Cell Hybrid Research Articles

Energy management strategy for fuel cell hybrid tractor considering demand power frequency characteristic compensation.

The application of fuel cell tractors is expected to drive technological upgrades and sustainable development in agricultural machinery. However, fuel cell hybrid systems face issues such as slow dynamic response, low efficiency, and short lifespan. This paper proposes an energy management strategy based on signal reconstruction methods, including Empirical Mode Decomposition (EMD) and Variational Mode Decomposition (VMD), to achieve optimal energy utilization and system efficiency based on frequency response characteristics. First, we collected data on the tractor's traction force and operating speed, and calculated the required traction power using a full-machine dynamics model built in MATLAB software. We conducted frequency response characteristic analysis of the fuel cell hybrid system based on EMD and VMD, establishing an energy management controller to sequentially meet the average power demand of the fuel cell under plowing load operations, the instantaneous acceleration power demand of the power battery, and the real-time compensation power demand of the supercapacitor. The results show that the EMD strategy exhibits good stability, while the VMD strategy performs better in terms of hydrogen consumption. Under the VMD strategy, the hybrid system achieves a maximum output efficiency of 55.0% with a total hydrogen consumption of 750g. Compared to the EMD strategy, the maximum efficiency of the system increases by 27.31%, and hydrogen consumption decreases by 3.49%. This study provides a new theoretical foundation and technological route for the application of fuel cell hybrid systems in the field of agricultural machinery.

BIOM-43. SOMATICTP53, PIK3R1, NF1 ORTERT MUTATIONS ARE ASSOCIATED WITH INFERIOR CLINICAL OUTCOME IN H3K27M-ALTERED DIFFUSE MIDLINE GLIOMA: AN INTERNATIONAL, MULTI-INSTITUTIONAL STUDY

Abstract BACKGROUND H3K27-altered diffuse midline gliomas (DMGs) have poor prognosis with no standard of care therapy beyond radiation (RT). While RT prolongs survival, not all patients respond. Molecular biomarkers can be a strategy to predict RT response and thus patient outcomes. METHODS We performed a retrospective analysis of patients with biopsy-proven H3K27M-altered DMG between 2013-2023 from six US medical centers and the DMG Center in Zurich. Patients with tumor genomic sequencing and completion of RT were eligible. Genomic alterations were evaluated for somatic mutations, fusions, and chromosomal instability. Cox proportional hazard models were used to evaluate associations between genomic alterations and survival. Multivariate analysis included all significant variables at P <0.1 in initial analysis, including age at diagnosis, tumor location, TP53 and PIK3R1 alterations. RESULTS 297 patients (135 female; median age 8.3 years; range 0.2-71.4 years) were included. Median progression-free survival (PFS) and overall survival (OS) was 7.6 months (95%CI: 6.9-8.6) and 14.0 months (95%CI: 12.9-15.7). In univariate analysis, TP53 or PIK3R1 status correlated with shorter OS (TP53-mutant 12.5 vs. TP53-wildtype 17.5 months; HR=1.5; 95%CI: (1.1-2.0), P= 0.005); (PIK3R1-mutant 12.6 vs. 14.5 months; HR=1.9, 95%CI: (1.1-3.4), P = 0.03). Multivariate analysis corroborated TP53-mutant status with poorer OS (HR=1.5; 95%CI: (1.2-2.0), P= 0.003). Subgroup univariate analysis of pontine DMG patients showed poorer OS with TP53, NF1, or TERT mutations (TP53-mutant 11.9 vs. 15.2 months; HR= 1.6; 95%CI: (1.1-2.3), P= 0.02); (NF1-mutant 8.4 vs. 13.6 months; HR= 2.3; 95%CI: (1.1-5.0), P=0.03); (TERT 8.5 vs. 13.6 months; HR= 2.5; 95%CI: (1.1-5.7), P= 0.03). Subsequent multivariate analysis corroborated the association of TERT mutations with poorer OS in pontine DMG (HR=2.5; 95%CI:1.05, 5.9, P= 0.03). CONCLUSIONS This is the largest cohort of H3K27M-altered DMG patients with volumetric imaging analysis ongoing. Taken together, our data suggests these genetic alterations can risk-stratify patients for RT and future investigations of radiosensitizers targeting these alterations may improve survival.

