The Crucible Essay Research Paper The CrucibleThe free essay sample

The Crucible Essay, Research Paper The Crucible The Crucible is a drama written by Arthur Miller. It is about a clip of mundane people who use lying as a arm while stating they are pure and spiritual. This narrative is a premier illustration of how people will make anything to acquire what they want, even if it means aching other people. This narrative is about a smattering of misss who use other people to salvage themselves. These misss were caught dancing around a fire and what seemed to be a charming brew. Although these misss were making nil wrong they felt to be on the safe side to state that other people made them dance and subscribe the book of the Satan. Although all the people in this drama is acquiring betrayed or bewraying other people. One of the chief people in this narrative was Abigail Williams. She was average and would make anything to acquire what she wanted. She was in love with an older married adult male and she was covetous of the married woman. Abigail wanted nil more in her unrecorded so to happen a manner to acquire rid of his married woman so she can take her topographic point. In the procedure of non acquiring in problem for dancing in the forests and seeking to acquire the love of her live, she convinced the other misss of aching alot if guiltless people. She got all kinds of people thrown in prison such as Rebecca Nurse and Mary Warren. She got all these people in problem by stating she saw them with the Satan and accused them of enchantress trade. John Proctor started to be a nice cat, but subsequently in the book when his married woman was accused of witchery he seemed to hold changed. He ended up being average and started to mistreat his power to the family amah named Mary Warren who was one of the childs stating prevarications. He told her that if she did non get down stating the truth about her and the other misss so he would kill her. By stating this he showed that he doesn? T attention for anyone except him and his married woman. Even though he knew from the beginning that the misss were lying he allow other guiltless people die. When Mary Warren went to the justice to state the truth she knew either manner that she was ruined. She knew that if she told the truth that the other misss would turn on her and impeach her, but if she didn? T so she knew the John Proctor would make what he said and kill her. She went to the tribunal and merely as she thought the misss said they saw her as a xanthous bird. The lone thing she could believe of to take back what she said and acquire the misss to halt was to state that John Proctor was into witchery and he put a enchantment on her. The misss eventually stopped with their prevarications when they went to far, they started to fault the city managers married woman. After that the justice eventually relized that all of the misss have told nil but prevarications and guiltless people died for nil. That was the terminal of the Salem enchantress tests. This narrative was a good illustration of how life was back so, and how everyone thought they could hold their manner. The Salem enchantress tests killed 100s of guiltless people who died by nil more than prevarications.

Special Interests Groups and Political Participation

Special Interests Groups and Political Participation Special InterestAs children growing up we learn to fight hunger from our parents and schools. The schools would announce a hunger drive, typically around the holidays and parents would give their children a couple of cans of corn to bring to school and for most of us, that was the extent of our contribution to the fight against hunger.We need to do more. The fight against hunger should not stop within the walls of our schools. It begins with each individual person, one can at a time and with the help of communities around the nation we can achieve the fight against hunger.Approximately 16.2% of children in the U.S. live in poverty (U.S. Census Bureau, Poverty in the United States: 2000, Current Population Reports, September 2001). The U.S. child poverty rate is higher than that of most other industrialized nations.In 2000, slightly more than half of all food stamp recipients were children.The Connecticut State Capitol, in downtown Hartfor...About 68% of these children were school age . Most of the food stamp households with children were headed by single adults, with half of these households receiving cash assistance in addition to food stamp benefits. (United State Department of Agriculture Food and Nutrition Service, Characteristics of Food Stamp Households: Fiscal Year 2000, October 2001).The target demographic area of this report is in the state of Connecticut. According to the U.S. Census Bureau the Percent of Persons in Poverty by in the state of Connecticut in 2000, 2001, and 2002 is a follows:2000 - 2001 (2-year average) 7.5%2001 - 2002 (2-year average) 7.8%2000 - 2002 (3-year average) 7.8%Arkansas was listed as the highest at 18%, the lowest is New Hampshire at 5.6%.Democratic ProcessThe Connecticut Public Interest Research Group (ConnPIRG) is a special interest group that...

Internet Essay Example | Topics and Well Written Essays - 500 words - 1

Internet - Essay Example D by the name of Gloria Kindell. The author has divided the article in three different sections mainly with the names "what's the fuss", what is an endangered language", and "so, what should we do" These three parts can be scientifically termed as the problem, the matter and the recommendations. In the first part the author gives a brief overlook of the problem that is the extinction of language due to globalization. As people move towards languages that are more widely spoken dumping their own language. In the second part the writer explains the symptoms of languages that are endangered to extinction. And in the third part the author explains how does SIL International helps in preserving the languages that are endangered. The website also has three more sections that enable a user to browse through links to other resources plus a FAQ's section that helps a user in getting answers to the same questions that have been previously asked. The website content is very interesting but is o f a level for a user that is completely unaware of the language extinction problem which is a major threat and only provides a brief over view of the problem. The website has been designed using the simple Java Language. The website also gives the contact information of SIL their address, telephone number, and fax number.

New World Order Essay Example | Topics and Well Written Essays - 750 words

New World Order - Essay Example The continuous struggle for the natural resources, and going about to different places in the name of intervention for democracy and establishing the people’s elected government all amass to the real motives and agendas that are set forth in the New World Order. Apart from the ongoing elements and incidents, there are areas that have been subjected to political motives and interests which speak of the entire conspiracy against the free mankind.The usage of satellites networks and the functions fulfilled by the surveillance systems in the form of the Drone and other notable actions leave the ordinary citizens void of their basic rights and privacy that is promised to them under the charter of human rights. The presence and establishment of governments that are pro imperialistic superpowers is another motive and move that is in place.The subsequent output of these actions come in the form of the starvation, hunger, inequality, lack of true democracy, religious values distortion, religion being used as a subject of extremism and exploitation. The natural resources have become a trouble for a number of states on account of the interests that are shown by the imperialistic superpowers.If the trends keep going this way, the world may well see further deterioration and direct damages being suffered by the different people in the different parts of the world.Under the New World Order, the media has been used as a tool for the propagation of agendas and conspiracy has been done so through this process.

