Search results for: cost of farming

Authors: Naveed Ahmed Khan

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The ever increasing importance of food industry cannot be denied and especially in the wake of escalating population and prices both in developing and developed nations. Thus, this issue demands the attention of researchers especially in the area of supply chain to identify cost diminution waste eliminating supply chain practices in the said industry. For such purpose the 'Dairy Division' of Engro Foods Limited, one of the biggest food companies in Pakistan was taken into consideration in a case study manner. Based on the literature review and interviews following variables were obtained: energy, losses, maintenance, taxes, and logistics. Having studied the said variables, it was concluded that management of relevant industries operating in a comparable environment need to efficiently manage two major areas: energy and taxes. On the other hand, similar kind of other organizations could be benefited by adopting the proficient supply chain practices being observed at dairy division of Engro foods limited.

Keywords: cost diminution, supply chain, dairy industry, Engro Foods Limited

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Authors: Wondmnew Derebe Yohannis

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Farming irrigation plays a crucial role in rural development strategies, impacting both annual household income and livelihood. This research aims to evaluate the factors influencing irrigation participation and assess the impact of small-scale irrigation on rural households' annual income. The study collected data from 287 farmers in the Dahana district of northern Ethiopia. The research investigates the driving forces behind farmers' decisions to adopt small-scale irrigation and its effect on annual income gain. The findings reveal that several factors positively influence the probability of adoption, including access to credit, cultivated land size, livestock holding, extension contact, and the education level of the household head. Conversely, the distance to local markets and water schemes negatively affects the likelihood of adoption. To understand the differences in annual income between farm households that adopted irrigation and those that did not, a simultaneous equations model with endogenous switching regression is estimated. This accounts for the heterogeneity in the adoption decision and unobservable characteristics of farmers and their farms. The analysis compares the expected income gain under actual and counterfactual scenarios, considering whether the farm household adopted irrigation or not. The study reveals that the group of farm households that adopted irrigation has distinct characteristics compared to those that did not adopt it. Furthermore, the research demonstrates that the adoption of irrigation practices leads to an increase in annual income. Interestingly, the impact of small-scale irrigation on annual income is greater for the farm households that actually adopted irrigation compared to those in the counterfactual scenario where they did not adopt. Based on the findings, the researcher concludes that small-scale irrigation is a practical solution for meeting household financial needs in the study area. It is recommended that investments in small-scale irrigation continue to further improve the livelihoods of rural farming communities by enhancing annual income gains.

Keywords: small-scale irrigation, income, rural farm households, endogenous switching regression, user, non-user

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Authors: Himanshu Shekhar Maharana, S. K .Dash

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Economic Load Dispatch (ELD) proves to be a vital optimization process in electric power system for allocating generation amongst various units to compute the cost of generation, the cost of emission involving global warming gases like sulphur dioxide, nitrous oxide and carbon monoxide etc. In this dissertation, we emphasize ramp rate constriction factor based particle swarm optimization (RRCPSO) for analyzing various performance objectives, namely cost of generation, cost of emission, and a dual objective function involving both these objectives through the experimental simulated results. A 6-unit 30 bus IEEE test case system has been utilized for simulating the results involving improved weight factor advanced ramp rate limit constraints for optimizing total cost of generation and emission. This method increases the tendency of particles to venture into the solution space to ameliorate their convergence rates. Earlier works through dispersed PSO (DPSO) and constriction factor based PSO (CPSO) give rise to comparatively higher computational time and less good optimal solution at par with current dissertation. This paper deals with ramp rate and constriction factor based well defined ramp rate PSO to compute various objectives namely cost, emission and total objective etc. and compares the result with DPSO and weight improved PSO (WIPSO) techniques illustrating lesser computational time and better optimal solution. 

Keywords: economic load dispatch (ELD), constriction factor based particle swarm optimization (CPSO), dispersed particle swarm optimization (DPSO), weight improved particle swarm optimization (WIPSO), ramp rate and constriction factor based particle swarm optimization (RRCPSO)

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Authors: Danica Maljkovic, Igor Balen, Bojana Dalbelo Basic

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Following the regulation of EU Directive on Energy Efficiency, specifically Article 9, individual metering in district heating systems has to be introduced by the end of 2016. These directions have been implemented in member state’s legal framework, Croatia is one of these states. The directive allows installation of both heat metering devices and heat cost allocators. Mainly due to bad communication and PR, the general public false image was created that the heat cost allocators are devices that save energy. Although this notion is wrong, the aim of this work is to develop a model that would precisely express the influence of installation heat cost allocators on potential energy savings in each unit within multifamily buildings. At the same time, in recent years, a science of machine learning has gain larger application in various fields, as it is proven to give good results in cases where large amounts of data are to be processed with an aim to recognize a pattern and correlation of each of the relevant parameter as well as in the cases where the problem is too complex for a human intelligence to solve. A special method of machine learning, decision tree method, has proven an accuracy of over 92% in prediction general building consumption. In this paper, a machine learning algorithms will be used to isolate the sole impact of installation of heat cost allocators on a single building in multifamily houses connected to district heating systems. Special emphasises will be given regression analysis, logistic regression, support vector machines, decision trees and random forest method.

