Search results for: introspective agent

Authors: Sukla Saha, Alok Sinha

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Acid mine drainage (AMD) is the generation of acidic water from active as well as abandoned mines. AMD generates due to the oxidation of pyrites present in the rock in mining areas. Sulfur oxidizing bacteria such as Thiobacillus ferrooxidans acts as a catalyst in this oxidation process. The characteristics of AMD is extreme low pH (2-3) with elevated concentration of different heavy metals such as Fe, Al, Zn, Mn, Cu and Co and anions such sulfate and chloride. AMD contaminate the ground water as well as surface water which leads to the degradation of water quality. Moreover, it carries detrimental effect for aquatic organism and degrade the environment. In the present study, AMD is treated with fly ash, modified with alkaline agent (NaOH). This modified fly ash (MFA) was experimentally proven as a very effective neutralizing agent for the treatment of AMD. It was observed that pH of treated AMD raised to 9.22 from 1.51 with 100g/L of MFA dose. Approximately, 99% removal of Fe, Al, Mn, Cu and Co took place with the same MFA dose. The treated water comply with the effluent discharge standard of (IS: 2490-1981).

Keywords: acid mine drainage, heavy metals, modified fly ash, neutralization

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Authors: Han Wang, Zhuoxian Dai, Zhe Zhu, Hui Zhang, Zhenyu Zeng

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In traditional microscopic traffic simulation, various approaches have been suggested to implement the single-agent behaviors about lane changing and intelligent driver model. However, when it comes to very large metropolitan areas, microscopic traffic simulation requires more resources and become time-consuming, then macroscopic traffic simulation aggregate trends of interests rather than individual vehicle traces. In this paper, we describe the architecture and implementation of the actor system of microscopic traffic simulation, which exploits the distributed architecture of modern-day cloud computing. The results demonstrate that our architecture achieves high-performance and outperforms all the other traditional microscopic software in all tasks. To the best of our knowledge, this the first system that enables single-agent behavior in macroscopic traffic simulation. We thus believe it contributes to a new type of system for traffic simulation, which could provide individual vehicle behaviors in microscopic traffic simulation.

Keywords: actor system, cloud computing, distributed system, traffic simulation

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Authors: Zou Xuan, Liu Hongchen

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The ideal dentin desensitizing agent should not only have good biological safety, simple clinical operation mode, the superior treatment effect, but also should have a durable effect to resist the oral environmental temperature change and oral mechanical abrasion, so as to achieve a persistent desensitization effect. Also, when using desensitizing agent to prevent the post-operative hypersensitivity, we should not only prevent it from affecting crowns’ retention, but must understand its effects on bond strength of dentin adhesives. There are various of desensitizers and dentin adhesives in clinical treatment. They have different chemical or physical properties. Whether the use of desensitizing agent would affect the bond strength of dentin adhesives still need further research. In this in vitro study, we built the hypersensitive dentin model and post-operative dentin model, to evaluate the sealing effects and durability on exposed tubule by three different dentin desensitizers and to evaluate the sealing effects and the bond strength of dentin adhesives after using three different dentin desensitizers on post-operative dentin. The result of this study could provide some important references for clinical use of dentin desensitizing agent. 1. As to the three desensitizers, the hypersensitive dentin model was built to evaluate their sealing effects on exposed tubule by SEM observation and dentin permeability analysis. All of them could significantly reduce the dentin permeability. 2. Test specimens of three groups treated by desensitizers were subjected to aging treatment with 5000 times thermal cycling and toothbrush abrasion, and then dentin permeability was measured to evaluate the sealing durability of these three desensitizers on exposed tubule. The sealing durability of three groups were different. 3. The post-operative dentin model was built to evaluate the sealing effects of the three desensitizers on post-operative dentin by SEM and methylene blue. All of three desensitizers could reduce the dentin permeability significantly. 4. The influences of three desensitizers on the bonding efficiency of total-etch and self-etch adhesives were evaluated with the micro-tensile bond strength study and bond interface morphology observation. The dentin bond strength for Green or group was significantly lower than the other two groups (P<0.05).

Keywords: dentin, desensitizer, dentin permeability, thermal cycling, micro-tensile bond strength

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Authors: Samy M. Shaban, Ismail Aiad, Mohamed M. El-Sukkary, E. A. Soliman, Moshira Y. El-Awady

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In situ and green synthesis of cubic and spherical silver nanoparticles were developed using sun light as reducing agent in the presence of newly prepared cationic surfactant which acting as capping agents. The morphology of prepared silver nanoparticle was estimated by transmission electron microscope (TEM) and the size distribution determined by dynamic light scattering (DLS). The hydrophobic chain length of the prepared surfactant effect on the stability of the prepared silver nanoparticles as clear from zeta-potential values. Also by increasing chain length of the used capping agent the amount of formed nanoparticle increase as indicated by increasing the absorbance. Both prepared surfactants and surfactants capping silver nanoparticles showed high antimicrobial activity against gram positive and gram-negative bacteria.

