Search results for: data transfer optimization

Authors: G. R. Kavitha, T. S. Indumathi

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With the constantly rising demands of the multimedia services, the requirements of long haul transport network are constantly changing in the area of optical network. Maximum data transmission using optimization of the communication channel poses the biggest challenge. Although there has been a constant focus on this area from the past decade, there was no evidence of a significant result that has been accomplished. Hence, after reviewing some potential design of optical network from literatures, it was understood that optical signal to noise ratio was one of the elementary attributes that can define the performance of the optical network. In this paper, we propose a framework termed as ECON (Enhanced Constraint-based Optical Network) that primarily optimize the optical signal to noise ratio using ROADM. The simulation is performed in Matlab and optical signal to noise ratio is extracted considering the system matrix. The outcome of the proposed study shows that optimized OSNR as compared to the existing studies.

Keywords: component, optical network, reconfigurable optical add-drop multiplexer, optical signal-to-noise ratio

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Authors: Melaku Tsehay

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The Consortium of Christian Relief and Development Associations (CCRDA), and the CORE Group Polio Partners (CGPP) Secretariat have been working with Global Alliance for Vac-cines and Immunization (GAVI) to improve the immunization data quality in Afar and Somali Regional States. The main aim of this study was to compare the quality of immunization data before and after the above interventions in health facilities in the pastoralist communities in Ethiopia. To this end, a comparative-cross-sectional study was conducted on 51 health facilities. The baseline data was collected in May 2019, while the end line data in August 2021. The WHO data quality self-assessment tool (DQS) was used to collect data. A significant improvment was seen in the accuracy of the pentavalent vaccine (PT)1 (p = 0.012) data at the health posts (HP), while PT3 (p = 0.010), and Measles (p = 0.020) at the health centers (HC). Besides, a highly sig-nificant improvment was observed in the accuracy of tetanus toxoid (TT)2 data at HP (p < 0.001). The level of over- or under-reporting was found to be < 8%, at the HP, and < 10% at the HC for PT3. The data completeness was also increased from 72.09% to 88.89% at the HC. Nearly 74% of the health facilities timely reported their respective immunization data, which is much better than the baseline (7.1%) (p < 0.001). These findings may provide some hints for the policies and pro-grams targetting on improving immunization data qaulity in the pastoralist communities.

Keywords: data quality, immunization, verification factor, pastoralist region

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Authors: Mohsen Azarmjoo, Navid Sharifi, Zahra Alikhani Koopaei

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This article aims to conserve energy and optimize the performance of solar water heaters using modern modeling systems. In this study, a large-scale solar water heater is modeled using an AVR microcontroller, which is a digital processor from the AVR microcontroller family. This mechatronic system will be used to analyze the performance and design of solar collectors, with the ultimate goal of improving the efficiency of the system being used. The findings of this research provide insights into optimizing the performance of solar water heaters. By manipulating the arrangement of solar panels and controlling the water flow through them using the AVR microcontroller, researchers can identify the optimal configurations and operational protocols to achieve the desired temperature and flow conditions. These findings can contribute to the development of more efficient and sustainable heating and cooling systems. This article investigates the optimization of solar water heater performance. It examines the impact of solar panel layout on system efficiency and explores methods of controlling water flow to achieve the desired temperature and flow conditions. The results of this research contribute to the development of more sustainable heating and cooling systems that rely on renewable energy sources.

Keywords: energy conservation, solar water heaters, solar cooling, simulation, mechatronics

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Authors: Hsu-Yung Cheng, Kuo-Chang Hsu, Chi-Chang Chan, Mei-Hui Tseng, Chih-Chang Yu, Ya-Sheng Liu

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Photovoltaics modules are usually not installed horizontally to avoid water or dust accumulation. However, the measured irradiance data on tilted surfaces are rarely available since installing pyranometers with various tilt angles induces high costs. Therefore, estimating solar irradiance on tilted surfaces is an important research topic. In this work, artificial neural networks (ANN) are utilized to construct the transfer model to estimate solar irradiance on tilted surfaces. Instead of predicting tilted irradiance directly, the proposed method estimates the differences between the horizontal irradiance and the irradiance on a tilted surface. The outputs of the ANNs in the proposed design are differential values. The experimental results have shown that the proposed ANNs with differential outputs can substantially improve the estimation accuracy compared to ANNs that estimate the titled irradiance directly.

