Search Results (352)

The integration of renewable energy sources (RESs) into power systems, particularly in microgrids, is becoming a prominent trend aimed at reducing dependence on traditional energy sources. Replacing conventional synchronous generators with grid-connected RESs through power electronic converters has significantly reduced the inertia of microgrids. This reduction negatively impacts the dynamics and operational performance of microgrids when confronted with uncertainties, posing challenges to frequency and voltage stability, especially in a standalone operating mode. To address this issue, this research proposes enhancing microgrid stability through frequency control based on virtual inertia (VI). Additionally, the Iterative Learning Control (ILC) method is employed, leveraging iterative learning strategies to improve the quality of output response control. Accordingly, the ILC-VI control method is introduced, integrating the iterative learning mechanism into the virtual inertia controller to simultaneously enhance the system’s inertia and damping coefficient, thereby improving frequency stability under varying operating conditions. The effectiveness of the ILC-VI method is evaluated in comparison with the conventional VI (C-VI) control method through simulations conducted on the MATLAB/Simulink platform. Simulation results demonstrate that the ILC-VI method significantly reduces the frequency nadir, the rate of change of frequency (RoCoF), and steady-state error across iterations, while also enhancing the system’s robustness against substantial variations from renewable energy sources. Furthermore, this study analyzes the effects of varying virtual inertia values, shedding light on their role in influencing response quality and convergence speed. This research underscores the potential of the ILC-VI control method in providing effective support for low-inertia microgrids. Full article

Due to the complexity of modeling tumor-host interactions within the tumor microenvironment in vitro, we developed a 3D heterotypic cellular breast cancer (BC) model. We generated spheroid models using MCF7, MDA-MB-231, and SK-BR-3 cell lines alongside cancer-associated (BrC4f) and normal (BN120f) fibroblasts in ultra-low attachment plates. Stromal spheroids (3Df) were formed using a liquid overlay technique (graphical abstract). The YT cell line and peripheral blood NK (PB-NK) cells were used as immune components in our 3D model. In this study, we showed that stromal cells promoted tumor cell aggregation into spheroids, regardless of the initial proliferation rates, with NK cells accumulating in fibroblast-rich regions. The presence of CAFs within the model induced alterations in the expression levels of MICA/B and PD-L1 by tumor cells within the 3D-2 model. The feasibility of utilizing a 3D cell BC model in combination with cytokines and PB-NKs was evaluated. We observed that IL-15 and IL-2 enhanced NK cell activity within spheroids, whereas TGFβ had varying effects on proliferation depending on the cell type. Stimulation with IL-2 and IL-15 or TGFβ1 altered PB-NK markers and stimulated their differentiation into ILC1-like cells in 3D models. These findings underscore the regulatory function of CAFs in shaping the response of the tumor microenvironment to immunotherapeutic interventions. Full article

Chickpea is a legume that grows in most parts of the world. It is negatively affected by abiotic and biotic factors like drought and fungal diseases, respectively. One of the most important soil-borne pathogens affecting chickpeas is Fusarium oxysporum f.sp. ciceris (Foc). Its population dynamics in the soil are affected by fluctuations in soil water content and host characteristics. For the last three decades, drought has been common in most areas of the world due to global warming. Drought stress decreases the quality and quantity of the chickpeas, particularly where soil-borne pathogens are the main stress factor for plants. The use of both drought-tolerant and disease-resistant cultivars may be the only option for cost-effective yield production. In this study, we screened the seeds of twelve chickpea genotypes WR-315, JG-62, C-104, JG-74, CPS-1, BG-212, ANNIGERI, CHAFFA, BG-215, UC-27, ILC-82, and K-850 for drought tolerance at increasing polyethylene glycol (PEG) concentrations (0-, 5-, 7.5-, 10-, 15-, 20-, 25-, 30- and 50%) to create drought stress conditions at different severities. The performances of genotypes that were previously tested in Foc resistance/susceptibility studies were assessed in terms of percentage of germination, radicle and hypocotyl length, germination energy, germination rate index, mean germination time, and vigor index in drought conditions. We determined the genotypes of C-104, CPS-1, and WR-315 as drought-susceptible, moderately drought-tolerant, and drought-tolerant, respectively. We then elucidated the stress levels of selected genotypes (20-day-old seedlings) at 0–15% PEG conditions via measuring proline and malondialdehyde (MDA) contents. Our findings showed that genotypes that were resistant to Foc also exhibited drought tolerance. The responses of chickpea genotypes infected with Foc under drought conditions are the next step to assess the combined stress on chickpea genotypes. Full article

