As one of the flow-based passive sorting, the hydrodynamic filtration using a microfluidic-chip has shown to effectively separate into different sizes of subpopulations from cell or particle suspensions. Its model framework involving two-phase Newtonian or generalized Newtonian fluid (GNF) was developed, by performing the complete analysis of laminar flow and complicated networks of main and multiple branch channels. To predict rigorously what occurs in flow fields, we estimated pressure drop, velocity profile, and the ratio of the flow fraction at each branch point, in which the analytical model was validated with numerical flow simulations. As a model fluid of the GNF, polysaccharide solution based on Carreau type was examined. The objective parameters aiming practical channel design include the number of the branches and the length of narrow section of each branch for arbitrary conditions. The flow fraction and the number of branches are distinctly affected by the viscosity ratio between feed and side flows. As the side flow becomes more viscous, the flow fraction increases but the number of branches decreases, which enables a compact chip designed with fewer branches being operated under the same throughput. Hence, our rational design analysis indicates the significance of constitutive properties of each stream.
Human bone marrow-derived mesenchymal stem cells (hMSCs) consist of heterogeneous subpopulations with different multipotent properties: small and large cells with high and low multipotency, respectively. Accordingly, sorting out a target subpopulation from the others is very important to increase the effectiveness of cell-based therapy. We performed flow-based sorting of hMSCs by using optimally designed microfluidic chips based on the hydrodynamic filtration (HDF) principle. The chip was designed with the parameters rigorously determined by the complete analysis of laminar flow for flow fraction and complicated networks of main and multi-branched channels for hMSCs sorting into three subpopulations: small (<25>40 μm) cells. By focusing with a proper ratio between main and side flows, cells migrate toward the sidewall due to a virtual boundary of fluid layers and enter the branch channels. This opens the possibility of sorting stem cells rapidly without damage. Over 86% recovery was achieved for each population of cells with complete purity in small cells, but the sorting efficiency of cells is slightly lower than that of rigid model particles, due to the effect of cell deformation. Finally, we confirmed that our method could successfully fractionate the three subpopulations of hMSCs by analyzing the surface marker expressions of cells from each outlet.
The ability of antimicrobial peptides (AMPs) for effective binding to multiple target microbes has drawn lots of attention as an alternative to antibodies for detecting whole bacteria. We investigated pathogenic Escherichia coli (E. coli) detection by applying a microfluidic based biosensing device embedded with AMP-labeled beads. According to a new channel design, our device is reusable by the repeated operation of detection and regeneration modes, and the binding rate is more enhanced due to even distribution of the bacterial suspension inside the chamber by implementing influx side channels. We observed higher binding affinity of pathogenic E. coli O157:H7 for AMP-labeled beads than nonpathogenic E. coli DH5α, and the fluorescence intensity of pathogenic E. coli was about 3.4 times higher than the nonpathogenic one. The flow rate of bacterial suspension should be applied above a certain level for stronger binding and rapid detection by attaining a saturation level of detection within a short time of less than 20 min. A possible improvement in the limit of detection in the level of 10 cells per mL for E. coli O157:H7 implies that the AMP-labeled beads have high potential for the sensitive detection of pathogenic E. coli at an appropriate flow rate.
Many people are distracted from the normal lifestyle, because of the hearing loss they have. Most of them do not use the hearing aids due to various discomforts in wearing them. The main and the foremost problem available in it is; the device introduces unpleasant whistling sounds, caused by the changing environmental noise, which is faced by the user daily. This paper describes the development of an algorithm, which focuses on the adaptive feedback cancellation, that improves the listening effort of the user. The genetic algorithm is one of the computational techniques, that is used in enhancing the above features. The performance can also be compared with other comprehensive analysis methods, to evaluate its standards.
Despite the evolution of modern technology, the users of hearing aids do not realize the persistence of feedback, while wearing the device until the condition becomes worse. The feedback cancellation algorithms, instead of cancelling the acoustic feedback, limits speech intelligibility. The paper presents a novel method for estimation of SNR based adaptive-feedback equalizers (SBAFE) algorithm to develop an optimized hearing aid for the feedback less sound transmission and achieving better speech discrimination. The data gathered for the optimization is visualized and compared with the traditional technology, which provides the subjective and objective quality of the hearing aids.
Alzheimer Disease is a chronic neurological brain disease. Early diagnosis of Alzheimer illness may the prevent the occurrence of memory cellular injury. Neuropsychological tests are commonly used to diagnose Alzheimer’s disease. The above technique, has a limited specificity and sensitivity. This article suggests solutions to this issue an early diagnosis model of Alzheimer’s disease based on a hybrid meta-heuristic with a multi-feed-forward neural network. The proposed Alzheimer’s disease detection model includes four major phases: pre-processing, feature extraction, feature selection and classification (disease detection). Initially, the collected raw data is pre-processed using the SPMN12 package of MATLAB. Then, from the pre-processed data, the statistical features (mean, median and standard deviation) and DWT are extracted. Then, from the extracted features, the optimal features are selected using the new Hybrid Sine cosine firefly (HSCAFA). This HSCAFA is a conceptual improvement of standard since cosine optimization and firefly optimization algorithm, respectively. Finally, the disease detection is accomplished via the new regression- based multi-faith neighbors’ network (MFNN). The final detected outcome is acquired from regression-based MFNN. The proposed methodology is performed on the PYTHON platform and the performances are evaluated by the matrices such as precision, recall, and accuracy.
