Search results for: safety stock optimization

Authors: C. A. Akaash Emmanuel Raj, V. R. Sanal Kumar

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In this paper using smart materials we have proposed a specially manufactured variable volume spin tub for loading clothes for negating the vibration to a certain extent for getting better operating performance. Additionally, we have recommended a variable spinning speed rotor for handling varieties of garments for an efficient washing, aiming for increasing the life span of both the garments and the machine. As a part of the conflicting dynamic constraints and demands of the customer friendly design optimization of a lucrative and cosmetic washing machine we have proposed a drier and a desalination system capable to supply desirable heat and a pleasing fragrance to the garments. We thus concluded that while incorporating variable volume and variable spinning speed tub integrated with a drier and desalination system, the washing machine could meet the varieties of domestic requirements of the customers cost-effectively.

Keywords: customer friendly washing machine, drier design, quick cloth cleaning, variable tub volume washing machine, variable spinning speed washing machine

Procedia PDF Downloads 242

Authors: Sholeh Motaghian, Pekka Toivanen, Keiji Haataja

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The swift expansion of the transportation industry and its associated emissions have captured the focus of policymakers who are dedicated to upholding ecological sustainability. As a result, understanding the key contributors to transportation emissions is of utmost significance. Amidst the escalating transportation emissions, the significance of electric vehicles cannot be overstated. Electric vehicles play a critical role in steering us towards a low-carbon economy and a sustainable ecological setting. The effective integration of electric vehicles hinges on the development of energy consumption models capable of accurately and efficiently predicting energy usage. Enhancing the energy efficiency of electric vehicles will play a pivotal role in reducing driver concerns and establishing a vital framework for the efficient operation, planning, and management of charging infrastructure. In this article, the works done in this field are reviewed, and the advantages and disadvantages of each are stated.

Keywords: deep learning, electrical vehicle, energy consumption, machine learning, smart grid

Procedia PDF Downloads 59

Authors: Zefry Darmawan, Shigeyuki Haruyama, Ken Kaminishi

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Product development is a big issue in the industrial competition and takes a serious part in development of technology. Product development process could adapt high changes of market needs and transform into engineering concept in order to produce high-quality product. One of the latest methods in product development is Analysis-Led-Design (ALD). It utilizes digital engineering design tools with finite analysis to perform product robust analysis and valuable for product reliability assurance. Heavy machinery which operates under severe condition should maintain safety to the customer when faced with potential hazard. Cab frame should able to absorb the energy while collision. Through ALD, a series of improvement of cab frame to increase energy absorption was made and analyzed. Improvement was made by modifying shapes of frame and-or install absorption device in certain areas. Simulation result showed that install absorption device could increase absorption energy than modifying shape.

Keywords: ALD, ROPS, energy absorption, cab frame

Procedia PDF Downloads 359

Authors: V. Giacometti, R. Mirabelli, V. Patera, D. Pinci, A. Sarti, A. Sciubba, G. Traini, M. Marafini

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The particle therapy (PT) is a very modern technique of non invasive radiotherapy mainly devoted to the treatment of tumours untreatable with surgery or conventional radiotherapy, because localised closely to organ at risk (OaR). Nowadays, PT is available in about 55 centres in the word and only the 20\% of them are able to treat with carbon ion beam. However, the efficiency of the ion-beam treatments is so impressive that many new centres are in construction. The interest in this powerful technology lies to the main characteristic of PT: the high irradiation precision and conformity of the dose released to the tumour with the simultaneous preservation of the adjacent healthy tissue. However, the beam interactions with the patient produce a large component of secondary particles whose additional dose has to be taken into account during the definition of the treatment planning. Despite, the largest fraction of the dose is released to the tumour volume, a non-negligible amount is deposed in other body regions, mainly due to the scattering and nuclear interactions of the neutrons within the patient body. One of the main concerns in PT treatments is the possible occurrence of secondary malignant neoplasm (SMN). While SMNs can be developed up to decades after the treatments, their incidence impacts directly life quality of the cancer survivors, in particular in pediatric patients. Dedicated Treatment Planning Systems (TPS) are used to predict the normal tissue toxicity including the risk of late complications induced by the additional dose released by secondary neutrons. However, no precise measurement of secondary neutrons flux is available, as well as their energy and angular distributions: an accurate characterization is needed in order to improve TPS and reduce safety margins. The project MONDO (MOnitor for Neutron Dose in hadrOntherapy) is devoted to the construction of a secondary neutron tracker tailored to the characterization of that secondary neutron component. The detector, based on the tracking of the recoil protons produced in double-elastic scattering interactions, is a matrix of thin scintillating fibres, arranged in layer x-y oriented. The final size of the object is 10 x 10 x 20 cm3 (squared 250µm scint. fibres, double cladding). The readout of the fibres is carried out with a dedicated SPAD Array Sensor (SBAM) realised in CMOS technology by FBK (Fondazione Bruno Kessler). The detector is under development as well as the SBAM sensor and it is expected to be fully constructed for the end of the year. MONDO will make data tacking campaigns at the TIFPA Proton Therapy Center of Trento, at the CNAO (Pavia) and at HIT (Heidelberg) with carbon ion in order to characterize the neutron component and predict the additional dose delivered on the patients with much more precision and to drastically reduce the actual safety margins. Preliminary measurements with charged particles beams and MonteCarlo FLUKA simulation will be presented.