Objectives: Innate lymphoid cells (ILCs) and natural killer (NK) cells represent a diverse group of innate immune populations that modulate immune responses and tissue equilibrium across various diseases, including cancer. In the present study, we analyzed single-cell RNA sequencing (scRNA-seq) data to explore the landscape and functional status of ILC subsets in patients with head and neck squamous cell carcinoma (HNSCC). Methods: The GSE164690 dataset, which includes preprocessed scRNA-seq and clinical data, was acquired from the Gene Expression Omnibus database. The Cancer Genome Atlas database was used to develop the survival prediction model. Results: A total of 95,809 immune cells were clustered into 16 immune cell clusters, among which 7278 NK cells were further subdivided into 11 clusters. Among the 11 clusters, eight NK cell clusters, two intraepithelial ILC1 (ieILC1) clusters, and one ieILC1–NK-intermediate (ieILC1-NK-int) cluster were identified. Among the ieILC1/NK clusters, ieILC1-1 exhibited the highest immunological activity and was mainly derived from human papillomavirus-positive samples. Further, ieILC1s showed higher enrichment of pathways related to inflammation and effector functions—such as inflammatory response, interferon-gamma response, and interferon-alpha response—compared to the other clusters. Moreover, we developed prognostic prediction models based on differentially expressed genes in the ieILC1/NK clusters. Risk scores of the ieILC1-1, ieILC1-NK-int, and NK clusters were identified as independent prognostic factors for shorter overall survival (OS) and progression-free survival (PFS). Recursive partitioning revealed that combining ieILC1-1 and the NK clusters strongly predicted shorter OS and PFS. Conclusions: Our findings highlight the diverse landscape and prognostic significance of ieILC1/NK cells in patients with HNSCC. Full article

This paper aims to solve the spatial trajectory tracking control problem of underactuated autonomous underwater vehicles (AUVs) in the presence of system parameter uncertainties and complex external disturbances. To accomplish this goal, a model–data-driven learning adaptive robust control (LARC) strategy is introduced for AUVs. Firstly, a serial iterative learning control (ILC) approach is introduced as feedforward compensation, and then the corresponding trajectory tracking error dynamics model, the Feedforward Compensation–Line of Sight (FFC-LOS) guidance law, and the feedforward compensation-based kinematics controller are designed. Secondly, the dynamics controller is designed for AUVs, which consists of a linear feedback term, a nonlinear robust feedback term, an adjustable model compensation term, and a fast dynamic compensation term. In this control framework, the robust control and fast dynamic compensation parts are utilized to deal with nonlinear uncertainties and disturbances, the projection-type adaptive control part solves the influence caused by the uncertainty of system parameters, and the serial ILC part that is a data-driven learning method can further improve the trajectory tracking accuracy for repetitive tasks. Finally, comparative simulations under different scenarios and different types of disturbances are performed to verify the effectiveness of the proposed control strategy for AUVs. Full article

