Search results for: multiple distribution supply chain network

Authors: Amine M. Benmehaia, Amine Oulmane

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The supply response of agricultural commodities to changes in price incentives is an important issue for the success of any policy reform in the agricultural sector. This study aims to quantify the responsiveness of producers of some livestock commodities to price incentives in Algerian context. Time series analysis is used on annual data for a period of 52 years (1966-2018). Both co-integration and vector error correction model (VECM) are used through the Nerlove model of partial adjustment. The study attempts to determine the long-run and short-run relationships along with the magnitudes of disequilibria in the selected commodities. Results show that the short-run price elasticities are low in cow and sheep meat sectors (8.7 and 8% respectively), while their respective long-run elasticities are 16.5 and 10.5, whereas eggs and milk have very high short-run price elasticities (82 and 90% respectively) with long-run elasticities of 40 and 46 respectively. The error correction coefficient, reflecting the speed of adjustment towards the long-run equilibrium, is statistically significant and have the expected negative sign. Its estimates are 12.7 for cow meat, 33.5 for sheep meat, 46.7 for eggs and 8.4 for milk. It seems that cow meat and milk producers have a weak feedback of about 12.7% and 8.4% respectively of the previous year's disequilibrium from the long-run price elasticity, whereas sheep meat and eggs producers adjust to correct long run disequilibrium with a high speed of adjustment (33.5% and 46.7 % respectively). The implication of this is that much more in-depth research is needed to identify those factors that affect agricultural supply and to describe the effect of factors that shift supply in response to price incentives. This could provide valuable information for government in the use of appropriate policy measures.

Keywords: Algeria, cointegration, livestock, supply response, vector error correction model

Procedia PDF Downloads 121

Authors: Jiin-Yuh Jang, Yu-Feng Gan

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In order to improve the comprehensive mechanical properties of the steel, the cooling rate, and the temperature distribution must be controlled in the cooling process. A three-dimensional numerical model for the prediction of the heat transfer coefficient distribution of H-beam in the controlled cooling process was performed in order to obtain the uniform temperature distribution and minimize the maximum stress and the maximum deformation after the controlled cooling. An algorithm developed with a simplified conjugated-gradient method was used as an optimizer to optimize the heat transfer coefficient distribution. The numerical results showed that, for the case of air cooling 5 seconds followed by water cooling 6 seconds with uniform the heat transfer coefficient, the cooling rate is 15.5 (℃/s), the maximum temperature difference is 85℃, the maximum the stress is 125 MPa, and the maximum deformation is 1.280 mm. After optimize the heat transfer coefficient distribution in control cooling process with the same cooling time, the cooling rate is increased to 20.5 (℃/s), the maximum temperature difference is decreased to 52℃, the maximum stress is decreased to 82MPa and the maximum deformation is decreased to 1.167mm.

Keywords: controlled cooling, H-Beam, optimization, thermal stress

Procedia PDF Downloads 354

Authors: Omojokun G. Aju, Adedayo O. Sule

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ZigBee wireless sensor and control network is one of the most popularly deployed wireless technologies in recent years. This is because ZigBee is an open standard lightweight, low-cost, low-speed, low-power protocol that allows true operability between systems. It is built on existing IEEE 802.15.4 protocol and therefore combines the IEEE 802.15.4 features and newly added features to meet required functionalities thereby finding applications in wide variety of wireless networked systems. ZigBee‘s current focus is on embedded applications of general-purpose, inexpensive, self-organising networks which requires low to medium data rates, high number of nodes and very low power consumption such as home/industrial automation, embedded sensing, medical data collection, smart lighting, safety and security sensor networks, and monitoring systems. Although the ZigBee design specification includes security features to protect data communication confidentiality and integrity, however, when simplicity and low-cost are the goals, security is normally traded-off. A lot of researches have been carried out on ZigBee technology in which emphasis has mainly been placed on ZigBee network performance characteristics such as energy efficiency, throughput, robustness, packet delay and delivery ratio in different scenarios and applications. This paper investigate and analyse the data accuracy, network implementation difficulties and security challenges of ZigBee network applications in star-based and mesh-based topologies with emphases on its home monitoring application using the ZigBee ProBee ZE-10 development boards for the network setup. The paper also expose some factors that need to be considered when designing ZigBee network applications and suggest ways in which ZigBee network can be designed to provide more resilient to network attacks.

