The Quality Caring Model of Nursing Term Paper Example | Topics and Well Written Essays - 2500 words

The Quality Caring Model of Nursing - Term Paper Example It is not difficult to imagine that an uncaring hospital staff and bureaucracy lead to an enhanced potential for medical errors and other dysfunctions. When the perception exists that the staff is simply doing the job, that they have become hardened to the suffering around them, and that each patient that enters their doors is simply a number and a disease, the consequences to patients’ morale could be as damaging as delivering the wrong dosage of morphine. Here, the prospect exists of elevated stress levels, pain or discomfort that may go ignored, and general disappointment and/or dissatisfaction. It is a noteworthy issue in the sense that not only could patients’ medical outcomes be negatively impacted, but also, even from the most pragmatic and cynical perspective, extremely dissatisfied patients – even when they do recover – may lead to a bad reputation of the hospital, which could impact potential donations. Even if serious complications do not occur, inconsistent or inattentive medical staff and procedures can also delay healing and recovery, and this can create a financial burden for the patient and increase the risk of nosocomial infections through extended hospital stays, if surgery and healing are not expedited with efficiency. Thus, the issue of caring in nursing is not only a matter of bolstering spirits but also a matter of added medical burdens that can be avoided by the staff which is deeply passionate both about the job and the patients’ health.... On the one hand, some level of detachment is a necessity in the medical field. Excessive emotional investment in any particular patient could compromise judgment or debilitate the healthcare provider if that patient should in fact perish or take a turn for the worst despite the implementation of all appropriate procedural measures. There is always another patient, so doctors, nurses, and allied health professionals would be doing a disservice to other patients if the previous one consumed too much of their attention and emotion. Yet detachment is also an enemy promoting the carelessness described above, making the patient feel uncared for and unappreciated, and enhancing the statistical likelihood of a medical error. Thus, there must be a corporate culture that institutes an appropriate level of attention and compassion in the implementation and management of medicine. It is a standard to teach nurses and doctors to 'care', but with the constant demands of a large hospital, with an e ndless patient-after-patient time consuming process and a great deal to be accomplished independently, the reality rarely lives up to the rhetoric. Time constraints in a large setting also limit the prospect of getting to know a particular patient on an individual basis. Bereft of the potential for attachment, one is easily dehumanized. Thus, a balance must be sought between an emotional entanglement, especially in terminal patients that can emotionally damage doctors or nurses, and a laissez-faire factory farm attitude where no outcome has any emotional impact, leading to frequent mistakes and a clear perception of indifference on part of the patients, enhancing the negative

Child Protection Task Essay Example | Topics and Well Written Essays - 1250 words

Child Protection Task - Essay Example sores, serious nappy rash and urine scalds, significant dental decay. Compromised standards of hygiene i.e. child consistently unwashed. Poverty-stricken complexion and hair texture. No adequate supervision for their age. Searching or stealing food and focus on basic survival. Extensive stays at school, public places and other homes. Longs for or erratically seeks adult affection. Rocking, sucking, head-banging. Poor school attendance (Recognizing Child Abuse and Neglect). Signs in children due to physical abuse: Streak marks on face, head or neck, other staining marks depicting the shape of the object that caused it e.g. belt buckle, hand print etc. the child show drowsiness, vomiting, fits or pooling of blood in the eyes, suggesting that the physical abuse has resulted in head injury. Some may display adult bite marks and scratches and in severe conditions fractures of bones, dislocations, sprains, twisting, burns and scalds encompassing cigarette burns. In some conditions child may complain about the abdominal pain caused by broken internal organs, swallowing of poisonous substances, alcohol or other harmful drugs (Recognizing Child Abuse and Neglect). Possible signs of sexual abuse depicted by the child: bruising or hemorrhage in the genital area, suffering from STD (sexually transmitted disease). In female child, bruising to breasts, buttocks, lower abdomen or thighs may be depicted. Child telling about the sexual acts, sexual knowledge shown by the child not appropriate for her age. Fear of going to bed and if going then with full clothes. Signs of bed wetting or soiling, showing changed behavior pattern of self destruction, attempting suicide, anorexia or over-eating, adolescent pregnancy, child not able to explain about the accumulation of money and gifts, enduring running away from home (Recognizing Child Abuse and Neglect). Possible signs of psychological abuse: Any kind of abuse, gives a great impact on the psychology

Optimization of Benchmark Functions using VTS-ABC Algorithm

Optimization of Benchmark Functions using VTS-ABC Algorithm Performance Optimization of Benchmark Functions using VTS-ABC Algorithm Twinkle Gupta  and Dharmender Kumar Abstract  A new variant based on tournament selection called VTS-ABC algorithm is provided in this paper. Its performance is compared with standard ABC algorithm with different size of data on several Benchmark functions and results show that VTS-ABC provides better quality of solution than original ABC algorithm in every case. Keywords— Artificial Bee Colony Algorithms, Nature-Inspired Meta-heuristics,Optimizations, Swarm Intelligence Algorithms, Tournament selection. NOMENCLATURE ABC – Artificial Bee Colony ACO – Ant Colony Optimization BFS – Blocking Flow-Shop Scheduling DE – Differential Evolution EA – Evolutionary Algorithm GA – Genetic Algorithm MCN – Maximum Cycle Number PSO – Particle Swarm Optimization TS – Tournament size TSP – Travelling Salesman Problem 1.INTRODUCTION For optimization problems, various algorithms havebeendesigned which are basedonnature-inspiredconcepts [1].Evolutionary algorithms(EA) and swarmoptimizationalgorithmsare two different classes in which nature inspired algorithms are classified.Evolutionary algorithms like Geneticalgorithms (GA)andDifferentialevolution (DE) attempt to carry out the phenomenon ofnaturalevolution [2]. However, a swarm like ant colony, a flock of birds can be described as collection of interacting agents and their intelligence lieintheir way of interactions with other individuals andtheenvironment [3]. Swarm optimization includes Particle swarm optimization (PSO) modelon socialbehaviorofbirdflocking [4], Antcolony optimization (ACO) model on swarmofants and Artificial Bee Colony (ABC) model on the intelligent foraging behaviour of honey bees [5]. Some important characteristics of ABC algorithm which makesitmoreattractivethanotheroptimizationalgorithms are: Employs only three control parameters (population size, maximum cycle number and limit) [6]. Fastconvergencespeed. Quite simple, flexible and robust [7] [8]. Easyintegrationwithotheroptimizationalgorithms. Therefore, ABC algorithm is a very popular nature inspired meta-heuristic algorithm used to solve various kinds of optimization problems. In recent years, ABC has earned so much popularity and used widely in various application such as: Constrained optimization, Image processing, Clustering, Engineering Design, Blocking flow shop scheduling (BFS), TSP, Bioinformatics, Scheduling and many others [9]-[18].Similar to other stochastic population-based approaches like GA, Ant Colony etc. ABC algorithm also applied Roulette Wheel selection mechanism which chooses best solution always with high selection pressure and leads the algorithm into premature convergence. With ever-growing size of dataset, optimization of algorithm has become a big concern. This calls for a need of better algorithm. The aim of this paper is to create such an algorithm named VTS-ABC algorithm. This new variant is based on tournament selection mechanism and selects variable tournament size each time in order to select the employed bees sharing their information with onlooker bees. Onlooker bees select solution from selected tournament size of solutions with less selection pressure so that high fitness solutions can’t dominate and give better quality of solutions with large data set as well. A worst solution is also replaced by better solution generated randomly in each cycle. Rest of the paper is divided in different sections as follows: Introduction to standard ABC algorithm is described in section 2. Section 3 describes the proposed VTS-ABC algorithm. Experiments and its simulation results to show performance on several Benchmark functions are described in section 4 and in the last; Conclusion of the paper is discussed. 