Research on the Comparison between the Different Policies by Service Level and Inventory Level Performance of Auto Parts in N.A.C.C. (North Automobile Components Company)

As after sales services become more and more popular, particularly preventive or corrective maintenance, the intervention and repair of the customer’s goods in a timely and efficient manner ensure customer satisfaction and contribute to the establishment of brand image in the market of the suppliers. The availability and quality of spare parts are key elements of this strategy while ensuring minimal management costs. The reuse of spare parts retrieved from customer systems is a growing maintenance strategy practice which impacts the traditional spare parts supply chain. This reuse is primarily driven by extending the economic life of goods, initially regarded as waste and therefore without added value, by transforming them into valuable spare parts that can be reused; secondly, for environmental or regulatory reasons, demanding responsibility for the treatment of products at the end of their life; and thirdly, to improve the availability of parts for maintenance, especially parts that the organization can no longer purchase or that are impacted by other issues. It also involves the analysis of their condition and their eventual return to working order as they are retrieved from the customer’s systems in a defective condition. In this paper, we will identify and classify the different customers and spare parts by estimating the critical level of rationing policy based on forecasts, identify the thresholds of inventory management policies, and finally, compare the different policies by service level and inventory level performance for the N.A.C.C. company

Ten golden rules for optimal antibiotic use in hospital settings: the WARNING call to action

Antibiotics are recognized widely for their benefits when used appropriately. However, they are often used inappropriately despite the importance of responsible use within good clinical practice. Effective antibiotic treatment is an essential component of universal healthcare, and it is a global responsibility to ensure appropriate use. Currently, pharmaceutical companies have little incentive to develop new antibiotics due to scientific, regulatory, and financial barriers, further emphasizing the importance of appropriate antibiotic use. To address this issue, the Global Alliance for Infections in Surgery established an international multidisciplinary task force of 295 experts from 115 countries with different backgrounds. The task force developed a position statement called WARNING (Worldwide Antimicrobial Resistance National/International Network Group) aimed at raising awareness of antimicrobial resistance and improving antibiotic prescribing practices worldwide. The statement outlined is 10 axioms, or “golden rules,” for the appropriate use of antibiotics that all healthcare workers should consistently adhere in clinical practice

THE FINITE DIFFERENCE EQUATIONS OF SUCCESSIVE APPROXIMATION METHOD FOR THE CALCULATION OF BENDING BEAMS OF VARIABLE THICKNESS UNDER THE STATIC ACTION OF LOADS

The finite difference equations of the successive approximation method (SAM) which substitute the differential equations ofbending beam of a variable stiffness are obtained. Difference equations of SAM, which approximate the limit conditions of the hand ends of the beam, are also obtained: simply supported hand end; rigidly fixed hand end and free hand end. On the basis ofthe obtained equations, a numerical algorithm was developed for calculating beams of constant and variable thickness under theaction of various static loads. According to this algorithm, a program for calculating beams on a computer was performed. Variable stiffness simple supported hand ends beams, rigidly fixed hand ends beams with uniformly distributed loads along theirlengths, with concentrated force, were calculated. A cantilever beam of variable thickness was also calculated under the action ofthe uniformly distributed load over its entire length. The examples presented here show the accuracy of the results and thesimplicity of the algorithm. Checks for integral equilibrium conditions of beams were performed to validate the newly obtainedresult

SUCCESSIVE APPROXIMATION METHOD FOR THE CALCULATION OF BENT-COMPRESSED BEAMS OF CONSTANT STIFFNESS

The finite difference equations of the successive approximation method (SAM) which substitute the differential equations ofbending-compressed of constant stiffness are obtained. Difference equations of SAM, which approximate the limit conditions ofthe hand ends of the beam, are also obtained: simply supported hand end; rigidly fixed hand end and freehand end. On the basisof the obtained equations, a numerical algorithm was developed for calculating beams of constant thickness under the action ofvarious static loads. According to this algorithm, a program for calculating beams on a computer was performed. The examplespresented here show the accuracy of the results and the simplicity of the algorithm. Checks for integral equilibrium conditions ofbeams were performed to validate the newly obtained results

On the multi-agent learning neural and Bayesian methods in skin detector and pornography classifier: An automated anti-pornography system

The main objective on this study proposed anti-pornography system works on four machine learning methods in two different stages namely skin detector stage and pornography classifier stage. A multi-agent learning is used twice. In the first stage, we propose a multi-agent learning method that combines the Bayesian method with a grouping histogram (GH) technique and the back-propagation neural network with a segment adjacent-nested (SAN) technique based on the YCbCr and RGB colour spaces respectively, to extract skin regions from the image accurately with taking into consideration the problems of the light-changing conditions, skin-like colour and reflection from glass and water. In the second stage, the features from the skin are extracted to classify the images into either pornographic or non-pornographic. Inaccurate classification occurs when different image sizes are used in the existing anti-pornography systems. Thus, this paper proposes a multi-agent learning that combines the Bayesian method with a grouping histogram technique again to extract the features from the skin detection based on YCbCr colour space and the back propagation neural network method using shape features extracted again from skin detection. The classification of the pornographic images becomes more robust to the variation in images sizes. The findings from this study have shown that the proposed multi-agent learning system for skin detection has produced a significant rate of true positives (TP) (i.e., 98.44%). In addition, it has achieved a significant low average rate for the false positives (FP) (i.e., only 0.14%) while the proposed multi-agent learning for pornography classifier has produced significant rates of TP (i.e., 96%). Moreover, it has achieved a significant low average rate of FP (i.e., only 2.67%). The experimental results show that multi-agent learning in the skin detector and pornography classifier are more efficient than other approaches

