1,725,628 research outputs found
Calpe y al-Askar (Alicante): sobre el hábitat medieval del Peñón de Ifach y al-Askar o Madinat al-Askar
Full text linkEstudios y reflexiones arqueológicas sobre el Peñón de Ifach de Calpe (Alicante), con restos de muralla y torres de los siglos XIII y XIV, y la voz árabe al ‘Askar que las Crónicas árabes del siglo X aplican a una ciudad campamento (Madinat al ‘Askar). Se insiste en la tesis de M.J. Rubiera / M. de Epalza y otros autores que sitúan esa ciudad en la amplia comarca del actual Callosa d’En Sarrià, donde se registran topónimos cristianos (s. XIII) derivados de al ‘Askar árabe del siglo X. En esa comarca se encuentran restos de cerámica vidriada tipo califal.Archaeological studies and reflections above Ifach Rock at Calpe (Alicante) with walls and towers remains of the Thirteen and Fourteen centuries, and the Arab Voice al ‘Askar which refering to the Tenth century Islamic chronicles are identified with a town-camping (Madinat al ‘Askar). According to M.J. Rubiera / M. de Epalza and other authors’ thesis, I insist on the idea that madinat al ‘Askar is a wide district of the present village Callosa D’En Sarrià. Here appear Islamic glazed ceramics remains of the Tenth Century and also Christian place-names (XIII C.) derived from the Islamic al ‘Askar
Jessica Askar, Urban School Educator
Full text linkJessica Askar knows that successful students become successful adults. She has served children and families for seven years working as an elementary school teacher in an urban district in Chicago. So far, fourth grade is her favorite grade to teach. Ms. Askar\u27s teaching revolves around culturally relevant pedagogy and critical thinking. Due to her leadership and passion for education, Ms. Askar was awarded the Harrison Fellowship at National Louis University where she majored in elementary education with a concentration in mathematics. She is an experienced math tutor and mentor. Throughout her education career, she worked as the lead math tutor for the Center for Academic Success (CAD) and mentored young adults. She is currently working on her Master of Education degree at National Louis University.https://digitalcommons.nl.edu/hforalhistories/1000/thumbnail.jp
Predicting Norwegian elderly hospitalizations using Machine Learning
Full text linkThis thesis focuses on developing a Machine Learning (ML) model to predict the first hospital admission in older Norwegian patients.
Addressing the complexity of predicting all-cause hospitalizations required a comprehensive approach, beginning with a systematic review of existing work. The review highlighted some important gaps in the use of ML for predicting hospitalization such as challenges in representing Health Code Systems (HCSs), the quality of reporting, and individual clinical interpretations. These insights were starting points for the methodological and practical frameworks presented in this thesis.
To tackle the representation of HCSs, while taking into consideration the trade-off between model performance and meaningful clinical interpretations, we proposed a methodology based on Network Analysis (NA) modularity detection. The idea was to group these codes after their prevalence in the population. HCSs such as the Internation Classification of Disease (ICD), were modeled as a network, where nodes represent the codes and edges quantify co-occurrence among patients. The methodology demonstrated good predicting performance and several advantages over traditional grouping approaches. Building on that, and to validate the clinical relevance of this methodology, we demonstrated a framework for detecting and interpreting Multimorbidity Patterns (MPs) using data from the Norwegian elderly hospitalized population.
This thesis focuses on developing a Machine Learning (ML) model to predict the first hospital admission in older Norwegian patients.
Addressing the complexity of predicting all-cause hospitalizations required a comprehensive approach, beginning with a systematic review of existing work. The review highlighted some important gaps in the use of ML for predicting all-cause hospitalization such as challenges in representing high-dimensional Health Code Systems (HCSs), the quality of reporting, clinical interpretations on the individual patient level, and models’ deployment. These insights were the starting point for the methodological and practical framework presented in this thesis.
