Search results for: cost estimate

Authors: Haileyesus Tessema Alemneh, Abiyu Enyew Molla, Oluwole Daniel Makinde

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Introduction: Faba bean is one of the most important grown plants worldwide for humans and animals. Several biotic and abiotic elements have limited the output of faba beans, irrespective of their diverse significance. Many faba bean pathogens have been reported so far, of which the most important yield-limiting disease is chocolate spot disease (Botrytis fabae). The dynamics of disease transmission and decision-making processes for intervention programs for disease control are now better understood through the use of mathematical modeling. Currently, a lot of mathematical modeling researchers are interested in plant disease modeling. Objective: In this paper, a deterministic mathematical model for chocolate spot disease (CSD) on faba bean plant with an optimal control model was developed and analyzed to examine the best strategy for controlling CSD. Methodology: Three control interventions, quarantine (u2), chemical control (u3), and prevention (u1), are employed that would establish the optimal control model. The optimality system, characterization of controls, the adjoint variables, and the Hamiltonian are all generated employing Pontryagin’s maximum principle. A cost-effective approach is chosen from a set of possible integrated strategies using the incremental cost-effectiveness ratio (ICER). The forward-backward sweep iterative approach is used to run numerical simulations. Results: The Hamiltonian, the optimality system, the characterization of the controls, and the adjoint variables were established. The numerical results demonstrate that each integrated strategy can reduce the diseases within the specified period. However, due to limited resources, an integrated strategy of prevention and uprooting was found to be the best cost-effective strategy to combat CSD. Conclusion: Therefore, attention should be given to the integrated cost-effective and environmentally eco-friendly strategy by stakeholders and policymakers to control CSD and disseminate the integrated intervention to the farmers in order to fight the spread of CSD in the Faba bean population and produce the expected yield from the field.

Keywords: CSD, optimal control theory, Pontryagin’s maximum principle, numerical simulation, cost-effectiveness analysis

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Authors: Kaddour Ziani, Méghit Boumédiène Khaled

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Forty Hamra single non-castrated male lambs were included in the present study. Traits analyzed were weighted at birth (BW) every 20 days. At 99.15±1.07 days old, the animals were weaned, then divided in two identical groups: control and experimental lambs (n=20) according to their live weight; 24.63±0.47 and 24.35±0.64 Kg respectively. During 59 days, two varieties of feed were given to assess the growth performance. The feeding system consisted of supplying a commercial concentrate (corn based) for control lambs. However, a similar amount of experimental concentrate (barley based) was given to the experimental ones. Both diets were supplemented with 200g straw of barley/animal/ration. 10 lambs fed with experimental concentrate were slaughtered at 37.85±0.78 Kg live weight. The growth performance, the diet cost, and some of the carcass and meat characteristics were evaluated. Chemical analysis of both given diets showed an elevated crude fibre content in the commercial concentrate. However, the experimental concentrate contained higher amounts of calcium. Both groups grew at a similar rate (p > 0.05) and showed the same final body weight. Concerning the cost of the given diet, a significant difference has been found (p ≤ 0.001), between both diets. This could affect the price of the produced meat. The dressing percentage was 46.65%, with 2.49% of carcass shrink. Furthermore, an interesting percentage of total muscle was obtained (63.73%) with a good carcass conformation scoring 9.56. Compared to other breed sheep, “Hamra” carcass could be considered as the most valuable economically.

Keywords: Carcass characteristics, feeding cost, growth performance, Hamra lamb, meat

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Authors: Byung Ho Jung, Dong Hoon Lim

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Logistic regression is a statistical method for analyzing a dataset in which there are one or more independent variables that determine an outcome. Logistic regression is used extensively in numerous disciplines, including the medical and social science fields. In this paper, we address the problem of estimating parameters in the logistic regression based on MapReduce framework with RHadoop that integrates R and Hadoop environment applicable to large scale data. There exist three learning algorithms for logistic regression, namely Gradient descent method, Cost minimization method and Newton-Rhapson's method. The Newton-Rhapson's method does not require a learning rate, while gradient descent and cost minimization methods need to manually pick a learning rate. The experimental results demonstrated that our learning algorithms using RHadoop can scale well and efficiently process large data sets on commodity hardware. We also compared the performance of our Newton-Rhapson's method with gradient descent and cost minimization methods. The results showed that our newton's method appeared to be the most robust to all data tested.

Keywords: big data, logistic regression, MapReduce, RHadoop

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Authors: Thiago M. R Dias, Gray F. Moita

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The analysis of scientific collaboration networks has contributed significantly to improving the understanding of how does the process of collaboration between researchers and also to understand how the evolution of scientific production of researchers or research groups occurs. However, the identification of collaborations in large scientific databases is not a trivial task given the high computational cost of the methods commonly used. This paper proposes a method for identifying collaboration in large data base of curriculum researchers. The proposed method has low computational cost with satisfactory results, proving to be an interesting alternative for the modeling and characterization of large scientific collaboration networks.