Keywords: photovoltaics, artificial neural networks, tilted irradiance, solar energy

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Authors: Zohreh Khojasteh-Ghamari, Takashi Irohara

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One of the efficient ways in supply chain risk management is avoiding the interruption in Supply Chain (SC) before it occurs. Although the majority of the organizations focus on their first-tier suppliers to avoid risk in the SC, studies show that in only 60 percent of the disruption cases the reason is first tier suppliers. In the 40 percent of the SC disruptions, the reason is downstream SC, which is the second tier and lower. Due to the increasing complexity and interrelation of modern supply chains, the SC elements have become difficult to trace. Moreover, studies show that there is a vital need to better understand the integration of risk and visibility, especially in the context of multiple objectives. In this study, we propose a multi-objective programming model to avoid disruption in SC. The objective of this study is evaluating the effect of downstream SCV on managing supply chain risk. We propose a multi-objective mathematical programming model with the objective functions of minimizing the total cost and maximizing the downstream supply chain visibility (SCV). The decision variable is supplier selection. We assume there are several manufacturers and several candidate suppliers. For each manufacturer, our model proposes the best suppliers with the lowest cost and maximum visibility in downstream supply chain. We examine the applicability of the model by numerical examples. We also define several scenarios for datasets and observe the tendency. The results show that minimum visibility in downstream SC is needed to have a safe SC network.

Keywords: downstream supply chain, optimization, supply chain risk, supply chain visibility

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Authors: Sami Meddeb, M. L Giorgi, J. L. Courouau

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This work presents the progress of studies aiming to guarantee the lifetime of 316L(N) steel in a sodium-cooled fast reactor by determining the elementary corrosion mechanism, which is akin to an accelerated dissolution by dissolved oxygen. The mechanism involving iron, the main element of steel, is particularly studied in detail, from the viewpoint of the data available in the literature, the modeling of the various mechanisms hypothesized. Experiments performed in the CORRONa facility at controlled temperature and dissolved oxygen content are used to test both literature data and hypotheses. Current tests, performed at various temperatures and oxygen content, focus on specifying the chemical reaction at play, determining its free enthalpy, as well as kinetics rate constants. Specific test configuration allows measuring the reaction kinetics and the chemical equilibrium state in the same test. In the current state of progress of these tests, the dissolution of iron accelerated by dissolved oxygen appears as directly related to a chemical complexation reaction of mixed iron-sodium oxide (Na-Fe-O), a compound that is soluble in the liquid sodium solution. Results obtained demonstrate the presence in the solution of this corrosion product, whose kinetics is the limiting step under the conditions of the test. This compound, the object of hypotheses dating back more than 50 years, is predominant in solution compared to atomic iron, presumably even for the low oxygen concentration, and cannot be neglected for the long-term corrosion modeling of any heat transfer system.

Keywords: corrosion, sodium fast reactors, iron, oxygen

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Authors: Mostafa Mjahed

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Tilt Rotor UAVs (Unmanned Aerial Vehicles) are naturally unstable and difficult to maneuver. The purpose of this paper is to design controllers for the stabilization and trajectory tracking of this type of UAV. To this end, artificial intelligence methods have been exploited. First, the dynamics of this UAV was modeled using the Lagrange-Euler method. The conventional method based on Proportional, Integral and Derivative (PID) control was applied by decoupling the different flight modes. To improve stability and trajectory tracking of the Tilt Rotor, the fuzzy approach and the technique of multilayer neural networks (NN) has been used. Thus, Fuzzy Proportional Integral and Derivative (FPID) and Neural Network-based Proportional Integral and Derivative controllers (NNPID) have been developed. The meta-heuristic approach based on Particle Swarm Optimization (PSO) method allowed adjusting the setting parameters of NNPID controller, giving us an improved NNPID-PSO controller. Simulation results under the Matlab environment show the efficiency of the approaches adopted. Besides, the Tilt Rotor UAV has become stable and follows different types of trajectories with acceptable precision. The Fuzzy, NN and NN-PSO-based approaches demonstrated their robustness because the presence of the disturbances did not alter the stability or the trajectory tracking of the Tilt Rotor UAV.

Keywords: neural network, fuzzy logic, PSO, PID, trajectory tracking, tilt-rotor UAV

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Authors: Maryam Haghi, Ivan Contreras, Nadia Bhuiyan

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Chemotherapy is one of the most popular and effective cancer treatments offered to patients in outpatient oncology centers. In such clinics, patients first consult with an oncologist and the oncologist may prescribe a chemotherapy treatment plan for the patient based on the blood test results and the examination of the health status. Then, when the plan is determined, a set of chemotherapy and consultation appointments should be scheduled for the patient. In this work, a comprehensive mathematical formulation for planning and scheduling different types of chemotherapy patients over a planning horizon considering blood test, consultation, pharmacy and treatment stages has been proposed. To be more realistic and to provide an applicable model, this study is focused on a case study related to a major outpatient cancer treatment clinic in Montreal, Canada. Comparing the results of the proposed model with the current practice of the clinic under study shows significant improvements regarding different performance measures. These major improvements in the patients’ schedules reveal that using optimization techniques in planning and scheduling of patients in such highly demanded cancer treatment clinics is an essential step to provide a good coordination between different involved stages which ultimately increases the efficiency of the entire system and promotes the staff and patients' satisfaction.