To tackle the representation of HCSs, while taking into consideration the trade-off between model performance and meaningful clinical interpretations, we proposed a methodology based on Network Analysis (NA) modularity detection. The idea was to group these codes after their prevalence in the population. HCSs such as the Internation Classification of Disease (ICD), were modeled as a network, where nodes represent the codes and edges quantify co-occurrence among patients. The methodology demonstrated good prediction performance and several advantages over traditional grouping approaches. Building on that, and to validate the clinical relevance of this methodology, we demonstrated a framework for detecting and interpreting Multimorbidity Patterns (MPs) using data from the Norwegian older patient hospitalized population.
We finally developed an ML model to predict all-cause somatic hospitalizations. We applied a pipeline to achieve good model performance and to find the most influential features for predicting hospitalizations. We also aimed to address some of the identified gaps in the literature and integrate the usage of the proposed methodology of representing HCSs. The model pipeline incorporated diverse data samples for model training, feature selection technique, and algorithm groups. The model was deployed as a web application to demonstrate the potential use of this work in practice. The thesis provides a clinically relevant framework for healthcare systems investigating similar outcomes and puts the foundation for future research on the Norwegian national level to refine predictive models, expand multimorbidity analyses, and address challenges in clinical deployment.Denne avhandlingen fokuserer på å utvikle en maskinlæringsmodell (ML) for å predikere den første sykehusinnleggelsen hos eldre norske pasienter.
Håndteringen av kompleksiteten av predikering alle typer sykehusinnleggelser krevde en omfattende tilnærming. Vi begynte med en systematisk gjennomgang av eksisterende arbeid. Gjennomgangen avdekket flere viktige svakheter i bruken av ML til å forutsi sykehusinnleggelser, blant annet utfordringer med representasjon av Health Code Systems (HCS), kvaliteten på rapportering og individuelle kliniske tolkninger. Disse funnene dannet utgangspunktet for de metodiske og praktiske rammene som presenteres i denne avhandlingen.
For å takle representasjon av HCS og balansere hensynet til både modellens ytelse og meningsfulle kliniske tolkninger, foreslo vi en metodikk basert på modulæritetsdeteksjon i nettverksanalyse (NA). Målet var å gruppere kodene ut fra hvor vanlige de er i befolkningen. HCS-er, som for eksempel International Classification of Disease (ICD), ble modellert som et nettverk der noder representerer kodene og kantene viser samforekomst blant pasienter. Metodikken ga god modellytelse og viste flere fordeler sammenlignet med tradisjonelle grupperingsmetoder. For å bekrefte den kliniske relevansen av denne tilnærmingen, lagde vi et rammeverk for å oppdage og tolke multimorbiditetsmønstre (MP-er) ved bruk av data fra eldre sykehuspasienter i Norge.
