Search results for: multi period

Authors: Tewele Gerlase Haile

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Sustainable development is development that meets the needs of the present without compromising the ability of future generations to meet their own needs. The global development landscape underwent a transformative shift in 2015 as the international community pivoted from the MDGs to the more ambitious and comprehensive SDGs. The NDGs were a set of eight international development goals established by the United Nations in 2000, with the aim of improving the lives of people around the world by 2015. SDGs are a continuation of the MDGs. Unlike on the other development goals, progress on eradication of extreme hunger and poverty (MDG 1) has been slow at a continental level. The implementation of the MDGs was uneven: some countries have already achieved many of them, while the others have not started any of them yet. With its Poverty Reduction Strategic Papers (PRSPs), Ethiopia has been given special attention to the first MDG since 1993. The Ethiopian government was actively engaged in anti-poverty political campaign leaving other agendas as secondary issues. Poverty in Ethiopia progressively reduced over the years; it was 44.2% in 2000, 38.7% in 2007, 29.6 % in 2011, and it is projected to further reduce to 16.7% by the end of 2020. The long-term impact of war on the sustainability and effectiveness of SDG-related initiatives in post-conflict regions, particularly in how local governance and community resilience are affected. This could involve exploring how war interrupts progress, which specific SDGs are most vulnerable, and what strategies might mitigate these impacts. Reviewing a transitional period enables policy makers to align global or national development goals into local development goals with an uninterrupted policy continuity. The existing literature on development economics often neglects the importance of reviewing the transitional period of consecutive global development goals in a local or regional perspective. Reviewing a transitional period enables policy makers to align global or national development goals into local development goals with an uninterrupted policy continuity. Using a Policy Coherence for Development (PCD) approach as analytical tool, this paper is intended to retrospectively review what happened to the local economy of Tigrai Regional State during the transitional period from MDGs (2000-2015) to SDGs (2015-2030). Taking a retrospective facts and observations into account, policy discontinuity is witnessed in Tigrai following the dissolution of the EPRDF that followed with a terrible war that claimed about a million human lives and worth of over a hundred Billion US dollars economic costs. The unhealthy political reform caused not only a terrible war but also breaks the promising SDGs. Unlike other regional states, Tigrai left unprivileged to translate the ambitious SDGs into its local development policies.

Keywords: local development, political reform, war, MDGs, SDGs, Ethiopia, tigrai

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Authors: Andrew R. Winters, Gregor J. Gassner

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A high-order numerical magentohydrodynamics (MHD) solver built upon a non-linear entropy stable numerical flux function that supports eight traveling wave solutions will be described. The method is designed to treat the divergence-free constraint on the magnetic field in a similar fashion to a hyperbolic divergence cleaning technique. The solver is especially well-suited for flows involving strong discontinuities due to its strong stability without the need to enforce artificial low density or energy limits. Furthermore, a new formulation of the numerical algorithm to guarantee positivity of the pressure during the simulation is described and presented. By construction, the solver conserves mass, momentum, and energy and is entropy stable. High spatial order is obtained through the use of a third order limiting technique. High temporal order is achieved by utilizing the family of strong stability preserving (SSP) Runge-Kutta methods. Main attributes of the solver are presented as well as details on an implementation of the new solver into the multi-physics, multi-scale simulation code FLASH. The accuracy, robustness, and computational efficiency is demonstrated with a variety of numerical tests. Comparisons are also made between the new solver and existing methods already present in FLASH framework.

Keywords: entropy stability, finite volume scheme, magnetohydrodynamics, pressure positivity

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Authors: Seyyed Masoud Seyyedoshohadaei, Borhanuddin Mohd Ali

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To support group mobility, the NEMO Basic Support Protocol has been standardized as an extension of Mobile IP that enables an entire network to change its point of attachment to the Internet. Using NEMO in IEEE 802.16e (WiMax) networks causes latency in handover procedure and affects seamless communication of real-time applications. To decrease handover latency and service disruption time, an integrated scheme named Optimized Fast NEMO (OFNEMO) was introduced by authors of this paper. In OFNEMO a pre-establish multi tunnels concept, cross function optimization and cross layer design are used. In this paper, an analytical model is developed to evaluate total cost consisting of signaling and packet delivery costs of the OFNEMO compared with RFC3963. Results show that OFNEMO increases probability of predictive mode compared with RFC3963 due to smaller handover latency. Even though OFNEMO needs extra signalling to pre-establish multi tunnel, it has less total cost thanks to its optimized algorithm. OFNEMO can minimize handover latency for supporting real time application in moving networks.

Keywords: fast mobile IPv6, handover latency, IEEE802.16e, network mobility

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Authors: Dylan Santos De Pinho, Arnaud Gay De Combes, Matthieu Steuhlet, Claude Jeannerat, Nabil Ouerhani