Keywords: photosynthesis, hexaonal shapes, zetapotential, biological activity

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Authors: Brenda Cecilia Ledesma, Andrea Beltramone

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This work deals with the bio-waste valorization approach for catalyst development, the use of products derived from biomass as raw material and the obtaining of biofuels. In this research, activated carbons were synthesized from the orange peel using different synthesis conditions. With the activated carbons obtained with the best structure and texture, PtIr bimetallic catalysts were prepared. Carbon activation was carried out through a chemical process with phosphoric acid as an activating agent, varying the acid concentration, the ratio substrate/activating agent and time of contact between them. The best support was obtained using a carbonization time of 1 h, the temperature of carbonization of 470oC, the phosphoric acid concentration of 50 wt.% and a BET area of 1429 m2/g. Subsequently, the metallic nanoparticles were deposited in the activated carbon to use the solid as a catalytic material for the hydrogenation of HMF to 2,5-DMF. The catalyst presented an excellent performance for biofuels generation.

Keywords: orange peel, bio-waste valorization, platinum, iridium, 5-hydroxymethylfurfural

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Authors: Anushaa A.

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In this work, silver nanoparticles (AgNP) were manufactured directly without harmful chemicals utilising methanol extract (SNLME) Solanum nigrume leaves. We are using nigrum leaf extract from Solanum, which converts silver nitrate to silver ions, for synthesization purposes. An examination of the AgNP produced was performed using ultraviolet (UV-VIS) spectroscopy, infrared spectroscopy (FTIR) transformed from Fourier and scanning electrons (SEM). Biological activity was also tested. UV-VIS has proven that biosynthesized AgNP exists (420-450 nm). The FTIR spectrum has been utilised to confirm the presence of different functional groups within the biomolecules, which are a nanoparticular capping agent and the spectroscopic and crystal nature of AgNP. The viability of the silver nanoparticles was evaluated using zeta potential calculations. Negative zeta potential of -33.4 mV demonstrated the stability of silver-nanoparticles. The morphology of AgNP was examined using a scanning electron microscope. Greenly generated AgNP showed significant anti-Staphylococcus aureus, Candida, and Escherichia coli action. The green AgNP demonstration indicated that the IC50 for the human teratocarcinoma cell line was 29.24 μg/ml during 24 hours of therapy (PA1 Ovarian cell line). The dose-dependent effects were reported in both antibacterial and cytotoxicity assays and as an effective agent. Finally, the findings of this research showed that silver nanoparticles generated might serve as a viable therapeutic agent to combat microorganisms killing and curing cancer.

Keywords: antimicrobial activity, PA1 ovarian cancer cell line, silver nanoparticles, Solanum nigrum

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Authors: Abdulla D. Alblooshi

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The stock market is a representation of human behavior and psychology, such as fear, greed, and discipline. Those are manifested in the form of price movements during the trading sessions. Therefore, predicting the stock movement and prices is a challenging effort. However, those trading sessions produce a large amount of data that can be utilized to train an AI agent for the purpose of predicting the stock movement. Predicting the stock market price action will be advantageous. In this paper, the stock movement data of three DFM listed stocks are studied using historical price movements and technical indicators value and used to train an agent using KNN and SVM methods to predict the future price movement. MATLAB Toolbox and a simple script is written to process and classify the information and output the prediction. It will also compare the different learning methods and parameters s using metrics like RMSE, MAE, and R².

Keywords: KNN, ANN, style, SVM, stocks, technical indicators, RSI, MACD, moving averages, RMSE, MAE

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Authors: Mok-Tan Ahn, Hyeok Choi, Joon-Hong Park

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Multi-stage drawing process is an important technique for forming a shape that cannot be molded in a single process. multi-stage drawing process in number of passes and the shape of the die are an important factor influencing the productivity and moldability of the product. The number and shape of the multi-path in the mold of the drawing process is very influencing the productivity and moldability of the product. Half angle of the die and mandrel affects the drawing force and it also affects the completion of the final shape. Thus reducing the number of pass and the die shape optimization are necessary to improve the formability of the billet. The purpose of this study, Analyzing the load on the die through the FEM analysis and in consideration of the formability of the material presents a die model.

Keywords: automotive reinforcing agent, multi-pass shape drawing, automotive parts, FEM analysis

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Authors: Khalil Bachiri, Ali Yahyaouy, Nicoleta Rogovschi

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Improved user experience is a goal of contemporary recommender systems. Recommender systems are starting to incorporate reinforcement learning since it easily satisfies this goal of increasing a user’s reward every session. In this paper, we examine the most effective Reinforcement Learning agent tactics on the Movielens (1M) dataset, balancing precision and a variety of recommendations. The absence of variability in final predictions makes simplistic techniques, although able to optimize ranking quality criteria, worthless for consumers of the recommendation system. Utilizing the stochasticity of Leonard-Ornstein processes, our suggested strategy encourages the agent to investigate its surroundings. Research demonstrates that raising the NDCG (Discounted Cumulative Gain) and HR (HitRate) criterion without lowering the Ornstein-Uhlenbeck process drift coefficient enhances the diversity of suggestions.