Multiculturalism in America and Its Impact on the American Identity Essay - 2

Multiculturalism in America and Its Impact on the American Identity - Essay Example It is evidently clear from the discussion that America is a multicultural society, where different cultures coexist alongside each other. Multiculturalism started as a movement at the end of the 19th century in the United States and Europe. The mass immigration of southern and eastern Europeans and Latin Americans were the driving forces behind it. The genesis of multiculturalism was the concept of cultural pluralism. The different features of different cultures often combine and incorporate. In this way, a cultural blend is formed which creates an environment of tolerance and respect for each other. In a multicultural society, individuals have the freedom to practice their own religion, follow their own dressing code, to eat what they want and participate in cultural practices despite its variance from the mainstream cultural norms. Since the first half of the 19th century, United States has witnessed a constant mass immigration. These immigrants have played a pivotal role in shapin g the cultural landscape of America. The immigrants having their own values, beliefs and attitudes, created their own perception of the adopted home. The passing of these beliefs and attitudes to their children, made them experience not only the cultural practices of their parents but also those followed by the wider society. The effect that it creates, is a more tolerant and open-minded society. In a globalized world, ideas such as isolation and discrimination are not acknowledged anymore. Multiculturalism fosters the idea of inclusiveness, where the society is vibrant and open to change. The different individuals bring their own experiences, tastes, and flavors thus making the society diverse and rich. Cultural tends to conflict create conflict, which creates divisions within a nation. Multiculturalism, on the other hand, strengthens the nation due to its cohesive nature.

