Cadmium in Shellfish Essay Example | Topics and Well Written Essays - 1500 words

Cadmium in Shellfish - Essay Example The figures for both types of shellfish was well within the tolerable limits at 7 g/kg body weight for adults as per WHO and Australian guidelines. Nevertheless, since there was observed high standard deviation among the scallop samples and there was also high deviation between the scallop and mussel cadmium/body-weight ratios it has been suggested that a future experiment be conducted during which the origin of the two types of shellfish be ascertained first to ensure that water contamination levels for both types of shellfish are the same. This shall ensure better conformation among the results. Correlation analysis was not conducted because of the small number of samples. This too should be rectified in future experiments to ascertain how each type of shellfish responds to the same level of cadmium contamination in the water. Thus, the report finds that it is highly essential to ascertain contamination in particular harvesting areas so that only those areas from where the bivalves have tolerable metal contents can be allowed harvestable. Other non-conforming areas should be declared out-of-limits. Only this can assure safety in supply. Cadmium is a heavy metal that is commonly found in many types of soil and rocks. At birth, primates like humans have no cadmium in their bodies but, with age, some humans induct the metal from their environment. The metal is a contaminant and classified as possibly carcinogenic under World Health Organisation (WHO) guidelines (CHEC, Cadmium, 2007). The metal is variously used in industrial applications and commonly found in everyday use objects like paints, plastics, some types of coatings, batteries and other electrical components (CHEC, Cadmium, 2007). The metal is also let out into the atmosphere from burning fuels, especially fossil fuels. It is present in rubber tires and is emitted when tires burn. Also, many industries let out the metal into the atmosphere trough flue gases (CHEC, Cadmium, 2007). Another significant source of cadmium contamination is tobacco smoke. Children are especially susceptible to the metal if they are near smokers (CHEC, Cadmium, 2007). Children also a re more prone to cadmium contamination because ration of the metal intake by body weight is much higher than in adults with larger body weight. Thus, more care has to be taken to preclude such contamination in children. Also, it is estimated that cadmium build-up in the body is faster in the early years than later if the individual is subjected to such contamination (WHO, Cadmium, Series 24, 1972). In this particular context it is noted that shellfish like mussels and scallops are bottom-dwellers and are non-mobile filter feeders (Moffett, 1993). In all likelihood, if the metal becomes evident in seawater, it settles down towards the bottom and the shellfish induct it into their systems. Since there is evidence that the metal is not easily evicted from organic tissues. Over time the metal may accrue in shellfish tissue and if these are ingested by humans poisoning may result if the metal levels in the tissue are

Rites of Passage Essay Example | Topics and Well Written Essays - 500 words

Rites of Passage - Essay Example The cost of providing subsidized housing was high therefore the state would latter change to provide a relatively cheaper ‘outdoor relief’ to disadvantaged individuals who lived with their relatives or friends. This relief worked efficiently in providing quality living standards, despite the economic depression where massive lay-off occurred resulting to high levels of unemployment of able- bodied people (Joanne, 1966). The article ‘Rites of Passage’ represents a beneficiary of public relief trying to give back to the society. Cephas Ribble, a sixty eight year old man, enters into County Department of Public Welfare with the sole intention of donating farm product to the welfare so that they foodstuff can be distributed to the poor and needy people. On arrival he seems not sure whether he is presentable or not. Additionally, the staff present in the office had the assumption that all elderly people visiting the office are in need of assistance as it had been the norm. Mr. Ribble does not realize that the public assistance once given to him no longer exists. He explains how the relief aid provided to farmers in 1934 had sustained his family. He further explains how he worked hard to buy his house and pay off the debts that his family had. He requests the woman to bring men to his truck to off load his massive food donation. However, he does not realize that laws had changed and food donations were no longer necessary. Despite his good conscience and massive donation, Cephas’s truck blocks all the workers’ cars and all workers leave the office to assist him offload. The donation suffers sharp criticism due to disorganized distribution of the food with some protesting that the food contains contraceptive medicine, others claim racism in the mode of distribution (Joanne, 1966). Mr. Cephas is a wise and

Factor Language Model Programming