Keywords: secondary neutrons, particle therapy, tracking detector, elastic scattering

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Authors: Raymond Limen Njinga

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The comparator factors (Fc) for miniature research reactors are of great importance in the field of nuclear physics as it provide accurate bases for the evaluation of elements in all form of samples via ko-NAA techniques. The Fc was initially simulated theoretically thereafter, series of experiments were performed to validate the results. In this situation, the experimental values were obtained using the alloy of Au(0.1%) - Al monitor foil and a neutron flux setting of 5.00E+11 cm-2.s-1. As was observed in the inner irradiation position, the average experimental value of 7.120E+05 was reported against the theoretical value of 7.330E+05. In comparison, a percentage deviation of 2.86 (from theoretical value) was observed. In the large case of the outer irradiation position, the experimental value of 1.170E+06 was recorded against the theoretical value of 1.210E+06 with a percentage deviation of 3.310 (from the theoretical value). The estimation of equivalent dose rate at 5m from neutron flux of 5.00E+11 cm-2.s-1 within the neutron energies of 1KeV, 10KeV, 100KeV, 500KeV, 1MeV, 5MeV and 10MeV were calculated to be 0.01 Sv/h, 0.01 Sv/h, 0.03 Sv/h, 0.15 Sv/h, 0.21Sv/h and 0.25 Sv/h respectively with a total dose within a period of an hour was obtained to be 0.66 Sv.

Keywords: neutron flux, comparator factor, NAA techniques, neutron energy, equivalent dose

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Authors: Andrés Durán, Brian Castellanos, Marta Quicazán, Carlos Zuluaga-Domínguez

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Bee-pollen is an apicultural-derived food product, with a growing appreciation among consumers given the remarkable nutritional and functional composition, in particular, protein (24%), dietary fiber (15%), phenols (15 – 20 GAE/g) and carotenoids (600 – 900 µg/g). These properties are given by the geographical and climatic characteristics of the region where it is collected. There are several countries recognized by their pollen production, e.g. China, United States, Japan, Spain, among others. Beekeepers use traps in the entrance of the hive where bee-pollen is collected. After the removal of foreign particles and drying, this product is ready to be marketed. However, in countries located along the equator, the absence of seasons and a constant tropical climate throughout the year favors a more rapid spoilage condition for foods with elevated water activity. The climatic conditions also trigger the proliferation of microorganisms and insects. This, added to the factor that beekeepers usually do not have adequate processing systems for bee-pollen, leads to deficiencies in the quality and safety of the product. In contrast, the Andean region of South America, lying on equator, typically has a high production of bee-pollen of up to 36 kg/year/hive, being four times higher than in countries with marked seasons. This region is also located in altitudes superior to 2500 meters above sea level, having extremes sun ultraviolet radiation all year long. As a mechanism of defense of radiation, plants produce more secondary metabolites acting as antioxidant agents, hence, plant products such as bee-pollen contain remarkable more phenolics and carotenoids than collected in other places. Considering this, the improvement of bee-pollen processing facilities by technical modifications and the implementation of an integrated cleaning and drying system for the product in an apiary in the area was proposed. The beehives were modified through the installation of alternative bee-pollen traps to avoid sources of contamination. The processing facility was modified according to considerations of Good Manufacturing Practices, implementing the combined use of a cabin dryer with temperature control and forced airflow and a greenhouse-type solar drying system. Additionally, for the separation of impurities, a cyclone type system was implemented, complementary to a screening equipment. With these modifications, a decrease in the content of impurities and the microbiological load of bee-pollen was seen from the first stages, principally with a reduction of the presence of molds and yeasts and in the number of foreign animal origin impurities. The use of the greenhouse solar dryer integrated to the cabin dryer allowed the processing of larger quantities of product with shorter waiting times in storage, reaching a moisture content of about 6% and a water activity lower than 0.6, being appropriate for the conservation of bee-pollen. Additionally, the contents of functional or nutritional compounds were not affected, even observing an increase of up to 25% in phenols content and a non-significant decrease in carotenoids content and antioxidant activity.

Keywords: beekeeping, drying, food processing, food safety

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Authors: Abobaker Mohammed Qasem Farhan, Olof M. A. Saif

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The demand for parking spaces in urban areas, particularly in developing countries, has led to a significant issue in the absence of sufficient parking spaces in crowded areas, which results in daily traffic congestion as drivers search for parking. This not only affects the appearance of the city but also has indirect impacts on the economy, society, and environment. In response to these challenges, researchers from various countries have sought technical and intelligent solutions to mitigate the problem through the development of smart parking systems. This paper aims to analyze and design three models of parking lots, with a focus on parking time and security. The study used computer software and Uppaal tools to simulate the models and determine the best among them. The results and suggestions provided in the paper aim to reduce the parking problems and improve the overall efficiency and safety of the parking process. The conclusion of the study highlights the importance of utilizing advanced technology to address the pressing issue of insufficient parking spaces in urban areas.

