Make a Fake Neon Sign Using Fluorescence

Do you love the look of neon signs, but want an inexpensive alternative that you can customize to say whatever you want? You can make a fake neon sign using fluorescence to make inexpensive common materials glow. Fake Neon Sign Materials You only need a few basic materials for this project. Flexible plastic tubing (usually sold as aquarium tubing)Glue gunCardboard or other stiff backing for your signFluorescent highlighter pen or laundry detergentWaterBlack light Make the Fake Neon The plastic tubing will glow blue under a black light, so technically this project will work if you simply form a sign with the tubing and illuminate it with a black light (ultraviolet lamp). However, youll get a much brighter glow if you fill the tubing with a fluorescent liquid, such as a small amount of laundry detergent dissolved in water (bright blue) or a fluorescent highlighter ink pad in water (available in various colors). Tip: A lot of highlighter pens called fluorescent markers arent actually fluorescent. Write a quick note on paper and shine a black light on it to determine whether or not the ink fluoresces. Yellow almost always glows. Blue rarely does. Make the Sign Design Practice forming the word you want on your sign so that you can get an idea of how much tubing will be required.Cut the tubing somewhat longer than what you think you will need.Fill the plastic tubing with your fake neon. Put one end of the tubing into the fluorescent liquid and raise it higher than the other end of the tubing. Place the lower end of the tubing into a cup so you wont have a big mess. Let gravity pull the liquid down the tube.When the tubing is filled with liquid, seal its ends with beads of hot glue. Allow the glue to cool before proceeding to make sure you have a good seal on your neon.Apply hot glue to stick the tubing to the backing you have selected. Form the word for your sign. If you are making a sign that uses multiple words, you will need separate tubes for each word.If you have excess tubing, carefully cut the end and seal it with hot glue.Illuminate the sign by turning on a black light. A fluorescent light fixture will provide some glow, but for a bright ne on appearance, use a black light.

Susan Glaspell s The Play Trifles - 1410 Words

The play Trifles by Susan Glaspell is a very powerful play that conveys a strong meaning to the audience. The meaning that Susan Glaspell conveys through this play is the importance of women to stick together and rise up against the suppression of their gender. This message can be felt strongly while reading this play. Susan Glaspell does an outstanding job incorporating this message into an interesting, captivating plot. This play was written around the time where woman’s social equality was becoming more of an important civil issue. Women were starting to take action against social inequality. This play displays the life of which many woman of that era had lived. This social inequality had pretty much confined woman to the household, taking care of their husband, and obeying every command. Mrs. Wright is clearly conveying the feelings of suppressed woman of this time period. The main idea that Susan Glaspell is trying to convey in this play is that woman need to stick togeth er in order to rise up against social inequality. This can be seen in the way that Mrs. Peters and Mrs. Hale protect and stick up for Mrs. Wright. Mrs. Peters and Mrs. Hale sympathize with Mrs. Wright; they know how she was treated in the house and the type of life that she lived. Susan Glaspell does a very good job conveying the condition in which Mrs. Wright lived. The way that the Wright’s home is described makes it seem gloomy, boring, and just an awful place to live. These conditions cause Mrs.Show MoreRelatedSusan Glaspell s Play Trifles870 Words   |  4 PagesIn the Susan Glaspell s play Trifles, gender plays a huge roll in everyday life. Trifles is an exemplary example of the war between male and female. It creates a scene where women are less deserving than the men. Women are used as stepping stones or told to remain dedicated to the male power. Females are the lesser creatures compared to the men who have the power. The play paints a scene where women are in compl iance to this unwritten code of conduct expected for them. Towards the end of the playRead MoreSusan Glaspell s The Play Trifles1499 Words   |  6 Pageshave today. Susan Glaspell wrote the play Trifles to embed the thematic focuses about the contrast between the two sexes, the practiced culture of social structure and household subjugation, females forced labor, and the oppression of women in order to explain that society should stop overlooking powerful women and their