Dehydration as a Universal Mechanism for Ion Selectivity in Graphene and Other Atomically Thin Pores

Ion channels play a key role in regulating cell behavior and in electrical signaling. In these settings, polar and charged functional groups -- as well as protein response -- compensate for dehydration in an ion-dependent way, giving rise to the ion selective transport critical to the operation of cells. Dehydration, though, yields ion-dependent free-energy barriers and thus is predicted to give rise to selectivity by itself. However, these barriers are typically so large that they will suppress the ion currents to undetectable levels. Here, we establish that graphene displays a measurable dehydration-only mechanism for selectivity of $\mathrm{K}^+$ over $\mathrm{Cl}^-$. This fundamental mechanism -- one that depends only on the geometry and hydration -- is the starting point for selectivity for all channels and pores. Moreover, while we study selectivity of $\mathrm{K}^+$ over $\mathrm{Cl}^-$, we find that dehydration-based selectivity functions for all ions, i.e., cation over cation selectivity (e.g., $\mathrm{K}^+$ over $\mathrm{Na}^+$). Its likely detection in graphene pores resolves conflicting experimental results, as well as presents a new paradigm for characterizing the operation of ion channels and engineering molecular/ionic selectivity in filtration and other applications.Comment: 27 page

A Pre-experimental Study to Assess the Effect of Structured Teaching Program on Knowledge regarding Intravenous Cannulation and Its Complications among Staff Nurses Working in a Selected Hospital of Bhopal, M.P.

Intravenous therapy is important in modern medicine. Millions of patients are receiving infusion therapy for life saving and for correcting the metabolic disorders through drugs, nutrition, solutions and blood products

Experimental and numerical study of process parameters effects towards part quality and porosity during powder bed additive manufacturing processes.

Powder bed additive manufacturing (PB-AM) process utilizes an electron beam or a laser as a heat source to melt the metallic powder particles. These processes have the capability of freeform fabrication, however certain defects such as porosity, high surface roughness, etc. would hinder its application. It is important to understand the effect of the process parameters and the underlying physical phenomena, which lead to the formation of such defects. In this regard, a three-dimensional (3D) thermo-fluid model is developed to study the effect of beam speed on the surface morphology during powder bed electron beam additive fabrication (PB-EBAF). Besides, the surfaces of PB-EBAF fabricated Ti-6Al-4V parts are analyzed using a white-light interferometer. The results show that in general, the build surface roughness along the beam moving direction slightly increases with the scanning speed. On the other hand, the hatch spacing noticeably affects the surface roughness in the transverse direction. In addition, the numerical model was modified to incorporate powder particles and study the effect of powder distribution towards the single-track formation during the laser powder bed fusion (LPBF) process. The numerical results show that the single-track morphology and density depend on the process parameters: scanning speed and laser power. Besides, micro-computed tomography (micro-CT) is utilized to characterize the pores formed during the LPBF process. Single tracks were fabricated with linear energy density (LED) ranging from 0.1 J/mm to 0.98 J/mm, and the samples were then scanned using micro-CT to measure keyhole porosity. The results show that the severity of the keyhole porosity increases with the increase of the LED. By keeping the LED constant in another single-track scanning experiment, different combinations of the power and the speed were tested to investigate the individual effect. The results show that for the same LED, the pore number and volume increased with increasing the power to a certain critical level, beyond which, the further increase and power resulted in fewer pore number and lower pore volume. The experimental results suggested that the dynamic phenomenon of a melt pool during the LPBF process is complex and sensitive to process parameters. Hence, a discrete element method (DEM) is utilized to obtain a powder distribution, which is then used to perform a thermo-fluid simulation using FLOW-3D software. The numerical results indicated that for a constant LED, the keyhole size increases with the increase in the laser power. The keyhole becomes stable at a higher power, which may reduce the occurrence of pores during laser scanning. In addition, a back and forth raster scanning is performed to form three tracks to investigate the effect of scan length on the melt pool size at different locations along the laser travel direction. Three scan speeds (375 mm/s, 750 mm/s, and 1500 mm/s) are used with laser power of 195 W, and three tracks are fabricated with 0.5 mm, 1 mm, and 1.5 mm scan lengths. Besides, hatch spacings of 80 µm and 120 µm are used. The fabricated samples are analyzed using white light interferometer and metallography. Moreover, a powder scale numerical model is developed to understand the residual heat effect on the melt pool. The results show that the region where the laser changes the direction is the most affected zone where a significant increase in the melt pool size is observed. The depth of the melt pool increased with increasing track number. The delay in the successive laser scan needed to minimize the residual heat effect is calculated for different process parameters

An Expectation Maximization Method to Learn the Group Structure of Deep Neural Network