Keywords: district heating, heat cost allocator, energy efficiency, machine learning, decision tree model, regression analysis, logistic regression, support vector machines, decision trees and random forest method

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Authors: Randa Boukhris, Alima Damak, Dorra Sellami

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Fingervein biometric authentication is one of the most popular and accurate technologies. However, low cost embedded solution is still an open problem. In this paper, a real-time implementation of fingervein recognition process embedded in Raspberry-Pi has been proposed. The use of Raspberry-Pi reduces overall system cost and size while allowing an easy user interface. Implementation of a target technology has guided to opt some specific parallel and simple processing algorithms. In the proposed system, we use four structural directional kernel elements for filtering finger vein images. Then, a Top-Hat and Bottom-Hat kernel filters are used to enhance the visibility and the appearance of venous images. For feature extraction step, a simple Local Directional Code (LDC) descriptor is applied. The proposed system presents an Error Equal Rate (EER) and Identification Rate (IR), respectively, equal to 0.02 and 98%. Furthermore, experimental results show that real-time operations have good performance.

Keywords: biometric, Bottom-Hat, Fingervein, LDC, Rasberry-Pi, ROI, Top-Hat

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Authors: Mary Moner Botros Fanawel

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Many companies use the statistical tool named as statistical quality control, and which can have a high cost for the companies interested on these statistical tools. The evaluation of the quality of products and services is an important topic, but the reduction of the cost of the implantation of the statistical quality control also has important benefits for the companies. For this reason, it is important to implement a economic design for the various steps included into the statistical quality control. In this paper, we describe some relevant aspects related to the economic design of a quality control chart for the proportion of defective items. They are very important because the suggested issues can reduce the cost of implementing a quality control chart for the proportion of defective items. Note that the main purpose of this chart is to evaluate and control the proportion of defective items of a production process.

Keywords: model predictive control, hierarchical control structure, genetic algorithm, water quality with DBPs objectives proportion, type I error, economic plan, distribution function bootstrap control limit, p-value method, out-of-control signals, p-value, quality characteristics

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Authors: Chih-Hsing Lin, Wen-Ching Chen, Chih-Ting Kuo, Gang-Neng Sung, Chih-Chyau Yang, Chien-Ming Wu, Chun-Ming Huang

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This paper proposes a cellular-based structure health monitoring device (HMD) for temporary bridge monitoring without the requirement of power line and internet service. The proposed HMD includes sensor node, power module, cellular gateway, and rechargeable batteries. The purpose of HMD focuses on short-term collection of civil infrastructure information. It achieves the features of low cost by using three 1-axis accelerometers with data synchronization problem being solved. Furthermore, instead of using data acquisition system (DAQ) sensed data is transmitted to Host through cellular gateway. Compared with 3-axis accelerometer, our proposed 1-axis accelerometers based device achieves 50.5% cost saving with high sensitivity 2000mv/g. In addition to fit different monitoring environments, the proposed system can be easily replaced and/or extended with different PCB boards, such as communication interfaces and sensors, to adapt to various applications. Therefore, with using the proposed device, the real-time diagnosis system for civil infrastructure damage monitoring can be conducted effectively.

Keywords: cellular-based structural health monitoring, cost-effective 1-axis accelerometers, short-term monitoring, structural engineering

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Authors: Miroslav Popelka, Jakub Nožička

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In this paper we deal with using Lego Mindstorms in simulation of robotic systems with respect to cost reduction. Lego Mindstorms kit contains broad variety of hardware components which are required to simulate, program and test the robotics systems in practice. Algorithm programming went in development environment supplied together with Lego kit as in programming language C# as well. Algorithm following the line, which we dealt with in this paper, uses theoretical findings from area of controlling circuits. PID controller has been chosen as controlling circuit whose individual components were experimentally adjusted for optimal motion of robot tracking the line. Data which are determined to process by algorithm are collected by sensors which scan the interface between black and white surfaces followed by robot. Based on discovered facts Lego Mindstorms can be considered for low-cost and capable kit to simulate real robotics systems.