extraordinary minds. Furthermore, Glaspell was a member of a group of intellectuals who questioned marriage and women s role in society, and she also wrote the play that was basedRead MoreFeminist in Susan Glaspell ´s Play Trifles999 Words   |  4 Pages Trifles In Susan Glaspell’s play Trifles a man has been murdered by his wife, but the men of the town who are in charge of investigating the crime are unable solve the murder mystery through logic and standard criminal procedures. Instead, two women (Mrs. Hale and Mrs. Peters) who visit the home are able to read a series of clues that the men cannot see because all of the clues are embedded in domestic items that are specific to women. The play at first it seems to be about mystery, but itRead More A Comparison of Feminism in Trifles and A Jury of Her Peers Essay example1104 Words   |  5 PagesFeminism in Trifles and A Jury of Her Peers  Ã‚     Ã‚  Ã‚   As a strong feminist, Susan Glaspell wrote â€Å"Trifles† and then translated it to a story called â€Å"A Jury of Her Peers.†Ã‚   These works express Glaspell’s view of the way women were treated at the turn of the century.   Even though Glaspell is an acclaimed feminist, her story does not contain the traditional feminist views of equal rights for both sexes.   Ã‚  Ã‚  Ã‚   The short story and the play written by Susan Glaspell are very much alike.   The storyRead MoreTrifles : A Dramatic Examination Of Gender Role1031 Words   |  5 PagesTrifles: A Dramatic Examination of Gender Role Trifles is a dramatic one act play written by American female playwright Susan Glaspell. The play examines through the framework of a murder mystery how rigid gender role dynamics in the early 20th century not only shaped people s thinking, but blinded them from seeing what would otherwise be clear as day to someone else. During the time the play was written the women s liberation movement had yet to take place. Women were strongly stereotyped andRead MoreWomen In Susan Glaspells Trifles931 Words   |  4 PagesSusan Glaspell’s â€Å"Trifles† attempts to answer a single question for the public. Why do women, a stereotypically quiet and submissive group, turn to murder? The male dominated society of the 1900’s found answers by simply branding them as insane; men were never to blame because only a crazy women would turn on a man. However, Glaspell empowers the women of her play in their submissive roles by utilizing the oppression by men to point out the holes in the male-dominated legal system. Linda Ben-ZviRead MoreTrifles981 Words   |  4 PagesReview of â€Å"Trifles† Susan Glaspell play, â€Å"Trifles†, revolves around Mrs. Wright, a woman who seeks revenge on her husband for oppressing her through their years of marriage. During the time of Glaspell’s play, early 1900’s, men are the dominant figures in society and women are expected to cook, clean, raise children and care for their husbands. Glaspell’s play, â€Å"Trifles†, main goal is portraying a theme of women being oppressed through marriage by the use of symbolism through a canary and a birdRead MoreLiterary Analysis of Susan Glaspells Trifles1788 Words   |  7 PagesAn Analysis of Natures in Susan Glaspells Trifles A trifle is something that has little value or importance, and there are many seeming trifles in Susan Glaspells one-act play Trifles. The irony is that these trifles carry more weight and significance than first seems to be the case. Just as Glaspells play ultimately reveals a sympathetic nature in Mrs. Peters and Mrs. Hale, the evidence that the men investigators fail to observe, because they are blind to the things that have importanceRead MoreSusan Glaspell s Trifles 1732 Words   |  7 PagesSusan Glaspell (1876-1948) was an American-born Pulitzer Prize winning writer of both plays and fiction. Glaspell came from humble beginnings and went on to study at Drake University and the University of Chicago. Much of Glaspell s work dealt with the relationships between men and women and the negative effects they have on women. In Glaspell s play Trifles, it is revealed that the operations of patriarchy are just an illusion that men have created to make themselves feel superior to w omenRead MoreFeminism Is Not About Making Women Stronger1441 Words   |  6 PagesFeminism is not about making women stronger. Women are already strong. It s about changing the way the world perceives that strength. - G.D Anderson      Our culture in the early Twentieth Century was biased in many ways, as it still is to this day in the Twenty-first Century. One of the major struggles were men s biased writing about women. Many women then and to this day still stand up and try to fight for equality. Women used to be  given certain roles to be a part of society in our history.

