Search Results (532)

Fault diagnosis is a core technology ensuring the safe and efficient operation of industrial systems. A paradigm shift has been observed wherein traditional signal analysis has been replaced by intelligent, algorithm-driven approaches. In recent years, large language models, digital twins, and knowledge graphs have been introduced. A new stage of intelligent integration has been reached that is characterized by data-driven methods, knowledge guidance, and physical–virtual fusion. In the present paper, the evolutionary context of fault diagnosis technologies was systematically reviewed, with a focus on the theoretical methods and application practices of traditional machine learning, digital twins, knowledge graphs, and large language models. First, the research background, core objectives, and development history of fault diagnosis were described. Second, the principles, industrial applications, and limitations of supervised and unsupervised learning were analyzed. Third, innovative uses were examined involving physical–virtual mapping in digital twins, knowledge modeling in knowledge graphs, and feature learning in large language models. Subsequently, a multi-dimensional comparison framework was constructed to analyze the performance indicators, applicable scenarios, and collaborative potential of different technologies. Finally, the key challenges faced in the current fault diagnosis field were summarized. These included data quality, model generalization, and knowledge reuse. Future directions driven by the fusion of large language models, digital twins, and knowledge graphs were also outlined. A comprehensive technical map was established for fault diagnosis researchers, as well as an up-to-date reference. Theoretical innovation and engineering deployment of intelligent fault diagnosis are intended to be supported. Full article

Programmable mechanical metamaterials enable precise regulation of mechanical responses through geometric design, ushering in transformative paradigms for transformable structures. To systematically map the knowledge landscape and development trends in this field, this study employs knowledge mapping methods to analyze the current research status, core hotspots, and future directions of programmable mechanical metamaterials. During the research process, we expanded keywords using the litsearchr tool to optimize the retrieval strategy. Bibliometric tools, including CiteSpace 6.3.R3 and bibliometrix, were utilized to conduct multidimensional analyses on 2017 original papers related to mechanical metamaterials in transformable architecture from 2015 to 2025. These analyses encompass co-word analysis, co-citation clustering, and structural variation analysis. Key aspects include (1) identifying core journals and their attributes to clarify interdisciplinary dynamics, (2) mapping research themes and evolutionary trends through keyword analysis and clustering, and (3) pinpointing research hotspots and future directions based on citation networks and clustering results. The results reveal significant interdisciplinary characteristics, with core knowledge emerging from the intersection of materials science, mechanics, and civil engineering. Mathematical system theory provides a cross-scale modeling foundation for metamaterial microstructure design. The field is evolving from static structural design toward environment-adaptive intelligent systems. Future efforts should prioritize multi-physics collaborative regulation, engineering integration, and technical chain refinement. These findings offer a theoretical reference for the innovative development of transformable architecture. Full article

Background: The zinc finger-homeodomain (ZF-HD) transcription factor family exerts pivotal regulatory functions in plant development and stress responses, yet a systematic genome-wide survey is lacking for Rosa chinensis. Methods: In this study, we performed a comprehensive genome-wide identification and analysis of RcZF-HD genes in R. chinensis using bioinformatics approaches. Nine RcZF-HD loci were mined from the rose genome and comprehensively profiled for physicochemical parameters, phylogenetic affiliations, chromosomal positions, exon–intron architectures, conserved motifs, and spatiotemporal expression landscapes. Results: The results showed that RcZF-HD genes were unevenly distributed across four chromosomes (Chr2, Chr4, Chr6, and Chr7), with tandem duplication events detected on chromosomes 2 and 7, suggesting their contribution to gene family expansion. Maximum-likelihood phylogeny placed RcZF-HD proteins within nine well-supported sub-clades alongside Arabidopsis orthologs, implying both evolutionary conservation and lineage-specific divergence. All members retain canonical zinc-finger domains, yet acquire unique motif signatures predictive of functional specialization. Gene structure analysis revealed considerable diversity in exon–intron organization. Expression profiling across six different tissues (root, stem, leaf, bud, flower, and fruit) demonstrated remarkable tissue-specific expression patterns. Notably, RchiOBHm_Chr2g0168531 exhibited extremely high expression in stem tissue, while RchiOBHm_Chr7g0181371 showed preferential expression in flower tissue, suggesting specialized roles in stem development and floral organ formation, respectively. The cold-stress challenge of ‘Old Blush’ petals further disclosed pronounced up-regulation of seven RcZF-HD genes, attesting to their critical contribution to low-temperature tolerance. Conclusions: Integrative analyses furnish a multidimensional blueprint of the rose RcZF-HD repertoire, providing molecular landmarks for future functional dissection and ornamental trait engineering. Full article

