•A new CCHP configuration is proposed based on PEMFC.•A new improved version for butterfly optimization algorithm is proposed.•Exergy and Energy performances are analyzed.•A Multi-objective ...methodology is proposed for the case study.
This study presents an optimal design of a combined cooling, heating and power generation system consists of a heat recovery system, a 5 kW proton exchange membrane stack, a small absorption chiller, a humidifier, and a gas compressor based on multi-criteria assessment for apartment simultaneously. The system is analyzed in terms of economics, environment, and thermodynamics. An improved version of butterfly optimization algorithm is employed for optimizing the system performance. The system efficiency is analyzed based on annual cost, exergy and energy efficiencies, and pollutant emission reduction. Final results illustrated that high relative humidity, pressure of inlet gases, and low operating temperature develop GHG emission reduction and system exergy performance. The optimized values of annual GHG reduction and exergy efficiency are 2.67e7 g and 47.1%, respectively, meanwhile, annual cost can be decreased to 3.139e3 $.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
This paper proposes a new methodology for the optimal selection of the parameters for proton exchange membrane fuel cell (PEMFC) models. The proposed method is to optimal parameter selection of the ...circuit-based model of the PEMFC model to minimize the sum of squared error (SSE) value between the estimated and the actual output voltage of the PEMFC stack. For minimizing the SSE, a newly developed model of the Sunflower Optimization Algorithm (DSFO) is proposed. Performance analysis is performed based on two practical models including NedSstack PS6 PEMFC and Horizon 500-W PEMFCs from the literature and the results have been compared with the empirical data and also some state of art methods including Seagull Optimization Algorithm (SOA), Multi-verse optimizer (MVO), and Shuffled Frog-Leaping Algorithm (SFLA). Final results indicate 2.18 and 0.014 SSE value for NedSstack PS6 PEMFC and Horizon 500-W open cathode PEMFC, respectively which are the minimum values compared with the other compared methods.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
In this paper, a new approach has been introduced for optimal parameter estimation of a proton exchange membrane fuel cell (PEMFC) model. The main purpose is to minimize the total error between the ...empirical data and the proposed method by optimal parameter selection of the model. The methodology is based on using a newly introduced developed version of the Coyote Optimization Algorithm (DCOA) for determining the value of the unknown parameters in the model. Two different PEMFC models including 2 kW Nexa FC and 6kW NedSstack PS6 FC are adopted for validation and the results are compared with the empirical data and some well-known methods including conventional COA, Seagull Optimization Algorithm, and (N + λ) - ES algorithm to show the proposed method's superiority toward the literature methods. The final results declared a satisfying agreement between the proposed DCOA and the empirical data. The results also declared the excellence of the presented method toward the other compared methods.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
Single-cell RNA sequencing (scRNA-seq) strives to capture cellular diversity with higher resolution than bulk RNA sequencing. Clustering analysis is critical to transcriptome research as it allows ...for further identification and discovery of new cell types. Unsupervised clustering cannot integrate prior knowledge where relevant information is widely available. Purely unsupervised clustering algorithms may not yield biologically interpretable clusters when confronted with the high dimensionality of scRNA-seq data and frequent dropout events, which makes identification of cell types more challenging.
We propose scSemiAAE, a semi-supervised clustering model for scRNA sequence analysis using deep generative neural networks. Specifically, scSemiAAE carefully designs a ZINB adversarial autoencoder-based architecture that inherently integrates adversarial training and semi-supervised modules in the latent space. In a series of experiments on scRNA-seq datasets spanning thousands to tens of thousands of cells, scSemiAAE can significantly improve clustering performance compared to dozens of unsupervised and semi-supervised algorithms, promoting clustering and interpretability of downstream analyses.
scSemiAAE is a Python-based algorithm implemented on the VSCode platform that provides efficient visualization, clustering, and cell type assignment for scRNA-seq data. The tool is available from https://github.com/WHang98/scSemiAAE .