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The Industry 4.0 initiative has been launched to address huge challenges related to ever-smaller batch sizes. The end-user need for highly customized products requires highly adaptive production systems in order to keep the same efficiency of shop floors. Most of the classical Software solutions that operate the manufacturing processes in a shop floor are based on rigid Manufacturing Execution Systems (MES), which are not capable to adapt the production order on the fly depending on changing demands and or conditions. In this paper, we present a highly modular and flexible solution to orchestrate a set of production systems composed of a micro-milling machine-tool, a polishing station, a cleaning station, a part inspection station, and a rough material store. The different stations are installed according to a novel matrix configuration of a 3x3 vertical shelf. The different cells of the shelf are connected through horizontal and vertical rails on which a set of shuttles circulate to transport the machined parts from a station to another. Our software solution for orchestrating the tasks of each station is based on a Multi-Agent System. Each station and each shuttle is operated by an autonomous agent. All agents communicate with a central agent that holds all the information about the manufacturing order. The core innovation of this paper lies in the path planning of the different shuttles with two major objectives: 1) reduce the waiting time of stations and thus reduce the cycle time of the entire part, and 2) reduce the disturbances like vibration generated by the shuttles, which highly impacts the manufacturing process and thus the quality of the final part. Simulation results show that the cycle time of the parts is reduced by up to 50% compared with MES operated linear production lines while the disturbance is systematically avoided for the critical stations like the milling machine-tool.

Keywords: multi-agent systems, micro-manufacturing, flexible manufacturing, transfer systems

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Authors: Willian L. G. Silva, Fabio R. M. Batista, Matthieu Tubino

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Microalgae can be considered a potential source of oil for biodiesel synthesis since this microorganism can grow rapidly in either fresh or salty water, not competing with food production. There are several favorable conditions in Brazil for this type of culture due to the country’s great amount of water. Another very positive aspect of this type of culture is its ability to fix atmospheric CO2, contributing to the reduction of greenhouse gases and their effects on global warming. Despite this biodiesel environmental advantages it degrades resulting in changes in its physical and chemical properties. In this work, the methyl and ethyl microalgae biodiesel oxidative stability was studied in the absence and presence of a synthetic antioxidant. The synthetic antioxidants used were propyl gallate (PG) and tert-butylhydroquinone (TBHQ), at a 0,12% (w/w) concentration. The biodiesel mixture was kept in a sealed glass flask, sheltered from light, and at room temperature (about 25 ºC) for 180 days. During this period, aliquots from this biodiesel were subjected to induced degradation by the Rancimat method, which determines an important quality parameter, provided in the current methods, and is used to monitor the degradation processes that occur in the biodiesel over time. The induction period (IP) expresses the biodiesel oxidative stability. It was stablished that the minimum accepted IP value for biodiesel is 8 hours. The results show that ethylic biodiesel increased its IP value from 7,6 hours to 31 hours when using PG, and to 67 hours when using TBHQ, exceeding the minimum accepted IP value. When the antioxidants were added to the methylic biodiesel samples, the IP was raised to 28 hours when using PG, and to 62 hours when using TBHQ. These values were maintained throughout the entire period of study (180 days). On the other hand, the biodiesel samples without additives maintained an IP above the allowed value for only 30 days. Therefore, in order to preserve microalgae biodiesel for longer periods of time, it is necessary to add antioxidants to both derivatives, i.e., the ethylic and methylic.

Keywords: biodiesel, microalgae, oxidative stability, storage, synthetic antioxidants

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Authors: Elsayed Amer, Tarek Essam, Abdullah Hella, Mohammed Al-Ajmi

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Hydrocarbon production decline in mature gas fields is inevitable, and mitigating these circumstances is essential to ensure a longer production period. Production decline is not only influenced by reservoir pressure and wellbore integrity; however, associated liquids in the reservoir rock have a considerable impact on the production process. The associated liquid may result in liquid loading, near wellbore damage, condensate banking, fine sand migration, and wellhead pressure depletion. Consequently, the producing well will suffocate, and the liquid column will seize the well from flowing. A common solution in such circumstances is reducing the surface pressure by opening the well to the atmospheric pressure and flaring the produced liquids. This practice may not be applicable to many cases since the atmospheric pressure is not low enough to create a sufficient driving force to flow the well. In addition, flaring the produced hydrocarbon is solving the issue on account of the environment, which is against the world's efforts to mitigate the impact of climate change. This paper presents a novel approach and a case study that utilizes a multi-phase mobile wellhead gas compression unit (MMWGC) to reduce surface pressure to the sub-atmospheric level and transfer the produced hydrocarbons to the sales line. As a result, the liquid column will unload in a zero-flaring manner, and the life of the producing well will extend considerably. The MMWGC unit was able to successfully kick off a dead well to produce up to 10 MMSCFD after reducing the surface pressure for 3 hours. Applying such novelty on a broader scale will not only extend the life of the producing wells yet will also provide a zero-flaring, economically and environmentally preferred solution.

Keywords: petroleum engineering, zero-flaring, liquid loading, well revival

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Authors: Abhijitsinh Gohil, Kaushal Wadhvaniya, Kuldipsinh Jadeja

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Road Safety is a multi-sectoral and multi-dimensional issue. An effective model can assess the risk associated with highway safety. A questionnaire survey is very essential to identify the events or activities which are causing unsafe condition for traffic on an urban highway. A questionnaire of standard questions including vehicular, human and infrastructure characteristics can be made. Responses from the age wise group of road users can be taken on field. Each question or an event holds a specific risk weightage, which contributes in creating an inappropriate and unsafe flow of traffic. The probability of occurrence of an event can be calculated from the data collected from the road users. Finally, the risk score can be calculated by considering the risk factor and the probability of occurrence of individual event and addition of all risk score for the individual event will give the total risk score of a particular road. Standards for risk score can be made and total risk score can be compared with the standards. Thus road can be categorized based on risk associated and traffic safety on it. With this model, one can assess the need for traffic safety improvement on a given road, and qualitative data can be analysed.

