Latihan bab6 cikgugeog

Dec 9, 2012Download as PPTX, PDF0 likes383 views

Kila Shakila

A Sumber manusia
B Sumber budaya
C Sumber tenaga
D Sumber semulajadi

A Sumber manusia
B Sumber budaya
C Sumber tenaga
D Sumber semulajadi

CUBA LAGI MAAF..JAWAPAN NEXT
ANDA SALAH

A Sumber manusia
B Sumber budaya
C Sumber tenaga
D Sumber semulajadi

TAHNIAH NEXT

A PIUTER
B
TAYAR

C BATANG PAIP

D WAYAR

A PIUTER
B
TAYAR

C BATANG PAIP

D WAYAR

CUBA LAGI MAAF..JAWAPAN NEXT
ANDA SALAH

A PIUTER
B
TAYAR

C BATANG PAIP

D WAYAR

TAHNIAH NEXT

BAHAN MINERAL INI DIGUNAKAN DALAM INDUSTRI KACA
DAN PEMBINAAN.

3) HURAIAN DI ATAS ADALAH MENGENAI SUMBER
MINERAL…
A PASIR

B TANIH LIAT

C KAOLIN

D BATU KAPUR

I. PERABOT A 1,11 dan 111
II. CERUCUK
III. ARANG KAYU B 1,11 dan 1V
IV. MENYAMAK KULIT
BINATANG C 1,111 dan IV

D 11,111 dan 1V

I. PERABOT A 1,11 dan 111
II. CERUCUK
III. ARANG KAYU B 1,11 dan 1V
IV. MENYAMAK KULIT
BINATANG C 1,111 dan IV

D 11,111 dan 1V

CUBA LAGI MAAF..JAWAPAN NEXT
ANDA SALAH

I. PERABOT A 1,11 dan 111
II. CERUCUK
III. ARANG KAYU B 1,11 dan 1V
IV. MENYAMAK KULIT
BINATANG C 1,111 dan IV

D 11,111 dan 1V

TAHNIAH NEXT

A i dan ii

B ii dan iii

C iii dan iv

D i dan iv

A i dan ii

B ii dan iii

C iii dan iv

D i dan iv

CUBA LAGI MAAF..JAWAPAN NEXT
ANDA SALAH

A i dan ii

B ii dan iii

C iii dan iv

D i dan iv

TAHNIAH NEXT

This document discusses defining one's leadership legacy. It encourages leaders to clarify what legacy means to them, determine goals to accomplish that legacy, and develop an action plan with first steps. Attendees are asked to pair up and discuss their proudest accomplishments, why leaving a legacy is important, and their strengths to make their campus better. The document provides tips for effective goal setting, including making goals specific, measurable, attainable, realistic, timely and ethical. Overall it focuses on helping leaders reflect on their impact and develop goals and plans to further their leadership legacy.

[DDBJing31] 軽量仮想環境を用いてNGSデータの解析再現性を担保するDNA Data Bank of Japan center

軽量仮想環境を用いてNGSデータの解析再現性を担保する講師：大田達郎（ライフサイエンス統合データベースセンター特任研究員） 2015年6月12日「第31回 DDBJing 講習会 in 東京」＠国立研究開発法人科学技術振興機構東京本部別館 YouTube：YouTube：http://youtu.be/pGtAGOjN2rw DDBJing 講習会：http://www.ddbj.nig.ac.jp/ddbjing/ddbjing.html

Flowers cl1Jalaja Kumari

DDBJ Nucleotide Sequence Submission System の紹介DNA Data Bank of Japan center

[DDBJing31] Mass Submission System の紹介DNA Data Bank of Japan center

Gamification1Jiye Park

Industrial Programming Language (IPL) Reference Manual for Quantities, Logic/...Alkis Vazacopoulos