Keywords: chemotherapy patients scheduling, integer programming, integrated scheduling, staff balancing

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Authors: Maria Paula Santos, Ana Lucas

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Organizations support their operations and decision making on the data they have at their disposal, so the quality of these data is remarkably important and Data Quality (DQ) is currently a relevant issue, the literature being unanimous in pointing out that poor DQ can result in large costs for organizations. The literature review identified and described 24 Critical Success Factors (CSF) for Data Quality Management (DQM) that were presented to a panel of experts, who ordered them according to their degree of importance, using the Delphi method with the Q-sort technique, based on an online questionnaire. The study shows that the five most important CSF for DQM are: definition of appropriate policies and standards, control of inputs, definition of a strategic plan for DQ, organizational culture focused on quality of the data and obtaining top management commitment and support.

Keywords: critical success factors, data quality, data quality management, Delphi, Q-Sort

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Authors: Yul Y. Nazaruddin, Anas Y. Widiaribowo, Satriyo Nugroho

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Boiler is one of the critical unit in a petrochemical plant. Steam produced by the boiler is used for various processes in the plant such as urea and ammonia plant. An alternative method to optimize the boiler combustion system is presented in this paper. Adaptive Neuro-Fuzzy Inference System (ANFIS) approach is applied to model the boiler using real-time operational data collected from a boiler unit of the petrochemical plant. Nonlinear equation obtained is then used to optimize the air to fuel ratio using Genetic Algorithm, resulting an optimal ratio of 15.85. This optimal ratio is then maintained constant by ratio controller designed using inverse dynamics based on ANFIS. As a result, constant value of oxygen content in the flue gas is obtained which indicates more efficient combustion process.

Keywords: ANFIS, boiler, combustion process, genetic algorithm, optimization.

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Authors: Stefana Francisca Lupu, Jasmin Chantah, Mara Krone, Ingo Roden, Stephan Bongard, Gunter Kreutz

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Long-term music interventions could enhance both musical and nonmusical skills. The present study was designed to explore cognitive, socio-emotional, and musical development in a longitudinal setting. Third-graders (N = 184: 87 male, 97 female; mean age = 8.61 years; 115 native German and 69 migrant children) were randomly assigned to two intervention groups (music and maths) and a control group over a period of one school-year. At baseline, children in these groups were similar in basic cognitive skills, with a trend of advantage in the control group. Dependent measures included the culture fair intelligence test CFT 20-R; the questionnaire of emotional and social school experience for grade 3 and 4 (FEESS 3-4), the test of resources in childhood and adolescence (FRKJ 8-16), the test of language proficiency for German native and non-native primary school children (SFD 3), the reading comprehension test (ELFE 1-6), the German math test (DEMAT 3+) and the intermediate measures of music audiation (IMMA). Data were collected two times at the beginning (T1) and at the end of the school year (T2). A third measurement (T3) followed after a six months retention period. Data from baseline and post-intervention measurements are currently being analyzed. Preliminary results of all three measurements will be presented at the conference.

Keywords: musical training, primary-school German and migrant children, socio-emotional skills, transfer

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Authors: Hammad Majeed, Hanna Knuutila, Magne Hilestad, Hallvard Svendsen

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Formation of aerosols can cause serious complications in industrial exhaust gas CO2 capture processes. SO3 present in the flue gas can cause aerosol formation in an absorption based capture process. Small mist droplets and fog formed can normally not be removed in conventional demisting equipment because their submicron size allows the particles or droplets to follow the gas flow. As a consequence of this aerosol based emissions in the order of grams per Nm3 have been identified from PCCC plants. In absorption processes aerosols are generated by spontaneous condensation or desublimation processes in supersaturated gas phases. Undesired aerosol development may lead to amine emissions many times larger than what would be encountered in a mist free gas phase in PCCC development. It is thus of crucial importance to understand the formation and build-up of these aerosols in order to mitigate the problem.Rigorous modelling of aerosol dynamics leads to a system of partial differential equations. In order to understand mechanics of a particle entering an absorber an implementation of the model is created in Matlab. The model predicts the droplet size, the droplet internal variable profiles and the mass transfer fluxes as function of position in the absorber. The Matlab model is based on a subclass method of weighted residuals for boundary value problems named, orthogonal collocation method. The model comprises a set of mass transfer equations for transferring components and the essential diffusion reaction equations to describe the droplet internal profiles for all relevant constituents. Also included is heat transfer across the interface and inside the droplet. This paper presents results describing the basic simulation tool for the characterization of aerosols formed in CO2 absorption columns and gives examples as to how various entering droplets grow or shrink through an absorber and how their composition changes with respect to time. Below are given some preliminary simulation results for an aerosol droplet composition and temperature profiles. Results: As an example a droplet of initial size of 3 microns, initially containing a 5M MEA, solution is exposed to an atmosphere free of MEA. Composition of the gas phase and temperature is changing with respect to time throughout the absorber.