Keywords: extraction, data integration, information retrieval, scientific collaboration

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Authors: A. Arshad, M. Lehtonen. M. Humayun

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With the increasing Distributed Generation (DG) penetration, distribution systems are advancing towards the smart grid technology for least latency in tackling voltage control problem in a distributed manner. This paper proposes a Multi-agent based distributed voltage level control. In this method a flat architecture of agents is used and agents involved in the whole controlling procedure are On Load Tap Changer Agent (OLTCA), Static VAR Compensator Agent (SVCA), and the agents associated with DGs and loads at their locations. The objectives of the proposed voltage control model are to minimize network losses and DG curtailments while maintaining voltage value within statutory limits as close as possible to the nominal. The total loss cost is the sum of network losses cost, DG curtailment costs, and voltage damage cost (which is based on penalty function implementation). The total cost is iteratively calculated for various stricter limits by plotting voltage damage cost and losses cost against varying voltage limit band. The method provides the optimal limits closer to nominal value with minimum total loss cost. In order to achieve the objective of voltage control, the whole network is divided into multiple control regions; downstream from the controlling device. The OLTCA behaves as a supervisory agent and performs all the optimizations. At first, a token is generated by OLTCA on each time step and it transfers from node to node until the node with voltage violation is detected. Upon detection of such a node, the token grants permission to Load Agent (LA) for initiation of possible remedial actions. LA will contact the respective controlling devices dependent on the vicinity of the violated node. If the violated node does not lie in the vicinity of the controller or the controlling capabilities of all the downstream control devices are at their limits then OLTC is considered as a last resort. For a realistic study, simulations are performed for a typical Finnish residential medium-voltage distribution system using Matlab ®. These simulations are executed for two cases; simple Distributed Voltage Control (DVC) and DVC with optimized loss cost (DVC + Penalty Function). A sensitivity analysis is performed based on DG penetration. The results indicate that costs of losses and DG curtailments are directly proportional to the DG penetration, while in case 2 there is a significant reduction in total loss. For lower DG penetration, losses are reduced more or less 50%, while for higher DG penetration, loss reduction is not very significant. Another observation is that the newer stricter limits calculated by cost optimization moves towards the statutory limits of ±10% of the nominal with the increasing DG penetration as for 25, 45 and 65% limits calculated are ±5, ±6.25 and 8.75% respectively. Observed results conclude that the novel voltage control algorithm proposed in case 1 is able to deal with the voltage control problem instantly but with higher losses. In contrast, case 2 make sure to reduce the network losses through proposed iterative method of loss cost optimization by OLTCA, slowly with time.

Keywords: distributed voltage control, distribution system, multi-agent systems, smart grids

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Authors: Andrey V. Timofeev

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The practical efficient approach is suggested to estimate the high-speed objects instant bounds in C-OTDR monitoring systems. In case of super-dynamic objects (trains, cars) is difficult to obtain the adequate estimate of the instantaneous object localization because of estimation lag. In other words, reliable estimation coordinates of monitored object requires taking some time for data observation collection by means of C-OTDR system, and only if the required sample volume will be collected the final decision could be issued. But it is contrary to requirements of many real applications. For example, in rail traffic management systems we need to get data off the dynamic objects localization in real time. The way to solve this problem is to use the set of statistical independent parameters of C-OTDR signals for obtaining the most reliable solution in real time. The parameters of this type we can call as 'signaling parameters' (SP). There are several the SP’s which carry information about dynamic objects instant localization for each of C-OTDR channels. The problem is that some of these parameters are very sensitive to dynamics of seismoacoustic emission sources but are non-stable. On the other hand, in case the SP is very stable it becomes insensitive as a rule. This report contains describing the method for SP’s co-processing which is designed to get the most effective dynamic objects localization estimates in the C-OTDR monitoring system framework.

Keywords: C-OTDR-system, co-processing of signaling parameters, high-speed objects localization, multichannel monitoring systems

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Authors: Sharmin Nasrin

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Dhaka is the capital of Bangladesh. In Dhaka among other trips, significant percentages of trips comprise education trips. This paper explores significant factors for private university students’ education trip to the University. A paper pencil based survey has been conducted on Asia Pacific University student in Dhaka from May 2016 to July 2016. Participants were chosen randomly for the survey. Exploratory analysis showed that about 50% chose bus, 33% chose Rickshaw, 2% chose car and 15% chose to walk for travel to their University. Results from Multinomial Logit model revealed that travel cost, travel time and comfort are the significant factors for private university students to choose different modes. However, magnitude of coefficient of attribute comfort is significantly higher compared to travel cost and travel time. Result from this paper can be used by policymakers and Government agencies to provide more cost effective, comfortable journey to their University.

Keywords: private university student's education trip, mode choice mode, Dhaka, developing country

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Authors: Maria Arechavaleta, Mark Halpin

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In the United States, costs of solar energy systems have declined to the point that they are viable options for most consumers. However, there are no consistent procedures for specifying sufficient systems. The factors that must be considered are energy consumption, potential solar energy production, and cost. The traditional method of specifying solar energy systems is based on assumed daily levels of available solar energy and average amounts of daily energy consumption. The mismatches between energy production and consumption are usually mitigated using battery energy storage systems, and energy use is curtailed when necessary. The main consumer decision question that drives the total system cost is how much unserved (or curtailed) energy is acceptable? Of course additional solar conversion equipment can be installed to provide greater peak energy production and extra energy storage capability can be added to mitigate longer lasting low solar energy production periods. Each option increases total cost and provides a benefit which is difficult to quantify accurately. An approach to quantify the cost-benefit of adding additional resources, either production or storage or both, based on the statistical concepts of loss-of-energy probability and expected unserved energy, is presented in this paper. Relatively simple calculations, based on site-specific energy availability and consumption data, can be used to show the value of each additional increment of production or storage. With this incremental benefit-cost information, consumers can select the best overall performance combination for their application at a cost they are comfortable paying. The approach is based on a statistical analysis of energy consumption and production characteristics over time. The characteristics are in the forms of curves with each point on the curve representing an energy consumption or production value over a period of time; a one-minute period is used for the work in this paper. These curves are measured at the consumer location under the conditions that exist at the site and the duration of the measurements is a minimum of one week. While greater accuracy could be obtained with longer recording periods, the examples in this paper are based on a single week for demonstration purposes. The weekly consumption and production curves are overlaid on each other and the mismatches are used to size the battery energy storage system. Loss-of-energy probability and expected unserved energy indices are calculated in addition to the total system cost. These indices allow the consumer to recognize and quantify the benefit (probably a reduction in energy consumption curtailment) available for a given increase in cost. Consumers can then make informed decisions that are accurate for their location and conditions and which are consistent with their available funds.