Factor Language Model Programming Language Model Language model helps a speech recognizer figure out how likely a word sequence is, independent of acoustics. There is a linguistic and statistical approach to calculate the probability. The linguistic technique tries to understand the syntactic and semantic structure of a language and derive the probabilities of word sequences using this knowledge. The challenge here is to have proper co occurrence statistics of the unit of recognition. The approach in use evaluates a huge text corpus in a statistical way and word transitions. Current language models make no use of the syntactic properties of natural language but rather use very simple statistics such as word co-occurrences. Recent results show that incorporating syntactic constraints in a statistical language model reduces the word error rate on a conventional dictation task by 10% [M.S.Salam, 2009]. Proposed Language Model The approach proposed here uses factored language model which incorporates the morphological knowledge. Factored language models have recently been proposed for incorporating morphological knowledge in the modeling lexicon. As suffix and compound words are the cause of the growth of the vocabulary, a logical idea is to split the words into shorter units. The language model proposed in this research is based on morphology. A morphological analyser obtains and verifies the internal structure of a given complete word form [Rosenfield, 2000]. Building a morphological analyser for highly inflecting, agglutinative languages is a challenging task. It is very difficult to build a high performance analyser for such languages. The main idea here is to divide a given word form into a stem and single suffix. Morphology plays a much greater role in Telugu. An inflected Telugu word starts with a stem and may have suffix(s) added to the right according to complex rules of saMdhi. This research proposes a new data structure based on Inverted Index and an efficient algorithm for accessing its elements. Few researchers have used tries for efficient retrieval from dictionary, earlier. This research work is different from earlier work in two ways: a) variation to the structure of trie b) the method of identifying and combining inflections. Modified Trie Structure A trie is a tree based data structure for storing strings in order to support fast pattern matching. A trie T represents the strings of set S of n strings with paths from root to the external node of T. Fig 5.1: Original Trie Structure The trie considered here is different from standard trie in two ways: 1) A standard trie does not allow a word to be prefix of another, but the proposed trie structure allows a word to be prefix of another word. The node structure and search algorithm also is given according to this new property. 2) Each word in a standard trie ends at an external node, where as in the modified trie a word may end at either an external node, or the internal node. Irrespective of whether the word ends at internal node or external node, the node stores the index of the associated word in the occurrence list. The node structure is changed such that, each node of the trie is represented by a triplet C,R,Ind>. C represents character stored at that node. R represents whether the concatenation of characters from root till that node forms a meaningful stem word. Its value is 1, if characters from root node to that node form a stem, 0 otherwise. Ind represents index of the occurrence list. Its value depends on the value of R. Its value is -1 (negative 1), if R=0, indicating it is not a valid stem. So no index of occurrence list matches with it. If R=1, its value is index of occurrence list of associated stem. Fig 5.2: Modified Trie Structure Advantages relative to binary search tree: The following are the main advantages of tries overbinary search trees(BSTs): Looking up keys is faster. Looking up a key of lengthmtakes worst caseO(m) time. A BST performs O(log(n)) comparisons of keys, wherenis the number of elements in the tree, because lookups depend on the depth of the tree, which is logarithmic in the number of keys if the tree is balanced. Hence in the worst case, a BST takes O(mlogn) time. Moreover, in the worst case log(n) will approachm. Also, the simple operations tries use during lookup, such as array indexing using a character, are fast on real machines. Tries can require less space when they contain a large number of short strings, because the keys are not stored explicitly and nodes are shared between keys with common initial subsequences. Tries facilitatinglongest-prefix matching, helping to find the key sharing the longest possible prefix of characters all unique. Corpus structure of proposed Language Model The corpus consists of the following modules: Stem word dictionary This accommodates all the stems of the language. Stem word dictionary is implemented as an Inverted Index for better efficiency. The Inverted index will have the following two data structures in it: 1) Occurrence list: It is an array of pairs, 2) Stem trie: consisting of stem words Occurrence list is constructed based on the grammar of the language, where each entry of the list contains the pair (ii) Inflection Dictionary This dictionary contains the list of all possible inflections of the Telugu language. Each entry of Stem word dictionary lists the indexes of this dictionary to indicate which all inflections are possible with that stem. The proposed corpus structure helps in reducing the corpus size drastically. Every stem word may have number of inflections possible. If the inflected words are stored as it is, then corpus size would be m*n, where m is number of stem words and n is number of inflections. Instead of storing all the inflected words, the proposed corpus structure stores stem words and inflections separately, and handles the inflected words through morphology. Hence the corpus size required is for m stem words and n inflections i.e., m+n. Thus there is a great reduction in the corpus size. For a corpus of 1000 stem words and 10 inflections, the required corpus size is 1000+10=1010, which otherwise would have required 1000*10=10000. Fig 5.3 : Corpus structure of proposed Language Model Textual Word Segmentation using Proposed Language Model The proposed language model is used to develop a textual word segmenter. A word segmenter is used to divide the given inflected word into a stem and single inflection. This is required as the corpus stores stems and inflections separately. Input the word segmenter is an Inflected word. Syllabifier takes this word and divides the word into syllables and identifies if the letter is a vowel or a consonant. After applying the rules syllabified form of the input will be obtained. Once the process of syllabification is done, this will be taken up by the analyzer. Analyzer separates the