High rock slopes are extensively distributed in areas of major engineering constructions, such as transportation infrastructure, hydraulic projects, and mining operations. The stability and failure evolution mechanism during their multi-stage excavation process have consistently been a crucial research topic in geotechnical engineering. In this paper, a series of two-dimensional rock slope models, incorporating various combinations of slope height and slope angle, were established utilizing the Discrete Element Method (DEM) software PFC2D. This systematic investigation delves into the meso-mechanical response of the slopes during multi-stage excavation. The Parallel Bond Model (PBM) was employed to simulate the contact and fracture behavior between particles. Parameter calibration was performed to ensure that the simulation results align with the actual mechanical properties of the rock mass. The research primarily focuses on analyzing the evolution of displacement, the failure modes, and the changing characteristics of the force chain structure under different geometric conditions. The results indicate that as both the slope height and slope angle increase, the inter-particle deformation of the slope intensifies significantly, and the shear band progressively extends deeper into the slope mass. The failure mode transitions from shallow localized sliding to deep-seated overall failure. Prior to instability, the force chain system exhibits an evolutionary pattern characterized by “bundling–reconfiguration–fracturing,” serving as a critical indicator for characterizing the micro-scale failure mechanism of the slope body. Full article

The evolution of type I polyketide synthase (T1PKS) assembly lines remains poorly understood. Through systematic mining of polyene biosynthetic gene clusters, we identified a novel eurocidin biosynthetic pathway capable of producing identical compounds with divergent loading module architectures, thereby capturing an evolutionary transitional state. Biochemical analysis revealed unprecedented functional reprogramming of acyltransferase (AT) domains, shifting substrate specificity from extender units (malonyl-CoA) to starter units (acyl-CoA). This paradigm shift enables direct initiation of polyketide chain assembly via AT-mediated loading of starter units, thereby elucidating the origin of extant AT-initiated assembly lines and establishing AT functional plasticity as a novel mechanism for polyketide structural diversification. Parallel evolution of ketosynthase (KS) domains through KSS→KSQ mutations further diversified initiation strategies. Applying this evolutionary insight, we engineered the candicidin pathway by replacing its native aromatic-starting bimodule with a starter-selective monomodule from eurocidin, generating aliphatic-starting analogs. This demonstrates that evolution-inspired AT reprogramming provides a rational framework for modifying polyketide starter units, expanding structural diversity, and enhancing therapeutic potential. Full article

Cryptochromes (CRYs) are a conserved class of blue light and near-ultraviolet light receptors that regulate diverse processes, including photomorphogenesis in plants. In the extreme Antarctic environment, ice algae endure intense UV radiation, prolonged darkness, and low temperatures, where cryptochromes play a vital role in light sensing and stress response. In this study, we cloned the complete open reading frame (ORF) of the cryptochrome gene CiCRY-DASH1 from the Antarctic microalga Chlamydomonas sp. ICE-L. Both in vivo and in vitro DNA photorepair assays showed that CiCRY-DASH1 effectively repairs cyclobutane pyrimidine dimer (CPD) and 6-4 photoproducts (6-4PPs) induced by UV radiation. Furthermore, deletion of the N-terminal and C-terminal loop regions, combined with activity assays, revealed that the C-terminal loop region plays a crucial role in photorepair activity. These findings elucidate the adaptive photorepair mechanisms of Antarctic microalgae and establish CiCRY-DASH1 as a valuable genetic resource. Specifically, the high catalytic efficiency and evolutionary robustness of the engineered variants position it as a promising marine bioactive agent for photoprotective therapeutics and a strategic target for constructing microbial chassis to enable sustainable drug biomanufacturing. Full article

The context of food science and biotechnology is undergoing a profound transformation, characterized by an evolutionary shift from conventional large-scale fermentation to precision biomanufacturing, positioning Lactic Acid Bacteria (LAB) as versatile cellular biofactories for next-generation functional foods. This review analyzes the evolutionary role of LAB, their utilization as probiotics, and the technological advances driving this shift. This work also recognizes the fundamental contributions of pioneering women in the field of biotechnology. The primary methodology relies on the seamless integration of synthetic biology (CRISPR-Cas editing), Multi-Omics analysis, and advanced Artificial Intelligence/Machine Learning, enabling the precise, rational design of LAB strains. This approach has yielded significant findings, including successful metabolic flux engineering to optimize the biosynthesis of high-value nutraceuticals such as Nicotinamide Mononucleotide and N-acetylglucosamine, and the development of Live Biotherapeutic Products using native CRISPR systems for the expression of human therapeutic peptides (e.g., Glucagon-like Peptide-1 for diabetes). From an industrial perspective, this convergence enhances strain robustness and supports the digitalized circular bioeconomy through the valorization of agri-food by-products. In conclusion, LAB continue to consolidate their position as central agents for the development of next-generation functional foods. Full article