New Mind in Data Mining Free Essays

Content mining has turned into an energizing examination field as it tries to find profitable data from unstructured writings. The unstructured writings which contain huge measure of data can’t just be utilized for additionally preparing by PCs. Thusly, correct preparing strategies, calculations and methods are fundamental keeping in mind the end goal to separate this profitable data which is finished by utilizing content mining. We will write a custom essay sample on New Mind in Data Mining or any similar topic only for you Order Now In this paper, we have talked about general thought of content mining and correlation of its procedures. What’s more, we quickly talk about various content mining applications which are utilized directly and in future. Index Terms Retrieval, Extraction, Categorization, Clustering, Summa- rization. INTRODUCTION Content mining has turned out to be imperative research region. Countless put away in better places in unstructured structure. Around 80% of the world’s information is in unstructured content [1]. This unstructured content can’t be effortlessly utilized by PC for all the more preparing. So there is a requirement for some procedure that is valuable to remove some valuable data from unstructured content. These data are then put away in content database design which contains organized and couple of unstructured fields. Content can be sited in sends, visits, SMS, daily paper articles, diaries, item audits, and association records [2]. Relatively every one of the organizations, government divisions. Text Mining Steps Gather data from unstructured information. Change over this data got into organized information Identify the example from organized information Analyze the example Extract the profitable data and store in the database. Information Retrieval The most well known information retrieval (IR) systems are Google search engines which recognize those documents on the World Wide Web that are associated to a set of given words. It is measured as an extension to document retrieval where the documents that are returned are processed to extract the useful information crucial for the user [3]. Thus document retrieval is followed by a text summarization stage that focuses on the query posed by the user, or an information extraction stage. IR in the broader sense deals with the whole range of information processing, from information retrieval to knowledge retrieval [8]. It is a relatively old research area where first attempts for automatic indexing where made in 1975. It gained increased attention with the grow of the World Wide Web and the need for classy search engines. Information Extraction The objective of data extraction (IE) techniques is the extraction of helpful data from content. It recognizes the extraction of elements, occasions and connections from semi-organized or unstructured content. Most valuable data, for example, name of the individual, area and association are extricated without legitimate comprehension of the content [4]. IE is worried about extraction of semantic data from the text.IE can be portrayed as the development of an organized picture of chose important piece data drawn from writings. 4. Clustering Grouping is a standout amongst the most fascinating and vital subjects in content mining. Its point is to discover inborn structures in data, and organize them into noteworthy subgroups for additionally study and examination. It is an unsupervised procedure through which objects are ordered into bunches called groups. The issue is to gather the given unlabeled accumulation into significant bunches with no earlier data. Any names related with objects are acquired exclusively from the information. For instance, archive grouping aids recovery by making joins between related records, which thus enables related reports to be recovered once one of the archives has been regarded pertinent to a question [8]. Grouping is helpful in numerous application regions, for example, science, information mining, design acknowledgment, record recovery, picture division, design order, security, business insight and Web seek. Bunch examination can be utilized as an independent content mining device to accomplish information conveyance, or as a pre-preparing venture for other content mining calculations working on the identified groups. Internet Security The utilization of content mining device in security field has turned into a critical issue. A considerable measure of content mining programming bundles is showcased for security applications, especially observing and examination of online plain content sources, for example, Internet news, sites, mail and so on for security purposes 7. It is additionally associated with the investigation of content encryption/unscrambling. Government offices are putting significant assets in the reconnaissance of a wide range of correspondence, for example, email, online talks. Email is utilized as a part of numerous true blue exercises, for example, messages and reports trade.6. ConclusionContent mining for the most part alludes to the way toward separating profitable data from unstructured content. In this overview of content mining, a few content mining strategies and its applications in different fields have been talked about. A correlation of vary ent content mining has been indicated which can be additionally upgraded. Content mining calculations will give us valuable and organized information which can decreases time and cost. Shrouded data in