Number Of Different Cells Research Articles

Hepatocyte Growth Factor Delivery to Injured Cervical Spinal Cord Using an Engineered Biomaterial Protects Respiratory Neural Circuitry and Preserves Functional Diaphragm Innervation.

A major portion of spinal cord injury (SCI) cases occur in the cervical region, where essential components of the respiratory neural circuitry are located. Phrenic motor neurons (PhMNs) housed at cervical spinal cord level C3-C5 directly innervate the diaphragm, and SCI-induced damage to these cells severely impairs respiratory function. In this study, we tested a biomaterial-based approach aimed at preserving this critical phrenic motor circuitry after cervical SCI by locally delivering hepatocyte growth factor (HGF). HGF is a potent mitogen that promotes survival, proliferation, migration, repair, and regeneration of a number of different cell and tissue types in response to injury. We developed a hydrogel-based HGF delivery system that can be injected into the intrathecal space for local delivery of high levels of HGF without damaging the spinal cord. Implantation of HGF hydrogel after unilateral C5 contusion-type SCI in rats preserved diaphragm function, as assessed by invivo recordings of both compound muscle action potentials and inspiratory electromyography amplitudes. HGF hydrogel also preserved PhMN innervation of the diaphragm, as assessed by both retrograde PhMN tracing and detailed neuromuscular junction morphological analysis. Furthermore, HGF hydrogel significantly decreased lesion size and degeneration of cervical motor neuron cell bodies, as well as reduced levels surrounding the injury site of scar-associated chondroitin sulfate proteoglycan molecules that limit axon growth capacity. Our findings demonstrate that local biomaterial-based delivery of HGF hydrogel to injured cervical spinal cord is an effective strategy for preserving respiratory circuitry and diaphragm function.

Hierarchical Symmetry-Breaking Model for Stem Cell Differentiation

Waddington envisioned stem cell differentiation as a marble rolling down a hill, passing through hierarchically branched valleys representing the cell’s temporal state. The terminal valleys at the bottom of the hill indicate the possible committed cells of the multicellular organism. Although originally proposed as a metaphor, Waddington’s hypothesis establishes the fundamental principles for characterizing the differentiation process as a dynamic system: the generated equilibrium points must exhibit hierarchical branching, robustness to perturbations (homeorhesis), and produce the appropriate number of cells for each cell type. This article aims to capture these characteristics using a mathematical model based on two fundamental hypotheses. First, it is assumed that the gene regulatory network consists of hierarchically coupled subnetworks of genes (modules), each modeled as a dynamical system exhibiting supercritical pitchfork or cusp bifurcation. Second, the gene modules are spatiotemporally regulated by feedback mechanisms originating from epigenetic factors. Analytical and numerical results show that the proposed model exhibits self-organized multistability with hierarchical branching. Moreover, these branches of equilibrium points are robust to perturbations, and the number of different cells produced can be determined by the system parameters.

High-quality flatband resonances in few-cell moiré superlattices by band-offset tuning

Wave localization by flatband mechanisms underlies prominent moiré physics and relevant applications. While typically studied in periodic superlattices, the wave-confining capability of finite-size superlattices is important both fundamentally and practically. Here, we investigate wave localization in few-cell moiré superlattices through fine tuning of band offset, which is accomplished by jointly adjusting structural parameters of constitutive lattice. Remarkably, the quality factor Q, which reflects the capability of few-cell superlattices to localize wave, can reach quite high levels with appropriate band offset. Thus, superior wave localization is feasible in few-cell superlattices, indicated by the very high Q up to 104−105 for single-cell superlattices and extremely high Q up to >107 for double-cell superlattices. Moreover, the Q patterns are distinct for different flatband resonances and different number of cells, while narrow high-Q (∼108) branches appear for the triple-cell superlattices. The narrow high-Q branches are hard to be reached in practice, since it requires stringent control on structural parameters. In this respect, the double-cell superlattices are promising for exploiting unconventional effects induced by wave localization, since it can achieve extremely high-Q (>107) resonances within a sufficiently large tuning range. These results demonstrate the profound role of band-offset tuning in achieving strong wave localization in few-cell superlattices, which is useful for on-chip applications such as lasing, optical filters, and optical harmonic generation.