Achieving a dynamic equilibrium among feasibility, convergence, and diversity remains a fundamental challenge in Constrained Multi-objective Optimization Problems (CMOPs). To address the limitations of conventional methods in handling complex constraints and resource allocation, this paper proposes a Dual-Population Cooperative Evolutionary Algorithm based on Relaxed Feasibility Selection and Shrinking Contribution Resource Allocation (RFSCMOEA). First, a relaxed feasibility selection strategy is designed with a dynamically shrinking threshold, allowing near-feasible solutions to survive in early stages to enhance boundary exploration. Second, a dual-criterion environmental selection mechanism integrates non-dominated sorting with k-nearest neighbor density estimation to prevent premature convergence and ensure solution uniformity. Furthermore, a dynamic resource allocation model optimizes computational configuration by adjusting offspring generation ratios based on the real-time evolutionary contribution of each population. Extensive experiments on 47 benchmark functions and 12 real-world engineering problems demonstrate that RFSCMOEA significantly outperforms eight state-of-the-art algorithms in Feasibility Rate, Inverted Generational Distance, and Hypervolume. Full article

Constrained optimization problems (COPs) are frequent in engineering design yet remain challenging due to complex search spaces and strict feasibility requirements. Existing swarm-based optimizers often rely on penalty functions or algorithm-specific control parameters, whose performance is sensitive to problem-dependent tuning and may lead to premature convergence or infeasible solutions when feasible regions are narrow. This paper introduces the Constrained Team-Oriented Swarm Optimizer (CTOSO), a tuning-free metaheuristic that adapts the ETOSO framework by replacing linear exploiter movement with spiral search and integrating Deb’s feasibility rule. The population divides into Explorers, promoting diversity through neighbor-guided navigation, and Exploiters, performing intensified local search around the global best solution. Extensive evaluation on twelve constrained engineering benchmark problems shows that CTOSO achieves a 100% feasibility rate and attains the highest overall composite performance score among the compared algorithms under limited function-evaluation budgets. On the CEC 2017 constrained benchmark suite, CTOSO attains an average feasibility rate of 79.78%, generating feasible solutions on 14 out of 15 problems. Statistical analysis using Wilcoxon signed-rank tests and Friedman ranking with Nemenyi post hoc comparison indicates that CTOSO performs significantly better than several baseline optimizers, while exhibiting no statistically significant differences with leading evolutionary methods under the same experimental conditions. The algorithm’s design, requiring no tuning of algorithm-specific control parameters, makes it suitable for real-world engineering applications where tuning effort must be minimized. Full article

Modern high-bypass turbofan engines often operate near their structural natural frequencies, posing significant challenges for vibration isolation in aircraft engine mount systems. This study presents a comprehensive modal optimization framework to enhance vibration isolation performance by maximizing the separation between excitation and natural frequencies. A dynamic model of a typical single-aisle airliner engine mount system is formulated. Modal analysis is conducted via finite element modeling in Abaqus, extracting 20 modes and identifying dominant modes based on effective mass criteria. To avoid resonance within the excitation range of 72–336 Hz, a genetic algorithm is employed in MATLAB R2023a (9.14) to optimize key geometric parameters, including mount thicknesses and thrust link dimensions. The optimized configuration achieves a 16.84% increase in minimum frequency separation and a 21.51% reduction in vibration transmissibility. The results demonstrate the efficacy of combining modal analysis with evolutionary algorithms in designing advanced engine mounting systems for improved vibration isolation in next-generation aircraft. Full article

To address the imbalance between user evaluation noise and algorithmic autonomy in Interactive Evolutionary Design (IED), this study proposes an optimization method integrating subjective and objective weights to alleviate user fatigue and enhance evolutionary efficiency of Genetic Algorithms (GAs). Building upon the Interactive Genetic Algorithm (IGA), an AHP-CRITIC combined proxy model is introduced, leveraging the bidirectional complementarity of AHP and CRITIC to suppress evaluation noise. An interactive evolutionary design model integrating AHP-CRITIC-IGA is constructed, with armchair design as a case study. Compared to traditional IGA, the combined-weighting-based IGA shows faster convergence and more stable fitness trajectories under the same parameter settings. To avoid over-interpretation, evaluation-noise claims are tied to explicit variability metrics (e.g., fitness standard deviation and inter-rater disagreement) reported in the revised experimental section. The proposed method effectively balances the contradiction between human interaction and algorithmic autonomy in interactive evolutionary design. Furthermore, the framework is inherently generalizable and demonstrates significant potential for adaptation and electronic system design, where optimizing complex, multi-criteria problems under user feedback is paramount. This establishes its relevance to the broader field of intelligent and interactive engineering systems. Full article