Keywords: LEGO Mindstorms, PID controller, low-cost robotics systems, line follower, sensors, programming language C#, EV3 Home Edition Software

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Authors: Usha S. Adiga, Sachidanada Adiga

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Introduction: Diabetes Mellitus (DM) is one of the most common non-communicable diseases which imposes a large economic burden on the global health-care system. Cost of illness studies in India have assessed the health care cost of DM, but have certain limitations due to lack of standardization of the methods used, improper documentation of data, lack of follow up, etc. The objective of the study was to estimate the cost of illness of uncomplicated versus complicated type 2 diabetes mellitus in Coastal Karnataka, India. The study also aimed to find out the trend of cost of illness of the disease over a decade. Methodology: A prevalence based bottom-up approach study was carried out in two tertiary care hospitals located in Coastal Karnataka after ethical approval. Direct Medical costs like annual laboratory costs, pharmacy cost, consultation charges, hospital bed charges, surgical /intervention costs of 238 diabetics and 340 diabetic patients respectively from two hospitals were obtained from the medical record sections. Patients were divided into six groups, uncomplicated diabetes, diabetic retinopathy(DR), nephropathy(DN), neuropathy(DNeu), diabetic foot(DF), and ischemic heart disease (IHD). Different costs incurred in 2008 and 2017 in these groups were compared, to study the trend of cost of illness. Kruskal Wallis test followed by Dunn’s test were used to compare median costs between the groups and Spearman's correlation test was used for correlation studies. Results: Uncomplicated patients had significantly lower costs (p <0.0001) compared to other groups. Patients with IHD had highest Medical expenses (p < 0.0001), followed by DN and DF (p < 0.0001 ). Annual medical costs incurred were 1.8, 2.76, 2.77, 1.76, and 4.34 times higher in retinopathy, nephropathy, diabetic foot, neuropathy and IHD patients as compared to the cost incurred in managing uncomplicated diabetics. Other costs also showed a similar pattern of rising. A positive correlation was observed between the costs incurred and duration of diabetes, a negative correlation between the glycemic status and cost incurred. The cost incurred in the management of DM in 2017 was found to be elevated 1.4 - 2.7 times when compared to that in 2008. Conclusion: It is evident from the study that the economic burden due to diabetes mellitus is substantial. It poses a significant financial burden on the healthcare system, individual and society as a whole. There is a need for the strategies to achieve optimal glycemic control and operationalize regular and early screening methods for complications so as to reduce the burden of the disease.

Keywords: COI, diabetes mellitus, a bottom up approach, economics

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Authors: B. Falah, N. E. Abakouy

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Due to its high computational cost, mutation testing has been neglected by researchers. Recently, many cost and mutants’ reduction techniques have been developed, improved, and experimented, but few of them has relied the possibility of reducing the cost of mutation testing on the program type of the application under test. This paper is a comparative study between four operators’ selection techniques (mutants sampling, class level operators, method level operators, and all operators’ selection) based on the program code type of each application under test. It aims at finding an alternative approach to reveal the effect of code type on mutation testing score. The result of our experiment shows that the program code type can affect the mutation score and that the programs using polymorphism are best suited to be tested with mutation testing.

Keywords: equivalent mutant, killed mutant, mutation score, mutation testing, program code type, software testing

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Authors: Saba Rahimi, Ozan Oktay, Javier Alvarez-Valle, Sujeeth Bharadwaj

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Successful application of deep learning in medical image analysis necessitates unprecedented amounts of labeled training data. Unlike conventional 2D applications, radiological images can be three-dimensional (e.g., CT, MRI), consisting of many instances within each image. The problem is exacerbated when expert annotations are required for effective pixel-wise labeling, which incurs exorbitant labeling effort and cost. Active learning is an established research domain that aims to reduce labeling workload by prioritizing a subset of informative unlabeled examples to annotate. Our contribution is a cost-effective approach for U-Net 3D models that uses Monte Carlo sampling to analyze pixel-wise uncertainty. Experiments on the AAPM 2017 lung CT segmentation challenge dataset show that our proposed framework can achieve promising segmentation results by using only 42% of the training data.

Keywords: image segmentation, active learning, convolutional neural network, 3D U-Net

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Authors: Adithya Subramanian, S. Ananthan, T. Prasanth, S. P. Selvabharathi

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This project involves extraction of grapefruit essential oil from grapefruit peels using various oils like castor oil, gingelly oil, olive oil as carrier oils. The main aim of this project is to extract the oil which has numerous medicinal uses. The extraction can be performed by two methods. Project involves extraction of the oil with various carrier oil in a view to reduce the cost of production and the physical properties of the extracted oil are examined.