Air flow prediction and evaluation of ventilation effectiveness with different zonal configurations

This paper proposes a numerical method for the study of ventilation efficiency in buildings. The developed model is validated with the experimental results of Nielsen [5] who tested the isothermal flow in a scaled model of a ventilated room. A zonal method is used to predict airflow patterns in the same ventilated room. The different equations governing the flow in the room were coded in Matlab for different operating conditions, different zonal configurations of the room and different number of cells (control volumes). The efficiency of the ventilation was determined by calculating the number of air changes per hour (ACH) for each cell. The present results show the importance of the inlet air flow rate, the space resolution and the jet inlet dimensions on the determination of air quality.

Feline calicivirus and natural killer cells: A study of its relationship in chronic gingivostomatitis.

Feline chronic gingivostomatitis (FCGS) is a frequent chronic inflammatory condition in the oral cavity with an etiopathogenesis not completely identified. This study aimed to contribute to the knowledge of FCGS by identifying the presence of feline calicivirus (FCV) antigens and natural killer (NK) cells and comparing them. Forty biopsies from the oral mucosa of cats diagnosed with chronic gingivostomatitis were subjected to immunohistochemical techniques to evaluate cells with FCV antigens and NK cells positive for CD56. NK cells were identified in all samples, with an average of 725.3 ± 409.1 cells. Regarding FCV, it was identified in 18 out of 30 samples (60%), with a different number of cells with virus in between the analyzed cases. In all cases, the number of cells infected with FCV was lower than the number of NK cells present in the same samples, but there was no statistical association between them. This preliminary study shows that NK cells are present in gingivostomatitis lesions not exclusively caused by FCV-stimulus, as only 60% of all cases were positive for this virus, but other antigens should be considered in the etiology of FCGS.

Maternal saliva visfatin level in term and preterm labor: A case control study.

Visfatin, a colony-enhancing factor (pre-B-cell), is an inflammatory biomarker that is secreted from a different number of cells and appears to have some proinflammatory or immune-regulating effects. The aim of this study was to compare maternal saliva visfatin levels in women with preterm and term delivery. In This case-control study, women in labor before 37 weeks of gestation were the case group (n = 40) and women in labor after 37 weeks of gestation were in control group (n = 40). The saliva of the participants was sampled and maternal saliva visfatin level was measured by ELISA test. In this study, 80 pregnant women were studied in case and control groups. The mean age of case and control group was 29.1±6.9 and 30.55±5.3 years, respectively. The results revealed that the mean maternal saliva visfatin level in the preterm group (4.75±2.2) is significantly (p = 0.001) lower than that in term birth group (6.7±3.1). The results of adjusted logistic regression revealed that after adjusting for GDM, preeclampsia, pre pregnancy BMI and weight gain, the mean of maternal saliva visfatin level in the preterm group is significantly lower than that in the term group (p = 0.026). Considering that during the term pregnancy visfatin levels increase and visfatin may play a role in initiating labor, in our study due to the high visfatin level in case group although the level of maternal saliva visfatin was lower than the control group but high levels of visfatin in the case group can represent the role of visfatin in initiating labor and due to this issue can be use the role of this adipokine for early diagnosis of preterm delivery can be used to prevent, treat and improve the prognosis of this disease. Also, this study is the first study to compare the maternal saliva visfatin level between SGA and AGA group and there is no difference between these groups.

