scholarly journals Water poverty assessment based on the random forest algorithm: application to Gansu, Northwest China

Water Policy ◽  
2021 ◽  
Author(s):  
Xiang Gao ◽  
Ke Wang ◽  
Kevin Lo ◽  
Ruiyang Wen ◽  
Xingxing Huang ◽  
...  
Keyword(s):  
Random Forest ◽  
Northwest China ◽  
Random Forest Algorithm ◽  
Poverty Assessment ◽  
Significant Difference ◽  
Level 3 ◽  
Level 1 ◽  
Poverty Levels ◽  
High Dimensional Datasets

Abstract This study proposes a random forest algorithm to evaluate water poverty. It shows how the machine learning technique can be used to classify the degree of water poverty into five levels: very severe, severe, moderate, mild, and very mild. The strengths of the proposed random forest method include a high classification accuracy, good operational efficiency, and the ability to handle high-dimensional datasets. The success of the proposed method is empirically illustrated through a case study in Gansu, Northwest China. The analysis shows that from 2000 to 2017, the severity of water poverty in the study area declined. In 2000, most municipalities were classified as level 1 (very severe) or level 2 (severe). In 2017, level 1 water poverty disappeared, with most municipalities classified in as level 3 (moderate) and level 4 (mild). Spatially, there is a significant difference between the water poverty levels of the western, central, and eastern parts of Gansu, and the eastern part is affected by serious water poverty problems.

scholarly journals Analysis of Student Problem-Solving Ability Topics 3D Reviewed from Geometry Thinking Level

2020 ◽  
Vol 3 ◽  
pp. 525-530
Author(s):  
Bagas Ardiyanto ◽  
Gunawan ◽  
Maryam Abdulloh ◽  
Safrilia Septiasari
Keyword(s):  
Problem Solving ◽  
Problem Solving Skills ◽  
Study Approach ◽  
Data Collection Instrument ◽  
Level 3 ◽  
Master Level ◽  
Level 1 ◽  
Level 2 ◽  
Student Problem

Each student has different problem-solving skills on the 3D topic. The study aims to find out the student problem-solving skills in the 3D Topics reviewed from geometry thinking level. This type of research is qualitative research with a case study approach. The subject in this study is as many as 20 subjects consisting of high problem-solving capabilities, moderate problem-solving capabilities, and low problem-solving capabilities. The data collection Instrument on this research is a level test of geometry thinking. The results showed that students in the category of high problem-solving skills were able to master level 0, Level 1, Level 2, and level 3 despite not being perfect, students in the category of problem-solving skills were able to master level 0, Level 1, and Level 2, students in the category of low problem-solving skills are only capable of mastering level 0 and Level 1.

scholarly journals General Practitioner Use of Generically Substitutable Inhaler Devices and the Impact of Training on Device Mastery and Maintenance of Correct Inhaler Technique

2020 ◽  
Vol 6 (2) ◽  
pp. 315-331
Author(s):  
Biljana Cvetkovski ◽  
Charlotte Hespe ◽  
Rachel Tan ◽  
Vicky Kritikos ◽  
Elizabeth Azzi ◽  
...  
Keyword(s):  
Generic Substitution ◽  
Instructional Video ◽  
Inhaler Technique ◽  
Dry Powder ◽  
Paired Data ◽  
Significant Difference ◽  
Level 3 ◽  
Level 1 ◽  
Level Of Training ◽  
Inhaler Devices