Constructing Social Knowledge Graph from Twitter Data

Constructing Social Knowledge Graph from Twitter Data   Yue Han Loke 1.1 Introduction The current era of technology allows its users to post and share their thoughts, images, and content via networks through different forms of applications and websites such as Twitter, Facebook and Instagram. With the emerging of social media in our daily lives and it is becoming a norm for the current generation to share data, researchers are starting to perform studies on the data that could be collected from social media [1] [2].The context of this research will be solely dedicated to Twitter data due to its publicly available wealth of data and its public Stream API. Twitters tweets can be used to discover new knowledge, such as recommendations, and relationships for data analysis. Tweets in general are short microblogs consisting of maximum 140 characters that can consists of normal sentences to hashtags and tags with @, other short abbreviation of words (gtg, 2night), and different form of a word (yup, nope). Observing how tweets are posted shows the noisy and short lexical natu re of these texts. This presents a challenge to the flexibility of Twitter data analysis. On the other hand, the availability of existing research conducted on entity extraction and entity linking has decreased the gap between entities extracted and the relationships that could be discovered. Since 2014, the introduction of the Named Entity rEcognition and Linking (NEEL) Challenge [3] has proved the significance of automated entity extraction, entity linking and classification appearing in different event streams of English tweets in the research and commercial communities to design and develop systems that could solve the challenging nature in tweets and to mine semantics from them. 1.2 Project Aim The focus of this research aims to construct a social knowledge graph (Knowledge Base) from Twitter data. A knowledge graph is a technique to analyse social media networks using the method of mapping and measurement for both relationships and information flows among group, organizations, and other connected entities in social networks [4]. A few tasks are required to successfully create a knowledge graph based on Twitter data A method to aid in the construction of knowledge graph is by extracting named entitiessuch as persons, organizations, locations, or brands from the tweets [5]. In the domain of this research, the named entity to be referenced in the tweet is defined as a proper noun or acronym if it is found in the NEEL Taxonomy in the Appendix A of [3], and is linked to an English DBpedia [6] referent and a NIL referent. The second component in creating a social knowledge graph is to utilize those extracted entities and link them to their respective entities in a knowledge base. For example, Tweet: The ITEE department is organizing a pizza gettogether at UQ. #awesome ITEE refers to an organization and UQ refers to an organization as well. The annotation for this is [ITEE, organization, NIL1], where NIL1 refers to the unique NIL referent describing the real-world entity ITEE that does not have the equivalent entry in DBpedia and [UQ, Organization, dbp:University_of_Queensland] which represents the RDF triple (subject, predicate, object). 1.3 Project Goals Firstly, getting the Twitter tweets. This can be achieved by crawling Twitter data using Public Stream API[1] available in the Twitter developer website. The Public Stream API allows extraction of Twitter data in real time. Next, entity extraction and typing with the aid of a specifically chosen information extraction pipeline called TwitIE[2] open-source and specific to social media and has been tested most extensively on microblog sentences. This pipeline receives the tweets as input and recognises the entities in the same tweet. The third task is to link those entities mined from tweets to the entities in the available knowledge base. The knowledge base that has been selected for the context of this project is DBpedia. If there is a referent in DBpedia, the entity extracted will be linked to that referent. Thus, the entity type is retrieved based on the category received from the knowledge base. In the event of the unavailability of a referent, a NIL identifier is given as shown in section 1.2. The selection of an entity linking system with the appropriate entity disambiguation and candidate entity generation that receives the extracted entities from the same Tweet and produce a list with all the candidate entities in the knowledge base. The task is to accurately link the correct entity extracted to one of the candidates. The social knowledge graph is an entity-entity graph combining two extracted sources of entities. The first is the analysis of the co-occurrence of those entities in same tweet or same sentence. Besides that, the existing relationships or categories extracted from DBpedia. Thus, the project aims to combine the extraction of co-occurrence of extracted entities and the extracted relationships to create a social knowledge graph to unlock new knowledge from the fusion of the two data sources. Named Entity Recognition (NER), Information Extraction (IE) are generally well researched in the domain of longer text such as newswire. However, overall, microblogs are possibly the hardest kind of content to process. For Twitter, some methods have been proposed by the research community such as [7] that uses a pipeline approach to perform the first tokenisation and POS tagging and topic models were used to find named entities. [8] propose a gradient-descent graph-based method for doing joint text normalisation and recognition, reaching 83.6% F1 measure. Besides that, entity linking in knowledge graphs have been studied in [9] using graph-based method by collectively gather the referent entities of all named entities in the same document and by modelling and exploiting the global interdependence between Entity Linking decisions. However, the combination of NER, and Entity Linking in Twitter tweets is still a new area of research since the NEEL challenge was first established in 2013 . Based on the evaluation conducted in [10] on the NEEL challenge, lexical similarity mention detection strategy that exploit the popularity of the entities and apply a distance similarity functions to rank entities efficiently, and n-gram [11] features are used. Besides that, Conditional Random Forest (CRF) [12] is another mentioned entity extraction strategy. In the entity detection context, graph distances and various ranking features were used. 2.1. Twitter crawling [13] defined the public Twitter Streaming API provides the ability of collecting a sample of user tweets. Using the statuses/filter API provides a constant stream of public Tweets. Multiple optional parameters may be specified such as language and locations. Applying the method CreateStreamingConnection,a POST request to the API has the capability of returning the public statuses as a stream. The rate limit of the Streaming API allows each application to submit up to 5,000 Twitter. [13] Based on the documentation, Twitter currently allows the public to retrieve at most a 1% sample of their data posted on Twitter at a specific time. Twitter will begin to return the sample data to the user when the number of tweets reaches 1% of all tweets on Twitter. According to [14] research comparing Twitter Streaming API and Twitter Firehouse, the final results of the Streaming API depends strongly on the coverage and the type of analysis that the researcher wishes to perform. For example, the researchers found that if given a set of parameters and the number of tweets matching them increases, the coverage of the Streaming API is reduced. Thus, if the research is concerning a filtered content, the Twitter Firehose would be a better choice with regards to its drawback of restrictive cost. However, since our project requires random sampling of Twitter data without filters except for English language, Twitter Streaming API would be an appropriate choice since it is freely available. 2.2. Entity Extraction [15] suggested an open-source pipeline, called TwitIE which is solely dedicated for social media components in GATE [16]. TwitIE consists for 7 parts: tweet import, language identification, tokenisation, gazetteer, sentence splitter, normalisation, part-of-speech tagging, and named entity recogniser. Twitter data is delivered from the Twitter Streaming API in JSON format. TwitIE included a new Format_Twitter plugin in the most recent GATE codebase which converts the tweets in JSON format automatically into GATE documents. This converter is automatically associated with documents names that end in .json, if not text/x-json-twitter should be specified. The TwitIE system uses TextCat a language processing and identification algorithm for its language identification. It has the capability to provide reliable tweet language identification for tweets written in English using the English POS tagger and named entity recogniser. Tokenisation oversees different characters, class sequence and rules. Since the TwitIE system is dealing with microblogs, it treats abbreviations and URLs as one token each by following the Ritters tokenisation scheme. Hashtags and user mentions are considered as two tokens and is covered by a separate annotation hashtags. Normalisation in TwitIE system is divided into two task: the identification of orthographic errors and correction of the errors found. The TwitIE Normaliser is designed specific to social media. TwitIE reuses the ANNIE gazetteer lists which contain lists such as cities, organisations, days of the week, etc. TwiTie uses the adapted version of the Stanford Part-of speech tagger which is tweets tagged with Penn TreeBank(PTB) tagset trained. The results of using the combination of normalisation, gazetteer name lookup, and POS tagger, the performance was increased to 86.93%. It was further increased to 90.54% token accuracy when the PTB tagset was used. Named entity recognition in TwitIE has a +30% absolute precision and +20% abso lute performance increase as compare to ANNIE, mainly respect to date, Organizations and Person. [7] proposed an innovative approach to distant supervision using topic models that pulls large amount of entities gathered from Freebase, and large amount of unlabelled data. Using those entities gathered, the approach combines information about an entitys context across its mentions. T-NER POS Tagging system called T-POS has added new tags for Twitter specific phenomenal retweets such as usernames, urls and hashtags. The system uses clustering to group together distributionally similar words for lexical variations and OOV words. T-POS utilizes the Brown Clusters and Conditional Random Fields. The combination of both features results in the ability to model strong dependencies between adjacent POS tags and make use of highly correlated features. The results of the T-POS are shown on a 4-fold cross validation over 800 tweets. It is proved that T-POS outperforms the Standford tagger, obtaining a 26% reduction in error. Besides that, when trained on 102K tokens, there is an error reduct ion of 41%. The system includes shallow parsing which can identify non-recursive phrases such as noun, verb and prepositional phrases in text. T-NERs shallow parsing component called T-CHUNK, obtained a better performance at shallow parsing of tweets as compared against the off the shelf OpenNLP chunker. As reported, a 22% reduction in error. Another component of the T-NER is the capitalization classifier, T-CAP, which analyse a tweet to predict capitalization. Named entity recognition in T-NER is divided into two components: Named Entity Segmentation using T-SEG, and classifying named entities by applying LabeledLDA. T-SEG uses IOB encoding on sequence-labelling task to represent segmentations. Furthermore, Conditional Random Fields is used for learning and inference. Contextual, dictionary and orthographic features: a set of type lists is included in the in-house dictionaries gathered from Freebase. Additionally, outputs of T-POS, T-CHUNK and T-CAP, and the Brown clusters are used to generate features. The outcome of the T-SEG as stated in the research paper, Compared with the state-of-the-art news-trained Stanford Named Entity Recognizer. T-SEG obtains a 52% increase in F1 score. To address the issues of lack of context in tweets to identify the types of entities they contain and excessive distinctive named entity types present in tweets, the research paper presented and assessed a distantly supervised approach based on LabeledLD. This approach utilizes modelling of every entity as a combination of types. This allows information about an entitys distribution over types to be shared across mentions, naturally handling ambiguous entity strings whose mentions could refer to different types. Based on the empirical experiments conducted, there is a 25% increase in F1 score over the co-training approach to Named Entity Classification suggested by Collins and Singer (1999) when applie d to Twitter. [17] proposed a Twitter adapted version of Kanopy called Kanopy4Tweets that uses the approach of interlinking text documents with a knowledge base by using the relations between concepts and their neighbouring graph structure. The system consists of four parts: Name Entity Recogniser (NER), Named Entity Linking (NEL), Named Entity Disambiguation(NED) and Nil Resources Clustering(NRC). The NER of Kanopy4Tweets uses a TwitIE a Twitter information extraction pipeline mentioned above. For the Named Entity Linking. For NEL, a DBpedia index is build using a selection of datasets to search for suitable DBpedia resource candidates for each extracted entity. The datasets are store in a single binary file using HDT RDF format. This format has compact structures due to its binary representation of RDF data. It allows for faster search functionality without the need of decompression. The datasets can be quickly browse and scan through for a specific object, subject or predicate at glance. For e ach named entity found by NER component, a list of resource candidates retrieved from DBpedia can be obtain using the top-down strategy. One of the challenges found is the large volume of found resource candidates impacts negatively on the processing time for disambiguation process. However, this problem can be resolved by reducing the number of candidates using a ranking method. The proposed ranking method ranks the candidates according to the document score assigned by the indexing engine and selects the top-x elements. The NED takes an input of a list of named entities which are candidate DBpedia resources after the previous NEL process. The best candidate resource for each named entity is selected as output. A relatedness score is calculated based on the number of paths between the resources weighted by the exclusivity of the edges of these paths which is applied to candidates with respect to the candidate resources of all other entities. The input named entities are jointly dis ambiguated and linked to the candidate resources with the highest combined relatedness. NRC is a stage whereby if there are no resource in the knowledge base that can be linked to a named entity extracted. Using the Monge-Elkan similarity measure, the first NIL element is assign into a new cluster, then the next element is used to differentiate from the previous ones. An element is added to a cluster when the similarity between an element and the present clusters is above a fixed threshold, the element is added to that particular cluster, whereas a new cluster is formed if there are no current cluster with a similarity above the threshold is found. 