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Material cost reduction using cutting optimization Pro5 and enhanced N-BEATS deep learning forecasting: a case study
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Manufacturing industry is constantly looking for ways to improve the efficiency of production processes so as to lower production costs, maintain product quality, and improve production planning. This study aims to provide an integrated optimization and forecasting framework that combines Cutting Optimization Pro5, linear programming, and an Enhanced N-BEATS deep learning model to minimize material waste and manufacturing cost and to predict the number of production items with a price-reduction strategy. The real industrial case study is based on production and pricing data from the Akad factory of the General Company for Electrical and Electronic Products in Iraq for the period from 2014 to 2025, concerning the manufacture of 80 L and 120 L electric water heaters. That is, Cutting Optimization Pro5 was used to specify optimal sheet sizes and cutting plans, minimizing waste and reducing production costs. At the same time, the Enhanced N-BEATS deep learning model was implemented to forecast future production quantities. The optimization results showed a significant reduction in wasted materials and production costs, allowing lower product prices. Thus, the forecasting results proved that the performance of the Enhanced N-BEATS model was much better than that of SARIMAX and Simple Linear Regression since the model showed the lowest prediction errors (pMAPE value for the 120 L product is equal to 0.040 and nMSE is equal to 0.016, while pMAPE for the 80 L product equals 0.091, and nMSE equals 0.007). The results show that combining cutting optimization and deep learning can create an efficient decision-support framework to improve production efficiency, lower production costs, and enhance production planning in industrial environments.

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Publication Date
Fri Jun 01 2007
Journal Name
Al-khwarizmi Engineering Journal
Reduction of the error in the hardware neural network
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Specialized hardware implementations of Artificial Neural Networks (ANNs) can offer faster execution than general-purpose microprocessors by taking advantage of reusable modules, parallel processes and specialized computational components. Modern high-density Field Programmable Gate Arrays (FPGAs) offer the required flexibility and fast design-to-implementation time with the possibility of exploiting highly parallel computations like those required by ANNs in hardware. The bounded width of the data in FPGA ANNs will add an additional error to the result of the output. This paper derives the equations of the additional error value that generate from bounded width of the data and proposed a method to reduce the effect of the error to give

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Publication Date
Sun Mar 30 2003
Journal Name
Iraqi Journal Of Chemical And Petroleum Engineering
Reduction of Aromatic Content from Hexane by Silica Gel
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Publication Date
Sun Jun 30 2002
Journal Name
Iraqi Journal Of Chemical And Petroleum Engineering
Catalytic Reduction of Nitric Oxide in Automobile Exhaust Gases
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Publication Date
Wed Jun 20 2012
Journal Name
Journal Of Biotechnology Research Center
Evaluation of toxic effects of two weight reduction pills
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Abstract his study involved evaluation of side effects of two weight reduction pills that had been widely distributed in the last period. Two weight reduction compounds are studied, Reductil (containing chemical substances) and Chinese’s weight reduction herbs (containing natural substances). Two doses for each compound are used in this research; 5mg/ml and 0.5mg/ml for Reductil, while 30mg/ml and 10mg/ml for Chinese weight reduction herbs. To evaluate the toxic effects of these compounds, the following parameters were determined which include mitotic index (cytogenetic analysis), serum FSH and LH hormones level (follicles stimulation hormone/FSH and lutenising hormone/LH) and histological examination of female mice ovaries. Control group

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Publication Date
Wed Mar 10 2021
Journal Name
Baghdad Science Journal
Compression-based Data Reduction Technique for IoT Sensor Networks
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Energy savings are very common in IoT sensor networks because IoT sensor nodes operate with their own limited battery. The data transmission in the IoT sensor nodes is very costly and consume much of the energy while the energy usage for data processing is considerably lower. There are several energy-saving strategies and principles, mainly dedicated to reducing the transmission of data. Therefore, with minimizing data transfers in IoT sensor networks, can conserve a considerable amount of energy. In this research, a Compression-Based Data Reduction (CBDR) technique was suggested which works in the level of IoT sensor nodes. The CBDR includes two stages of compression, a lossy SAX Quantization stage which reduces the dynamic range of the