Scale-adaptive model for detection and grading of age-related macular degeneration from color retinal fundus images

Age-related Macular Degeneration (AMD), a retinal disease that affects the macula, can be caused by aging abnormalities in number of different cells and tissues in the retina, retinal pigment epithelium, and choroid, leading to vision loss. An advanced form of AMD, called exudative or wet AMD, is characterized by the ingrowth of abnormal blood vessels beneath or into the macula itself. The diagnosis is confirmed by either fundus auto-fluorescence imaging or optical coherence tomography (OCT) supplemented by fluorescein angiography or OCT angiography without dye. Fluorescein angiography, the gold standard diagnostic procedure for AMD, involves invasive injections of fluorescent dye to highlight retinal vasculature. Meanwhile, patients can be exposed to life-threatening allergic reactions and other risks. This study proposes a scale-adaptive auto-encoder-based model integrated with a deep learning model that can detect AMD early by automatically analyzing the texture patterns in color fundus imaging and correlating them to the vasculature activity in the retina. Moreover, the proposed model can automatically distinguish between AMD grades assisting in early diagnosis and thus allowing for earlier treatment of the patient’s condition, slowing the disease and minimizing its severity. Our model features two main blocks, the first is an auto-encoder-based network for scale adaption, and the second is a convolutional neural network (CNN) classification network. Based on a conducted set of experiments, the proposed model achieves higher diagnostic accuracy compared to other models with accuracy, sensitivity, and specificity that reach 96.2%, 96.2%, and 99%, respectively.

Flexible and high-throughput approach for capturing large number of single cells simultaneously using microwell-based technology

Abstract Single-cell RNA-seq is a powerful method for studying complex cell populations. As opportunities grow for single-cell multiomics methods in translational research, there is an increased need for high-throughput approaches capable of efficiently capturing and analyzing large numbers of cells simultaneously. Current assays on scRNA-seq platforms have limited throughput due to the low cell recovery and high multiplet rates with high cell inputs. Here we demonstrate the feasibility of profiling the single-cell gene expression of hundreds of thousands of cells in a single experiment using an eight-lane microwell based cartridge. We also increased the cell label diversity of cell capture beads to enable high cell loading with a lower rate of barcode collisions. To show the range of cell capture capabilities of the eight-lane microwell cartridge, we loaded different numbers of cells per lane ranging from 100 to over 40,000 cells. To evaluate the multiplet rates and sensitivity, we labeled two different cell types with antibody-based sample multiplexing prior to cell capture. Cell multiplet rates were also assessed through Sample Tag analysis. Detection of Sample Tags used showed no contamination of Sample Tag between samples. In addition, less than 0.5% barcode collision rate was detected from 360,000 viable cells captured with a bead. Targeted mRNA profiling had high correlation (R 2> 0.95) of gene expression across cell capture lanes. This study demonstrates that the eight-lane microwell-based cartridge technology provides flexibility and high-throughput cell capture of thousands of cells and could be readily extended to other existing single-cell technologies such as surface protein marker detection and immune profiling.

Abstract 3385: A pipeline for mRNA analysis on circulating tumor cells (CTCs) collected and stored in preservative tubes

