Missing value estimation using clustering and deep learning within multiple imputation framework

Missing values in tabular data restrict the use and performance of machine learning, requiring the imputation of missing values. Arguably the most popular imputation algorithm is multiple imputation by chained equations (MICE), which estimates missing values from linear conditioning on observed values. This paper proposes methods to improve both the imputation accuracy of MICE and the classification accuracy of imputed data by replacing MICE’s linear regressors with ensemble learning and deep neural networks (DNN). The imputation accuracy is further improved by characterizing individual samples with cluster labels (CISCL) obtained from the training data. Our extensive analyses of six tabular data sets with up to 80% missing values and three missing types (missing completely at random, missing at random, missing not at random) reveal that ensemble or deep learning within MICE is superior to the baseline MICE (b-MICE), both of which are consistently outperformed by CISCL. Results show that CISCL + b-MICE outperforms b-MICE for all percentages and types of missing values. In most experimental cases, our proposed DNN-based MICE and gradient boosting MICE plus CISCL (GB-MICE-CISCL) outperform seven state-of-the-art imputation algorithms. The classification accuracy of GB-MICE-imputed data is further improved by our proposed GB-MICE-CISCL imputation method across all percentages of missing values. Results also reveal a shortcoming of the MICE framework at high percentages of missing values (50%) and when the missing type is not random. This paper provides a generalized approach to identifying the best imputation model for a tabular data set based on the percentage and type of missing values

Cu(II) Ions Adsorption Using Activated Carbon Prepared From Pithecellobium Jiringa (Jengkol) Shells with Ultrasonic Assistance: Isotherm, Kinetic and Thermodynamic Studies

Adsorption of Cu(II) ions from aqueous solution onto activated carbon (AC) prepared from Pithecellobium jiringa shell (PJS) waste was investigated by conducting batch mode adsorption experiments. The activation with ultrasound assistance removed almost all functional groups in the PJS-AC, while more cavities and pores on the PJS-AC were formed, which was confirmed by FTIR and SEM analyses. The Cu(II) ion adsorption isotherm fitted best to the Freundlich model with average R2 at 0.941. It was also correlated to the Langmuir isotherm with average R2 at 0.889. This indicates that physical sorption took place more than chemical sorption. The maximum Cu(II) ion adsorption capacity onto the PJS-AC for a dose of 1 g was 104.167 mg/g at 30 °C and pH 4.5, where the Langmuir constant was 0.523 L/mg, the Freundlich adsorption intensity was 0.523, and the Freundlich constant was 5.212 L/mg. Cu(II) adsorption followed the pseudo second-order kinetic (PSOKE) model with average R2 at 0.998, maximum adsorption capacity at 96.154 mg/g, PSOKE adsorption rate constant at 0.200 g/mg.min, temperature at 30 °C and pH at 4.5. The changes in enthalpy, entropy, free energy and activation energy were determined, and the results confirmed that Cu(II) adsorption onto the PJS-AC was exothermic chemical adsorption in part. There was a decrease in the degree of freedom and the adsorption was non-spontaneous