stem and inflection part of the given word. This stem word will be validated by comparing it with the stem words present in stem dictionary. If the stem word is present, then the inflection of the input word will be compared with the inflections present in inflection dictionary of the given stem word. If both the inflections get matched then it will directly displays the output otherwise it takes the appropriate inflection(s) through comparison and then displays. Syllabification is the separation of the words into syllables, where syllables are considered as phonological building blocks of words. It is dividing the word in the way of our pronunciation. The separation is marked by hyphen. In the morphological analyzer, the main objective is to divide the given word into root word and the inflection. For this, we divide the given input word into syllables and we compare the syllables with the root words and inflections to get the root word and appropriate inflection. Fig 5.4: Block diagram of Word Segmentr for text Steps for word segmentation Receiving the inflected word as an input from the user. Syllabify the input Analyze the input and validating the stem word. Identify the appropriate inflection for the given stem word by comparing the inflection of given word with the inflections present in inflection dictionary of the stem word. Displaying the appropriate inflected word. For example, considering the word â€Å"nAnnagariki† (à  Ã‚ °Ã‚ ¨Ãƒ  Ã‚ °Ã‚ ¾Ãƒ  Ã‚ °Ã‚ ¨Ãƒ  Ã‚ ±Ã‚ Ãƒ  Ã‚ °Ã‚ ¨Ãƒ  Ã‚ °-à  Ã‚ °Ã‚ ¾Ãƒ  Ã‚ °Ã‚ °Ãƒ  Ã‚ °Ã‚ ¿Ãƒ  Ã‚ °Ã¢â‚¬ ¢Ãƒ  Ã‚ °Ã‚ ¿) meaning â€Å"to father†, the input is given the user in Roman transliteration format. This input is basically divided into lexemes as: Now, the array is processed which gives the type of lexeme by applying the rules of syllabification one by one. Applying Rule 1: â€Å" No two vowels come together in Telugu literature.† The given user input does not have two vowels together. Hence this rule is satisfied by the given user input. The output after applying this rule is same as above. If the rule is not satisfied, an error message is displayed that the given input is incorrect. Now the array is: c – v – c – c – v – c – v – c – v – c – v Applying Rule 2: â€Å" Initial and final consonants in a word go with the first and last vowel respectively.† Telugu literature rarely has the words which end up with a consonant. Mostly all the Telugu words end with a vowel. So this rule does not mean the consonant that ends up with the string, but it means the last consonant in string. The application of this rule2 changes the array as following: c – v – c – c– v – c – v – c – v – c – v cv – c – c – v – c – v – c – v – cv This generated output is further processed by applying the other rules. Applying Rule 3: â€Å" VCV: The C goes with the right vowel.† The string wherever has the form of VCV, then this rule is applied by dividing it as V – CV. In the above rule the consonant is combined with the vowel, but here in this rule the consonant is combined with the right vowel and separated from the left vowel. To the output generated by the application of rule2, this rule is applied and the output will be as: cv – c – c – v – c – v – c – v – cv cv – c – c – v – cv – cv – cv This output is not yet completely syllabified, one more rule is to be applied which finishes the syllabification of the given user input word. Applying Rule 4: â€Å" Two or more Cs between Vs First C goes to the left and the rest to right.† It is the string which is in the form of VCCC*V, then according to this rule it is split as VC – CC*V. In the above output VCCV in the string can be syllabified as VC – CV. Then the output becomes: cv – c – c – v – cv – cv – cv cvc– cv – cv – cv – cv Now this output is converted to the respective consonants and vowels. Thus giving the complete syllabified form of the given user input. nAn – na –cA – ri – ku cvc – cv – cv – cv – cv Hence, for the given user input, â€Å"nAnnagAriki†, the generated syllabified form is, â€Å"nAn – na – gA – ri – ki†. Fig 5.5: Word Segmenter showing an inflected word without change in stem form Fig 5.6: Word Segmenter showing an inflected word with a change in stem form SCIL Speech Corrector for Indian Languages In inflectional language every word consists of one or several morphemes into which the word can be segmented. The approach used here aims at reducing the above mentioned problem of having a very huge corpus for good recognition accuracy. It exploits the characteristic of Telugu language that every word consists of one or several morphemes into which the word can be segmented. SCIL is a procedure To deal with complex word forms applied after recognition Using which misrecognized words are corrected Architecture of SCIL The design of Speech Corrector for Indian Languages, consists of the Syllable Identifier, Phone Sequence Generator, Word Segmenter, and Morpho- Syntactic Analyzer modules. Input speech is decoded by a normal ASR system which gives the identified word as a string. The sequence of phones would be the input to the Word Segmenter module which matches the phonetized input with the root words stored in dictionary module, and generates a possible set of root words. Morpho-Syntactic Analyzer compares the inflection part of the signal with the possible inflections list from the database and gives correct inflection. This will be given to Morph Analyzer to apply morpho-syntactic rules of the language and gives the correct inflected word. Fig 5.7: Block diagram of SCIL i) Syllable Identifier Syllable identifier marks the rough boundaries of the syllables and labels them. At this stage , we get list of syllables separated with hyphen. The user input is syllabified and this would be the input to the next module. E.g. dE-vA-la-yA-ku ii) Phone Sequence Generator As the words in the dictionary are stored at phone level transcription, this module generates the phone sequences from the syllables. E.g. d-E-v-A-l-a-y-A-k-u iii) Word Segmentor This module compares the phonetized