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To address the parameter instability issues in hazardous materials handling during multi-machine loading and unloading operations, we propose a Full-Scale Smart Parameter Optimization Control (FSPOC) system specifically designed for multi-machine coordination. This system leverages a novel fish scale prediction algorithm tailored for cooperative multi-machine environments. Initially, the fish scale prediction algorithm, inspired by bionic fish scales, is developed to predict future system behavior by analyzing historical data. Building on this algorithm, we introduce a disturbance cancellation control theorem and design a parameter optimization controller to enhance stability in high-dimensional nonlinear spaces. The FSPOC method is then applied to a multi-machine cooperative system, enabling online distributed parameter optimization for complex systems with multiple degrees of freedom. The effectiveness of the proposed method was validated through simulations, where it was compared with two other optimization techniques: Genetic Algorithm-based PID (GAPID) and Chaotic Atomic Search Algorithm-based PID (CHASO). The simulation results confirm the superiority of the FSPOC method. [ABSTRACT FROM AUTHOR]

To establish and validate a predictive multi-machine learning model for the long-term efficacy of uterine artery embolization (UAE) in the treatment of adenomyosis based on habitat subregions. Patients who underwent UAE for adenomyosis at institution A between November 2015 and June 2018 were included in the training cohort and those at institution B between June 2017 and June 2019 were included in the test cohort. The regions of interest (ROI) were manually segmented on the T2-weighted images (T2WI). The ROIs were subsequently partitioned into habitat subregions using k-means clustering. Radiomic features were extracted from each subregion on T1WI, T2WI, apparent diffusion coefficient, and contrast-enhanced images. The least absolute shrinkage and selection operator (LASSO) was used to select the subregion radiomics features. With the improvement in patients' symptoms at 36 months post-UAE, the habitat subregion features were trained using six machine-learning classifiers. The most suitable classifier was chosen based on model performance to establish the habitat radiomics model (HRM). The efficacy of the model was validated using both the training and test cohorts. Finally, a whole-region radiomics model (WRM) and clinical model (CM) were established. The Delong test was used to compare the predictive performance of the habitat subregion model and the two other models. The study included 258 patients, 191 in the training cohort and 67 in the test cohort. The ROIs were divided into four habitat subregions. Radiomics features were extracted from different sequence images of the subregions. After LASSO regression, 24 habitat subregion features were included in the model. Based on the receiver operating characteristic curve analysis, the area under the curve (AUC) of the HRM was 0.921 (95% CI, 0.857–0.985, training) and 0.890 (95% CI, 0.736–1.000, test). The AUCs for the WRM were 0.805 (95% CI, 0.737–0.872, training) and 0.693 (95% CI, 0.497–0.889, test). Compared to the HRM, the difference in predictive performance was statistically significant (p = 0.008, training; p = 0.007, test). The AUCs for the CM were 0.788 (95% CI, 0.711–0.866, training) and 0.735 (95% CI, 0.566–0.903, test). Compared to the HRM, there was a statistically significant difference in the training cohort (p = 0.014) but not in the test cohort (p = 0.186). The HRM can predict the long-term efficacy of UAE in the treatment of adenomyosis. The predictive performance was superior to that of both the WRM and CM, serving as an effective tool to assist interventional physicians in clinical decision-making. [ABSTRACT FROM AUTHOR]

