Here are 3 questions with their corresponding SQL queries using the hospital database:
1. Display the IDs of doctors that do not receive annual bonuses.
SELECT doc_id FROM doctor WHERE annual_bonus IS NULL;
2. Display doctor names whose names start with 'J' or end with 'n'.
SELECT doc_name FROM doctor WHERE doc_name LIKE 'J%' OR doc_name LIKE '%n';
3. Display doctor IDs whose annual bonus is less than 2000 or greater than 4000.
SELECT doc_id FROM doctor WHERE annual_bonus < 2000 OR annual_bonus > 4000;
Buddi health class imbalance based deep learningRam Swaminathan
BUDDI Health is a disruptive Deep Learning Platform which helps automate healthcare administrative functions such as Medical Coding, Claims Denial Prediction, Clinical Documentation Improvement and more.
Examination is the process by which the ability and the quality of the examinees can be measured. It is necessary to ensure the quality of the examinees. Online examination system is the process by which the participants can appear at the examination irrespective of their locations by connecting to examination site via Internet using desktop computers, laptops or smart phones. Automated score generation is the process by which the answer scripts of the examinations are evaluated automatically to generate scores. Although, there are many existing online examination systems, the main drawback of these systems is that they cannot compute automated score accurately, especially from the text-based answers. Moreover, most of them are unilingual in nature. As a result, examinees can appear at the examination in a particular language. Considering this fact, in this paper, we present a framework that can take Multiple Choice Questions (MCQ) examinations and written examinations in two different languages English and Bangla. We develop a database where the questions and answers are stored. The questions from the database are displayed in the web page with answering options for the MCQ questions and text boxes for the written questions. For generating the scores of the written questions, we performed several types of analysis of the answers of the written questions. However, for generating the scores of the MCQ questions, we simply compared between the database answers and the user’s answers. We conducted several experiments to check the accuracy of score generation by our system and found that our system can generate 100% accurate scores for MCQ questions and more than 90% accurate scores from text based questions.
Every year the most prestigious and biggest University of India, Delhi University conducts the JAT entrance exam. Delhi University JAT Entrance Exam is conducted for selecting students for their professional courses which are BMS, BBA-FIA, and BBE.
Analysis of sms feedback and online feedback using sentiment analysis for ass...eSAT Journals
Abstract In this, the system will collect SMS’s and online Feedback from students in free text format. To get instance response of lecturer feedback by analyzing free text messages using sentiment analysis. It is very low cost and plat form independent. It will show you three different outputs, i.e. Good, Bad and Average/Neutral. The result will be shown in graphical format. Key Words: Sentiment analysis, online feed-back.
This paper introduces the competency models for Operations Manager, User Interface
Designer, and Application Developers. It will serve as a guide for Information Systems students
to identify which among the three of the offered tracks would be most suited for them to pursue
according to their knowledge, skills, values and interests. The Holland’s RIASEC model and the
Values Search model of Bronwyn and Holt were utilized to determine the most dominant interest
and most dominant values of the industry computing experts. Survey assessment forms were sent
to IT Operations Manager, User Interface Designer, and Application Developer. Most dominant
values and interests of industry computing experts were determined as well as the knowledge
and skills which are mostly required by the industry in their particular area. Based on the result
of the survey, it shows that application developer and user interface designer have a closely
related values. Thus a second round of a survey would be needed to come up with the most
exclusive dominant values for the particular information systems specialization track.
Buddi health class imbalance based deep learningRam Swaminathan
BUDDI Health is a disruptive Deep Learning Platform which helps automate healthcare administrative functions such as Medical Coding, Claims Denial Prediction, Clinical Documentation Improvement and more.
Examination is the process by which the ability and the quality of the examinees can be measured. It is necessary to ensure the quality of the examinees. Online examination system is the process by which the participants can appear at the examination irrespective of their locations by connecting to examination site via Internet using desktop computers, laptops or smart phones. Automated score generation is the process by which the answer scripts of the examinations are evaluated automatically to generate scores. Although, there are many existing online examination systems, the main drawback of these systems is that they cannot compute automated score accurately, especially from the text-based answers. Moreover, most of them are unilingual in nature. As a result, examinees can appear at the examination in a particular language. Considering this fact, in this paper, we present a framework that can take Multiple Choice Questions (MCQ) examinations and written examinations in two different languages English and Bangla. We develop a database where the questions and answers are stored. The questions from the database are displayed in the web page with answering options for the MCQ questions and text boxes for the written questions. For generating the scores of the written questions, we performed several types of analysis of the answers of the written questions. However, for generating the scores of the MCQ questions, we simply compared between the database answers and the user’s answers. We conducted several experiments to check the accuracy of score generation by our system and found that our system can generate 100% accurate scores for MCQ questions and more than 90% accurate scores from text based questions.