input from starting with the root words stored in dictionary module and lists the possible set of root words. The possible root word is dEvAlayamu. iv) Dictionary Dictionary contains stems and inflections separately. It does not store inflected words as it is very difficult, if not impossible, to cover all inflected words of the language. The database consists of 2 dictionaries: Stem Dictionary Inflection Dictionary Stem dictionary contains the stem words of the language, signal information for that stem which includes the duration and location of that utterance and list of indices of inflection dictionary which are possible with that stem word. Inflection Dictionary contains the inflections of the language, signal information for that inflection which includes the duration and location of that utterance. Both the dictionaries are implemented using trie structure in order to reduce the search space. v) Morpho Syntactic Analyzer This module compares the inflection part of the signal with the possible inflections list from the database and gives correct inflection. This will be given to Morph Analyzer to apply morpho-syntactic rules of the language and gives the correct inflected word. Post Recognition Procedure Capture the utterance, an isolated inflected word. Get its syllabified form. Generate phone sequence from the syllabified word. Compare the phone sequences with stem words in the dictionary and identify the stem. Segment the word into stem and inflection. Get the list of possible inflections. Compare the inflection signals possible with that stem one by one and apply morpho-syntactic rules of the language to combine stem and inflection. Display the inflected word. Using the rules the possible set of root words are combined with possible set of inflections and the obtained results are compared with the given user input and the nearest possible root word and inflection are displayed if the given input is correct. If the given input is not correct then the inflection part of the given input word is compared with the inflections of that particular root word and identifies the nearest possible inflection and combines the root word with those identified inflections, applies sandhi rules and displays the output. When there is more than one root word or more than one inflection has minimum edit distance then the model will display all the possible options. User can choose the correct one from that. For example, when the given word is pustakaMdO (à  Ã‚ °Ã‚ ªÃƒ  Ã‚ ±Ã‚ Ãƒ  Ã‚ °Ã‚ ¸Ãƒ  Ã‚ ±Ã‚ Ãƒ  Ã‚ °Ã‚ ¤Ãƒ  Ã‚ °Ã¢â‚¬ ¢Ãƒ  Ã‚ °Ã¢â‚¬Å¡Ãƒ  Ã‚ °Ã‚ ¦Ãƒ  Ã‚ ±Ã¢â‚¬ ¹), the inflections tO making it pustakaMtO (à  Ã‚ °Ã‚ ªÃƒ  Ã‚ ±Ã‚ Ãƒ  Ã‚ °Ã‚ ¸Ãƒ  Ã‚ ±Ã‚  à  Ã‚ °Ã‚ ¤Ãƒ  Ã‚ °Ã¢â‚¬ ¢Ãƒ  Ã‚ °Ã¢â‚¬Å¡Ãƒ  Ã‚ °Ã‚ ¤Ãƒ  Ã‚ ±Ã¢â‚¬ ¹) meaning ‘with the book’ and lO making it pustakaMlO (à  Ã‚ °Ã‚ ªÃƒ  Ã‚ ±Ã‚ Ãƒ  Ã‚ °Ã‚ ¸Ãƒ  Ã‚ ±Ã‚ Ãƒ  Ã‚ °Ã‚ ¤Ãƒ  Ã‚ °Ã¢â‚¬ ¢Ãƒ  Ã‚ °Ã¢â‚¬Å¡Ãƒ  Ã‚ °Ã‚ ²Ãƒ  Ã‚ ±Ã¢â‚¬ ¹) meaning ‘in the book’) mis are possible. Present work will list both the words and user is given the option. We are working on improving this by selecting the appropriate word based on the context. SCIL Algorithm W=Utterance.wav Syl[]=SyllableIdentifier(W) Phone[]=phonetizer(Syl[]) Stem=getStem(Syl[]) Infl[]=getInflections(Stem) While (not exactMatch) word=MorphAnalyzer(stem,inflMatch) display word Stop Working of SCIL Once possible root words identified the given word is segmented into two parts, first being the root word and second part inflection. Now the inflection part is compared in the reverse direction for a match in the inflection dictionary. It will consider only the inflections that are mentioned against the possible root words, thus reducing the search space and making the algorithm faster. For example consider â€Å"nAnnagariki† (à  Ã‚ °Ã‚ ¨Ãƒ  Ã‚ °Ã‚ ¾Ãƒ  Ã‚ °Ã‚ ¨Ãƒ  Ã‚ ±Ã‚ Ãƒ  Ã‚ °Ã‚ ¨Ãƒ  Ã‚ °-à  Ã‚ °Ã‚ ¾Ãƒ  Ã‚ °Ã‚ °Ãƒ  Ã‚ °Ã‚ ¿Ãƒ  Ã‚ °Ã¢â‚¬ ¢Ãƒ  Ã‚ °Ã‚ ¿) meaning â€Å"to father†, is misrecognized as nAn-na-cA-ri-ku (à  Ã‚ °Ã‚ ¨Ãƒ  Ã‚ °Ã‚ ¾Ãƒ  Ã‚ °Ã‚ ¨Ãƒ  Ã‚ ±Ã‚ Ãƒ  Ã‚ °Ã‚ ¨Ãƒ  Ã‚ °Ã… ¡Ãƒ  Ã‚ °Ã‚ ¾Ãƒ  Ã‚ °Ã‚ °Ãƒ  Ã‚ °Ã‚ ¿Ãƒ  Ã‚ °Ã¢â‚¬ ¢Ãƒ  Ã‚ ±Ã‚ ) then SCIL is applied and will correct the recognition error as follows: The output from ASR is nAn-na-cA-ri-ku. The phone sequence generator will generate the phone sequence as n-A-n-n-a-c-A-r-i-k-u. Now, match it with the set of root words stored in dictionary module. This process will identify the possible set of root words from the Stem dictionary as follows: Once possible root words identified the given word is segmented into two parts, first being the root word and second part inflection. Now the inflection part is compared for a match in the inflection dictionary. It will consider only the inflections that are mentioned against the possible root words, thus reducing the search space and making the algorithm faster. Possible set of inflections in inflections dictionary After getting the possible set of root words and possible set of inflections they are combined with the help of SaMdhi formation rules. Here in this example cA-ri-ku is compared with the inflections of the root word nAnna After comparing it identifies gAriki as the nearest possible inflection and combines the root word with the inflection and displays the output as â€Å"nAnnagAriki†. Conclusions Language model proposed in this work results in reduction in corpus size by using factored approach. The search process is fastened by use of trie based structure. A change to standard trie is proposed. A post recognition procedure SCIL, is designed which uses the proposed language model and corrects the words misrecognized at inflections. The approach is tested using 1500 speech samples. These samples consist of 100 distinct words , each word repeated 3 times and recorded by 5 speakers in the age group 18-50. It is implemented as a speaker dependent system. An average model is built from the three utterances of each word for each speaker. Each speaker is given a unique ID, using which average model of that speaker is used for testing.