2.ARTIFICIAL BEE COLONY ALGORITHM In 2005, Karaboga firstly proposed Artificial Bee Colony algorithm for optimizing numerical problems [19] which includes employed bees, onlooker bees and scouts. The bee carrying out search randomly is known as a scout. The bee going to the food source visited by it before and sharing its information with onlooker bees is known as employed bee and the bee waiting on the dance area called onlooker bee. ABC algorithm as a collective intelligence searching model has three essential components: Employed bees, Unemployed bees (onlooker and scout bees) and Food sources. In the view of optimization problem, a food source represents a possible solution. The position of a good food source indicates the solution providing better results to the given optimization problem. The quality of nectar of a food source represents the fitness value of the associated solution. Initially, a randomly distributed food source position of SNsize, the size of employed bees or onlooker bees is generated. Each solution xi is a D-dimensional vector that represents the number of optimized parameters and produced usingthe equation 1: where,xmaxandxminare the upper and lower bound of the parameterxi,respectively and j denotes the dimension. The fitness of food sources to find the global optimal is calculated by the following formula: where, fm(xm)is the objective function value of xm. Then the employed bee phase starts. In this phase, each employed bee xi finds a new food source viin its neighborhood using the equation 3: where, t: Cycle number; : Randomly chosen employed bee and k is not equal to i ; ( ): A series of random variable in the range [-1, 1]. The fitness of new solution produced is compared with that of current solution and memorizes the better one by means of a greedy selection mechanism. Employed bees share their information about food sources with onlooker bees waiting in the hive and onlooker bees probabilistically choose their food sources using fitness based selection technique such as roulette wheel selection shown in equation 4: where, Pi: Probability of selecting the ith employed bee, S: Size of employed bees, ÃŽ ¸i: Position of the ith employed bee and F : Fitness value. Afterthatonlookerbeescarried outrandomly searchintheirneighborhood similar to employed bees and memorize the better one. Employed bees whose solutions can’t be improved through a predetermined number of cycles, called limit, become scouts and their solutions are abandoned. Then, they find a new random food source position using the following equation 5: Where, r: A random number between 0 and 1 and these steps are repeated through a predetermined number of cycles called Maximum Cycle Number (MCN). 3.PROPOSED WORK: VTS-ABC ALGORITHM In every meta-heuristic algorithm mainly two factors need to be balanced for global optimization outcome i.e. Exploration and Exploitation but ABC is a poor balance of these two factors. Various variants of ABC have been modelled for its improvement in different phases by number of researchers like Sharma and Pant have proposed a variant of ABC called RABC for solving the numerical optimization problem [20] and Tsai et al. have presented an interactive ABC optimization algorithm to solve combinational optimization problem [21] in which the concept of universal gravitational force for the movement of onlooker bees is introduced to enhance the exploration ability of the ABC algorithm. D. Kumar and B. Kumar also reviewed various papers on ABC and give a modified RABC algorithm based on topology for optimization of benchmark functions [22] [23]. Intelligence of ABC algorithm mainly depends upon the communication between individual agents. Employed beesshare their information with onlooker bees waiting in the hive and flow of this information from one individual to another depends on the selection mechanism used. Different selection schemes select different individuals to share the information which affect the communication ability of individuals and primarily the outcome of the algorithm. ABC algorithm uses Roulette wheel selection mechanism in which each onlooker bee selects the food source based on certain probability. Each onlooker bee selects the best food source with high selection pressure and lead to premature convergence. To overcome this problem, its new variant is proposed in which Tournament Selection method is applied based on Cycle number and number