The genomic landscape of papillary thyroid carcinoma on next-generation sequencing in patients undergoing total thyroidectomy.

Thyroidectomy is increasingly performed for suspected malignancy. This cohort study aimed to identify genetic markers associated with malignancy and determine the molecular landscape of papillary thyroid carcinoma (PTC) through next-generation sequencing (NGS) in patients undergoing total thyroidectomy. Among 116 surgical candidates, 103 patients (age=42.9±13.7years; Male: Female=14:89) with benign or malignant thyroid nodules were eligible. Live thyroid tissue samples harvested intraoperatively with adequate DNA and RNA yield were subjected to NGS on the Illumina NovaSeq 6000 platform for genomic and transcriptomic analysis, respectively. Histopathology comprised 20 malignant (19.4%) and 83 benign (80.6%) cases, including 16 PTC (15.5%) cases. On NGS, single nucleotidepolymorphisms (SNPs) in NTRK1 at NC_000001.11:156879016 on chromosome 1 and ALK at NC_000002.12:29717663 on chromosome 2 were frequent in malignant lesions (p<0.05). A SNP in ALK at NC_000002.12:29193706 was consistently a homozygous alternate alleleacross the cohort. DNA-sequencing of PTC lesions identified recurrentsomatic mutations in BRAF (100%), ALK (56.3%), RET (18.8%), PIK3CA (12.5%), NTRK1 (12.5%), NTRK2 (87.5%), NTRK3(12.5%), NRAS(6.3%), and PTCH1 (31.3%) genes. RNA sequencing revealednovelfusiongenes, including MKRN1-BRAF, RN7SL1-CDH1, IRF2BPL-MED12, MED12-IRF2BPL, CPM-MDM2, and AC005895.3-PDGFRB. Inreceiveroperative characteristics analysis, the AUCs ofALKmutationpredictingrecurrence and metastases were 0.818 and 0.783. This Indian study identified novel somatic mutations and fusion genes in PTC, revealing a distinct genomic landscape with implications in precision diagnostics and personalized therapies. NGS with intraoperative live sampling shows promise in prognostication and therapeutic optimization of advanced/metastatic PTC cases. CTRI/2020/09/027607dt 04/09/2020; REF/2020/08/036119.

De novo detection of somatic variants in high-quality long-read single-cell RNA sequencing data.

In cancer, genetic and transcriptomic variations generate clonal heterogeneity, leading to treatment resistance. Long-read single-cell RNA sequencing (LR scRNA-seq) has the potential to detect genetic and transcriptomic variations simultaneously. Here, we present LongSom, a computational workflow leveraging high-quality LR scRNA-seq data to call de novo somatic single-nucleotide variants (SNVs), including in mitochondria (mtSNVs), copy-number alterations (CNAs), and gene fusions, to reconstruct the tumor clonal heterogeneity. Before somatic variants calling, LongSom re-annotates marker gene based cell types using cell mutational profiles. LongSom distinguishes somatic SNVs from noise and germline polymorphisms by applying an extensive set of hard filters and statistical tests. Applying LongSom to human ovarian cancer samples, we detected clinically relevant somatic SNVs that were validated against matched DNA samples. Leveraging somatic SNVs and fusions, LongSom found subclones with different predicted treatment outcomes. In summary, LongSom enables de novo variant detection without the need for normal samples, facilitating the study of cancer evolution, clonal heterogeneity, and treatment resistance.