Keywords: amine solvents, emissions, global climate change, simulation and modelling, aerosol generation

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Authors: Hongshan Guo

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Ground-source heat pumps provide stable and reliable heating and cooling when designed properly. The confounding effect of the borehole depth for a GSHP system, however, is rarely taken into account for any optimization: the determination of the borehole depth usually comes prior to the selection of corresponding system components and thereafter any optimization of the GSHP system. The depth of the borehole is important to any GSHP system because the shallower the borehole, the larger the fluctuation of temperature of the near-borehole soil temperature. This could lead to fluctuations of the coefficient of performance (COP) for the GSHP system in the long term when the heating/cooling demand is large. Yet the deeper the boreholes are drilled, the more the drilling cost and the operational expenses for the circulation. A controller that reads different building load profiles, optimizing for the smallest costs and temperature fluctuation at the borehole wall, eventually providing borehole depth as the output is developed. Due to the nature of the nonlinear dynamic nature of the GSHP system, it was found that between conventional optimal controller problem and model predictive control problem, the latter was found to be more feasible due to a possible history of both the trajectory during the iteration as well as the final output could be computed and compared against. Aside from a few scenarios of different weighting factors, the resulting system costs were verified with literature and reports and were found to be relatively accurate, while the temperature fluctuation at the borehole wall was also found to be within acceptable range. It was therefore determined that the MPC is adequate to optimize for the investment as well as the system performance for various outputs.

Keywords: geothermal borehole, MPC, dynamic modeling, simulation

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Authors: Saif Ullah, Guan Zailin, Xu Xianhao, He Zongdong, Wang Baoxi

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Assembly line balancing problem is aimed to divide the tasks among the stations in assembly lines and optimize some objectives. In assembly lines the workload on stations is different from each other due to different tasks times and the difference in workloads between stations can cause blockage or starvation in some stations in assembly lines. Buffers are used to store the semi-finished parts between the stations and can help to smooth the assembly production. The assembly line balancing and buffer sizing problem can affect the throughput of the assembly lines. Assembly line balancing and buffer sizing problems have been studied separately in literature and due to their collective contribution in throughput rate of assembly lines, balancing and buffer sizing problem are desired to study simultaneously and therefore they are considered concurrently in current research. Current research is aimed to maximize throughput, minimize total size of buffers in assembly line and minimize workload variations in assembly line simultaneously. A multi objective optimization objective is designed which can give better Pareto solutions from the Pareto front and a simple example problem is solved for assembly line balancing and buffer sizing simultaneously. Current research is significant for assembly line balancing research and it can be significant to introduce optimization approaches which can optimize current multi objective problem in future.

Keywords: assembly line balancing, buffer sizing, Pareto solutions

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Authors: Apurva Patil, Sujatha Srinivasan

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Reducing actuator loads is important for applications in which human effort is required for actuation. The potential benefit of applying spring balancing to rehabilitation devices which work against gravity on a nonhorizontal plane is well recognized, but practical applications have been elusive. Although existing methods provide exact spring balance, they require additional masses or auxiliary links, or all the springs used originate from the ground, which makes the resulting device bulky and space-inefficient. This paper uses a method of static balancing of mechanisms with conservative loads such as gravity and spring loads using non-zero-free-length springs and no auxiliary links. Application of this method to a manually operated swimming pool lift mechanism which lowers and raises the physically challenged users into or out of the swimming pool is presented here. Various possible configurations using extension and compression springs as well as gas spring in the mechanism are compared. This work involves approximate spring balancing of the mechanism using minimization of potential energy variance. It uses the approach of flattening the potential energy distribution over the workspace and fuses it with numerical optimization. The results show the considerable reduction in actuator torque requirement with practical spring design and arrangement. Although the method provides only an approximate balancing, it is versatile, flexible in choosing appropriate control variables that are relevant to the design problem and easy to implement. The true potential of this technique lies in the fact that it uses a very simple optimization to find the spring constant, free length of the spring and the optimal attachment points subject to the optimization constraints. Also, it uses physically realizable non-zero-free-length springs directly, thereby reducing the complexity involved in simulating zero-free-length springs from non-zero-free-length springs. This method allows springs to be attached inside the mechanism, which makes the implementation of spring balancing practical. Because auxiliary linkages can be avoided, the resultant swimming pool lift mechanism is compact. The cost benefits and reduced complexity can be significant advantages in the development of this user-actuated swimming pool lift for developing countries.

