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The Double Permanent Magnet Vernier (DPMV) machine is well known for its high torque density and magnet utilization ratio. This paper aims to investigate the torque generation mechanism and its improved design in DPMV machines for hub propulsion based on the field modulation principle. Firstly, the topology of the proposed DPMV machine is introduced, and a commercial PM machine is used as a benchmark. Secondly, the rotor PM, stator PM, and armature magnetic fields are derived and analyzed considering the modulation effect, respectively. Meanwhile, the contribution of each harmonic to average torque is pointed out. It can be concluded that the 7th-, 12th-, 19th- and 24th-order flux density harmonics are the main source of average torque. Thanks to the multi-working harmonic characteristics, the average torque of DPMV machines has significantly increased by 31.8% compared to the counterpart commercial PM machine, while also reducing the PM weight by 75%. Thirdly, the auxiliary barrier structure and dual three-phase winding configuration are proposed from the perspective of optimizing the phase and amplitude of working harmonics, respectively. The improvements in average torque are 9.9% and 5.4%, correspondingly.

In view of the terminal distribution link that has several shortcomings in the whole logistics chain such as the highest cost, the lowest efficiency and the most serious pollution, a kind of intelligent machine nest hub in this thesis, which carries out the analysis on the working principle of the machine nest hub system. In the meantime, the structure composition and control system of the machine nest hub is also designed. Based on the system designed in this thesis, it can enlighten the uninterrupted unmanned and automatic operation of the package delivery assembly end of UAV, and is able to handle the huge growth of orders in an effective way, so as to make the express delivery of UAV become more efficient and convenient.

This paper contributes to the integrated design issue of urban and rural logistics networks under demand uncertainty. A hierarchical hub location model is proposed, which minimizes the expected total system cost by optimizing the locations, number and capacities of “urban-town‒village” hierarchical logistics hubs. The interactions among the logistics hubs and among the hub‒and‒spoke connections, as well as the hub capacity constraints are explicitly considered in the presence of logistics demand uncertainty. A demand scenario‒based branch‒and‒Benders‒cut algorithm is developed to solve the proposed model. A case study of Jiangling urban‒rural region in Hubei province of China is conducted for the illustration of the model and solution algorithm. The results generated by the proposed algorithm are benchmarked against those obtained by GUROBI solver and the practical scheme being currently implemented in the region. The results showed that the proposed methodology can greatly improve the efficiency of the urban‒rural logistics system in terms of expected total system cost. It is important to explicitly model the demand uncertainty, otherwise a significant decision bias may emerge. The proposed algorithm outperforms the GUROBI solver in terms of problem size solved and computational time.

Tendon injury is a common and challenging clinical problem, and its healing process involves complex cellular and biological factors. Therefore, this study aims to reveal the mechanism of tendon healing and provide theoretical basis for clinical treatment. We first selected GSE26051 dataset from the GEO database and used R language to obtain 721 DEGs (459 up-regulated and 262 down-regulated). Subsequently, the 7378 genes of tendon injury obtained from the GeneCards database were intersected with DEGs to obtain 228 common genes. We constructed a PPI network of common genes using the STRING database, visualized it using Cytoscape software, and selected the top 10 (MYH6, MYL3, MYH1, MYH8, MYL1, TTN, TCAP, PKP2, ACTN2, CSRP3) genes through the CytoHubba plugin. We further identified hub genes (MYH1, MYH6, PKP2, MYH8) via machine learning models. Afterwards, the cytoskeleton in muscle cells and IL-17 signaling pathways were obtained by GO and KEGG analysis of common genes. Finally, the macrophages M2 was screened through immune infiltration analysis. This study revealed that hub genes such as MYH1, MYH6, MYH8 and PKP2 were mainly enriched in the cytoskeleton in muscle cells signaling pathway, and macrophage M2 played an important role in the inflammatory phase of tendon healing.

[This corrects the article DOI: 10.3389/fimmu.2024.1511824.].

In this article, a solution is proposed through a population-based metaheuristic for the Two-level Hub Location Routing Problem with Directed Tours (THLRP-DT). Hubs are facilities used to handle and dispatch resources on a given network. The goal of the THLRP-DT is to locate a set of hubs on a network and to route resources from sources to destinations, where the hubs are connected by means of an oriented cycle, and the spokes form clusters. Each cluster is composed of a unique hub, including none or some spoke nodes, connected in an oriented cycle structure. This problem appears in transportation logistics, where the flow of demands can be aggregated, resulting in economies of scale, and the orientations of arcs model a one way flow direction, which speeds up the distribution. We propose a Biased Random-Key Genetic Algorithm (BRKGA) metaheuristic, where the parameters have been calibrated using a machine learning package, which makes use of a machine learning mechanism. The results obtained using the BRKGA metaheuristic are of high quality compared to the ones found in the literature, improving solutions for instances with unknown optimal values.

Rheumatoid arthritis (RA) is a systemic immune-related disease characterized by synovial inflammation and destruction of joint cartilage. The pathogenesis of RA remains unclear, and diagnostic markers with high sensitivity and specificity are needed urgently. This study aims to identify potential biomarkers in the synovium for diagnosing RA and to investigate their association with immune infiltration. We downloaded four datasets containing 51 RA and 36 healthy synovium samples from the Gene Expression Omnibus database. Differentially expressed genes were identified using R. Then, various enrichment analyses were conducted. Subsequently, weighted gene co-expression network analysis (WGCNA), random forest (RF), support vector machine-recursive feature elimination (SVM-RFE), and least absolute shrinkage and selection operator (LASSO) were used to identify the hub genes for RA diagnosis. Receiver operating characteristic curves and nomogram models were used to validate the specificity and sensitivity of hub genes. Additionally, we analyzed the infiltration levels of 28 immune cells in the expression profile and their relationship with the hub genes using single-sample gene set enrichment analysis. Three hub genes, namely, ribonucleotide reductase regulatory subunit M2 ( ), DLG-associated protein 5 ( ), and kinesin family member 11 ( ), were identified through WGCNA, LASSO, SVM-RFE, and RF algorithms. These hub genes correlated strongly with T cells, natural killer cells, and macrophage cells as indicated by immune cell infiltration analysis. , , and could serve as potential diagnostic indicators and treatment targets for RA. The infiltration of immune cells offers additional insights into the underlying mechanisms involved in the progression of RA.