The IPL code can be programmed in any computer programming language that can interact with dynamic link or shared object libraries and then can be used to call our IMPL modeling and solving platform. IMPL is an acronym for Industrial Modeling and Programming Language provided by Industrial Algorithms LLC. The IPL code allows the user to configure and capture the necessary data to model and solve large-scale and complex industrial optimization problems (IOP's) such as planning, scheduling, control and data reconciliation and regression in either off or on-line environments. IPL, also known as the IMPL Interacter, is a complement to IML (known as the IMPL Interfacer) which means that they can be combined together in any arrangement or combination. That is, a portion of the model can be configured in IML/IPL and the remaining portion can be configured in IPL/IML. In addition, all terminology and nomenclature used in IPL are consistent with IML given that their data is interchangeable and exchangeable. Ultimately, once all of the static and dynamic model data have been configured using IML and IPL, then IMPL’s Modeler will create or generate the necessary IMPL sets, lists, parameters, formulas, variables and constraints (as well as the derivatives and expressions). All integers are 4-bytes (long), all reals are 8-bytes (double precision) and all strings are 64-bytes unless otherwise stated. The return status for the integer functions is zero (0) for successful and non-zero for unsuccessful which usually implies that the unit-operation-port-state names and/or the quality name was not recognized.

Muhammad and sandraeso1113enricborras

The document contains biographical information about several famous people presented as puzzles to guess who each person is based on clues about their life and career. The people described are The Rock, Albert Einstein, Bill Gates, Michael Phelps, and Michael Jackson. For each, clues are given about their date of birth, family background, achievements, and occupations to allow the reader to deduce who the person is.

Cpm2011 pengurusan kokurikulum (a131pjj)Bahari Zakaria

[DDBJing30] Mass Submission System の紹介DNA Data Bank of Japan center

[DDBJing33] ゲノムワイド多型を利用した遺伝解析の実際DNA Data Bank of Japan center

ゲノムワイド多型を利用した遺伝解析の実際講師：白澤健太（かずさDNA研究所先端研究部植物ゲノム?遺伝学研究室主任研究員） 2015年11月11日「第33回 DDBJing 講習会 in 東京」＠国立研究開発法人科学技術振興機構東京本部別館 YouTube：https://youtu.be/AL8gkTCLj_w DDBJing 講習会：http://www.ddbj.nig.ac.jp/ddbjing/ddbjing.html

[DDBJ Challenge 2016] 機械学習コンペティションにおける予測モデリング手法の傾向DNA Data Bank of Japan center

機械学習コンペティションにおける予測モデリング手法の傾向講師：馬場雪乃（京都大学大学院情報学研究科知能情報学専攻助教） 2016年7月6日「DDBJデータ解析チャレンジ 2016 キックオフ講習会」＠国立研究開発法人科学技術振興機構東京本部別館 YouTube：https://www.youtube.com/watch?v=Vb6xWOrbZgU&index=4&list=PL_dbAF_dbOEpSFAZx35F66sH39SLortcR DDBJデータ解析チャレンジ：http://www.ddbj.nig.ac.jp/ddbj-challenge2016-j.html

[DDBJ Challenge 2016] DDBJデータ解析事例「ChIP-Atlasデータベース」の紹介DNA Data Bank of Japan center

DDBJデータ解析事例「ChIP-Atlasデータベース」の紹介講師：沖真弥（九州大学大学院医学研究院発生再生医学分野助教） 2016年7月6日「DDBJデータ解析チャレンジ 2016 キックオフ講習会」＠国立研究開発法人科学技術振興機構東京本部別館 YouTube：https://www.youtube.com/watch?v=_jw2VRvb_Vo&feature=youtu.be DDBJデータ解析チャレンジ：http://www.ddbj.nig.ac.jp/ddbj-challenge2016-j.html