Keywords: home monitoring, IEEE 802.14.5, topology, wireless security, wireless sensor network (WSN), ZigBee

Procedia PDF Downloads 366

Authors: Ci Lin, Tet Yeap, Iluju Kiringa

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This paper introduces the subspace rotation algorithm (SRA) to train the Restricted Hopfield Network (RHN) as an auto-associative memory. Subspace rotation algorithm is a gradient-free subspace tracking approach based on the singular value decomposition (SVD). In comparison with Backpropagation Through Time (BPTT) on training RHN, it is observed that SRA could always converge to the optimal solution and BPTT could not achieve the same performance when the model becomes complex, and the number of patterns is large. The AUTS case study showed that the RHN model trained by SRA could achieve a better structure of attraction basin with larger radius(in general) than the Hopfield Network(HNN) model trained by Hebbian learning rule. Through learning 10000 patterns from MNIST dataset with RHN models with different number of hidden nodes, it is observed that an several components could be adjusted to achieve a balance between recovery accuracy and noise resistance.

Keywords: hopfield neural network, restricted hopfield network, subspace rotation algorithm, hebbian learning rule

Procedia PDF Downloads 104

Authors: V. Morenov, E. Leusheva

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Power supplying of well drilling on oil and gas fields at ambient air low temperatures is characterized by increased requirements of electric and heat energy. Power costs for heating of production facilities, technological and living objects may several times exceed drilling equipment electric power consumption. Power supplying of prospecting and exploitation drilling objects is usually done by means of local electric power structures based on diesel power stations. In the meantime, exploitation of oil fields is accompanied by vast quantities of extracted associated petroleum gas, and while developing gas fields there are considerable amounts of natural gas and gas condensate. In this regard implementation of gas-powered self-sufficient power units functioning on produced crude products for power supplying is seen as most potential. For these purposes gas turbines (GT) or gas reciprocating engines (GRE) may be used. In addition gas-powered units are most efficiently used in cogeneration mode - combined heat and power production. Conducted research revealed that GT generate more heat than GRE while producing electricity. One of the latest GT design are microturbines (MT) - devices that may be efficiently exploited in combined heat and power mode. In conditions of ambient air low temperatures and high velocity wind sufficient heat supplying is required for both technological process, specifically for drilling mud heating, and for maintaining comfortable working conditions at the rig. One of the main heat regime parameters are the heat losses. Due to structural peculiarities of the rig most of the heat losses occur at cold air infiltration through the technological apertures and hatchways and heat transition of isolation constructions. Also significant amount of heat is required for working temperature sustaining of the drilling mud. Violation of circulation thermal regime may lead to ice build-up on well surfaces and ice blockages in armature elements. That is why it is important to ensure heating of the drilling mud chamber according to ambient air temperature. Needed heat power will be defined by heat losses of the chamber. Noting heat power required for drilling structure functioning, it is possible to create combined heat and power complex based on MT for satisfying consumer power needs and at the same time lowering power generation costs. As a result, combined power supplying scheme for multiple well drilling utilizing heat of MT flue gases was developed.

Keywords: combined heat, combined power, drilling, electric supply, gas-powered units, heat supply

Procedia PDF Downloads 569

Authors: Zhizhong Yuan, Lili Wang, Chenya Zheng, Wuqi Yang

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How to encourage and support peer-to-peer (P2P) network lending, and effectively monitor the risk of P2P network lending, has become the focus of the Chinese government departments, industrialists, experts and scholars in recent years. The reason is that this convenient online micro-credit service brings a series of credit risks and other issues. Avoiding the risks brought by the P2P network lending model, it can better play a benign role and help China's small and medium-sized private enterprises with vigorous development to solve the capital needs; otherwise, it will bring confusion to the normal financial order. As a form of financial services, P2P network lending has injected new blood into China's non-government finance in the past ten years, and has found a way out for idle funds and made up for the shortage of traditional financial services in China. However, it lacks feasible measures in credit evaluation and government supervision. This paper collects a large amount of data about P2P network lending of China. The data collection comes from the official media of the Chinese government, the public achievements of existing researchers and the analysis and collation of correlation data by the authors. The research content of this paper includes literature review; the current situation of China's P2P network lending development; the risk analysis of P2P network lending in China; the risk prevention strategy of P2P network lending in China. The focus of this paper is to try to find a specific program to strengthen supervision and avoid risks from the perspective of government regulators, operators of P2P network lending platform, investors and users of funds. These main measures include: China needs to develop self-discipline organization of P2P network lending industry and formulate self-discipline norms as soon as possible; establish a regular information disclosure system of P2P network lending platform; establish censorship of credit rating of borrowers; rectify the P2P network lending platform in compliance through the implementation of bank deposition. The results and solutions will benefit all the P2P network lending platforms, creditors, debtors, bankers, independent auditors and government agencies of China and other countries.