Every year the most prestigious and biggest University of India, Delhi University conducts the JAT entrance exam. Delhi University JAT Entrance Exam is conducted for selecting students for their professional courses which are BMS, BBA-FIA, and BBE.
Analysis of sms feedback and online feedback using sentiment analysis for ass...eSAT Journals
Abstract In this, the system will collect SMS’s and online Feedback from students in free text format. To get instance response of lecturer feedback by analyzing free text messages using sentiment analysis. It is very low cost and plat form independent. It will show you three different outputs, i.e. Good, Bad and Average/Neutral. The result will be shown in graphical format. Key Words: Sentiment analysis, online feed-back.
This paper introduces the competency models for Operations Manager, User Interface
Designer, and Application Developers. It will serve as a guide for Information Systems students
to identify which among the three of the offered tracks would be most suited for them to pursue
according to their knowledge, skills, values and interests. The Holland’s RIASEC model and the
Values Search model of Bronwyn and Holt were utilized to determine the most dominant interest
and most dominant values of the industry computing experts. Survey assessment forms were sent
to IT Operations Manager, User Interface Designer, and Application Developer. Most dominant
values and interests of industry computing experts were determined as well as the knowledge
and skills which are mostly required by the industry in their particular area. Based on the result
of the survey, it shows that application developer and user interface designer have a closely
related values. Thus a second round of a survey would be needed to come up with the most
exclusive dominant values for the particular information systems specialization track.
Artificial intelligence involves two basic ideas. First, it involves studying the thought processes of human beings. Second, it deals with representing those processes via machines (like computers, robots, etc.). Artificial intelligence (AI) technologies and techniques have useful purposes in every domain of mental health care including clinical decision-making, treatments, assessment, self-care, mental health care management and more. This application involves an AI based fuzzy expert system which helps the students to give a basic idea or insight of possible career opportunities, to enable them to move forward towards the path most suitable for them in all respects. This project will give a personal aid to the students taking into consideration, the student’s interest and aptitude test result. The fuzzy expert in our project will choose accurate careers for the user accordingly.
Holistic Approach for Arabic Word RecognitionEditor IJCATR
Optical Character Recognition (OCR) is one of the important branches. One segmenting words into character is one of the
most challenging steps on OCR. As the results of advances in machine speeds and memory sizes as well as the availability of large
training dataset, researchers currently study Holistic Approach “recognition of a word without segmentation”. This paper describes a
method to recognize off-line handwritten Arabic names. The classification approach is based on Hidden Markov models.. For each
Arabic word many HMM models with different number of states have been trained. The experiments result are encouraging, it also
show that best number of state for each word need careful selection and considerations.
Result generation system for cbgs scheme in educational organizationeSAT Journals
generation system. We will try to implement the rules as per University norms
according to the CBGS system. The necessity of this system is to ease the process of result generation and once the data is fed in
the system, could be used to calculate the result and generate it in the desired format. We can analyze this data and generate
various reports needed using some data mining techniques. The objective is to generate result that will be semester wise for each
student. Analyzing these reports would give various parameters such as students passing or failing, semester wise as well as
subject wise. The software will be accessible only to authorized users so that security could be maintained.
Key Words: CBGS System
Towards a new ontology of the Moroccan Post-baccalaureate learner profile for...iosrjce
IOSR Journal of Computer Engineering (IOSR-JCE) is a double blind peer reviewed International Journal that provides rapid publication (within a month) of articles in all areas of computer engineering and its applications. The journal welcomes publications of high quality papers on theoretical developments and practical applications in computer technology. Original research papers, state-of-the-art reviews, and high quality technical notes are invited for publications.
AT&T Voice Over IP Connect Service (AVOICS) provides Wholesale customers with transport and termination of domestic and international VoIP traffic. I collaborated with the Sales, Product and Contracting teams on many custom pricing solutions for AVOICS.
Artificial intelligence involves two basic ideas. First, it involves studying the thought processes of human beings. Second, it deals with representing those processes via machines (like computers, robots, etc.). Artificial intelligence (AI) technologies and techniques have useful purposes in every domain of mental health care including clinical decision-making, treatments, assessment, self-care, mental health care management and more. This application involves an AI based fuzzy expert system which helps the students to give a basic idea or insight of possible career opportunities, to enable them to move forward towards the path most suitable for them in all respects. This project will give a personal aid to the students taking into consideration, the student’s interest and aptitude test result. The fuzzy expert in our project will choose accurate careers for the user accordingly.