Abstract Introduction Generic substitution of inhaler devices is a relatively new phenomenon. The best patient outcomes associated with generic substitution occur when prescribers obtain consent from their patients to prescribe a generic inhaler and also teach their patient how to correctly use the new device. To date, no prospective observational study has assessed the level of training required for general practitioners (GPs) to demonstrate correct inhaler technique using two dry powder inhaler devices delivering fixed-dose combination budesonide/formoterol therapy. This study aims to (1) determine the level of training required for GPs to master and maintain correct IT when using two different dry powder inhalers that are able to be substituted in clinical practice and (2) determine the number and types of errors made by GPs on each device and inhaler device preference at each training visit. Method A randomized, parallel-group cross-over study design was used to compare the inhaler technique of participants with a Spiromax® placebo device and a Turbuhaler® placebo device. This study consisted of two visits with each participant over a period of 4 ± 1 weeks (visit 1 and visit 2). A total of six levels of assessment and five levels of training were implemented as required. Level 1, no instruction; level 2, following use of written instruction; level 3, following viewing of instructional video; level 4, expert tuition from the researcher; level 5/level 6, repeats of expert tuition from the researcher when required. Participants progressed through each level and stopped at the point at which they demonstrated device mastery. At each level, trained researchers assessed the inhaler technique of the participants. Participants were also surveyed about their previous inhaler use and training. Results In total, 228 GPs participated in this study by demonstrating their ability to use a Turbuhaler® and a Spiromax® device. There was no significant difference between the proportion of participants who demonstrated device mastery with the Turbuhaler® compared with the Spiromax® at level 1, (no instruction), (119/228 (52%) versus 131/228 (57%), respectively, n = 228, p = 0.323 (McNemar’s test of paired data). All but one participant had demonstrated correct inhaler technique for both devices by level 3(instructional video). There was a significant difference between the proportion of participants who demonstrated maintenance of device mastery with the Turbuhaler® compared with the Spiromax® at visit 2, level 1 (127/177 (72%) versus 151/177 (85%) respectively, p = 0.003; McNemar’s test of paired data). All but two participants achieved device mastery by level 3, visit 2. More participants reported previous training with the Turbuhaler® than with Spiromax®. Discussion This study demonstrates that GPs are able to equally demonstrate correct use of the Turbuhaler® and Spiromax® devices, even though most had not received training on a Spiromax® device prior to this study. The significance of being able to demonstrate correct technique on these two devices equally has ramifications on practice and supported generic substitution of inhaler devices at the point of prescribing, as the most impactful measure a GP can take to ensure effective use of inhaled medicine is the correct demonstration of inhaler technique.

Isolation gowns as a potential work hazard

2020 ◽  
Vol 64 (6) ◽  
pp. 596-603
Author(s):  
Abigail Schirmer ◽  
Renard Sessions ◽  
Nikolaus Gravenstein ◽  
Kenneth Rand ◽  
Monika Oli ◽  
...  
Keyword(s):  
Skin Surface ◽  
Physical Structure ◽  
Patient Contact ◽  
Sterile Saline ◽  
Significant Difference ◽  
Microscope Imaging ◽  
Level 3 ◽  
Level 1 ◽  
Level 2 ◽  
Scanning Electron

Abstract Objectives Isolation gowns are used as a barrier to bacterial transmission from patient to provider and vice versa. If an isolation gown is ineffective, the patient and provider have a potential breach of safety and increased infection risk. This study compared the bacterial permeability of differently rated, commonly uses isolation gowns to assess their effectiveness in preventing simulated bacterial transmittance, and thus contamination, from patient to provider. Methods Serial dilutions of Staphylococcus epidermidis in sterile saline were applied to a simulated skin surface. Unrated and Levels 1 through 4 non-sterile isolation gowns contacted the solution, simulating patient contact. Both sides of the contaminated gowns were then cultured on blood agar by rolling a sterile swab across the gown and evenly inoculating the culture plate. Colony counts from inside and outside of the gowns were compared. Separately, S. epidermidis was placed on a sample of each gown and scanning electron microscopy was used to visualize the contaminated gowns’ physical structure. Results Mean bacterial transmittance from outside of the gown (i.e. patient contact side) to inside of the gowns (i.e. provider clothing or skin side) based on gown rating was as follows: unrated: 50.4% (SD 9.0%); Level 1: 39.7% (SD 11.2%); Level 2: 16.3% (SD 10.3%); Level 3: 0.3% (SD 0.8%); Level 4: 0.0% (SD 0.0%). Scanning electron microscope imaging of unrated, Level 1, and Level 2 gowns revealed gown pore sizes much larger than the bacteria. The Welch one-way analysis of variance statistic showed significant difference dependent on gown-level rating. Conclusions Unrated, Level 1, and Level 2 isolation gowns do not provide effective bacterial isolation barriers when bacteria like S. epidermidis make contact with one side of the gown material. Not studied, but implied, is that unrated and lower rated isolation gowns would be as or even more physically permeable to virus particles, which are much smaller than bacteria.

scholarly journals Preliminary Study of Landslide Susceptibility Modeling with Random Forest Algorithm Using R (Case Study: the Cisangkuy Sub-watershed)

2021 ◽  
Vol 936 (1) ◽  
pp. 012015
Author(s):  
S Sukristiyanti ◽  
K Wikantika ◽  
I A Sadisun ◽  
L F Yayusman ◽  
E Soebowo
Keyword(s):  
Random Forest ◽  
Spatial Data ◽  
Landslide Susceptibility ◽  
Google Earth ◽  
Landslide Susceptibility Mapping ◽  
Random Forest Algorithm ◽  
Visual Interpretation ◽  
Susceptibility Modeling ◽  
Initial Measure

