Effect of water temperature on air-core generation and disappearance during draining

Understanding the air-core development during the draining of liquid from a cylindrical tank is of particular interest because this phenomenon has multiple engineering applications. In this study, experiments are performed for draining water of different temperatures from a cylindrical tank. Results show that the air-core generation and disappearance during draining depend on the temperature of the water in the tank, in which the total draining time increases with the water temperature. A correlation is proposed to predict the water height with time during draining. A numerical study is then performed for some of the temperature cases. Numerical and correlation results show a good agreement with the experimental data. Findings show that viscosity plays an important role behind the mechanism of air-core generation and disappearance phenomena.     

Drug Usage Safety from Drug Reviews with Hybrid Machine Learning Approach

With the increasing usage of drugs to remedy different diseases, drug safety has become crucial over the past few years. Often medicine from several companies is offered for a single disease that involves the same/similar substances with slightly different formulae. Such diversification is both helpful and dangerous as such medicine proves to be more effective or shows side effects to different patients. Despite clinical trials, side effects are reported when the medicine is used by the mass public, of which several such experiences are shared on social media platforms. A system capable of analyzing such reviews could be very helpful to assist healthcare professionals and companies for evaluating the safety of drugs after it has been marketed. Sentiment analysis of drug reviews has a large potential for providing valuable insights into these cases. Therefore, this study proposes an approach to perform analysis on the drug safety reviews using lexicon-based and deep learning techniques. A dataset acquired from the ‘Drugs.Com’ containing reviews of drug-related side effects and reactions, is used for experiments. A lexicon-based approach, Textblob is used to extract the positive, negative or neutral sentiment from the review text. Review classification is achieved using a novel hybrid deep learning model of convolutional neural networks and long short-term memory (CNN-LSTM) network. The CNN is used at the first level to extract the appropriate features while LSTM is used at the second level. Several well-known machine learning models including logistic regression, random forest, decision tree, and AdaBoost are evaluated using term frequency-inverse document frequency (TF-IDF), a bag of words (BoW), feature union of (TF-IDF + BoW), and lexicon-based methods. Performance analysis with machine learning models, long short term memory and convolutional neural network models, and state-of-the-art approaches indicate that the proposed CNN-LSTM model shows superior performance with an 0.96 accuracy. We also performed a statistical significance T-test to show the significance of the proposed CNN-LSTM model in comparison with other approaches.     

Skin-Inspired Nanofluid-Filled Surfaces with Tunable Icephobic,Photothermal, and Energy Absorption Properties

Ice buildup can significantly and negatively impact system performance in various industrial sectors, and has remained a persistent challenge for decades. Many compliant materials exhibit excellent de-icing performance but are easily eroded by impacts from supercooled water droplets, sand, dust, and debris. A composite panel inspired by animal skin, consisting of a facesheet protecting a nanofluid layer beneath, which exhibits durable anti-icing and tunable photothermal properties is proposed. The viscous liquid layer beneath the facesheet increases flexural rigidity, preventing large deflections and increasing deformation resistance, which alters ice's adhesion to the surface. The non-uniform fluid pressure exerted by the viscous nanofluid-filled composite panels facilitates ice detachment, resulting in ice adhesion strengths as low as τ_ice ≈ 10 kPa. Further, by altering the fluid properties, different additional functionalities can be endowed to the system. Incorporating fumed silica in a fluid-filled composite panel results in rheopectic behavior, and this doubles their impact resistance when the shear thickening properties are properly tuned. Additionally, the combination of a transparent facesheet and a solar light absorbent nanofluid allows for tunable photothermal properties, further enhancing the anti-icing performance of the system. This durable and tunable nanofluid-filled composite panel shows great promise as a multifunctional de-icing material.     