Performance improvements for green hydrogen-ammonia fueled SOFC hybrid system through dead-end anode recirculation and split transcritical CO2 power cycle

This paper proposes a novel green hydrogen-ammonia fueled solid oxide fuel cell (SOFC) hybrid power system by coupling dead-end anode recirculation with split transcritical CO2 power cycle (STCC). The STCC with dual-temperature heaters adopts split heat absorbing concept to recover cathode off-gas waste heat. The thermodynamic and economic performances of novel system are investigated and compared. Furthermore, the impacts of fuel ammonia mixing ratio, cell stack fuel utilization ratio, STCC CO2 split ratio, fuel price etc. on system performance are revealed. The results show that the introduction of STCC results in a 3.96% improvement in total system energy efficiency and a 0.6% reduction in total system levelized cost of electricity (LCOE) compared with standalone dead-end anode recirculated SOFC unit. The extremely high current density is not beneficial to enhance hybrid system thermo-economic performance. For every 0.1 increase in fuel ammonia mixing ratio, total system energy efficiency increases by 0.26∼0.85% and LCOE decreases by 4.6∼7.4%. The optimal STCC CO2 split ratio of recuperator declines with increasing fuel ammonia mixing ratio. Novel hydrogen-ammonia fueled hybrid system offers a high total energy efficiency of 0.7406, and its economic performance advantage over dead-end anode recirculated SOFC declines with decreasing fuel price.

Research progress on topology and energy management of fuel cell hybrid power ships

Abstract With the increasing awareness of environmental protection, the maritime industry is moving towards green and low-carbon development. Fuel cells, due to their pollution-free and high-efficiency characteristics, are one of the key technologies driving the green transformation of traditional ships. However, fuel cells have relatively soft output characteristics and often require other energy storage components to provide supplementary power. This paper reviews the types, advantages, and disadvantages of the topologies of fuel cell hybrid power ships, analyses the current development status of energy management goals and methods, and finally provides an outlook on the development of fuel cell hybrid power ships.

Simulation of Hybrid PV Solar System with Fuel Cell in MATLAB Simulink

This paper discusses the simulation of a fuel cell hybrid solar photovoltaic system in MATLAB Simulink. To achieve the stated objective, it is proposed to dynamically model a hybrid system using Simulink to analyze its performance and optimize its operation, improving the efficiency and stability of the power supply under different operating conditions, then the dynamic simulation model employs energy conversion equations of the solar PV panel and the fuel cell to meet the electrical load requirements, where the solar panel uses solar radiation and ambient temperature, while the fuel cell produces electricity by electrolysis of water, which separates hydrogen from oxygen. In the first graph, the power at load increases sharply at 0.5 seconds and stabilizes between 0.5 and 1.5 seconds, with a sharp drop at 1.6 seconds. In the second graph, PV power increases at 0.5 seconds, reaching 80 kW between 0.5 and 1.5 seconds, dropping to zero at 1.6 seconds. The fuel cell shows similar behavior. These results indicate a coordinated operation for a stable power supply to the load, responding efficiently to changes in demand and generation.

Macrophage-T cell hybrid membrane-camouflaged biomimetic nanoparticles ameliorate autoimmune myocarditis via suppressing macrophage pyroptosis by target gene silencing