Keywords: recommender systems, reinforcement learning, deep learning, DDPG, Leonard-Ornstein process

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Authors: Migena Mana, Ahmed Khalid Syed, Abdul Malik, Nikhil Cherian

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In recent years, advancements in deep learning enabled researchers to tackle the problem of self-driving cars. Car companies use huge datasets to train their deep learning models to make autonomous cars a reality. However, this approach has certain drawbacks in that the state space of possible actions for a car is so huge that there cannot be a dataset for every possible road scenario. To overcome this problem, the concept of reinforcement learning (RL) is being investigated in this research. Since the problem of autonomous driving can be modeled in a simulation, it lends itself naturally to the domain of reinforcement learning. The advantage of this approach is that we can model different and complex road scenarios in a simulation without having to deploy in the real world. The autonomous agent can learn to drive by finding the optimal policy. This learned model can then be easily deployed in a real-world setting. In this project, we focus on three RL algorithms: Q-learning, Deep Deterministic Policy Gradient (DDPG), and Proximal Policy Optimization (PPO). To model the environment, we have used TORCS (The Open Racing Car Simulator), which provides us with a strong foundation to test our model. The inputs to the algorithms are the sensor data provided by the simulator such as velocity, distance from side pavement, etc. The outcome of this research project is a comparative analysis of these algorithms. Based on the comparison, the PPO algorithm gives the best results. When using PPO algorithm, the reward is greater, and the acceleration, steering angle and braking are more stable compared to the other algorithms, which means that the agent learns to drive in a better and more efficient way in this case. Additionally, we have come up with a dataset taken from the training of the agent with DDPG and PPO algorithms. It contains all the steps of the agent during one full training in the form: (all input values, acceleration, steering angle, break, loss, reward). This study can serve as a base for further complex road scenarios. Furthermore, it can be enlarged in the field of computer vision, using the images to find the best policy.

Keywords: autonomous driving, DDPG (deep deterministic policy gradient), PPO (proximal policy optimization), reinforcement learning

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Authors: P. Gomathi Priya, M. E. Thenmozhi

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Iron nanoparticles were used to cleanup effluents. This paper involves synthesis of iron nanoparticles chemically by sodium borohydride reduction of ammonium ferrous sulfate solution (FAS). Iron oxide nanoparticles have lesser efficiency of adsorption than Zero Valent Iron nanoparticles (nZVI). Glucosamine acts as a stabilizing agent and chelating agent to prevent Iron nanoparticles from oxidation. nZVI particles were characterized using Scanning Electron Microscopy (SEM). Thus, the synthesized nZVI was subjected to entrapment in biopolymer, viz. barium (Ba)-alginate beads. The beads were characterized using SEM. Batch dye degradation studies were conducted using Reactive black Water soluble Nontoxic Natural substances (WNN) dye which is one of the most hazardous dyes used in textile industries. Effect of contact time, effect of pH, initial dye concentration, adsorbent dosage, isotherm and kinetic studies were carried out.

Keywords: ammonium ferrous sulfate solution, barium, alginate beads, reactive black WNN dye, zero valent iron nanoparticles

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Authors: Chuan Yang, Jing Bie, Panagiotis Psimoulis, Zhong Wang

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Modelling and understanding vehicle volume distribution over the urban network are essential for urban design and transport planning. The space syntax approach was widely applied as the main conceptual and methodological framework for contemporary vehicle volume models with the help of the statistical method of multiple regression analysis (MRA). However, the MRA model with space syntax variables shows a limitation in vehicle volume predicting in accounting for the crossed effect of the urban configurational characters and socio-economic factors. The aim of this paper is to construct models by interacting with the combined impact of the street network structure and socio-economic factors. In this paper, we present a multilevel linear (ML) and an agent-based (AB) vehicle volume model at an urban scale interacting with space syntax theoretical framework. The ML model allowed random effects of urban configurational characteristics in different urban contexts. And the AB model was developed with the incorporation of transformed space syntax components of the MRA models into the agents’ spatial behaviour. Three models were implemented in the same urban environment. The ML model exhibit superiority over the original MRA model in identifying the relative impacts of the configurational characters and macro-scale socio-economic factors that shape vehicle movement distribution over the city. Compared with the ML model, the suggested AB model represented the ability to estimate vehicle volume in the urban network considering the combined effects of configurational characters and land-use patterns at the street segment level.