With human guidance, computers now use machine learning (ML) in artificial intelligence (AI) to learn from data, detect trends, and make predictions. Software can adapt and improve with new information. Imaging scans leverage pattern recognition to predict outcomes, diagnose disorders, and suggest treatments. Tuberculosis (TB) remains the most common bacterial disease affecting humans. The World Health Organisation reported that in 2022, 1.3 million people died from tuberculosis, with the death rate potentially reaching 66% if proper treatment isn’t provided. We trained ML-supervised algorithms like XG Boost, Logistic Regression, Random Forest Classifier, Ad- aBoost, and Support Vector Machine to help classify TB patients from large RNA-sequence count data. Such algorithms provided prediction accuracies of 0.963, 0.739, 0.773, 0.866, and 0.866 sequentially. This article highlights feature importance techniques using the ML model, XGBoost, with the highest prediction accuracy of 0.963, identifying significant genes in TB RNA sequence count data. Using key machine learning features, we here identified 20 pathways, 24 gene ontologies, 20 hub genes, and 22 drugs. Next, we applied advanced computational techniques, including pathway analysis, GO, hub-protein and protein–protein interactions (PPI), transcriptomic and miRNA interactions, and drug-protein interactions, to help analyze 100 highly expressed genes.

This paper contributes to the development of a Next Generation First Responder (NGFR) communication platform with the key goal of embedding it into a smart city technology infrastructure. The framework of this approach is a concept known as SmartHub, developed by the US Department of Homeland Security. The proposed embedding methodology complies with the standard categories and indicators of smart city performance. This paper offers two practice-centered extensions of the NGFR hub, which are also the main results: first, a cognitive workload monitoring of first responders as a basis for their performance assessment, monitoring, and improvement; and second, a highly sensitive problem of human society, the emergency assistance tools for individuals with disabilities. Both extensions explore various technological-societal dimensions of smart cities, including interoperability, standardization, and accessibility to assistive technologies for people with disabilities. Regarding cognitive workload monitoring, the core result is a novel AI formalism, an ensemble of machine learning processes aggregated using machine reasoning. This ensemble enables predictive situation assessment and self-aware computing, which is the basis of the digital twin concept. We experimentally demonstrate a specific component of a digital twin of an NGFR, a near-real-time monitoring of the NGFR cognitive workload. Regarding our second result, a problem of emergency assistance for individuals with disabilities that originated as accessibility to assistive technologies to promote disability inclusion, we provide the NGFR specification focusing on interactions based on AI formalism and using a unified hub platform. This paper also discusses a technology roadmap using the notion of the Emergency Management Cycle (EMC), a commonly accepted doctrine for managing disasters through the steps of mitigation, preparedness, response, and recovery. It positions the NGFR hub as a benchmark of the smart city emergency service.

Background The immune system plays a vital role in the pathological process of ischaemic stroke. However, the exact immune-related mechanism remains unclear. The current research aimed to identify immune-related key genes associated with ischaemic stroke. Methods CIBERSORT was utilized to reveal the immune cell infiltration pattern in ischaemic stroke patients. Meanwhile, a weighted gene coexpression network analysis (WGCNA) was utilized to identify meaningful modules significantly correlated with ischaemic stroke. The characteristic genes correlated with ischaemic stroke were identified by the following two machine learning methods: the support vector machine-recursive feature elimination (SVM-RFE) algorithm and least absolute shrinkage and selection operator (LASSO) logistic regression. Results The CIBERSORT results suggested that there was a decreased infiltration of naive CD4 T cells, CD8 T cells, resting mast cells and eosinophils and an increased infiltration of neutrophils, M0 macrophages and activated memory CD4 T cells in ischaemic stroke patients. Then, three significant modules (pink, brown and cyan) were identified to be significantly associated with ischaemic stroke. The gene enrichment analysis indicated that 519 genes in the above three modules were mainly involved in several inflammatory or immune-related signalling pathways and biological processes. Eight hub genes ( ADM , ANXA3 , CARD6 , CPQ , SLC22A4 , UBE2S , VIM and ZFP36 ) were revealed to be significantly correlated with ischaemic stroke by the LASSO logistic regression and SVM-RFE algorithm. The external validation combined with a RT‒qPCR analysis revealed that the expression levels of ADM , ANXA3 , SLC22A4 and VIM were significantly increased in ischaemic stroke patients and that these key genes were positively associated with neutrophils and M0 macrophages and negatively correlated with CD8 T cells. The mean AUC value of ADM , ANXA3 , SLC22A4 and VIM was 0.80, 0.87, 0.91 and 0.88 in the training set, 0.85, 0.77, 0.86 and 0.72 in the testing set and 0.87, 0.83, 0.88 and 0.91 in the validation samples, respectively. Conclusions These results suggest that the ADM , ANXA3 , SLC22A4 and VIM genes are reliable serum markers for the diagnosis of ischaemic stroke and that immune cell infiltration plays a crucial role in the occurrence and development of ischaemic stroke.