Holistic Approach for Arabic Word RecognitionEditor IJCATR
Optical Character Recognition (OCR) is one of the important branches. One segmenting words into character is one of the
most challenging steps on OCR. As the results of advances in machine speeds and memory sizes as well as the availability of large
training dataset, researchers currently study Holistic Approach “recognition of a word without segmentation”. This paper describes a
method to recognize off-line handwritten Arabic names. The classification approach is based on Hidden Markov models.. For each
Arabic word many HMM models with different number of states have been trained. The experiments result are encouraging, it also
show that best number of state for each word need careful selection and considerations.
Result generation system for cbgs scheme in educational organizationeSAT Journals
generation system. We will try to implement the rules as per University norms
according to the CBGS system. The necessity of this system is to ease the process of result generation and once the data is fed in
the system, could be used to calculate the result and generate it in the desired format. We can analyze this data and generate
various reports needed using some data mining techniques. The objective is to generate result that will be semester wise for each
student. Analyzing these reports would give various parameters such as students passing or failing, semester wise as well as
subject wise. The software will be accessible only to authorized users so that security could be maintained.
Key Words: CBGS System
Towards a new ontology of the Moroccan Post-baccalaureate learner profile for...iosrjce
IOSR Journal of Computer Engineering (IOSR-JCE) is a double blind peer reviewed International Journal that provides rapid publication (within a month) of articles in all areas of computer engineering and its applications. The journal welcomes publications of high quality papers on theoretical developments and practical applications in computer technology. Original research papers, state-of-the-art reviews, and high quality technical notes are invited for publications.
AT&T Voice Over IP Connect Service (AVOICS) provides Wholesale customers with transport and termination of domestic and international VoIP traffic. I collaborated with the Sales, Product and Contracting teams on many custom pricing solutions for AVOICS.
SQL provides powerful but reasonably simple tools for data analysis and handling. Mike McClellan, the Senior Product Manager for Paddle8, took beginners through the basics of SQL. He talked about the SQL queries needed to collect data from a database, even if it lives in different places and analyze it to find the answers you’re looking for.
He taught the understanding of essential SQL skills that allow developers to write queries against single and multiple tables, manipulate data in tables, and create database objects.
This paper presents a comparative analysis of various machine learning classification models for
structured query language injection prevention. The objective is to identify the best-performing model in
terms of accuracy on a given dataset. The study utilizes popular classifiers such as Logistic Regression,
Naive Bayes, Decision Tree, Random Forest, K-Nearest Neighbors, and Support Vector Machine. Based on
the tests used to evaluate the performance of the classifiers, the Naïve Bayes gets the highest level of
accurate detection. The results show a 97.06% detection rate for the Naïve Bayes, followed by
LogisticRegression (0.9610), Support Vector Machine (0.9586), RandomForest (0.9530), DecisionTree
(0.9069), and K-Nearest Neighbor (0.6937). The code snippet provided demonstrates the implementation
and evaluation of these models.
Skill Gap Analysis for Improved Skills and Quality DeliverablesIJERA Editor
With a growing pressure in identifying the skilled resources in Clinical Data Management (CDM) world of clinical research organizations, to provide the quality deliverables most of the CDM organizations are planning to improve the skills within the organization. In changing CDM landscape the ability to build, manage and leverage the skills of clinical data managers is very critical and important. Within CDM to proactively identify, analyze and address skill gaps for all the roles involved. In addition to domain skills, the evolving role of a clinical data manager demands diverse skill sets such as project management, six sigma, analytical, decision making, communication etc. This article proposes a methodology of skill gap analysis (SGA) management as one of the potential solutions to the big skill challenge that CDM is gearing up for bridging the gap of skills. This would in turn strength the CDM capability, scalability, consistency across geographies along with improved productivity and quality of deliverables
Structured Query Language for Data Management 2 Sructu.docxjohniemcm5zt
Structured Query Language for Data Management 2
Sructured Query Language for Data Management 6
Table of Contents
Phase 1- Database Design and DDL 3
Business Rules & Entity Tables 3
Entity Tables: 4
SQL CODE: 4
Screenshots: 8
Phase 2 – Security and DML 13
Task 1 14
Task 2 15
Task 3 16
Task 4 17
Task 5 18
Phase 3 - DML (Select) and Procedures 19
Task 1 19
Task 2 20
Task 3 21
Task 4 22
Task 5 23
Phase 4 – Architecture, Indexes 27
Step 1: CREATE TABLE [Degrees] 27
Step 2: Re-create ‘Classes’ TABLE to add ‘DegreeID’ column and INSERT 6 classes 29
Step 3: ALTER TABLE [Students] 31
Step 5: DML script to INSERT INTO the ‘Students’ table ‘DegreeID’ data 33
Step 6: Display ERD 36
Phase 5 – Views, Transactions, Testing and Performance 37
References 38
Phase 1- Database Design and DDL
My team was recently contracted to design and develop a database for CTU that will store personal and confidential university data. This database is expected to provide the back-end architecture for a front-end web application with an intuitive User/Interface (U/I) to be used by the university HR department. We’ve decided to use Microsoft SQL Server 2012 given the nature of data to be stored because it will be more secure, and it also provides a suite of server maintenance tools to be left behind with the IT Department once the database and web application have been tested and accepted by university stakeholders.