Keywords: space syntax, vehicle volume modeling, multilevel model, agent-based model

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Authors: M. Taheri Tehrani, H. Ajorloo

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In this paper, an intelligent multi-agent framework is developed for each router in which agents have two vital functionalities, traffic shaping and buffer allocation and are positioned in the ports of the routers. With traffic shaping functionality agents shape the traffic forward by dynamic and real time allocation of the rate of generation of tokens in a Token Bucket algorithm and with buffer allocation functionality agents share their buffer capacity between each other based on their need and the conditions of the network. This dynamic and intelligent framework gives this opportunity to some ports to work better under burst and more busy conditions. These agents work intelligently based on Reinforcement Learning (RL) algorithm and will consider effective parameters in their decision process. As RL have limitation considering much parameter in its decision process due to the volume of calculations, we utilize our novel method which invokes Principle Component Analysis (PCA) on the RL and gives a high dimensional ability to this algorithm to consider as much as needed parameters in its decision process. This implementation when is compared to our previous work where traffic shaping was done without any sharing and dynamic allocation of buffer size for each port, the lower packet drop in the whole network specifically in the source routers can be seen. These methods are implemented in our previous proposed intelligent simulation environment to be able to compare better the performance metrics. The results obtained from this simulation environment show an efficient and dynamic utilization of resources in terms of bandwidth and buffer capacities pre allocated to each port.

Keywords: principal component analysis, reinforcement learning, buffer allocation, multi- agent systems

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Authors: Massra M. Sabeima, Myriam lamolle, Mohamedade Farouk Nanne

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Adapting the learning resources of an e-learning system to the characteristics of the learners is an important aspect to consider when designing an adaptive e-learning system. However, this adaptation is not a simple process; it requires the extraction, analysis, and modeling of user information. This implies a good representation of the user's profile, which is the backbone of the adaptation process. Moreover, during the e-learning process, collaboration with similar users (same geographic province or knowledge context) is important. Productive collaboration motivates users to continue or not abandon the course and increases the assimilation of learning objects. The contribution of this work is the following: we propose an adaptive e-learning semantic platform to recommend learning resources to learners, using ontology to model the user profile and the course content, furthermore an implementation of a multi-agent system able to progressively generate the learning graph (taking into account the user's progress, and the changes that occur) for each user during the learning process, and to synchronize the users who collaborate on a learning object.

Keywords: adaptative learning, collaboration, multi-agent, ontology

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Authors: Jang kyun Cho, Jeong-dong Lee

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The advent of the Internet, mobile communications, and social network services has stimulated social interactions among consumers, allowing people to affect one another’s innovation adoptions by exchanging information more frequently and more quickly. Previous diffusion models, such as the Bass model, however, face limitations in reflecting such recent phenomena in society. These models are weak in their ability to model interactions between agents; they model aggregated-level behaviors only. The agent based model, which is an alternative to the aggregate model, is good for individual modeling, but it is still not based on an economic perspective of social interactions so far. This study assumes the presence of social utility from other consumers in the adoption of innovation and investigates the effect of individual interactions on innovation diffusion by developing a new model called the interaction-based diffusion model. By comparing this model with previous diffusion models, the study also examines how the proposed model explains innovation diffusion from the perspective of economics. In addition, the study recommends the use of a small-world network topology instead of cellular automata to describe innovation diffusion. This study develops a model based on individual preference and heterogeneous social interactions using utility specification, which is expandable and, thus, able to encompass various issues in diffusion research, such as reservation price. Furthermore, the study proposes a new framework to forecast aggregated-level market demand from individual level modeling. The model also exhibits a good fit to real market data. It is expected that the study will contribute to our understanding of the innovation diffusion process through its microeconomic theoretical approach.

Keywords: innovation diffusion, agent based model, small-world network, demand forecasting

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Authors: Yasser F. Hassan

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Social networking involves use of an online platform or website that enables people to communicate, usually for a social purpose, through a variety of services, most of which are web-based and offer opportunities for people to interact over the internet, e.g. via e-mail and ‘instant messaging’, by analyzing the voting behavior and ratings of judges in a popular comments in social networks. While most of the party literature omits the electorate, this paper presents a model where elites and parties are emergent consequences of the behavior and preferences of voters. The research in artificial intelligence and psychology has provided powerful illustrations of the way in which the emergence of intelligent behavior depends on the development of representational structure. As opposed to the classical voting system (one person – one decision – one vote) a new voting system is designed where agents with opposed preferences are endowed with a given number of votes to freely distribute them among some issues. The paper uses ideas from machine learning, artificial intelligence and soft computing to provide a model of the development of voting system response in a simulated agent. The modeled development process involves (simulated) processes of evolution, learning and representation development. The main value of the model is that it provides an illustration of how simple learning processes may lead to the formation of structure. We employ agent-based computer simulation to demonstrate the formation and interaction of coalitions that arise from individual voter preferences. We are interested in coordinating the local behavior of individual agents to provide an appropriate system-level behavior.