interpersonal organization locales, bioinformatics and web security and so on are distinguished utilizing content mining is a noteworthy test in these fields. The progression of web innovations has lead toa colossal enthusiasm for the order of content records containing joins or other data.7. References R. Agrawal and R. Srikant. Rapid calculations for mining affiliation ideas. In proceedings of the twentieth global convention on Very tremendous Databases (VLDB-94), pages 487– 499, Santiago, Chile, Sept. 1994. R. Baeza-Yates and B. Ribeiro-Neto. Current information Retrieval. ACM Press, the big apple,1999. S. Basu, R. J. Mooney, ok. V. Pasupuleti, and J. Ghosh. Assessing the oddity of content mined ideas utilising lexical expertise. In court cases of the Seventh ACM SIGKDD worldwide assembly on advantage Discovery and data Mining (KDD-2001), pages 233– 239, San Francisco, CA, 2001. M. W. Berry, editorial supervisor. Approaches of the 0.33 SIAM global conference on knowledge Mining(SDM-2003) Workshop on text Mining, San Francisco, CA, may 2003. M. E. Califf, editorial manager. Papers from the Sixteenth countrywide conference on synthetic Intelligence (AAAI-99) Workshop on laptop learning for knowledge Extraction, Orlando, FL, 1999. AAAI Press. M. E. Califf and R. J. Mooney. Social studying of illustration coordinate standards for knowledge How to cite New Mind in Data Mining, Papers

Significant Benefits Organizations Business-Myassignmenthelp.Com

Question: Discuss About The Significant Benefits Organizations Business? Answer: Introductions The case study discusses the threats and the security issues associated with the process of data mining and use of big data. Big data refers to the storage of large amount of Data that can be mined for significant benefits in organizations and business (Wu et al., 2014). These data can be a very useful for business related decision marketing and therefore holds utmost importance. Thus, there are certain risks associated with the mining of big data. The case study discusses the different threats associated with ENISA and the various processes that can be implemented in elimination of the threats. The different threats agents and the threat mitigation processes are discussed in the following paragraphs (Inukollu, Arsi Ravuri, 2014)- Overview of the case study and illustration of the big data security Infrastructure The case study focuses on the big data landscape and the ENISA big data threat. The content of the case study puts a light to the different use of big data and the associated risk with the process of data mining (Wright De Hert, 2012). The key threat agents associated with the data breaches due the use of big data is clearly depicted in the case study. It further elaborates the impact of the big data, which is huge in thriving of the data driven economy. The use of big data is wide in fields such as military applications, fighting terrorism and research work (Gonzalez et al., 2012). Therefore, protecting the business process of data mining and ensuring a secure data mining is essential. The case study discusses the process of ETL ( ENISA threat landscape) and suggests different risk management and mitigation processes for mitigating the risks associated with the big data landscape ( ENISA 2017). The case study aims at giving providing a clear picture of the risks and threats associ ated with the big data landscape. The risk is widespread due to the involvement of cloud storage in order to store the big data. The threat infrastructure diagram of ENISA is illustrated below- Top and most significant threats The storage and the access of huge amount of data is subjected to different types of risks. The top threats associated with ENISA are elaborated below ( ENISA, 2017)- 1) Malicious code/ software activity: One of the top threats associated with the big data is the use of malicious code and software in order to extract information unethically. These are doe by infusing different threat agents into the system, which includes, viruses, Trojan horses, trapdoors, backdoors, ransomware and so on. These threats are infused with the system with the help of certain malicious codes and software. The attacker installs these programs into the system and gains access to the entire system by running these codes (Theoharidou, Tsalis Gritzalis, 2013). The risk from these malicious activity is high since the malware can easily spread to different systems. The assets that are mainly targeted by this threat include database and computing infrastructure model. 2) Data leak due to unsecure API: Big data is based on cloud storage as it helps in easy access of the data. however, cloud storage is a very unsecure platform and the use of unsecure API further leads to significant data breaches and data loss. Different types of injection attacks can be launched making use of unsecure API and therefore this can be considered as a significant threat agent. The assets that are targeted by this threat includes software and computing models of the information system. 3) Denial of service attack: Denial of service attack freezes the system thus making the resources unavailable for the legitimate users. A severe denial of service attack may lead to the permanent unavailability of the resources. These attacks can however be controlled by implying effective measures (Tan et al., 2014). The assets that are mainly targeted by this attack includes networks and servers of the system. 