北京若生智能科技有限公司
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Trinity ELITE (Trinity ELITE)

✓ Approved

Orthofix · 细胞治疗 · 细胞治疗

什么是 Trinity ELITE？

Trinity ELITE 是一种细胞治疗，由Orthofix研发。该药已获批，用于治疗相关适应症，给药途径：Injectable (Others)。

药物档案

商品名Trinity ELITE
公司Orthofix
药物类别细胞治疗
给药途径Injectable (Others)
状态Approved
来源： ClinicalTrials.gov, Orthofix

治疗适应症

Trinity ELITE 针对 1 个适应症，涉及 1 个治疗领域。

治疗领域疾病/病症分期
Musculoskeletal and connective tissue disordersMusculoskeletal disorder✓ Approved

相关研究文献

PubMedBiomimetics (Basel, Switzerland)2026-10-26

Elite-Guided Nutcracker Optimization Algorithm with Memory-Differential Spiral Search for Feature Selection.

Pang Baowei B, Kang Chunying C

Feature selection can effectively remove redundant and irrelevant features in classification tasks; however, feature subset search on datasets with different dimensionalities still faces challenges such as strong initialization randomness, complex search spaces, a single local exploitation direction, and difficulty in effectively updating inferior individuals. This paper proposes an Elite-Guided Nutcracker Optimization Algorithm with Memory-Differential Spiral Search for Feature Selection (EGRNOA). The proposed method first employs Minimum Redundancy Maximum Relevance (mRMR) to preselect candidate features, thereby reducing the interference of redundant features in the subsequent search process. Then, Logistic-Tent chaotic initialization is used to enhance the uniformity of the initial population distribution. In the foraging phase of NOA, a memory-differential spiral search strategy is designed in which global best memory, individual historical best memory, and random differential directions are jointly incorporated into the spiral updating process so that the search no longer contracts only around a single optimal individual, thereby enhancing local exploitation capability while maintaining directional perturbation. A diversity-adaptive elite-guided reconstruction mechanism for inferior individuals is constructed; this mechanism uses elite population information to directionally repair inferior individuals, which improves population update efficiency while alleviating premature convergence. Experimental results on the CEC2020 benchmark functions show that EGRNOA achieves the best or tied-best average results on all ten functions and obtains the best Friedman average ranking of 1.050. On ten classification datasets with different dimensionalities, EGRNOA achieves the highest average classification accuracy on nine; across datasets, its average accuracy on the movement dataset is improved by 6.41 percentage points when compared with the second-best algorithm. Convergence curves, box plots, critical difference diagrams, and ablation experiments further verify the effectiveness of the memory-differential spiral search and diversity-adaptive elite-guided reconstruction mechanism in improving search quality and enhancing the robustness of feature selection.

PMID 42930236
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PubMedBiomimetics (Basel, Switzerland)2026-10-26

Adaptive Reinforced Elite Gray Wolf Optimization for Robotic Path Planning and Football Pass-Quality Prediction.

Liu Sheng S, Wang Huijuan H, Yuan Yijuan Y, Zhou Min M et al.

Complex optimization problems in engineering and data-driven modeling require effective exploration, stable convergence, and adaptability across different decision representations. This study proposes an Adaptive Reinforced Elite Gray Wolf Optimizer (AREGWO) that combines opposition-based initialization, nonlinear convergence control, reinforced elite guidance, adaptive weak perturbation, stagnation-aware partial restart, and greedy elite inheritance. AREGWO is evaluated through CEC2022 benchmark optimization, synthetic 2D constrained robotic path planning, and football pass-quality prediction. On CEC2022, AREGWO ranks second among nine algorithms in the Friedman test and significantly outperforms seven of eight competitors in the Wilcoxon signed-rank test, while L-SHADE remains the strongest baseline. Component-wise ablation shows that the six mechanisms provide complementary and problem-dependent contributions. In robotic path planning, 30 independent runs on six random maps yield a six-map average path length of 134.38 m, which is 3.61-5.12% shorter than those obtained by the four population-based baselines and 1.08% shorter than A*, with a 100% success rate in all tested environments. For football pass-quality prediction, AREGWO is coupled with TabM for outer-level hyperparameter optimization of a predefined pass-quality reference score. AREGWO-TabM achieves an RMSE of 0.445, an MSE of 0.198, and an R2 of 0.920, providing the best RMSE, MSE, and R2 among the evaluated neural models, although ResNet attains a lower MAE. These results demonstrate the competitive search capability of AREGWO across the numerical, synthetic spatial, and model-level optimization settings evaluated in this study.

PMID 42930300
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PubMedBiomimetics (Basel, Switzerland)2026-10-26

A Multi-Strategy Crested Porcupine Optimizer for Work-Family Resource Allocation: Childcare Facility Siting and Family-Friendly Shift Scheduling.

Shi Yang Y, Cai Xinyu X

The time cost of care constrains female labour supply; two policy levers act on it: where childcare capacity is sited and how shifts are scheduled, both large combinatorial problems. The crested porcupine optimizer (CPO) initialises at random, guides exploitation by a single random or best individual and resizes its population on a fixed cycle. The proposed multi-strategy CPO (MSCPO) keeps CPO's four defence operators and replaces those original components with Sobol quasi-opposition initialisation, elite-guided exploitation and monotone fitness-based population reduction. On the 29 CEC2017 functions at 10, 30, 50 and 100 dimensions (30 runs of 30,000 evaluations), MSCPO had the best Friedman mean rank among ten algorithms at every dimension (1.29 against 2.53 for CPO; χ2(9) = 878.5, p < 0.001), and pooled Holm tests separated it from all nine competitors (p ≤ 0.002). A factorial ablation attributed about 70% of the gain to elite-guided exploitation. The advantage over CPO vanished at the full budget of 104D evaluations. In childcare siting, MSCPO ranked first, leaving the dispersion of access 28-35% below the most equal siting rule with at least 99% of the best rule's employment; in shift scheduling, it ranked fifth, with instrumentation suggesting a limitation of large steps under a rank-key decoder.