Keywords: gas spring, rehabilitation device, spring balancing, swimming pool lift

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Authors: Navpreet Kaur, Himkusha Thakur, Nirmal Prabhakar

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The most controversial issue in crop production is the use of Organophosphate insecticides. This is evident in many reports that Organophosphate (OP) insecticides, among the broad range of pesticides are mainly involved in acute and chronic poisoning cases. OPs detection is of crucial importance for health protection, food and environmental safety. In our study, a nanocomposite of poly (3,4 ethylenedioxythiophene) (PEDOT) and multi-walled carbon nanotubes (MWCNTs) has been deposited electrochemically onto the surface of fluorine doped tin oxide sheets (FTO) for the analysis of malathion OP. The -COOH functionalization of MWCNTs has been done for the covalent binding with amino groups of AChE enzyme. The use of PEDOT-MWCNT films exhibited an excellent conductivity, enables fast transfer kinetics and provided a favourable biocompatible microenvironment for AChE, for the significant malathion OP detection. The prepared biosensors were characterized by Fourier transform infrared spectrometry (FTIR), Field emission-scanning electron microscopy (FE-SEM) and electrochemical studies. Various optimization studies were done for different parameters including pH (7.5), AChE concentration (50 mU), substrate concentration (0.3 mM) and inhibition time (10 min). Substrate kinetics has been performed and studied for the determination of Michaelis Menten constant. The detection limit for malathion OP was calculated to be 1 fM within the linear range 1 fM to 1 µM. The activity of inhibited AChE enzyme was restored to 98% of its original value by 2-pyridine aldoxime methiodide (2-PAM) (5 mM) treatment for 11 min. The oxime 2-PAM is able to remove malathion from the active site of AChE by means of trans-esterification reaction. The storage stability and reusability of the prepared biosensor is observed to be 30 days and seven times, respectively. The application of the developed biosensor has also been evaluated for spiked lettuce sample. Recoveries of malathion from the spiked lettuce sample ranged between 96-98%. The low detection limit obtained by the developed biosensor made them reliable, sensitive and a low cost process.

Keywords: PEDOT-MWCNT, malathion, organophosphates, acetylcholinesterase, biosensor, oxime (2-PAM)

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Authors: Pablo Oteiza, Ricardo Alvarez, Mehrdad Pirnia, Fuat Can

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To effectively combat climate change, many countries around the world have committed to a decarbonisation of their electricity, along with promoting a large-scale integration of renewable energy sources (RES). While this trend represents a unique opportunity to effectively combat climate change, achieving a sound and cost-efficient energy transition towards low-carbon power systems poses significant challenges for the multi-year Transmission Network Expansion Planning (TNEP) problem. The objective of the multi-year TNEP is to determine the necessary network infrastructure to supply the projected demand in a cost-efficient way, considering the evolution of the new generation mix, including the integration of RES. The rapid integration of large-scale RES increases the variability and uncertainty in the power system operation, which in turn increases short-term flexibility requirements. To meet these requirements, flexible generating technologies such as energy storage systems must be considered within the TNEP as well, along with proper models for capturing the operational challenges of future power systems. As a consequence, TNEP formulations are becoming more complex and difficult to solve, especially for its application in realistic-sized power system models. To meet these challenges, there is an increasing need for developing efficient algorithms capable of solving the TNEP problem with reasonable computational time and resources. In this regard, a promising research area is the use of artificial intelligence (AI) techniques for solving large-scale mixed-integer optimization problems, such as the TNEP. In particular, the use of AI along with mathematical optimization strategies based on decomposition has shown great potential. In this context, this paper presents an efficient algorithm for solving the multi-year TNEP problem. The algorithm combines AI techniques with Column Generation, a traditional decomposition-based mathematical optimization method. One of the challenges of using Column Generation for solving the TNEP problem is that the subproblems are of mixed-integer nature, and therefore solving them requires significant amounts of time and resources. Hence, in this proposal we solve a linearly relaxed version of the subproblems, and trained a binary classifier that determines the value of the binary variables, based on the results obtained from the linearized version. A key feature of the proposal is that we integrate the binary classifier into the optimization algorithm in such a way that the optimality of the solution can be guaranteed. The results of a study case based on the HRP 38-bus test system shows that the binary classifier has an accuracy above 97% for estimating the value of the binary variables. Since the linearly relaxed version of the subproblems can be solved with significantly less time than the integer programming counterpart, the integration of the binary classifier into the Column Generation algorithm allowed us to reduce the computational time required for solving the problem by 50%. The final version of this paper will contain a detailed description of the proposed algorithm, the AI-based binary classifier technique and its integration into the CG algorithm. To demonstrate the capabilities of the proposal, we evaluate the algorithm in case studies with different scenarios, as well as in other power system models.

Keywords: integer optimization, machine learning, mathematical decomposition, transmission planning

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Authors: Ivana Lolic, Petar Soric, Mirjana Cizmesija

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Based on Business and Consumer Survey (BCS) data, the European Commission (EC) regularly publishes the monthly Economic Sentiment Indicator (ESI) for each EU member state. ESI is conceptualized as a leading indicator, aimed ad tracking the overall economic activity. In calculating ESI, the EC employs arbitrarily chosen weights on 15 BCS response balances. This paper raises the predictive quality of ESI by applying nonlinear programming to find such weights that maximize the correlation coefficient of ESI and year-on-year GDP growth. The obtained results show that the highest weights are assigned to the response balances of industrial sector questions, followed by questions from the retail trade sector. This comes as no surprise since the existing literature shows that the industrial production is a plausible proxy for the overall Croatian economic activity and since Croatian GDP is largely influenced by the aggregate personal consumption.