Keywords: essential oil, carrier oil, medicinal uses, cost of production

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Authors: Mehrab Gholami Zangalani, Siavash Rajabpour

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In compliance with the Statistical Centre of Iran (SCI)’s results, construction and housing section in Iran is consuming 40% of energy, which is 5 times more than the world average consumption. By considering the climate in Iran, the solutions in terms of design, construction and exploitation of the buildings by utilizing the LEED rating system (LRS) is presented, regarding to the reasons for the high levels of energy consumption during construction and housing in Iran. As a solution, innovative Work Break Structure (WBS) in accordance with LRS and Iranian construction’s methods is unveiled in this research. Also, by amending laws pertaining to the construction in Iran, the huge amount of energy and cost can be saved. Furthermore, with a scale-up of these results to the scale of big cities such as Tehran (one of the largest metropolitan areas in the middle east) in which the license to build more than two hundred and fifty units each day is issued, the amount of energy and cost that can be saved is estimated.

Keywords: costs reduction, energy statistics, leed rating system (LRS), work brake structure (WBS)

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Authors: Dimitrios Ntardas, Alexandros Tzanetos, Georgios Dounias

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High Voltage Substations (HVS) are the intermediate step between production of power and successfully transmitting it to clients, making them one of the most important checkpoints in power grids. Nowadays - renewable resources and consequently distributed generation are growing fast, the construction of HVS is of high importance both in terms of quality and time completion so that new energy producers can quickly and safely intergrade in power grids. The resources needed, such as machines and workers, should be carefully allocated so that the construction of a HVS is completed on time, with the lowest possible cost (e.g. not spending additional cost that were not taken into consideration, because of project delays), but in the highest quality. In addition, there are milestones and several checkpoints to be precisely achieved during construction to ensure the cost and timeline control and to ensure that the percentage of governmental funding will be granted. The management of such a demanding project is a NP-hard problem that consists of prerequisite constraints and resource limits for each task of the project. In this work, a hybrid meta-heuristic method is implemented to solve this problem. Meta-heuristics have been proven to be quite useful when dealing with high-dimensional constraint optimization problems. Hybridization of them results in boost of their performance.

Keywords: hybrid meta-heuristic methods, substation construction, resource allocation, time-cost efficiency

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Authors: Stephan Printz, Kristina Lahl, René Vossen, Sabina Jeschke

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Strategic investment decisions are characterized by high innovation potential and long-term effects on the competitiveness of enterprises. Due to the uncertainty and risks involved in this complex decision making process, the need arises for well-structured support activities. A method that considers cost and the long-term added value is the cost-benefit effectiveness estimation. One of those methods is the “profitability estimation focused on benefits – PEFB”-method developed at the Institute of Management Cybernetics at RWTH Aachen University. The method copes with the challenges associated with strategic investment decisions by integrating long-term non-monetary aspects whilst also mapping the chronological sequence of an investment within the organization’s target system. Thus, this method is characterized as a holistic approach for the evaluation of costs and benefits of an investment. This participation-oriented method was applied to business environments in many workshops. The results of the workshops are a library of more than 96 cost aspects, as well as 122 benefit aspects. These aspects are preprocessed and comparatively analyzed with regards to their alignment to a series of risk levels. For the first time, an accumulation and a distribution of cost and benefit aspects regarding their impact and probability of occurrence are given. The results give evidence that the PEFB-method combines precise measures of financial accounting with the incorporation of benefits. Finally, the results constitute the basics for using information technology and data science for decision support when applying within the PEFB-method.

Keywords: cost-benefit analysis, multi-criteria decision, profitability estimation focused on benefits, risk and uncertainty analysis

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Authors: Gyeong-Sung Kim, In-soo Ahn, Yong Cho

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SBR (Sequencing Batch Reactor) process is usually applied to membrane water treatment plants to treat its concentrated wastewater. The role of SBR process is to remove COD (Chemical Oxygen Demand) and NH3 from wastewater before discharging it outside of the water treatment plant using microorganism. Microorganism’s nitrification capability is influenced by water temperature because the nitrification rate of the concentrated wastewater becomes ‘zero’ as water temperature approach 0℃. Heating system is necessary to operate SBR in winter season even though the operating cost increase sharply. The operating cost of SBR at ‘D’ RO water treatment plant in Korea was 51.8 times higher in winter (October to March) compare to summer (April to September) season in 2014. Otherwise the effluent water temperature maintained around 8℃ constantly in winter. This study focuses on application heat pump system to recover the thermal energy from the effluent water of ‘D’ RO plant so that the operating cost will be reduced.