Mouse models serve as a means of examining immune changes when genes of interest are knocked out (KO). One group of immune gene-producing cells that have been identified is type 2 innate lymphoid cells (Ilc2). These cells are involved in the production of Th2 equivalent immune responses and signal cytokine production during the resolution of Nippostrongylus brasiliensis parasite infection in mice lungs. However, many questions about Ilc2 activity in the gut remain. To study this, retinoic acid receptor (RAR)-related orphan receptor alpha (RORα)-deficient mice were infected with adult N. brasiliensis and arranged into four treatment groups. Ten days post-infection (dpi), mouse ileum tissue was extracted for RNA-Seq. The RORα-deficient mice showed little change in gene expression at 10 dpi (N = 51) when compared to the WT mice at 10 dpi (N = 915), displaying dysregulation within the mouse gut. Based on the results, the gene expression in the gut of Ilc2-deficient mice denoted that the inability to craft Ilc2 cells left the mice unable to mount classical helminth immune responses involving humoral, mast cell, and antibody Th2-driven reactions. Overall, the results showed the importance of Ilc2 in the gut during N. brasiliensis infections and the effect that the lack of these cells had on immunity. Full article

Inflammatory bowel disease (IBD), including Crohn’s disease and ulcerative colitis, is a chronic inflammatory disorder of the gastrointestinal tract with rising incidence and an unclear etiology. Innate lymphoid cells (ILCs) have recently emerged as key regulators of mucosal immunity and tissue homeostasis and are increasingly implicated in IBD. Unlike adaptive lymphocytes, ILCs do not require antigen recognition and clonal expansion to respond rapidly to environmental cues and shape immune responses. In a healthy gut, ILCs maintain intestinal homeostasis by guarding the epithelial barrier, protecting against pathogens, and mounting proper responses to external insults. However, their altered differentiation, proliferation, recruitment, activation, and interaction with other host cells, microbiota, and environmental stimuli may contribute to IBD. In this review, we discuss recent advances in understanding murine and human ILCs in the context of intestinal inflammation and IBD. A deeper understanding of ILC-mediated immune mechanisms may offer novel therapeutic strategies for restoring intestinal homeostasis and improving personalized management of IBD. Full article

Dendritic cell (DC) vaccines stimulate the immune system to target cancer antigens, representing a promising option for immunotherapy. However, clinical trials have demonstrated limited effectiveness, emphasizing the need for enhanced immune responses. Improving the production of DC vaccines, assessing their impact on immune components, and observing responses could improve the results of DC-based therapies. Innate lymphoid cells (ILCs) represent a heterogeneous population of innate immune components that generate cytokines and modulate the immune system, potentially enhancing immunotherapies. Recent research highlights the different functions of ILCs in cancer, demonstrating their dual capabilities to promote tumors and exhibit anti-tumor actions. DCs and ILCs actively communicate under physiological and pathological conditions, and the activation of ILCs by DCs or DC vaccines has been shown to influence ILC cytokine production and function. Gaining insights into the interaction between DC-activated ILCs and tumors is essential for creating exciting new therapeutic strategies. These strategies aim to boost anti-tumor immunity while reducing the support that tumors receive. This review examines the effect of DC vaccination on host ILCs, illustrating the complex relationship between DC-based vaccines and ILCs. Furthermore, it explores some exciting strategies to enhance DC vaccines, aiming to boost anti-tumor immune responses by fostering better engagement with ILCs. Full article

Iterative learning control (ILC) typically requires strict repeatability in initial states, trajectory length, external disturbances, and system dynamics. However, these assumptions are often difficult to fully satisfy in practical applications. While most existing studies have achieved limited progress in relaxing either one or two of these constraints simultaneously, this work aims to eliminate the restrictions imposed by all four strict repeatability conditions in ILC. For general finite-duration multi-input multi-output (MIMO) linear discrete-time systems subject to multiple non-repetitive uncertainties—including variations in initial states, external disturbances, trajectory lengths, and system dynamics—an innovative open-closed loop robust iterative learning control law is proposed. The feedforward component is used to make sure the tracking error converges as expected mathematically, while the feedback control part compensates for missing tracking data from previous iterations by utilizing real-time tracking information from the current iteration. The convergence analysis employs an input-to-state stability (ISS) theory for discrete parameterized systems. Detailed explanations are provided on adjusting key parameters to satisfy the derived convergence conditions, thereby ensuring that the anticipated tracking error will eventually settle into a compact neighborhood that meets the required standards for robustness and convergence speed. To thoroughly assess the viability of the proposed ILC framework, computer simulations effectively illustrate the strategy’s effectiveness. Further simulation on a real system, a piezoelectric motor system, verifies that the ILC tracking error converges to a small neighborhood in the sense of mathematical expectation. Extending the ILC to complex real-world applications provides new insights and approaches. Full article