Keywords: battery energy storage systems, loss of load probability, residential renewable energy, solar energy systems

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Authors: Nahed Al-Hajeri

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Time and cost is an integral part of every construction plan and can affect each party’s contractual obligations. The performance of both time and cost are usually important to the client and contractor during the project. Almost all construction projects are experiencing time overrun. These time overruns always contributed as expensive to both client and contractor. Construction of any project inside the gathering centers involves complex management skills related to work force, materials, plant, machineries, new technologies etc. It also involves many agencies interdependent on each other like the vendors, structural and functional designers including various types of specialized engineers and it includes support of contractors and specialized contractors. This paper mainly highlights the types of construction delays due to which project suffer time and cost overrun. This paper also speaks about the delay causes and factors that contribute to the construction sequence delay for the oil and gas projects. Construction delay is supposed to be one of the repeated problems in the construction projects and it has an opposing effect on project success in terms of time, cost and quality. Some effective methods are identified to minimize delays in construction projects such as: 1. Site management and supervision, 2. Effective strategic planning, 3. Clear information and communication channel. Our research paper studies the types of delay with some real examples with statistic results and suggests solutions to overcome this problem.

Keywords: non-compensable delay, delays caused by force majeure, compensable delay, delays caused by the owner or the owner’s representative, non-excusable delay, delay caused by the contractor or the contractor’s representative, concurrent delay, delays resulting from two separate causes at the same time

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Authors: Nuraddeen Usman, Khiruddin Abdullah, Mohd Nawawi, Amin Khalil Ismail

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A magnetic survey is carried out in order to locate the remains of construction items, specifically concrete pillars. The conventional Euler deconvolution technique can perform the task but it requires the use of fixed structural index (SI) and the construction items are made of materials with different shapes which require different SI (unknown). A Euler deconvolution technique that estimate background, horizontal coordinate (xo and yo), depth and structural index (SI) simultaneously is prepared and used for this task. The synthetic model study carried indicated the new methodology can give a good estimate of location and does not depend on magnetic latitude. For field data, both the total magnetic field and gradiometer reading had been collected simultaneously. The computed vertical derivatives and gradiometer readings are compared and they have shown good correlation signifying the effectiveness of the method. The filtering is carried out using automated procedure, analytic signal and other traditional techniques. The clustered depth solutions coincided with the high amplitude/values of analytic signal and these are the possible target positions of the concrete pillars being sought. The targets under investigation are interpreted to be located at the depth between 2.8 to 9.4 meters. More follow up survey is recommended as this mark the preliminary stage of the work.

Keywords: concrete pillar, magnetic survey, geotechnical investigation, Euler Deconvolution

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Authors: Narendra Nath Patra

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Several recent studies reinforce the existence of a thick molecular disc in galaxies along with the dynamically cold thin disc. Assuming a two-component molecular disc, we model the disc of NGC 6946 as a four-component system consists of stars, HI, thin disc molecular gas, and thick disc molecular gas in vertical hydrostatic equilibrium. Following, we set up the joint Poisson-Boltzmann equation of hydrostatic equilibrium and solve it numerically to obtain a three-dimensional density distribution of different baryonic components. Using the density solutions and the observed rotation curve, we further build a three-dimensional dynamical model of the molecular disc and consecutively produce simulated CO spectral cube and spectral width profile. We find that the simulated spectral width profiles distinguishably differs for different assumed thick disc molecular gas fraction. Several CO spectral width profiles are then produced for different assumed thick disc molecular gas fractions and compared with the observed one to obtain the best fit thick disc molecular gas fraction profile. We find that the thick disc molecular gas fraction in NGC 6946 largely remains constant across its molecular disc with a mean value of 0.70 +/- 0.09. We also estimate the amount of extra-planar molecular gas in NGC 6946. We find 60% of the total molecular gas is extra-planar at the central region, whereas this fraction reduces to ~ 35% at the edge of the molecular disc. With our method, for the first time, we estimate the thick disc molecular gas fraction as a function of radius in an external galaxy with sub-kpc resolution.

Keywords: galaxies: kinematics and dynamic, galaxies: spiral, galaxies: structure , ISM: molecules, molecular data

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Authors: Arash Esmaeili, Zhibang Liu, Yang Xiang, Jimmy Yun, Lei Shao

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A high-pressure carbon dioxide (CO₂) absorption from a specific off-gas in a conventional column has been evaluated for the environmental concerns by the Aspen HYSYS simulator using a wide range of single absorbents and piperazine (PZ) blended solutions to estimate the outlet CO₂ concentration, CO₂ loading, reboiler power supply, and regeneration heat duty to choose the most efficient solution in terms of CO₂ removal and required heat duty. The property package, which is compatible with all applied solutions for the simulation in this study, estimates the properties based on the electrolyte non-random two-liquid (E-NRTL) model for electrolyte thermodynamics and Peng-Robinson equation of state for vapor phase and liquid hydrocarbon phase properties. The results of the simulation indicate that piperazine, in addition to the mixture of piperazine and monoethanolamine (MEA), demands the highest regeneration heat duty compared with other studied single and blended amine solutions, respectively. The blended amine solutions with the lowest PZ concentrations (5wt% and 10wt%) were considered and compared to reduce the cost of the process, among which the blended solution of 10wt%PZ+35wt%MDEA (methyldiethanolamine) was found as the most appropriate solution in terms of CO₂ content in the outlet gas, rich-CO₂ loading, and regeneration heat duty.