During our preliminary meetings, CTU’s requirements were defined and adequately scoped to begin creation of the database. The following sections contain the business rules and entity tables developed during the preliminary meetings, as well as copies of all the SQL code used to build the database and create the Entity Relationship Diagram (ERD). Business Rules & Entity Tables
Business Rules:
· A student has a name, a birth date, and gender.
· You must track the date the student started at the university and his or her current GPA, as well as be able to inactivate him or her without deleting information.
· For advising purposes, store the student's background/bio information. This is like a little story.
· An advisor has a name and an e-mail address.
· Students are assigned to one advisor, but one advisor may service multiple students.
· A class has a class code, name, and description.
· You need to indicate the specific classes a student is taking/has taken at the university. Track the date the student started a specific class and the grade earned in that class.
· Each class that a student takes has 4 assignments. Each assignment is worth 100 points.Entity Tables:
SQL CODE:
Create Database:
CREATE DATABASE [Cameron_CTU]
CONTAINMENT = NONE
ON PRIMARY
( NAME = N'Cameron_CTU', FILENAME = N'c:\Program Files\Microsoft SQL Server\MSSQL11.SCAMERON_CTU\MSSQL\DATA\Cameron_CTU.mdf' , SIZE = 3072KB , FILEGROWTH = 1024KB )
LOG ON
( NAME = N'Cameron_CTU_log', FILENAME = N'c:\Program Files\Microsoft SQL Server\MSSQL11.SCAMERON_CTU\MSSQL\DATA\Cameron_CTU_.
Ethanol (CH3CH2OH), or beverage alcohol, is a two-carbon alcohol
that is rapidly distributed in the body and brain. Ethanol alters many
neurochemical systems and has rewarding and addictive properties. It
is the oldest recreational drug and likely contributes to more morbidity,
mortality, and public health costs than all illicit drugs combined. The
5th edition of the Diagnostic and Statistical Manual of Mental Disorders
(DSM-5) integrates alcohol abuse and alcohol dependence into a single
disorder called alcohol use disorder (AUD), with mild, moderate,
and severe subclassifications (American Psychiatric Association, 2013).
In the DSM-5, all types of substance abuse and dependence have been
combined into a single substance use disorder (SUD) on a continuum
from mild to severe. A diagnosis of AUD requires that at least two of
the 11 DSM-5 behaviors be present within a 12-month period (mild
AUD: 2–3 criteria; moderate AUD: 4–5 criteria; severe AUD: 6–11 criteria).
The four main behavioral effects of AUD are impaired control over
drinking, negative social consequences, risky use, and altered physiological
effects (tolerance, withdrawal). This chapter presents an overview
of the prevalence and harmful consequences of AUD in the U.S.,
the systemic nature of the disease, neurocircuitry and stages of AUD,
comorbidities, fetal alcohol spectrum disorders, genetic risk factors, and
pharmacotherapies for AUD.
micro teaching on communication m.sc nursing.pdfAnurag Sharma
Microteaching is a unique model of practice teaching. It is a viable instrument for the. desired change in the teaching behavior or the behavior potential which, in specified types of real. classroom situations, tends to facilitate the achievement of specified types of objectives.
New Drug Discovery and Development .....NEHA GUPTA
The "New Drug Discovery and Development" process involves the identification, design, testing, and manufacturing of novel pharmaceutical compounds with the aim of introducing new and improved treatments for various medical conditions. This comprehensive endeavor encompasses various stages, including target identification, preclinical studies, clinical trials, regulatory approval, and post-market surveillance. It involves multidisciplinary collaboration among scientists, researchers, clinicians, regulatory experts, and pharmaceutical companies to bring innovative therapies to market and address unmet medical needs.
Title: Sense of Taste
Presenter: Dr. Faiza, Assistant Professor of Physiology
Qualifications:
MBBS (Best Graduate, AIMC Lahore)
FCPS Physiology
ICMT, CHPE, DHPE (STMU)
MPH (GC University, Faisalabad)
MBA (Virtual University of Pakistan)
Learning Objectives:
Describe the structure and function of taste buds.