Keywords: voting system, rough sets, multi-agent, social networks, emergence, power indices

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Authors: Nina Bijedic, Drazena Gaspar, Mirsad Hadzikadic

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Corruption is an influential and widespread problem. One part of it is so-called petty corruption, related to large-scale bribe giving by ordinary citizens trying to influence the works of public administration or public services. As it is with all means of corruption, petty corruption is related to the level of democracy (or administration efficiency) in a society. The developed model captures some of the factors related to corruptive behavior, as well as people’s attitude towards petty corruption. It has four basic elements: user’s perception of corruption in the society of interest, the influence of social interactions, the influence of penalizing mechanism, and influence of campaigns against petty corruption. The model is agent-based, developed in NetLogo, with a lot of random settings that provide a wider scope of responses. Interactions of different settings for variables of elements provide insight into the influence of each element on attitude towards petty corruption, as well as petty corruptive behavior.

Keywords: agent-based model, attitude, influence, petty corruption, society

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Authors: M. Rehan, H. Mashaly, H. Emam, A. Abou El-Kheir, S. Mowafi

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In this work, we will study a simple highly efficient technique to impart multi functional properties to different fabric substrates by in situ Ag NPs incorporation into fabric matrix. Ag NPs as a coloration and antimicrobial agent were prepared in situ incorporation into fabric matrix (Cotton and Wool) by using trisodium citrate as reducing and stabilizing agent. The Ag NPs treated fabric (Cotton and Wool) showed different color because of localized surface Plasmon resonance (LSPR) property of Ag NPs. The formation of Ag NPs was confirmed by UV/Vis spectra for the supernatant solutions and The Ag NPs treated fabric (Cotton and Wool) were characterized by scanning electron microscopy (SEM) and X-ray photo electron spectroscopy (XPS). The dependence of color properties characterized by colorimetric, fastness and antibacterial properties evaluated by Escherichia coli using counting method and the reaction parameters were studied. The results indicate that, the in situ Ag NPs incorporation into fabric matrix approach can simultaneously impart colorant and antimicrobial properties into different fabric substrates.

Keywords: Ag NPs, coloration, antibacterial, wool, cotton fabric

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Authors: Mojtaba Eshraghi Arani

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The freight forwarders, who are known as the Architect of Transportation, play a vital role in the supply chain management. The package of various services which they provide has made the legal nature of freight forwarders very controversial, so that they might be qualified once as principal or carrier and, on other occasions, as agent of the shipper as the case may be. They could even be involved in the transportation process as the agent of shipping line, which makes the situation much more complicated. The courts in all countries have long had trouble in distinguishing the “forwarder as agent” from “forwarder as principal” (as it is outstanding in the prominent case of “Vastfame Camera Ltd v Birkart Globistics Ltd And Others” 2005, Hong Kong). It is not fully known that in the case of a claim against the forwarder, what particular parameter would be used by the judge among multiple, and sometimes contradictory, tests for determining the scope of the forwarder liability. In particular, every country has its own legal parameters for qualifying the freight forwarders that is completely different from others, as it is the case in France in comparison with Germany and England. The unpredictability of the courts’ decisions in this regard has provided the freight forwarders with the opportunity to impose any limitation or exception of liability while pretending to play the role of a principal, consequently making the cargo interests incur ever-increasing damage. The transportation industry needs to remove such uncertainty by unifying national laws governing freight forwarders liability. A long time ago, in 1967, The International Institute for Unification of Private Law (UNIDROIT) prepared a draft convention called “Draft Convention on Contract of Agency for Forwarding Agents Relating to International Carriage of Goods” (hereinafter called “UNIDROIT draft convention”). The UNIDROIT draft convention provided a clear and certain framework for the liability of freight forwarder in each capacity as agent or carrier, but it failed to transform to a convention, and eventually, it was consigned to oblivion. Today, after nearly 6 decades from that era, the necessity of such convention can be felt apparently. However, one might reason that the same grounds, in particular, the resistance by forwarders’ association, FIATA, exist yet, and thus it is not logical to revive a forgotten draft convention after such long period of time. It is argued in this article that the main reason for resisting the UNIDROIT draft convention in the past was pending efforts for developing the “1980 United Nation Convention on International Multimodal Transport of Goods”. However, the latter convention failed to become in force on due time in a way that there was no new accession since 1996, as a result of which the UNIDROIT draft convention must be revived strongly and immediately submitted to the relevant diplomatic conference. A qualitative method with the concept of interpretation of data collection has been used in this manuscript. The source of the data is the analysis of international conventions and cases.

Keywords: freight forwarder, revival, agent, principal, uidroit, draft convention

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Authors: Nais Pinta Adetya, H. Hadiyanto

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Microalgae has a potential to be utilized as food and natural colorant. The microalgae components consists of three main parts, these are lipid, protein, and carbohydrate. Crucial step in producing lipid and protein from microalgae is extraction. Microalgae has high water level (70-90%), it causes drying process of biomass needs much more energy and also has potential to distract lipid and protein from microalgae. Extraction of lipid from wet biomass is able to take place efficiently with cell disruption of microalgae by osmotic shock method. In this study, osmotic shock method was going to be integrated with ultrasound to maximalize the extraction yield of lipid and protein from wet biomass Spirulina sp. with osmotic shock method assisted ultrasound. This study consisted of two steps, these were osmotic shock process toward wet biomass and ultrasound extraction assisted. NaCl solution was used as osmotic agent, with the variation of concentrations were 10%, 20%, and 30%. Extraction was conducted in 40°C for 20 minutes with frequency of ultrasound wave was 40kHz. The optimal yield of protein (2.7%) and (lipid 38%) were achieved at 20% osmotic agent concentration.