Pengaruh Kadar Karbon pada Proses Gasifikasi

Investigasi proses gasifikasi dilakukan dengan pemodelan termodinamik. Kuantifikasi unjuk kerja proses gasifikasi dinyatakan dengan kadar H2 dan kadar CO dalam gas produser, temperatur dan efisiensi termal. Investigasi pengaruh kadar karbon terhadap unjuk kerja proses gasifikasi dilakukan dengan simulasi menggunakan batubara: lignit, bituminus dan antrasit. Ketiga jenis batubara diharapkan mewakili tingkatan kadar karbon. Kajian termodinamika digunakan sebagai piranti prediksi kinerja gasifikasi dan dapat melihat efek berbagai faktor secara cepat. Penyimpangan kinerja gasifier aktual terhadap hasil prediksi termodinamika sering ditemui dan biasanya dianggap sebagai akibat faktor-faktor teknis yang berhubungan dengan laju proses, misalnya pengontakan partikel dengan medium gasifikasi. Pada makalah ini, kajian termodinamika disempurnakan dengan melibatkan pemodelan dekomposisi batubara yang sangat tergantung pada jenis batubara dalam hal ini mewakili kadar karbon. Harapannya, pengabungan model dekomposisi batubara yang diusulkan dalam penelitian ini dan model kesetimbangan reaksi konvensional menghasilkan kajian termodinamika yang lebih rasional. Hasil dari kajian termodinamika digunakan sebagai piranti prediksi kinerja gasifikasi dengan mempertimbangkan kadar karbon batubara. Fraksi mol gas hidrogen maksimum yang dihasilkan lignit lebih tinggi daripada antrasit dan bituminus, berturut-turut 0,43 dan 0,25. Fraksi mol maksimum gas hidrogen dari lignit berada pada laju udara/batubara sekitar 1,2 kg/kg sedangkan antrasit dan bituminus berada pada sekitar 3 kg/kg. Temperatur proses gasifikasi, seperti diduga sangat dipengaruhi oleh jenis batubara. Sesuai dengan kadar H2, temperatur gasifikasi sebaiknya dijaga sekitar 1000 oC (Ru = 2,4 dan 4 berturut-turut untuk batubara lignit, antrasit dan bituminus). Keuntungan pada temperatur sekitar 1000 oC, tar secara praktis sudah terdekomposisi lebih lanjut, sehingga gas produser hanya sedikit mengandung tar

Gated Ensemble of Spatio-temporal Mixture of Experts for Multi-task Learning in Ride-hailing System

Designing spatio-temporal forecasting models separately in a task-wise and city-wise manner poses a burden for the expanding transportation network companies. Therefore, a multi-task learning architecture is proposed in this study by developing gated ensemble of spatio-temporal mixture of experts network (GESME-Net) with convolutional recurrent neural network (CRNN), convolutional neural network (CNN), and recurrent neural network (RNN) for simultaneously forecasting spatio-temporal tasks in a city as well as across different cities. Furthermore, a task adaptation layer is integrated with the architecture for learning joint representation in multi-task learning and revealing the contribution of the input features utilized in prediction. The proposed architecture is tested with data from Didi Chuxing for: (i) simultaneously forecasting demand and supply-demand gap in Beijing, and (ii) simultaneously forecasting demand across Chengdu and Xian. In both scenarios, models from our proposed architecture outperformed the single-task and multi-task deep learning benchmarks and ensemble-based machine learning algorithms.Comment: arXiv admin note: text overlap with arXiv:2012.0886

Effect of Vertical Canard Location on Skin Friction Drag

This study investigates the viscous skin friction drag generation due to the three different vertical canard locations on the mid winger un-swept aircraft scaled-down model by using boundary layer measurements in the wind tunnel. The N22 airfoil was selected for the canard and the modified S1223 airfoil was selected for the wing. The laser cutting technique was employed for the fabrication of the wing, and canard airfoils, which gave sufficient dimensional accuracy to the model. The canard, wing, and fuselage were fabricated by balsa wood and strengthened by Aluminum stripes. The assembled model is tested in an open subsonic wind tunnel a fixed chord Reynolds number 3.8*106. The boundary layers were measured at 70% of the chord and at three different wingspan locations i.e. 30%, 60%, and 90% with 00 incidence angle. The canards were positioned at three vertical positions one at fuselage reference line (FRL) and the remaining two locations at ± 0.16 c from the FRL. The results were compared with wing-body alone and with three canard locations and found that the high canard configuration outperformed the other two configurations and also wing-body alone configuration as it provides half of the total drag. However, the high canard produces 15% more drag than the wing-body alone at the wing tip (90%).The aerodynamic performance of the high canard configuration was found to be significantly promising for the future use in drones and other small aircrafts

Polarity of T Cell Shape, Motility, and Sensitivity to Antigen

AbstractT cell activation requires contact with APCs. We used optical techniques to demonstrate T cell polarity on the basis of shape, motility, and localized sensitivity to antigen. An intracellular Ca2+ clamp showed that T cell shape and motility are extremely sensitive to changes in [Ca2+]i (Kd = 200 nM), with immobilization and rounding occurring via a calcineurin-independent pathway. Ca2+-dependent immobilization prolonged T cell contact with the antigen-presenting B cell; buffering the [Ca2+]i signal prevented the formation of stable cell pairs. Optical tweezers revealed spatial T cell sensitivity to antigen by controlling placement on the T cell surface of either B cells or α-CD3 MAb-coated beads. T cells were 4-fold more sensitive to contact made at the leading edge of the T cell compared with the tail. We conclude that motile T cells are polarized antigen sensors that respond physically to [Ca2+]i signals to stabilize their interaction with APCs