Jing Yu,1 Yike Zhang,1 Zhixiong Ye,1 Kun Tang,1 Yiming Ma,1 Linlin Fu,1 Tongtong Cui,1 Hening Kang,2 Yadong Yuan,1 Wensen Pan1 1Second Department of Respiratory and Critical Care Medicine, The Second Hospital of Hebei Medical University, Shijiazhuang, People’s Republic of China; 2College of Basic Medicine, Hebei Medical University, Shijiazhuang, People’s Republic of ChinaCorrespondence: Wensen Pan, Email 27400010@hebmu.edu.cnBackground: Despite ongoing interventions, SARS-CoV-2 continues to cause significant global morbidity and mortality. Early diagnosis and intervention are crucial for effective clinical management. However, prognostic features based on transcriptional data have shown limited effectiveness, highlighting the need for more precise biomarkers to improve COVID-19 treatment outcomes.Methods: We retrospectively analyzed 149 clinical features from 189 COVID-19 patients, identifying prognostic features via univariate Cox regression. The cohort was split into training and validation sets, and 77 prognostic models were developed using seven machine learning algorithms. Among these, the least absolute shrinkage and selection operator (Lasso) method was employed to refine the selection of prognostic variables by ten-fold cross-validation strategy, which were then integrated with random survival forests (RSF) to build a robust COVID-19-related prognostic model (CRM). Model accuracy was evaluated across training, validation, and entire cohorts. The diagnostic relevance of interleukin-10 (IL-10) was confirmed in bulk transcriptional data and validated at the single-cell level, where we also examined changes in cellular communication between mononuclear cells with differing IL-10 expression and other immune cells.Results: Univariate Cox regression identified 43 prognostic features. Among the 77 machine learning models, the combination of Lasso and RSF produced the most robust CRM. This model consistently performed well across training, validation, and entire cohorts. IL-10 emerged as a key prognostic feature within the CRM, validated by single-cell transcriptional data. Transcriptome analysis confirmed the stable diagnostic value of IL-10, with mononuclear cells identified as the primary IL-10 source. Moreover, differential IL-10 expression in these cells was linked to altered cellular communication in the COVID-19 immune microenvironment.Conclusion: The CRM provides accurate prognostic predictions for COVID-19 patients. Additionally, the study underscores the importance of early IL-10 level testing upon hospital admission, which could inform therapeutic strategies.Keywords: SARS-CoV-2, machine learning, IL-10, therapeutic guidance, clinical decision making

BackgroundUstekinumab (UST), a biologic against interleukin (IL)-12/23, is commonly used to treat Crohn’s disease (CD). Myofibroblast (MF) is known as one of the most important factors causing intestinal fibrosis, and UST has been reported to alleviate this condition. However, the genetic mechanisms underlying UST’s effects on CD remain unclear. This study uses bioinformatics tools to analyze the genes and potential pathways affected by UST in CD, with a focus on its anti-fibrosis effects, providing insights into new therapeutic targets.MethodsThe data downloaded from the Gene Expression Omnibus (GEO) database were analyzed to screen for differentially expressed genes (DEGs). Various machine learning strategies, including the least absolute shrinkage and selection operator (LASSO), support vector machine (SVM), and random forest (RF), were employed to screen for key genes among the DEGs. Functional and pathway enrichment analyses were conducted, and key genes associated with myofibroblast (MF) activity were screened. Finally, endoscopic surgical specimens from CD patients and healthy participants were collected to assess the expression levels of collagen and key genes in intestinal tissues using hematoxylin–eosin (H&E), Masson staining, and immunohistochemistry.ResultsA total of 1,341 DEGs associated with CD were identified. Among them, 738 genes showed low expression in healthy populations but high expression in patients with CD, reduced expression after the treatment of UST. In contrast, 603 genes exhibited high expression in healthy individuals, showed low expression in CD patients, and increased expression after UST treatment. Functional and pathway analysis showed that DEGs were mainly concentrated in response to foreign biological stimuli and bacterial-derived molecules. DEGs are mainly enriched in chemokines, TNF, IL-17, and other signaling pathways. Seven key genes were identified: NCRNA00236, LOC730101, ORP3, XG, UBFD1, KDELC1, and RBP7. Single-cell analysis revealed that KDELC1 was closely related to MF activity. MFs with high KDELC1 expression were significantly enriched in biological functions, signaling pathways, and metabolic processes that promote fibrosis. The experiment showed that UST treatment helped maintain the integrity of intestinal tissue structure, reducing the expression levels of collagen I, KDELC1, and the severity of intestinal fibrosis. The functional and pathway analysis reiterated that DEGs were largely focused on responses to foreign biological stimuli and bacterial-derived molecules, as well as signaling pathways such as chemokines, TNF, and IL-17. Of the identified genes, KDELC1 showed a particularly strong correlation with MF activity in single-cell analysis (R = 0.33, p = 3.2e-07). MFs with high KDELC1 expression were closely linked to pathways promoting fibrosis progression, including TGF-β, epithelial-mesenchymal transformation, TNF/NF-κB, and related metabolic pathways such as vitamin B6 and arginine.ConclusionKDELC1 plays a key role in regulating multiple biological functions, including signaling pathways related to MF. UST alleviates intestinal fibrosis by targeting KDELC1, thereby influencing intramuscular fat metabolism and intercellular communication.