Keywords: probability of occurrence, questionnaire, risk factor, risk score

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Authors: Rizwan Rizwan

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This paper provides an overview of Federated Learning (FL) and its application in enhancing data security, privacy, and efficiency. FL utilizes three distinct architectures to ensure privacy is never compromised. It involves training individual edge devices and aggregating their models on a server without sharing raw data. This approach not only provides secure models without data sharing but also offers a highly efficient privacy--preserving solution with improved security and data access. Also we discusses various frameworks used in FL and its integration with machine learning, deep learning, and data mining. In order to address the challenges of multi--party collaborative modeling scenarios, a brief review FL scheme combined with an adaptive gradient descent strategy and differential privacy mechanism. The adaptive learning rate algorithm adjusts the gradient descent process to avoid issues such as model overfitting and fluctuations, thereby enhancing modeling efficiency and performance in multi-party computation scenarios. Additionally, to cater to ultra-large-scale distributed secure computing, the research introduces a differential privacy mechanism that defends against various background knowledge attacks.

Keywords: federated learning, differential privacy, gradient descent strategy, convergence, stability, threats

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Authors: Kelsey Kimball

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Generalized anxiety disorder (GAD) is a common mental health illness affecting approximately 10% of the perinatal population. Research examining cognitive behavioral therapy in this population has only recently become more prevalent though exploring this subject is long overdue. This research examines the impact of cognitive behavioral therapy (CBT) on GAD during the perinatal period. The aim of this project was to identify the most effective way to manage GAD during the perinatal period to provide clinicians with evidence-based methods of caring for this population’s mental health. The research was conducted using several databases to identify ten primary research articles involving anxiety management. A critique and a systematic review of the literature was done. The results of the systematic literature review suggested that CBT had a significant positive impact on perinatal anxiety. Three main themes were derived from the literature: CBT for managing GAD in the general population, CBT for managing GAD in the perinatal population, and CBT’s effect on worry and problematic behaviors in both populations. Nurse practitioners are central in improving access to and treatment of perinatal anxiety disorders.

Keywords: anxiety, cognitive behavioral therapy, nurse practitioner, perinatal

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Authors: Liu Yi Hui

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The COVID-19 pandemic in 2020 has globally affected all aspects of life, with social distancing and quarantine orders causing turmoil and learning in community colleges being temporarily paused. In fact, this is the first time that adult education has faced such a severe challenge. It forces researchers to reflect on the impact of pandemics on adult education and ways to respond. Distance learning appears to be one of the pedagogical tools capable of dealing with interpersonal isolation and social distancing caused by the pandemic. This research aims to examine whether the impact of social distancing during COVID-19 will lead to increased acceptance of technology and, subsequently, an increase in adults ’ willingness to participate in distance learning. The hypothesis that social distancing and the desire to participate in distance learning affects learners’ tendency to accept technology is investigated. Teachers ’ participation in distance education and acceptance of technology are used as adjustment variables with the relationship to “social distancing,” “participation in distance learning,” and “acceptance of technology” of learners. A questionnaire survey was conducted over a period of twelve months for teachers and learners at all community colleges in Taiwan who enrolled in a basic unit course. Community colleges were separated using multi-stage cluster sampling, with their locations being metropolitan, non-urban, south, and east as criteria. Using the G*power software, 660 samples were selected and analyzed. The results show that through appropriate pedagogical strategies or teachers ’ own acceptance of technology, adult learners’ willingness to participate in distance learning could be influenced. A diverse model of participation can be developed, improving adult education institutions’ ability to plan curricula to be flexible to avoid the risk associated with epidemic diseases.

Keywords: social distancing, adult learning, community colleges, technology acceptance model

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Authors: Tesfaye Fenta Boka, Niu Zhendong

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Recommender systems play a crucial role in connecting individuals with the items they require, as is particularly evident in the rapid growth of online grocery shopping platforms. These systems predominantly rely on user-centered recommendations, where items are suggested based on individual preferences, garnering considerable attention and adoption. However, our focus lies on the item-centered recommendation task within the grocery shopping context. In the reverse next item period recommendation (RNPR) task, we are presented with a specific item and challenged to identify potential users who are likely to consume it in the upcoming period. Despite the ever-expanding inventory of products on online grocery platforms, the cold start item problem persists, posing a substantial hurdle in delivering personalized and accurate recommendations for new or niche grocery items. To address this challenge, we propose a Graph Neural Network (GNN)-based approach. By capitalizing on the inherent relationships among grocery items and leveraging users' historical interactions, our model aims to provide reliable and context-aware recommendations for cold-start items. This integration of GNN technology holds the promise of enhancing recommendation accuracy and catering to users' individual preferences. This research contributes to the advancement of personalized recommendations in the online grocery shopping domain. By harnessing the potential of GNNs and exploring item-centered recommendation strategies, we aim to improve the overall shopping experience and satisfaction of users on these platforms.