Keywords: extraction, lipid, osmotic shock, protein, ultrasound

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Authors: A. Hol

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Even in the well developed countries like Australia, Small to Medium Farmers do not often have the power over the market prices as they are more often than not set by the farming agents. This in turn creates problems as farmers only get to know for how much their produce has been sold for by the agents three to four weeks after the sale has taken the place. To see and identify if and how peri-urban Sydney farmers could be assisted, carefully selected group of peri-urban Sydney farmers of the stone fruit has been interviewed. Following the case based interviews collected data was analyzed in detail using the Scenario Based Transformation principles. Analyzed data was then used to create a most common transformation case. The case identified that a mobile web based system could be develop so that framers can monitor agent earnings and in turn gain more power over the markets. It is expected that after the system has been in action for six months to a year, farmers will become empowered and they will gain means to monitor the market and negotiate agent prices.

Keywords: mobile applications, farming, scenario-based analysis, scenario-based transformation, user empowerment

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Authors: Ahmed Samy Elnoby

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Ailanthus altissima native to Asia which belongs to the family Simaroubaceae was subjected to phytochemical screening and biological investigations. Phytochemical screening revealed the presence of carbohydrates, tannins, sterols, flavonoids and traces of saponins. In addition, quantitative determination of phenolics and flavonoid content were performed. The antimicrobial activity of methanolic extract of the leaves was determined against gram-positive, gram-negative bacteria in addition to fungi using a modified Kirby-Bauer disc diffusion method that was compared with standard discs ampicillin which acts as an antibacterial agent and amphotericin B which acts as an antifungal agent. A high potency was observed against gram-positive bacteria mainly staphylococcus aureus, gram-negative bacteria mainly Escherichia coli and showed no potency against fungi mainly Aspergillus flavus and candida albicans. On the other hand, the antioxidant activity of the extract was determined by 1, 1-diphenyl-2- diphenyl-2-picryl-hydrazil (DPPH). A very low potency was shown by using DPPH for the antioxidant effect so IC50 = 0 ug/ml, IC90 =0 ug /ml and remark gave 47.2 % at 100 ug/ml which is very weak. Cytotoxic activity was determined by using MTT assay (3-4, 5-Dimethylthiazol-2-yl)-2, 5-Diphenyltetrazolium Bromide) against MCF7 (Human Caucasian breast adenocarcinoma) cell line. A moderate potency was shown by using MCF7 cell line for cytotoxic effect so LC50= 90.2 ug/ml, LC90=139.9 ug/ml and the remark gave 55.2% at 100 ug/ml which is of moderate activity so, Ailanthus altissima can be considered to be a promising antimicrobial agent from natural origin.

Keywords: Ailanthus altissima, TLC, HPLC, anti-microbial activity, antifungal activity, antioxidant, cytotoxic activity

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Authors: Abdulwakeel B. Raji

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This paper introduces a concept of an intelligent virtual learning environment that involves communication between learners and an artificially intelligent teaching agent in an attempt to replicate classroom learning interactions. The benefits of this technology over current e-learning practices is that it creates a virtual classroom where real time adaptive learning interactions are made possible. This is a move away from the static learning practices currently being adopted by e-learning systems. Over the years, artificial intelligence has been applied to various fields, including and not limited to medicine, military applications, psychology, marketing etc. The purpose of e-learning applications is to ensure users are able to learn outside of the classroom, but a major limitation has been the inability to fully replicate classroom interactions between teacher and students. This study used comparative surveys to gain information and understanding of the current learning practices in Nigerian universities and how they compare to these practices compare to the use of a developed e-learning system. The study was conducted by attending several lectures and noting the interactions between lecturers and tutors and as an aftermath, a software has been developed that deploys the use of an artificial intelligent teaching-agent alongside an e-learning system to enhance user learning experience and attempt to create the similar learning interactions to those found in classroom and lecture hall settings. Dialogflow has been used to implement a teaching-agent, which has been developed using JSON, which serves as a virtual teacher. Course content has been created using HTML, CSS, PHP and JAVASCRIPT as a web-based application. This technology can run on handheld devices and Google based home technologies to give learners an access to the teaching agent at any time. This technology also implements the use of definite clause grammars and natural language processing to match user inputs and requests with defined rules to replicate learning interactions. This technology developed covers familiar classroom scenarios such as answering users’ questions, asking ‘do you understand’ at regular intervals and answering subsequent requests, taking advanced user queries to give feedbacks at other periods. This software technology uses deep learning techniques to learn user interactions and patterns to subsequently enhance user learning experience. A system testing has been undergone by undergraduate students in the UK and Nigeria on the course ‘Introduction to Database Development’. Test results and feedback from users shows that this study and developed software is a significant improvement on existing e-learning systems. Further experiments are to be run using the software with different students and more course contents.