Keywords: peer-to-peer(P2P), regulation, risk prevention, supervision

Procedia PDF Downloads 152

Authors: V. A. Doss, R. Sowndarya, K. Juila Rose Mary

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Objective: Amino acid neurotransmitter system dysfunction plays a major role in the pathophysiology of depression. The objective of the present study was to identify the amino acids as possible metabolite biomarkers for depression using GCMS (Gas Chromatography Mass Spectrometry) before and after exercise regimen in brain samples of depression induced animal models. Methods: Depression-like behaviour was induced by Chronic Unpredictable mild stress (CUMS). Severity of depression was measured by forced swim test (FST) and sucrose consumption test (SCT). Swimming protocol was followed for 4 weeks of exercise treatment. Brain obtained from depressed and exercise treated rats were used for the metabolite analysis by GCMS. Subsequent statistical analysis obtained by ANOVA followed by post hoc test revealed significant metabolic changes. Results: Amino acids such as alanine, glycine, serine, glutamate, homocysteine, proline and branched chain aminoacids (BCAs) Leucine, Isoleucine, Valine were determined in brain samples of control, depressed and exercised groups. Among these amino acids, the levels of D-Serine and branched chain amino acids were found to be decreased in depression induced rats. After four weeks of swimming exercise regimen, there were improvements in the levels of serine and Branched chain amino acids. Conclusion: We suggest that Serine and BCAs may be investigated as potential metabolite markers using GCMS and their beneficial metabolic changes in Exercise.

Keywords: metabolomics, depression, forced swim test, exercise, amino acid metabolites, GCMS, biomarker

Procedia PDF Downloads 310

Authors: Katsumi Hirata

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Certain systems can function well only if they recognize the sound environment as humans do. In this research, we focus on sound classification by adopting a convolutional neural network and aim to develop a method that automatically classifies various environmental sounds. Although the neural network is a powerful technique, the performance depends on the type of input data. Therefore, we propose an approach via a slice bispectrogram, which is a third-order spectrogram and is a slice version of the amplitude for the short-time bispectrum. This paper explains the slice bispectrogram and discusses the effectiveness of the derived method by evaluating the experimental results using the ESC‑50 sound dataset. As a result, the proposed scheme gives high accuracy and stability. Furthermore, some relationship between the accuracy and non-Gaussianity of sound signals was confirmed.

Keywords: environmental sound, bispectrum, spectrogram, slice bispectrogram, convolutional neural network

Procedia PDF Downloads 111

Authors: Kai Qian, Lixin Tao

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Mobile devices such as smartphones are getting more and more popular in our daily lives. The security vulnerability and threat attacks become a very emerging and important research and education topic in computing security discipline. There is a need to have an innovative mobile security hands-on laboratory to provide students with real world relevant mobile threat analysis and protection experience. This paper presents an authentic teaching and learning mobile security approach with smartphone devices which covers most important mobile threats in most aspects of mobile security. Each lab focuses on one type of mobile threats, such as mobile messaging threat, and conveys the threat analysis and protection in multiple ways, including lectures and tutorials, multimedia or app-based demonstration for threats analysis, and mobile app development for threat protections. This authentic learning approach is affordable and easily-adoptable which immerse students in a real world relevant learning environment with real devices. This approach can also be applied to many other mobile related courses such as mobile Java programming, database, network, and any security relevant courses so that can learn concepts and principles better with the hands-on authentic learning experience.

Keywords: mobile computing, Android, network, security, labware

Procedia PDF Downloads 389

Authors: Bin Wang, Jian Kang

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After years of development, the study of soundscape has been refined to the types of urban space and building. Traffic complex takes traffic function as the core, with obvious design features of architectural space combination and traffic streamline. The acoustic environment is strongly characterized by function, space, material, user and other factors. Traffic complex integrates various functions of business, accommodation, entertainment and so on. It has various forms, complex and varied experiences, and its acoustic environment is turned rich and interesting with distribution and coordination of various functions, division and unification of the mass, separation and organization of different space and the cross and the integration of multiple traffic flow. In this study, it made field recordings of each space of various traffic complex, and extracted and analyzed different acoustic elements, including changes in sound pressure, frequency distribution, steady sound source, sound source information and other aspects, to make cluster analysis of each independent traffic complex buildings. It divided complicated traffic complex building space into several typical sound space from acoustic environment perspective, mainly including stable sound space, high-pressure sound space, rhythm sound space and upheaval sound space. This classification can further deepen the study of subjective evaluation and control of the acoustic environment of traffic complex.

Keywords: soundscape, traffic complex, cluster analysis, classification

Procedia PDF Downloads 240

Authors: Zhou Mo, Dennis Chow

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In the dynamic positioning system (DPS) for vessels, the reliable information transmission between each note basically relies on the wireless protocols. From the perspective of cluster-based routing pro-tocols for wireless sensor networks, the data fusion technology based on the sleep scheduling mechanism and remaining energy in network layer is proposed, which applies the sleep scheduling mechanism to the routing protocols, considering the remaining energy of node and location information when selecting cluster-head. The problem of uneven distribution of nodes in each cluster is solved by the Equilibrium. At the same time, Classified Forwarding Mechanism as well as Redelivery Policy strategy is adopted to avoid congestion in the transmission of huge amount of data, reduce the delay in data delivery and enhance the real-time response. In this paper, a simulation test is conducted to improve the routing protocols, which turns out to reduce the energy consumption of nodes and increase the efficiency of data delivery.

