Search Results (20)

To address the limitations of the traditional Balancing Composite Motion Optimization (BCMO) algorithm—namely weak directional global exploration, insufficient local exploitation accuracy, and a tendency to fall into local optima with reduced population diversity in feature selection tasks—this paper proposes a Multi-Strategy Enhanced Balancing Composite Motion Optimization algorithm (MEBCMO). From a symmetry perspective, MEBCMO exploits the symmetric and asymmetric relationships among candidate solutions in the search space to achieve a better balance between exploration and exploitation. The performance of MEBCMO is enhanced through three complementary strategies. First, an adaptive heat-conduction search mechanism is introduced to simulate thermal transmission behavior, where a Sigmoid function adjusts the heat-conduction coefficient α_T from 0.9 to 0.2 during iterations. By utilizing the symmetric fitness–distance relationship between the current solution and the global best, this mechanism improves the directionality and efficiency of global exploration. Second, a quadratic interpolation search strategy is designed. By constructing a quadratic model based on the current individual, a randomly selected individual, and the global best, the algorithm exploits local symmetric characteristics of the fitness landscape to strengthen local exploitation and alleviate performance degradation in high-dimensional spaces. Third, an elite population genetic strategy is incorporated, in which the top three individuals generate new candidates through symmetric linear combinations with non-elite individuals and Gaussian perturbations, preserving population diversity and preventing premature convergence. To evaluate MEBCMO, extensive global optimization experiments are conducted on the CEC2017 benchmark suite with dimensions of 30, 50, and 100, and comparisons are made with eight mainstream algorithms, including PSO, DE, and GWO. Experimental results demonstrate that MEBCMO achieves superior performance across unimodal, multimodal, hybrid, and composite functions. Furthermore, MEBCMO is combined with LightGBM to form the MEBCMO-LightGBM model for feature selection on 14 public datasets, yielding lower fitness values, higher classification accuracy, and fewer selected features. Statistical tests and convergence analyses confirm the effectiveness, stability, and rapid convergence of MEBCMO in symmetric and complex optimization landscapes. Full article

Hereditary predisposition substantially shapes prevention and management across urologic oncology. This narrative review synthesizes contemporary, practice-oriented guidance on whom to test, what to test, how to act on results, and how to implement care equitably for hereditary forms of prostate cancer, renal cell carcinoma (RCC), urothelial carcinoma, pheochromocytoma/paraganglioma (PPGL), and adrenocortical carcinoma (ACC). We delineate between forms of indication-driven germline testing (e.g., universal testing in metastatic prostate cancer; early-onset, bilateral/multifocal, or syndromic RCC; reflex tumor mismatch repair (MMR)/microsatellite instability (MSI) screening in upper-tract urothelial carcinoma (UTUC); universal testing in PPGL; universal TP53 testing in ACC) and pair these strategies with minimum actionable gene sets and syndrome-specific surveillance frameworks. Key points include targeted prostate-specific antigen screening in BRCA2 carriers and the impact of BRCA/ATM variants on reclassification during active surveillance; major hereditary RCC syndromes with genotype-tailored surveillance and pathway-directed therapy (e.g., HIF-2α inhibition for von Hippel–Lindau disease); UTUC/bladder cancer in Lynch syndrome with tumor MMR/MSI screening, annual urinalysis (selective cytology), and immunotherapy opportunities in deficient MMR disease/MSI-H; PPGL management emphasizing universal germline testing, intensified surveillance for SDHB, cortical-sparing adrenalectomy, and emerging HIF-2α inhibition; and ACC care modified by Li–Fraumeni syndrome (minimization of radiation/genotoxic therapy with whole-body imaging surveillance). Testicular germ cell tumor remains largely polygenic, with no routine germline testing in typical presentations. Finally, we provide pre-/post-test genetic-counseling checklists and mainstreamed workflows with equity metrics to operationalize precision care and close real-world access gaps. Full article