Keywords: recommender systems, cold start item recommendations, online grocery shopping platforms, graph neural networks

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Authors: Achut Manandhar, Kenneth D. Morton, Peter A. Torrione, Leslie M. Collins

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The current trends in affect recognition research are to consider continuous observations from spontaneous natural interactions in people using multiple feature modalities, and to represent affect in terms of continuous dimensions, incorporate spatio-temporal correlation among affect dimensions, and provide fast affect predictions. These research efforts have been propelled by a growing effort to develop affect recognition system that can be implemented to enable seamless real-time human-computer interaction in a wide variety of applications. Motivated by these desired attributes of an affect recognition system, in this work a multi-dimensional affect prediction approach is proposed by integrating multivariate Relevance Vector Machine (MVRVM) with a recently developed Output-associative Relevance Vector Machine (OARVM) approach. The resulting approach can provide fast continuous affect predictions by jointly modeling the multiple affect dimensions and their correlations. Experiments on the RECOLA database show that the proposed approach performs competitively with the OARVM while providing faster predictions during testing.

Keywords: dimensional affect prediction, output-associative RVM, multivariate regression, fast testing

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Authors: Soheil Tavakolpour

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Enhance Oil Recovery (EOR) can be considered as an undeniable action in reservoirs life period. Different kind of EOR methods are available, but suitable EOR method depends on reservoir properties, like rock and fluid properties. In this paper, we nominated fifth SPE’s Comparative Solution Projects (CSP) for testing different scenarios. We used seven EOR scenarios for this reservoir and we simulated it for 10 years after 2 years production without any injection. The first scenario is waterflooding for whole of the 10 years period. The second scenario is gas injection for ten years. The third scenario is Water-Alternation-Gas (WAG). In the next scenario, water injected for 4 years before starting WAG injection for the next 6 years. In the fifth scenario, water injected after 6 years WAG injection for 4 years. For sixth and last scenarios, all the things are similar to fourth and fifth scenarios, but gas injected instead of water. Results show that fourth scenario was the most efficient method for 10 years EOR, but it resulted very high water production. Fifth scenario was efficient too, with little water production in comparison to the fourth scenario. Gas injection was not economically attractive. In addition to high gas production, it produced less oil in comparison to other scenarios.

Keywords: WAG, SPE’s comparative solution projects, numerical simulation, EOR scenarios

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Authors: Kaushik Sathupadi, Sandesh Achar

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Human action recognition modeling is a critical task in machine learning. These systems require better techniques for recognizing body parts and selecting optimal features based on vision sensors to identify complex action patterns efficiently. Still, there is a considerable gap and challenges between images and videos, such as brightness, motion variation, and random clutters. This paper proposes a robust approach for classifying human actions over cloud-based image data. First, we apply pre-processing and detection, human and outer shape detection techniques. Next, we extract valuable information in terms of cues. We extract two distinct features: fuzzy local binary patterns and sequence representation. Then, we applied a greedy, randomized adaptive search procedure for data optimization and dimension reduction, and for classification, we used a random forest. We tested our model on two benchmark datasets, AAMAZ and the KTH Multi-view football datasets. Our HMR framework significantly outperforms the other state-of-the-art approaches and achieves a better recognition rate of 91% and 89.6% over the AAMAZ and KTH multi-view football datasets, respectively.

Keywords: computer vision, human motion analysis, random forest, machine learning

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Authors: Christo Nicholls

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The National Electricity Grid (NEG) in South Africa has been under strain for the last five years. This overburden of the NEG led Eskom (the State-Owned Entity responsible for the NEG) to implement a blunt methodology to assist them in reducing the maximum demand (MD) on the NEG, when required, called Loadshedding. The challenge of this methodology is that not only does it lead to immense technical issues with the distribution network equipment, e.g., transformers, due to the frequent abrupt off and on switching, it also has a broader negative fiscal impact on the distributors, as their key consumers (commercial & industrial) are now grid defecting due to the lack of Electricity Security Provision (ESP). This paper provides a pragmatic alternative methodology utilizing specific functionalities embedded within direct-connect single and three-phase Advanced Meter Infrastructure (AMI) Solutions deployed within the distribution network, in conjunction with a Multi-Agent Systems Based AI implementation focused on Automated Negotiation Peer-2-Peer trading. The results of this research clearly illustrate, not only does methodology provide a factual percentage contribution towards the NEG MD at the point of consideration, it also allows the distributor to leverage the real-time MD data from key consumers to activate complex, yet impact-measurable Demand Response (DR) programs.

Keywords: AI, AMI, demand response, multi-agent

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Authors: Radovan Cobanovic, Milica Rankov-Sicar

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The fresh cake is in the group of perishable food which cannot be kept a long period of time. The study of sustainability has been done in order to extend the shelf-life of the product which was 10 days. According to the plan of sustainability, it was defined that 5 samples had to be stored for 20 days at max +8°C and analyzed every 5th day from the day of reception until the 20th day. The shelf life of cake has expired during the study of sustainability in the period between 10th and 20th day of analyses. Cake samples were subjected to sensory analysis (appearance, odor, taste, color, aroma) and bacteriological analysis (Listeria monocytogenes, Salmonella spp. and Enterobacteriaceae) according to Serbian state regulation. All analysis were tested according to ISO methodology: sensory analysis ISO 6658, Listeria monocytogenes ISO 11290-1, Salmonella spp ISO 6579, and Enterobacteriaceae ISO 21258-2. Analyses showed that after ten days of storage at a temperature defined by the manufacturers and within the product's shelf life, the cake did not have any noticeable changes in sensory characteristics. Smell and taste are unaffected there was no presence of strange smell or taste. As far as microbiological analyses are concerned, neither one pathogen was detected and number of Enterobacteriaceae was at level less than 102 cfu/g. After expiry of shelf life in a period of 15th and 20th day of storage, the sensory analysis showed the presence of strange sour-milky smell and rancid taste. Concerning microbiological analyses, there still were not positive results for pathogen microorganisms but the number of Enterobacteriaceae was at level more than 103cfu/g. Reviewing the results of sensory analysis indicates that it is not recommended to extend the shelf-life of the product comparing to the already defined shelf-life because occurred changes may adversely affect the consumer desire for the choice of this product.