Keywords: absorption, amine solutions, aspen HYSYS, CO₂ loading, piperazine, regeneration heat duty

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Authors: Valdir Moura, Raniele dos Anjos de Souza, Fernando Gomes de Souza, Jose Vagner da Silva, Jerry Adriani Johann

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The estimate of the Area of Land to be planted with annual crops and its stratification by the municipality are important variables in crop forecast. Nowadays in Brazil, these information’s are obtained by the Brazilian Institute of Geography and Statistics (IBGE) and published under the report Assessment of the Agricultural Production. Due to the high cloud cover in the main crop growing season (October to March) it is difficult to acquire good orbital images. Thus, one alternative is to work with remote sensing data from dates before the crop growing season. This work presents the use of multitemporal Landsat data gathered on July and September (before the summer growing season) in order to estimate the area of land to be planted with summer crops in an area of São Paulo State, Brazil. Geographic Information Systems (GIS) and digital image processing techniques were applied for the treatment of the available data. Supervised and non-supervised classifications were used for data in digital number and reflectance formats and the multitemporal Normalized Difference Vegetation Index (NDVI) images. The objective was to discriminate the tracts with higher probability to become planted with summer crops. Classification accuracies were evaluated using a sampling system developed basically for this study region. The estimated areas were corrected using the error matrix derived from these evaluations. The classification techniques presented an excellent level according to the kappa index. The proportion of crops stratified by municipalities was derived by a field work during the crop growing season. These proportion coefficients were applied onto the area of land to be planted with summer crops (derived from Landsat data). Thus, it was possible to derive the area of each summer crop by the municipality. The discrepancies between official statistics and our results were attributed to the sampling and the stratification procedures. Nevertheless, this methodology can be improved in order to provide good crop area estimates using remote sensing data, despite the cloud cover during the growing season.

Keywords: area intended for summer culture, estimated area planted, agriculture, Landsat, planting schedule

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Authors: Rafael Martins Manzano Silva, Jean-Francois Tremong

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Electrification of railway lines allows to increase speed, power, capacity and energetic efficiency of rolling stocks. However, this process of electrification is complex and costly. An electrification project is not just about design of catenary. It also includes installation of structures around electrification, as substation installation, electrical isolation, signalling, telecommunication and civil engineering structures. France has more than 30,000 km of railways, whose only 53% are electrified. The others 47% of railways use diesel locomotive and represent only 10% of the circulation (tons.km). For this reason, a new type of electrification, less expensive than the usual, is requested to enable the modernization of these railways. One solution could be the use of hybrids trains. This technology opens up new opportunities for less expensive infrastructure development such as the partial electrification of railway lines. In a partially electrified railway, the power supply of theses hybrid trains could be made either by the catenary or by the on-board energy storage system (ESS). Thus, the on-board ESS would feed the energetic needs of the train along the non-electrified zones while in electrified zones, the catenary would feed the train and recharge the on-board ESS. This paper’s objective deals with the technical and economic potential identification of partial electrification of railway lines. This study provides different scenarios of electrification by replacing the most expensive places to electrify using on-board ESS. The target is to reduce the cost of new electrification projects, i.e. reduce the cost of electrification infrastructures while not increasing the cost of rolling stocks. In this study, scenarios are constructed in function of the electrification’s cost of each structure. The electrification’s cost varies considerably because of the installation of catenary support in tunnels, bridges and viaducts is much more expensive than in others zones of the railway. These scenarios will be used to describe the power supply system and to choose between the catenary and the on-board energy storage depending on the position of the train on the railway. To identify the influence of each partial electrification scenario in the sizing of the on-board ESS, a model of the railway line and of the rolling stock is developed for a real case. This real case concerns a railway line located in the south of France. The energy consumption and the power demanded at each point of the line for each power supply (catenary or on-board ESS) are provided at the end of the simulation. Finally, the cost of a partial electrification is obtained by adding the civil engineering costs of the zones to be electrified plus the cost of the on-board ESS. The study of the technical and economic potential ends with the identification of the most economically interesting scenario of electrification.

Keywords: electrification, hybrid, railway, storage

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Authors: Serdal Pamuk, Irem Cay

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Our model is based on the theory of reinforced random walks coupled with Michealis-Menten mechanisms which view endothelial cell receptors as the catalysts for transforming both tumor and macrophage derived tumor angiogenesis factor (TAF) into proteolytic enzyme which in turn degrade the basal lamina. The model consists of two main parts. First part has seven differential equations (DE’s) in one space dimension over the capillary, whereas the second part has the same number of DE’s in two space dimensions in the extra cellular matrix (ECM). We connect these two parts via some boundary conditions to move the cells into the ECM in order to initiate capillary formation. But, when does this movement begin? To address this question we estimate the thresholds that activate the transport equations in the capillary. We do this by using steady-state analysis of TAF equation under some assumptions. Once these equations are activated endothelial, pericyte and macrophage cells begin to move into the ECM for the initiation of angiogenesis. We do believe that our results play an important role for the mechanisms of cell migration which are crucial for tumor angiogenesis. Furthermore, we estimate the long time tendency of these three cells, and find that they tend to the transition probability functions as time evolves. We provide our numerical solutions which are in good agreement with our theoretical results.