This paper presents an adaptive Kalman filter (AKF)-enhanced iterative learning control (ILC) scheme to improve trajectory tracking in non-repetitive time-varying systems (NTVSs), particularly in industrial applications. Unlike traditional ILC methods that assume fixed system dynamics, gradual parameter variations in NTVSs require adaptive approaches to address factors such as tool wear and sensor drift, which significantly affect tracking accuracy. By integrating AKF, the proposed method continuously estimates time-varying parameters and uncertainties in real time, thus improving the robustness and adaptability of trajectory tracking. Theoretical analysis is conducted to confirm the robust convergence and stability of the AKF-enhanced ILC scheme under uncertain and time-varying conditions. Experimental results demonstrate that the proposed approach significantly outperforms conventional ILC methods, ensuring precise and reliable tracking performance in dynamic industrial scenarios. Full article

Background/Objectives: Immune cells determine the role of the tumor microenvironment during tumor progression, either suppressing tumor formation or promoting tumorigenesis. This study aimed to fully characterize immune cell responses during skin tumor progression. Methods: Using single-cell RNA sequencing, we analyzed the profile of immune cells in the tumor microenvironment of control mouse skins and skin tumors at the single-cell level. Results: We identified 15 CD45⁺ immune cell clusters, which broadly represent the most functionally characterized immune cell types including macrophages, Langerhans cells (LC), conventional type 1 dendritic cells (cDC1), conventional type 2 dendritic cells (cDC2), migratory/mature dendritic cells (mDC), dendritic epidermal T cells (DETC), dermal γδ T cells (γδT), T cells, regulatory T cells (Tregs), natural killer cells (NK), type 2 innate lymphoid cells (ILC2), neutrophils (Neu), mast cells (Mast), and two proliferating populations (Prolif.1 and Prolif.2). Skin tumor progression reprogramed immune cells and led to a marked increase in the relative percentages of macrophages, cDC2, mDC, Tregs, and Neu. Macrophages, the largest cell cluster of immune cells in skin tumors. In addition, macrophages emerged as the predominant communication ‘hub’ in skin tumors, highlighting the importance of macrophages during skin tumor progression. In contrast, other immune cell clusters decreased during skin tumor progression, including DETC, γδT, ILC2, and LC. In addition, skin tumor progression dramatically upregulated Jak2/Stat3 expression and the interferon response across various immune cell clusters. Further, skin tumor progression activated T cells and NK cells indicated by elevated expression of IFN-γ and Granzyme B in skin tumors. Meanwhile, a pronounced infiltration of M2-macrophages and Tregs in skin tumors created an immunosuppressive microenvironment, consistent with the elevated expression of the Stat3 pathway in skin tumors. Conclusions: Our study elucidates the immune cell landscape of epidermal neoplasms, offering a comprehensive understanding of the immune response during skin tumor progression and providing new insights into cancer immune evasion mechanisms. Full article

The uterine decidua contains NK cells differing in their characteristics from classical NK cells, as well as other populations of innate lymphoid cells (ILCs). ILC differentiation depends on the active transcription factors: ILC1 is characterized by T-bet expression, ILC2 is defined by RORα and GATA3, ILC3 expresses RORγt and AhR. We analyzed in vitro the expression of transcription factors by NK cells in the presence of trophoblast cells and cytokines and changes in NK cell cytotoxic activity. We used NK-92 and JEG-3 cell lines, which we cocultured in the presence of IFNγ, IL-10, IL-15, and TGFβ. Then, cells were treated with antibodies to AhR, Eomes, GATA-3, RORα, RORγt, and T-bet and were analyzed. We determined NK cell cytotoxicity towards K562 cells. To characterize the functional state of trophoblast cells, we estimated their secretion of TGFβ and βhCG. We showed that in the presence of trophoblasts, the expression of the classical NK cell transcription factors—Eomes, T-bet, as well as RORα, regulating ILC2 differentiation, and AhR, participating in NCR+ ILC3 formation—decreased in NK cells. RORγt expression typical for NCR- ILC3 remained unchanged. IFNγ inhibited AhR expression. IL-10 stimulated an increase in the number of T-bet+ ILC1-like cells. Both IL-10 and IFNγ suppressed RORα expression by NK cells and stimulated TGFβ secretion by trophoblasts. After coculture with trophoblast cells, NK cells reduced their cytotoxicity. These results indicated trophoblast cell influence on the acquisition of ILC1 and ILC3 characteristics by NK cells. Full article

