Search Results (988)

The heterogeneous automatic guided vehicle (AGV) system, composed of several AGVs with different load capability and handling function, has good flexibility and agility to operational requirements. Accurate task travel time prediction (T3P) is vital for the efficient operation of heterogeneous AGV systems. However, T3P remains a challenging problem due to individual task correlations and dynamic changes in model input/output dimensions. To address these challenges, a biomimetics-inspired learning framework based on a radial basis function (RBF) neural network with an improved mayfly algorithm and a selective update strategy (IMA-SURBF) is proposed. Firstly, a T3P model is constructed by using travel-influencing factors as input and task travel time as output of the RBF neural network, where the input/output dimension is determined dynamically. Secondly, the improved mayfly algorithm (IMA), a biomimetic metaheuristic method, is adopted to optimize the initial parameters of the RBF neural network, while a selective update strategy is designed for parameter updates. Finally, simulation experiments on model design, parameter initialization, and comparison with deep learning-based models are conducted in a complex assembly line scenario to validate the accuracy and efficiency of the proposed method. Full article

MXenes, a class of two-dimensional transition metal carbides and nitrides, emerged as a promising material for next-generation energy storage and corresponding applications due to their unique combination of high electrical conductivity, tunable surface chemistry, and lamellar structure. This review highlights recent advances in MXene-based composites, focusing on their integration into electrode architectures for the development of supercapacitors, batteries, and multifunctional devices, including triboelectric nanogenerators. It serves as a comprehensive overview of the multifunctional capabilities of MXene-based composites and their role in advancing efficient, flexible, and sustainable energy and sensing technologies, outlining how MXene-based systems are poised to redefine multifunctional energy platforms. Electrochemical performance optimization strategies are discussed by considering surface functionalization, interlayer engineering, scalable synthesis techniques, and integration with advanced electrolytes, with particular attention paid to the development of hybrid supercapacitors, triboelectric nanogenerators (TENGs), and wearable sensors. These applications are favored due to improved charge storage capability, mechanical properties, and the multifunctionality of MXenes. Despite these aspects, challenges related to long-term stability, sustainable large-scale production, and environmental degradation must still be addressed. Emerging approaches such as three-dimensional self-assembly and artificial intelligence-assisted design are identified as key challenges for overcoming these issues. Full article

This paper addresses text simplification task for Kazakh, a morphologically rich, low-resource language, by introducing KazSim, an instruction-tuned model built on multilingual large language models (LLMs). First, we develop a heuristic pipeline to identify complex Kazakh sentences, manually validating its performance on 400 examples and comparing it against a purely LLM-based selection method; we then use this pipeline to assemble a parallel corpus of 8709 complex–simple pairs via LLM augmentation. For the simplification task, we benchmark KazSim against standard Seq2Seq systems, domain-adapted Kazakh LLMs, and zero-shot instruction-following models. On an automatically constructed test set, KazSim (Llama-3.3-70B) achieves BLEU 33.50, SARI 56.38, and F1 87.56 with a length ratio of 0.98, outperforming all baselines. We also explore prompt language (English vs. Kazakh) and conduct human evaluation with three native speakers: KazSim scores 4.08 for fluency, 4.09 for meaning preservation, and 4.42 for simplicity—significantly above GPT-4o-mini. Error analysis shows that remaining failures cluster into tone change, tense change, and semantic drift, reflecting Kazakh’s agglutinative morphology and flexible syntax. Full article

Recombinant protein hydrogels have emerged as transformative biomaterials that overcome the bioinertness and unpredictable degradation of traditional synthetic systems by leveraging genetically engineered backbones, such as elastin-like polypeptides, SF, and resilin-like polypeptides, to replicate extracellular matrix (ECM) dynamics and enable programmable functionality. Constructed through a hierarchical crosslinking strategy, these hydrogels integrate reversible physical interactions with covalent crosslinking approaches, collectively endowing the system with mechanical strength, environmental responsiveness, and controlled degradation behavior. Critically, molecular engineering strategies serve as the cornerstone for functional precision: domain-directed self-assembly exploits coiled-coil or β-sheet motifs to orchestrate hierarchical organization, while modular fusion of bioactive motifs through genetic encoding or site-specific conjugation enables dynamic control over cellular interactions and therapeutic release. Such engineered designs underpin advanced applications, including immunomodulatory scaffolds for diabetic wound regeneration, tumor-microenvironment-responsive drug depots, and shear-thinning bioinks for vascularized bioprinting, by synergizing material properties with biological cues. By uniting synthetic biology with materials science, recombinant hydrogels deliver unprecedented flexibility in tuning physical and biological properties. This review synthesizes emerging crosslinking paradigms and molecular strategies, offering a framework for engineering next-generation, adaptive biomaterials poised to address complex challenges in regenerative medicine and beyond. Full article