Bioinformatics - Solving Biological Problems Using DNA and Amino Acid

Bioinformatics - Solving Biological Problems Using DNA and Amino Acid Sequences 1. Introduction In the wake of Genomic revolution, biology that used to be a lab-based science has transformed to embrace Information science. Human Genome Project is a 13-year project focusing on identifying approximately 30,000 genes in human DNA. The information found is stored in databases, analyzed and used for different purposes like simplifying diagnosis of disease, earlier detection of genetic predisposition to specific disease, custom drugs, gene therapy, gene replacement technologies [1]. Technological advancement has been one of the contributors for the early completion of this project. Computer technology has facilitated in managing and using the deluge of biological data, and various software tools are used to model biological structures in biotechnology. The simplest definition of the biotechnology industry is that it deals with the application of biological knowledge and techniques pertaining to molecular, cellular and genetic processes to develop products and services. The applications range from agriculture (genetically modified food, insect resistant fibre, food processing), industrial (biofuels, bioenzymes in pollution control) and medical biotechnology (diagnosing diseases, developing new drugs). The ethical issues of Bioinformatics data collection and use of human biological data is being analyzed in this paper. 2. What is Bioinformatics Fredj Tekaia at the Institut Pasteur offers this definition of bioinformatics: "The mathematical, statistical and computing methods that aim to solve biological problems using DNA and amino acid sequences and related ... ...e/umlnews/viewarticle.asp?articleid=16 8) Human Genome Project Information, Genetics and Patenting, http://www.ornl.gov/sci/techresources/Human_Genome/elsi/patents.shtml#4 9) The Golden Cusp by Samar Halarnkar and Venkatesha Babu , Business Today, http://www.renodis.com/media/businesstoday/bustoday_article.htm 10) Pankaj Sohaney, Asian Student Medical Journal, Recent Techniques in Biological Research: Bioinformatics http://www.asmj.netfirms.com/article3.html 11) Nature http://www.nature.com/genetics Bibliography 1) www.bioinformatics.org 2) Rob Blatchey, Ethical issues related to the Collection, Storage, and Use of Data Obtained through Bioinformatics. http://www.acsu.buffalo.edu/~rdb2/bioinformatics.htm 3) Human Genome Project Information: Ethical, Legal, and Social Issues. http://www.ornl.gov/sci/techresources/Human_Genome/elsi/elsi.shtml

Federation of Automobile Dealers Associations Report

|Federation of Automobile Dealers Associations | | | | | |   | |Home   | | | |Truck Freightage Defies Trends in Economy: IFTRT Report | |   | |Defying the buoyant data being released by various agencies about the expansion of economic activities and soaring corporate profits, | |the trucking business, consisting of 3 million trucks, has failed to look up in last 4 weeks. The truck freightage has remained flat | |on most of the trunk routes despite increase in cost of operation due to 2 diesel price hikes (Dec 31 and Dec 15†² 03) totaling Rs. 2/- | |per litre (10%) and have, in fact, sharply declined on trunk routes passing through Uttar Pradesh due to resumption of overloading by | |trucks. | | |On the other hand various State Governments had withdrawn Gold token/ passes/ cards, which permitted over-loading of trucks in excess | |of permitted weight, in order to get release of their share of Central Road Fund withheld since April 2003. The Central Government has| |stop ped Central Road Fund to those State Governments, which were not only permitting but sponsoring overloading of trucks in | |contravention of Central Motor Vehicle Act, 1988 by issuing Gold Cards/ passes/ tokens to truckers against fixed monthly/ quarterly | |fee. The Central Government has taken a firm view that â€Å"over-loading of the vehicles cause significant damage to road surface†¦ | | | |Recently, the State Government of Uttar Pradesh withdrew the Gold Card scheme w. e. f. Dec. 15, 03 and a month later Rajasthan did the | |same w. e. f. 1, Jan 04. The subsequent stringent enforcement of CMV Act, 1988 by U. P. Transport Department by not permitting the | |entry/passing through of overloaded trucks from the State resulted in sharp increase in truck freightage by 4% – 7. 5% during the | |fortnight (Dec 1, – Dec 15, 03) and unsettled the trucking business in region. However, this anti-overloading drive was short-lived | |and has collapsed in the U. P. State, by and large. Now, overloaded trucks are plying and passing through the State merrily. Thus, | |truck freightage once again is being dictated by the over-loading of vehicles that existed before Dec. 1, 03. In the last four weeks | |the truck freightage for the trunk routes passing through U. P. has dropped by 3. 5% – 6. 