Keywords: preliminaries, system requirements, timed Au- tomata, Uppaal

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Authors: G. Blugan, B. Jiang, J. Thornberry, P. Sturzenegger, U. Gonzenbach, M. Misson, D. Cartlidge, R. Stenerud, J. Kuebler

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Low-cost ceramic spheres were developed and manufactured from the engineering ceramic aluminium oxide. Hollow spheres of 50 mm diameter with a wall thickness of 0.5-1.0 mm were produced via an adapted slip casting technique. It was possible to produce the spheres with good repeatability and with no defects or failures in the spheres due to the manufacturing process. The spheres were developed specifically for use in buoyancy devices for deep-sea exploration conditions at depths of 3000 m below sea level. The spheres with a 1.0 mm wall thickness exhibit a buoyancy of over 54% while the spheres with a 0.5 mm wall thickness exhibit a buoyancy of over 73%. The mechanical performance of the spheres was confirmed by performing a hydraulic burst pressure test on individual spheres. With a safety factor of 3, all spheres with 1.0 mm wall thickness survived a hydraulic pressure of greater than 150 MPa which is equivalent to a depth of more than 5000 m below sea level. The spheres were then incorporated into a buoyancy module. These hollow aluminium oxide ceramic spheres offer an excellent possibility of deep-sea exploration to depths greater than the currently used technology.

Keywords: buoyancy, ceramic spheres, deep-sea, oil exploration

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Authors: Milorad Pavlovic, Anna Manzato, Antonella Cecchi

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Cultural heritage conservation is a challenge for contemporary society. In recent decades, significant resources have been allocated for the conservation and restoration of architectural heritage. Historical buildings were restored, protected and reinforced with the intent to limit the risks of degradation or loss, due to phenomena of structural damage and to external factors such as differential settlements, earthquake effects, etc. The wide diffusion of historic masonry constructions in Italy, Europe and the Mediterranean area requires reliable tools for the evaluation of their structural safety. In this paper is presented a free modal analysis performed on a minor historical architecture located in the village of Bagno Grande, near the city of L’Aquila in Italy. The location is characterized by a complex urban context, seriously damaged by the earthquake of 2009. The aim of this work is to check the structural behavior of a masonry building characterized by several boundary conditions imposed by adjacent buildings and infrastructural facilities.

Keywords: FEM, masonry, minor historical architecture, modal analysis

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Authors: Arun Goel

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Prediction of maximum local scour is necessary for the safety and economical design of the bridges. A number of equations have been developed over the years to predict local scour depth using laboratory data and a few pier equations have also been proposed using field data. Most of these equations are empirical in nature as indicated by the past publications. In this paper, attempts have been made to compute local depth of scour around bridge pier in dimensional and non-dimensional form by using linear regression, simple regression and SVM (Poly and Rbf) techniques along with few conventional empirical equations. The outcome of this study suggests that the SVM (Poly and Rbf) based modeling can be employed as an alternate to linear regression, simple regression and the conventional empirical equations in predicting scour depth of bridge piers. The results of present study on the basis of non-dimensional form of bridge pier scour indicates the improvement in the performance of SVM (Poly and Rbf) in comparison to dimensional form of scour.

Keywords: modeling, pier scour, regression, prediction, SVM (Poly and Rbf kernels)

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Authors: Wanchalerm Patanacharoenwong, Panaya Sudta, Prachya Bumrungkun

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The paper focuses on the development of a system that combines Internet of Things (IoT) technologies and deep learning algorithms for anomaly detection in residential Electric Vehicle (EV) charging in smart cities. With the increasing number of EVs, ensuring efficient and reliable charging systems has become crucial. The aim of this research is to develop an integrated IoT and deep learning system for detecting anomalies in residential EV charging and enhancing EV load profiling and event detection in smart cities. This approach utilizes IoT devices equipped with infrared cameras to collect thermal images and household EV charging profiles from the database of Thailand utility, subsequently transmitting this data to a cloud database for comprehensive analysis. The methodology includes the use of advanced deep learning techniques such as Recurrent Neural Networks (RNN) and Long Short-Term Memory (LSTM) algorithms. IoT devices equipped with infrared cameras are used to collect thermal images and EV charging profiles. The data is transmitted to a cloud database for comprehensive analysis. The researchers also utilize feature-based Gaussian mixture models for EV load profiling and event detection. Moreover, the research findings demonstrate the effectiveness of the developed system in detecting anomalies and critical profiles in EV charging behavior. The system provides timely alarms to users regarding potential issues and categorizes the severity of detected problems based on a health index for each charging device. The system also outperforms existing models in event detection accuracy. This research contributes to the field by showcasing the potential of integrating IoT and deep learning techniques in managing residential EV charging in smart cities. The system ensures operational safety and efficiency while also promoting sustainable energy management. The data is collected using IoT devices equipped with infrared cameras and is stored in a cloud database for analysis. The collected data is then analyzed using RNN, LSTM, and feature-based Gaussian mixture models. The approach includes both EV load profiling and event detection, utilizing a feature-based Gaussian mixture model. This comprehensive method aids in identifying unique power consumption patterns among EV owners and outperforms existing models in event detection accuracy. In summary, the research concludes that integrating IoT and deep learning techniques can effectively detect anomalies in residential EV charging and enhance EV load profiling and event detection accuracy. The developed system ensures operational safety and efficiency, contributing to sustainable energy management in smart cities.