Isolation of Enterobacteriaceae Bacteria Species From Feces of Sumatran Orangutans (Pongo Abelii)

The porpouse of this research aimed to isolate enterobacteriaceae bacteria from feces of Pongo abelii. The samples of feces were collected from 15 captive orangutans in Orangutan Sumatera Batu Mbelin Sibolangit Qurantine, North Sumatra. Of each sample was cultured in nutrient broth media using sterilecotton swabs or Pasteur pipettes, and incubated at 37°C temperature for 24 hours. Culture was spared on Methylene Blue Agar (Oxoid), examined by Gram staining, and tested by biochemically. The result showed that significantly more common appear Escherichia sp. (93,33%) and fewer Edwardsiella sp. (66,67%) wereisolated from feces samples of P. abelii. Others enterobacteriaceae found in feces of P. Abelii were Shigella sp. (46,67%), Klebsiella sp. (33,33%), Citrobacter sp., and Salmonella sp. (13,33%), respectivel

Treatment recommendations for psoriatic arthritis

Objective: To develop comprehensive recommendations for the treatment of the various clinical manifestations of psoriatic arthritis (PsA) based on evidence obtained from a systematic review of the literature and from consensus opinion. Methods: Formal literature reviews of treatment for the most significant discrete clinical manifestations of PsA (skin and nails, peripheral arthritis, axial disease, dactylitis and enthesitis) were performed and published by members of the Group for Research and Assessment of Psoriasis and Psoriatic Arthritis (GRAPPA). Treatment recommendations were drafted for each of the clinical manifestations by rheumatologists, dermatologists and PsA patients based on the literature reviews and consensus opinion. The level of agreement for the individual treatment recommendations among GRAPPA members was assessed with an online questionnaire. Results: Treatment recommendations were developed for peripheral arthritis, axial disease, psoriasis, nail disease, dactylitis and enthesitis in the setting of PsA. In rotal, 19 recommendations were drafted, and over 80% agreement was obtained on 16 of them. In addition, a grid that factors disease severity into each of the different disease manifestations was developed to help the clinician with treatment decisions for the individual patient from an evidenced-based perspective. Conclusions: Treatment recommendations for the cardinal physical manifestations of PsA were developed based on a literature review and consensus between rheumatologists and dermatologists. In addition, a grid was established to assist in therapeutic reasoning and decision making for individual patients. It is anticipated that periodic updates will take place using this framework as new data become available

Deep learning-based post-processing of real-time MRI to assess and quantify dynamic wrist movement in health and disease

While morphologic magnetic resonance imaging (MRI) is the imaging modality of choice for the evaluation of ligamentous wrist injuries, it is merely static and incapable of diagnosing dynamic wrist instability. Based on real-time MRI and algorithm-based image post-processing in terms of convolutional neural networks (CNNs), this study aims to develop and validate an automatic technique to quantify wrist movement. A total of 56 bilateral wrists (28 healthy volunteers) were imaged during continuous and alternating maximum ulnar and radial abduction. Following CNN-based automatic segmentations of carpal bone contours, scapholunate and lunotriquetral gap widths were quantified based on dedicated algorithms and as a function of wrist position. Automatic segmentations were in excellent agreement with manual reference segmentations performed by two radiologists as indicated by Dice similarity coefficients of 0.96 ± 0.02 and consistent and unskewed Bland–Altman plots. Clinical applicability of the framework was assessed in a patient with diagnosed scapholunate ligament injury. Considerable increases in scapholunate gap widths across the range-of-motion were found. In conclusion, the combination of real-time wrist MRI and the present framework provides a powerful diagnostic tool for dynamic assessment of wrist function and, if confirmed in clinical trials, dynamic carpal instability that may elude static assessment using clinical-standard imaging modalities