Bacteriophages (phages), the most abundant biological entities on Earth, have long served as both model systems and therapeutic tools. Recent advances in synthetic biology and genetic engineering have revolutionized the capacity to tailor phages with enhanced functionality beyond their natural capabilities. This review outlines the current landscape of synthetic and functional engineering of phages, encompassing both in-vivo and in-vitro strategies. We describe in-vivo approaches such as phage recombineering systems, CRISPR-Cas-assisted editing, and bacterial retron-based methods, as well as synthetic assembly platforms including yeast-based artificial chromosomes, Gibson, Golden Gate, and iPac assemblies. In addition, we explore in-vitro rebooting using TXTL (transcription–translation) systems, which offer a flexible alternative to cell-based rebooting but are less effective for large genomes or structurally complex phages. Special focus is given to the design of customized phages for targeted applications, including host range expansion via receptor-binding protein modifications, delivery of antimicrobial proteins or CRISPR payloads, and the construction of biocontained, non-replicative capsid systems for safe clinical use. Through illustrative examples, we highlight how these technologies enable the transformation of phages into programmable bactericidal agents, precision diagnostic tools, and drug delivery vehicles. Together, these advances establish a powerful foundation for next-generation antimicrobial platforms and synthetic microbiology. Full article

Pilot production is a critical transitional phase in the process of new product development or manufacturing, aiming at ensuring that products are thoroughly validated and optimized before entering full-scale production. During this stage, a key challenge is how to leverage limited resources to build data infrastructure and conduct data analysis to establish and verify quality control. This paper presents the implementation of a cyber–physical system (CPS) for a lithium battery pilot assembly line. A machine learning-based predictive model was employed to establish quality control mechanisms. Process knowledge-guided data analysis was utilized to build a quality prediction model based on the collected battery data. The model-centric concept of ‘virtual quality’ enables early quality judgment during production, which allows for flexible quality control and the determination of optimal process parameters, thereby reducing production costs and minimizing energy consumption during manufacturing. Full article

Climate concerns are driving the automotive industry to adopt advanced manufacturing technologies that aim to improve energy efficiency and reduce vehicle weight. In this context, lightweight structural materials such as aluminium alloys have gained significant attention due to their favorable strength-to-weight ratio. Laser welding plays a crucial role in assembling such materials, offering high flexibility and fast joining capabilities for thin aluminium sheets. However, welding these materials presents specific challenges, particularly in controlling heat input to minimize distortions and ensure consistent weld quality. As a result, numerical simulations based on the Finite Element Method (FEM) are essential for predicting weld-induced phenomena and optimizing process performance. This study investigates welding-induced distortions in laser butt welding of 1.5 mm-thick Al 6061 samples through FEM simulations performed in the SYSWELD 2024.0 environment. The methodology provided by the software is based on the Moving Heat Source (MHS) model, which simulates the physical movement of the heat source and typically requires extensive calibration through destructive metallographic testing. This transient approach enables the detailed prediction of thermal, metallurgical, and mechanical behavior, but it is computationally demanding. To improve efficiency, the Imposed Thermal Cycle (ITC) model is often used. In this technique, a thermal cycle, extracted from an MHS simulation or experimental data, is imposed on predefined subregions of the model, allowing only mechanical behavior to be simulated while reducing computation time. To avoid MHS-based calibration, this work proposes using thermal cycles acquired in-line during welding via infrared thermography as direct input for the ITC model. The method was validated experimentally and numerically, showing good agreement in the prediction of distortions and a significant reduction in workflow time. The distortion values from simulations differ from the real experiment by less than 0.3%. Our method exhibits a slight decrease in performance, resulting in an increase in estimation error of 0.03% compared to classic approaches, but more than 85% saving in computation time. The integration of real process data into the simulation enables a virtual representation of the process, supporting future developments toward Digital Twin applications. Full article