Abstract Background Current management of myocarditis mainly relies on conventional approaches and immunosuppressive therapies. However, these strategies often yield limited efficacy. Our previous research revealed the pivotal role of macrophage pyroptosis in myocarditis pathogenesis. Therefore, targeted intervention to inhibit macrophage pyroptosis may provide new insights into myocarditis treatment. Purpose We previously identified interferon regulatory factor 1 (IRF1) as the key modulator of macrophage pyroptosis in myocarditis. In this study, we synthesized a nano-delivery platform consisting of a macrophage-T cell hybrid membrane-coated zeolitic imidazolate framework-8 (ZIF-8) encapsulating IRF1-small interfering RNA (siRNA@ZIF@HM). We aimed to evaluate its targeting capability and therapeutic efficacy for macrophage pyroptosis in myocarditis. Methods The fabrication of siRNA@ZIF@HM was validated using transmission electron microscopy (TEM) and dynamic light scattering (DLS). Cellular uptake, cytotoxicity, endolysosome escape, IRF1 silencing, and anti-pyroptotic effect were assessed using mouse bone marrow-derived macrophages and the mouse macrophage cell line J774A.1 and RAW264.7. An experimental autoimmune myocarditis (EAM) mouse model was induced in Balb/c mice for in vivo investigations. Pharmacokinetics and targeting capability were determined with ex vivo fluorescence imaging and immunofluorescence staining. Subsequently, mice were randomly allocated into control, EAM + siRNA, EAM + siRNA@ZIF, and EAM + siRNA@ZIF@HM groups to assess the therapeutic efficacy. Additionally, the biodistribution and biosafety of siRNA@ZIF@HM were also evaluated. Results TEM, DLS, and element mapping confirmed the successful fabrication of siRNA@ZIF@HM, with a diameter of approximately 130 nm. The nanoparticle exhibited pH responsiveness and membrane markers of macrophage and T lymphocytes (Figure 1). It demonstrated high targeting specificity for pro-inflammatory macrophages and myocarditis. Immunofluorescence microscopy revealed successful endolysosome escape of IRF1-siRNA in macrophages. Consequently, the nanoparticle significantly silenced IRF1 protein expression and suppressed macrophage pyroptosis both in vivo and in vitro, resulting in the amelioration of autoimmune myocarditis (Figure 2). Furthermore, the nanoparticle showed good biocompatibility with no significant changes observed in other organs. Conclusions The pH-responsive, macrophage-T cell hybrid membrane-coated biomimetic nanoparticle demonstrates promising capability for targeted siRNA delivery to myocardial inflammation sites, particularly pro-inflammatory macrophages. Additionally, targeting IRF1-mediated macrophage pyroptosis represents a potential therapeutic strategy for myocarditis treatment.Figure 1Figure 2

Performance evaluation and energy management strategy of drone methanol reforming fuel cell hybrid power system: A case study

The performance of hydrogen production by methanol steam reforming and high temperature proton exchange membrane fuel cells on drone lacks the comparison of experimental data, resulting in the overall performance improvement of the system is difficult to evaluate. Therefore, the energy management strategy of methanol reforming high temperature proton exchange membrane fuel cell hybrid power system and state machine is designed and optimized. The performance of this system is analyzed and compared with the flight experiment data of a proton exchange membrane fuel cell hybrid UAV. The results show that the SOC of the system can be stabilized between 70 and 80 during the long cruise phase. The test results show that the maximum hydrogen storage mass density of the hydrogen supply system is increased by 67.6%. The flight time of the system under economic cruise flight of the drone is improved by 29.1%–44.9% with different quality of fuel. Under the maximum flight speed of a certain wind speed, the system can meet the flight requirements and increase the flight time by 31.9% and 17.6% respectively under the wind speed of 1.5 m/s and 4 m/s. Its flight time has been greatly improved, and it has great potential as a drone system.

Energy management with adaptive moving average filter and deep deterministic policy gradient reinforcement learning for fuel cell hybrid electric vehicles

Fuel cell hybrid electric vehicles (FCHEV) with battery (BAT) and supercapacitor (SC) advance in flexible configuration and high energy efficiency. However, the complex coupling relationship among various power sources poses a severe challenge to the design of the energy management system (EMS), including multi-degrees of freedom power allocation, fuel economy, and power sources lifespan of the FCHEV. This paper proposes an EMS based on a dual-layer power distribution structure. In the upper layer, adaptive moving average filter (AMAF) is designed to separate different frequency power, where the energy supply of the SC is managed to attenuate fluctuating power and simplifies the optimization problem and reduces computational costs. The lower layer is constructed by the deep deterministic policy gradient (DDPG) algorithm, where fuel cell system (FCS) hydrogen consumption and degradation rewards are designed to simultaneously enhance fuel efficiency and degradation performance by regulating the FCS real-time power variation. The proposed strategy has been evaluated regarding FCHEV fuel economy and FCS durability under combined driving cycle simulation, which shows AMAF + DDPG strategy reduces fuel consumption by 7.24 % and 1.3 %, also the degradation reduces by 0.04 % and 0.02 % compared with different EMS. Simulation results demonstrate that AMAF + DDPG optimizes the output characteristics of power sources.