Keywords: virtual learning, natural language processing, definite clause grammars, deep learning, artificial intelligence

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Authors: Paul Pistone

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Nihilism claims that there are no actual intrinsic goods. Desire, however, directly contradicts this claim. To desire, something is more than to be motivated to bring about the desired ends. It is more than to take pleasure in it, seeming that one has obtained her desired end. Desire is, further, more than believing that something is good. Desire is the perception that something is good for the self. In this paper, it is argued that desire is an agent-relative value seeming. This implies that there are intrinsic values. It will be argued that: (1) there are intrinsic values related to life and flourishing, (2) that it is metaphysically impossible that there are no intrinsic values, (3) that desire is our psychological mechanism which enables us to perceive a state of affairs or event as an agent-relative good, and (4) while we can be wrong about the large scale object of desire (i.e., the instrumental desire) we cannot be wrong about what is at the root of our desire (i.e., the intrinsic desire). The method of this paper will be to examine the claims of nihilism and moral realism in recent literature, present a case for moral realism, discuss a few theories of desire, connect moral realism to an evaluative perceptual model of desire, and conclude that not only is this the best theory of desire but that this psychological faculty offers a clear counterexample to nihilism.

Keywords: desire, moral realism, nihilism, perception

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Authors: Krishnakant Bhole, Neerakallu D. Shivakumar, Shakti Singh Chauhan, Sanketh Tonannavar, Rajath S

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Synthesis of natural-based composite materials is state of the art. This work discusses the preparation and characterization of cellulose nanofibers (CNF) extracted from the bamboo pulp using TEMPO-oxidization and high-pressure homogenization methods. Bio-composites are prepared using synthesized CNF and bamboo particles. Nanocellulose prepared is characterized using SEM and XRD for morphological and crystallinity analysis, and the formation of fibers at the nano level is ensured. Composite specimens are fabricated using these natural sources and subjected to tensile and flexural tests to characterize the mechanical properties such as modulus of elasticity (MOE), modulus of rupture (MOR), and interfacial strength. Further, synthesized nanocellulose is used as a binding agent to prepare particleboards using various natural sources like bamboo, areca nut, and banana in the form of fibers. From the results, it can be inferred that nanocellulose prepared from bamboo pulp acts as a binding agent for making bio-composites. Hence, the concept of using matrix and reinforcement derived from natural sources can be used to prepare green composites that are highly degradable.

Keywords: nanocellulose, biocomposite, CNF, bamboo

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Authors: Abdelhadi Adel, Kadri Ouahab

Abstract:

This paper proposes an improved approach based on MAS Architecture and Heuristic Algorithm for systematic maintenance to minimize makespan. We have implemented a problem-solving approach for optimizing the processing time, methods based on metaheuristics. The proposed approach is inspired by the behavior of the human body. This hybridization is between a multi-agent system and inspirations of the human body, especially genetics. The effectiveness of our approach has been demonstrated repeatedly in this paper. To solve such a complex problem, we proposed an approach which we have used advanced operators such as uniform crossover set and single point mutation. The proposed approach is applied to three preventive maintenance policies. These policies are intended to maximize the availability or to maintain a minimum level of reliability during the production chain. The results show that our algorithm outperforms existing algorithms. We assumed that the machines might be unavailable periodically during the production scheduling.

Keywords: multi-agent systems, emergence, genetic algorithm, makespan, systematic maintenance, scheduling, hybrid flow shop scheduling

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Authors: Sang Hun Park, Moung Young Lee, Su Min Yu, Hyun Sang Jo, Ji Hyeon Kim, Chul Gyu Song

Abstract:

A near-infrared light source used as a light source in the fluorescence imaging system is suitable for use in real-time during the operation since it has no interference in surgical vision. However, fluorescence images do not have depth information. In this paper, we configured the device with the research on molecular imaging systems for monitoring thrombus imaging using fluorescence and photoacoustic. Fluorescence imaging was performed using a phantom experiment in order to search the exact location, and the Photoacoustic image was in order to detect the depth. Fluorescence image obtained when evaluated through current phantom experiments when the concentration of the contrast agent is 25μg / ml, it was confirmed that it looked sharper. The phantom experiment is has shown the possibility with the fluorescence image and photoacoustic image using an indocyanine green contrast agent. For early diagnosis of cardiovascular diseases, more active research with the fusion of different molecular imaging devices is required.