During the process of flexible aerial refueling, the flexible structure of the hose drogue assembly is affected by internal and external interference, such as docking maneuvering, deformation of the hose, attitude changes, and body vibrations, causing the hose to swing and the whipping phenomenon, which greatly limits the success rate and safety of aerial refueling operations. Based on a 2.4 m transonic wind tunnel, high-speed wind tunnel test technology of a flexible aerial refueling hose–drogue system was established to carry out experimental research on the coupling characteristics of aerodynamics and multi-body dynamics. Based on the aid of Videogrammetry Model Deformation (VMD), high-speed photography, dynamic balance, and other wind tunnel test technologies, the dynamic characteristics of the hose–drogue system in a high-speed airflow and during the approach of the receiver are obtained. Adopting flexible multi-body dynamics, a dynamic system of the tanker, hose, drogue, and receiver is modeled. The cable/beam model is based on an arbitrary Lagrange–Euler method, and the absolute node coordinate method is used to describe the deformation, movement, and length variation in the hose during both winding and unwinding. The aerodynamic forces of the tanker, receiver, hose, and drogue are modeled, reflecting the coupling influence of movement of the tanker and receiver, the deformation of the hose and drogue, and the aerodynamic forces on each other. The tests show that during the approach of the receiver (distance from 1000 mm to 20 mm), the sinking amount of the drogue increases by 31 mm; due to the offset of the receiver probe, the drogue moves sideways from the symmetric plane of the receiver. Meanwhile, the oscillation magnitude of the drogue increases (from 33 to 48 and from 48 to 80 in spanwise and longitudinal directions, respectively). The simulation results show that the shear force induced by the oscillation of the hose and the propagation velocity of both the longitudinal and shear waves are affected by the hose stiffness and Mach number. The results presented in this work can be of great reference to further increase the safety of aerial refueling. Full article

Janus hydrogels, defined by their asymmetric architectures and bifunctional interfaces, have emerged as a transformative class of solid-state electrolytes in electrochemical energy storage. By integrating spatially distinct chemomechanical and ionic functionalities within a single matrix, they overcome the intrinsic limitations of conventional isotropic hydrogels, offering enhanced interfacial stability, directional ion transport, and dendrite suppression in lithium- and zinc-based batteries. This mini-review systematically highlights recent breakthroughs in Janus hydrogel design, including interfacial polymerization and layer-by-layer assembly, which collectively enable precise modulation of crosslinking gradients and ion transport pathways. This review uniquely frames Janus hydrogels from a battery-centric and interface-engineering perspective. It elucidates key structure–function correlations, identifies current limitations in scalable fabrication and electrochemical longevity, and outlines future directions toward intelligent, multifunctional platforms for next-generation flexible and biointegrated energy systems. Full article

Study design: The advent of the Matrix Wave^TM System (Depuy-Synthes)—a bone-anchored Mandibulo-Maxillary Fixation (MMF) System—merits closer consideration because of its peculiarities. Objective: This study alludes to two preliminary stages in the evolution of the Matrix Wave^TM MMF System and details its technical and functional features. Results: The Matrix Wave^TM System (MWS) is characterized by a smoothed square-shaped Titanium rod profile with a flexible undulating geometry distinct from the flat plate framework in Erich arch bars. Single MWS segments are Omega-shaped and carry a tie-up cleat for interarch linkage to the opposite jaw. The ends at the throughs of each MWS segment are equipped with threaded screw holes to receive locking screws for attachment to underlying mandibular or maxillary bone. An MWS can be partitioned into segments of various length from single Omega-shaped elements over incremental chains of interconnected units up to a horseshoe-shaped bracing of the dental arches. The sinus wave design of each segment allows for stretch, compression and torque movements. So, the entire MWS device can conform to distinctive spatial anatomic relationships. Displaced fragments can be reduced by in-situ-bending of the screw-fixated MWS/Omega segments to obtain accurate realignment of the jaw fragments for the best possible occlusion. Conclusion: The Matrix Wave^TM MMF System is an easy-to-apply modular MMF system that can be assembled according to individual demands. Its versatility allows to address most facial fracture scenarios in adults. The option of “omnidirectional” in-situ-bending provides a distinctive feature not found in alternate MMF solutions. Full article

Process control and quality assurance of wire + arc additive manufacturing (WAAM) and automated welding rely heavily on in-process monitoring videos to quantify variables such as melt pool geometry, location and size of droplet transfer, arc characteristics, etc. To enable feedback control based upon this information, an automatic and robust segmentation method for monitoring of videos and images is required. However, video segmentation in WAAM and welding is challenging due to constantly fluctuating arc brightness, which varies with deposition and welding configurations. Additionally, conventional computer vision algorithms based on greyscale value and gradient lack flexibility and robustness in this scenario. Deep learning offers a promising approach to WAAM video segmentation; however, the prohibitive time and cost associated with creating a well-labelled, suitably sized dataset have hindered its widespread adoption. The emergence of large computer vision models, however, has provided new solutions. In this study a semi-automatic annotation tool for WAAM videos was developed based upon the computer vision foundation model SAM and the video object tracking model XMem. The tool can enable annotation of the video frames hundreds of times faster than traditional manual annotation methods, thus making it possible to achieve rapid quantitative analysis of WAAM and welding videos with minimal user intervention. To demonstrate the effectiveness of the tool, three cases are demonstrated: online wire position closed-loop control, droplet transfer behaviour analysis, and assembling a dataset for dedicated deep learning segmentation models. This work provides a broader perspective on how to exploit large models in WAAM and weld deposits. Full article