This study answers how bibliometrics and the analysis of terminology in selected theoretical books and reference sources can allow domain analysis in interdisciplinary fields of knowledge, taking as a case study the global studies (GS) field. A mixed-methods approach was applied to answer this. First, an analysis of GS’s lexicon from three sources: (1) The Encyclopedia of Global Studies (2012), (2) The Global Studies Encyclopedic Dictionary (2014), and (3) The Palgrave Dictionary of Transnational History (2009). Second, the analysis of GS topic tendencies using bibliometrics. The results show (1) the validity of the methods used for domain analysis under the lenses of library and information science (LIS) and (2) the importance of a manual selection of sources for domain analysis and the correspondence between the methods and the application of results using integrative level classification (ILC). The author concludes that domain analysis for emergent interdisciplinary fields of knowledge benefit from quantitative approaches based on a methodology that considers terminology in various formats and can be applied not just for the global studies field. Finally, we emphasize the need for collaboration between librarians and scholars for a better understanding of the dynamics of interdisciplinary vocabularies in science. Full article

In this paper, a multi-objective mixed integer linear programming (MOMILP) approach is proposed for the optimal planning of battery energy storage systems (BESSs) and the interlink converter (ILC) in hybrid AC/DC microgrids (HMGs). The ILC is the backbone of the HMG, facilitating power exchange between the sub-grids. It plays a vital role in enhancing the stability of the HMG by balancing power between subsystems. Economically, the ILC enables the transfer of surplus power and lower-cost energy between the AC and DC microgrids. Therefore, selecting an optimal size for the ILC is critical from both technical and economic perspectives. However, existing studies have overlooked the optimal sizing of the ILC and its associated stress factors in the planning of HMGs. This paper proposes a multi-objective planning approach for HMGs that considers both calendar and cyclic ageing of BESSs. The performance of the proposed strategy is compared with the most widely used existing methods. The results confirm the superiority of the proposed planning approach in terms of both technical performance and economic efficiency. Full article

This paper presents a model predictive controller (MPC) based on dynamic models generated using the Particle Swarm Optimisation method for accurate motion control of a pneumatic artificial muscle (PAM) for application in rehabilitation robotics. The physical compliance and lightweight nature of PAMs make them desirable for use in the field but also introduce nonlinear dynamic properties which are difficult to accurately model and control. As well as the MPC, three other control systems were examined for a comparative study: a particle-swarm optimised proportional-integral-derivative controller (PSO-PID), an iterative learning controller (ILC), and classical PID control. A series of different waveforms were used as setpoints for each controller, including addition of external loading and simulated disturbance, for a system consisting of a single PAM. Based on the displacement error measured for each experiment, the PID controller performed worst with the largest error values and an issue with oscillating about the setpoint. PSO-PID performed better but still poorly compared with the other intelligent controllers, as well as still exhibiting oscillation, which is undesirable in any human–robot interaction as it can heavily impact the comfort and safety of the system. ILC performed well with rapid convergence to steady-state and low-error values, as well as mitigation of loads and disturbance; however, it performed poorly under changing frequency of input. MPC generally performed the best of the controllers tested here, with the lowest error values and a rapid response to changes in setpoint, as well as no required learning period due to the predictive algorithm. Full article