Keywords: cloud computing framework, recurrent neural networks, long short-term memory, Iot, EV charging, smart grids

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Authors: Ali Pour Yazdanpanah, Farideh Foroozandeh Shahraki, Emma Regentova

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The reconstruction from sparse-view projections is one of important problems in computed tomography (CT) limited by the availability or feasibility of obtaining of a large number of projections. Traditionally, convex regularizers have been exploited to improve the reconstruction quality in sparse-view CT, and the convex constraint in those problems leads to an easy optimization process. However, convex regularizers often result in a biased approximation and inaccurate reconstruction in CT problems. Here, we present a nonconvex, Lipschitz continuous and non-smooth regularization model. The CT reconstruction is formulated as a nonconvex constrained L1 − L2 minimization problem and solved through a difference of convex algorithm and alternating direction of multiplier method which generates a better result than L0 or L1 regularizers in the CT reconstruction. We compare our method with previously reported high performance methods which use convex regularizers such as TV, wavelet, curvelet, and curvelet+TV (CTV) on the test phantom images. The results show that there are benefits in using the nonconvex regularizer in the sparse-view CT reconstruction.

Keywords: computed tomography, non-convex, sparse-view reconstruction, L1-L2 minimization, difference of convex functions

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Authors: Fatemeh Khalili Varnamkhasti

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In recent times, there has been a notable shift in the application of Total Quality Management (TQM) from manufacturing to service organizations, prompting numerous studies on the subject. TQM has firmly established itself across various sectors, emerging as an approach to process improvement, waste reduction, business optimization, and quality performance. Many researchers and academics have recognized the relevance of TQM for sustainable competitive advantage, particularly in service organizations. In light of this, the purpose of this research study is to explore the applicability of TQM within the service framework. The study delves into existing literature on TQM in service organizations and examines the reasons for its occasional shortcomings. Ultimately, the paper provides systematic guidelines for the effective implementation of TQM in service organizations. The findings of this study offer a much-improved understanding of TQM and its practices, shedding light on the evolution of service organizations. Additionally, the study highlights key insights from recent research on TQM in service organizations and proposes a ten-step approach for the successful implementation of TQM in the service sector. This framework aims to provide service managers and professionals with a comprehensive understanding of TQM fundamentals and encourages a deeper exploration of TQM theory.

Keywords: quality, control, service, management, teamwork

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Authors: Nadia Belu, Laurenţiu Mihai Ionescu, Agnieszka Misztal

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The automotive industry is one of the most important industries in the world that concerns not only the economy, but also the world culture. In the present financial and economic context, this field faces new challenges posed by the current crisis, companies must maintain product quality, deliver on time and at a competitive price in order to achieve customer satisfaction. Two of the most recommended techniques of quality management by specific standards of the automotive industry, in the product development, are Failure Mode and Effects Analysis (FMEA) and Control Plan. FMEA is a methodology for risk management and quality improvement aimed at identifying potential causes of failure of products and processes, their quantification by risk assessment, ranking of the problems identified according to their importance, to the determination and implementation of corrective actions related. The companies use Control Plans realized using the results from FMEA to evaluate a process or product for strengths and weaknesses and to prevent problems before they occur. The Control Plans represent written descriptions of the systems used to control and minimize product and process variation. In addition Control Plans specify the process monitoring and control methods (for example Special Controls) used to control Special Characteristics. In this paper we propose a computer-aided solution with Genetic Algorithms in order to reduce the drafting of reports: FMEA analysis and Control Plan required in the manufacture of the product launch and improved knowledge development teams for future projects. The solution allows to the design team to introduce data entry required to FMEA. The actual analysis is performed using Genetic Algorithms to find optimum between RPN risk factor and cost of production. A feature of Genetic Algorithms is that they are used as a means of finding solutions for multi criteria optimization problems. In our case, along with three specific FMEA risk factors is considered and reduce production cost. Analysis tool will generate final reports for all FMEA processes. The data obtained in FMEA reports are automatically integrated with other entered parameters in Control Plan. Implementation of the solution is in the form of an application running in an intranet on two servers: one containing analysis and plan generation engine and the other containing the database where the initial parameters and results are stored. The results can then be used as starting solutions in the synthesis of other projects. The solution was applied to welding processes, laser cutting and bending to manufacture chassis for buses. Advantages of the solution are efficient elaboration of documents in the current project by automatically generating reports FMEA and Control Plan using multiple criteria optimization of production and build a solid knowledge base for future projects. The solution which we propose is a cheap alternative to other solutions on the market using Open Source tools in implementation.