Abstract Introduction: Circulating tumor cells (CTCs) are associated with prognosis in breast cancer. In addition to enumeration, there is great interest in the molecular characterization of CTCs. Nonetheless, this is technically challenging, especially with regards to RNA analysis, which requires immediate CTC processing to avoid RNA degradation. The purpose of this study was to evaluate the performance of the CellRescueTM preservative tubes in preserving RNA, and to optimize a method for mRNA expression analysis on CTCs up to 4 days after blood collection. Methods: Ten blood sample from one healthy donor (10 mL each) were collected into CellRescueTM tubes and spiked-in with 5 (n=4) or 500 (n=4) MCF7; two tubes were used for white blood cell (WBC) collection on day (d) 1, 2, 4, and 8 (4 mL used for each timepoint). The spiked-in samples were processed for CTC enrichment on d2 and d4 using CellSearch® (cs5d2, cs500d2, cs5d4, cs500d4) or Parsortix® (p5d2, p500d2, p5d4, p500d4). RNA was extracted, quantified, and subjected to reverse transcription to cDNA. Half cDNA from each sample was analyzed through TaqMan real-time PCR (in technical duplicates) for the detection of 3 transcripts of interest: ACTB (housekeeping), CD45 (WBC marker), and EPCAM (tumor cell marker). Considering the low amount of RNA, the second half of cDNA for each sample underwent preamplification of the 3 target transcripts, followed by real-time PCR (with technical triplicates) to evaluate whether this could increase sensitivity. Results: RNA quantification by Qubit was 11.6, 23, 9.9 and 4.8 ng/uL in WBC samples collected on d1, d2, d4 and d8, respectively. Thus, using CellRescueTM tubes, RNA was preserved up to 8 days, with a slight reduction in RNA quantity on d8. For spiked-in samples, RNA was below detection at any timepoint. Real-time PCR analysis of CD45 and ACTB in WBC samples confirmed a similar quantity of transcripts across all timepoints. For spiked-in samples processed with CellSearch®, CTCs were detected only after preamplification and when 500 MCF7 were present. For spiked-in samples processed with Parsortix®, 500 CTCs were detected both at d2 and d4, regardless of preamplification. Preamplification allowed for the detection of 5 CTCs as well, but only at d2. The spiked-in samples confirmed that by using CellRescueTM tubes, RNA is preserved up to 4 days, with slight transcript signal decrease between D2 and D4. Conclusions: With the developed pipeline, it is possible to detect 500 CTCs after blood storage for up to 4 days, and as low as 5 CTCs after 2-days storage. The enrichment methods can however affect the detection. More tests need to be performed to define the pipeline’s sensitivity using different enrichment methods and with different numbers of cells. Using this pipeline, up to a total of 100 gene expression assays of interest can be evaluated in a single sample. Citation Format: Eleonora Nicolo, Marwa Manai, Youbin Zhang, Laura Munoz Arcos, Strickland Kaylan, Paolo D'Amico, Huiping Liu, Giuseppe Curigliano, Carolina Reduzzi, Massimo Cristofanilli. A pipeline for mRNA analysis on circulating tumor cells (CTCs) collected and stored in preservative tubes [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 3385.

Autonomous UAV object Avoidance with Floyd-warshall differential evolution approach

Unmanned Aerial Vehicles (UAVs) are recently focused with significant research attention from commercial to military industries. Due to its wide range of applications such as traffic monitoring, surveillance, aerial photograph and rescue mission, many research studies were conducted related to UAV development. UAV are commonly called as ‘drones’ used to suit dull, dangerous and dirty missions that can be suited by manned aircraft. UAV can be controlled either remotely or using automation approaches so that it can be travelled into predefined path. To make the autonomous UAV, the most complex issue that is faced by UAV is obstacle / object avoidance. Obstacle detection and avoidance are important for UAV and it is the complex problem to solve due to the payload restriction. This will limit the sensor count mounted on the vehicle. Radar was used to find the distance between the object and vehicle. This can help to detect and track the moving objects speed and direction towards the vehicle. This paper considered the object avoidance problem as path planning problem. There were many path planning methods related to UAV which formulates the path planning as an optimization problem to avoid the obstacles. With the consideration, this paper proposed an efficient and optimal approach called Floyd Warshall- Differential evolution (FWDE) approach to detect the frontal obstacles of UAV. Finally, statistical analysis of the simulated environment reveals that the proposed evolutionary method can efficiently avoid both static and dynamic objects for UAVs. This efficient avoidance algorithm for UAV can be experimented with simulation environment with three kinds of scenarios having different number of cells. The obtained accuracy and recall value of the proposed system is 95.21% and 91.56%.