8%, according to the monthly update released by| |Indian Foundation of Transport Training & Research (IFTRT). | | | |Union Government outsmarted by Rajasthan | | | |Followed by U. P. the Rajasthan Government, too, had withdrawn its Gold card/token scheme from 1, Jan 04 to get the Central Road Fund | |released immediately. However, the State Government, very smartly has replaced the earlier special Gold Token Scheme with another | |†AMNESTY SCHEME† by charging multi slab fee to permit unhindered over-loading of trucks in excess of prescribed weight limit. The 1st | |Jan. '04 notification has been â€Å"modified† to pacify the Central Government, which had again refused to release the money from Central | |Road Fund. But, plyin g of over loaded commercial vehicles continues under the patronage of State Transport Department, points out the | |report. |   | | | |TRUCK (16. 2 TON GVW) Hire charges/rates (Rs. per round trip 21 Dec'03 – 21 Jan'04 | |Route / Round Trip |Period |Truck Hire charges |Diesel Price increase Impact | |9 ton pay load | |change/Round Trip/Period |(per round trip) 31 Dec'03 | | | |21 Dec'03 – 21 Jan'04 | | | Hire Charges |Hire Charges | | | | |21 Dec'03 |21 Jan'04 | | | | | | |Rs. |% |Rs. |% | |Delhi – Mumbai – Delhi |28,800/- |28,800/- |No Change |NC |(+) 620/- |(+) 5 | |Delhi – Nagpur – Delhi |28,400/- |28,400/- |No Change |NC |(+) 610/- |(+) 5 | |Delhi – Kolkata – Delhi |34,700/- |33,000/- |(-) 1,700/- |(-) 5. 0 |(+) 630/- |(+) 5 | |Delhi – Guwahati – Delhi |69,400/- |67,000/- |(-) 2,400/- |(-) 3. |(+) 850/- |(+) 5 | |Delhi – Hyderabad – Delhi |43,100/- |43,100/- |No Change |NC |(+) 770/- |(+) 5 | |Delhi – Chennai – Delhi |59,000/- |59,000/- |No Change |NC |(+) 870/- |(+) 5 | |Delhi – Bangalore – Delhi |44,800/- |45,000/- |(+) 200/- |(+) 0. 5 |(+) 770/- |(+) 5 | |Delhi – Ranchi – Delhi |33,300/- |31,300/- |(-) 2,000/- |(-) 6. 8 |(+) 620/- |(+) 5 | |Delhi – Raipur – Delhi |29,300/- |29,300/- |No Change |NC |(+) 630/- |(+) 5 | |Delhi – Kandla – Delhi |18,300/- |18,300/- |No Change |NC |(+) 340/- |(+) 5 | |Delhi – Bilaspur – Delhi |30,300/- |30,300/- |No Change |NC |(+) 620/- |(+) 5 |

Ptcl Report

1. Introduction: Pakistan Telecommunication Company Limited  (PTCL) is a  mega corporation  and a leading telecommunication authority in the  State of Pakistan. The corporation provides and enforces policies for the telephonic services nation-wide and is the backbone for country's telecommunication infrastructure despite arrival of a dozens other telecommunication corporations, including  Telenor Corps  and  China Mobile Ltd. The corporation managed and operates around ~2000 telephone exchanges across the country, providing the largest fixed line network.Data and backbone services such as GSM, CDMA, Broadband Internet, and IPTV, wholesale are an increasing part of its business. From the beginnings of Posts ; Telegraph Department in 1947 and establishment of Pakistan Telephone ; Telegraph Department in 1962, PTCL has been a major player in telecommunication in Pakistan. Despite having established a network of enormous size, PTCL workings and policies have attracted regul ar criticism from other smaller operators and the civil society of Pakistan.Pakistan Telecommunication Corporation (PTC) took over operations and functions from Pakistan Telephone and Telegraph Department under Pakistan Telecommunication Corporation Act 1991. In 1995, Pakistan Telecommunication (Reorganization) Ordinance formed the basis for PTCL monopoly over basic telephony in the country. The provisions of the Ordinance were lent permanence in October 1996 through Pakistan Telecommunication (Reorganization) Act.The same year, Pakistan Telecommunication Company Limited was formed and listed on all stock exchanges of Pakistan PTCL launched its mobile and data services subsidiaries in 2001 by the name of Ufone and PakNet respectively. None of the brands made it to the top slots in the respective competitions Lately, however, Ufone had increased its market share in the cellular sector. The PakNet brand has effectively dissolved over the period of time. Recent DSL services launched by PTCL reflect this by the introduction of a new brand name and operation of the service being directly supervised by PTCL. . 1 Vision To be the leading Information and Communication Technology Service Provider in the region by achieving customer satisfaction and maximizing shareholders' value’. The future is unfolding around us. In times to come, we will be the link that allows global communication. We are striving towards mobilizing the world for the future. By becoming partners in innovation, we are ready to shape a future that offers telecom services that bring us closer. 1. 2 Mission To achieve our mission by having: An organizational environment that fosters professionalism, motivation and quality * An environment that is cost effective and quality conscious * Services that are based on the most optimum technology * â€Å"Quality† and â€Å"Time† conscious customer service * Sustained growth in earnings and profitability 4. SERVICES OF PTCL Pakistan Telecom munication Company Limited not only Provides Conventional telephone facilities, it also offers optical fiber services to the private sector. We will briefly discuss below the product lines being offered by the PTCL.Basically PTCL divide their services into two parts. 1 Services for consumers2. Services for corporate customers 4. 