Keywords: automotive industry, FMEA, control plan, automotive technology

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Authors: Rakan A. Ahmed, Raja S. Khan, Mohammed M. Qahtani

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This paper explores the transformative impact of laser cladding technology on the circular economy, emphasizing its role in reducing environmental impact compared to traditional welding methods. Laser cladding, an innovative manufacturing process, optimizes resource efficiency and sustainability by significantly decreasing power consumption and minimizing material waste. The study explores how laser cladding operates within the framework of the circular economy, promoting energy efficiency, waste reduction, and emissions control. Through a comparative analysis of energy and material consumption between laser cladding and conventional welding methods, the paper highlights the significant strides in environmental conservation and resource optimization made possible by laser cladding. The findings highlight the potential for this technology to revolutionize industrial practices and propel a more sustainable and eco-friendly manufacturing landscape.

Keywords: laser cladding, circular economy, carbon emission, energy

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Authors: M. A. Aleem, M. S. Awan

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Flat spiral coil for electromagnetic forming system has been modelled in FEMM 4.2 software. Copper strip was chosen as the material for designing the actuator coil. Relationship between height to width ratio (S-factor) of the copper strip and coil’s performance has been studied. Magnetic field intensities, eddy currents, and Lorentz force were calculated for the coils that were designed using six different 'S-factor' values (0.65, 0.75, 1.05, 1.25, 1.54 and 1.75), keeping the cross-sectional area of strip the same. Results obtained through simulation suggest that actuator coil with S-factor ~ 1 shows optimum forming performance as it exerts maximum Lorentz force (84 kN) on work piece. The same coils were fabricated and used for electromagnetic sheet forming experiments. Aluminum 6061 sheets of thickness 1.5 mm have been formed using different voltage levels of capacitor bank. Smooth forming profiles were obtained with dome heights 28, 35 and 40 mm in work piece at 800, 1150 and 1250 V respectively.

Keywords: FEM modelling, electromagnetic forming, spiral coil, Lorentz force

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Authors: Sagir M. Yusuf, Chris Baber

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We describe issues bedeviling the coordination of heterogeneous (different sensors carrying agents) multi-agent missions such as belief conflict, situation reasoning, etc. We applied Bayesian and agents' presumptions inferential reasoning to solve the outlined issues with the heterogeneous multi-agent belief variation and situational-base reasoning. Bayesian Belief Network (BBN) was used in modeling the agents' belief conflict due to sensor variations. Simulation experiments were designed, and cases from agents’ missions were used in training the BBN using gradient descent and expectation-maximization algorithms. The output network is a well-trained BBN for making inferences for both agents and human experts. We claim that the Bayesian learning algorithm prediction capacity improves by the number of training data and argue that it enhances multi-agents robustness and solve agents’ sensor conflicts.

Keywords: distributed constraint optimization problem, multi-agent system, multi-robot coordination, autonomous system, swarm intelligence

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Authors: Shrestha Sharma, Jasjeet K. Sahni, Javed Ali, Sanjula Baboota

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Introduction: Rutin is a naturally occurring strong antioxidant molecule belonging to bioflavonoid category. Due to its free radical scavenging properties, it has been found to be beneficial in the treatment of various diseases including inflammation, cancer, diabetes, allergy, cardiovascular disorders and various types of microbial infections. Despite its beneficial effects, it suffers from the problem of low aqueous solubility which is responsible for low oral bioavailability. The aim of our study was to optimize and characterize self-nanoemulsifying drug delivery system (SNEDDS) of rutin using Box-Behnken design (BBD) combined with a desirability function. Further various antioxidant, pharmacokinetic and pharmacodynamic studies were performed for the optimized rutin SNEDDS formulation. Methodologies: Selection of oil, surfactant and co-surfactant was done on the basis of solubility/miscibility studies. Sefsol+ Vitamin E, Solutol HS 15 and Transcutol P were selected as oil phase, surfactant and co-surfactant respectively. Optimization of SNEDDS formulations was done by a three-factor, three-level (33)BBD. The independent factors were Sefsol+ Vitamin E, Solutol HS15, and Transcutol P. The dependent variables were globule size, self emulsification time (SEF), % transmittance and cumulative percentage drug released. Various response surface graphs and contour plots were constructed to understand the effect of different factor, their levels and combinations on the responses. The optimized Rutin SNEDDS formulation was characterized for various parameters such as globule size, zeta potential, viscosity, refractive index , % Transmittance and in vitro drug release. Ex vivo permeation studies and pharmacokinetic studies were performed for optimized formulation. Antioxidant activity was determined by DPPH and reducing power assays. Anti-inflammatory activity was determined by using carrageenan induced rat paw oedema method. Permeation of rutin across small intestine was assessed using confocal laser scanning microscopy (CLSM). Major findings:The optimized SNEDDS formulation consisting of Sefsol+ Vitamin E - Solutol HS15 -Transcutol HP at proportions of 25:35:17.5 (w/w) was prepared and a comparison of the predicted values and experimental values were found to be in close agreement. The globule size and PDI of optimized SNEDDS formulation was found to be 16.08 ± 0.02 nm and 0.124±0.01 respectively. Significant (p˂0.05) increase in percentage drug release was achieved in the case of optimized SNEDDS formulation (98.8 %) as compared to rutin suspension. Furthermore, pharmacokinetic study showed a 2.3-fold increase in relative oral bioavailability compared with that of the suspension. Antioxidant assay results indicated better efficacy of the developed formulation than the pure drug and it was found to be comparable with ascorbic acid. The results of anti-inflammatory studies showed 72.93 % inhibition for the SNEDDS formulation which was significantly higher than the drug suspension 46.56%. The results of CLSM indicated that the absorption of SNEDDS formulation was considerably higher than that from rutin suspension. Conclusion: Rutin SNEDDS have been successfully prepared and they can serve as an effective tool in enhancing oral bioavailability and efficacy of Rutin.