Keywords: angiogenesis, capillary formation, mathematical analysis, steady-state, transition probability function

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Authors: Shohei Maruyama, Yasuo Matsuyama, Sachiyo Aburatani

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The development of the method to annotate unknown gene functions is an important task in bioinformatics. One of the approaches for the annotation is The identification of the metabolic pathway that genes are involved in. Gene expression data have been utilized for the identification, since gene expression data reflect various intracellular phenomena. However, it has been difficult to estimate the gene function with high accuracy. It is considered that the low accuracy of the estimation is caused by the difficulty of accurately measuring a gene expression. Even though they are measured under the same condition, the gene expressions will vary usually. In this study, we proposed a feature extraction method focusing on the variability of gene expressions to estimate the genes' metabolic pathway accurately. First, we estimated the distribution of each gene expression from replicate data. Next, we calculated the similarity between all gene pairs by KL divergence, which is a method for calculating the similarity between distributions. Finally, we utilized the similarity vectors as feature vectors and trained the multiclass SVM for identifying the genes' metabolic pathway. To evaluate our developed method, we applied the method to budding yeast and trained the multiclass SVM for identifying the seven metabolic pathways. As a result, the accuracy that calculated by our developed method was higher than the one that calculated from the raw gene expression data. Thus, our developed method combined with KL divergence is useful for identifying the genes' metabolic pathway.

Keywords: metabolic pathways, gene expression data, microarray, Kullback–Leibler divergence, KL divergence, support vector machines, SVM, machine learning

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Authors: Dixit Garg, S. Luthra, A. Haleem

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During recent years, the natural environment has become a challenging topic that business organizations must consider due to the economic and ecological impacts and increasing awareness of environment protection among society. Organizations are trying to achieve the goals of improvement in environment, low cost, high quality, flexibility and more customer satisfaction. Performance measurement frameworks are very useful to monitor the performance of any organization. The basic goal of this paper is to identify performance measures and ranking of these performance measures of GSCM performance measurement towards sustainability framework. Five perspectives (Environment, Economic, Social, Operational and Cost performances) and nineteen performance measures of GSCM performance towards sustainability have been have been identified from extensive literature review. Analytical Hierarchy Process (AHP) technique has been utilized for ranking of these performance perspectives and measures. All pair comparisons in AHP have been made on the basis on the experts’ opinions (selected from academia and industry). Ranking of these performance perspectives and measures will help to understand the importance of environmental, economic, social, operational performances, and cost performances in the supply chain.

Keywords: analytical hierarchy process, green supply chain management, performance measures, sustainability

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Authors: Shobha Ramalingam

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The majority of the infrastructure projects are affected by time overrun, resulting in project delays and subsequently cost overruns. Time overrun may vary from a few months to as high as five or more years, placing the project viability at risk. One of the probable reasons noted in the literature for this outcome in projects is due to poor productivity. Researchers contend that productivity in construction has only marginally increased over the years. While studies in the literature have extensively focused on time and cost parameters in projects, there are limited studies that integrate time and cost with productivity to assess project performance. To this end, a study was conducted to understand the project delay factors concerning cost, time and productivity. A case-study approach was adopted to collect rich data from a nuclear power plant project site for two months through observation, interviews and document review. The data were analyzed using three different approaches for a comprehensive understanding. Foremost, a root-cause analysis was performed on the data using Ishikawa’s fish-bone diagram technique to identify the various factors impacting the delay concerning time. Based on it, a questionnaire was designed and circulated to concerned executives, including project engineers and contractors to determine the frequency of occurrence of the delay, which was then compiled and presented to the management for a possible solution to mitigate. Second, a productivity analysis was performed on select activities, including rebar bending and concreting through a time-motion study to analyze product performance. Third, data on cost of construction for three years allowed analyzing the cost performance using earned value management technique. All three techniques allowed to systematically and comprehensively identify the key factors that deter project performance and productivity loss in the construction of the nuclear power plant project. The findings showed that improper planning and coordination between multiple trades, concurrent operations, improper workforce and material management, fatigue due to overtime were some of the key factors that led to delays and poor productivity. The findings are expected to act as a stepping stone for further research and have implications for practitioners.

Keywords: earned value analysis, time performance, project costs, project delays, construction productivity

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Authors: Alireza Gholami, Amir H. D. Markazi

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In this paper, an observer-based direct adaptive fuzzy sliding mode (OAFSM) algorithm is proposed. In the proposed algorithm, the zero-input dynamics of the plant could be unknown. The input connection matrix is used to combine the sliding surfaces of individual subsystems, and an adaptive fuzzy algorithm is used to estimate an equivalent sliding mode control input directly. The fuzzy membership functions, which were determined by time consuming try and error processes in previous works, are adjusted by adaptive algorithms. The other advantage of the proposed controller is that the input gain matrix is not limited to be diagonal, i.e. the plant could be over/under actuated provided that controllability and observability are preserved. An observer is constructed to directly estimate the state tracking error, and the nonlinear part of the observer is constructed by an adaptive fuzzy algorithm. The main advantage of the proposed observer is that, the measured outputs is not limited to the first entry of a canonical-form state vector. The closed-loop stability of the proposed method is proved using a Lyapunov-based approach. The proposed method is applied numerically on a multi-link robot manipulator, which verifies the performance of the closed-loop control. Moreover, the performance of the proposed algorithm is compared with some conventional control algorithms.