Keywords: confectionary product, extension of shelf life, sensory and microbiological analyses, sustainability

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Authors: José Luis Carrillo-Medina, Roberto Latorre

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Experimental evidence has revealed that different living neural systems can sign their output signals with some specific neural signature. Although experimental and modeling results suggest that neural signatures can have an important role in the activity of neural networks in order to identify the source of the information or to contextualize a message, the functional meaning of these neural fingerprints is still unclear. The existence of cellular mechanisms to identify the origin of individual neural signals can be a powerful information processing strategy for the nervous system. We have recently built different models to study the ability of a neural network to process information based on the emission and recognition of specific neural fingerprints. In this paper we further analyze the features that can influence on the information processing ability of this kind of networks. In particular, we focus on the role that the duration of a refractory period in each neuron after emitting a signed message can play in the network collective dynamics.

Keywords: neural signature, neural fingerprint, processing based on signal identification, self-organizing neural network

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Authors: Salim Yusif

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Fiber optic technology has revolutionized telecommunications and data transmission, providing unmatched speed, bandwidth, and reliability. This paper presents the latest advancements in fiber optic technology, focusing on innovations in fiber materials, transmission techniques, and network architectures that enhance the performance of high-speed data transmission systems. Key advancements include the development of ultra-low-loss optical fibers, multi-core fibers, advanced modulation formats, and the integration of fiber optics into next-generation network architectures such as Software-Defined Networking (SDN) and Network Function Virtualization (NFV). Additionally, recent developments in fiber optic sensors are discussed, extending the utility of optical fibers beyond data transmission. Through comprehensive analysis and experimental validation, this research offers valuable insights into the future directions of fiber optic technology, highlighting its potential to drive innovation across various industries.

Keywords: fiber optics, high-speed data transmission, ultra-low-loss optical fibers, multi-core fibers, modulation formats, coherent detection, software-defined networking, network function virtualization, fiber optic sensors

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Authors: Faik Derya Ince, Ugur Topgul, Alp Gunay, Can Bayoglu, Dante J. Dorantes-Gonzalez

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Explosive attacks are arguably the most lethal threat that may occur in terrorist attacks. In order to counteract this issue, explosive ordnance disposal operators put their lives on the line to dispose of a possible improvised explosive device. Robots can make the disposal process more accurately and saving human lives. For this purpose, there is a demand for more accurate and dexterous manipulating robotic hands that can be teleoperated from a distance. The aim of this project is to design a robotic hand that contains two active and two passive DOF for each finger, as well as a minimum set of tools for mechanical cutting and screw driving within the same robotic hand. Both hand and toolset, are teleoperated from a distance from a haptic robotic glove in order to manipulate dangerous objects such as improvised explosive devices. SolidWorks® Computer-Aided Design, computerized dynamic simulation, and MATLAB® kinematic and static analysis were used for the robotic hand and toolset design. Novel, dexterous and robust solutions for the fingers were obtained, and six servo motors are used in total to remotely control the multi-tooled robotic hand. This project is still undergoing and presents currents results. Future research steps are also presented.

Keywords: Explosive Manipulation, Robotic Hand, Tele-Operation, Tool Integration

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Authors: E. U. Dampage, Ovindi D. Bandara, Vinushi S. Waraketiya, Samitha S. R. De Silva, Yasiru S. Gunarathne

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The knowledge of water inflow figures is paramount in decision making on the allocation for consumption for numerous purposes; irrigation, hydropower, domestic and industrial usage, and flood control. The understanding of how reservoir inflows are affected by different climatic and hydrological conditions is crucial to enable effective water management and downstream flood control. In this research, we propose a method using a Long Short Term Memory (LSTM) Artificial Neural Network (ANN) to assist the aforesaid decision-making process. The Kotmale reservoir, which is the uppermost reservoir in the Mahaweli reservoir complex in Sri Lanka, was used as the test bed for this research. The ANN uses the runoff in the Kotmale reservoir catchment area and the effect of Sea Surface Temperatures (SST) to make a forecast for seven days ahead. Three types of ANN are tested; Multi-Layer Perceptron (MLP), Convolutional Neural Network (CNN), and LSTM. The extensive field trials and validation endeavors found that the LSTM ANN provides superior performance in the aspects of accuracy and latency.