Modal Selection for Inclined Darcy-Bénard Convection in a Rectangular Cavity

Nonlinear free convection in an inclined rectangular porous cavity heated from below has been studied using a two-dimensional spectral decomposition. The code uses pseudo-arclength continuation to follow solution curves around fold bifurcations. The evolution with inclination of the pattern of convection is complicated and it relies strongly on both the Darcy–Rayleigh number and the aspect ratio of the cavity. When the inclination is large it is generally true that only one cell appears, and that it has a circulation that is consistent with the direction of the buoyancy forces along the heated and cooled boundaries. However, as the inclination decreases back towards the horizontal, this unicellular pattern evolves, sometimes initially via fold bifurcations, into patterns with different numbers of cells. Such evolutions always conserve the parity of the number of cells (such as one cell becoming three and then five, or two cells becoming four), but bifurcations also arise between patterns with different parities. These phenomena are illustrated using a suitable selection of solution curves that show the dependence of the Nusselt number on the inclination.

Collagenase-Induced Mouse Model of Osteoarthritis-A Thorough Flow Cytometry Analysis.

Osteoarthritis (OA) is a chronic degenerative disorder of the joint characterized by cartilage breakdown and synovial inflammation. A number of different cells of innate and adaptive immunity contribute to joint pathology during OA inflammation. The interaction between the local synovial and systemic inflammatory cellular response and the structural changes in the joint is still unknown. The objective of this study was to investigate the role of the different types of immune cells in the development of OA. Collagenase-induced osteoarthritis was induced in Balb/c mice; flow cytometry analysis; and histopathological damages were assessed in histological sections stained with H&E, Toluidine blue, and Safranin O. Flow cytometry analysis showed B lymphocyte infiltration in the active phase of inflammation and an increase in the effector T cell population into the synovium. An increased activation state of cytotoxic T cells and of NK cell populations in the spleen and synovium was also found. The differentiation of NK cells from a cytotoxic phenotype in early OA to cells with an effector phenotype in the chronic phase of the disease followed. A number of different cells contribute to inflammatory processes in OA. The correlation between their phenotype and the inflammatory pathophysiology could result in the development of novel approaches to suppress destructive changes in the joint.

Evaluation of PV Panel Power Loss Using Gabor Filter Bank

Photovoltaic panels are subject to thermomechanical stresses during their production and subsequent life stages. These conditions give rise to cracks and other defects in panels that can affect power output. Cell cracking is one of the most important causes of power loss in photovoltaic panels. Therefore, photovoltaic panels and cells need to be monitored to achieve the maximum output during production and further downstream. Electroluminescent imaging is a powerful and established technique consisting of many electrically connected solar cells arranged on a grid, and it is employed in order for assessing the quality of photovoltaic panels. In this study, the detection of photovoltaic panel defects in electroluminescent images was examined through image processing methods. PV panels can consist of different numbers of cells. Performance evaluation is made on a cell-based and whole module basis. In PV panel production, unlike EL images taken in standard environments in the factory environment, EL images taken under field conditions require preprocessing before actually being processed. Features were extracted from the preprocessed EL images by exploiting Gabor filter. The obtained features were evaluated as cell-based and the stability of the cells was determined. The performance of the panel was calculated according to the power loss of the cells of the panel. When the calculated performance values were compared with the power values obtained by I-V measurement, the highest error was found to be 0.059, the lowest error was 0.004, and the average error was 0.0213. As a result, the highest success rate was 99.99%.

Modeling electrospun PLGA nanofibers’ diameter using response surface methodology and artificial neural networks