Keywords: rutin, oral bioavilability, pharamacokinetics, pharmacodynamics

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Authors: Binbin Chen, Dennis Y. C. Leung

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Aluminium (Al) -air cell holds a high volumetric capacity density of 8.05 Ah cm-3, benefit from the trivalence of Al ions. Additional benefits of Al-air cell are low price and environmental friendliness. Furthermore, the Al energy conversion process is characterized of 100% recyclability in theory. Along with a large base of raw material reserve, Al attracts considerable attentions as a promising material to be integrated within the global energy system. However, despite the early successful applications in military services, several problems exist that prevent the Al-air cells from widely civilian use. The most serious issue is the parasitic corrosion of Al when contacts with electrolyte. To overcome this problem, super-pure Al alloyed with various traces of metal elements are used to increase the corrosion resistance. Nevertheless, high-purity Al alloys are costly and require high energy consumption during production process. An alternative approach is to add inexpensive inhibitors directly into the electrolyte. However, such additives would increase the internal ohmic resistance and hamper the cell performance. So far these methods have not provided satisfactory solutions for the problem within Al-air cells. For the operation of alkaline Al-air cell, there are still other minor problems. One of them is the formation of aluminium hydroxide in the electrolyte. This process decreases ionic conductivity of electrolyte. Another one is the carbonation process within the gas diffusion layer of cathode, blocking the porosity of gas diffusion. Both these would hinder the performance of cells. The present work optimizes the above problems by building an Al-air cell operation system, consisting of four components. A top electrolyte tank containing fresh electrolyte is located at a high level, so that it can drive the electrolyte flow by gravity force. A mechanical rechargeable Al-air cell is fabricated with low-cost materials including low grade Al, carbon paper, and PMMA plates. An electrolyte waste tank with elaborate channel is designed to separate the hydrogen generated from the corrosion, which would be collected by gas collection device. In the first section of the research work, we investigated the performance of the mechanical rechargeable Al-air cell with a constant flow rate of electrolyte, to ensure the repeatability experiments. Then the whole system was assembled together and the feasibility of operating was demonstrated. During experiment, pure hydrogen is collected by collection device, which holds potential for various applications. By collecting this by-product, high utilization efficiency of aluminum is achieved. Considering both electricity and hydrogen generated, an overall utilization efficiency of around 90 % or even higher under different working voltages are achieved. Fluidic electrolyte could remove aluminum hydroxide precipitate and solve the electrolyte deterioration problem. This operation system provides a low-cost strategy for harvesting energy from the abundant secondary Al. The system could also be applied into other metal-air cells and is suitable for emergency power supply, power plant and other applications. The low cost feature implies great potential for commercialization. Further optimization, such as scaling up and optimization of fabrication, will help to refine the technology into practical market offerings.

Keywords: aluminium-air cell, high efficiency, hydrogen, mechanical recharge

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Authors: Nitin Singh, Meng Ling, Talha Ahmed, Tianxia Zhao, Reinier van de Pol

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We propose the use of reinforcement learning (RL) as a viable alternative for optimizing supply chain management, particularly in scenarios with stochasticity in product demands. RL’s adaptability to changing conditions and its demonstrated success in diverse fields of sequential decision-making makes it a promising candidate for addressing supply chain problems. We investigate the impact of demand fluctuations in a multi-product supply chain system and develop RL agents with learned generalizable policies. We provide experimentation details for training RL agents and statistical analysis of the results. We study the generalization ability of RL agents for different demand uncertainty scenarios and observe superior performance compared to the agents trained with fixed demand curves. The proposed methodology has the potential to lead to cost reduction and increased profit for companies dealing with frequent inventory movement between supply and demand nodes.

Keywords: inventory management, reinforcement learning, supply chain optimization, uncertainty

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Authors: Muhammet Dursun Kaya, Hasan Asil

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In recent years, different methods have been proposed to optimize question processing in database. Although different methods have been proposed to optimize the query, but the problem which exists here is that most of these methods destroy the query execution plan after executing the query. This research attempts to solve the above problem by using a combination of methods of communicating with the database (the present questions in the programming code and using store procedures) and making query processing adaptive in database, and proposing a new approach for optimization of query processing by introducing the idea of dynamic store procedures. This research creates dynamic store procedures in the database according to the proposed algorithm. This method has been tested on applied software and results shows a significant improvement in reducing the query processing time and also reducing the workload of DBMS. Other advantages of this algorithm include: making the programming environment a single environment, eliminating the parametric limitations of the stored procedures in the database, making the stored procedures in the database dynamic, etc.