Describe the relationship between the taste threshold and taste index of common substances.
Explain the chemical basis and signal transduction of taste perception for each type of primary taste sensation.
Recognize different abnormalities of taste perception and their causes.
Key Topics:
Significance of Taste Sensation:
Differentiation between pleasant and harmful food
Influence on behavior
Selection of food based on metabolic needs
Receptors of Taste:
Taste buds on the tongue
Influence of sense of smell, texture of food, and pain stimulation (e.g., by pepper)
Primary and Secondary Taste Sensations:
Primary taste sensations: Sweet, Sour, Salty, Bitter, Umami
Chemical basis and signal transduction mechanisms for each taste
Taste Threshold and Index:
Taste threshold values for Sweet (sucrose), Salty (NaCl), Sour (HCl), and Bitter (Quinine)
Taste index relationship: Inversely proportional to taste threshold
Taste Blindness:
Inability to taste certain substances, particularly thiourea compounds
Example: Phenylthiocarbamide
Structure and Function of Taste Buds:
Composition: Epithelial cells, Sustentacular/Supporting cells, Taste cells, Basal cells
Features: Taste pores, Taste hairs/microvilli, and Taste nerve fibers
Location of Taste Buds:
Found in papillae of the tongue (Fungiform, Circumvallate, Foliate)
Also present on the palate, tonsillar pillars, epiglottis, and proximal esophagus
Mechanism of Taste Stimulation:
Interaction of taste substances with receptors on microvilli
Signal transduction pathways for Umami, Sweet, Bitter, Sour, and Salty tastes
Taste Sensitivity and Adaptation:
Decrease in sensitivity with age
Rapid adaptation of taste sensation
Role of Saliva in Taste:
Dissolution of tastants to reach receptors
Washing away the stimulus
Taste Preferences and Aversions:
Mechanisms behind taste preference and aversion
Influence of receptors and neural pathways
Impact of Sensory Nerve Damage:
Degeneration of taste buds if the sensory nerve fiber is cut
Abnormalities of Taste Detection:
Conditions: Ageusia, Hypogeusia, Dysgeusia (parageusia)
Causes: Nerve damage, neurological disorders, infections, poor oral hygiene, adverse drug effects, deficiencies, aging, tobacco use, altered neurotransmitter levels
Neurotransmitters and Taste Threshold:
Effects of serotonin (5-HT) and norepinephrine (NE) on taste sensitivity
Supertasters:
25% of the population with heightened sensitivity to taste, especially bitterness
Increased number of fungiform papillae
Title: Sense of Smell
Presenter: Dr. Faiza, Assistant Professor of Physiology
Qualifications:
MBBS (Best Graduate, AIMC Lahore)
FCPS Physiology
ICMT, CHPE, DHPE (STMU)
MPH (GC University, Faisalabad)
MBA (Virtual University of Pakistan)
Learning Objectives:
Describe the primary categories of smells and the concept of odor blindness.
Explain the structure and location of the olfactory membrane and mucosa, including the types and roles of cells involved in olfaction.
Describe the pathway and mechanisms of olfactory signal transmission from the olfactory receptors to the brain.
Illustrate the biochemical cascade triggered by odorant binding to olfactory receptors, including the role of G-proteins and second messengers in generating an action potential.
Identify different types of olfactory disorders such as anosmia, hyposmia, hyperosmia, and dysosmia, including their potential causes.
Key Topics:
Olfactory Genes:
3% of the human genome accounts for olfactory genes.
400 genes for odorant receptors.
Olfactory Membrane:
Located in the superior part of the nasal cavity.
Medially: Folds downward along the superior septum.
Laterally: Folds over the superior turbinate and upper surface of the middle turbinate.
Total surface area: 5-10 square centimeters.
Olfactory Mucosa:
Olfactory Cells: Bipolar nerve cells derived from the CNS (100 million), with 4-25 olfactory cilia per cell.
Sustentacular Cells: Produce mucus and maintain ionic and molecular environment.
Basal Cells: Replace worn-out olfactory cells with an average lifespan of 1-2 months.
Bowman’s Gland: Secretes mucus.
Stimulation of Olfactory Cells:
Odorant dissolves in mucus and attaches to receptors on olfactory cilia.
Involves a cascade effect through G-proteins and second messengers, leading to depolarization and action potential generation in the olfactory nerve.
Quality of a Good Odorant:
Small (3-20 Carbon atoms), volatile, water-soluble, and lipid-soluble.
Facilitated by odorant-binding proteins in mucus.
Membrane Potential and Action Potential:
Resting membrane potential: -55mV.
Action potential frequency in the olfactory nerve increases with odorant strength.