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Summary
A new dynamic condition approximation method, which provides the current feedback, in collaboration with STATCOM regulation approach is presented to diminish the variation in voltage of solid ...oxide fuel cell (SOFC) electrical energy generators in connection to the compound power network in the time of faults. The suggested regulation approach is contrasted with the other present regulation techniques and the results show its advantage over the others in mitigating voltage fluctuation, inaccuracy and guarding the membranes inside the fuel cells. Because the dynamic internal conditions in the SOFC can precisely represent the transient conditions and dynamicity of the SOFC, their usage in designing the voltage regulator will provide more desirable control of voltage in the SOFC compared to the other approaches. Nonetheless, this model is not enough to mitigate AC voltage fluctuation which is caused by faults. Hence, STATCOM, which is a power electronic equipment, is applied to improve the alleviation of voltage variation and inaccuracy. Electricity network in the presence of the suggested regulation approach has shown stability in the linear assessments. Linear assessment of the system demonstrates that the final energy system embedded with the proposed regulation can maintain stability. Obtained numerical analysis demonstrates the validity of suggested approach.
Diagrammatic presentation of the solid oxide fuel cell.
Abstract
Grid-connection of new energy is highly important in promoting the use of clean and renewable energy. However, it will bring huge risks to the power grid operation security, such as ...frequency stability, voltage stability, small signal stability, and transient stability, etc.,. In the study, SWOT (Strengths, Weaknesses, Opportunities, and Threats) analysis has been employed to construct 24 kinds of internal and external evaluation factors and 8 kinds of improvement strategies, for assessing operation security prospective with new energy power system of HM in China. The weights of SWOT factors are determined with the fuzzy-AHP method. Moreover, the fuzzy-MARCOS approach is used to select the most suitable strategies for power system operation security effective implementation. The reported research reveals that new energy in HM area not only has an ample potential for full development and generating electricity, but also brings operation security problems due to large-scale grid connection. Therefore, 8 kinds of improvement strategies are suggested to encourage the government to exploit and develop new resources, improve the investment pay, power generation and transmission technologies to mitigate the current energy crisis, and increase the energy security for sustainable development of the country. The methodology proposed herein is applicable with a case study concerning the operation security prospective of HM power grid, and all phases of the comparative analysis and sensitivity analysis illustrate the validity of MARCOS method. Furthermore, the ranked order of strategies is obtained as
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, i.e., “improving the technical establishment to encourage efficient and cheap electricity production”, “strive to build local permanent load, and reduce the risk of long-distance and high-capacity transmission”, “taking advantage of government incentives and investment to modify the irrational energy policies and energy planning”, respectively.
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The objective of this article is to evaluate the effect of dexmedetomidine on emergence agitation (EA) and recovery profiles in children after sevoflurane anesthesia and its pharmacological ...mechanisms. Standard bibliographic databases, including MEDLINE, EMBASE, PsycINFP, Springer and ISI Web of Knowledge, were artificially searched to identify all randomized controlled trials (RCTs) comparing the impact of dexmedetomidine with placebo, fentanyl and midazolam on EA and recovery profiles after sevoflurane anesthesia in post-anesthesia care unit (PACU). Two authors assessed the quality of each study independently in accordance with strict inclusion criteria and extracted data. RevMan 5.0 software was applied for performing statistic analysis. The outcomes analyzed included: 1) incidence of EA, 2) emergence time, 3) time to extubation, 4) incidence of post-operation nausea and vomiting, 5) number of patients requiring an analgesic, and 6) time to discharge from PACU. A total of 1364 patients (696 in the dexmedetomidine group and 668 in the placebo, fentanyl and midazolam group) from 20 prospective RCTs were included in the meta-analysis. Compared with placebo, dexmedetomidine decreased the incidence of EA (risk ratio RR 0.37; 95% CI 0.30 to 0.46), incidence of nausea and vomiting (RR 0.57; 95% CI 0.38 to 0.85) and number of patients requiring an analgesic (RR 0.43; 95% CI 0.31 to 0.59). However, dexmedetomidine had a significantly delayed effect on the emergence time (weighted mean differences WMD 1.16; 95% CI 0.72 to 1.60), time to extubation (WMD 0.61; 95% CI 0.27 to 0.95), and time to discharge from recovery room (WMD 2.67; 95% CI 0.95 to 4.39). Compared with fentanyl (RR 1.39; 95% CI 0.78 to 2.48) and midazolam (RR 1.12; 95% CI 0.54 to 2.35), dexmedetomidine has no significantly difference on the incidence of EA. However, the analgesia effect of dexmedetomidine on postoperation pain has no significantly statistical differences compared with fentanyl (RR 1.12; 95% CI 0.66 to 1.91), which implied that its analgesia effect might play an important role in decreasing the incident of EA. No evidence of publication bias was observed.