2.3. Entity Extraction and Entity Linking [18]proposed a lexicon-based joint Entity Extraction and Entity Linking approach, where n-grams from tweets are mapped to DBpedia entities. A pre-processing stage cleans and classifies the part-of-speech tags, and normalises the initial tweets converting alphabetic, numeric, and symbolic Unicode characters to ASCII equivalents. Tokenisation is performed on non-characters except special characters joining compound words. The resulting list of tokens is fed into a shingle filter to construct token n-grams from the token stream. In the candidate mapping component, a gazetteer is used to map each token that is compiled from DBpedia redirect labels, disambiguation labels and entities labels that is linked to their own DBpedia entities. All labels are lowercase indexed and linked by exact matches only to the list of candidate entities in the form of tokens. The researcher used a method of prioritizing longer tokens than shorter ones to remove possible overlaps of tokens. For each entity ca ndidate, it considers both local and context-related features via a pipeline of analysis scorers. Examples of local features included are string distance between the candidate labels and the n-gram, the origin of the label, its DBpedia type, the candidates link graph popularity, the level of uncertainty of the token, and the surface form that matches best. On the other hand, the relation between a candidate entity and other candidates with a given context is accessed by the context-related features. Examples of mentioned context-related features are direct links to other context candidates in the DBpedia link graph, co-occurrence of other tokens surface forms in the corresponding Wikipedia article of the candidate under consideration, co-references in Wikipedia article, and further graph based feature of the link graph induced by all candidates of the context graph which includes graph distance measurements, connected component analysis, or centrality and density observations. Besid es that, the candidates are sorted per their confidence score based on how an entity describes a mention. If the confidence score is lower than the threshold chosen, a NIL referent is annotated. [19] proposed a lexical based and n-grams features to look up resources in DBpedia. The role of the entity type was assigned by a Conditional Random Forest (CRF) classifier, that is specifically trained using DBpedia related feature (local features), word embedding (contextual features), temporal popularity knowledge of an entity extracted from Wikipedia page view data, string similarity measures to measure the similarity between the title of the entity and the mention (string distance), and linguistic features, with additional pruning stage to increase the precision of Entity Linking. The whole process of the system is split into five stages: pre-processing, mention candidate generation, mention detection and disambiguation (candidate selection), NIL detection and entity mention typing prediction. In the pre-processing stage, tweet tokenisation and part-of-speech tags were used based on ARK Twitter Part-of-Speech Tagger, together with the tweet timestamps extracted from tweet ID. Th e researchers used an in-house mention-entity dictionary of acronyms. This dictionary computes the n-grams (n [20] research paper proposed an entity linking technique to link named entity mentions appearing in Web text with their corresponding entities in a knowledge base. The solution mentioned is by employing a knowledge base. Due to the vast knowledge shared among communities and the development of information extraction techniques, the existence of automated large scale knowledge bases has been ensured. Thus, this rich information about the worlds entities, their relationships, and their semantic classes which are all possibly populated into a knowledge base, the method of relation extraction techniques is vital to obtain those web data that promotes discovery of useful relationships between entities extracted from text and their extracted relation. Once possible way is to map those entities extracted and associated them to a knowledge base before it could be populated into a knowledge base. The goal of entity linking is to map ever textual entity mention m à ¢Ã‹â€ Ã‹â€  M to its corres ponding entry e à ¢Ã‹â€ Ã‹â€  E in the knowledge base. In some cases, when the entity mentioned in text does not have its corresponding entity record in the given knowledge base, a NIL referent is given to indicate a special label of un-linkable. It is mentioned in the paper that named entity recognition and entity linking o be jointly perform for both processes to strengthen one another. A method proposed in this paper is candidate entity generation. The objective of the entity linking system is to filter out irrelevant entities in the knowledge base that for each entity extracted. A list of candidates which might be the possible entities that the extracted entity is referring to is retrieved. The paper suggested three techniques to handle this goal such as name based dictionary techniques entity pages, redirect pages, disambiguation pages, bold phrases from the first paragraphs, and hyperlinks in Wikipedia articles. Another method proposed is the surface form expansion from the local document that consists of heuristics based methods and supervised learning methods, and methods based on search engine. In the context of candidate entity ranking method, five categories of methods are advised. The supervised ranking methods, unsupervised ranking methods, independent ranking methods, collective ranking methods and collaborative ranking methods. Lastly, the research paper mentioned ways to evaluate entity linking systems using precision, recall, F1-measure and accuracy. Despite all these methods used in the three main approaches is proposed to handle entity linking system, the paper clarified that it is still unclear which are the best techniques and systems. This is since different entity linking system react or perform differently according to datasets and domains. [21] proposed a new versatile algorithm based on multiple addictive regression trees called S-MART (Structured Multiple Additive Regression Trees) which emphasized on non-linear tree-based models and structured learning. The framework is a generalized Multiple Addictive Regression Trees (MART) but is adapted for structured learning. This proposed algorithm was tested on entity linking primarily focused on tweet entity linking. The evaluation of the algorithm is based on both IE and IR situations. It is shown that non-linear performs better than linear during IE. However, for the IR setting, the results are similar except for LambdaRank, a neural network based model. The adoption of polynomial kernel further improves the performance of entity linking by non-LINEAR SSVM. The paper proved that entity linking of tweets perform better using tree-based non-linear models rather than the alternative linear and non-linear methods in IE and IR driven evaluations. Based on the experiments condu cted, the S-MART framework outperforms the current up-to-date entity linking systems. 2.4. Entity Linking and Knowledge Base Based on [22], an approach to free text relation extraction was proposed. The system was trained to extract the entities from the text from existing large scale knowledge base in a cooperatively manner. Furthermore, it utilizes the learning of low-dimensional embedding of words, entities and relationships from a knowledge base with regards to score functions. Built upon the norm of employing weakly labelled text mention data but with a modified version which extract triples from the existing knowledge bases. Thus, by generalizing from knowledge base, it can learn the plausibility of new triples (h, r, t); h is the left-hand side entity (or head), the right-hand side entity (or tail) and r the relationship linking them, even though this specific triple does not exist. By using all knowledge base triples rather than training only on (mention, relationship), the precision on relation extraction was proved to be significantly improved. [1] presented a novel system for named entity linking over microblog posts by leveraging the linked nature of DBpedia as knowledge base and using graph centrality scoring as disambiguation methods to overcome polysemy and synonymy problems. The motivation for the authors to create this method is because linked entities tend to appear in the same tweets because tweets are topic specific and together with the assumption since tweets are topic specific, related entities tend to appear in the same tweet. Since the system is tackling noisy tweets acronyms handling and Hashtags in the process of entity linking were integrated. The system was compared with TAGME, a state-of-the-art system for named entity linking designed for short text. The results shown that it outperformed TAGME in Precision, Recall and F1 metrics with 68.3%, 70.8% and 69.5%. [23] presented an automated method to populate a Web-scale probabilistic knowledge base called Knowledge Vault (KV) that uses the combination of extractions from the Web such as text documents (TXT), HTML trees (DOM), Html tables (TBL), and Human Annotated pages (ANO). By using RDF triples (subject, predicate, object) with association to a confidence score that represents the probability that KV believes the triple is correct. In addition, all 4 extractors are merged together to form one system called FUSED-EX by constructing a feature vector for each extracted triple. Next, a binary classifier is applied to compute the formula. The advantages of using this fusion extractor is that it can learn the relative reliabilities of each system as well as creating a model of the reliabilities. The benefits of combining multiple extractors include 7% higher confidence triples and a high AUC score (the higher probability that a classifier will choose a randomly chosen positive instance to be ra nked) of 0.927. To overcome the unreliability of facts extracted from the Web, prior knowledge is used. In the domain of this paper, Freebase is used to fit the existing models. Two ways were proposed in the paper which are Path ranking algorithm with AUC scores of 0.884 and the Neural network model with a AUC score of 0.882. A fusion of both methods stated was conducted to increase performance with an increased AUC score of 0.911. With the evidence of the benefits of fusion quantitatively, the authors of the paper proposed another fusion of the prior methods and the extractors to gain additional performance boost. The result of the fusion is a generation of 271M high confidence facts with 33% new facts that are unavailable in Freebase. [24]proposed TremenRank, a graph based model to tackle the target entity disambiguation challenge, task of identifying target entities of the same domain. The motivation of this system is due to the challenges and unreliability of current methods that relies on knowledge resources, the shortness of the context which a target word occurs, and the large scale of the document collected. To overcome these challenges, first TremenRank was built upon the notion of collectively identity target entities in short texts. This reduces memory storage because the graph is constructed locally and is continuously scale-up linearly as per the number of target entities. This graph was created locally via inverted index technology. There are two types of indexes used: the document-to-word index and the word-to-document index. Next, the collection of documents (the shorts texts) are modelled as a multi-layer directed graph that holds various trust scores via propagation. This trust score provided an in dication of the possibility of a true mention in a short text. A series of experiments was conducted on TremenRank and the model is more superior than the current advanced methods with a difference of 24.8% increase in accuracy and 15.2% increase in F1. [25]introduced a probabilistic fusion system called SIGMAKB that integrates strong, high precision knowledge base and weaker, and nosier knowledge bases into a single monolithic knowledge base. The system uses the Consensus Maximization Fusion algorithm to validate, aggregate, and ensemble knowledge extracted from web-scale knowledge bases such as YAGO and NELL and 69 Knowledge Base Population. The algorithm combines multiple supervised classifiers (high-quality and clean KBs), motivated by distant supervision and unsupervised classifiers (noisy KBs) Using this algorithm, a probabilistic interpretation of the results from complementary and conflicting data values can be shown in a singular response to its user. Thus, using a consensus maximization component, the supervised and unsupervised data collected from the method stated above produces a final combined probability for each triple. The standardization of string named entities and alignment of different ontologies is done in the pre-processing stage. Project plan Semester 1 Task Start End Duration(days) Milestone Research: 23/03/2017 Twitter Call 27/02/2017 02/03/2017 4 Entity Recognition 27/02/2017 02/03/2017 4 Entity Extraction 02/03/2017 02/03/2017 7 Entity Linking 09/03/2017 16/03/2017 7 Knowledge Base Fusion 16/03/2017 23/03/2017 7 Proposal 27/02/2017 30/03/2017 30 30/03/2017 Crawling Twitter data using Public Stream API 31/03/2017 15/04/2017 15 15/04/2017 Collect Twitter data for training purp