of employed bees. In Tournament selection, a tournament size (TS) is chosen to select the number of employed bees sharing the information with onlooker bees. For better exploration, TS=2 i.e. Binary Tournament is applied in early stages and for better exploitation, variable tournament size is applied based on the current cycle number (CYL) and size of employed bee in middle stages. As the stages grow, this method works similar to Roulette wheel method in the end. Hence, the selection pressure is less in early stages and more in final stages which provide us better quality of solution. As variable size of tournament is used at different stages of the algorithm, hence the algorithm named VTS-ABC (Variable Tournament Size Artificial Bee Colony) algorithm. Method used for calculating TS is shown in equation 6 and equation 7: If SN >= 20 If SN Where Here, two equations are shown for calculating tournament size of tournament selection method. The purpose of using these two equations is to increase the speed of algorithm. When the size of employed bee i.e. given population of food source positions is small like 10, a solution can be easily found by changing the tournament size by 1 but as the size grows i.e. when best food source position is to be found in large set of population for example when SN=40 or more than 40, increasing size of tournament by 1 and 2 only is a very tedious task as it will take more time to run the algorithm. Hence, in order to increase speed of algorithm, the tournament size based on current cycle and size of population is increased. One more concept is applied to increase its convergence speed. At each iteration or cycle, a new solution is generated randomly similar to scout and its fitness value is calculated. Greedy selection mechanism is applied between new solution and worst one and the better solution is memorized. Hence, it helps in finding good quality of solution as well as improving the convergence speed and provides better balance between exploration and exploitation. 4.experiments and simulation results 4.1 Benchmark Functions The Benchmark Functions used to compare the performance of VTS-ABC algorithm with original ABC algorithm are illustrated below: Sphere Function: Schwefel Function: Griewank Function: Where Ackley Function: Here, ObjVal is the function value calculated for each food source position. A food source is represented by X and population size is taken of n*p matrix where n is the no. of possible food source positions and p represents the dimension of each position. 4.2 Performance Measures Simulation Result The experimental results of VTS-ABC and ABC algorithm in MATLAB are taken under the parameter of size of food source positions (n*p) i.e. different size of population with different dimensions are taken to run and compare both algorithms. MCN is set as 2000 and each algorithm is run for 3 iteration i.e. Runtime=3. Limit for scouts is set equals to 300. In order to provide the quantitative assessment of the performance of an optimization algorithm, Mean of Global Minimum i.e. mean of minimum objective function value at each cycle of all iterations are taken as performance measure whose values are shown in table1and figure 1-4. Table1: Mean of Global minimum on different size of data Fig. 1: Mean of Sphere function values on different size of data Fig. 2: Mean of Schwefel function values on different size of data Fig. 3: Mean of Griewank function values on different size of data Fig. 4: Mean of Ackley function values on different size of data Figure 1 to 4 show simulation results of ABC and VTS-ABC algorithm with different size of data on Sphere, Schwefel, Griewank, Ackley respectively and reveal that VTS-ABC algorithm provides us better quality of solution than original ABC algorithm by minimizing objective function value or producing higher fitness solutions. 5. DISCUSSION AND CONCLUSION In this paper, a new algorithm VTS-ABC is presented. In this algorithm, firstly variable tournament size (TS) is applied to select the food source position for onlooker bees which helps to achieve diversity in solution. Then to increase convergence speed, a new solution is generated in each cycle which replaced the worst one. In order to demonstrate the performance of proposed algorithm, it is applied on several Benchmark functions with different size of data set as input. Simulation results show that it provides better quality of solution than original ABC algorithm in every case. Therefore, it can be applied in different fields of optimization with large and higher dimensions