Department of Computer Science and EngineeringAnalyzing multivariate time series data is important for many applications such as automated control, sensor fault diagnosis and financial data analysis. One of the key challenges is to learn latent features automatically from dynamically changing multivariate input. Convolutional neural networks (CNNs) have been successful to learn generalized feature extractors with shared parameters over the spatial domain in visual recognition tasks. For high-dimensional multivariate time series, designing an appropriate CNN model structure is challenging because the kernels may need to be extended through the full dimension of the input volume. To address this issue, we propose an Expectation Maximization (EM) method to learn the group structure of deep neural networks so that we can process the multiple high-dimensional kernels efficiently. This algorithm groups the kernels for each channel using the EM method and partition the kernel matrix into a block matrix. The EM method assumes the Gaussian Mixture Model (GMM) and the parameters of the GMM is updated together with the parameters of deep neural network by end-to-end backpropagation learning.ope

FAKTOR RISIKO HBsAg PADA KELUARGA HBsAg POSITIF DI KELURAHAN HAMBALA SUMBA TIMUR - NTT

Kejadiaan hepatitis B di Kabupaten Sumba Timur NTT cukup tinggi. Hasil pemeriksaan Donor darah di RSUD Umbu Meha Waingapu pada masyarakat Kelurahan Hambala Kabupaten Sumba Timur dari Tahun 2003-2005 didapatkan 26-29% HBsAg positif. Tujuan penelitian ini adalah untuk mengetahui faktor yang berhubugan dengan kejadiaan HBsAg pada keluarga penderita HBsAg positif di wilayah Kelurahan Hambala. Penelitian menggunakan penelitian survei dengan pendekatan cross sectional stusy. Sampel penelitian ini adalah keluarga penderita HBsAg positif sebanyak 81 orang; yang dinyatakan positif HBsAg sebanyak 23 orang. Hasil penelitian ini menujukkan bahwa status gizi p=0,023 ,RP=0455, ada hubungan dengan kejadiaan HBsAg positif. Sedangkan umur p=0,223 p=1,538. Jenis kelamin . p=0,263, RP=0,828, tingkat pendidikan p=0,263 RP=1,607); Status imunisasi p=0,823, RP =1,151; dan pengetauan p=0,198, RP=2,194, Tidak ada hubungan dengan kejadiaan HBsAg positif. Kesimpulan penelitian ini adalah status gizi buruk merupakan faktor risiko kejadian HBsAg pada keluarga penderita HBsAg positif di Kelurahan Hambala Sumba Timur NTT. Kata Kunci: Hepatitis B, HBsAg,Faktor Risiko

Integration Of Multispectral Camera Systems For Enhanced Visualization Biological Studies Using UAS

The purpose of this research is to enhance visualization of warm-blooded animals and analyze the vegetation in which they are located using a camera system mounted on Unmanned Aircraft System (UAS). The results are coherently displayed in a single image at the same spatial location so that biologists will have accurate animal counts along with vegetation conditions. Application of aerial imagery has been used to analyze the vegetation health and determining the number of the animals by the wildlife service. However, a major obstacle of the research is to combine the two imaging systems to obtain the same spatial image with enhanced visualization. The two camera systems used in this research are the Tetracam ADC lite multispectral and the FLIR Photon 320 infrared camera. These two camera systems each have a different lens, field of view and sensor array size. The project involves the alignment of the two cameras to pixel level for the spectral image analysis. The spectral image analysis provides both vegetation information, such as Normalized Difference Vegetation Index (NDVI), along with enhanced visualization of warm-blooded targets. The system was miniaturized for the standalone payload for aerospace applications including UAS. The FLIR Photon 320 was used to capture the infrared image and a Tetracam ADC lite multispectral camera was used to capture the near infrared, red and green spectral band images. A laboratory experimental setup was designed to mechanically align the two camera systems to get close identical spatial imagery. Spatial registration of the two images was performed using reverse image warping method by finding affine transformation matrix using point correspondences. Both camera systems were calibrated using Camera Calibration Toolbox for Matlab to reduce any distortion due to the lenses. A single board computer is used to capture and store the image data from FLIR Photon 320 infrared camera while the Tetracam image data is stored internally on board. The image capture time was set by continuous timed delay triggering within the Tetracam camera. The single board computer follows the Tetracam signals and matches the FLIR Photon 320 still image acquisition time with the Tetracam ADC lite. Once the images are captured and stored by the camera systems, the files are downloaded and image processing is conducted. The data was analyzed to calculate the NDVI to observe the plant health. The infrared spectral band was used to identify the warm-blooded animals. In addition, various false color combinations of spectral bands and normalized difference ratios are processed to observe the visual enhancement capabilities on the vegetation and warm-blooded animal. It was determined that detected warm-blooded animal in the infrared band registered on top of NDVI image to show the vegetation health in a single image produced effective results. The combined image data from the FLIR Photon 320 and Tetracam ADC lite produced meaningful vegetation and animal information in the single image. This enhanced the capacity to identify and count animals while simultaneously characterize the vegetation environment, which is highly desired in ecosystem studie