Keywords: fluorescence, photoacoustic, indocyanine green, carotid artery

Procedia PDF Downloads 566

Authors: V. P. Muhamed Shajudheen, K. Viswanathan, K. Anitha Rani, A. Uma Maheswari, S. Saravana Kumar

Abstract:

We are reporting a simple and low-cost chemical precipitation method adopted to prepare zinc oxide nanoparticles (ZnO) using polyvinyl pyrrolidone (PVP) as a capping agent. The Differential Scanning Calorimetry (DSC) and Thermo Gravimetric Analysis (TGA) was applied on the dried gel sample to record the phase transformation temperature of zinc hydroxide Zn(OH)2 to zinc oxide (ZnO) to obtain the annealing temperature of 800C. The thermal, structure, morphology and optical properties have been employed by different techniques such as DSC-TGA, X-Ray Diffraction (XRD), Fourier Transform Infra-Red spectroscopy (FTIR), Micro Raman spectroscopy, UV-Visible absorption spectroscopy (UV-Vis), Photoluminescence spectroscopy (PL) and Field Effect Scanning Electron Microscopy (FESEM). X-ray diffraction results confirmed the wurtzite hexagonal structure of ZnO nanoparticles. The two intensive peaks at 160 and 432 cm-1 in the Raman Spectrum are mainly attributed to the first order modes of the wurtzite ZnO nanoparticles. The energy band gap obtained from the UV-Vis absorption spectra, shows a blue shift, which is attributed to increase in carrier concentration (Burstein Moss Effect). Photoluminescence studies of the single crystalline ZnO nanoparticles, show a strong peak centered at 385 nm, corresponding to the near band edge emission in ultraviolet range. The mixed shape of grapes, sphere, hexagonal and rock like structure has been noticed in FESEM. The results showed that PVP is a suitable capping agent for the preparation of ZnO nanoparticles by simple chemical precipitation method.

Keywords: ZnO nanoparticles, simple chemical precipitation route, mixed shape morphology, UV-visible absorption, photoluminescence, Fourier transform infra-Red spectroscopy

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Authors: Jun Hee Lee, Young Kyu Kim, Seong Jae Hong, Chamroeun Chhorn, Seung Woo Lee

Abstract:

Roller-compacted concrete pavement (RCCP), an environmental friendly pavement of which load carry capacity benefitted from both hydration and aggregate interlock from roller compacting, demonstrated a superb structural performance for a relatively small amount of water and cement content. Even though an excellent structural performance can be secured, it is required to investigate roller-compacted concrete (RCC) under environmental loading and its long-term durability under critical conditions. In order to secure long-term durability, an appropriate internal air-void structure is required for this concrete. In this study, a method for improving the long-term durability of RCCP is suggested by analyzing the internal air-void structure and corresponding durability of RCC. The method of improving the long-term durability involves measurements of air content, air voids, and air-spacing factors in RCC that experiences changes in terms of type of air-entraining agent and its usage amount. This test is conducted according to the testing criteria in ASTM C 457, 672, and KS F 2456. It was found that the freezing-thawing and scaling resistances of RCC without any chemical admixture was quite low. Interestingly, an improvement of freezing-thawing and scaling resistances was observed for RCC with appropriate the air entraining (AE) agent content; Relative dynamic elastic modulus was found to be more than 80% for those mixtures. In RCC with AE agent mixtures, large amount of air was distributed within a range of 2% to 3%, and an air void spacing factor ranging between 200 and 300 μm (close to 250 μm, recommended by PCA) was secured. The long-term durability of RCC has a direct relationship with air-void spacing factor, and thus it can only be secured by ensuring the air void spacing factor through the inclusion of the AE in the mixture.

Keywords: durability, RCCP, air spacing factor, surface scaling resistance test, freezing and thawing resistance test

Procedia PDF Downloads 222

Authors: Zhiguo Yang, Xiangan Yue, Minglu Shao, Yue Yang, Rongjie Yan

Abstract:

It is difficult to realize deep profile control because of the small pore-throats and easy water channeling in low-permeability heterogeneous reservoir, and the traditional polymer microspheres have the contradiction between injection and plugging. In order to solve this contradiction, the controllable self-aggregating colloidal particles (CSA) containing amide groups on the surface of microspheres was prepared based on emulsion polymerization of styrene and acrylamide. The dispersed solution of CSA colloidal particles, whose particle size is much smaller than the diameter of pore-throats, was injected into the reservoir. When the microspheres migrated to the deep part of reservoir, , these CSA colloidal particles could automatically self-aggregate into large particle clusters under the action of the shielding agent and the control agent, so as to realize the plugging of the water channels. In this paper, the morphology, temperature resistance and self-aggregation properties of CSA microspheres were studied by transmission electron microscopy (TEM) and bottle test. The results showed that CSA microspheres exhibited heterogeneous core-shell structure, good dispersion, and outstanding thermal stability. The microspheres remain regular and uniform spheres at 100℃ after aging for 35 days. With the increase of the concentration of the cations, the self-aggregation time of CSA was gradually shortened, and the influence of bivalent cations was greater than that of monovalent cations. Core flooding experiments showed that CSA polymer microspheres have good injection properties, CSA particle clusters can effective plug the water channels and migrate to the deep part of the reservoir for profile control.

Keywords: heterogeneous reservoir, deep profile control, emulsion polymerization, colloidal particles, plugging characteristic

Procedia PDF Downloads 193