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Single cell RNA sequencing (scRNA-seq) enables researchers to characterize transcriptomic profiles at the single-cell resolution with increasingly high throughput. Clustering is a crucial step in ...single cell analysis. Clustering analysis of transcriptome profiled by scRNA-seq can reveal the heterogeneity and diversity of cells. However, single cell study still remains great challenges due to its high noise and dimension. Subspace clustering aims at discovering the intrinsic structure of data in unsupervised fashion. In this paper, we propose a deep sparse subspace clustering method scDSSC combining noise reduction and dimensionality reduction for scRNA-seq data, which simultaneously learns feature representation and clustering via explicit modelling of scRNA-seq data generation. Experiments on a variety of scRNA-seq datasets from thousands to tens of thousands of cells have shown that scDSSC can significantly improve clustering performance and facilitate the interpretability of clustering and downstream analysis. Compared to some popular scRNA-deq analysis methods, scDSSC outperformed state-of-the-art methods under various clustering performance metrics.
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Elderly individuals undergoing surgical procedures are often confronted with the peril of experiencing postoperative cognitive dysfunction (POCD). Prior research has demonstrated the exacerbating ...effect of sevoflurane anesthesia on neuroinflammation, which can further deteriorate the condition of POCD in elderly patients. Intermittent fasting (IF) restricts food consumption to a specific time window and has been demonstrated to ameliorate cognitive dysfunction induced by neuropathic inflammation. We subjected 18-month-old male mice to 16 hours of fasting and 8 hours of unrestricted eating over a 24-hour period for 0, 1, 2, and 4 weeks, followed by abdominal exploration under sevoflurane anesthesia. In this study, we aim to explore the potential impact of IF on postoperative cognitive function in aged mice undergoing sevoflurane surgery through the preoperative implementation of IF measures. The findings indicate two weeks of IF leads to a significant enhancement of learning and memory capabilities in mice following surgery. The cognitive performance, as determined by the novel object recognition and Morris water maze tests, as well as the synaptic plasticity, as measured by in vivo electrophysiological recordings, has demonstrated marked improvements. Furthermore, the administration of IF markedly enhances the expression of synaptic-associated proteins in hippocampal neurons, concomitant with a decreasing expression of pro-inflammatory factors and a reduced density of microglial cells within the hippocampal brain region. To summarize, the results of this study indicate that IF may mitigate inflammation in the hippocampal area of the brain. Furthermore, IF appears to provide a safeguard against cognitive impairment and synaptic plasticity impairment brought on by sevoflurane anesthesia.
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•Anesthesia and surgery lead to postoperative cognitive dysfunction (POCD) in aged mice.•Preoperative intermittent fasting (≥2 weeks) can improve POCD.•Intermittent fasting reduces neuroinflammation in the hippocampus region of the brain.•Intermittent fasting increases hippocampal synaptic protein expression and improves synaptic plasticity.
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The ability to predict the response of a cancer patient to a therapeutic agent is a major goal in modern oncology that should ultimately lead to personalized treatment. Existing approaches to ...predicting drug sensitivity rely primarily on profiling of cancer cell line panels that have been treated with different drugs and selecting genomic or functional genomic features to regress or classify the drug response. Here, we propose a dual-layer integrated cell line-drug network model, which uses both cell line similarity network (CSN) data and drug similarity network (DSN) data to predict the drug response of a given cell line using a weighted model. Using the Cancer Cell Line Encyclopedia (CCLE) and Cancer Genome Project (CGP) studies as benchmark datasets, our single-layer model with CSN or DSN and only a single parameter achieved a prediction performance comparable to the previously generated elastic net model. When using the dual-layer model integrating both CSN and DSN, our predicted response reached a 0.6 Pearson correlation coefficient with observed responses for most drugs, which is significantly better than the previous results using the elastic net model. We have also applied the dual-layer cell line-drug integrated network model to fill in the missing drug response values in the CGP dataset. Even though the dual-layer integrated cell line-drug network model does not specifically model mutation information, it correctly predicted that BRAF mutant cell lines would be more sensitive than BRAF wild-type cell lines to three MEK1/2 inhibitors tested.
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