Judaism Essay example -- essays research papers

  Ã‚  Ã‚  Ã‚  Ã‚  Ã¢â‚¬Å"When people around the world were worshiping thunder and wind, the Jews had but one word to say - God.† Judaism is one of the three major religions in our society today along with Islam and Christianity. Judaism believes there is only one God who created and presides over the world. Their God is all powerful, all knowing and is in all places at all times. He is also compassionate and just. The Jewish religion is passed on via the mother of a child. If the mother is Jewish, the child is 100% Jewish. According to Jewish law, one will remain a Jew even if they don’t practice Judaism or they do not believe in God.  Ã‚  Ã‚  Ã‚  Ã‚     Ã‚  Ã‚  Ã‚  Ã‚  The Israelites accepted the Ten Commandments from God at Mount Sinai therefore they devoted themselves to following a code of law which regulates both how they worship and how they should treat other people. The Ten Commandments were given to Abraham and they serve as a moral code not only for the Jews but for all of society. The Ten Commandments are as follows: 1.  Ã‚  Ã‚  Ã‚  Ã‚  I am the Lord your God 2.  Ã‚  Ã‚  Ã‚  Ã‚  You shall not recognize the gods of others in My presence 3.  Ã‚  Ã‚  Ã‚  Ã‚  You shall not take the Name of the Lord your God in vain 4.  Ã‚  Ã‚  Ã‚  Ã‚  Remember the day of Shabbat to keep it holy 5.  Ã‚  Ã‚  Ã‚  Ã‚  Honor your father and your mother 6.  Ã‚  Ã‚  Ã‚  Ã‚  You shall not murder 7.  Ã‚  Ã‚  Ã‚  Ã‚  You shall not commit adultery 8.  Ã‚  Ã‚  Ã‚  Ã‚  You shall not steal 9.  Ã‚  Ã‚  Ã‚  Ã‚  Do not give false testimony against your neighbor 10.  Ã‚  Ã‚  Ã‚  Ã‚  You shall not covet your fellow's possessions (http://godstenlaws.com/commandments.htm) The Torah is the Jewish holy book. Jews believe that it is God's instructions to the Jews with guidelines on how they should act, think and even feel about life. It includes every aspect of life, from birth through death. The Torah contains 613 commandments, but the Ten Commandments are considered the most important commandments. There are two parts to the Torah; The written and the oral Torah. The Written Torah contains:  Ã‚  Ã‚  Ã‚  Ã‚  1. Five Books of Moses   Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  &nb... ... and ending after dusk on the day of Yom Kippur. Jews refrain from eating and drinking anything on Yom Kippur. The Jews fast to atone for the sins they have committed through out the past year. Passover is the most widely observed Jewish holiday. On Passover, Jews all over the world conduct a Passover Seder. Seder means order or organization. The Passover Seder is a celebratory meal that is performed in an organized way so that all the commandments of Pesach will be performed. The Torah commands Jews on Passover to tell the story of the Exodus and to eat matzah. On Passover Jews must eat bitter herbs this is done to remind them of the Israelites' suffering. They must also eat extra matzah called afikoman to remind them of the sacrifice of Passover. They must recite Hallel psalms of praise, drink four cups of wine, and demonstrate acts of freedom such as sitting with a pillow.   Ã‚  Ã‚  Ã‚  Ã‚  Believers of Judaism await the coming of the Messiah. The importance placed on a future occurrence is one of the strongest factors that is responsible for the continuance of any religion. It supports the need to follow the customs, ethics, morals of the particular belief system.