Keywords: relational database, agent, query processing, adaptable, communication with the database

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Authors: Adel El-Hadidy

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For any industry, the production and quality management or wastages reductions have major impingement on overall factory economy. This work discusses the quality improvement of garment industry by applying Pareto analysis, cause and effect diagram and Taguchi experimental design. The main purpose of the work is to reduce the stitching defects, which will also minimize the rejection and reworks rate. Application of Pareto chart, fish bone diagram and Process Sigma Level/and or Performance Level tools helps solving those problems on priority basis. Among all, only sewing, defects are responsible form 69.3% to 97.3 % of total defects. Process Sigma level has been improved from 0.79 to 1.3 and performance rate improved, from F to D level. The results showed that the new set of sewing parameters was superior to the original one. It can be seen that fabric size has the largest effect on the sewing defects and that needle size has the smallest effect on the stitching defects.

Keywords: garment, sewing defects, cost of rework, DMAIC, sigma level, cause and effect diagram, Pareto analysis

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Authors: Simangele Dlamini

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Smart city development is seen as a way of facing the challenges brought about by the growing urban population the world over. Research indicates that cities have a role to play in combating urban challenges like crime, waste disposal, greenhouse gas emissions, and resource efficiency. These solutions should be such that they do not make city management less sustainable but should be solutions-driven, cost and resource-efficient, and smart. This study explores opportunities on how the City of Johannesburg, South Africa, can use Geographic Information Systems, Big Data and the Internet of Things (IoT) in identifying opportune areas to initiate smart city initiatives such as smart safety, smart utilities, smart mobility, and smart infrastructure in an integrated manner. The study will combine Big Data, using real-time data sources to identify hotspot areas that will benefit from ICT interventions. The GIS intervention will assist the city in avoiding a silo approach in its smart city development initiatives, an approach that has led to the failure of smart city development in other countries.

Keywords: smart cities, internet of things, geographic information systems, johannesburg

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Authors: Krassimir Todorov

Abstract:

The purpose of the paper is to redefine the levels of structure of corporate, business and functional strategies that were established over the past several decades, to a conceptual model, consisting of corporate, business and operations strategies, that are reinforced by functional strategies. We will propose a conceptual framework of different perspectives in the role of strategic operations as a separate strategic place and reposition the remaining functional strategies as supporting tools, existing at all three levels. The proposed model is called ‘the strategic engine’, since the mutual relationships of its ingredients are identical with main elements and working principle of the internal combustion engine. Based on theoretical essence, related to every strategic level, we will prove that the strategic engine model is useful for managers seeking to safeguard the competitive advantage of their companies. Each strategy level is researched through its basic elements. At the corporate level we examine the scope of firm’s product, the vertical and geographical coverage. At the business level, the point of interest is limited to the SWOT analysis’ basic elements. While at operations level, the key research issue relates to the scope of the following performance indicators: cost, quality, speed, flexibility and dependability. In this relationship, the paper provides a different view for the role of operations strategy within the overall strategy concept. We will prove that the theoretical essence of operations goes far beyond the scope of traditionally accepted business functions. Exploring the applications of Resource-based theory and Market-based theory within the strategic levels framework, we will prove that there is a logical consequence of the theoretical impact in corporate, business and operations strategy – at every strategic level, the validity of one theory is substituted to the level of the other. Practical application of the conceptual model is tested in automotive industry. Actually, the proposed theoretical concept is inspired by a leading global automotive group – Inchcape PLC, listed on the London Stock Exchange, and constituent of the FTSE 250 Index.

Keywords: business strategy, corporate strategy, functional strategies, operations strategy

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Authors: Khin Fai Chen, Jee-Hou Ho, Eng Hwa Yap

Abstract:

An acoustic micro-energy harvester (AMEH) is developed to convert wasted acoustical energy into useful electrical energy. AMEH is mathematically modeled using lumped element modelling (LEM) and Euler-Bernoulli beam (EBB) modelling. An experiment is designed to validate the mathematical model and assess the feasibility of AMEH. Comparison of theoretical and experimental data on critical parameter value such as Mm, Cms, dm and Ceb showed the variances are within 1% to 6%, which is reasonably acceptable. Hence, AMEH mathematical model is validated. Then, AMEH undergoes bandwidth tuning for performance optimization for further experimental work. The AMEH successfully produces 0.9 V⁄(m⁄s^2) and 1.79 μW⁄(m^2⁄s^4) at 60Hz and 400kΩ resistive load which only show variances about 7% compared to theoretical data. By integrating a capacitive load of 200µF, the discharge cycle time of AMEH is 1.8s and the usable energy bandwidth is available as low as 0.25g. At 1g and 60Hz resonance frequency, the averaged power output is about 2.2mW which fulfilled a range of wireless sensors and communication peripherals power requirements. Finally, the design for AMEH is assessed, validated and deemed as a feasible design.