Vi utviklet til slutt en ML-modell for å predikere somatiske sykehusinnleggelser av alle årsaker. Hensikten var å fylle noen av kunnskapshullene i litteraturen og inkludere den foreslåtte metodikken for å representere HCS. Modellen ble bygget ved hjelp av ulike datahåndteringsteknikker, metoder for utvelgelse av funksjoner og flere algoritmegrupper. Den ble deretter gjort tilgjengelig som en nettapplikasjon for å illustrere hvordan den kan tas i bruk i praksis. Avhandlingen presenterer et klinisk relevant rammeverk for helsesektorer som ønsker å undersøke tilsvarende problemstillinger, og legger grunnlaget for videre forskning på nasjonalt nivå i Norge for å forbedre prediksjonsmodeller, utvide multimorbiditetsanalyser og adressere utfordringer ved klinisk implementering
Askar Wataniah mampu pupuk semangat cinta negara
Full text linkKUANTAN: Penuntut institusi pengajian tinggi(IPT) perlu menyertai pasukan unit beruniform terutama Askar Wataniah untuk memupuk semangat cintakan negara, kata Pegawai Memerintah Batalion Pertama Rejimen 505 Askar Wataniah, Leftenan Kolonel Datuk Seri Tengku Kamarulzaman Sultan Abu Bakar
[Jorge Abdo Askar, técnico da FJP]
No full textJorge Abdo Askar, técnico da Fundação João Pinheiro (FJP)
Askar wataniah bentuk disiplin
Full text linkAskar Wataniah perlu terus mengekalkan tahap disiplin yang tinggi selain memiliki semangat kental bagi memastikan imej sebagai benteng pertahanan kedua negara digalas dengan penuh rasa tanggungjawab
Aspects of Designing Sustainable Development Policies in Developing Countries
Full text linkThis thesis is composed of the following four articles organized in a book format. 1. ‘Energy subsidy reform for growth and equity in Egypt: The approach matters’ by Clemens Breisinger, Askar Mukashov, Mariam Raouf, and Manfred Wiebelt. The paper is published in Energy Policy (2019, Vol. 129, 661-671, doi:10.1016/j.enpol.2019.02.059). 2. ‘Modeling conflict impact and post-conflict reconstruction: The case of Yemen’ by Clemens Breisinger, Wilfried Engelke, Askar Mukashov, and Manfred Wiebelt, 2020. 3. ‘Parameter Uncertainty in Policy Planning Models: Using Portfolio Management Methods to Choose Optimal Policies under World Market Volatility’ by Askar Mukashov, 2021. 4. ‘The Role of Global Climate Change in Structural Transformation of Sub-Saharan Africa: Case Study of Senegal’ by Askar Mukashov, Christian Henning, Richard Robertson, and Manfred Wiebelt, 202
[Radios Askar, si es Askar es mejor, en Electro Hogar : anuncio publicitario]
No full textJingle publicitario Askar en Electrohogar, calle San Francisco 26 de Alco
Transkrip wawancara bersama Mantan Sarjan Mejar Rejimen Askar Diraja Malaysia Haji Mohd Din Bin Sulaiman Askar Veteran / Nor Amiza Mat Akhir and Nor Atiqah Mohd Din
Full text linkSatu temubual antara Nor Amiza binti Mat Akhir dan Nor Atiqah binti Mohd Din bersama dengan bekas askar Rejimen Askar Melayu Diraja (RAMD). Beliau telah dilahirkan pada 14/08/1949 di Kampung Gating, Johol. Beliau ialah seorang bekas askar yang telah berkhidmat pada tahun 1969 sehingga 1992. Sesi temubual telah dijalankan di rumah beliau sendiri Kampung Senaling. Temubual ini telah dijalankan bagi mengetahui mengenai pengalaman manis dan pahit beliau sebagai anggota tentera yang telah berkhidmat selama 22 tahun sepanjang beliau berkhidmat. Temubual ini juga sebagai salah satu kajian yang perlu kami jalankan bagi memenuhi kajian kami di dalam subjek IMR 604 iaitu Oral History. Selain itu juga, temubual yang dijalankan ini memberikan suatu pandangan terhadap askar-askar zaman dahulu yang sanggup mengorbankan nyawa, masa, keluarga demi mempertahankan tanah air kita, tumpahnya darah kita iaitu Malaysia. Melalui temubual ini juga, kesedaran dapat di peroleh melalui perjuangan yang di pamerkan oleh Encik Mohd Din bersama dengan rakan-rakan ketika berkhidmat sebagai Rejimen Askar Diraja Melayu
d) Bâb Qâdi 'Askar
No full textSimaïka Marcus H., Greg Robert Hyde, Home John, Verrucci Ernesto, Mahbub Mahmud Sabri, Mustafa Fahmy, Sayed Metoualli, Pauty Edmond. d) Bâb Qâdi 'Askar. In: Comité de Conservation des Monuments de l'Art Arabe. Fascicule 36, exercice 1930-1932, 1936. pp. 249-250
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