Keywords: adaptive algorithm, fuzzy systems, membership functions, observer

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Authors: Ali Zaker, Zhi Chen

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Due to the concerns about the depletion of fossil fuel sources and the deteriorating environment, the attempt to investigate the production of renewable energy will play a crucial role as a potential to alleviate the dependency on mineral fuels. One particular area of interest is the generation of bio-oil through sewage sludge (SS) pyrolysis. SS can be a potential candidate in contrast to other types of biomasses due to its availability and low cost. However, the presence of high molecular weight hydrocarbons and oxygenated compounds in the SS bio-oil hinders some of its fuel applications. In this context, catalytic pyrolysis is another attainable route to upgrade bio-oil quality. Among different catalysts (i.e., zeolites) studied for SS pyrolysis, activated chars (AC) are eco-friendly alternatives. The beneficial features of AC derived from SS comprise the comparatively large surface area, porosity, enriched surface functional groups, and presence of a high amount of metal species that can improve the catalytic activity. Hence, a sludge-based AC catalyst was fabricated in a single-step pyrolysis reaction with NaOH as the activation agent and was compared with HZSM5 zeolite in this study. The thermal decomposition and kinetics were invested via thermogravimetric analysis (TGA) for guidance and control of pyrolysis and catalytic pyrolysis and the design of the pyrolysis setup. The results indicated that the pyrolysis and catalytic pyrolysis contains four obvious stages, and the main decomposition reaction occurred in the range of 200-600°C. The Coats-Redfern method was applied in the 2nd and 3rd devolatilization stages to estimate the reaction order and activation energy (E) from the mass loss data. The average activation energy (Em) values for the reaction orders n = 1, 2, and 3 were in the range of 6.67-20.37 kJ for SS; 1.51-6.87 kJ for HZSM5; and 2.29-9.17 kJ for AC, respectively. According to the results, AC and HZSM5 both were able to improve the reaction rate of SS pyrolysis by abridging the Em value. Moreover, to generate and examine the effect of the catalysts on the quality of bio-oil, a fixed-bed pyrolysis system was designed and implemented. The composition analysis of the produced bio-oil was carried out via gas chromatography/mass spectrometry (GC/MS). The selected SS to catalyst ratios were 1:1, 2:1, and 4:1. The optimum ratio in terms of cracking the long-chain hydrocarbons and removing oxygen-containing compounds was 1:1 for both catalysts. The upgraded bio-oils with AC and HZSM5 were in the total range of C4-C17, with around 72% in the range of C4-C9. The bio-oil from pyrolysis of SS contained 49.27% oxygenated compounds, while with the presence of AC and HZSM5 dropped to 13.02% and 7.3%, respectively. Meanwhile, the generation of benzene, toluene, and xylene (BTX) compounds was significantly improved in the catalytic process. Furthermore, the fabricated AC catalyst was characterized by BET, SEM-EDX, FT-IR, and TGA techniques. Overall, this research demonstrated AC is an efficient catalyst in the pyrolysis of SS and can be used as a cost-competitive catalyst in contrast to HZSM5.

Keywords: catalytic pyrolysis, sewage sludge, activated char, HZSM5, bio-oil

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Authors: Peyman Sindareh Esfahani, Jeffery Kurt Pieper

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In this paper, the problem of robust model predictive control (MPC) for discrete-time linear systems in linear fractional transformation form with structured uncertainty and norm-bounded disturbance is investigated. The problem of minimization of the cost function for MPC design is converted to minimization of the worst case of the cost function. Then, this problem is reduced to minimization of an upper bound of the cost function subject to a terminal inequality satisfying the l₂-norm of the closed loop system. The characteristic of the linear fractional transformation system is taken into account, and by using some mathematical tools, the robust predictive controller design problem is turned into a linear matrix inequality minimization problem. Afterwards, a formulation which includes an integrator to improve the performance of the proposed robust model predictive controller in steady state condition is studied. The validity of the approaches is illustrated through a robust control benchmark problem.

Keywords: linear fractional transformation, linear matrix inequality, robust model predictive control, state feedback control

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Authors: Anastasia Constantinou, Panayiotis Laouris, Stephen Morris

Abstract:

Background: Climate change has been reported as one of the worst threats to healthcare. The healthcare sector is a significant contributor to greenhouse gas emissions with primary care being responsible for 23% of the NHS’ total carbon footprint. Digital interventions, primarily focusing on telemedicine, offer a route to change. This systematic review aims to quantify and characterize the carbon footprint savings associated with the implementation of digital interventions in the setting of primary care. Methods: A systematic review of published literature was conducted according to PRISMA (Preferred Reporting Item for Systematic Reviews and Meta-Analyses) guidelines. MEDLINE, PubMed, and Scopus databases as well as Google scholar were searched using key terms relating to “carbon footprint,” “environmental impact,” “sustainability”, “green care”, “primary care,”, and “general practice,” using citation tracking to identify additional articles. Data was extracted and analyzed in Microsoft Excel. Results: Eight studies were identified conducted in four different countries between 2010 and 2023. Four studies used interventions to address primary care services, three studies focused on the interface between primary and specialist care, and one study addressed both. Digital interventions included the use of mobile applications, online portals, access to electronic medical records, electronic referrals, electronic prescribing, video-consultations and use of autonomous artificial intelligence. Only one study carried out a complete life cycle assessment to determine the carbon footprint of the intervention. It estimate that digital interventions reduced the carbon footprint at primary care level by 5.1 kgCO2/visit, and at the interface with specialist care by 13.4 kg CO₂/visit. When assessing the relationship between travel-distance saved and savings in emissions, we identified a strong correlation, suggesting that most of the carbon footprint reduction is attributed to reduced travel. However, two studies also commented on environmental savings associated with reduced use of paper. Patient savings in the form of reduced fuel cost and reduced travel time were also identified. Conclusion: All studies identified significant reductions in carbon footprint following implementation of digital interventions. In the future, controlled, prospective studies incorporating complete life cycle assessments and accounting for double-consulting effects, use of additional resources, technical failures, quality of care and cost-effectiveness are needed to fully appreciate the sustainable benefit of these interventions