[DDBJ Challenge 2016] 機械学習と予測モデルコンペティションDNA Data Bank of Japan center

機械学習と予測モデルコンペティション講師：鹿島久嗣（京都大学大学院情報学研究科知能情報学専攻教授） 2016年7月6日「DDBJデータ解析チャレンジ 2016 キックオフ講習会」＠国立研究開発法人科学技術振興機構東京本部別館 YouTube：https://www.youtube.com/watch?v=ndJ735cJJco DDBJデータ解析チャレンジ：http://www.ddbj.nig.ac.jp/ddbj-challenge2016-j.html

[DDBJing30] メタゲノム解析と微生物統合データベースDNA Data Bank of Japan center

[DDBJ Challenge 2016] 遺伝研スーパーコンピュータのビッグデータ解析環境DNA Data Bank of Japan center

遺伝研スーパーコンピュータのビッグデータ解析環境講師：石川直史（国立遺伝学研究所スパコン担当SE） 2016年7月6日「DDBJデータ解析チャレンジ 2016 キックオフ講習会」＠国立研究開発法人科学技術振興機構東京本部別館 YouTube： DDBJデータ解析チャレンジ：http://www.ddbj.nig.ac.jp/ddbj-challenge2016-j.html

Talis Insight Asia-Pacific 2017: Craig Milne, Griffith UniversityTalis

The document discusses how Griffith University Library is using reading list data to support its scholarly resource strategy and drive changes to collections. Key points include: - Targeting print to electronic conversions and using reading list data to prioritize and negotiate bulk e-text purchases. - Supporting discussions with publishers to trial and acquire e-texts. - Working to increase the availability of required readings for Open Universities Australia courses online. - Monitoring progress towards the goals of 95% of required readings available online by 2018 and reducing the physical collection footprint by 50% by 2020. - Using reading list data to inform deselection projects and influence future reading selection.

Talis Insight Asia-Pacific 2017: Caroline Ondracek, Griffith UniversityTalis

The document discusses La Trobe University Library's approach to managing reading lists through its partnership with academic staff. Key points: - The library aims to improve student experience by building relationships with subject coordinators and helping create reading lists. - Two library teams work together to populate lists and train staff, with over 900 active lists now. - Initiatives like the "Gold Star Reading List service" help check lists are complete before semesters start. - Feedback has been positive, though some staff still prefer managing lists independently. - The library seeks continued list growth and better integration with learning systems, while addressing challenges like copyright and list display issues.

Talis Insight Asia-Pacific 2017: Reading lists: academic adoption at GriffithTalis

The document outlines Griffith University's adoption of academic reading lists, including agreeing to the adoption with Deans and the Academic Registrar, creating a change management and communication plan, developing a reading list support site, adjusting the "List publisher" role, and providing change management and communication strategies such as drop-in sessions, videos, guides, and updates to the reading list website and teaching community.

Deakin University Presentation - Talis Aspire User Group February 2015Talis

Deakin University implemented Talis Aspire after a successful pilot program. They proposed a two-year transition to a "digital preferred" policy for course readings using the Talis Resource List module. In 2015, they focused on creating reading lists for core units across various faculties manually or from information provided by academics. Their objectives for 2015 were to establish a foundation of lists and train academics. Their next challenges included integrating Equella links, cloud services, and improving bookmarking. Academic survey feedback was positive about the user-friendliness and accessibility of lists. For the user group meeting, Deakin wanted to learn about other universities' data migration, library control levels, workflows, and training approaches for Talis Aspire.

Talis Insight Asia-Pacific 2017: Simon Bedford, University of WollongongTalis

This document provides an overview of learning analytics at the University of Wollongong. It discusses how learning analytics is being used to assist with student retention, personalizing learning, and improving teaching and learning through near real-time data collection and analysis. A case study examines how learning analytics has been implemented in chemistry subjects to provide early student interventions, predict student performance, and inform curriculum improvements based on data. The document emphasizes that learning analytics requires interpreting multiple data sources and partnering with subject specialists to avoid assumptions and optimize student learning.

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