Modern: Technology and Social Networking Essay

â€Å"It has become appallingly obvious that our technology has exceeded our humanity.† – Albert Einstein  © 3.1. INTRODUCTION How are we to obtain the measure of the distance between basic research and the essential technologies of the modern age? Are we in the process of building the bridge that will unite the two domains or is the gulf between them growing wider by the day? Reconciling the interested parties in any definitive way remains difficult as each side can furnish multiple examples to support their perspective on the matter. Perhaps the best illumination can be provided through a retrospective approach that highlights numerous pertinent discoveries and in doing so clear up some of the fog that surrounds the debate. 3.2. BACKGROUND CONDITIONS Modern Technologies have made us complete slaves to machines. There is no work which cannot be done without the assistance of machines and there is not a single area of human activity where machines don’t have to be used. No one can deny the fact that gadgets have not only simplified our lives but also made them more comfortable and luxurious. But on the contrary man’s dependence on them has increased so much that we just cannot do without them at all. If cabs go off the road we cannot reach our destinations. No cooking without LPG cylinder or cooking flame. No, we can’t do even simple calculations, what to talk of washing without washing machine or electricity. If electricity fails, life for each one of us comes to a standstill as all gadgets are operated with it be it AC, TV, computer, a telephone, or any other modern appliance. Perhaps there were times when every work was done with hands be it grinding or travelling far off places. People were tough who could walk for miles and work ceaselessly. In modern times we can’t ascend the stairs without feeling a burden over our stamina. Modern gadgets have completely transformed the human life and health to a great extent. It a fact that machines have become like servants without which life comes to a standstill. Thus we can say that our dependence on modern gadgets has made us complete slaves to machines and that we have lost our spirit to work and vitality, vigor and stamina and therefore no more good health and cheerfulness and endurance prevails. This dependence on machines has transformed the very human psychology. â€Å"All the biggest technological inventions created by man-the airplane, the automobile, the computer- says little with his intelligence, but speaks volume about his laziness† A warm greetings of peace and love ladies and gentlemen We have come to an era where everything inconceivable for the past 50 years has been made possible and actual. Once a dream, now a commodity. Once a prospect, now outdated. Once a thing of imagination, now an item of sensation. We can say that everything, I mean, everything, is now made available and accessible. Modernization takes a great part in the life of people. Effects that these products brought affects every aspect of human life. The effects of these technologies can be bothh positive and negative. Technologies are designed to make man’s life more easier. Technology makes communication more faster and easier. With the modern and improved equipments in our hospitals and other medical facilities, it saves more innocent lives. Transportation is also improved and more faster. Modern technologies are also used for security purposes. Crimes and other cases are easily solved with the help of these gadgets. But did we ever think that it could also destroy and degrades our very own life? Benfits also has its price, while it makes our lives and works easier to deal with, it can also have disturbing impacts to our lives. While some technologies are used in security purposes, some are also used to destroy security and peacefulness in the society. Let’s talk about social networking now. Social networking is rampant these days. I am too have my own facebook account. Social networking helps communication more fasters, but are we aware of news regarding social networking? Man use social networkings to find prospective victims of their selfish desires. I had read news about these in the web. Security of users are not assured and privacy is being invaded. Women are more prone to this kind of schemes. Man is the only living specie with boundless needs, but thankfully, with immeasurable capacity to invent things and satisfy his insatiability. Technology accommodates every human and inhuman want – pampers every fancy of human fiber. Look around. People manipulate the environment to achieve practical goals – goals that respond to their physiological drive. Technology is always about satisfaction, gratification and indulgence; technology is about excess, as in excessive entertainment of human needs. While new technology can provide advances for humanity, it can also have disturbing impacts. Our youth is the most vulnerable to any unfavorable bearing technology may bring. With the flood of modern equipment, gadgets and devices, we are deprived of the basics and fundamentals and essentials of things; we are estranged by sound judgment to determine what is right or wrong, what is appropriate or not, what is effective and not so. With the advent of PlayStation and the likes, who would prefer to gather around grandmother’s cradle and listen to her old-age stories? Who would have the thought of grabbing a book and have the religious habit of reading when internet is inviting? Who would flip pages of encyclopedia if they can just surf at Wikipedia and other educational portals? Gone are the days for serenades – cellular phones radically take over the courtship activity; say goodbye to airmail – electronic mails revolutionize the mailing system.