4) Rogue Certificate usage: This is other threat agent associated with the illegal usage of data and data breach. Rogue or false certificate can be used unethically to gain access to the systems the attackers are unauthorized to access (Pearson, 2013). This may result in severe data loss, data leakage and data modification and misuse of data The assets that are mainly targeted by this threat include hardware , software and its associated data. 5) Improper designing of the security systems: This is another major reason of the data security issues associated with the big data. Improper designing of security system or using an out of date security may lead to severe data loss. An inadequate system may further lead to improper data update thus giving rise to data redundancy (Theoharidou et al., 2013). The assets that are targeted by this threat include data and applications. 6) Identity fraud: Accessing the data by unauthorized person, by impersonating someone one else can be termed as identity theft. This is a significant threat as it might result in loss of confidential data and information (Roberts, Indermaur Spiranovic, 2013). The assets that are mainly targeted by the threat identity fraud includes personal identifiable information , back end services and the servers associated with the system. Most significant threat identified Out of the top threats identified in the previous section of the report, the top threat is definitely the threat associated with the usage or infusion of malicious codes and programs into the system. This is because, with the help of this threat, the attacker may easily gain access to the system and manipulate the data stored in them (Chen Zhao, 2012). This risk associated with this type of threat is very high and therefore, this threat agent is the most significant threat agent in the big data landscape (Pavlyushchik, 2014). Threat Agents, Their Impact and threat probability The top threat agents associated with the big data landscape, identified from the case study are elaborated below- 1) Corporation: The corporation or the organization that uses big data for its business benefits is a major threat agent associated with the security concerns related to the big data. This is because it is easier for them to manipulate and misuse these data fro their business benefits and gain competitive advantage in the market. 2)Cyber criminals: This is one of the most significant threat agents associated with the data breach and data loss. The main objective of cyber criminals is financial benefit by making the use of the mined data and therefore the impact of the attack by these threat agents is very high. 3) Cyber Terrorist: Cyber terrorists are more dangerous than cyber criminals as the methods used in the launching the attacks are more sophisticated in case of cyber terrorists. These threat agents mainly target large organizations, as impact over these organization effects a large part of the society as well (Taylor, Fritsch Liederbach, 2014). 4) Employees: One of the major threat agents is employees of the organization. They can be termed as threat insiders as well. Employee posses a sound knowledge of the data and security system of a particular organization and therefore manipulation of data by the threat insider is easier and sometimes unrecognizable. 5) Nation States: Nation States is one of the most significant threat agent associated with the security issues of big data landscape. Nation states are the most sophisticated cyber criminals and have high-level skill and expertise. 6) Script Kiddies: This threat agents uses ready-made code and programs in order to launch an attack. Therefore, this type of attack and the threat agent is less dangerous and can be eliminated by implementing proper security measures. Minimizing the Impact of the Threat In order to minimize the threats associated with the big data, the recommended measures that can be taken are elaborated below- 1) Using an effective security system coupled with the cryptographic methods of encryption limits the use and access of the data thus preventing the data loss and data breaches (Stallings Tahiliani, 2014). 2) Access control can be implied to limit the access of data only to authorized person. These may reduce the data breach considerably (Brucker et al., 2012). Access control only enables a registered person to access and data. 3) Training the staffs and employees in order to build awareness among them can be an effective method of preventing any sorts of threats by threat insider. Probability Trend of the Threats The probability of the threat is high as the attacker is coming up with different methods of implementing an attack. In order to prevent these attacks, proper security measures are needed to be taken. The associated threats are increasing in number and therefore, it becomes essential to eliminate it as soon as possible. ETL process Improvement ETL or the ENISA Threat landscape investigates and reports about the threats associated with different organizations. The document or report by ETL mainly deals with the threats associated with the information and communication technology assets (ENISA, 2017). The major loophole in the process of ETL is that, I only focuses on the technology issue and not the issues cause by the threat agents. The process of ETL can be improved by including a detailed and a structured report of the all types of threats associated with the big data and their possible effects. ENISA threat landscape or ETL provides a structure and the overview of the threats associated with the merging trends. It is mainly based on the publicly available data and reports the identified threats, and threat agents with the threats prioritized according to the frequency of appearance. Now this process can be improved by prioritizing the threat according to the impact caused and not by the frequency of appearance as minor threats such as denial of service attack can appear a several number of times but can cause less damage than some other threats whose frequency of appearance is less (Cherdantseva et al,. 