Keywords: business and consumer survey, economic sentiment indicator, leading indicator, nonlinear optimization with constraints

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Authors: Azeez Balogun

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Inexperienced building score practices are continually evolving and vary across areas. Yet, a few middle ideas stay steady, such as website selection, design, energy efficiency, water and material conservation, indoor environmental great, operational optimization, and waste discount. The essence of green building lies inside the optimization of 1 or more of those standards. This paper conducts a comparative analysis of 7 extensively recognized sustainable score structures—BREEAM, CASBEE, green GLOBES, inexperienced superstar, HK-BEAM, IGBC green homes, and LEED—based totally on the perceptions and opinions of stakeholders in Nigeria certified in green constructing rating systems. The purpose is to pick out and adopt an appropriate green building rating device for Nigeria. Numerous components of those systems had been tested to determine the high-quality health of the Nigerian built environment. The findings imply that LEED, the important machine within the USA and Canada, is the most suitable for Nigeria due to its sturdy basis, extensive funding, and confirmed blessings. LEED obtained the highest rating of eighty out of one hundred points on this assessment.

Keywords: structure, built surroundings, inexperienced building score gadget, Nigeria Inexperienced Constructing Council, sustainability

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Authors: Myriam Ben Said, Dhekra Trabelsi, Faouzi Achouri, Marwa Ben Saad, Latifa Bousselmi, Ahmed Ghrabi

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This present study suggests the use of biological and natural bactericide, cheap, safe to handle, natural, environmentally benign agents to enhance the conventional wastewater treatment process. In the same sense, to highlight the enhancement of wastewater photocatalytic treatability, we were used virulent bacteriophage(s) and essential oils (EOs). The pre-phago-treatment of wastewater with lytic phage(s), leads to a decrease in bacterial density and, consequently, limits the establishment of intercellular communication (QS), thus preventing biofilm formation and inhibiting the expression of other virulence factors after photocatalysis. Moreover, to increase the photocatalytic efficiency, we were added to the secondary treated wastewater 1/1000 (w/v) of EO of thyme (T. vulgaris). This EO showed in vitro an anti-biofilm activity through the inhibition of plonctonic cell mobility and their attachment on an inert surface and also the deterioration of the sessile structure. The presence of photoactivatable molecules (photosensitizes) in this type of oil allows the optimization of photocatalytic efficiency without hazards relayed to dyes and chemicals reagent. The use of ‘biological and natural tools’ in combination with usual water treatment process can be considered as a safety procedure to reduce and/or to prevent the recontamination of treated water and also to prevent the re-expression of virulent factors by pathogenic bacteria such as biofilm formation with friendly processes.

Keywords: biofilm, essential oil, optimization, phage, photocatalysis, wastewater

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Authors: Sirirat Choophan Atthaphonphiphat

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The objective of the study is to analyze errors in English essay writing of Thai (Suratthani Rajabhat University)’s students with different English language experiences. 16 subjects were divided into 2 groups depending on their English language experience. The data were collected from English essay writing about 'My daily life'. The finding shows that 275 tokens of errors were found from 240 English sentences. The errors were categorized into 4 types based on frequency counts: grammatical errors, mechanical errors, lexical errors, and structural errors, respectively. The findings support all of the researcher’s hypothesizes, i.e. 1) the students with low English language experience made more errors than those with high English language experience; 2) all errors in English essay writing of Suratthani Rajabhat University’s students, the interlingual errors are more than the intralingual ones; 3) systemic and structural differences between English (target language) and Thai (mother-tongue language) lead to the errors in English essays writing of Suratthani Rajabhat University’s students.

Keywords: applied linguistics, error analysis, interference, language transfer

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Authors: M. Imran Hossen Khan, M. Mahiuddin, M. A. Karim

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Drying is a food processing technique where simultaneous heat and mass transfer take place from surface to the center of the sample. Deformation of food materials during drying is a common physical phenomenon which affects the textural quality and taste of the dried product. Most of the plant-based food materials are porous and hygroscopic in nature that contains about 80-90% water in different cellular environments: intercellular environment and intracellular environment. Transport of this cellular water has a significant effect on material deformation during drying. However, understanding of the scale of deformation is very complex due to diverse nature and structural heterogeneity of food material. Knowledge about the effect of transport of cellular water on deformation of material during drying is crucial for increasing the energy efficiency and obtaining better quality dried foods. Therefore, the primary aim of this work is to investigate the effect of intracellular water transport on material deformation during drying. In this study, apple tissue was taken for the investigation. The experiment was carried out using 1H-NMR T2 relaxometry with a conventional dryer. The experimental results are consistent with the understanding that transport of intracellular water causes cellular shrinkage associated with the anisotropic deformation of whole apple tissue. Interestingly, it is found that the deformation of apple tissue takes place at different stages of drying rather than deforming at one time. Moreover, it is found that the penetration rate of heat energy together with the pressure gradient between intracellular and intercellular environments is the responsible force to rupture the cell membrane.