Almost, But Not Quite

Almost, But Not Quite One of the most popular features on this site has been the list of false friends, those words that look the same or almost the same as English words but have different meanings. However, such words arent the only dangerous ones for those who believe (usually correctly) that knowing English gives them a head start on Spanish vocabulary. For there also are a number of words that might be called fickle friends, words that are roughly synonymous with English words but have a different connotation, or that are synonymous some of the time but not always. These words can be confusing to anyone with a knowledge of English who is speaking Spanish as a second language. (Although technically not accurate, false friends are often referred to as false cognates. Presumably, that would make fickle friends known as partial cognates.) To take an extreme example of a fickle friend, one so extreme it is on the list of false friends, look at molestar, which is related to the English verb to molest. In English, the verb can mean to bother, which is its Spanish meaning, as in the sentence they continued on their journey unmolested. But far more often, almost always, the English word has a sexual connotation that is absent in Spanish. Many of the words on the following list are something like that, in that they have a meaning similar to an English one but often mean something different. Translating them as the English cognates may make sense some of the time but frequently it wont. Accià ³n: It is usually synonymous with action in its various meanings. But to a stock broker it can also mean a share, and to an artist it can be posture or pose. Adecuado: This word can mean adequate in the sense of being appropriate. But adequate can have a negative connotation that adecuado doesnt. Its usually better to translade adecuado as suitable or fitting. Admirar: It can mean to admire. But it frequently means to surprise or to astonish. Afeccià ³n: Once in a while, this word does refer to a fondness toward somebody or something. But far more commonly it refers to a disease or some other sort of medical condition. Better words for affection are another cognate, afecto, and a separate word, carià ±o. Agonà ­a: Nobody wants to be in agony, but the Spanish agonà ­a is much worse, usually suggesting that someone is in the final stages of death. Americano: The understanding of this word varies from place to place. If youre from the United States, its safest to say soy de los Estados Unidos. Aparente: It can mean the same as the English apparent. However, the Spanish usually carries a strong implication that things arent what they appear to be. Thus, aparentemente fue a la tienda would usually be understood not as he apparently went to the store but as it appeared like he had gone to the store but he didnt. Aplicar: Yes, this word does mean apply, as in applying an ointment or a theory. But if youre applying for a job, use solicitar (although there is some regional usage of aplicar). Similarly, an application for a job or something else you would apply for is a solicitud. Apologà ­a: The Spanish word doesnt have anything to do with saying youre sorry. But it is synonymous with the English word apology only when it means a defense, as in a defense of the faith. An apology in the usual sense of the word is excusa or disculpa. Arena: In sports, arena can refer to an arena. But it is more commonly used as the word for sand. Argumento: This word and its verb form, argumenta r, refer to the type of argument a lawyer might make. It can also refer to the theme of a book, play or similar work. On the other hand, a quarrel could be a discusià ³n or disputa. Balance, balanceo, balancear: Although these words can sometimes be translated as balance, they most often refer to a swinging or oscillation. Words with meanings more closely related to the English balance include balanza, equilibrio, saldo, equilibrar, contrapesar  and saldar. Cndido: Although this word can mean frank, it more often means naively innocent. Colegio: The Spanish word can refer to almost any school, not just ones that provide university-level classes. Collar: This word is used when referring to the collar a pet (such as a dog) might wear, and it also can refer to a ringlike mechanical item known as a collar. But the collar of a shirt, jacket or similar type of apparel is a cuello (the word for neck). Collar can also refer to a necklace or similar item worn around the neck. Conducir: It can mean to conduct or (in the reflexive form conducirse) to conduct oneself. But it more often means to drive or to transport. For that reason, a conductor on a train (or other veh icle) is the person in the driving seat, not someone who handles tickets. Confidencia: Its meaning is related to the English meaning of confidence as a secret. If youre referring to trust in someone, confianza would be more appropriate. Criatura: Most commonly it means creature or being, including humans. But it is also commonly used to refer to babies and even to fetuses. Debate: This word often does refer to a debate, particularly one in a legislative body. But it also frequently refers to a discussion, one that doesnt have to include opposing viewpoints. Defraudar: This verb doesnt have to imply wrongdoing. Although it can mean to defraud, it more often means to disappoint. Demandar: As a legal term only, demandar and the noun form, la demanda, are similar to the English demand. But to demand something in a less formal situation, use exigir or exigencia. Direccià ³n: It usually means direction in most of the ways it is used in English. But it is also the most common way of referring to a postal or email address. Discusià ³n: The Spanish word often ca rries the connotation that a discussion has become heated. Alternatives include conversacià ³n and debate (which doesnt have to refer to a formal debate). En efecto: This phrase can mean in effect. But it also can mean in fact, not quite the same thing. Estupor: In medical usage, this word refers to a stupor. But in everyday meaning it refers to a state of amazement or astonishment. Usually the context will make clear what meaning is meant. Etiqueta: It can refer to etiquette and the requirements of formality. However, it also frequently means tag or label. The verb form, etiquetar, means to label. Excitado: This adjective can be synonymous with excited, but a closer equivalent is aroused - which doesnt necessarily have to do with sexual overtones but usually does. Better translations of excited include emocionado and agitado. Experimentar: This is what scientists and other people do when theyre trying something out. However, the word also often means to suffer or to experience. Familiar: In Spanish, the adjective is more closely connected with the meaning of family than in English. Often a better word to use for something youre fami liar with is conocido (known) or comà ºn (common). Habitual: The word often does mean habitual and it is a common translation for the English word. But it can refer to something that is normal, typical or customary. Hindà º: Hindà º can refer to a Hindu, but it can also refer to someone from India regardless of the persons religion. Someone from India can also be called an indio, a word also used to refer to indigenous people of North and South America. An American Indian is also often called an indà ­gena (a word both masculine and feminine). Historia: This word is obviously related to the English word history, but it is also similar to story. It can mean either one. Honesto: It can mean honest. But honesto and its negative form, deshonesto, more often have sexual overtones, meaning chaste and lewd or slutty, respectively. Better words for honest are honrado and sincero. Intentar: Like the English cognate, it can mean to plan or want to do something. But it also is frequently used to indicate more than a mental state, referring to an actual attempt. It thus is often a good translation for to try. Intoxicado, intoxicar: These words refer to almost any kind of poisoning. To refer specifically to the symptoms of alcohol poisoning, use borracho or any number of slang terms. Introducir: This verb can be translated as, among other things, to introduce in the sense of to bring in, to begin, to put or to place. For example, se introduce la ley en 1998, the law was introduced (put in effect) in 1998. But its not the verb to use to introduce someone. For that purpose, use presentar. Marcar: While it usually means to mark in some way, it also can mean to dial a telephone, to score in a game, and to notice. Marca is most often brand (with origins similar to the English trademark), while marco can be a window frame or picture frame. Miserà ­a: In Spanish, the word more often carries the connotation of extreme poverty than does the English misery. Notorio: Like the English notorious, it means well-known, but in Spanish it usually doesnt have the negative connotation. Opaco: It can mean opaque, but it can also mean dark or gloomy. Oracià ³n: Like the English oration, an oracià ³n can refer to a speech. But it also can refer to a prayer or a sentence (in the grammatical sense). Oscuro: It can mean obscure, but it more often means dark. Parientes: All of ones relatives are parientes in Spanish, not just parents. To refer to parents specifically, use padres. Peticià ³n: In English, petition as a noun most often means a list of names or a legal demand of some sort. Peticià ³n (among other words) can be used as a Spanish translation in such cases, but most often peticià ³n refers to almost any kind of request. Pimienta, pimiento: Although the English words pimento and pimiento come from the Spanish words pimienta and pimiento, they arent all interchangeable. Depending on region and speaker, the English terms can refer to allspice (malageta in Spanish) or a type of sweet garden pepper known as pimiento morrà ³n. Standing alone, both pimiento and pimienta are general words meaning pepper. More specifically, pimienta usually refers to a black or white pepper, while pimiento refers to a red or green pepper. Unless the context is clear, Spanish usually uses these words as part of a phrase such as pimiento de Padrà ³na (a type of small green pepper) or pimienta negra (black pepper). Preservativo: You might find yourself embarrassed if you go to a store and ask for one of these, because you could end up with a condom (sometimes referred to as a condà ³n in Spanish). If you want a preservative, ask for a conservante (although the word preservativo is also used at times). Probar: It can mean to probe or to test. But it is frequently used to mean to taste or to try on clothes. Profundo: It can have some of the meanings of the English profound. But it more often means deep. Propaganda: The Spanish word can have the negative implications of the English word, but it often doesnt, simply meaning advertising. Punto: Point often works as a translation of this word, but it also has a variety of other meanings such as dot, period, a type of stitch, belt hole, cog, opportunity, and taxi stand. Real, realismo: Real and realism are the obvious meanings, but these words also can mean royal and regalism. Similarly, a realista can be either a realist or a royalist. Fortunately, realidad is reality; to say royalty, use realeza.Relativo: As an adjective, relativo and relative are often synonymous. But there is no Spanish noun relativo corresponding to the English relative when it refers to a family member. In that case, use pariente.Rentar: In some areas of Latin America, rentar can indeed mean to rent. But it also has a more common meaning, to yield a profit. Similarly, the most common meaning of rentable is profitable.Rodeo: In the right context, it can mean rodeo, although there are differences between the typical rodeos of the United States and of Mexico. But it can also mean an encirclement, a stockyard, or an indirect path. Figuratively, it also can mean an evasive reply, a beating around the bush.Rumor: When used in a figurative sense, it indeed does mean rumor. But it als o often means a low, soft sound of voices, commonly translated as murmurring, or any soft, vague sound, such as the gurgling of a creek. Soportar: Although it can be translated as to support in some usages, it often is better translated as to tolerate or to endure. Some of the verbs that are better used to mean to support include sostener or aguantar in the sense of supporting weight, and apoyar or ayudar in the sense of supporting a friend.Suburbio: Both suburbs and suburbios can refer to areas outside a city proper, but in Spanish the word usually has a negative connotation, referring to slums. A more neutral word to refer to suburbs is las afueras.Tà ­pico: This word usually does mean typical, but it doesnt have the negative connotation that the English word often has. Also, tà ­pico often means something along the lines of traditional or having the characteristics of the local area. Thus if you see a restaurant offering comidas tà ­picas, expect food that is characteristic for the region, not merely typical food.Tortilla: In Spanish, the word can refer not only to a tortilla but also to an omelet.Último: Alt hough something that is the best can be referred to as lo à ºltimo, the word more commonly means last or most recent. Vicioso: Although this word is sometimes translated as vicious, it more often means depraved or simply faulty.Violar, violador: These words and words related to them have a sexual connotation more often than they do in English. While in English a violator may simply be someone who drives too fast, in Spanish a violador is a rapist.