Abstract: Recent studies on hydrogels have shown them as promising biomaterials for numerous applications involving tissue engineering, drug-screening, drug-delivery, and 3-D bioprinting because they show unique physicochemical properties. The ability of these structures to hold large amounts of water is because of their hydrophilic nature that provides a soft and hydrated environment like natural tissues. This makes them ideal for mimicking the extracellular matrix and supporting cell growth and proliferation. In tissue engineering, hydrogels might be used to create scaffolds that promote cell growth and facilitate tissue regeneration. Hydrogels can also be engineered in such a way that they intimate the mechanical and biochemical in vivo characteristics making them a versatile tool for applications in tissue engineering. Hydrogels are being used in drug screening, as they can be functionalized with different biochemicals in order to match the microenvironment of specific tissues. This allows researchers to study how drugs interact with cells and tissues in-vitro conditions, which can lead to more efficient strategies for drug development. For applications in drug delivery hydrogels are designed to release drugs in a sustainable and controlled way, improving the drug efficacy and reducing the toxicity of drugs. Designing can also be done in a way that they can target specific tissues and cells making them a promising tool for personalized medicine. Hydrogels are being used in 3-D bioprinting, where they serve as bio-inks that can be fabricated into complex structures with high precision. In comparison to conventional technologies, this is a promising technique that allows the construction of complex three-dimensional structures in a sequential manner by a computeraided system. One major challenge in bioprinting is finding such material that is suitable for printing and also satisfies the mechanical strength requisite for tissue engineering applications. That is where hydrogels serve as the most appropriate model and have encouraging or favorable operation potential as cell-affable materials. This technique has revolutionized tissue engineering by allowing researchers to create functional tissues and organoids and spheroids. Overall, hydrogel-based tissue engineering, drug screening, drug delivery, and 3D bioprinting are exciting areas of research with great potential to significantly impact different areas of medicine and biology.
The domestication of animals and cultivation of crops have been essential to human development throughout history, with the agricultural sector playing a pivotal role. Insufficient nutrition often leads to plant diseases, such as those affecting rice crops, resulting in yield losses of 20-40% of total production. These losses carry significant global economic consequences. Timely disease diagnosis is critical for implementing effective treatments and mitigating financial losses. However, despite technological advancements, rice disease diagnosis primarily depends on manual methods. In this study, we present a novel Self-Attention Network (SANET) based on the ResNet50 architecture, incorporating a kernel attention mechanism for accurate AI-assisted rice disease classification. We employ attention modules to extract contextual dependencies within images, focusing on essential features for disease identification. Using a publicly available rice disease dataset comprising four classes (three disease types and healthy leaves), we conducted cross-validated classification experiments to evaluate our proposed model. The results reveal that the attention-based mechanism effectively guides the Convolutional Neural Network (CNN) in learning valuable features, resulting in accurate image classification and reduced performance variation compared to state-of-the-art methods. Our SANET model achieved a test set accuracy of 98.71%, surpassing current leading models. These findings highlight the potential for widespread AI adoption in agricultural disease diagnosis and management, ultimately enhancing efficiency and effectiveness within the sector.
As the demand for renewable energy continues to rise, it becomes crucial to discover effective ways to enhance grid-connected photovoltaic (PV) battery energy storage systems. The Institute of Petroleum Studies (IPS) complex at the University of Port Harcourt in Rivers State, Nigeria, embarked on a quest to determine the optimal approach for optimizing their PV battery energy storage system. This research aimed to fulfill this need by employing a diverse research methodology, incorporating the innovative MOALO theory. To begin with, the research gathered primary and secondary data to construct models for the power grid, solarPV, and battery. Furthermore, it meticulously analyzed the load profile of the IPS complex, at the University of Port Harcourt. Leveraging the power of the MOALO theory.The researchers accurately sized the system and evaluated the potential outcomes of simultaneously interconnecting all loads. To gauge the system's performance, there was a calculation of various parameters such as economics, random walk, boundary conditioning, entrapping ants, and ant trap development. Remarkably, the outcome showed that the fitness responses between the two trial runs, facilitated by the integration of MOALO, were strikingly similar, revealing a typical concaveconnected shape, which is characteristic of a multi-objective solver. The optimal multi-objective cost implication of the system was estimated to be around 4,300 USD, with a power mismatch performance of approximately -1.7819e+09. Based on these compelling findings, the study concluded that MOALO serves as an impressive optimization tool capable of minimizing power mismatches and optimizing costs. Moreover, it recommended the generation of excess power as a means to achieve sustainability.