Adaptation Towards the Sense of Smell:
Rapid adaptation within the first second, with further slow adaptation.
Psychological adaptation greater than receptor adaptation, involving feedback inhibition from the central nervous system.
Primary Sensations of Smell:
Camphoraceous, Musky, Floral, Pepperminty, Ethereal, Pungent, Putrid.
Odor Detection Threshold:
Examples: Hydrogen sulfide (0.0005 ppm), Methyl-mercaptan (0.002 ppm).
Some toxic substances are odorless at lethal concentrations.
Characteristics of Smell:
Odor blindness for single substances due to lack of appropriate receptor protein.
Behavioral and emotional influences of smell.
Transmission of Olfactory Signals:
From olfactory cells to glomeruli in the olfactory bulb, involving lateral inhibition.
Primitive, less old, and new olfactory systems with different path
Lung Cancer: Artificial Intelligence, Synergetics, Complex System Analysis, S...Oleg Kshivets
RESULTS: Overall life span (LS) was 2252.1±1742.5 days and cumulative 5-year survival (5YS) reached 73.2%, 10 years – 64.8%, 20 years – 42.5%. 513 LCP lived more than 5 years (LS=3124.6±1525.6 days), 148 LCP – more than 10 years (LS=5054.4±1504.1 days).199 LCP died because of LC (LS=562.7±374.5 days). 5YS of LCP after bi/lobectomies was significantly superior in comparison with LCP after pneumonectomies (78.1% vs.63.7%, P=0.00001 by log-rank test). AT significantly improved 5YS (66.3% vs. 34.8%) (P=0.00000 by log-rank test) only for LCP with N1-2. Cox modeling displayed that 5YS of LCP significantly depended on: phase transition (PT) early-invasive LC in terms of synergetics, PT N0—N12, cell ratio factors (ratio between cancer cells- CC and blood cells subpopulations), G1-3, histology, glucose, AT, blood cell circuit, prothrombin index, heparin tolerance, recalcification time (P=0.000-0.038). Neural networks, genetic algorithm selection and bootstrap simulation revealed relationships between 5YS and PT early-invasive LC (rank=1), PT N0—N12 (rank=2), thrombocytes/CC (3), erythrocytes/CC (4), eosinophils/CC (5), healthy cells/CC (6), lymphocytes/CC (7), segmented neutrophils/CC (8), stick neutrophils/CC (9), monocytes/CC (10); leucocytes/CC (11). Correct prediction of 5YS was 100% by neural networks computing (area under ROC curve=1.0; error=0.0).
CONCLUSIONS: 5YS of LCP after radical procedures significantly depended on: 1) PT early-invasive cancer; 2) PT N0--N12; 3) cell ratio factors; 4) blood cell circuit; 5) biochemical factors; 6) hemostasis system; 7) AT; 8) LC characteristics; 9) LC cell dynamics; 10) surgery type: lobectomy/pneumonectomy; 11) anthropometric data. Optimal diagnosis and treatment strategies for LC are: 1) screening and early detection of LC; 2) availability of experienced thoracic surgeons because of complexity of radical procedures; 3) aggressive en block surgery and adequate lymph node dissection for completeness; 4) precise prediction; 5) adjuvant chemoimmunoradiotherapy for LCP with unfavorable prognosis.
Basavarajeeyam is an important text for ayurvedic physician belonging to andhra pradehs. It is a popular compendium in various parts of our country as well as in andhra pradesh. The content of the text was presented in sanskrit and telugu language (Bilingual). One of the most famous book in ayurvedic pharmaceutics and therapeutics. This book contains 25 chapters called as prakaranas. Many rasaoushadis were explained, pioneer of dhatu druti, nadi pareeksha, mutra pareeksha etc. Belongs to the period of 15-16 century. New diseases like upadamsha, phiranga rogas are explained.
Knee anatomy and clinical tests 2024.pdfvimalpl1234
This includes all relevant anatomy and clinical tests compiled from standard textbooks, Campbell,netter etc..It is comprehensive and best suited for orthopaedicians and orthopaedic residents.
Report Back from SGO 2024: What’s the Latest in Cervical Cancer?bkling
Are you curious about what’s new in cervical cancer research or unsure what the findings mean? Join Dr. Emily Ko, a gynecologic oncologist at Penn Medicine, to learn about the latest updates from the Society of Gynecologic Oncology (SGO) 2024 Annual Meeting on Women’s Cancer. Dr. Ko will discuss what the research presented at the conference means for you and answer your questions about the new developments.
Explore natural remedies for syphilis treatment in Singapore. Discover alternative therapies, herbal remedies, and lifestyle changes that may complement conventional treatments. Learn about holistic approaches to managing syphilis symptoms and supporting overall health.