Keywords: heat and mass transfer, food material, intracellular water, cell rupture, deformation

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Authors: Nicolae Bold, Daniel Nijloveanu

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The idea of cropping-system is a method used by farmers. It is an environmentally-friendly method, protecting the natural resources (soil, water, air, nutritive substances) and increase the production at the same time, taking into account some crop particularities. The combination of this powerful method with the concepts of genetic algorithms results into a possibility of generating sequences of crops in order to form a rotation. The usage of this type of algorithms has been efficient in solving problems related to optimization and their polynomial complexity allows them to be used at solving more difficult and various problems. In our case, the optimization consists in finding the most profitable rotation of cultures. One of the expected results is to optimize the usage of the resources, in order to minimize the costs and maximize the profit. In order to achieve these goals, a genetic algorithm was designed. This algorithm ensures the finding of several optimized solutions of cropping-systems possibilities which have the highest profit and, thus, which minimize the costs. The algorithm uses genetic-based methods (mutation, crossover) and structures (genes, chromosomes). A cropping-system possibility will be considered a chromosome and a crop within the rotation is a gene within a chromosome. Results about the efficiency of this method will be presented in a special section. The implementation of this method would bring benefits into the activity of the farmers by giving them hints and helping them to use the resources efficiently.

Keywords: chromosomes, cropping, genetic algorithm, genes

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Authors: Ya L. V., Benjamin Ong Wei Lin, Wanli Niu, Benjamin Seah Chin Tat

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This article introduces a methodology that improves reliability and cyclability of 2nd-life Li-ion battery system repurposed as an energy storage system (ESS). Most of the 2nd-life retired battery systems in the market have module/pack-level state-of-health (SOH) indicator, which is utilized for guiding appropriate depth-of-discharge (DOD) in the application of ESS. Due to the lack of cell-level SOH indication, the different degrading behaviors among various cells cannot be identified upon reaching retired status; in the end, considering end-of-life (EOL) loss and pack-level DOD, the repurposed ESS has to be oversized by > 1.5 times to complement the application requirement of reliability and cyclability. This proposed battery grading algorithm, using non-invasive methodology, is able to detect outlier cells based on historical voltage data and calculate cell-level historical maximum temperature data using semi-analytic methodology. In this way, the individual battery cell in the 2nd-life battery system can be graded in terms of SOH on basis of the historical voltage fluctuation and estimated historical maximum temperature variation. These grades will have corresponding DOD grades in the application of the repurposed ESS to enhance system reliability and cyclability. In all, this introduced battery grading algorithm is non-invasive, compatible with all kinds of retired Li-ion battery systems which lack of cell-level SOH indication, as well as potentially being embedded into battery management software for preventive maintenance and real-time cyclability optimization.

Keywords: battery grading algorithm, 2nd-life repurposing battery system, semi-analytic methodology, reliability and cyclability

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Authors: Farina Riaz, Shahab Abdulla, Srinjoy Ganguly, Hajime Suzuki, Ravinesh C. Deo, Susan Hopkins

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Recently, quantum machine learning has received significant attention. For various types of data, including text and images, numerous quantum machine learning (QML) models have been created and are being tested. Images are exceedingly complex data components that demand more processing power. Despite being mature, classical machine learning still has difficulties with big data applications. Furthermore, quantum technology has revolutionized how machine learning is thought of, by employing quantum features to address optimization issues. Since quantum hardware is currently extremely noisy, it is not practicable to run machine learning algorithms on it without risking the production of inaccurate results. To discover the advantages of quantum versus classical approaches, this research has concentrated on colored image data. Deep learning classification models are currently being created on Quantum platforms, but they are still in a very early stage. Black and white benchmark image datasets like MNIST and Fashion MINIST have been used in recent research. MNIST and CIFAR-10 were compared for binary classification, but the comparison showed that MNIST performed more accurately than colored CIFAR-10. This research will evaluate the performance of the QML algorithm on the colored benchmark dataset CIFAR-10 to advance QML's real-time applicability. However, deep learning classification models have not been developed to compare colored images like Quantum Convolutional Neural Network (QCNN) to determine how much it is better to classical. Only a few models, such as quantum variational circuits, take colored images. The methodology adopted in this research is a hybrid approach by using penny lane as a simulator. To process the 10 classes of CIFAR-10, the image data has been translated into grey scale and the 28 × 28-pixel image containing 10,000 test and 50,000 training images were used. The objective of this work is to determine how much the quantum approach can outperform a classical approach for a comprehensive dataset of color images. After pre-processing 50,000 images from a classical computer, the QCNN model adopted a hybrid method and encoded the images into a quantum simulator for feature extraction using quantum gate rotations. The measurements were carried out on the classical computer after the rotations were applied. According to the results, we note that the QCNN approach is ~12% more effective than the traditional classical CNN approaches and it is possible that applying data augmentation may increase the accuracy. This study has demonstrated that quantum machine and deep learning models can be relatively superior to the classical machine learning approaches in terms of their processing speed and accuracy when used to perform classification on colored classes.