Keywords: piezoelectric, acoustic, energy harvester

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Authors: Zeeshan Hamid, Asher Ramish

Abstract:

The purpose of this paper is to focus on security and safety issues facing by pharmaceutical industry globally when counterfeit drugs are in question. Hence, there is an intense need to secure and authenticate pharmaceutical products in the emerging counterfeit product market. This paper will elaborate the application of radio frequency identification (RFID) in pharmaceutical industry and to identify its key benefits for patient’s care. The benefits are: help to co-ordinate the stream of supplies, accuracy in chains of supplies, maintaining trustworthy information, to manage the operations in appropriate and timely manners and finally deliver the genuine drug to patient. It is discussed that how RFID supported supply chain information sharing (SCIS) helps to combat against counterfeit drugs. And a solution how to tag pharmaceutical products; since, some products prevent RFID implementation in this industry. In this paper, a proposed model for pharma industry distribution suggested to combat against the counterfeit drugs when they are in supply chain.

Keywords: supply chain, RFID, pharmaceutical industry, counterfeit drugs, patients care

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Authors: Ashish James, Anusha James

Abstract:

The impressive predictive performance of deep learning techniques on a wide range of tasks has led to its widespread use. Estimating the confidence of these predictions is paramount for improving the safety and reliability of such systems. However, the uncertainty estimates provided by neural networks (NNs) tend to be overconfident and unreasonable. Ensemble of NNs typically produce good predictions but uncertainty estimates tend to be inconsistent. Inspired by these, this paper presents a framework that can quantitatively estimate the uncertainties by leveraging the advances in transfer learning through slight modification to the existing training pipelines. This promising algorithm is developed with an intention of deployment in real world problems which already boast a good predictive performance by reusing those pretrained models. The idea is to capture the behavior of the trained NNs for the base task by augmenting it with the uncertainty estimates from a supplementary network. A series of experiments with known and unknown distributions show that the proposed approach produces well calibrated uncertainty estimates with high quality predictions.

Keywords: uncertainty estimation, neural networks, transfer learning, regression

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Authors: Ankur Sachan

Abstract:

Carbon dioxide being the most anthropogenic greenhouse gas,it needs to be isolated from entering into atmosphere. Carbon capture and storage is process that captures CO2 emitted from various sources, separates it from other gases and stores it in a safe place preferably in underground geological formations for large period of time. It is then purified and monitored so that can be made to reuse. Monoethanolamine, zeolitic imidazolate framework, microalgae, membranes etc are utilized to capture CO2. Post-combustion, pre-combustion and oxyfuel combustion along with chemical looping combustion are technologies for scrubbing CO2. The properties of CO2 being easily miscible and readily dissolving in oil with impurities makes it capable for numerous applications such as in producing oil by enhanced oil recovery (EOR), Bio CCS Algal Synthesis etc. CO2-EOR operation is capable to produce million barrels of oil and extend the field's lifetime as in case of Weyburn Oil Field in Canada. The physical storage of CO2 is technically the most feasible direction provided that the associated safety and sustainability issues can be met and new materials for CCUS process at low cost are urgently found so that so that fossil based systems with carbon capture are cost competitive.

Keywords: carbon capture, CCUS, sustainability, oil

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Authors: Arefeh Lotfi, Mahdi Babaei, Ayda Mashhadizadeh, Samira Nikpour, Morteza Bagheri

Abstract:

The main objective of this study is to investigate the key factors in accidents’ severity at rail-road level crossings. The data required for this study is obtained from both accident and inventory database of Iran Railways during 2009-2015. The Ordered Probit model is developed using SPSS software to identify the significant factors in the accident severity at rail-road level crossings. The results show that 'train speed', 'vehicle type' and 'weather' are the most important factors affecting the severity of the accident. The results of these studies assist to allocate resources in the right place. This paper suggests mandating the regulations to reduce train speed at rail-road level crossings in bad weather conditions to improve the safety of rail-road level crossings.

Keywords: rail-road level crossing, ordered probit model, accidents’ severity, significant factors

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Authors: M. Jerold, V. Sivasubramanian

Abstract:

In this study, nano zero valent iron Sargassum swartzii (nZVI-SS) biocomposite a marine algal based biosorbent was used for the removal of simulated crystal violet (CV) in batch and continuous fixed bed operation. The Box-Behnen design (BBD) experimental results revealed the biosoprtion was maximum at pH 7.5, biosorbent dosage 0.1 g/L and initial CV concentration of 100 mg/L. The effect of various column parameters like bed depth (3, 6 and 9 cm), flow rate (5, 10 and 15 mL/min) and influent CV concentration (5, 10 and 15 mg/L) were investigated. The exhaustion time increased with increase of bed depth, influent CV concentration and decrease of flow rate. Adam-Bohart, Thomas and Yoon-Nelson models were used to predict the breakthrough curve and to evaluate the model parameters. Out of these models, Thomas and Yoon-Nelson models well described the experimental data. Therefore, the result implies that nZVI-SS biocomposite is a cheap and most promising biosorbent for the removal of CV from wastewater.

Keywords: algae, biosorption, zero-valent, dye, wastewater

Procedia PDF Downloads 177