A tensor-based hierarchical process monitoring approach for anomaly detection in additive manufacturing

Additive manufacturing (AM) is a technology that enables the creation of complex shapes with advanced structural and functional properties. It has transformed the traditional manufacturing operations into a more flexible and efficient process, reshaping the whole value chain and allowing new levels of product customization. AM is a layer-by-layer manufacturing process, in which materials are deposited in each layer to create the object of interest. Due to the layer-wise nature of the process, anomalies and defects might occur within each layer, across several layers or throughout the whole sample. An accurate and responsive detection strategy that enables the detection of various types of anomalies is essential for ensuring the quality and integrity of the manufactured product. In this paper, a hierarchical in situ process monitoring approach, namely, a three level monitoring strategy, is proposed to detect local, layer-wise, and sample-wise anomalies using thermal videos acquired during the manufacturing process. The proposed approach integrates hierarchical low-rank tensor decomposition methods with statistical monitoring techniques to effectively detect anomalies at different levels, namely, the within-layer level, the layer level, and the sample level. Simulations are used to evaluate the performance of the method and compare with existing benchmarks. The proposed approach is also applied to thermal videos acquired during the laser powder bed fusion (L-PBF) process to illustrate its effectiveness in practice.     

Growth,Yield and Physiological Characteristics of Maize (Zea mays L.) at Two Different Soil Moisture Regimes by Supplying Silicon and Chitosan

Silicon - Water deficit is an important factor to challenge food security, particularly in arid and semi-arid climates. In present experiment, silicon (Si) and chitosan were evaluated to affect the...     

De-Noising Brain MRI Images by Mixing Concatenation and Residual Learning (MCR)

Brain magnetic resonance images (MRI) are used to diagnose the different diseases of the brain, such as swelling and tumor detection. The quality of the brain MR images is degraded by different noises, usually salt & pepper and Gaussian noises, which are added to the MR images during the acquisition process. In the presence of these noises, medical experts are facing problems in diagnosing diseases from noisy brain MR images. Therefore, we have proposed a de-noising method by mixing concatenation, and residual deep learning techniques called the MCR de-noising method. Our proposed MCR method is to eliminate salt & pepper and gaussian noises as much as possible from the brain MRI images. The MCR method has been trained and tested on the noise quantity levels 2% to 20% for both salt & pepper and gaussian noise. The experiments have been done on publically available brain MRI image datasets, which can easily be accessible in the experiments and result section. The Structure Similarity Index Measure (SSIM) and Peak Signal-to-Noise Ratio (PSNR) calculate the similarity score between the denoised images by the proposed MCR method and the original clean images. Also, the Mean Squared Error (MSE) measures the error or difference between generated denoised and the original images. The proposed MCR de-noising method has a 0.9763 SSIM score, 84.3182 PSNR, and 0.0004 MSE for salt & pepper noise; similarly, 0.7402 SSIM score, 72.7601 PSNR, and 0.0041 MSE for Gaussian noise at the highest level of 20% noise. In the end, we have compared the MCR method with the state-of-the-art de-noising filters such as median and wiener de-noising filters.     

Improved Cytotoxicity and Induced Apoptosis in HeLa Cells by Co-loading Vitamin E Succinate and Curcumin in Nano-MIL-88B-NH2

One of the strategies for improved therapeutic effects in cancer therapy is combination chemotherapy. In this study, a flexible nano-MOF (Fe-MIL-88B-NH₂) was synthesized in a sonochemical process, then co-loaded with α-tocopheryl succinate (TOS) and curcumin (CCM). The anticancer activity of co-loaded Fe-MIL-88B-NH₂ (Fe-MIL-88B-NH₂/TOS@CCM) against the HeLa cells was compared with that of the single-loaded counterpart (Fe-MIL-88B-NH₂@CCM). MTT analysis indicates improved cytotoxicity of Fe-MIL-88B-NH₂/TOS@CCM. The data from the cell apoptosis assay indicated more apoptosis in the case of the co-loaded nano-MOF. This study indicates the positive effect of the presence of TOS on enhancing the anticancer effect of Fe-MIL-88B-NH₂@CCM to prepare a more efficient drug delivery nanosystem.