determining the SWCC, however, are difficult, expensive, and time-consuming. During the past few decades, researchers have laid a major focus on developing empirical equations for predicting the SWCC, with a large number of empirical models suggested. One of the most crucial questions is how precisely existing equations can represent the SWCC. As different models have different ranges of capability, it is essential to evaluate the precision of the SWCC models used for each particular soil type for better SWCC estimation. It is expected that better estimation of SWCC would be achieved via a thorough statistical analysis of its distribution within a particular soil class. With this in view, a statistical analysis was conducted in order to evaluate the reliability of the SWCC prediction models against laboratory measurement. Optimization techniques were used to obtain the best-fit of the model parameters in four forms of SWCC equation, using laboratory data for relatively coarse-textured (i.e., sandy) soil. The four most prominent SWCCs were evaluated and computed for each sample. The result shows that the Brooks and Corey model is the most consistent in describing the SWCC for sand soil type. The Brooks and Corey model prediction also exhibit compatibility with samples ranging from low to high soil water content in which subjected to the samples that evaluated in this study.

Keywords: soil-water characteristic curve (SWCC), statistical analysis, unsaturated soil, geotechnical engineering

Procedia PDF Downloads 325

Authors: Mehrnoosh Abouzari, Shahrokh Shahraei

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With the development of artificial intelligence in the present age, intelligent machines and systems have proven their actual and potential capabilities and are mindful of increasing their presence in various fields of human life in the fields of industry, financial transactions, marketing, manufacturing, service affairs, politics, economics and various branches of the humanities .Therefore, despite the conservatism and prudence of law enforcement, the traces of artificial intelligence can be seen in various areas of law. Including judicial robotics capability estimation, intelligent judicial decision making system, intelligent defender and attorney strategy adjustment, dissemination and regulation of different and scattered laws in each case to achieve judicial coherence and reduce opinion, reduce prolonged hearing and discontent compared to the current legal system with designing rule-based systems, case-based, knowledge-based systems, etc. are efforts to apply AI in law. In this article, we will identify the ways in which AI is applied in its laws and regulations, identify the dominant concerns in this area and outline the relationship between these two areas in order to answer the question of how artificial intelligence can be used in different areas of law and what the implications of this application will be. The authors believe that the use of artificial intelligence in the three areas of legislative, judiciary and executive power can be very effective in governments' decisions and smart governance, and helping to reach smart communities across human and geographical boundaries that humanity's long-held dream of achieving is a global village free of violence and personalization and human error. Therefore, in this article, we are going to analyze the dimensions of how to use artificial intelligence in the three legislative, judicial and executive branches of government in order to realize its application.

Keywords: artificial intelligence, law, intelligent system, judge

Procedia PDF Downloads 98

Authors: Sangwoo Oh, Jaewoo Kim, Dongmin Seo, Jaewon Park, Yongha Hwang, Sungkyu Seo

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Plasmonics has been regarded one of the most powerful bio-sensing modalities to evaluate bio-molecular interactions in real-time. However, most of the plasmonic sensing methods are based on labeling metallic nanoparticles, e.g. gold or silver, as optical modulation markers, which are non-recyclable and expensive. This plasmonic modulation can be usually achieved through various nano structures, e.g., nano-hole arrays. Among those structures, plasmonic lens has been regarded as a unique plasmonic structure due to its light focusing characteristics. In this study, we introduce a custom designed plasmonic lens array for bio-sensing, which was simulated by finite-difference-time-domain (FDTD) approach and fabricated by top-down approach. In our work, we performed the FDTD simulations of various plasmonic lens designs for bacteria sensor, i.e., Samonella and Hominis. We optimized the design parameters, i.e., radius, shape, and material, of the plasmonic lens. The simulation results showed the change in the peak intensity value with the introduction of each bacteria and antigen i.e., peak intensity 1.8711 a.u. with the introduction of antibody layer of thickness of 15nm. For Salmonella, the peak intensity changed from 1.8711 a.u. to 2.3654 a.u. and for Hominis, the peak intensity changed from 1.8711 a.u. to 3.2355 a.u. This significant shift in the intensity due to the interaction between bacteria and antigen showed a promising sensing capability of the plasmonic lens. With the batch processing and bulk production of this nano scale design, the cost of biological sensing can be significantly reduced, holding great promise in the fields of clinical diagnostics and bio-defense.

Keywords: plasmonic lens, FDTD, fabrication, bacteria sensor, salmonella, hominis

Procedia PDF Downloads 257

Authors: Prachi Bhatt

Abstract:

Motivated workforce is an important asset of an organisation. Intrinsic motivation is one of the key aspects of people operations and performance. Researches have emphasized the significance of internal factors in individuals’ motivation. In the changing business scenario, it is a challenge for the organizations’ leaders to inspire and motivate their workforce. The present study deals with the intrinsic motivation potential of an individual which govern the innate capability of an individual driving him or her to behave or perform in the changing work environment, tasks, teams. Differences at individual level significantly influence differences in levels of motivation. In the above context, the present research attempts to explore behavioral trait dimensions which influence motivational potential of an individual. The present research emphasizes the significance of intrinsic motivational potential and the significance of exploring the differences in the intrinsic motivational potential levels of individuals at work places. Thus, this paper empirically tests the framework of behavioral traits which affects motivational potential of an individual. With the help of two studies i.e., Study 1 and Study 2, exploratory factor analysis and confirmatory factor analysis, respectively, indicated a reliable measure assessing intrinsic motivational potential of an individual. Given the variety of challenges of motivating contemporary workforce, and with increasing importance of intrinsic motivation, the paper discusses the relevance of the findings and of the measure assessing intrinsic motivational potential. Assessment of such behavioral traits would assist in the effective realization of intrinsic motivational potential of individuals. Additionally, the paper discusses the practical implications and furnishes scope for future research.

Keywords: behavioral traits, individual differences, intrinsic motivational potential, intrinsic motivation, motivation, workplace motivation

Procedia PDF Downloads 182

Authors: Rabih Ghostine, Craig Kapfer, Viswanathan Kannan, Ibrahim Hoteit

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Urban flooding resulting from a sudden release of water due to dam-break or excessive rainfall is a serious threatening environment hazard, which causes loss of human life and large economic losses. Anticipating floods before they occur could minimize human and economic losses through the implementation of appropriate protection, provision, and rescue plans. This work reports on the numerical modelling of flash flood propagation in urban areas after an excessive rainfall event or dam-break. A two-dimensional (2D) depth-averaged shallow water model is used with a refined unstructured grid of triangles for representing the urban area topography. The 2D shallow water equations are solved using a second-order well-balanced discontinuous Galerkin scheme. Theoretical test case and three flood events are described to demonstrate the potential benefits of the scheme: (i) wetting and drying in a parabolic basin (ii) flash flood over a physical model of the urbanized Toce River valley in Italy; (iii) wave propagation on the Reyran river valley in consequence of the Malpasset dam-break in 1959 (France); and (iv) dam-break flood in October 1982 at the town of Sumacarcel (Spain). The capability of the scheme is also verified against alternative models. Computational results compare well with recorded data and show that the scheme is at least as efficient as comparable second-order finite volume schemes, with notable efficiency speedup due to parallelization.

Keywords: dam-break, discontinuous Galerkin scheme, flood modeling, shallow water equations

Procedia PDF Downloads 162