Keywords: water treatment, water thermal energy, energy saving, RO, SBR

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Authors: Manisha Dubey, Gaurav Gupta, Anoop Arya

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The demand for power supply increases day by day in developing countries like India henceforth demand of reactive power support in the form of ancillary services provider also has been increased. The multi-line and multi-type Flexible alternating current transmission system (FACTS) controllers are playing a vital role to regulate power flow through the transmission line. Unified power flow controller and interline power flow controller can be utilized to control reactive power flow through the transmission line. In a restructured power system, the demand of such controller is being popular due to their inherent capability. The transmission pricing by using reactive power cost allocation through modified matrix methodology has been proposed. The FACTS technologies have quite costly assembly, so it is very useful to apportion the expenses throughout the restructured electricity industry. Therefore, in this work, after embedding the FACTS devices into load flow, the impact on the costs allocated to users in fraction to the transmission framework utilization has been analyzed. From the obtained results, it is clear that the total cost recovery is enhanced towards the Reactive Power flow through the different transmission line for 5 bus test system. The fair pricing policy towards reactive power can be achieved by the proposed method incorporating FACTS controller towards cost recovery of the transmission network.

Keywords: interline power flow controller, transmission pricing, unified power flow controller, cost allocation

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Authors: Alireza Ghaffari, Hassan Saghi

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Project management is more complex than managing the day-to-day affairs of an organization. When the project dimensions are broad and multiple projects have to be monitored in different locations, the integrated management becomes even more complicated. One of the main concerns of project managers is the integrated project management, which is mainly rooted in the lack of accurate and accessible information from different projects in various locations. The collection of dispersed information from various parts of the network, their integration and finally the selective reporting of this information is among the goals of integrated information systems. It can help resolve the main problem, which is bridging the information gap between executives and senior managers in the organization. Therefore, the main objective of this study is to design and implement an important subset of a project management information system in order to successfully control the cost of construction projects so that its results can be used to design raw software forms and proposed relationships between different project units for the collection of necessary information.

Keywords: financial performance, cost subsystem, PMIS, project management

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Authors: Soheila Sadeghi

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Accurate cost prediction is essential for effective project management and decision-making in the construction industry. This study aims to develop a cost prediction model for construction projects using Machine Learning techniques and Particle Swarm Optimization (PSO). The research utilizes a comprehensive dataset containing project cost estimates, actual costs, resource details, and project performance metrics from a road reconstruction project. The methodology involves data preprocessing, feature selection, and the development of an Artificial Neural Network (ANN) model optimized using PSO. The study investigates the impact of various input features, including cost estimates, resource allocation, and project progress, on the accuracy of cost predictions. The performance of the optimized ANN model is evaluated using metrics such as Mean Squared Error (MSE), Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and R-squared. The results demonstrate the effectiveness of the proposed approach in predicting project costs, outperforming traditional benchmark models. The feature selection process identifies the most influential variables contributing to cost variations, providing valuable insights for project managers. However, this study has several limitations. Firstly, the model's performance may be influenced by the quality and quantity of the dataset used. A larger and more diverse dataset covering different types of construction projects would enhance the model's generalizability. Secondly, the study focuses on a specific optimization technique (PSO) and a single Machine Learning algorithm (ANN). Exploring other optimization methods and comparing the performance of various ML algorithms could provide a more comprehensive understanding of the cost prediction problem. Future research should focus on several key areas. Firstly, expanding the dataset to include a wider range of construction projects, such as residential buildings, commercial complexes, and infrastructure projects, would improve the model's applicability. Secondly, investigating the integration of additional data sources, such as economic indicators, weather data, and supplier information, could enhance the predictive power of the model. Thirdly, exploring the potential of ensemble learning techniques, which combine multiple ML algorithms, may further improve cost prediction accuracy. Additionally, developing user-friendly interfaces and tools to facilitate the adoption of the proposed cost prediction model in real-world construction projects would be a valuable contribution to the industry. The findings of this study have significant implications for construction project management, enabling proactive cost estimation, resource allocation, budget planning, and risk assessment, ultimately leading to improved project performance and cost control. This research contributes to the advancement of cost prediction techniques in the construction industry and highlights the potential of Machine Learning and PSO in addressing this critical challenge. However, further research is needed to address the limitations and explore the identified future research directions to fully realize the potential of ML-based cost prediction models in the construction domain.