Abstract Landslide susceptibility mapping is an initial measure in the landslide hazard mitigation. This study aims to evaluate landslide susceptibility in the Cisangkuy Sub-watershed, a part of Bandung Basin. Twenty-seven landslide variables were involved in this modeling derived from various data sources. As a target, 25 landslide polygons obtained through a visual interpretation of Google Earth timeseries images and 33 landslide points obtained from a field survey and an official landslide report, were used as landslide inventory data. All spatial data were prepared in the same cell size referring to the highest spatial resolution of data involved in this modeling, i.e., 8.34 m. Fifty-eight (58) landslide locations covering an area of 0.87 Ha are equivalent to 1040 cells in the raster format. In total, 2040 samples consisting of landslides and non-landslides with the same ratio, were trained using random forest algorithm. Non-landslides were sampled randomly from landslide-free cells. This modeling was executed using R environment. In this study, the result was two labels, susceptible and non-susceptible. This model provided an excellent performance, its accuracy reached 98.56%. This research needs an improvement to provide a probability that has a range of 0 to 1 to show the level of landslide susceptibility.

scholarly journals Penerapan PSO Over Sampling Dan Adaboost Random Forest Untuk Memprediksi Cacat Software

2020 ◽  
Vol 6 (2) ◽  
pp. 230-239
Author(s):  
Richky Faizal Amir ◽  
Irwan Agus Sobari ◽  
Rousyati Rousyati
Keyword(s):  
Particle Swarm Optimization ◽  
Random Forest ◽  
Particle Swarm ◽  
Training Data ◽  
Random Forest Algorithm ◽  
Swarm Optimization ◽  
Average Accuracy ◽  
Significant Difference ◽  
Imbalanced Class ◽  
Data Program

Abstract: The dataset of software metrics, in general, are not balanced (Imbalanced). Class imbalance in Dataset can reduce the performance of software defect prediction models, because it tends to produce majority class predictions from minority classes, the dataset used in this study uses the National Aeronautics and Space Administration (NASA) Metrics Data Program (MDP), dataset From Stages Pre-processing proposed the Particle Swarm Optimization (PSO). method to overcome the problem of attributes in the training data and the Random Over Sampling (ROS) Resampling method. to deal with class imbalances. This study proposes that the Random Forest method combined with Adaboost can estimate the level of disability of software through training data. The results of this study indicate that the Resampling + Adaboost + Random Forest algorithm can be used to predict software defects with an average accuracy of 94.70% and a value of AUC 0.939. While the PSO + Random Forest algorithm only has an average accuracy of 89.60% and AUC 0.636 the difference in the accuracy of the two models is 5.10% and AUC 0.303. Statistical tests show that there is a significant influence between the proposed model and the Random Forest model with a p-value (0.036) smaller than the alpha value (0.05), which means there is a significant difference between the two models.Keywords: Imbalanced Class, Resample, Particle Swarm Optimization, Random Forest, Adaboost, Software DefectAbstrak: Dataset dari software matrik secara umum bersifat tidak seimbang (Imbalanced). Ketidak seimbangan kelas yang ada dalam dataset dapat menurunkan kinerja model prediksi cacat software, karena cenderung menghasilkan prediksi kelas mayoritas dari kelas minoritas. Dataset yang digunakan pada penelitian ini menggunakan dataset National Aeronautics and Space Administration (NASA) Metrics Data Program (MDP). Dari tahapan pra pemrosesan diusulkan metode Particle Swarm Optimization (PSO) untuk mengatasi masalah attribute pada data training dan metode Resampling Random Over Sampling (ROS). untuk menangani ketidak seimbangan kelas. Penelitian ini mengusulkan metode Random Forest yang dikombinasikan dengan Adaboost dapat mengestimasi tingkat kecacatan suatu Software melalui data training, Dari Hasil penelitian ini menunjukan bahwa algoritma Resampling+Adaboost+Random Forest dapat digunakan untuk memprediksi cacat software dengan rata-rata akurasi 94,70% dan nilai AUC 0,939. Sementara algoritma PSO+Random Forest hanya memiliki rata-rata akurasi 89,60% dan AUC 0,636 perbedaan akurasi dari kedua model tersebut 5,10% dan AUC 0,303. Uji statistik menunjukan bahwa adanya pengaruh yang signifikan antara model usulan dengan model Random Forest dengan nilai p (0,036) lebih kecil dari nilai alpha (0,05) yang artinya terdapat perbedaan yang siginifkan antara kedua model.Kata kunci: Imbalanced Class, Resample, Particle Swarm Optimization, Random Forest, Adaboost, Kecacatan Software

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