2016). Current State of IT security in ENISA ENISA is not satisfied with the current security state of the organization as the organization is still exposed to the several cyber threats. The security essentials are needed to be updated in order to ensure the security of the big data. The threat agents and the attackers are growing stronger day by day and therefore updating the security systems becomes essential (Von Solms Van Niekerk, 2013). A stronger security system and proper supervision of the system is essential. The report identifies and discusses the risk associated with the information system of ENSA, which proves that there are certain loopholes associated with the structure of the security essentials in the organization. This is a major reason of ENISA being unsatisfied with the current security state. Different security measures can be undertaken by ENISA in order to remove the risks associated with the security system, which includes, using a proper intrusion detection system in order to prevent the data loss and d ata manipulation. Furthermore, the process of ETL can be improved by prioritizing the risks according to their impact in order to detect and eliminate several threats associated with the system (Albakri et al., 2014). Conclusion Therefore, from the above discussion, it can be concluded that ENISA is exposed to number of threats and the report discusses the different threat agents responsible for data breaches, data loss and data manipulation. The report suggests the different procedures by which the risk associated with the process of mining the big data can be eliminated. The ENISA Threat landscape deals with identifying and reporting different threats associated with various organizations. Use of big data is very significant in todays world and therefore, ensuring various security measures for the same is essential as well. The report concludes with the current state of IT security in ENISA and recommends few ways to address the issues. References Albakri, S. H., Shanmugam, B., Samy, G. N., Idris, N. B., Ahmed, A. (2014). Security risk assessment framework for cloud computing environments. Security and Communication Networks, 7(11), 2114-2124. Big Data Threat Landscape ENISA. (2017). Accounting. Retrieved 6 September 2017, from https://www.enisa.europa.eu/publications/bigdata-threat-landscape Brucker, A. D., Hang, I., Lckemeyer, G., Ruparel, R. (2012, June). SecureBPMN: Modeling and enforcing access control requirements in business processes. In Proceedings of the 17th ACM symposium on Access Control Models and Technologies (pp. 123-126). ACM. Chen, D., Zhao, H. (2012, March). Data security and privacy protection issues in cloud computing. In Computer Science and Electronics Engineering (ICCSEE), 2012 International Conference on (Vol. 1, pp. 647-651). IEEE. Cherdantseva, Y., Burnap, P., Blyth, A., Eden, P., Jones, K., Soulsby, H., Stoddart, K. (2016). A review of cyber security risk assessment methods for SCADA systems. computers security, 56, 1-27. Gonzalez, N., Miers, C., Redigolo, F., Simplicio, M., Carvalho, T., Nslund, M., Pourzandi, M. (2012). A quantitative analysis of current security concerns and solutions for cloud computing. Journal of Cloud Computing: Advances, Systems and Applications, 1(1), 11. Inukollu, V. N., Arsi, S., Ravuri, S. R. (2014). Security issues associated with big data in cloud computing. International Journal of Network Security Its Applications, 6(3), 45. Pavlyushchik, M. A. (2014). U.S. Patent No. 8,713,631. Washington, DC: U.S. Patent and Trademark Office. Pearson, S. (2013). Privacy, security and trust in cloud computing. In Privacy and Security for Cloud Computing (pp. 3-42). Springer London. Roberts, L. D., Indermaur, D., Spiranovic, C. (2013). Fear of cyber-identity theft and related fraudulent activity. Psychiatry, Psychology and Law, 20(3), 315-328. Seshardi, V., Ramzan, Z., Satish, S., Kalle, C. (2012). U.S. Patent No. 8,266,698. Washington, DC: U.S. Patent and Trademark Office. Stallings, W., Tahiliani, M. P. (2014). Cryptography and network security: principles and practice (Vol. 6). London: Pearson. Tan, Z., Jamdagni, A., He, X., Nanda, P., Liu, R. P. (2014). A system for denial-of-service attack detection based on multivariate correlation analysis. IEEE transactions on parallel and distributed systems, 25(2), 447-456. Taylor, R. W., Fritsch, E. J., Liederbach, J. (2014). Digital crime and digital terrorism. Prentice Hall Press. Theoharidou, M., Tsalis, N., Gritzalis, D. (2013, June). In cloud we trust: Risk-Assessment-as-a-Service. In IFIP International Conference on Trust Management (pp. 100-110). Springer, Berlin, Heidelberg. Von Solms, R., Van Niekerk, J. (2013). From information security operations. computers security, 38, 97-102. Wright, D., De Hert, P. (2012). Introduction to privacy impact assessment. In Privacy Impact Assessment (pp. 3-32). Springer Netherlands. Wu, X., Zhu, X., Wu, G. Q., Ding, W. (2014). Data mining with big data. IEEE transactions on knowledge and data engineering, 26(1), 97-107.