The present work is an attempt to model the diameter of Poly Lactic-co-Glycolic Acid (PLGA) nanofibers by utilizing response surface methodology (RSM) and artificial neural networks (ANNs). Hence, determining the optimal electrospinning process conditions to produce a minimum fiber diameter. For modelling the average diameter of nanofibers, RSM approach based on four parameters (polymer concentration, high voltage and needle tip to collector distance and spinning angle) with five-level was compared to ANN technique. In the RSM approach, central composite design (CCD) was used to determine the individual and interaction impacts of the parameters on the average diameter of nanofibers. Several ANNs of single and double hidden layers with different number of cells for each were tried to obtain the best network structure. The experimental and predicted PLGA fiber diameters using an ANN showed a strong correlation, indicating that the network topology of 4-14-1 has good predictability for analyzing factors impacting PLGA fiber diameter. The average absolute relative error for predicting PLGA nanofibers’ diameter using ANN (2.24%) is slightly less than that obtained from RSM (2.59%). The high regression coefficient between the variables and the response (R2 = 0.9636) shows a good second-order polynomial regression model for evaluating experimental data. The R2 value was 0.945, indicating that the ANN model was good fitting with the experimental results. The optimum combinations (PLGA concentration of 26 wt.%, high voltage 22 kV, needle tip to collector distance 20 cm, and spinning angle 60o) were developed by RSM model for electrospinning PLGA nanofiber that can produce fine, consistent, and high-quality nanofibers.

Prediction of Human Induced Pluripotent Stem Cell Formation Based on Deep Learning Analyses Using Time-lapse Brightfield Microscopy Images.

We use deep learning methods to predict human induced pluripotent stem cell (hiPSC) formation using time-lapse brightfield microscopy images taken from a cell identified as the beginning of entered into the reprogramming process. A U-net is used to segment cells and a CNN is used to classify the segmented cells into eight types of cells during the reprogramming and hiPSC formation based on cellular morphology on the microscopy images. The numbers of respective types of cells in cell clusters before the hiPSC formation stage are used to predict if hiPSC regions can be well formed lately. Experimental results show good prediction by the criteria using the numbers of different cells in the clusters. Time-series images with respective types of classified cells can be used to visualize and quantitatively analyze the growth and transition among dispersed cells not in cell clusters, various types of cells in the clusters before the hiPSC formation stage and hiPSC cells.

Revealing mobility pattern of taxi movements with its travel trajectory

Taxi movements contain much mobility information of passengers and taxi drivers in a city. However, previous researches often overlook the continuous movements of a single taxi. In this paper, we study the mobility pattern of taxi movements by analyzing the visiting sequences which record the cells (i.e., locations) being visited by taxis. At the collective level, we observe the weak power-law scaling exists in the relation between the number of visits of a cell and the corresponding number of taxis. At the individual level, we notice some unusual characteristics of continuous movements of a single taxi: (i) the number of different cells that have been visited increase with the visit number and shows a robust scaling behavior; (ii) the cell’s visiting frequency decreases with the rank of the cell and presents as a logarithm function; (iii) The distribution of the number of visits taken to revisit one cell presents as an exponential distribution. The empirical result demonstrates that the taxi movements do follow the unified pattern and show a stronger exploration tendency and weaker preferential return than other human dynamics. Finally, we utilize an agent-based model to reveal and understand the unified pattern of taxi movements. The model results indicate that the rank distribution is an intrinsic characteristic to explore the taxi destination choice and simulate the taxi movements.

Fully-Automated On-Chip Multi-Cell Arraying With Deterministic Quantities

Microfluidic devices for cell immobilization have significantly advanced the biological analysis at the single-cell level. However, the previous research on immobilization of multiple single cells, especially with deterministic quantities, is insufficient. In this paper, we proposed a novel microfluidic device based on the passive hydrodynamics and the uniform geometric design principle, which can array different numbers of cells in every capture cavity. The capture cavities could be stretched to accommodate more cells, and the trapping force was adjusted by modifying the related geometric parameters of the inside channel. The whole procedure was monitored and further automatized by integrating computer vision technology under a microscope. On the proposed integrated on-chip platform, we realized full-automated arraying of a single cell, two cells, and three cells on a single chip, achieving success rates up to 95%, 75%, and 72%, respectively. As a primary experimental demonstration, the cell viability test of arraying multiple cells with different quantities showed excellent biocompatibility and no significant association between trapping quantity and cell survivability. We envision that the proposed quantity-controllable, high-efficiency microfluidic devices for multiple cell arraying could be a powerful platform for an in-depth study of cell heterogeneity and cell communication between multiple cells.