Background: Germline genetic variants impacting splicing are a frequent cause of disease. The clinical interpretation of such variants is challenging for many reasons including the immense complexity of splicing mechanisms. While recent advances in splicing algorithms have improved the accuracy of splice prediction, predicting the nature and abundance of aberrant splicing remains challenging. As RNA testing becomes more mainstream in the clinical diagnostic setting, the complexities of interpretation are coming to light. Methods: Data from patients undergoing concurrent DNA and RNA testing were retrospectively reviewed for unusual splicing impacts to underscore some of these complexities and serve as exemplars in how to avoid pitfalls in the interpretation of sequence variants. Results: Seven rare variants with unusual splicing impacts are presented: a variant at a consensus donor nucleotide position lacking a splice impact (NF1 c.888+2T>C); a mid-exonic missense variant creating a novel donor site and a cryptic acceptor site resulting in pseudo-intronization (BRIP1 c.727A<G p.Ile243Val); one variant creating a spliceosome switch from U12 to U2 (LZTR1 c.2232G>A p.Ala744Ala); two variants that would be expected to result in nonsense-mediated-mRNA-decay triggering splicing impacts that obviated nonsense-mediated-decay (APC c.1042C>T p.Arg348Ter and BRCA2 c.6762del; c.6816_6841+1534del); and two variants causing splicing impacts through pyrimidine tract optimization (NF1 c.5750-184_5750-178dup and ATM c.3480G>T p.Val1160Val). Conclusions: Paired DNA and RNA testing revealed unexpected splice events altering variant interpretation, expanding our knowledge of clinically important splicing mechanisms and highlighting the benefit of RNA testing. Full article

Three-dimensional path planning in emergency logistics is a complex optimization problem, particularly challenging because it requires considering conflicting objectives such as flight time, energy consumption, and obstacle avoidance. Unlike most urban logistics research, this study examines emergency delivery path planning in mountainous environments during natural disasters. One of the most effective approaches to this problem is to employ multi-objective evolutionary algorithms. However, while multi-objective genetic algorithms can handle multiple conflicting objectives, they struggle when dealing with complex constraints. This paper proposes a multi-objective genetic optimization method, Adaptive Crossover-Mutation Multi-Objective Genetic Optimization (ACM-NSGA-II), based on the classic NSGA-II framework. Inspired by the principle of symmetry, this method dynamically adjusts the mutation and crossover rates based on population diversity to maintain a balanced exploration–exploitation trade-off. When population diversity is low, the mutation rate is increased to promote exploration of the solution space; when population diversity is high, the crossover rate is increased to promote better information exchange. The algorithm maintains symmetry by gradually adjusting the step size, balancing adaptability and stability. To address the obstacle avoidance problem, we introduced a dynamic path repair strategy that respects the symmetry of no-fly zone boundaries and terrain features, ensuring the safety and efficiency of Unmanned Aerial Vehicles. This algorithm jointly optimizes three objectives: safety cost, flight time, and energy consumption. The algorithm was tested in a mountainous environment model simulating a remote area. In experiments, ACM-NSGA-II was compared with several mainstream evolutionary algorithms. The Pareto set and hypervolume metrics of each method were recorded and statistically analyzed at a 5% significance level. The results show that ACM-NSGA-II outperforms the baseline algorithms in terms of diversity, convergence, and feasibility. Specifically, compared with the traditional NSGA-II, ACM-NSGA-II improved the average hypervolume metric by 53.39% and reduced the average flight time by 24.26%. ACM-NSGA-II also demonstrated significant advantages over other popular standard algorithms. Experimental results show that it can effectively solve the path planning challenge of emergency logistics Unmanned Aerial Vehicles in mountainous environments. Full article