Keywords: DPS for vessel, wireless sensor network, data fusion, routing protocols

Procedia PDF Downloads 437

Authors: Krisana Chueachainat

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The study is about the identity, typology and symbol using in communication through offline and online social network of each Thai football Premier League supporters. Also, study is about the factors that affected the sport to become the growing business and the communication factors that play the important role in the growth of the sport business. The Thai Premier League communicated with supporters in order to show the identity of each supporter and club in different ways. The expression of the identity was shown through online social network and offline told other people who they were. The study also about the factor that impacted the roles and communication factors that make football become the growing business. The factor that impact to the growth of football to the business, if clubs can action, the sport business would be to higher level and also push Thailand’s football to be effective and equal to other countries.

Keywords: online social network, offline social network, Thai football, supporters

Procedia PDF Downloads 290

Authors: K. Asanimoghadam, M. Salahi, A. Jamalian

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Data envelopment analysis is an approach to measure the efficiency of decision making units with multiple inputs and outputs. The structure of many decision making units also has decision-making subunits that are not considered in most data envelopment analysis models. Also, the inputs and outputs of the decision-making units usually are considered desirable, while in some real-world problems, the nature of some inputs or outputs are undesirable. In this thesis, we study the evaluation of the efficiency of two stage decision-making units, where some outputs are undesirable using two non-radial models, the SBM and the ASBM models. We formulate the nonlinear ASBM model as a second order cone optimization problem. Finally, we compare two models for both external and internal evaluation approaches for two real world example in the presence of undesirable outputs. The results show that, in both external and internal evaluations, the overall efficiency of ASBM model is greater than or equal to the overall efficiency value of the SBM model, and in internal evaluation, the ASBM model is more flexible than the SBM model.

Keywords: network DEA, conic optimization, undesirable output, SBM

Procedia PDF Downloads 182

Authors: Ravi Patel, Krishna K. Krishnan

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In global supply chains, appropriate and sustainable suppliers play a vital role in supply chain development and feasibility. In a larger organization with huge number of suppliers, it is necessary to divide suppliers based on their past history of quality and delivery of each product category. Since performance of any organization widely depends on their suppliers, well evaluated selection criteria and decision-making models lead to improved supplier assessment and development. In this paper, SCOR® performance evaluation approach and ISO standards are used to determine selection criteria for better utilization of supplier assessment by using hybrid model of Analytic Hierchchy Problem (AHP) and Fuzzy Techniques for Order Preference by Similarity to Ideal Solution (FTOPSIS). AHP is used to determine the global weightage of criteria which helps TOPSIS to get supplier score by using triangular fuzzy set theory. Both qualitative and quantitative criteria are taken into consideration for the proposed model. In addition, a multi-product and multi-time period model is selected for order allocation. The optimization model integrates multi-objective integer linear programming (MOILP) for order allocation and a hybrid approach for supplier selection. The proposed MOILP model optimizes order allocation based on manufacturing process and product tolerances as per manufacturer’s requirement for quality product. The integrated model and solution approach are tested to find optimized solutions for different scenario. The detailed analysis shows the superiority of proposed model over other solutions which considered individual decision making models.

Keywords: AHP, fuzzy set theory, multi-criteria decision making, multi-objective integer linear programming, TOPSIS

Procedia PDF Downloads 156

Authors: Hira Jabbar, Tanzeel-Ur Rehman

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Urban parks and open spaces play a significant role in improving physical and mental health of the citizens, strengthen the societies and make the cities more attractive places to live and work. As the world’s cities continue to grow, continuing to value green space in cities is vital but is also a challenge, particularly in developing countries where there is pressure for space, resources, and development. Offering equal opportunity of accessibility to parks is one of the important issues of park distribution. The distribution of parks should allow all inhabitants to have close proximity to their residence. Remote sensing and Geographic information systems (GIS) can provide decision makers with enormous opportunities to improve the planning and management of Park facilities. This study exhibits the capability of GIS and RS techniques to provide baseline knowledge about the distribution of parks, level of accessibility and to help in identification of potential areas for such facilities. For this purpose Landsat OLI imagery for year 2016 was acquired from USGS Earth Explorer. Preprocessing models were applied using Erdas Imagine 2014v for the atmospheric correction and NDVI model was developed and applied to quantify the land use/land cover classes including built up, barren land, water, and vegetation. The parks amongst total public green spaces were selected based on their signature in remote sensing image and distribution. Percentages of total green and parks green were calculated for each town of Lahore City and results were then synchronized with the recommended standards. ANGSt model was applied to calculate the accessibility from parks. Service area analysis was performed using Network Analyst tool. Serviceability of these parks has been evaluated by employing statistical indices like service area, service population and park area per capita. Findings of the study may contribute in helping the town planners for understanding the distribution of parks, demands for new parks and potential areas which are deprived of parks. The purpose of present study is to provide necessary information to planners, policy makers and scientific researchers in the process of decision making for the management and improvement of urban parks.