Aqueous Solution Definition

Aqueous Solution Definition Aqueous Definition Aqueous is a term used to describe a system which involves water. The word aqueous is also applied to describe a solution or mixture in which water is the solvent. When a chemical species has been dissolved in water, this is denoted by writing (aq) after the chemical name. Hydrophilic (waters of nonelectrolytes include sugar, glycerol, urea, and methylsulfonylmethane (MSM). Properties of Aqueous Solutions Aqueous solutions often conduct electricity. Solutions that contain strong electrolytes tend to be good electrical conductors (e.g., seawater), while solutions that contain weak electrolytes tend to be poor conductors (e.g., tap water). The reason is that strong electrolytes completely dissociate into ions in water, while weak electrolytes incompletely dissociate. When chemical reactions occur between species in an aqueous solution, the reactions are usually double displacement (also called metathesis or double replacement) reactions. In this type of reaction, the cation from one reactant takes the place for the cation in the other reactant, typically forming an ionic bond. Another way to think of it is that the reactant ions switch partners. Reactions in aqueous solution may result in products that are soluble in water or they may produce a precipitate. A precipitate is a compound with a low solubility that often falls out of solution as a solid. The terms acid, base, and pH only apply to aqueous solutions. For example, you can measure the pH of lemon juice or vinegar (two aqueous solutions) and they are weak acids, but you cant obtain any meaningful information from testing vegetable oil with pH paper. Will It Dissolve? Whether or not a substance forms an aqueous solution depends on the nature of its chemical bonds and how attracted the parts of the molecule are to the hydrogen or oxygen atoms in water. Most organic molecules wont dissolve, but there are solubility rules that can help identify whether or not an inorganic compound will produce an aqueous solution. In order for a compound to dissolve, the attractive force between a part of the molecule and hydrogen or oxygen has to be greater than the attractive force between water molecules. In other words, dissolution requires forces greater than those of hydrogen bonding. By applying the solubility rules, its possible to write a chemical equation for a reaction in aqueous solution. Soluble compounds are denoted using the (aq), while insoluble compounds form precipitates. Precipitates are indicated using (s) for solid. Remember, a precipitate does not always form! Also, keep in mind precipitation is not 100%. Small amounts of compounds with low solubility (considered insoluble) actually do dissolve in water.

Reflecting on critique Assignment Example | Topics and Well Written Essays - 1000 words

Reflecting on critique - Assignment Example We try to freeze the moment with the help of photography, capture the history that is why photography is something that shows us what happened and will never repeat again. That is why the art of photography as a genre is complicated in terms of time, authenticity, and artistic value. Photographic image is completely different from all the other types of images as it possesses great power and potency. It is capable of telling a story, serving as the evidence of the event or a person and at the same time retrieved from the context it can become a pure visual form. Traditionally photography served as a means of documentation of social and family life, and as soon as it appeared it became a privilege of certain classes. For a long time any portrait photography remained a luxury, an exclusive thing, a product that emphasized social status first of all and was affordable only to higher classes of society. Miniature or big family portraits adorned bedrooms of those who could afford going to salons to take a picture (Tagg, 1988, 53). In a broader sense documentary photography aimed to depict such events and circumstances that were inaccessible or not easily accessible. Documentary photography flourished during historically important events initially, such as American Civil War for instance, when whole photography archives were created. That type of photography was based on the principle of objectivity and trustfulness, and photography was and still remains a means of information transfer for a long time. With media empowering photojournalism became a separate type of documentation based on the capacity to demonstrate the information that is unknown or hidden (Stapp, 2007, 691). Later photography became a pure visual art in which depiction of reality and its documentation has lost its primary significance. Fashion, travelling magazines made artistic photography goods for selling. Photography has turned into a product as people learned how to evoke the

Intels corporate ethics Essay Example | Topics and Well Written Essays - 1250 words

Intels corporate ethics - Essay Example â€Å"This $2 million plant is only 0.1% of what Intel spent to build this facility.† (What Intel has done and is doing, n.d.). The US is the greatest inventor of the world and Intel is one of the greatest innovator of the world. There is no doubt it can make the most enormous contributions towards dealing with the world’s major problem—the environment problem. Yet the huge profits have caused Intel to act blindly in this area. Greed is no doubt the key factor in ignoring the environmental problems. Corporate heads get greedy and lust for profits and ignore citizens’ welfare. In Hamidi’s (n.d.) website, ‘Intel is being accused for causing air pollution by using toxic solvents. The air pollution in that area was about three times higher than the acceptable limit. The History of Intel’s Toxic Chemical Release in Corrales (n.d.) states, people have developed respiratory and skin problems. New Mexico has also been known to have droughts. It is extremely hot in the summer. Yet one of the largest consumers of their water is Intel. Intel has built such a huge facility with no water recycling system. On the Intel website, the US Environmental Protection Agency says this is a very dangerous practice. There was a debate on how to deal with this issue and they had conducted studies. In the site for NM risk assessment on the Intel website (n.d.), â€Å"the EPA wants manufacturers to take responsibility for the products, throughout their life, particularly when they contain hazardous materials.†