Are There Any Natural Remedies To Treat Syphilis.pdf
Info 2102 l4 basic select statement lab1
1. International Islamic University Malaysia
Department of Information Systems
Kulliyyah of Information & Communication Technology
LECTURE 4:
BASIC SELECT
STATEMENTS
Dr. Mira Kartiwi
2. Capabilities of SQL SELECT Statements
International Islamic University Malaysia
Kuliyyah of ICT – Department of Information Systems
Selection (Restriction) – Allows for the retrieval
of rows that satisfy certain specified condition
(predicate).
Projection – Allows for the retrieval of specified
columns (attributes).
Joining – Allows for the linking of data in
different tables.
3. More About SELECT
International Islamic University Malaysia
Kuliyyah of ICT – Department of Information Systems
SELECT is technically a Data Manipulation
Language (DML). However, Oracle does not
classify it as such.
You can write SELECT statements on multiple
lines. However, you are not allowed to split or
abbreviate keywords.
SELECT statements are not case sensitive.
4. More About SELECT
International Islamic University Malaysia
Kuliyyah of ICT – Department of Information Systems
Clauses are usually placed on separate
lines.
Indents are used to enhance readability.
5. Basic SELECT Statement
International Islamic University Malaysia
Kuliyyah of ICT – Department of Information Systems
SELECT *| { [DISTINCT] column | expression
[alias],...}
FROM table;
SELECT identifies what column
FROM identifies which table
8. Using Arithmetic Operations
International Islamic University Malaysia
Kuliyyah of ICT – Department of Information Systems
SELECT doc_name, (annual_bonus+ 500)
FROM doctor;
Four arithmetic operations according to precedence:
(*, /), (+, -)
Operators of the same priority are evaluated from left to
right.
Parentheses are used to force prioritized evaluation and
to clarify statements.
9. Operator Precedence and
Parantheses
International Islamic University Malaysia
Kuliyyah of ICT – Department of Information Systems
SELECT doc_name, annual_bonus,
10*annual_bonus+500
FROM doctor;
SELECT doc_name, annual_bonus,
10*(annual_bonus+500)
FROM doctor;
--What is the difference????
10. NULL Values
International Islamic University Malaysia
Kuliyyah of ICT – Department of Information Systems
How many doctor has Null value in the
result???
Why??
Arithmetic expressions containing a null value
evaluate to null
11. Column Alias
International Islamic University Malaysia
Kuliyyah of ICT – Department of Information Systems
Renames a column heading.
Useful with calculations.
Immediately follows the column name – there can also
be the optional AS keyword between column name and
alias.
Requires double quotation marks if it contains spaces or
special characters or is case sensitive.
12. Column Alias
International Islamic University Malaysia
Kuliyyah of ICT – Department of Information Systems
SELECT doc_name AS name, annual_bonus
FROM doctor
SELECT doc_name AS “Name”, annual_bonus AS
“Bonus”
FROM doctor
SELECT doc_name, annual_bonus AS “Bonus
Upgrade”
FROM doctor
13. Concatenation Operator
International Islamic University Malaysia
Kuliyyah of ICT – Department of Information Systems
SELECT doc_name||area AS “Doctor”
FROM doctor;
Concatenates columns or character strings to
other columns.
Is represented by two vertical bars (||).
Creates a resultant column that is a character
expression.
14. Literal Character String
International Islamic University Malaysia
Kuliyyah of ICT – Department of Information Systems
SELECT doc_name || ' is from ' || area AS “DOCTOR”
FROM doctor;
A literal is a character, a number, or a date included in
the SELECT statement.
Date and character literal values must be enclosed
within single quotation marks.
Each character string is output once for each row
returned.
15. Eliminating Duplicate Rows
International Islamic University Malaysia
Kuliyyah of ICT – Department of Information Systems
SELECT DISTINCT area
FROM doctor;
-- how many area??????
16. Limiting the Rows Selected
International Islamic University Malaysia
Kuliyyah of ICT – Department of Information Systems
SELECT *| { [DISTINCT] column | expression
[alias],...}
FROM table
WHERE condition(s);
17. The Where Clause
International Islamic University Malaysia
Kuliyyah of ICT – Department of Information Systems
The WHERE clause can be added to the
SELECT statement to restrict the results to
rows that satisfy a specified condition.
Rows that do not meet the condition will not be
included in the results.