Keywords: convolutional neural network, CNN, inflow, long short-term memory, LSTM, multi-layer perceptron, MLP, neural network

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Authors: Abdelkader Guendouz, Fatima Zohra Adel

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Even if both the economic and the human development are an axial pillar in its global policy, Algerian government seems to be more and more engaged in the international context aiming to reach of the so called millennium development goals, and this since its beginning. By looking closely at the Algerian economic policy, it is easy to mention the existence of several programs in which both economic and social realisations including among others, poverty reduction, enhancement of education level and conditions, woman statute and gender equity amelioration targets. The efforts of Algerian government in the field of these targets had been acheminated through three main plans, which are: -PSRE (Plan de Soutien à la Relance Economique), for the period of 2001 to 2004, initiated with about 7 billion US dollar, had been focused on three objectives, namely, poverty reduction, job creation and regional equilibrium with rural areas revitalization. -PCSC (le Programme complémentaire de soutien à la croissance économique), for the period of 2005 to 2009, with a starting funding of 114 billion US dollar. This program aims to develop public services and supporting public investments, especially in which concerns social infrastructures. Now, and at the end of the maturity of the MDGs agenda, an important question is to be asked: what are the main realizations regarding these MDGs? In order to answer this question, the present paper tries to examine the Algerian economic policy (but also the social one) by considering the MDGs challenges, for the period from 2000 to 2010, but also until 2015. This examination is focused on three main targets, namely poverty, education, and health. Firstly, statistical assessment for the Algerian economic and social situation shows that almost all MDGs had been reached during the period of 2000 to 2009 and it continues to maintain and improve them. This observation can be endorsed by invoking some achievements. Starting by the reduction of poverty, the proportion of population living with less than 1 US dollar per a day passed from 8.0 % in 2000 to 0.5 % in 2009, and 0.3 % in 2015. For education sphere, the enrolment ratio of six-year child, which is the most significant index for school attendance, is about 98 % for 2009 against 93 % in 1999, and only 43 % in 1966. Concluding with health care and relevant services; the Algerian government has accomplished big steps in providing easy access to this sector for the population. Moreover, the percentage of assisted accouchement had been raised from 91.2 % in 2000 to 97.2 % in 2009.

Keywords: Algerian economic policy, MDGs, poverty, education, health

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Authors: Akinsola Oluwatosin, Udofia Samuel, Odofin Mayowa

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The study is aimed at assessing the Geographic Information System (GIS)-based spatial analysis of Survival Pattern and Treatment Outcomes of Multi-Drug Resistant Tuberculosis (MDR-TB) cases for Lagos, Nigeria, with an objective to inform priority areas for public health planning and resource allocation. Multi-drug resistant tuberculosis (MDR-TB) develops due to problems such as irregular drug supply, poor drug quality, inappropriate prescription, and poor adherence to treatment. The shapefile(s) for this study were already georeferenced to Minna datum. The patient’s information was acquired on MS Excel and later converted to . CSV file for easy processing to ArcMap from various hospitals. To superimpose the patient’s information the spatial data, the addresses was geocoded to generate the longitude and latitude of the patients. The database was used for the SQL query to the various pattern of the treatment. To show the pattern of disease spread, spatial autocorrelation analysis was used. The result was displayed in a graphical format showing the areas of dispersing, random and clustered of patients in the study area. Hot and cold spot analysis was analyzed to show high-density areas. The distance between these patients and the closest health facility was examined using the buffer analysis. The result shows that 22% of the points were successfully matched, while 15% were tied. However, the result table shows that a greater percentage of it was unmatched; this is evident in the fact that most of the streets within the State are unnamed, and then again, most of the patients are likely to supply the wrong addresses. MDR-TB patients of all age groups are concentrated within Lagos-Mainland, Shomolu, Mushin, Surulere, Oshodi-Isolo, and Ifelodun LGAs. MDR-TB patients between the age group of 30-47 years had the highest number and were identified to be about 184 in number. The outcome of patients on ART treatment revealed that a high number of patients (300) were not ART treatment while a paltry 45 patients were on ART treatment. The result shows the Z-score of the distribution is greater than 1 (>2.58), which means that the distribution is highly clustered at a significance level of 0.01.

Keywords: tuberculosis, patients, treatment, GIS, MDR-TB

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Authors: Abdulmalik Altuwayjiri, Milad Pirhadi, Sina Taghvaee, Constantinos Sioutas

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A significant portion of PM₂.₅ mass concentration is carbonaceous matter (CM), which majorly exists in the form of organic carbon (OC). Ambient OC originates from a multitude of sources and plays an important role in global climate effects, visibility degradation, and human health. In this study, positive matrix factorization (PMF) was utilized to identify and quantify the long-term contribution of PM₂.₅ sources to total OC mass concentration in central Los Angeles (CELA) and Riverside (i.e., receptor site), using the chemical speciation network (CSN) database between 2005 and 2015, a period during which several state and local regulations on tailpipe emissions were implemented in the area. Our PMF resolved five different factors, including tailpipe emissions, non-tailpipe emissions, biomass burning, secondary organic aerosol (SOA), and local industrial activities for both sampling sites. The contribution of vehicular exhaust emissions to the OC mass concentrations significantly decreased from 3.5 µg/m³ in 2005 to 1.5 µg/m³ in 2015 (by about 58%) at CELA, and from 3.3 µg/m³ in 2005 to 1.2 µg/m³ in 2015 (by nearly 62%) at Riverside. Additionally, SOA contribution to the total OC mass, showing higher levels at the receptor site, increased from 23% in 2005 to 33% and 29% in 2010 and 2015, respectively, in Riverside, whereas the corresponding contribution at the CELA site was 16%, 21% and 19% during the same period. The biomass burning maintained an almost constant relative contribution over the whole period. Moreover, while the adopted regulations and policies were very effective at reducing the contribution of tailpipe emissions, they have led to an overall increase in the fractional contributions of non-tailpipe emissions to total OC in CELA (about 14%, 28%, and 28% in 2005, 2010 and 2015, respectively) and Riverside (22%, 27% and 26% in 2005, 2010 and 2015), underscoring the necessity to develop equally effective mitigation policies targeting non-tailpipe PM emissions.