Characterization of basaltic rock laterites in Dschang, West-Cameroon: Compressed Earth Bricks (CEB) for low-cost buildings

This study reports on the geotechnical characterization of lateritic soils upon basaltic rocks in Dschang, West-Cameroon. Six (06) soil samples from six (06) sites were taken for assessing their suitability in good quality CEB manufacturing. An analytical approach was adopted and consisted of standard physical and mechanical properties including water content, absolute density, Atterberg limits, grain size distribution and Optimum Proctor Modified (OPM). Mineralogical and geochemical contents of the materials were obtained by X-ray diffraction (XRD) and X-ray fluorescence (XRF) respectively. In addition, density, water absorption, abrasion resistance, and dry and wet compressive strengths were determined for manufactured brick specimens. Results indicate that the materials are rich in kaolinite (33–38%), gibbsite (21–27%) and goethite (22–26%) associated to low proportions of smectites (2–7%) and oxides such as hematite, ilmenite, anatase and bohmite. SiO2 (29.57–35.8%), Al2O3 (22.72–26.93%) and Fe2O3 (20.67–27.95%) are the most abundant oxides while basic cations are generally low in the materials (WIP: 2.25–5.89). The absolute density varies from 2.26 to 2.57 t/m3 and could be indicative of higher Fe2O3 contents. The plasticity index (7.80–18.95%) and the liquidity limit (57.80–64.50%) of materials allow their classification as inorganic silt and organic clay with high plasticity and compressibility that are not suitable for the production of CEB. Furthermore, these materials fall out the preferential plasticity domain recommended by Cameroonian standard for CEB. Based on grain size distribution, the studied materials also fall out the sprindle reference of the Cameroonian standard. For improving the characteristics of studied materials, a grain size correction was performed and provided an optimal mixture made up of 75% natural materials and 25% medium sand (0.05–0.5 mm) that is locally available. This is accompanied by considerable variations of consistency parameters allowing to pass from inorganic silt and organic clay to inorganic clay that display appropriate compressibility and plasticity for good quality CEB production. The using of cement at 4%, 6% and 8% revealed a progressive increase of the MDD (1.40–1.77%) and Wopt (12.60–28.39%). Moreover, the density and mechanical parameters of the manufactured specimens increase with the cement content. The density, physical and mechanical properties of the manufactured specimens fall within the Cameroonian standard of compressed earth bricks (CEB). Globally, the lateritic materials of Dschang improved with sand (25%) and Cement (8%) can be suitable for the production of good and low-cost CEB especially as the two additional materials are locally available and accessible

Image skin segmentation based on multi-agent learning Bayesian and neural network

Skin colour is considered to be a useful and discriminating spatial feature for many skin detection-related applications, but it is not sufficiently robust to address complex image environments because of light-changing conditions, skin-like colours and reflective glass or water. These factors can create major difficulties in face pixel-based skin detectors when the colour feature is used. Thus, this paper proposes a multi-agent learning method that combines the Bayesian method with a grouping histogram (GH) technique and the back-propagation neural network with a segment adjacent-nested (SAN) technique based on the YCbCr and RGB colour spaces, respectively, to improve skin detection performance. The findings from this study have shown that the proposed multi-agent learning for skin detector has produced significant true positive (TP) and true negative (TN) average rates (i.e. 98.44% and 99.86% respectively). In addition, it has achieved a significantly lower average rate for the false negative (FN) and false positive (FP) (i.e. only 1.56% and 0.14% respectively). The experimental results show that multi-agent learning in the skin detector is more efficient than other approaches

Image skin segmentation based on multi-agent learning Bayesian and neural network

Skin colour is considered to be a useful and discriminating spatial feature for many skin detectionrelated applications, but it is not sufficiently robust to address complex image environments because of light-changing conditions, skin-like colours and reflective glass or water. These factors can create major difficulties in face pixel-based skin detectors when the colour feature is used. Thus, this paper proposes a multi-agent learning method that combines the Bayesian method with a grouping histogram (GH) technique and the back-propagation neural network with a segment adjacent-nested (SAN) technique based on the YCbCr and RGB colour spaces, respectively, to improve skin detection performance. The findings from this study have shown that the proposed multi-agent learning for skin detector has produced significant true positive (TP) and true negative (TN) average rates (i.e. 98.44% and 99.86% respectively). In addition, it has achieved a significantlylower averagerate for the false negative (FN) and false positive (FP) (i.e. only 1.56% and 0.14% respectively). The experimental results show that multiagent learning in the skin detector is more efficient than other approaches. & 2014 Elsevier Ltd. All rights reserve