Keywords: cost prediction, construction projects, machine learning, artificial neural networks, particle swarm optimization, project management, feature selection, road reconstruction

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Authors: Pankhuri Dagaonkar, Charumani Singh, Poornima Krothapalli, Krishna Karthik

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The backbone of any retail e-commerce success story is a unique design of supply chain network, providing the business an unparalleled speed and scalability. Primary goal of the supply chain strategy is to meet customer expectation by offering fastest deliveries while keeping the cost minimal. Meeting this objective at the large market that India provides is the problem statement that we have targeted here. There are many models and optimization techniques focused on network design to identify the ideal facility location and size, optimizing cost and speed. In this paper we are presenting a tactical approach to optimize cost of an existing network for a predefined speed. We have considered both forward and reverse logistics of a retail e-commerce supply chain consisting of multiple fulfillment (warehouse) and delivery centers, which are connected via sortation nodes. The mathematical model presented here determines if the shipment from a node should get sorted directly for the last mile delivery center or it should travel as consolidated package to another node for further sortation (resort). The objective function minimizes the total cost by varying the resort percentages between nodes and provides the optimal resource allocation and number of sorts at each node.

Keywords: distribution strategy, mathematical model, network design, supply chain management

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Authors: Farnoosh Davarian

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The present research focuses on organizing the cooperation of units constrained by demand response (DR) programs, considering ancillary services in the P-Q power market. Moreover, it provides a comprehensive exploration of the effects of demand reduction and redistribution across several predefined scenarios (in three pre-designed demand response programs, for example, ranging from 5% to 20%) on system voltage and losses in a smart distribution system (in the studied network, distributed energy resources (DERs) such as synchronous distributed generators and wind turbines offer their active and reactive power for the proposed market).GAMS, a specialized software for high-powered modeling, is used for optimizing linear, nonlinear, and integer programming challenges. GAMS modeling is separate from its solution method, which is a notable feature. Thus, by providing changes in the solver, it is possible to solve the model using various methods (linear, nonlinear, integer, etc.). Finally, the combined active and reactive market challenge in smart distribution systems, considering renewable distributed sources and demand response programs in GAMS, will be evaluated. The active and reactive power trading by the distribution company is carried out in the wholesale market. What is demanded is active power. By using the buy-back/payment program, it is possible for responsive loads or aggregators to participate in the market. The objective function of the proposed market is to minimize the price of active and reactive power for DERs and distribution companies and the penalty cost for CO2 emissions and the cost of the buy-back/payment program. In this research, the objective function is to minimize the cost of active and reactive power from distributed generation sources and distribution companies, the cost of carbon dioxide emissions, and the cost of the buy-back/payment program. The effectiveness of the proposed method has been evaluated in a case study.

Keywords: consumer behavior, demand response, pollution cost, combined active and reactive market

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Authors: Ibrahim Ahmed Jajere

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Resource use conflicts capable of undermining of pastoralists’ livelihoods are on the increase in the North East Arid Zone of Nigeria. In order to expose socio - economic effects of conflicts and benefits of peace, this study assessed cost implications of farmers/pastoralists conflicts over natural resources. Interviews were conducted with 94 farmers, 90 agro-pastoralists and 91 pastoralists’ household heads. The farmers and agro-pastoralists were systematically sampled while pastoralists were located using snowballing. Both farmers and pastoralists suffered losses in the form of injuries to, and even death of household members, and loss of shelter. Farmers sustained losses of facilities and farm produce while pastoralists suffered loss and seizure of livestock, arrest of household members and forced migrations. The material losses in monetary terms amounted to 14,242,200.00 nairas for farmers, a figure higher than the 10,915,500.00 nairas incurred by pastoralists.

Keywords: cost, conflicts, farmers, pastoralists

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Authors: Anna Ching-Yu Wong

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Introduction: Textbooks play a sizable portion of the overall cost of higher education students. It is a board consent that open education resources (OER) reduce the te4xtbook costs and provide students a way to receive high-quality learning materials at little or no cost to them. However, there is less agreement over exactly how much. This study presents an approach for calculating OER savings by using SUNY Canton NON-OER courses (N=233) to estimate the potentially textbook savings for one semester – Fall 2022. The purpose in collecting data is to understand how much potentially saved from using OER materials and to have a record for future further studies. Literature Reviews: In the past years, researchers identified the rising cost of textbooks disproportionately harm students in higher education institutions and how much an average cost of a textbook. For example, Nyamweya (2018) found that on average students save $116.94 per course when OER adopted in place of traditional commercial textbooks by using a simple formula. Student PIRGs (2015) used reports of per-course savings when transforming a course from using a commercial textbook to OER to reach an estimate of $100 average cost savings per course. Allen and Wiley (2016) presented at the 2016 Open Education Conference on multiple cost-savings studies and concluded $100 was reasonable per-course savings estimates. Ruth (2018) calculated an average cost of a textbook was $79.37 per-course. Hilton, et al (2014) conducted a study with seven community colleges across the nation and found the average textbook cost to be $90.61. There is less agreement over exactly how much would be saved by adopting an OER course. This study used SUNY Canton as a case study to create an approach for estimating OER savings. Methodology: Step one: Identify NON-OER courses from UcanWeb Class Schedule. Step two: View textbook lists for the classes (Campus bookstore prices). Step three: Calculate the average textbook prices by averaging the new book and used book prices. Step four: Multiply the average textbook prices with the number of students in the course. Findings: The result of this calculation was straightforward. The average of a traditional textbooks is $132.45. Students potentially saved $1,091,879.94. Conclusion: (1) The result confirms what we have known: Adopting OER in place of traditional textbooks and materials achieves significant savings for students, as well as the parents and taxpayers who support them through grants and loans. (2) The average textbook savings for adopting an OER course is variable depending on the size of the college and as well as the number of enrollment students.