Dynamic Programming-Based ANFIS Energy Management System for Fuel Cell Hybrid Electric Vehicles

Reducing reliance on fossil fuels has driven the development of innovative technologies in recent years due to the increasing levels of greenhouse gases in the atmosphere. Since the automotive industry is one of the main contributors of high CO2 emissions, the introduction of more sustainable solutions in this sector is fundamental. This paper presents a novel energy management system for fuel cell hybrid electric vehicles based on dynamic programming and adaptive neuro fuzzy inference system methodologies to optimize energy distribution between battery and fuel cell, therefore enhancing powertrain efficiency and reducing hydrogen consumption. Three different approaches have been considered for performance assessment through a simulation platform developed in MATLAB/Simulink 2023a. Further validation has been conducted via a rapid control prototyping device, showcasing significant improvements in hydrogen usage and operational efficiency across different drive cycles. Results manifest that the developed controllers successfully replicate the optimal control trajectory, providing a robust and computationally feasible solution for real-world applications. This research highlights the potential of combining advanced control strategies to meet performance and environmental demands of modern heavy-duty vehicles.

Predictive Energy Management for Fuel Cell Hybrid Vehicles Considering Efficiency and Safety

Predictive Energy Management for Fuel Cell Hybrid Vehicles Considering Efficiency and Safety

Optimal design and dispatch of phosphoric acid fuel cell hybrid system with direct heat recovery through coupled calculation and artificial intelligence-based optimization

A phosphoric acid fuel cell (PAFC) is the first-generation of modern fuel cells that can use waste heat in various ways. This study presents a PAFC hybrid system with direct heat recovery. Direct heat recovery uses PAFC cooling water as the working fluid for the steam turbine, simplifying the system and improving performance. The PAFC in this hybrid system acts as a power generator and evaporator. The hybrid system operates in power generation mode (mode_pg) and combined heat and power mode (mode_chp). The overall behavior of the hybrid system was analyzed using a coupled calculation method. Under mode_pg, the electric power and efficiency of the hybrid system were 37.0% and 10.5% higher, respectively, than those of a conventional system. The thermal power and CHP efficiency in mode_chp were 35.7% and 8.8%p higher, respectively, than those of a conventional system. The optimal dispatch was obtained using an artificial intelligence-based optimization framework that considered two scenarios. As a result, the optimal demand was 25.8% higher when the hybrid system with the optimal dispatch was used. In addition, the economic and environmental indices were 7.3% and 6.8% lower, respectively, than the conventional system, highlighting the feasibility and eco-friendliness of the hybrid system.

Virtual Inertia-Based Control and Stability Analysis for Proton Exchange Membrane Fuel Cell Hybrid Electric Vehicles

Virtual Inertia-Based Control and Stability Analysis for Proton Exchange Membrane Fuel Cell Hybrid Electric Vehicles

Energy management of fuel cell hybrid electric bus in mountainous regions: A deep reinforcement learning approach considering terrain characteristics

Environmental characteristics, particularly in mountainous regions with significant slopes, substantially impact vehicle power demand and fuel consumption, thereby influencing both fuel economy and vehicle lifespan. However, existing research lacks energy management strategies specifically designed for these challenging terrains. This study presents an innovative energy management strategy (EMS) for fuel cell hybrid electric buses (FCHEB) utilizing a deep reinforcement learning algorithm considering terrain characteristics. A comprehensive model that incorporates road fluctuations and steep slopes was developed to accurately represent the terrain features of mountainous regions. Driving cycle and road condition data specific to hydrogen fuel cell buses in these areas were collected and processed as for the training sets of EMSs. Subsequently, an EMS based on the Proximal Policy Optimization (PPO) algorithm was devised to address the unique characteristics of these mountainous regions. Simulation results indicate a 26.16 % improvement in fuel economy and a 44.98 % enhancement in convergence efficiency compared to traditional methods that do not consider road slopes. Further validation through new bus route trials and standard driving cycle tests confirms the strategy's robustness and adaptability.