Scenario-based testing is a mainstream approach for evaluating the safety of automated driving systems (ADS). However, logical scenarios are defined through parameter spaces, and performance differences among systems under test make it difficult to ensure fairness and coverage using the same concrete parameters. Accordingly, an automated driving system testing method is proposed. Guided by the established full-coverage testing framework, a quantitative evaluation method for scenario representativeness is first proposed by jointly analyzing naturalistic driving probability distributions and hazard-related characteristics. Furthermore, a hybrid algorithm integrating heat-guided hierarchical search and genetic optimization is developed to address the non-uniform full-coverage problem, enabling efficient selection of representative parameters that ensure complete coverage of the logical scenario space. The proposed method is validated through empirical studies in representative use cases, including lead vehicle braking and cut-in scenarios. Experimental results show that the proposed method achieves 100% coverage of the logical scenario parameter space with an 8% boundary fitting error, outperforming mainstream baselines including monte carlo (84.3%, 19%), combinatorial testing (86.5%, 14%) and importance sampling (72.0%, 7%). The approach achieves exhaustive coverage of the logical scenario space with limited concrete scenarios, and effectively supports the development of consistent, reproducible and efficient scenario generation frameworks for testing organizations. Full article

Pathogenic variants in breast cancer predisposition genes are associated with poor clinical outcomes but also offer an opportunity for more individualized therapeutic pathways. Given increasing knowledge, improvements in germline genetic testing efficiency, and the availability of novel systemic targeted treatment options, the importance of appropriately identifying patients for testing has never been greater. A pan-Canadian expert working group (EWG) consisting of 10 healthcare professionals (HCPs) was convened to review recent international guidelines for germline genetic testing in breast cancer and develop Canadian recommendations. The group identified four clinical questions to address which patients should undergo testing, what approaches should be used, how patients should be counselled, and what steps are needed for implementation. In response to these questions, the EWG agreed upon 12 recommendations that emphasized broader incorporation of germline genetic testing and more standardized, streamlined testing and counselling approaches. The group also offered multiple suggestions to support effective and equitable implementation across Canada. These recommendations provide guidance for HCPs and represent a call to action for the Canadian government and other organizations to support genetic testing pathways, drug access, and ultimately improved outcomes for patients with breast cancer and their families. Full article

In response to the increasing demands for energy conservation and pollution reduction, optimizing transformer design to reduce operational losses and minimize raw material usage has become crucial. This paper introduces an innovative methodology that combines ensemble learning models with hybrid multi-objective optimization heuristic algorithms to optimize leakage impedance deviation, on-load loss, and raw material consumption in power transformers. The stacking ensemble model uses support vector machines, linear regression, decision tree regression, and K-nearest neighbors as base learners, with the extreme learning machine serving as the meta-learner to re-learn outputs from first-level learners. Given the significant impact of hyperparameters on the prediction performance of ensemble learning models, an improved particle swarm optimization method is proposed for effective hyperparameter optimization. To assess the uncertainty of the proposed ensemble learning model, a Kriging surrogate model-based analysis is outlined. Moreover, a powerful multi-objective algorithm that integrates the multi-objective grey wolf optimization (MOGWO) and the non-dominated sorting genetic algorithm-III (NSGA3) is presented for model optimization. This approach demonstrates superior performance compared to mainstream multi-objective optimization algorithms. The effectiveness of this method is further validated through the engineering tests of two real engineering cases. The proposed algorithm can accommodate various design requirements and, under the given constraints, achieve a multi-objective optimization design for power transformers, ensuring optimal performance in different operational scenarios. Full article

This paper introduces an optimization method for multi-robot automated control welding based on a Particle Swarm Genetic Algorithm (PSGA), aiming to address issues such as high costs, large footprint, and excessive production cycles in multi-robot welding production lines. The method first constructs a multi-axis robotic kinematic model to provide constraint conditions. Then, the PSO (particle swarm optimization) algorithm, which integrates penalty functions into the fitness evaluation, is used to determine the optimal welding path by simulating collective behavior within a group. The GA (genetic algorithm) encodes the position of the welding robot bases into chromosomes to find the optimal layout for coordinated control of multiple robots. The entire process is optimized according to welding standards and requirements. Additionally, a comprehensive production line performance estimation model was used to quantitatively analyze the new scheme. The results show that the optimized production line’s balance rate increased by 10%, the balance loss rate decreased by 10%, the smoothness index increased by 37.8%, the space costs reduced by 44.4%, the equipment demand reduced by 41.1%, the labor demand reduced by 50%, the total costs reduced by 10%, and the average product cycle time was reduced by 5.07 s. Finally, we tested the algorithm in various complex scenarios and compared its performance against mainstream algorithms within the context of this study. The results demonstrated that the optimized production line significantly improved efficiency while maintaining safety standards. Full article