Keywords: accessible natural green space standards (ANGSt), geographic information systems (GIS), remote sensing (RS), United States geological survey (USGS)

Procedia PDF Downloads 318

Authors: Xiaocong Liu, Huazhen Wang, Ting He, Xiaozheng Li, Weihan Zhang, Jian Chen

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The utilization of electronic medical record (EMR) data to establish the disease diagnosis model has become an important research content of biomedical informatics. Deep learning can automatically extract features from the massive data, which brings about breakthroughs in the study of EMR data. The challenge is that deep learning lacks semantic knowledge, which leads to impracticability in medical science. This research proposes a method of incorporating lexical-semantic knowledge from abundant entities into a convolutional neural network (CNN) framework for pediatric disease diagnosis. Firstly, medical terms are vectorized into Lexical Semantic Vectors (LSV), which are concatenated with the embedded word vectors of word2vec to enrich the feature representation. Secondly, the semantic distribution of medical terms serves as Semantic Decision Guide (SDG) for the optimization of deep learning models. The study evaluate the performance of LSV-SDG-CNN model on four kinds of Chinese EMR datasets. Additionally, CNN, LSV-CNN, and SDG-CNN are designed as baseline models for comparison. The experimental results show that LSV-SDG-CNN model outperforms baseline models on four kinds of Chinese EMR datasets. The best configuration of the model yielded an F1 score of 86.20%. The results clearly demonstrate that CNN has been effectively guided and optimized by lexical-semantic knowledge, and LSV-SDG-CNN model improves the disease classification accuracy with a clear margin.

Keywords: convolutional neural network, electronic medical record, feature representation, lexical semantics, semantic decision

Procedia PDF Downloads 118

Authors: Christian Meinecke, Bernd Scholz-Reiter

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The integrated problem of production and distribution scheduling is relevant in many industrial applications. Thus, many heuristics to solve this integrated problem have been developed in the last decade. Most of these heuristics use a sequential working principal or a single decomposition and integration approach to separate and solve sub-problems. A heuristic using a multi-step decomposition and integration approach is presented in this paper and evaluated in a case study. The result show significant improved results compared with sequential scheduling heuristics.

Keywords: production and outbound distribution, integrated planning, heuristic, decomposition, integration

Procedia PDF Downloads 416

Authors: Edward Guillén, Jhordany Rodriguez, Rafael Páez

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Anomaly detection techniques have been focused on two main components: data extraction and selection and the second one is the analysis performed over the obtained data. The goal of this paper is to analyze the influence that each of these components has over the system performance by evaluating detection over network scenarios with different setups. The independent variables are as follows: the number of system inputs, the way the inputs are codified and the complexity of the analysis techniques. For the analysis, some approaches of artificial neural networks are implemented with different number of layers. The obtained results show the influence that each of these variables has in the system performance.

Keywords: network intrusion detection, machine learning, artificial neural network, anomaly detection module

Procedia PDF Downloads 327

Authors: Farouk Shehu Abdulwahab

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The concept of farmer-farmer knowledge sharing was introduced to bridge the extension worker-farmer ratio gap in developing countries. However, the idea was poorly accepted, especially in typical agrarian communities. Therefore, the study explores the concept of a farmer-to-farmer knowledge-sharing network to enhance extension service delivery. The study collected data from 80 farmers randomly selected through a series of multiple stages. The Data was analysed using a 5-point Likert scale and descriptive statistics. The Likert scale results revealed that 62.5% of the farmers are satisfied with farmer-to-farmer knowledge-sharing networks. Moreover, descriptive statistics show that lack of capacity building and low level of education are the most significant problems affecting farmer-farmer sharing networks. The major implication of these findings is that the concept of farmer-farmer knowledge-sharing networks can work better for farmers in developing countries as it was perceived by them as a reliable alternative for information sharing. Therefore, the study recommends introducing incentives into the concept of farmer-farmer knowledge-sharing networks and enhancing the capabilities of farmers who are opinion leaders in the farmer-farmer concept of knowledge-sharing to make it more sustainable.