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Publication Date
Sun Sep 11 2022
Journal Name
Journal Of Petroleum Research And Studies
Non-Productive Time Reduction during Oil Wells Drilling Operations
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Often there is no well drilling without problems. The solution lies in managing and evaluating these problems and developing strategies to manage and scale them. Non-productive time (NPT) is one of the main causes of delayed drilling operations. Many events or possibilities can lead to a halt in drilling operations or a marginal decrease in the advancement of drilling, this is called (NPT). Reducing NPT has an important impact on the total expenditure, time and cost are considered one of the most important success factors in the oil industry. In other words, steps must be taken to investigate and eliminate loss of time, that is, unproductive time in the drilling rig in order to save time and cost and reduce wasted time. The data of

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Publication Date
Wed Jan 01 2020
Journal Name
2nd International Conference On Materials Engineering & Science (iconmeas 2019)
Viscosity reduction of Iraqi crude oil by different additives
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The high viscosity of heavy oil is a crucial factor that strongly affects its up-stream recovering, down-stream surface transporting and refining processes. Economical methods for recovering the heavy oil and reducing is very important and related to capital and/or operating cost. This research studies the treatment of Iraqi heavy crude oil, which characterize with high viscosity and low API which makes transportation of heavy crude oil a difficult mission, needs for treatment to reduce viscosity for facilitating transportation and processing. Iraqi heavy crude oil was used Sharqi Baghdad, which obtained from Baghdad east oil fields with API 22.2º.Many kinds of additives were used to reduce the viscosity, experiments were performed o

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Publication Date
Sun Dec 01 2019
Journal Name
Journal Of Economics And Administrative Sciences
Application of the Holonic Manufacturing System using the Genetic Algorithm : Case Study in Lab 7 of the General Company for the Leather Industry
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The study aims to achieve several objectives, including follow-up scientific developments and transformations in the modern concepts of the Holistic Manufacturing System for the purpose of identifying the methods of switching to the entrances of artificial intelligence, and clarifying the mechanism of operation of the genetic algorithm under the Holonic Manufacturing System, to benefit from the advantages of systems and to achieve the maximum savings in time and cost of machines Using the Holistic Manufacturing System method and the Genetic algorithm, which allows for optimal maintenance time and minimizing the total cost, which in turn enables the workers of these machines to control the vacations in th

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Publication Date
Wed Jan 25 2023
Journal Name
Molecular Simulation
Engineering promising A-π-D type molecules for efficient organic-based material solar cells
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Within this work, to promote the efficiency of organic-based solar cells, a series of novel A-π-D type small molecules were scrutinised. The acceptors which we designed had a moiety of N, N-dimethylaniline as the donor and catechol moiety as the acceptor linked through various conjugated π-linkers. We performed DFT (B3LYP) as well as TD-DFT (CAM-B3LYP) computations using 6-31G (d,p) for scrutinising the impact of various π-linkers upon optoelectronic characteristics, stability, and rate of charge transport. In comparison with the reference molecule, various π-linkers led to a smaller HOMO–LUMO energy gap. Compared to the reference molecule, there was a considerable red shift in the molecules under study (A1–A4). Therefore, based on

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Publication Date
Wed May 15 2019
Journal Name
Al-khwarizmi Engineering Journal
Mesoporous Silica MCM-41 as a Carriers Material for Nystatine Drug in Delivery System
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In the present study, MCM-41 was synthesis as a carrier for poorly drugs soluble in water, by the sol-gel technique. Textural and chemical characterizations of MCM-41 were carried out by X-ray diffraction (XRD), Fourier transform infrared (FTIR), scanning electron microscope (SEM), and thermal gravimetric analysis (TGA). The experimental results were analyzed mesoporous carriers MCM-41. With maximum drug loading efficiency in MCM-41 determined to be 90.74%. The NYS released was prudently studied in simulated body fluid (SBF) pH 7.4 and the results proved that the release of NYS from MCM-41 was (87.79%) after 18 hr. The data of NYS released was found to be submitted a Weibull model with a correlation coefficient of (0.995). The Historical

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