PMID 42930272
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PubMedBiomimetics (Basel, Switzerland)2026-10-26

Sooty Shearwater Migration Optimizer: A Novel Metaheuristic Algorithm for Engineering Optimization and Photovoltaic Parameter Identification.

Lin Xiaohan X, Song Xiangkun X, Zhao Jian J

This paper develops a new metaheuristic optimizer, named the Sooty Shearwater Migration Optimizer (SSMO), by drawing inspiration from the long-distance migration patterns of sooty shearwaters. The proposed SSMO integrates elite guidance, wind-driven migration, molting memory guidance, and adaptive foraging to coordinate global exploration with local exploitation. Its optimization performance is assessed in the CEC2017 benchmark suite under 30D, 50D, and 100D settings and in the CEC2022 benchmark suite under 10D and 20D settings. Compared with 11 representative optimization algorithms, SSMO achieves the lowest average Friedman ranks in all five-dimensional settings. The Wilcoxon rank-sum results also generally favor SSMO. Additional comparisons with four differential evolution variants further demonstrate competitive performance under the tested configurations. Its practical applicability is further assessed using five engineering design problems, on which it obtains the lowest mean fitness values under the adopted evaluation settings. In addition, four photovoltaic parameter identification cases are used to evaluate its effectiveness in practical parameter estimation. Compared with the selected general-purpose algorithms, SSMO obtains the lowest error values for the considered evaluation metrics. Further comparison with four specialized photovoltaic parameter identification algorithms shows that SSMO remains competitive in most test cases. These findings suggest that SSMO is a promising optimizer for numerical benchmarks and practical engineering applications.

PMID 42930303
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PubMedBiomimetics (Basel, Switzerland)2026-10-26

Biomimetic Coverage Optimization in WSNs via an Enhanced Spider-Wasp Optimizer with Adaptive Learning and Dual-Median-Point Guidance.

Nguyen Trong-The TT, Wang Longcun L, Dao Thi-Kien TK

Coverage optimization in wireless sensor networks (WSNs) is a non-convex deployment problem on which population-based optimizers often converge prematurely and lose diversity. This paper proposes the Enhanced Spider-Wasp Optimizer (ESWO), an extension of the Spider-Wasp Optimizer (SWO) conceptually inspired by the foraging and nesting behavior of Pompilidae wasps. ESWO integrates (i) an Adaptive Learning Strategy (ALS) that uses four types of positional gaps among individuals to sustain diversity, (ii) a dual-median-point guidance (DMPG) mechanism that steers exploitation towards the midpoint of two elite solutions, and (iii) a position-update rule with nest-overlap avoidance. Coverage is evaluated on a discretized target grid with a Euclidean binary-disk sensing model and an energy-penalized fitness. In four scenarios (25-65 nodes; 50 × 50 to 165 × 165 m2), ESWO was compared with seven algorithms, including SWO. It achieved coverage rates of 81%, 88%, 92% and 85%, the highest in every scenario (tied with MGWO at 81% in the smallest). In the 100 × 100 m2 scenario it reached 85% coverage after 523 iterations, against 601-698 for the competitors, although final convergence iteration counts were not uniformly the lowest. Over 15 runs, ESWO obtained a mean coverage of 90.2% (SD 1.2), a 95% success rate and the lowest estimated energy consumption (175.3 J).

PMID 42930312
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PubMedBiomimetics (Basel, Switzerland)2026-10-26

Adaptive Shannon Entropy-Driven Chaotic Grey Wolf Optimization (ASE-CGWO) for Combinatorial Optimization Problems.

Kalathodi Simi S, Jayan Sarada S, Alatas Bilal B, Das Mehmet M et al.

Combinatorial optimization problems include many NP-hard problems such as assignment, scheduling, and routing, for which conventional approaches to solving become computationally infeasible for large instances, thereby necessitating the use of metaheuristics. This study introduces an algorithm, Adaptive Shannon Entropy-Driven Chaotic Grey Wolf Optimization (ASE-CGWO), for combinatorial optimization problems. The proposed approach adapts the level of chaos using an entropy-based diversity measure to ensure an efficient trade-off between exploration and exploitation. It also uses an entropy-adapted hunting parameter that couples exploration breadth directly to the pack's information content. An Elite Memory Archive and swap, along with insertion and flip operators to enhance neighborhood searches, are also incorporated to maintain high-quality solutions. ASE-CGWO is benchmarked against Standard GWO, Chaotic GWO (fixed r = 3.9 logistic map), Entropy-Adaptive GWO (entropy adapts "a" only), Genetic Algorithm (GA), Particle Swarm Optimization (PSO), and Ant Colony Optimization (ACO) on three NP-hard combinatorial optimization problems: the Quadratic Assignment Problem instance chr15a, the Fisher-Thompson 10 × 10 Job Shop Scheduling Problem (FT10) and the 20-city Traveling Salesman Problem (TSP20) over 20 independent runs each. ASE-CGWO achieves the best mean assignment cost on chr15a (9921.6 ± 147.3 in native QAPLIB units), with its best run reaching the known optimum of 9896, and obtains the second-best mean makespan on FT10 (1182.05 ± 3.32). The adaptive and self-regulating characteristics of ASE-CGWO also suggest potential for future applications in complex engineering optimization problems, including the design and operation of solar energy systems.

PMID 42930241
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