Keywords: agricultural productivity, extension, farmer-to-farmer, livelihood, technology transfer

Procedia PDF Downloads 48

Authors: J. K. Adedeji, S. T. Ijatuyi

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The application of neural network using pattern recognition to study the fluid dynamics and predict the groundwater reservoirs properties has been used in this research. The essential of geophysical survey using the manual methods has failed in basement environment, hence the need for an intelligent computing such as predicted from neural network is inevitable. A non-linear neural network with an XOR (exclusive OR) output of 8-bits configuration has been used in this research to predict the nature of groundwater reservoirs and fluid dynamics of a typical basement crystalline rock. The control variables are the apparent resistivity of weathered layer (p₁), fractured layer (p₂), and the depth (h), while the dependent variable is the flow parameter (F=λ). The algorithm that was used in training the neural network is the back-propagation coded in C++ language with 300 epoch runs. The neural network was very intelligent to map out the flow channels and detect how they behave to form viable storage within the strata. The neural network model showed that an important variable g_r (gravitational resistance) can be deduced from the elevation and apparent resistivity p_a. The model results from SPSS showed that the coefficients, a, b and c are statistically significant with reduced standard error at 5%.

Keywords: gravitational resistance, neural network, non-linear, pattern recognition

Procedia PDF Downloads 202

Authors: Kgomotso Matobole, Noluzuko Monakali, Hilary Rutto, Tumisang Seodigeng

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On a global scale, there is an alarming generation of food waste. Food waste is generated across the food supply chain. Worldwide urbanization, as well as global economic growth, have contributed to this amount of food waste the environment is receiving. Food waste normally ends on illegal dumping sites when not properly disposed, or disposed to landfills. This results in environmental pollution due to inadequate waste management practices. Food waste is rich in organic matter and highly biodegradable; hence, it can be utilized for the production of bioethanol, a type of biofuel. In so doing, alternative energy will be created, and the volumes of food waste will be reduced in the process. This results in food waste being seen as a precious commodity in energy generation instead of a pollutant. The main aim of the project was to simulate a biorefinery, using a software called CHEMCAD 7.12. The resulting purity of the ethanol from the simulation was 98.9%, with the feed ratio of 1: 2 for food waste and water. This was achieved by integrating necessary unit operations and optimisation of their operating conditions.

Keywords: fermentation, bioethanol, food waste, hydrolysis, simulation, modelling

Procedia PDF Downloads 332

Authors: Sheng Fu, Yinbo Gao, Hao Lin

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In the current mine air compressors monitoring system, there are some difficulties in the installation and maintenance because of the wired connection. To solve the problem, this paper introduces a new air compressors monitoring system based on ZigBee in which the monitoring parameters are transmitted wirelessly. The collecting devices are designed to form a cluster network to collect vibration, temperature, and pressure of air cylinders and other parameters. All these devices are battery-powered. Besides, the monitoring software in PC is developed using MFC. Experiments show that the designed wireless sensor network works well in the site environmental condition and the system is very convenient to be installed since the wireless connection. This monitoring system will have a wide application prospect in the upgrade of the old monitoring system of the air compressors.

Keywords: condition monitoring, wireless sensor network, air compressor, zigbee, data collecting

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Authors: Ali Mohammad Mobaser Azad, Alireza Akhlaghinia

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Blockchain is changing our world and launching a new generation of decentralized networks. Meanwhile, Blockchain-based networks like Ethereum have been created and they will facilitate these processes using tools like smart contracts. The Ethereum has fundamental structures, each of which affects the activity of the nodes. Our purpose in this paper is to review similar research and examine various components to demonstrate the performance of the Ethereum network and to do this, and we used the data published by the Ethereum Foundation in different time spots to examine the number of changes that determine the status of network performance. This will help other researchers understand better Ethereum in different situations.

Keywords: blockchain, ethereum, smart contract, decentralization consensus algorithm

Procedia PDF Downloads 203

Authors: Alfredo Fortunato, Carmelofrancesco Origlia, Sara Laurita, Rossella Nicoletti