data set efficiently. References Yugal Kumar and Dharmender Kumar, â€Å"Parametric Analysis of Nature Inspired Optimization Techniques†International Journal of Computer Applications, vol. 32, no. 3, pp. 42-49, Oct. 2011. P. J. Angeline, J. B. Pollack and G.M. Saunders, â€Å"An evolutionary algorithm that constructs recurrent neural networks,† Neural Networks in IEEE Transactions on, vol. 5, no. 1, 1994, pp. 54-65. J. Kennedy and R. Eberhart, â€Å"Particle swarm optimization,† in Proceedings of IEEE international conference on neural networks, 1995, vol. 4, pp. 1942–1948. E. Bonabeau, M. Dorgio, and G. Theraulaz, â€Å"Swarm intelligence: from neural network to artificial intelligence,† NY: oxford university press, New York, 1999. D. Karaboga, â€Å"An idea based on honey bee swarm for numerical optimization,† Techn.Rep. TR06, Erciyes Univ. Press, Erciyes, 2005. D. Karaboga and B. Akay, â€Å"A comparative study of artificial bee colony algorithm,† Applied Mathematics and Computation, vol. 214, no. 1, pp. 108–132, 2009. R. S. Rao, S. V. L. Narasimham, and M. Ramalingaraju, â€Å"Optimization of distribution network configuration for loss reduction using artificial bee colony algorithm,† International Journal of Electrical Power and Energy Systems Engineering, vol. 1, no.2, pp. 116–122, 2008. A. Singh, â€Å"An artificial bee colony algorithm for the leaf-constrained minimum spanning tree problem,† Applied Soft Computing, vol. 9, no. 2, pp. 625–631, Mar. 2009. D. Karaboga and B. Basturk, â€Å"Artificial bee colony (ABC) optimization algorithm for solving constrained optimization problems,† in Foundations of Fuzzy Logic and Soft Computing, Springer, 2007, pp. 789–798. C. Chidambaram and H. S. Lopes, â€Å"A new approach for template matching in digital images using an Artificial Bee Colony Algorithm,† in World Congress on Nature Biologically Inspired Computing, 2009. NaBIC 2009, IEEE, 2009, pp. 146–151. N. K. Kaur Mann, â€Å"Review Paper on Clustering Techniques,† Global Journal of Computer Science and Technology, vol. 13, no. 5, 2013. S. Okdem, D. Karaboga, and C. Ozturk, â€Å"An application of Wireless Sensor Network routing based on Artificial Bee Colony Algorithm,† in 2011 IEEE Congress on Evolutionary Computation (CEC), 2011, pp. 326–330. T. K. Sharma, M. Pant, and J. C. Bansal, â€Å"Some modifications to enhance the performance of Artificial Bee Colony,† in 2012 IEEE Congress on Evolutionary Computation (CEC), 2012, pp. 1–8. L. Bao and J. Zeng, â€Å"Comparison and analysis of the selection mechanism in the artificial bee colony algorithm,† in Hybrid Intelligent Systems, 2009. HIS’09. Ninth International Conference on, 2009, vol. 1, pp. 411–41. C. M. V. Benà ­tez and H. S. Lopes, â€Å"Parallel Artificial Bee Colony Algorithm Approaches for Protein Structure Prediction Using the 3DHP-SC Model,† in Intelligent Distributed Computing IV, M. Essaaidi, M. Malgeri, and C. Badica, Eds. Springer Berlin Heidelberg, 2010, pp. 255–264. D. L. Gonzà ¡lez-à lvarez, M. A. Vega-Rodrà ­guez, J. A. Gà ³mez-Pulido, and J. M. Sà ¡nchez-Pà ©rez, â€Å"Finding Motifs in DNA Sequences Applying a Multiobjective Artificial Bee Colony (MOABC) Algorithm,† in Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics, C. Pizzuti, M. D. Ritchie, and M. Giacobini, Eds. Springer Berlin Heidelberg, 2011, pp. 89–100. L. Wang, G. Zhou, Y. Xu, S. Wang, and M. Liu, â€Å"An effective artificial bee colony algorithm for the flexible job-shop scheduling problem,† Int J Adv Manuf Technol, vol. 60, no. 1–4, pp. 303–315, Apr. 2012. S.-W. Lin and K.-C. Ying, â€Å"Increasing the total net revenue for single machine order acceptance and scheduling problems using an artificial bee colony algorithm,† J Oper Res Soc, vol. 64, no. 2, pp. 293–311, Feb. 2013. D. Karaboga, â€Å"An idea based on honey bee swarm for numerical optimization,† Techn.Rep. TR06, Erciyes Univ. Press, Erciyes, 2005. T. K. Sharma, M. Pant, and J. C. Bansal, â€Å"Some modifications to enhance the performance of Artificial Bee Colony,† in 2012 IEEE Congress on Evolutionary Computation (CEC), 2012, pp. 1–8. TSai, Pei-Wei, et al. , Enhanced artificial bee colony optimization.International Journal of Innovative Computing, Information and Control,vol. 5, no. 12, 2009, pp.5081-5092. B. K. Verma and D. Kumar, â€Å"A review on Artificial Bee Colony algorithm,† International Journal of Engineering Technology, vol. 2, no. 3, pp. 175–186, 2013. D. Kumar and B. Kumar, â€Å"Optimization of Benchmark Functions Using Artificial Bee Colony (ABC) Algorithm,† IOSR Journal of Engineering, vol. 3, no. 10, pp. 09-14, October 2013.