Keywords: textbook savings, open textbooks, textbook costs assessment, open access

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Authors: Wen Liang Chang, Mei Wei Wang, Ruey Huei Yeh

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This paper studies the comparisons of individual and group replacement policies for a series connection system with two machines. Suppose that manufacturer’s production system is a series connection system which is combined by two machines. For two machines, when machines fail within the operating time, minimal repair is performed for machines by the manufacturer. The manufacturer plans to a preventive replacement for machines at a pre-specified time to maintain system normal operation. Under these maintenance policies, the maintenance cost rate models of individual and group replacement for a series connection system with two machines is derived and further, optimal preventive replacement time is obtained such that the expected total maintenance cost rate is minimized. Finally, some numerical examples are given to illustrate the influences of individual and group replacement policies to the maintenance cost rate.

Keywords: individual replacement, group replacement, replacement time, two machines, series connection system

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Authors: Benga Ebouele, Thomas Tengen

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Implementing either periodic or continuous inventory review model within most manufacturing-companies-supply chains as a management tool may incur higher costs. These high costs affect the system flexibility which in turn affects the level of service required to satisfy customers. However, these effects are not clearly understood because the parameters of both inventory review policies (protection demand interval, order quantity, etc.) are not designed to be fully utilized under different and uncertain conditions such as poor manufacturing, supplies and delivery performance. Coming up with a hybrid model which may combine in some sense the feature of both continuous and a periodic inventory review models should be useful. Therefore, there is a need to build and evaluate such hybrid model on the annual total cost, stock out probability and system’s flexibility in order to search for the most cost effective inventory review model. This work also seeks to find the optimal sets of parameters of inventory management under stochastic condition so as to optimise each policy independently. The results reveal that a continuous inventory system always incurs lesser cost than a periodic (R, S) inventory system, but this difference tends to decrease as time goes by. Although the hybrid inventory is the only one that can yield lesser cost over time, it is not always desirable but also natural to use it in order to help the system to meet high performance specification.

Keywords: demand and lead time randomness, hybrid Inventory model, optimization, supply chain

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Authors: Tedros Asefaw Gebremeskel, Xaoyi Yang

Abstract:

The potential of solar energy in Eritrea is relatively high, based on this truth, there are a number of isolated and remote villages situated far away from the electrical national grid which don’t get access to electricity. The core objective of this work is to design a most favorable and cost-effective power by means of standalone PV system for the electrification of a single housing in the inaccessible area of Eritrea. The sizing of the recommended PV system is achieved, such as radiation data and electrical load for the typical household of the selected site is also well thought-out in the design steps. Finally, the life cycle cost (LCC) analysis is conducted to evaluate the economic viability of the system. The outcome of the study promote the use of PV system for a residential building and show that PV system is a reasonable option to provide electricity for household applications in the rural area of Eritrea.

Keywords: electrification, inaccessible area, life cycle cost, residential building, stand-alone PV system

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Authors: Jin Woo Hwang, Byung Kwan Oh, Yousok Kim, Hyo Seon Park

Abstract:

As greenhouse effect has been recognized as serious environmental problem of the world, interests in carbon dioxide (CO₂) emission which comprises major part of greenhouse gas (GHG) emissions have been increased recently. Since construction industry takes a relatively large portion of total CO₂ emissions of the world, extensive studies about reducing CO₂ emissions in construction and operation of building have been carried out after the 2000s. Also, performance based design (PBD) methodology based on nonlinear analysis has been robustly developed after Northridge Earthquake in 1994 to assure and assess seismic performance of building more exactly because structural engineers recognized that prescriptive code based design approach cannot address inelastic earthquake responses directly and assure performance of building exactly. Although CO₂ emissions and PBD approach are recent rising issues on construction industry and structural engineering, there were few or no researches considering these two issues simultaneously. Thus, the objective of this study is to minimize the CO₂ emissions and cost of building designed by PBD approach in structural design stage considering structural materials. 4 story and 4 span reinforced concrete building optimally designed to minimize CO₂ emissions and cost of building and to satisfy specific seismic performance (collapse prevention in maximum considered earthquake) of building satisfying prescriptive code regulations using non-dominated sorting genetic algorithm-II (NSGA-II). Optimized design result showed that minimized CO₂ emissions and cost of building were acquired satisfying specific seismic performance. Therefore, the methodology proposed in this paper can be used to reduce both CO₂ emissions and cost of building designed by PBD approach.

