IMPACT ANALYSIS OF COOPERATIVE LEARNING MODEL APPLICATION TYPE TWO STAY TWO STRAY (TSTS) TOWARD LEARNING OUTCOMES OF MATHEMATICS

This research is a comparative experiment. This study aims to determine the effect of the application of cooperative learning model Two Stay Two Stray (TSTS) towards mathematics learning outcomes through comparison with the results of studying mathematics taught using direct learning model. Population, namely the eighth grade students of SMP Negeri 2 Polong Bangkeng Utara Kabupaten Takalar semester of academic year 2016/2017, with a sample of students in grade VIIIA and VIIIB class. The data were analyzed using descriptive statistical analysis techniques and inferential statistics. Descriptive analysis showed that the average student learning outcomes experimental class at the high category, an average of 80.78 with a standard deviation of 11.28; and the average student learning outcomes control class in middle category with an average score of 73.82 with a standard deviation of 12.98. Inferential analysis results obtained H0 and H1 accepted. It can be concluded with 95% confidence that the results of students 'mathematics learning through cooperative learning model Two Stay Two Stray (TSTS) higher than the results of students' mathematics learning through direct learning model

Building Heat Demand Forecasting by Training a Common Machine Learning Model with Physics-Based Simulator

Accurate short-term forecasts of building energy consumption are necessary for profitable demand response. Short-term forecasting methods can be roughly classified into physics-based modelling and data-based modelling. Both of these approaches have their advantages and disadvantages and it would be therefore ideal to combine them. This paper proposes a novel approach that allows us to combine the best parts of physics-based modelling and machine learning while avoiding many of their drawbacks. A key idea in the approach is to provide a variety of building parameters as input for an Artificial Neural Network (ANN) and train the model with data from a large group of simulated buildings. The hypothesis is that this forces the ANN model to learn the underlying simulation model-based physics, and thus enables the ANN model to be used in place of the simulator. The advantages of this type of model is the combination of robustness and accuracy from a high-detail physics-based model with the inference speed, ease of deployment, and support for gradient based optimization provided by the ANN model. To evaluate the approach, an ANN model was developed and trained with simulated data from 900–11,700 buildings, including equal distribution of office buildings, apartment buildings, and detached houses. The performance of the ANN model was evaluated with a test set consisting of 60 buildings (20 buildings for each category). The normalized root mean square errors (NRMSE) were on average 0.050, 0.026, 0.052 for apartment buildings, office buildings, and detached houses, respectively. The results show that the model was able to approximate the simulator with good accuracy also outside of the training data distribution and generalize to new buildings in new geographical locations without any building specific heat demand data

SIGNIFICANCE OF BOTH INTERNAL AND EXTERNAL BOUNDARY CONDITIONS ON HUMAN THERMAL SENSATION

ABSTRACT This paper describes the basic features of a new advanced human thermal model (HTM), which is integrated with a building simulation tool. The thermal sensation calculation of the model has been validated using dynamical temperature step change test results. This new methodology seems promising, and significance of both internal (metabolic rate and clothing) and external (air and surface temperature levels, air velocity, and humidity) boundary conditions can be estimated. This is beneficial, for example, when evaluating new technical concepts for future energy-efficient buildings

Decision Support Tool to Enable Real-Time Data-Driven Building Energy Retrofitting Design

The availability of near-real-time data on energy performance is opening new opportunities to optimize buildings’ energy efficiency and flexibility capabilities and to support the decision-making and planning process of building retrofitting infrastructure investment. Existing tools can support retrofitting design and energy performance contracting. However, there are well-recognized shortcomings of these tools related to their usability, complexity, and ability to perform calculations based on the real-time energy performance of buildings. To address this gap, the advanced retrofitting decision support tool is developed and presented in this study. The strengths of our solution rely on easy usability, accuracy, and transparency of results. The automatic collection of real-time building energy consumption data gathered from the building management systems, combined with data analytics techniques, ensures ease of use and quickness of calculation. These results support step-by-step thinking for retrofitting design and hopefully enable a larger utilization rate for deep building retrofits

FAKTOR YANG BERHUBUNGAN DENGAN MOTIVASI KELUARGA DALAM MERAWAT PASIEN GANGGUANJIWA DI WILAYAH KERJA PUSKESMAS TANJUNG GADANG KABUPATEN SIJUNJUNG

PROGRAM STUDI S1 KEPERAWATAN FAKULTAS KEPERAWATAN UNIVERSITAS KEPERAWATAN JANUARI 2019 Nama : Yosi Novita No BP : 1711316007 Faktor yang Berhubungan dengan Motivasi Keluarga Dalam Merawat PasienGangguan Jiwa Di Wilayah Kerja Puskesmas TanjungGadang ABSTRAK Gangguan jiwa masih menjadi masalah serius pada kesehatan mental. Meningkatnya penderita gangguan jiwa di wilayah Tanjung Gadang perlu mendapatkan perhatian lebih untuk mengurangi dampak dari masalah gangguan jiwa, terutama peran serta dari keluarga dalam merawat anggota keluarga yang mengalami gangguan jiwa. Rendahnya peran serta keluarga dalam merawat pasien yang mengalami gangguan jiwa dipicu oleh rendahnya motivasi. Motivasirendahmenyebabkan pemberian asuhan keperawatan pada keluarga tidak terlaksana dengan baik.Motivasi ini berhubungan dengan umur, pendidikan, pengalaman, spritual, dukungan keluarga dan sosial ekonomi. Tujuan penelitianuntuk mengetahui faktor yang berhubungan dengan motivasi keluarga dalam merawat pasien gangguan jiwa. Penelitian ini menggunakan metode deskriptif analitik dengan desaincrosssectionalstudy, dilaksanakan dari Oktober sampai Januari 2019.Sampel pada penelitian ini berjumlah 96 keluarga (caregiver) dengan menggunakan teknik pengambilan sampel purposive samplingdengan analisa data menggunakan uji chi square. Hasil uji statistik menunjukkan adanya hubungan antara motivasi dengan pengalaman (p=0,025), spritual (p=0,000), dukungan keluarga (p=0,008), dan sosial ekonomi (p=0,012). Tidak terdapat hubungan antara motivasi dengan umur (p=0,197), dan tingkat pendidikan (p=0,911) dalam merawat pasien gangguan jiwa. Diharapkan kepada perawat komunitas untuklebih meningkatkan sarana informasi guna menambah wawasan keluarga perihal penanganan pasien gangguan jiwa yang dapat meningkatkan motivasikeluarga dalam merawat anggota keluarga yang mengalami gangguan jiwa. Kata Kunci : faktor motivasi, perawatan keluarga, gangguan jiwa Daftar Pustaka : 102 (2001-2018

Draft of innovation and research roadmap

sepponen2014ano abstrac

Draft of innovation and research roadmap

sepponen2014ano abstrac