Keywords: PM₂.₅, organic carbon, Los Angeles megacity, PMF, source apportionment, non-tailpipe emissions

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Authors: Galal H. Senussi, Muamar Benisa, Sanja Vasin

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In our business field today, one of the most important issues for any enterprises is cost minimization and profit maximization. Second issue is how to develop a strong and capable model that is able to give us desired forecasting of these two issues. Many researches deal with these issues using different methods. In this study, we developed a model for multi-criteria production program optimization, integrated with Artificial Neural Network. The prediction of the production cost and profit per unit of a product, dealing with two obverse functions at same time can be extremely difficult, especially if there is a great amount of conflict information about production parameters. Feed-Forward Neural Networks are suitable for generalization, which means that the network will generate a proper output as a result to input it has never seen. Therefore, with small set of examples the network will adjust its weight coefficients so the input will generate a proper output. This essential characteristic is of the most important abilities enabling this network to be used in variety of problems spreading from engineering to finance etc. From our results as we will see later, Feed-Forward Neural Networks has a strong ability and capability to map inputs into desired outputs.

Keywords: project profitability, multi-objective optimization, genetic algorithm, Pareto set, neural networks

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Authors: Tasir Khan, Yejuan Wang

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The study of extreme rainfall events is very important for flood management in river basins and the design of water conservancy infrastructure. Evaluation of quantiles of annual maximum rainfall (AMRF) is required in different environmental fields, agriculture operations, renewable energy sources, climatology, and the design of different structures. Therefore, the annual maximum rainfall (AMRF) was performed at different stations in Pakistan. Multiple probability distributions, log normal (LN), generalized extreme value (GEV), Gumbel (max), and Pearson type3 (P3) were used to find out the most appropriate distributions in different stations. The L moments method was used to evaluate the distribution parameters. Anderson darling test, Kolmogorov- Smirnov test, and chi-square test showed that two distributions, namely GUM (max) and LN, were the best appropriate distributions. The quantile estimate of a multi-parameter PD offers extreme rainfall through a specific location and is therefore important for decision-makers and planners who design and construct different structures. This result provides an indication of these multi-parameter distribution consequences for the study of sites and peak flow prediction and the design of hydrological maps. Therefore, this discovery can support hydraulic structure and flood management.

Keywords: RAMSE, multiple frequency analysis, annual maximum rainfall, L-moments

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Authors: Ali Berk Bozan, Reşat Atalay Oyguç

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The demand for precast reinforced concrete (RC) structures is growing, considering their certain benefits, including faster assembly, homogeneous materials, and high-quality labor. The structural integrity of precast reinforced concrete (RC) constructions is influenced by the effectiveness of the joints and connections. This paper contains an analytical study about four types of precast reinforced concrete frames, which vary according to the number of storeys and the number of bays with two different types of moment-resisting beam-to-column connection is investigated under cyclic displacement loading up to 5.6% drift rate by using ABAQUS software. The first connection type is the widely used moment-resisting connection that is defined as a wet connection in the Turkish Seismic Code (TBDY). The second connection type is known as Artificial Controllable Plastic Hinge. The goal of this connection is to defend reinforced concrete components from earthquake-related plastic deformations by keeping them in a specialized connecting section. It will be possible to repair the broken connections after the earthquake. The cyclic behavior of the four types of frames with the mechanical plastic hinge and wet connection was analytically investigated, and then comparisons and suggestions were made on period, ductility, and structural system behavior coefficient. The analytical study shows that the replaceable plastic hinge element provides a significant period increase. Especially in the case of two storeys and two bays, the change in the period was felt the most compared to other frames. The results for ductility show a significant change in the ductility of the frames with replaceable plastic hinges. For the structural system behavior coefficient, a recommendation between 3.90 and 4.52 values was made.

Keywords: precast structures, replaceable plastic hinge, beam to column connections, ductility

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Authors: Bekim Avdiaj

Abstract:

The transition period of Kosovo society brought fundamental changes in all the spheres of organizing life. This was the period when also in the cultural tradition the biggest movement and an emerging from ‘isolation’ or from the ‘shell’ occurred. Transformation of the traditional and embracing of the modern began here. The same was experienced and is currently being experienced also by Gjakova and its surrounding which is historically renowned for its great tradition and culture. The population of this region is actually facing a transition from the traditional system into the modern one and quite often with huge leaps. These ‘movements’ or ‘evolutions’ of the society of this region, besides the numerous positive things it ‘harvested’, also brought things that do not at all correspond with their tradition as well as new criminal occurrences which in the past were not present in this area. Furthermore, some of the ‘new’ behaviours that are embraced from other ‘cultures’ and ‘civilizations’, and which are often exceeded, are quite perturbing. The security situation is also worrying, particularly following the appearance of some new criminal occurrences. Therefore, with this research paper we will strive to analyse the new cultural “intersections” as well as the nature of the origin of some new very worrying criminal occurrences. We will present there also some factors inciting into these occurrences, which were confessed by the persons involved in these criminal occurrences and who come from this very region.