Shakespeares Much Ado About Nothing and Renaissance Italy Essay

Shakespeare's play "Much Ado About Nothing" takes place in Messina, a city-state of Renaissance Italy. The Renaissance was a period in European history believed to have been between AD 1300 and AD 1600 with a feudal society of agricultural economy and church dominated culture. It was during the Renaissance that Europe was transformed into a society dominated by central political institutions with education, arts and music heavily influenced by the Christian religion. In Italy, cities such as Florence, Ferrara, Milan, and Venice flourished. These cities were able to finance cultural achievements. Medieval Italian merchants developed commercial and financial techniques such as bookkeeping and bills of exchange. Italian city-states were transformed from communes into territorial states, each of which wanted to expand at the expense of others. Diplomacy was established when the Italian city-states began embassies in foreign courts. In general, the Italian Renaissance was a time of flo urishing ideas and prosperity. "Much Ado About Nothing" is a play intertwining two love stories. One story follows the romance of a young woman Hero (daughter of Leonato, governor of Messina) and a young officer Claudio. When Claudio returns from war, he realizes he's deeply in love with Hero. With the help of his commander, Don Pedro, Claudio proposes to her. The other is a less likely couple, Beatrice (Hero's cousin) and Bene*censored* (another officer). Both being witty, strong-willed and outspoken, the two seem to bare distaste for each other. However, when their friends arrange for them to overhear conversations revealing how much each is loved by the other, it doesn't take long before they declare their love for one another. Meanwhile, self... ...ce of Aragon, Don Pedro. The political systems are also shown by the fact Don John was denied claim to his family's wealth because he was born outside of marriage. Secondly, I learned about household positions in Renaissance Italy. As displayed by the play, a noble household was headed by a man (Leonato) with daughters waiting to be courted (Hero/Beatrice), and maidservants (Margaret/Ursula). A third lesson was in medieval courtship. In those times, the girls were to be wooed and events such as the dance in "Much Ado About Nothing" were thrown to do so. When a man wished to marry her, he would ask her father for permission, just as Claudio does for Hero. In conclusion, Shakespeare's play "Much Ado about Nothing" intertwines two love stories in Renaissance Italy. Through its characters, it portrays the political and household lifestyles of the Renaissance times.