1 Services for Consumers These services are basically for the common users (Individual/home users) those use telephone in their home/work place and they are basically non business users. a) New Telephone Connections: As mentioned earlier, PTCL is presently the only telecom company, who provided fixed-line telephony in the country. So whenever, any Private business concern or any individual needs a new telephone connection for provision of telephone service. ) Value Added Services: CLI (Caller’s Line Identification) Caller Line Identification (CLI): Calling line Identification (CLI) allow customers to identify the caller before picking up the phone rec eiver. To subscribe to CLI services, customer needs a telephone set with display capability or a CLI device attached to the phone. Thereby generating an account on I/N platform and any call made from that telephone will be charged to this account. The service will provide state of art technological facilities to the subscribers. 5. CUSTOMER CARE & CUSTOMER SERVICES DEPARTMENTPTCL has established its Customer Services Department at different levels the overview of the said department is as follows. Corporate  Customer  Care  Center  Operation  Region  Level Customer  Services  Centers  Tensile  Level Toll  Free  Help  Lines  for  Complaint  &  Enquiry now we briefly introduce the functions of these: Corporate Customer Care Center  to facilitate Corporate Customers PTCL has established Corporate Customer Care Centers at all Operation Regional Head Quarter Level, in all the meager cities countrywide. The Corporate Customers can get their problems res olved under one roof in a one window environment by dialing UAN 111-20 20 2.The Customer Relation Officers register the complaints & forward these to the related office. Customer Services Centers to facilitate consumers PTCL has established Customer Services Centers at all Tensile Level cities/offices. Here the consumers can use Fax Facility, Voice Telephony for  Local/NWD/ISD dialing. On divisional Offices Level duplicate phone bills may also be obtained from C. S. C’s. Toll Free Help Lines PTCL offers state-of-the-art call center network to its all type of valued customers for  convenient frequently asked Questions, Complaints regarding their services, T/No enquiry.The following three Toll Free T/Numbers are available for this purpose. a) 1236 (Service Activation) This toll free No is used to change the tariff packages of land line, WLL (V-fone),v PTCL phone n net service activation, & for Broad Band customers. The service activation is electronically ordered & activate d within 24 hours through concerned department) 1217 (Telephone Directory)This facility is also Toll Free & is used to obtain the telephone numbers of some specific subscribers (College, Govt. offices, Private offices etc. ).This is centralized & is being used as Telephone Directory) 1218 (Land Line Complaints 6. Projects and Assignments During Internship I was assigned to submit the daily market visit report to the consultant officer in which I had to find out the new costumers as well as to write down the complaints of the costumers regarding the products they use or any suggestions were always welcomed. Also I was assigned to meet at least 15 prospects and make them aware about Products and services like BB, Evo, IPtv, D-SET, H-set, Pstn, and Tab For this publicity I was trained for one week to make right publicity about the organization. . Recommendations: * Pakistan Telecommunication Company should Increase Publicity and Advertisement Activities. * Recruitment and selection opp ortunities should be increased. * Free Seminars should be organized. * They should not only focus on metropolitan cities but also should take close attention to the rural areas and small towns. * They should improve their Costumer care services. * To increase their sell activities they should create better strategies. Ptcl Report 1. Introduction: Pakistan Telecommunication Company Limited  (PTCL) is a  mega corporation  and a leading telecommunication authority in the  State of Pakistan. The corporation provides and enforces policies for the telephonic services nation-wide and is the backbone for country's telecommunication infrastructure despite arrival of a dozens other telecommunication corporations, including  Telenor Corps  and  China Mobile Ltd. The corporation managed and operates around ~2000 telephone exchanges across the country, providing the largest fixed line network.Data and backbone services such as GSM, CDMA, Broadband Internet, and IPTV, wholesale are an increasing part of its business. From the beginnings of Posts ; Telegraph Department in 1947 and establishment of Pakistan Telephone ; Telegraph Department in 1962, PTCL has been a major player in telecommunication in Pakistan. Despite having established a network of enormous size, PTCL workings and policies have attracted regul ar criticism from other smaller operators and the civil society of Pakistan.Pakistan Telecommunication Corporation (PTC) took over operations and functions from Pakistan Telephone and Telegraph Department under Pakistan Telecommunication Corporation Act 1991. In 1995, Pakistan Telecommunication (Reorganization) Ordinance formed the basis for PTCL monopoly over basic telephony in the country. The provisions of the Ordinance were lent permanence in October 1996 through Pakistan Telecommunication (Reorganization) Act.The same year, Pakistan Telecommunication Company Limited was formed and listed on all stock exchanges of Pakistan PTCL launched its mobile and data services subsidiaries in 2001 by the name of Ufone and PakNet respectively. None of the brands made it to the top slots in the respective competitions Lately, however, Ufone had increased its market share in the cellular sector. The PakNet brand has effectively dissolved over the period of time. Recent DSL services launched by PTCL reflect this by the introduction of a new brand name and operation of the service being directly supervised by PTCL. . 