18. Comparisons
International Islamic University Malaysia
Kuliyyah of ICT – Department of Information Systems
The two expressions must be of the same type
Character string literals should be enclosed in
single quotes
Date literals should be of the form 'DD-MON-
YY'
Numeric literals should consist of digits and
optionally, a decimal and/or sign (no commas
or dollar signs)
19. Comparison Conditions
International Islamic University Malaysia
Kuliyyah of ICT – Department of Information Systems
Equal (=)
Greater than (>)
Greater than or equal to (>=)
Less than (<)
Less than or equal to (<=)
Not equal to (<>, !=, ^=)
20. WHERE Clause
International Islamic University Malaysia
Kuliyyah of ICT – Department of Information Systems
SELECT doc_name
FROM doctor
WHERE chgperappt >= 40;
--What does it means?????
-- how many doctor name James?????
-- What area is Stevenson?????
21. Other Comparison Conditions
International Islamic University Malaysia
Kuliyyah of ICT – Department of Information Systems
Between two values (inclusive) –
BETWEEN... AND...
IN (set)
LIKE
IS NULL
IS NOT NULL
22. BETWEEN Condition
International Islamic University Malaysia
Kuliyyah of ICT – Department of Information Systems
Several special operators serve as shortcuts
for longer expressions.
One is the BETWEEN operator.
It is used to determine whether or not a value
lies within a specific range (including the end
points)
General syntax:Expression1 BETWEEN Expression2 AND
Expression 3
23. BETWEEN Condition
International Islamic University Malaysia
Kuliyyah of ICT – Department of Information Systems
SELECT doc_name, annual_bonus
FROM doctor
WHERE annual_bonus BETWEEN 2000 AND
4000;
-- how many people?
24. The IN Operator
International Islamic University Malaysia
Kuliyyah of ICT – Department of Information Systems
Used to see if a value occurs in a set of
possible values
The set of possible values is specified
within parentheses with commas between
values
25. IN Condition
International Islamic University Malaysia
Kuliyyah of ICT – Department of Information Systems
SELECT doc_id, doc_name, area
FROM doctor
WHERE doc_id IN (100,356, 558);
-- what area are they in???????
26. The IS NULL Operator
International Islamic University Malaysia
Kuliyyah of ICT – Department of Information Systems
Any comparison to a null value that uses the
standard comparison operators will not yield a
match.
If a check for null values is needed, the IS
NULL operator must be used.
27. The IS NULL Operator
International Islamic University Malaysia
Kuliyyah of ICT – Department of Information Systems
Display the IDs of doctors that do not receive
annual bonuses.
SELECT doc_id
FROM doctor
WHERE annual_bonus IS NULL;
--What is the name?---
28. The LIKE Operator
International Islamic University Malaysia
Kuliyyah of ICT – Department of Information Systems
Used in conjunction with wildcard characters
to match character string patterns
% is used to match zero or more characters
_ is used to match a single character
Wildcards cannot be used without the LIKE
operator
The LIKE operator should not be used without
wildcards
29. LIKE Condition
International Islamic University Malaysia
Kuliyyah of ICT – Department of Information Systems
SELECT *
FROM doctor
WHERE doc_name LIKE 'J%';
-- change to lowercase j
Note:
– Represents any sequence of zero or more characters
(%)
– Represents a single character (_)
30. The LIKE Operator
International Islamic University Malaysia
Kuliyyah of ICT – Department of Information Systems
Display the full name and phone number for
customers whose phone number begins with 549-67
SELECT pt_lname || ', ' || pt_fname "FULL NAME"
FROM patient
WHERE ptdob LIKE ’13-MAY-__' ;
31. Logical Operators
International Islamic University Malaysia
Kuliyyah of ICT – Department of Information Systems
3 standard logical operators (AND, OR, and
NOT) are used to combine expressions
AND will return a value of true only if both expressions
are true
OR will return a value of true if either or both of the
expressions are true
NOT will return the opposite value of the expression.
Order of precedence: NOT, AND, OR
32. Other NOT Operators
International Islamic University Malaysia
Kuliyyah of ICT – Department of Information Systems
The NOT operator can also be used in
conjunction with the special operators as
follows:
NOT BETWEEN
NOT IN
IS NOT NULL
NOT LIKE
33. NULL and NOT NULL Conditions
International Islamic University Malaysia
Kuliyyah of ICT – Department of Information Systems
SELECT docid, docname, annual_bonus
FROM doctor
WHERE annual_bonus IS NULL;
TO NEGATE ..
SELECT docid, docname, annual_bonus
FROM doctor
WHERE annual_bonus IS NOT NULL;
34. Lab Assignment 1
International Islamic University Malaysia
Kuliyyah of ICT – Department of Information Systems
Run the hospital script and create a question
in English as well as its SQL query that
generates:
NULL values
Values not within a specific range
List of names that started from or ended with
specific letter