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In the post-pandemic recovery phase of tourism, the role of a Destination Management Organization (DMO) as a coordinated management system of all the elements that make up a destination (attractions, access, marketing, human resources, brand, pricing, etc.) is also becoming relevant for local territories. The objective of a DMO is to maximize the visitor's perception of value and quality while ensuring the competitiveness and sustainability of the destination, as well as the long-term preservation of its natural and cultural assets, and to catalyze benefits for the local economy and residents. In carrying out the multiple functions to which it is called, the DMO can leverage a collective intelligence that comes from the ability to pool information, explicit and tacit knowledge, and relationships of the various stakeholders: policymakers, public managers and officials, entrepreneurs in the tourism supply chain, researchers, data journalists, schools, associations and committees, citizens, etc. The DMO potentially has at its disposal large volumes of data and many of them at low cost, that need to be properly processed to produce value. Based on these assumptions, the paper presents a conceptual framework for building an information system to support the DMO in the intelligent management of a tourist destination tested in an area of southern Italy. The approach adopted is data-informed and consists of four phases: (1) formulation of the knowledge problem (analysis of policy documents and industry reports; focus groups and co-design with stakeholders; definition of information needs and key questions); (2) research and metadatation of relevant sources (reconnaissance of official sources, administrative archives and internal DMO sources); (3) gap analysis and identification of unconventional information sources (evaluation of traditional sources with respect to the level of consistency with information needs, the freshness of information and granularity of data; enrichment of the information base by identifying and studying web sources such as Wikipedia, Google Trends, Booking.com, Tripadvisor, websites of accommodation facilities and online newspapers); (4) definition of the set of indicators and construction of the information base (specific definition of indicators and procedures for data acquisition, transformation, and analysis). The framework derived consists of 6 thematic areas (accommodation supply, cultural heritage, flows, value, sustainability, and enabling factors), each of which is divided into three domains that gather a specific information need to be represented by a scheme of questions to be answered through the analysis of available indicators. The framework is characterized by a high degree of flexibility in the European context, given that it can be customized for each destination by adapting the part related to internal sources. Application to the case study led to the creation of a decision support system that allows: •integration of data from heterogeneous sources, including through the execution of automated web crawling procedures for data ingestion of social and web information; •reading and interpretation of data and metadata through guided navigation paths in the key of digital story-telling; •implementation of complex analysis capabilities through the use of data mining algorithms such as for the prediction of tourist flows.

Keywords: collective intelligence, data framework, destination management, smart tourism

Procedia PDF Downloads 108

Authors: Zubair Khaliq, M. Bilal Qadir, Amir Shahzad, Zulfiqar Ali, Ahsan Nazir, Ali Afzal, Abdul Jabbar

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Carbon fibers are one of the most demanding materials on earth due to their potential application in energy, high strength materials, and conductive materials. The nanostructure of carbon fibers offers enhanced properties of conductivity due to the larger surface area. The next generation carbon nanofibers demand the porous structure as it offers more surface area. Multiple techniques are used to produce carbon fibers. However, electrospinning followed by carbonization of the polymeric materials is easy to carry process on a laboratory scale. Also, it offers multiple diversity of changing parameters to acquire the desired properties of carbon fibers. Polyacrylonitrile (PAN) is the most used material for the production of carbon fibers due to its promising processing parameters. Also, cellulose is one of the highest yield producers of carbon fibers. However, the electrospinning of cellulosic materials is difficult due to its rigid chain structure. The combination of PAN and cellulose can offer a suitable solution for the production of carbon fibers. Both materials are miscible in the mixed solvent of N, N, Dimethylacetamide and lithium chloride. This study focuses on the production of porous carbon fibers as a function of PAN/Cellulose blend ratio, solution properties, and electrospinning parameters. These single polymer and blend with different ratios were electrospun to give fine fibers. The higher amount of cellulose offered more difficulty in electrospinning of nanofibers. After carbonization, the carbon fibers were studied in terms of their blend ratio, surface area, and texture. Cellulose contents offered the porous structure of carbon fibers. Also, the presence of LiCl contributed to the porous structure of carbon fibers.

Keywords: cellulose, polyacrylonitrile, carbon nanofibers, electrospinning, blend

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Authors: Siminfar Samakoush Galougah

Abstract:

In this paper, we study two power allocation problems for an uplink user-centric (UC) cell-free massive multiple-input multiple-output (CF-mMIMO) system. Besides, we assume each access point (AP) is connected to a central processing unit (CPU) via a fronthaul link with limited capacity. To efficiently use the fronthaul capacity, two strategies for transmitting signals from APs to the CPU are employed, namely, compress-forward estimate (CFE), estimate-compress-forward (ECF). The capacity of the aforementioned strategies in user-centric CF-mMIMO is drived. Then, we solved the two power allocation problems with minimum Spectral Efficiency (SE) and sum-SE maximization objectives for ECF and CFE strategies.

Keywords: cell-free massive MIMO, limited capacity fronthaul, spectral efficiency

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Authors: Hanlin Liu, Hua Li, Yintan AI

Abstract:

Network performance prediction (NPP) is essential for the management and optimization of software-defined networking (SDN) and contributes to improving the quality of service (QoS) in SDN to meet the requirements of users. Although current deep learning-based methods can achieve high effectiveness, they still suffer from some problems, such as difficulty in capturing global information of the network, inefficiency in modeling end-to-end network performance, and inadequate graph feature extraction. To cope with these issues, our proposed Graphormer-based architecture for NPP leverages the powerful graph representation ability of Graphormer to effectively model the graph structure data, and a node-edge transformation algorithm is designed to transfer the feature extraction object from nodes to edges, thereby effectively extracting the end-to-end performance characteristics of the network. Moreover, routing oriented centrality measure coefficient for nodes and edges is proposed respectively to assess their importance and influence within the graph. Based on this coefficient, an enhanced feature extraction method and an advanced centrality encoding strategy are derived to fully extract the structural information of the graph. Experimental results on three public datasets demonstrate that the proposed GraphNPP architecture can achieve state-of-the-art results compared to current NPP methods.