Keywords: CO2 emissions, performance based design, optimization, sustainable design

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Authors: Maziyar Nouraee

Abstract:

Electronic markets in recent decades contribute remarkably in business transactions. Many organizations consider traditional ways of trade non-economical and therefore they do trade only through electronic markets. There are different categorizations of electronic markets functions. In one classification, functions of electronic markets are categorized into classes as information, transactions, and value added. In the present paper, effects of the three classes on the two major elements of the supply chain management are measured. The two elements are decrease in the product unit cost and reduction in response time to the customer. The results of the current research show that among nine minor elements related to the three classes of electronic markets functions, six factors and three factors influence on reduction of the product unit cost and reduction of response time to the customer, respectively.

Keywords: electronic commerce, electronic market, B2B trade, supply chain management

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Authors: Jyun-Huei Huang, Ting-I Lee

Abstract:

Urban heat island effect is derived from the reduction of vegetative cover by urban development. Because plants can improve air quality and microclimate, green walls have been applied as a sustainable design approach to cool building temperature. By using plants to green vertical surfaces, they decrease room temperature and, as a result, decrease the energy use for air conditioning. Based on their structures, green walls can be divided into two categories, green façades and living walls. A green façade uses the climbing ability of a plant itself, while a living wall assembles planter modules. The latter one is widely adopted in public space, as it is time-effective and less limited. Although a living wall saves energy spent on cooling, it is not necessarily cost-effective from the perspective of a lifecycle analysis. The Italian study shows that the overall benefit of a living wall is only greater than its costs after 47 years of its establishment. In Taiwan, urban greening policies encourage establishment of green walls by referring to their benefits of energy saving while neglecting their low performance on cost-effectiveness. Thus, this research aims at understanding the perception of appliers and consumers on the cost-effectiveness of their living wall products from the lifecycle viewpoint. It adopts semi-structured interviews and field observations on the maintenance of the products. By comparing the two results, it generates insights for sustainable urban greening policies. The preliminary finding shows that stakeholders do not have a holistic sense of lifecycle or cost-effectiveness. Most importantly, a living wall well maintained is often with high input due to the availability of its maintenance budget, and thus less sustainable. In conclusion, without a comprehensive sense of cost-effectiveness throughout a product’s lifecycle, it is very difficult for suppliers and consumers to maintain a living wall system while achieve sustainability.

Keywords: case study, maintenance, post-occupancy evaluation, vertical greening

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Authors: Soheila Sadeghi

Abstract:

Construction projects are complex and often subject to significant cost overruns due to the multifaceted nature of the activities involved. Accurate cost estimation is crucial for effective budget planning and resource allocation. Traditional methods for predicting overruns often rely on expert judgment or analysis of historical data, which can be time-consuming, subjective, and may fail to consider important factors. However, with the increasing availability of data from construction projects, machine learning techniques can be leveraged to improve the accuracy of overrun predictions. This study applied machine learning algorithms to enhance the prediction of cost overruns in a case study of a construction project. The methodology involved the development and evaluation of two machine learning models: Random Forest and Neural Networks. Random Forest can handle high-dimensional data, capture complex relationships, and provide feature importance estimates. Neural Networks, particularly Deep Neural Networks (DNNs), are capable of automatically learning and modeling complex, non-linear relationships between input features and the target variable. These models can adapt to new data, reduce human bias, and uncover hidden patterns in the dataset. The findings of this study demonstrate that both Random Forest and Neural Networks can significantly improve the accuracy of cost overrun predictions compared to traditional methods. The Random Forest model also identified key cost drivers and risk factors, such as changes in the scope of work and delays in material delivery, which can inform better project risk management. However, the study acknowledges several limitations. First, the findings are based on a single construction project, which may limit the generalizability of the results to other projects or contexts. Second, the dataset, although comprehensive, may not capture all relevant factors influencing cost overruns, such as external economic conditions or political factors. Third, the study focuses primarily on cost overruns, while schedule overruns are not explicitly addressed. Future research should explore the application of machine learning techniques to a broader range of projects, incorporate additional data sources, and investigate the prediction of both cost and schedule overruns simultaneously.

Keywords: resource allocation, machine learning, optimization, data-driven decision-making, project management

Procedia PDF Downloads 40