Keywords: carbon footprint, environmental impact, primary care, sustainable healthcare

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Authors: Wenjun Hou, Marek Perkowski

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The Traveling Salesman problem is famous in computing and graph theory. In short, it asks for the Hamiltonian cycle of the least total weight in a given graph with N nodes. All variations on this problem, such as those with K-bounded-degree nodes, are classified as NP-complete in classical computing. Although several papers propose theoretical high-level designs of quantum algorithms for the Traveling Salesman Problem, no quantum circuit implementation of these algorithms has been created up to our best knowledge. In contrast to previous papers, the goal of this paper is not to optimize some abstract complexity measures based on the number of oracle iterations, but to be able to evaluate the real circuit and time costs of the quantum computer. Using the emerging quantum programming language Q# developed by Microsoft, which runs quantum circuits in a quantum computer simulation, an implementation of the bounded-degree problem and its respective quantum circuit were created. To apply Grover’s algorithm to this problem, a quantum oracle was designed, evaluating the cost of a particular set of edges in the graph as well as its validity as a Hamiltonian cycle. Repeating the Grover algorithm with an oracle that finds successively lower cost each time allows to transform the decision problem to an optimization problem, finding the minimum cost of Hamiltonian cycles. N log₂ K qubits are put into an equiprobablistic superposition by applying the Hadamard gate on each qubit. Within these N log₂ K qubits, the method uses an encoding in which every node is mapped to a set of its encoded edges. The oracle consists of several blocks of circuits: a custom-written edge weight adder, node index calculator, uniqueness checker, and comparator, which were all created using only quantum Toffoli gates, including its special forms, which are Feynman and Pauli X. The oracle begins by using the edge encodings specified by the qubits to calculate each node that this path visits and adding up the edge weights along the way. Next, the oracle uses the calculated nodes from the previous step and check that all the nodes are unique. Finally, the oracle checks that the calculated cost is less than the previously-calculated cost. By performing the oracle an optimal number of times, a correct answer can be generated with very high probability. The oracle of the Grover Algorithm is modified using the recalculated minimum cost value, and this procedure is repeated until the cost cannot be further reduced. This algorithm and circuit design have been verified, using several datasets, to generate correct outputs.

Keywords: quantum computing, quantum circuit optimization, quantum algorithms, hybrid quantum algorithms, quantum programming, Grover’s algorithm, traveling salesman problem, bounded-degree TSP, minimal cost, Q# language

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Authors: Michelle Eliane Hernández-García, Angélica Lozano

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The traffic accidents study usually contemplates the relation between factors such as the type of vehicle, its operation, and the road infrastructure. Traffic accidents can be explained by different factors, which have a greater or lower relevance. Two zones are studied, a mixed industrial zone and the extended zone of it. The first zone has mainly residential (57%), and industrial (23%) land uses. Trucks are mainly on the roads where industries are located. Four sensors give information about traffic and speed on the main roads. The extended zone (which includes the first zone) has mainly residential (47%) and mixed residential (43%) land use, and just 3% of industrial use. The traffic mix is composed mainly of non-trucks. 39 traffic and speed sensors are located on main roads. The traffic mix in a mixed land use zone, could be related to traffic accidents. To understand this relation, it is required to identify the elements of the traffic mix which are linked to traffic accidents. Models that attempt to explain what factors are related to traffic accidents have faced multiple methodological problems for obtaining robust databases. Poisson regression models are used to explain the accidents. The objective of the Poisson analysis is to estimate a vector to provide an estimate of the natural logarithm of the mean number of accidents per period; this estimate is achieved by standard maximum likelihood procedures. For the estimation of the relation between traffic accidents and the traffic mix, the database is integrated of eight variables, with 17,520 observations and six vectors. In the model, the dependent variable is the occurrence or non-occurrence of accidents, and the vectors that seek to explain it, correspond to the vehicle classes: C1, C2, C3, C4, C5, and C6, respectively, standing for car, microbus, and van, bus, unitary trucks (2 to 6 axles), articulated trucks (3 to 6 axles) and bi-articulated trucks (5 to 9 axles); in addition, there is a vector for the average speed of the traffic mix. A Poisson model is applied, using a logarithmic link function and a Poisson family. For the first zone, the Poisson model shows a positive relation among traffic accidents and C6, average speed, C3, C2, and C1 (in a decreasing order). The analysis of the coefficient shows a high relation with bi-articulated truck and bus (C6 and the C3), indicating an important participation of freight trucks. For the expanded zone, the Poisson model shows a positive relation among traffic accidents and speed average, biarticulated truck (C6), and microbus and vans (C2). The coefficients obtained in both Poisson models shows a higher relation among freight trucks and traffic accidents in the first industrial zone than in the expanded zone.