A Sepic-Buck Topology for Remotely Piloted Aircraft Systems Battery Charger

The use of remotely piloted aircraft systems (RPAS) is already a reality in applications such as geographic mapping, surveillance, digital marketing, delivery, agriculture, infrastructure inspection, and others. Most of these aircraft are purely electric, being the only source of energy, packs of ion-lithium or lithium polymer batteries. These battery packs are conceived by the association of a different number of cells, usually ranging from three cells (3S) to twelve cells (12S). However, universal battery chargers for this range are not consolidated yet due to the recent emergence of the use of RPAS for different applications. To overcome this drawback, this paper introduces a topology to charge a wide range of low voltage battery packs (3S-12S) for RPAS. The circuit is composed of two power converters, one of them is a DCM SEPIC PFC rectifi er and another is a dc-dc Buck converter. The system is design for 400 W of rated power and the proposed solution is suggested to charge battery packs from 3S to 12S.

Omnidirectional-Wheel Conveyor Path Planning and Sorting Using Reinforcement Learning Algorithms

In this paper, path planning and sorting of packages for Omnidirectional-Wheel conveyor are presented using Reinforcement Learning (RL). Q-learning, Double Q-learning, Deep Q-learning, and the Double Deep Q-learning algorithms are investigated. The RL algorithms enable the conveyor to self-learn the packages path and sort them without using conventional control or path planning theories. The RL algorithms are used for two different case studies on conveyors structures with different numbers of cells to compare and evaluate their performances in large- and small-scale sized structures. To explore the proposed methods response to external environment effects, two types of collisions between multiple packages were considered, the proposed RL algorithms showed their ability to resolve both types successfully. Comparative study between multiple RL algorithms for path planning showed that the Q-learning and Double Q-learning algorithms had outperformed their Deep learning versions for path planning in the two case studies. Furthermore, the proposed RL methods are compared experimentally to classic control and path planning theories using a hardware prototype for one of the presented case studies. The hardware experimental results showed that the proposed RL methods were as successful as the conventional methods in path planning and sorting in much less processing time. Two types of sorting scenarios (Type I and II) were tested for same package type and for multiple ones. For Type I sorting the Q-learning algorithm performed better than the Q-learning with weights approach, achieving better mean and minimum rewards while maximum rewards remain the same for both techniques. As for Type II sorting, only the Q-learning with weights approach was able to achieve it and converge in a reasonable time.

Energy, exergy, environmental and economic analyzes (4E) and multi-objective optimization of a PEM fuel cell equipped with coolant channels

In the current research, the energy, exergy, environmental and economic (4E) analyzes of a PEM fuel cell are investigated. Parameters related to exergy are studied by considering the environmental effects and operating conditions of the PEM fuel cell. Multi-objective optimization is applied to maximize output power and efficiency and minimize environmental impacts and cost. The fuel cell is simulated with coolant channels and the effects of using porous metal foam inside the gas channels and coolant channels are investigated on the distribution of hydrogen and liquid water. The effect of using a hybrid nanofluid as a high potential liquid to keep the fuel cell cool is investigated. Optimal output power and optimal cost are provided according to the number of different cells. Optimal efficiencies (energy and exergy) and optimal environmental characteristics are likewise presented in a wide range of current densities. The results show that the simultaneous use of nanofluid and porous metal foam for cooling the fuel cell may not be appropriate. Furthermore, the effect of using metal foam is recommended for better cooling (increased heat transfer) of the fuel cell. Likewise, the use of porous metal foam inside the anode channel affects the distribution of hydrogen and liquid water. The effect of using porous foam in Re = 300 and above is significant so that in Re = 300, a 48% increase in heat transfer compared to the channel without porous foam can be seen. Besides, the percentage of reduction of nanofluid heat transfer in the presence of porous foam (in Re = 500) compared to pure water is 7.5%.