Keywords: software-defined networking, network performance prediction, Graphormer, graph neural network

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Authors: Waddah S. Abdullah, Khaled F. Al-Omari

Abstract:

Electrokinetic remediation is one of the most influential and effective methods to decontaminate contaminated soils. Electroosmosis and electromigration are the processes of electrochemical extraction of contaminants from soils. The driving force that causes removing contaminants from soils (electroosmosis process or electromigration process) is voltage gradient. Therefore, the electric field distribution throughout the soil domain is extremely important to investigate and to determine the factors that help to establish a uniform electric field distribution in order to make the clean-up process work properly and efficiently. In this study, small-sized passive electrodes (made of graphite) were placed at predetermined locations within the soil specimen, and the voltage drop between these passive electrodes was measured in order to observe the electrical distribution throughout the tested soil specimens. The electrokinetic test was conducted on two types of soils; a sandy soil and a clayey soil. The electrical distribution throughout the soil domain was conducted with different tests properties; and the electrical field distribution was observed in three-dimensional pattern in order to establish the electrical distribution within the soil domain. The effects of density, applied voltages, and degree of saturation on the electrical distribution within the remediated soil were investigated. The distribution of the moisture content, concentration of the sodium ions, and the concentration of the calcium ions were determined and established in three-dimensional scheme. The study has shown that the electrical conductivity within soil domain depends on the moisture content and concentration of electrolytes present in the pore fluid. The distribution of the electrical field in the saturated soil was found not be affected by its density. The study has also shown that high voltage gradient leads to non-uniform electric field distribution within the electroremediated soil. Very importantly, it was found that even when the electric field distribution is uniform globally (i.e. between the passive electrodes), local non-uniformity could be established within the remediated soil mass. Cracks or air gaps formed due to temperature rise (because of electric flow in low conductivity regions) promotes electrical tortuosity. Thus, fracturing or cracking formed in the remediated soil mass causes disconnection of electric current and hence, no removal of contaminant occur within these areas.

Keywords: contaminant removal, electrical tortuousity, electromigration, electroosmosis, voltage distribution

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Authors: Auliya Adzilatin Uzhma, Ismu Rini Dwi, I. Nyoman Suluh Wijaya

Abstract:

Corporate Social Responsibility (CSR) programme in Pantai Harapan Jaya village is cultivation of mangrove and fishery capital distribution, to achieve the goal the CSR programme needed participation from the society in it. Moeliono in Fahrudin (2011) mentioned that participation from society is based by intrinsic reason from inside people it self and extrinsic reason from the other who related to him. The fundamental connection who caused more boundaries from action which the organization can do called the social structure. The purpose of this research is to know the form of public participation and the social structure typology of the villager and people who is participated in CSR programme. The key actors of the society and key actors of the people who’s participated also can be known. This research use Social Network Analysis method by knew the Rate of Participation, Density and Centrality. The result of the research is people who is involved in the programme is lived in Dusun Pondok Dua and they work in fisheries field. The density value from the participant is 0.516 it’s mean that 51.6% of the people that participated is involved in the same step of CSR programme.

Keywords: social structure, social network analysis, corporate social responsibility, public participation

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Authors: Rania Alshikhe, Vinita Jindal

Abstract:

Digital innovation has played a crucial role in managing smart transportation. For this, big trajectory data collected from traveling vehicles, such as taxis through installed global positioning system (GPS)-enabled devices can be utilized. It offers an unprecedented opportunity to trace the movements of vehicles in fine spatiotemporal granularity. This paper aims to explore big trajectory data to measure the travel efficiency of road networks using the proposed statistical travel efficiency measure (STEM) across an entire city. Further, it identifies the cause of low travel efficiency by proposed least square approximation network-based causality exploration (LANCE). Finally, the resulting data analysis reveals the causes of low travel efficiency, along with the road segments that need to be optimized to improve the traffic conditions and thus minimize the average travel time from given point A to point B in the road network. Obtained results show that our proposed approach outperforms the baseline algorithms for measuring the travel efficiency of the road network.

Keywords: GPS trajectory, road network, taxi trips, digital map, big data, STEM, LANCE

Procedia PDF Downloads 148