Keywords: freight transport, industrial zone, traffic accidents, traffic mix, trucks

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Authors: Shogo Nagano, Yusuke Tanigaki, Hirokazu Fukuda

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Vegetables harvested early in the morning or late in the afternoon are valued in plant production, and so the time of harvest is important. The biological functions known as circadian clocks have a significant effect on this harvest timing. The purpose of this study was to non-destructively estimate the circadian clock and so construct a method for determining a suitable harvest time. We took eight samples of green busil (Perilla frutescens var. crispa) every 4 hours, six times for 1 day and analyzed all samples at the same time. A hyperspectral camera was used to collect spectrum intensities at 141 different wavelengths (350–1050 nm). Calculation of correlations between spectrum intensity of each wavelength and harvest time suggested the suitability of the hyperspectral camera for non-destructive estimation. However, even the highest correlated wavelength had a weak correlation, so we used machine learning to raise the accuracy of estimation and constructed a machine learning model to estimate the internal time of the circadian clock. Artificial neural networks (ANN) were used for machine learning because this is an effective analysis method for large amounts of data. Using the estimation model resulted in an error between estimated and real times of 3 min. The estimations were made in less than 2 hours. Thus, we successfully demonstrated this method of non-destructively estimating internal time.

Keywords: artificial neural network (ANN), circadian clock, green busil, hyperspectral camera, non-destructive evaluation

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Authors: Saifullahi Umar, Baba Mamman Yarima, Mohammed Bello Usman, Hassan Mohammed

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This study was conducted along with the Hadejia valley project irrigation under the Hadejia-Jama’are River Basin Development Authority (HRBDA) in Jigawa State. The multi-stage sampling procedure was used to select 72 rice farmers operating along with the Hadejia Valley Irrigation Project. Data for the study were collected using a structured questionnaire. The analytical tools employed for the study were descriptive statistics and Farm budget technique. The result shows that 55% of the farmers were between 31-40 years of age, 66.01% were male, and the result also revealed that the total cost of cultivation of an acre of land for rice production during the dry season was N73,900 with input cost accounting for 63.59% of the total cost of production. The gross return was N332,500, with a net return of N258,600 per acre. The estimated benefit-cost ratio of 3.449 indicates the strong performance of the dry season rice production. The leading constraints to dry season rice production were low access to quality extension services, low access to finance, poor quality fertilizers, and poor prices. The study, therefore, concludes that dry season rice production is a profitable enterprise in the study area hence, to productivity the farmers should be linked to effective extension service delivery institutions, expanding their access to productive sources of finances, the government should strengthen fertilizer quality control measures and comprehensive market linkages for the farmers.

Keywords: Auyo, dry season, Hadejia Valley, rice

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Authors: Z. Abdollahnejad, M. Mastali, F. Pacheco-Torgal

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Waste reuse is crucial in a context of circular economy and zero waste sustainable needs. Some wastes deserve further studies by the scientific community not only because they are generated in high amount but also because they have a low reuse rate. This paper reports results of 32 hybrid cement mortars based on fly ash and waste glass. They allow to explore the influence of mix design on the cost and on the embodied carbon of the hybrid cement mortars. The embodied carbon data for all constituents were taken from the database Ecoinvent. This study led to the development of a mixture with just 70 kg CO2e.

Keywords: waste reuse, fly ash, waste glass, hybrid cements, cost, embodied carbon

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Authors: Javed Kamal

Abstract:

A proposed field study of wheat-based cropping systems was conducted at Faisalabad (Post-Graduate Research Station). We used 7 treatments and Jantar as a green manuring crop to increase the fertility status of soil; after the vegetative phases of wheat, rice, sorghum, and mungbean, the agronomic parameters of these crops were recorded. Hopefully, all increased with jantar treatment when compared with controls. The benefit: cost ratio and physicochemical characteristics of the soil before and after the crop harvest were also calculated.

Keywords: benifit cost ratio, jantar, sunflower, rice, wheat

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Authors: Jian Cao

Abstract:

This paper describes a novel watermarking technique which we call the random direction embedding (RDE) watermarking. Unlike traditional watermarking techniques, the watermark energy after the RDE embedding does not focus on a fixed direction, leading to the security against the traditional unauthorized watermark removal attack. In addition, the experimental results show that when compared with the existing secure watermarking, namely natural watermarking (NW), the RDE watermarking gains significant improvement in terms of robustness. In fact, the security of the RDE watermarking is not at the cost of low robustness, and it can even achieve more robust than the traditional spread spectrum watermarking, which has been shown to be very insecure.

Keywords: robustness, spread spectrum watermarking, watermarking security, random direction embedding (RDE)

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Authors: Christos Spandonidis, Stefanos Tsantilas, Elias Sedikos, Nektarios Galiatsatos, Fotios Giannopoulos, Panagiotis Papadopoulos, Nikolaos Demagos, Dimitrios Reppas, Christos Giordamlis

Abstract:

In the present paper, a low cost, compact and modular Internet of Things (IoT) platform for air quality monitoring in urban areas is presented. This platform comprises of dedicated low cost, low power hardware and the associated embedded software that enable measurement of particles (PM2.5 and PM10), NO, CO, CO₂ and O₃ concentration in the air, along with relative temperature and humidity. This integrated platform acts as part of a greater air pollution data collecting wireless network that is able to monitor the air quality in various regions and neighborhoods of an urban area, by providing sensor measurements at a high rate that reaches up to one sample per second. It is therefore suitable for Big Data analysis applications such as air quality forecasts, weather forecasts and traffic prediction. The first real world test for the developed platform took place in Thessaloniki, Greece, where 16 devices were installed in various buildings in the city. In the near future, many more of these devices are going to be installed in the greater Thessaloniki area, giving a detailed air quality map of the city.

Keywords: distributed sensor system, environmental monitoring, Internet of Things, smart cities

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