In this work, three novel optimization algorithms—collectively referred to as the BxR algorithms—and their multi-objective versions, referred to as the MO-BxR algorithms, are applied to diverse heat transfer systems. Five representative case studies are presented: two single-objective problems involving a heat exchanger network and a jet-plate solar air heater; a two-objective optimization of Y-type fins in phase-change thermal energy storage units; and two three-objective problems involving TPMS–fin three-fluid heat exchangers and Tesla-valve evaporative cold plates for LiFePO₄ battery modules. The proposed algorithms are compared with leading evolutionary optimizers, including IUDE, εMAgES, iL-SHADEε, COLSHADE, and EnMODE, as well as NSGA-II, NSGA-III, and NSWOA. The results demonstrated improved convergence characteristics, better Pareto front diversity, and reduced computational burden. A decision-making framework is also incorporated to identify balanced, practically feasible, and engineering-preferred solutions from the Pareto sets. Overall, the results demonstrated that the BxR and MO-BxR algorithms are capable of effectively handling diverse thermal system designs and enhancing heat transfer performance. Full article

Our paper adopts a deep-roots approach to examining the Environmental Kuznets Curve (EKC) by tracing its origins beyond industrialization and into the dawn of human civilization. We hypothesize that the roots of environmental degradation lie not only in modern-day markets or technology, but in the evolutionary arc of societies themselves. Using a two-stage empirical framework applied to a sample of 130 countries, we show that early transitions into agriculture, technology adoption, and human settlement patterns shaped modern growth trajectories, which in turn influence environmental degradation in line with EKC dynamics. Our findings imply that climate change is not merely a policy failure but also a civilizational inheritance, and sustainable futures cannot be engineered solely through contemporary interventions. Therefore, climate policy must evolve from reactive governance to deep-time reengineering to realign humanity’s path with the planet’s limits, not just for today, but for millennia ahead. Full article

Buildings are under increasing pressure to address decarbonization and climate adaptation, which is pushing design practice from post hoc performance checks to performance-driven generative design (PDGD). This review maps the current state of PDGD in buildings and proposes an engineering-oriented framework that links research methods to deployable workflows. Using a PRISMA-based systematic search, we identify 153 core studies and code them along five dimensions: design objects and scales, objectives and metrics, algorithms and tools, workflows, and data and validation. The corpus shows a strong focus on facades, envelopes, and single-building massing, dominated by energy, daylight and thermal comfort objectives, and a widespread reliance on parametric platforms connected to performance simulation software with multi-objective optimization. From this evidence we extract three typical workflow routes: parametric evolutionary multi-objective optimization, surrogate or Bayesian optimization, and data- or model-driven generation. Persistent weaknesses include fragmented metric conventions, limited cross-case or field validation, and risks to reproducibility. In response, we propose a harmonized objective–metric system, an evidence pyramid for PDGD, and a reproducibility checklist with practical guidance, which together aim to make PDGD workflows more comparable, auditable, and transferable for design practice. Full article

Modern manufacturing robots must dynamically balance multiple conflicting objectives amid rapidly evolving production demands. Traditional control approaches lack the adaptability required for real-time decision-making in Industry 4.0 environments. This study presents an adaptive multi-objective reinforcement learning (MORL) framework integrating dynamic preference weighting with Pareto-optimal policy discovery for real-time adaptation without manual reconfiguration. Experimental validation employed a UR5 manipulator with RG2 gripper performing quality-aware object sorting in CoppeliaSim with realistic physics (friction μ = 0.4, Bullet engine), manipulating 12 objects across four geometric types on a dynamic conveyor. Thirty independent runs per algorithm (seven baselines, 30,000+ manipulation cycles) demonstrated +24.59% to +34.75% improvements (p < 0.001, d = 0.89–1.52), achieving hypervolume 0.076 ± 0.015 (19.7% coefficient of variation—lowest among all methods) and 95% optimal performance within 180 episodes—five times faster than evolutionary baselines. Four independent verification methods (WFG, PyMOO, Monte Carlo, HSO) confirmed measurement reliability (<0.26% variance). The framework maintains edge computing compatibility (<2 GB RAM, <50 ms latency) and seamless integration with Manufacturing Execution Systems and digital twins. This research establishes new benchmarks for adaptive robotic control in sustainable Industry 4.0/5.0 manufacturing. Full article