In Dr. Cloud’s book Essay

In Dr. Cloud’s book, the concept of â€Å"wake† is the next in a series of important concepts in business. This concept affects how a person works within an organization, and includes everything that he does, both in business and interpersonal relationships. A person’s wake is the most important thing about him or her in terms of business, and it will determine his or her overall success. The â€Å"wake† is the legacy that a person leaves behind in a business situation (or, really, in any situation). It consists of everything a person does, both in business and interpersonally. For example, a person may help his team meet their goals quickly and efficiently, achieve high sales numbers, and further the company’s other goals and missions. These would be examples from the business side. A person may also leave his team feeling happy to have worked with him, inspired to work harder, and feeling good about meeting their goals. These are examples from the interpersonal side. However, it is also possible that someone may not do so well in one or both of these areas. Someone may be a great salesman who is achieving goals quickly and making large amounts of money for the company, but who upsets almost everyone he works with, as in Dr. Cloud’s example. This type of person’s wake must be considered from all angles. What is the person really worth to the company? If his sales numbers are great, but no one wants to work with him and people spend a lot of time complaining about him, then the lost productivity may not be worth the amount of sales he is making. His interpersonal wake is poor. Dr. Cloud emphasizes the role of character is the wake. A person must have strong character in order to have a good wake. For example, the CEO in the book was not a bad person, or a bad leader in most respects. He treated his people fairly and people were glad to work with him. He also seemed (from what information there was) to be doing good things for the company. However, he had a weak character when it came to dealing with problems. Because the CEO did not react quickly enough by either disciplining or firing the sales VP, he lost the confidence of a large portion of his team and the board members, and had to leave the company. Had he been willing to handle the problem earlier, this might not have happened. As he was leaving, the company probably did not look favorably upon his wake, no matter how many other good things he had done for the company. Dr. Cloud goes on to emphasize that character is â€Å"the ability to meet the demands of reality. † This means a number of things. When a senior staff member is hiring a new team member, especially in a higher-importance job, the staff member needs to make sure that the person can meet the demands of the company’s reality. That means that regardless of the person’s abilities, they must be a good fit for the company. What’s even more important is that the person must be a good fit for the particular job and its responsibilities. There is a good example of this, too. Dr. Cloud called some references when he was hiring a new person for an important position. The references were very pleased with the work the woman had done, and people had worked well with her. Her weaknesses were not considered to be large issues, and most importantly, they would hire her again, knowing what they knew. Dr. Cloud ensured that this person would be a good fit by checking to see what wake she had left at her previous jobs. The primary point is this: it is the personal aspects of character, and the wake, that are most important. Business issues can be overcome by someone, under some set of circumstances. But whether or not a person is equipped to deal with the stress of those situations is another matter. A person’s character and the wake they leave behind when working with others is the most important part of whether or not they will succeed.

Pelts computerized sales and inventory system Essay

Pelts Corporation is a company that sells clothes for men, women, and children. They also have a wide variety of fashion accessories; they also have shoes, bags and eyewear. The company has two branches here in Bacolod, one is located in Lacson St. Corner B.S. Aquino drive and the other is inside the Robinson’s Mall Bacolod. The Pelts Corporation is still using the old software that is similar to the FoxPro MS Dos with barcode feature. That is why we came to a point to propose a new system that can help make their work easier and efficient. We want to change their Physical Count Inventory System to a Computerized Sales & Inventory System for easier and fast workflow, and can help improve their work like their growth as a company. Purpose & Description Pelts Computerized Sales & Inventory System has the following features: The Sales System this where the main transactions like the sales through cash, credit cards, Pelts Cards & check payments are being recorded. This system also helps them know the real-time transactions and helps them monitor their income if there is growth in the business. The Inventory System is the one tracking the incoming and outgoing stocks and the recorded and real-time stocks of the Company. Our team decided to combine the two separate systems into one for them to track real-time deficiency on both the sales and the inventory of their stocks. General Objective The General Objective of this proposal is to improve the existing system of Pelts Corporation through a computerized Sales & Inventory System. Specifically this proposal is to address the following: To develop a database that focuses on recording all units & items delivered to the company which is part of the Inventory System. To improve their Fox  Pro MS Dos software to a Visual Basic Software. To improve their archival system since both sales and inventory system are combined and is computerized there will be less paper works and hard copy of the files can be minimized in the warehouse facility. Scope & Limitation The proposed system will allow access that only authorized personnel can use thus it will have a Login feature for security. The system can generate a report to the files and records on the specific data and categories they require. The Computerized Sales and Inventory system can access the current rates, the cash and debit cards sales, credit cards and check charge, gift check conditional, payments, returns and memo. It can also access the gross sales total of the old and new brands. The Pelts Computerized Sales and Inventory system will update every year. The update will separate the old and the new records to avoid the files to overlap with one another. Limitations The system will also have minor limitations one of them is the storehouse of records that the system can handle a maximum of 50,000 data that can be stored and accessed since we use the Microsoft Office Access to store the data. The second is the system can inventory the credit line transactions but we cannot store it in the records since there is a different system that handles the recording in the condition of the mode of payment.