The wireless sensor network (WSN) is an essential technology of the Internet of Things (IoT) but has the problem of low coverage due to the uneven distribution of sensor nodes. This paper proposes a novel enhanced whale optimization algorithm (WOA), incorporating Lévy flight and a genetic algorithm optimization mechanism (WOA-LFGA). The Lévy flight technique bolsters the global search ability and convergence speed of the WOA, while the genetic optimization mechanism enhances its local search and random search capabilities. WOA-LFGA is tested with 29 mathematical optimization problems and a WSN coverage optimization model. Simulation results demonstrate that the improved algorithm is highly competitive compared with mainstream algorithms. Moreover, the practicality and the effectiveness of the improved algorithm in optimizing wireless sensor network coverage are confirmed. Full article

A global survey has revealed that genetic syndromes affect approximately 8% of the population, but most genetic diagnoses are typically made after birth. Facial deformities are commonly associated with chromosomal disorders. Prenatal diagnosis through ultrasound imaging is vital for identifying abnormal fetal facial features. However, this approach faces challenges such as inconsistent diagnostic criteria and limited coverage. To address this gap, we have developed FGDS, a three-stage model that utilizes fetal ultrasound images to detect genetic disorders. Our model was trained on a dataset of 2554 images. Specifically, FGDS employs object detection technology to extract key regions and integrates disease information from each region through ensemble learning. Experimental results demonstrate that FGDS accurately recognizes the anatomical structure of the fetal face, achieving an average precision of 0.988 across all classes. In the internal test set, FGDS achieves a sensitivity of 0.753 and a specificity of 0.889. Moreover, in the external test set, FGDS outperforms mainstream deep learning models with a sensitivity of 0.768 and a specificity of 0.837. This study highlights the potential of our proposed three-stage ensemble learning model for screening fetal genetic disorders. It showcases the model’s ability to enhance detection rates in clinical practice and alleviate the burden on medical professionals. Full article

Background: Up to 20% of women diagnosed with tubo-ovarian carcinoma carry a germline pathogenic variant in a cancer-predisposing gene (e.g., BRCA1/BRCA2). Identifying these variants can help to inform eligibility for therapies, guide surveillance and prevention of new primary cancers, and assess risk to family members. The Gynecologic Oncology-Initiated Genetic Testing Model (GOIGT) was initiated at the McGill University Health Centre (MUHC) to streamline universal germline genetic testing for this population, while addressing the limited resources in the public healthcare system. This study aimed to evaluate the patient experience of participating in this model. Methods: Study participants were patients diagnosed with high-grade non-mucinous epithelial tubo-ovarian cancer who underwent genetic testing through the GOIGT model between 1 January 2017 and 31 December 2020. Eligible participants completed the retrospective questionnaires at least one month after result disclosure. Results: A total of 126 patients were tested through the GOIGT model during the study period, of which 56 were invited to participate. Thirty-four participants returned the study questionnaire. Overall, participants did not report decision regret following the genetic testing and were satisfied with the GOIGT model. Participants reported low levels of uncertainty and distress related to the implications of their test results for themselves and their family members. Conclusions: The results of this study support the continued implementation of mainstreamed genetic testing models for women with high-grade non-mucinous tubo-ovarian cancer. Further studies are required to compare experiences for patients with different genetic test results. Full article