ObjectivesConsidering the difference of primary frequency regulation ability of wind turbines under different wind conditions in wind farms, the evaluation method of frequency regulation capability of wind turbines was optimized on the basis of sag control and inertia control strategy.MethodsAn improved multi-unit frequency modulation reference power cooperative control strategy was proposed.ResultsThe introduction of multi-unit cooperative control method can effectively improve the distribution of frequency modulation reference power among units, thus effectively adjust the degree of each unit participating in the primary frequency regulation of the system. An improved cooperative control strategy was introduced on the basis of inertia control and sag control. According to the operation state of the unit under the actual wind condition, the reference power of the unit which can effectively participate in the primary frequency modulation was evaluated. The introduction of frequency modulation capability coefficient can realize the energy distribution of the reference value of frequency modulation power among units.ConclusionsThe cooperative control strategy can effectively protect the speed of wind turbine and improve the frequency response of power grid.

This paper introduces the Adaptive Coordinated Fixed-Time Synergetic Controller (ACFSC) designed for the Thyristor Controlled Series Capacitor (TCSC) and the generator excitation system. The primary objective is to enhance power system transient stability and voltage regulation performance particularly in scenarios involving unknown generator parameters. For the controller design of both generator excitation and TCSC, a linear combination of local parameters is employed to achieve the control objectives. Control laws and update laws for unknown parameters are provided for both single-machine systems connected to the infinite bus (SMIB) and multi-machine power systems. In addition, an online parameter adaptation method is proposed to further improve transient stability and achieve satisfactory voltage regulation performance in multi-machine power systems. Ultimately, the proposed nonlinear controller’s implementation is simulated in both SMIB and WSCC-type 3 power systems. The numerical results underscore significant advancements in augmenting transient stability and voltage regulation performance especially when faced with the challenge of unknown generator parameters.

This work discusses the production of a novel hybrid algorithm by combining the gorilla troops optimizer (GTO) with the gradient-based optimizers (GBO) approach. The novel approach is called GTO-GBO, it is offered as a useful tool for optimizing the power system stabilizer (PSS) used in the IEEE four-generator, two-area multi-machine power system subjected to a three-phase short-circuit fault. MATLAB/Simulink software was utilized to carry out the assessments. The suggested approach is initially evaluated using multiple benchmark functions with unimodal and multimodal properties. The results are then compared to other competing algorithms (artificial ecosystem optimizer, artificial rabbits optimizer, Coati Optimization Algorithm, and northern goshawk optimization). The comparisons with various algorithms reveal the developed hybrid GTO-GBO algorithm’s considerable promise. This demonstrates the GTO-GBO algorithm’s improved balance of global and local search stages. The proposed GTO-GBO algorithm’s performance is also evaluated by developing an optimum performing PSS for further examination, allowing observation of its capabilities for difficult real-world engineering challenges. To illustrate the applicability and superior performance of the suggested hybrid algorithm for such a complicated real-world engineering problem, the PSS damping controller is formulated as an optimization problem, and the developed GTO-GBO algorithm is used to search for optimal controller parameters. The latter case’s findings are compared to the competitive optimization algorithms where the GTO-GBO demonstrates the efficiency and robustness of this suggested optimization algorithm to enhance power system stability.

Currently, the multi-machine collaboration of agricultural machinery is one of the international frontiers and a topic of research interest in the field of agricultural equipment. However, the multi-machine cooperative operation of agricultural machinery is mostly limited to the research on task goal planning and cooperative path optimization of a single operation. To address the mentioned shortcomings, this study addresses the problem of multi-machine cooperative operation of fertilizer applicators in fields with different fertility and fertilizer cooperative distribution of fertilizer trucks. The research uses the task allocation method of a multi-machine cooperative operation of applying fertilizer-transporting fertilizer. First, the problems of fertilizer applicator operation and fertilizer truck fertilizer distribution are defined, and the operating time and the distribution distance are used as optimization objectives to construct functions to establish task allocation mathematical models. Second, a Chaos–Cauchy Fireworks Algorithm (CCFWA), which includes a discretized decoding method, a population initialization with a chaotic map, and a Cauchy mutation operation, is developed. Finally, the proposed algorithm is verified by tests in an actual scenario of fertilizer being applied in the test area of Jimo District, Qingdao City, Shandong Province. The results show that compared to the Fireworks Algorithm, Genetic Algorithm, and Particle Swarm Optimization, the proposed CCFWA can address the problem of falling into a local optimum while guaranteeing the convergence speed. Also, the variance of the CCFWA is reduced by more than 48% compared with the other three algorithms. The proposed method can realize multi-machine cooperative operation and precise distribution of seeds and fertilizers for multiple seeding-fertilizer applicators and fertilizer trucks.