1 Vision To be the leading Information and Communication Technology Service Provider in the region by achieving customer satisfaction and maximizing shareholders' value’. The future is unfolding around us. In times to come, we will be the link that allows global communication. We are striving towards mobilizing the world for the future. By becoming partners in innovation, we are ready to shape a future that offers telecom services that bring us closer. 1. 2 Mission To achieve our mission by having: An organizational environment that fosters professionalism, motivation and quality * An environment that is cost effective and quality conscious * Services that are based on the most optimum technology * â€Å"Quality† and â€Å"Time† conscious customer service * Sustained growth in earnings and profitability 4. SERVICES OF PTCL Pakistan Telecom munication Company Limited not only Provides Conventional telephone facilities, it also offers optical fiber services to the private sector. We will briefly discuss below the product lines being offered by the PTCL.Basically PTCL divide their services into two parts. 1 Services for consumers2. Services for corporate customers 4. 1 Services for Consumers These services are basically for the common users (Individual/home users) those use telephone in their home/work place and they are basically non business users. a) New Telephone Connections: As mentioned earlier, PTCL is presently the only telecom company, who provided fixed-line telephony in the country. So whenever, any Private business concern or any individual needs a new telephone connection for provision of telephone service. ) Value Added Services: CLI (Caller’s Line Identification) Caller Line Identification (CLI): Calling line Identification (CLI) allow customers to identify the caller before picking up the phone rec eiver. To subscribe to CLI services, customer needs a telephone set with display capability or a CLI device attached to the phone. Thereby generating an account on I/N platform and any call made from that telephone will be charged to this account. The service will provide state of art technological facilities to the subscribers. 5. CUSTOMER CARE & CUSTOMER SERVICES DEPARTMENTPTCL has established its Customer Services Department at different levels the overview of the said department is as follows. Corporate  Customer  Care  Center  Operation  Region  Level Customer  Services  Centers  Tensile  Level Toll  Free  Help  Lines  for  Complaint  &  Enquiry now we briefly introduce the functions of these: Corporate Customer Care Center  to facilitate Corporate Customers PTCL has established Corporate Customer Care Centers at all Operation Regional Head Quarter Level, in all the meager cities countrywide. The Corporate Customers can get their problems res olved under one roof in a one window environment by dialing UAN 111-20 20 2.The Customer Relation Officers register the complaints & forward these to the related office. Customer Services Centers to facilitate consumers PTCL has established Customer Services Centers at all Tensile Level cities/offices. Here the consumers can use Fax Facility, Voice Telephony for  Local/NWD/ISD dialing. On divisional Offices Level duplicate phone bills may also be obtained from C. S. C’s. Toll Free Help Lines PTCL offers state-of-the-art call center network to its all type of valued customers for  convenient frequently asked Questions, Complaints regarding their services, T/No enquiry.The following three Toll Free T/Numbers are available for this purpose. a) 1236 (Service Activation) This toll free No is used to change the tariff packages of land line, WLL (V-fone),v PTCL phone n net service activation, & for Broad Band customers. The service activation is electronically ordered & activate d within 24 hours through concerned department) 1217 (Telephone Directory)This facility is also Toll Free & is used to obtain the telephone numbers of some specific subscribers (College, Govt. offices, Private offices etc. ).This is centralized & is being used as Telephone Directory) 1218 (Land Line Complaints 6. Projects and Assignments During Internship I was assigned to submit the daily market visit report to the consultant officer in which I had to find out the new costumers as well as to write down the complaints of the costumers regarding the products they use or any suggestions were always welcomed. Also I was assigned to meet at least 15 prospects and make them aware about Products and services like BB, Evo, IPtv, D-SET, H-set, Pstn, and Tab For this publicity I was trained for one week to make right publicity about the organization. . Recommendations: * Pakistan Telecommunication Company should Increase Publicity and Advertisement Activities. * Recruitment and selection opp ortunities should be increased. * Free Seminars should be organized. * They should not only focus on metropolitan cities but also should take close attention to the rural areas and small towns. * They should improve their Costumer care services. * To increase their sell activities they should create better strategies.