Background: Non-genetic healthcare professionals can provide pre-test counseling and order germline genetic tests themselves, which is called mainstream genetic testing. In this systematic review, we determined whether mainstream genetic testing was feasible in daily practice while maintaining quality of genetic care. Methods: PubMed, Embase, CINAHL, and PsychINFO were searched for articles describing mainstream genetic testing initiatives in cancer care. Results: Seventeen articles, reporting on 15 studies, met the inclusion criteria. Non-genetic healthcare professionals concluded that mainstream genetic testing was possible within the timeframe of a routine consultation. In 14 studies, non-genetic healthcare professionals completed some form of training about genetics. When referral was coordinated by a genetics team, the majority of patients carrying a pathogenic variant were seen for post-test counseling by genetic healthcare professionals. The number of days between cancer diagnosis and test result disclosure was always lower in the mainstream genetic testing pathway than in the standard genetic testing pathway (e.g., pre-test counseling at genetics department). Conclusions: Mainstream genetic testing seems feasible in daily practice with no insurmountable barriers. A structured pathway with a training procedure is desirable, as well as a close collaboration between genetics and other clinical departments. Full article

The installed capacity of photovoltaic power generation occupies an increasing proportion in the power system, and its stability is greatly affected by the fluctuation of solar radiation. Accurate prediction of solar radiation is an important prerequisite for ensuring power grid security and electricity market transactions. The current mainstream solar radiation prediction method is the deep learning method, and the structure design and data selection of the deep learning method determine the prediction accuracy and speed of the network. In this paper, we propose a novel long short-term memory (LSTM) model based on the attention mechanism and genetic algorithm (AGA-LSTM). The attention mechanism is used to assign different weights to each feature, so that the model can focus more attention on the key features. Meanwhile, the structure and data selection parameters of the model are optimized through genetic algorithms, and the time series memory and processing capabilities of LSTM are used to predict the global horizontal irradiance and direct normal irradiance after 5, 10, and 15 min. The proposed AGA-LSTM model was trained and tested with two years of data from the public database Solar Radiation Research Laboratory site of the National Renewable Energy Laboratory. The experimental results show that under the three prediction scales, the prediction performance of the AGA-LSTM model is below 20%, which effectively improves the prediction accuracy compared with the continuous model and some public methods. Full article

This study compares knowledge, experience and understanding of genetic testing, and psychological outcomes among breast and ovarian cancer patients undergoing multi-gene panel testing via genetic counselor-mediated (GMT) or oncologist-mediated (OMT) testing models. A pragmatic, prospective survey of breast and ovarian cancer patients pursuing genetic testing between January 2017 and August 2019 was conducted at the Princess Margaret Cancer Centre in Toronto, Canada. A total of 120 (80 GMT; 40 OMT) individuals completed a survey administered one week following consent to genetic testing. Compared to OMT, the GMT cohort had higher median knowledge (8 vs. 9; p = 0.025) and experience/understanding scores (8.5 vs. 10; p < 0.001) at the time of genetic testing. Significant differences were noted in the potential psychological concerns experienced, with individuals in the GMT cohort more likely to screen positive in the hereditary predisposition domain of the Psychosocial Aspects of Hereditary Cancer tool (55% vs. 27.5%; p = 0.005), and individuals in the OMT cohort more likely to screen positive in the general emotions domain (65.0% vs. 38.8%; p = 0.007). The results of this study suggest that OMT can be implemented to streamline genetic testing; however, post-test genetic counseling should remain available to all individuals undergoing genetic testing, to ensure any psychologic concerns are addressed and that individuals have a clear understanding of relevant implications and limitations of their test results. Full article

In advanced industrial automation, industrial robots have been widely utilized on assembly lines in order to reduce labor dependence. However, many related layout design approaches proposed are prone to generating unsafe layouts: there generally lacks a consideration regarding robots’ heights and assembly range, which will lead to costly collisions in the operation stage. In order to address the problem, we propose a three-dimensional (3D) optimization approach to a safe layout design for an assembly line with robots. We define modeling rules for robots to judge assembly ranges. A quantitative safety indicator is employed as a trigger for 3D collision detection in order to determine the positional relationship and status of the safe assembly collaboration. The optimization goals are established for minimizing the logistical cost and layout area in the model. A combined algorithm of differential evolution and nondominated sequencing genetic II is applied, which effectively enhances the poor diversity and convergence of the mainstream optimization method when solving this model. The benchmark tests and validation proved that our approach yields excellent convergence and distribution performance. The case study verifies that the safe layout model is valid and our approach can generate a safe layout in order to optimize economics and safety. Full article