Keywords: crime, occurrence, culture, Gjakova Region

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Authors: Y. D. Lim, K. S. Yap, K. T. Ooi

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Cross Vane Expander Compressor (CVEC) is a newly invented expander-compressor combined unit, where it is introduced to replace the compressor and the expansion valve in traditional refrigeration system. The mathematical model of CVEC has been developed to examine its performance, and it was found that the energy consumption of a conventional refrigeration system was reduced by as much as 18%. It is believed that energy consumption can be further reduced by optimizing the device. In this study, the coefficient of performance (COP) of CVEC has been optimized under predetermined operational parameters and constrained main design parameters. Several main design parameters of CVEC were selected to be the variables, and the optimization was done with theoretical model in a simulation program. The theoretical model consists of geometrical model, dynamic model, heat transfer model and valve dynamics model. Complex optimization method, which is a constrained, direct search and multi-variables method was used in the study. As a result, the optimization study suggested that with an appropriate combination of design parameters, a 58% COP improvement in CVEC R134a refrigeration system is possible.

Keywords: COP, cross vane expander-compressor, CVEC, design, simulation, refrigeration system, air-conditioning, R134a, multi variables

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Authors: Hossam Mohammed Abdelfattah

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In ancient Egypt, poverty, recognized as the foremost challenge, was extensively addressed in teachings, wisdom, and literary texts. These sources vividly depicted the suffering of a class deprived of life's pleasures. The ancient Egyptian language evolved to introduce terms reflecting poverty and hunger, underscoring the society's commitment to acknowledging and cautioning against this prevalent issue. Among the notable expressions, iwty.f emerged during the Middle Kingdom, symbolizing "the one without property" and signifying the destitute poor. iwty n.f traced back to the Pyramid Texts era, referred to "the one who has nothing" or simply, the poor. Another term, , iwty-sw emphasized the state of possessing nothing. rA-awy originating in the Middle Kingdom Period, initially meant "poverty and poor," expanding to signify poverty in various texts with the addition of the preposition "in," conveying strength given to the poor. During the First Intermediate Period, sny - mnt denoted going through a crisis or suffering, possibly referencing a widespread disease or plague. It encompassed meanings of sickness, pain, and anguish. The term .” sq-sn introduced in Middle Kingdom texts, conveyed the notion of becoming miserable. sp-Xsy . represented a temporal expression reflecting a period of misery or poverty, with Xsy ,indicating distress or misery. The term qsnt appearing in Middle Kingdom texts, held meanings of painful, difficult, harsh, miserable, emaciated, and in bad condition. Its feminine form, qsn denoted anxiety and turmoil. Finally, tp-qsn encapsulated the essence of misery and unhappiness. In essence, these expressions provide linguistic insights into the multifaceted experience of poverty in ancient Egypt, illustrating the society's keen awareness and efforts to address this pervasive challenge.

Keywords: poverty, poor, suffering, misery, painful, ancient Egypt

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Authors: Nilufer Turksoy

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Social media tools successfully connect people from different milieu to each other. This easy access allows politicians with populist attitude to circulate any kind of political opinion or message, which will hardly appear in conventional media. Populism is a relevant concept, especially, in political communication research. In the last decade, populism in social media has been researched extensively. The present study focuses on how social media is used as a playground by Turkish Cypriot politicians to perform populism in Northern Cyprus. It aims to determine and understand the relationship between politicians and social media, and how they employ social media in their political lives. Northern Cyprus’s multi-faced character provides politicians with many possible frames and topics they can make demagogy about ongoing political deadlock, international isolation, economic instability or social and cultural life in the north part of the island. Thus, Northern Cyprus, bizarrely branded as a 'no man's land', is a case par excellence to show how politically and economically unstable geographies are inclined to perform populism. Northern Cyprus is legally invalid territory recognized by no member of the international community other than Turkey and a phantom state, just like Abkhazia and South Ossetia or Nagorno-Karabakh. Five ideological key elements of populism are employed in the theoretical framework of this study: (1) highlighting the sovereignty of the people, (2) attacking the elites, (3) advocacy for the people, (4) excluding others, and (5) invoking the heartland. A qualitative text analysis of typical Facebook posts was conducted. Profiles of popular political leaders who occupy top positions (e.g. member of parliament, minister, chairman, party secretary), who have different political views, and who use their profiles for political expression on daily bases are selected. All official Facebook pages of the selected politicians are examined during a period of five months (1 September 2017-31 January 2018). This period is selected since it was prior to the parliamentary elections. Finding revealed that majority of the Turkish Cypriot politicians use their social media profile to propagate their political ideology in a populist fashion. Populist statements are found across parties. Facebook give especially the left-wing political actors the freedom to spread their messages in manipulative ways, mostly by using a satiric, ironic and slandering jargon that refers to the pseudo-state, the phantom state, the unrecognized Turkish Republic of Northern Cyprus state. While most of the political leaders advocate for the people, invoking the heartland are preferred by right-wing politicians. A broad range of left-wing politicians predominantly conducted attack on the economic elites and ostracism of others. The finding concluded that different politicians use social media differently according to their political standpoint. Overall, the study offers a thorough analysis of populism on social media. Considering the large role social media plays in the daily life today, the finding will shed some light on the political influence of social media and the social media usage among politicians.

Keywords: Northern Cyprus, populism, politics, qualitative text analysis, social media

Procedia PDF Downloads 143