Keywords: CIFAR-10, quantum convolutional neural networks, quantum deep learning, quantum machine learning

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Authors: Noreen Sajjad Ghulam Hussain

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Catalysts are prepared by simple physical mixing and thermal treatment of support and metal acetate precursors.The effect of metal ratio and metal loading to produce highly active catalyst for the oxidation of benzyl alcohol are studied.

Keywords: catalyst, acetates, benzyl alcohols

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Authors: Mohd Fairus Humar, Ibrahim Sulaiman, Pedro Cruz, Hasry Harun

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This paper presents the propose knowledge transfer program on railway signaling and communication by Original Equipment Manufacturer (OEM) Thales Portugal. The fundamental issue is that there is no rail related course offered by local universities and colleges in Malaysia which could be an option to pursue student career path. Currently, dedicated trainings related to the rail technology are provided by in-house training academies established by the respective rail operators such as Malaysia Railway Academy (MyRA) and Rapid Rail Training Centre. In this matter, the content of training and facilities need to be strengthened to keep up-to-date with the dynamic evolvement of the rail technology. This is because rail products have evolved to be more sophisticated and embedded with high technology components which no longer exist in the mechanical form alone but combined with electronics, information technology and others. These demand for a workforce imbued with knowledge, multi-skills and competency to deal with specialized technical areas. Talent is needed to support sustainability in Southeast Asia. Keeping the above factors in mind, an Industrial Collaboration Program (ICP) was carried out to transfer knowledge on curricula of railway signaling and communication to a selected railway operators and tertiary educational institution in Malaysia. In order to achieve the aim, a partnership was formed between Technical Depository Agency (TDA), Thales Portugal and MyRA for two years with three main stages of program implementation comprising of: i) training on basic railway signaling and communication for 1 month with Thales in Malaysia; ii) training on advance railway signaling and communication for 4 months with Thales in Portugal and; iii) a series of workshop. Two workshops were convened to develop and harmonize curricula of railway signaling and communication course and were followed by one training for installation equipment of railway signaling and Controlled Train Centre (CTC) system from Thales Portugal. With active involvement from Technical Depository Agency (TDA), railway operators, universities, and colleges, in planning, executing, monitoring, control and closure, the program module of railway signaling and communication course with a lab railway signaling field equipment and CTC simulator were developed. Through this program, contributions from various parties help to build committed societies to engage important issues in relation to railway signaling and communication towards creating a sustainable future.

Keywords: knowledge transfer program, railway signaling and communication, curricula, module and teaching aid simulator

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Authors: Reza Alikhani

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This research focuses on the establishment of horizontal cooperation among companies to enhance their operational efficiency and competitiveness. The study proposes an approach to horizontal collaboration, called coalition configuration, which involves partnering companies sharing distribution centers in a network design problem. The paper investigates which coalition should be formed in each distribution center to minimize the total cost of the network. Moreover, potential uncertainties, such as operational and disruption risks, are considered during the collaborative design phase. To address this problem, a two-stage robust optimization model for collaborative distribution network design under surging demand and facility disruptions is presented, along with a column-and-constraint generation algorithm to obtain exact solutions tailored to the proposed formulation. Extensive numerical experiments are conducted to analyze solutions obtained by the model in various scenarios, including decisions ranging from fully centralized to fully decentralized settings, collaborative versus non-collaborative approaches, and different amounts of uncertainty budgets. The results show that the coalition formation mechanism proposes some solutions that are competitive with the savings of the grand coalition. The research also highlights that collaboration increases network flexibility and resilience while reducing costs associated with demand and capacity uncertainties.

Keywords: logistics, warehouse sharing, robust facility location, collaboration for resilience

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Authors: Djamila Benhaddouche, Abdelkader Benyettou

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In this paper, we used data mining to extract biomedical knowledge. In general, complex biomedical data collected in studies of populations are treated by statistical methods, although they are robust, they are not sufficient in themselves to harness the potential wealth of data. For that you used in step two learning algorithms: the Decision Trees and Support Vector Machine (SVM). These supervised classification methods are used to make the diagnosis of thyroid disease. In this context, we propose to promote the study and use of symbolic data mining techniques.

Keywords: biomedical data, learning, classifier, algorithms decision tree, knowledge extraction

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Authors: Usama Ahmed

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Data mining is the process of analyze data which are used to predict helpful information. It is the field of research which solve various type of problem. In data mining, classification is an important technique to classify different kind of data. Diabetes is most common disease. This paper implements different classification technique using Waikato Environment for Knowledge Analysis (WEKA) on diabetes dataset and find which algorithm is suitable for working. The best classification algorithm based on diabetic data is Naïve Bayes. The accuracy of Naïve Bayes is 76.31% and take 0.06 seconds to build the model.

Keywords: data mining, classification, diabetes, WEKA

Procedia PDF Downloads 147