Simulation and Analysis of the Energy Consumption of a Fuel Cell Hybrid Electric Vehicle

Simulation and Analysis of the Energy Consumption of a Fuel Cell Hybrid Electric Vehicle

Reliable Energy Optimization Strategy for Fuel Cell Hybrid Electric Vehicles Considering Fuel Cell and Battery Health

Reliable Energy Optimization Strategy for Fuel Cell Hybrid Electric Vehicles Considering Fuel Cell and Battery Health

Biomimetic Cell Membrane-Coated Nanoparticles for Cancer Theranostics.

Nanoparticles can enhance drugs accumulating at the tumor site and hold tremendous promise for achieving effective tumor treatment. However, due to the complexity of cancer heterogeneity and suppressive tumor microenvironment, the delivery of traditional nanoparticles has poor infiltration and off-target effects, making it difficult to control the drug release rate and causing off-target toxicity. In recent years, cell membrane-coated biomimetic nanoparticles have been developed, which have both the natural characteristics of biomembranes and the physical characteristics of traditional nanoparticles, thus improving the homologous targeting ability of nanoparticles to tumor cells and better biocompatibility. In this paper, we reviewed the application of single cell membrane and hybrid cell membrane-coated biomimetic nanoparticles in the integration for tumor diagnosis and treatment. We talked about the preparation methods of cell membrane-coated nanoparticles, the targeting mechanisms, and the effects of imaging and therapeutic outcomes of different cell membrane-coated biomimetic nanoparticles in detail. Finally, we discussed the existing problems and prospects of cell membrane-coated biomimetic nanomaterials.

Capacity configuration of fuel cell hybrid vehicles using enhanced multi-objective particle swarm optimization with competitive mechanism

A well-designed hybrid powertrain is crucial for ensuring the safe, efficient, and durable operation of fuel cell hybrid vehicles. This paper introduces a modular design approach for powertrains, utilizing a Competitive mechanism-based Multi-Objective Particle Swarm Optimization (CMOPSO) algorithm integrated with nested dynamic programming. In this approach, the upper layer employs the CMOPSO algorithm to design the powertrain system, while the lower layer optimizes power coordination for each proposed design. This two-layer optimization framework considers factors such as vehicle economy and durability. Under WLTP conditions, the capacity configuration results are a fuel cell with a rated power of 22 kW, 100 batteries in series, and 7 batteries in parallel. Furthermore, the modular approach outperforms three other algorithms in terms of solution count, diversity, and overall performance metrics. The study also highlights that the vehicle’s power demand characteristics, influenced by different driving cycles, significantly affect capacity configuration results. Sensitivity analysis reveals that both the total operating cost and manufacturing cost of the vehicle are most sensitive to variations in the fuel cell rated power.

Multi-Objective Parameter Configuration Optimization of Hydrogen Fuel Cell Hybrid Power System for Locomotives

Conventional methods of parameterizing fuel cell hybrid power systems (FCHPS) often rely on engineering experience, which leads to problems such as increased economic costs and excessive weight of the system. These shortcomings limit the performance of FCHPS in real-world applications. To address these issues, this paper proposes a novel method for optimizing the parameter configuration of FCHPS. First, the power and energy requirements of the vehicle are determined through traction calculations, and a real-time energy management strategy is used to ensure efficient power distribution. On this basis, a multi-objective parameter configuration optimization model is developed, which comprehensively considers economic cost and system weight, and uses a particle swarm optimization (PSO) algorithm to determine the optimal configuration of each power source. The optimization results show that the system economic cost is reduced by 8.76% and 18.05% and the weight is reduced by 11.47% and 9.13%, respectively, compared with the initial configuration. These results verify the effectiveness of the proposed optimization strategy and demonstrate its potential to improve the overall performance of the FCHPS.