The research on ultra-thin inorganic chips is an important field in the development of inorganic flexible electronics. By thinning the inorganic (mainly silicon-based) chip to less than 50 μm, it will gain a certain degree of flexibility. After the ultra-thin chip is integrated into the flexible substrate, it is bent repeatedly during the operation of the system. When the bending angle is excessively large, the chip and substrate will debond, or the chip will break. In this process, whether the chip can be stably adhered to the substrate depends on many factors, and debonding can only be reduced by continuously adjusting the process parameters. From an energy method perspective, this study divides the bending process of flexible silicon-based chips and flexible films into two states: debonding and non-debonding. A debonding mechanical model of flexible chips is established, and the regulatory relationship between the adhesion coefficient between the chip and film, chip geometric size, and material parameters was established. Experiments were also conducted to verify the relevant theoretical results. The theoretical results show that the risk of chip debonding decreases with a reduction in chip thickness, an increase in interface adhesion, and an increase in bending radius. Improving the interface adhesion during the bending process can effectively stabilize the adhesion of flexible chips. This paper provides a theoretical basis for the integration and bending of ultra-thin flexible chips and flexible substrates, promoting the practical assembly and application of ultra-thin chips. Full article

This study presents a reinforcement learning–based approach to optimize replenishment policies in the presence of uncertainty, with the objective of minimizing total costs, including inventory holding, shortage, and ordering costs. The focus is on single-level assembly systems, where both component delivery lead times and finished product demand are subject to randomness. The problem is formulated as a Markov decision process (MDP), in which an agent determines optimal order quantities for each component by accounting for stochastic lead times and demand variability. The Deep Q-Network (DQN) algorithm is adapted and employed to learn optimal replenishment policies over a fixed planning horizon. To enhance learning performance, we develop a tailored simulation environment that captures multi-component interactions, random lead times, and variable demand, along with a modular and realistic cost structure. The environment enables dynamic state transitions, lead time sampling, and flexible order reception modeling, providing a high-fidelity training ground for the agent. To further improve convergence and policy quality, we incorporate local search mechanisms and multiple action space discretizations per component. Simulation results show that the proposed method converges to stable ordering policies after approximately 100 episodes. The agent achieves an average service level of 96.93%, and stockout events are reduced by over 100% relative to early training phases. The system maintains component inventories within operationally feasible ranges, and cost components—holding, shortage, and ordering—are consistently minimized across 500 training episodes. These findings highlight the potential of deep reinforcement learning as a data-driven and adaptive approach to inventory management in complex and uncertain supply chains. Full article

In advanced structural applications—aerospace and automotive—fiber-laminated composite (FRP) materials are increasingly used for their superior strength-to-weight ratios, making the reliability of their mechanical joints a critical concern. Mechanically fastened joints play a major role in ensuring the structural stability of FRP Composite structures; however, accurately predicting their failure behavior remains a major challenge due to the anisotropic and heterogeneous nature of composite materials. This paper presents a progressive damage modeling approach to investigate the failure modes and joint strength of mechanically fastened carbon fiber-laminated (CFRP) composite joints. A 3D constitutive model based on continuum damage mechanics was developed and implemented within a three-dimensional finite element framework. The joint model comprises a composite plate, a steel plate, a steel washer, and steel bolts, capturing realistic assembly behavior. Both single- and double-lap joint configurations, featuring single and double bolts, were analyzed under tensile loading. The influence of clamping force on joint strength was also investigated. Model predictions were validated against existing experimental results, showing a good correlation. It was observed that double-lap joints exhibit nearly twice the strength of single-lap joints and can retain up to 85% of the strength of a plate with a hole. Furthermore, double-lap configurations support higher clamping forces, enhancing frictional resistance at the interface and load transfer efficiency. However, the clamping force must be optimized, as excessive values can induce premature damage in the composite before external loading. The stiffness of double-bolt double-lap (3DD) joints was found to be approximately three times that of single-bolt single-lap (3DS) joints, primarily due to reduced rotational flexibility. These findings provide useful insights into the design and optimization of composite bolted joints under tensile loading. Full article

Hydrogel sensors show unique advantages in underwater detection, ocean monitoring, and human–computer interaction because of their excellent flexibility, biocompatibility, high sensitivity, and environmental adaptability. However, due to the water environment, hydrogels will dissolve to a certain extent, resulting in insufficient mechanical strength, poor long-term stability, and signal interference. In this paper, a double-network structure was constructed by polyvinyl alcohol (PVA) and poly([2-(methacryloyloxy) ethyl]7 dimethyl-(3-sulfopropyl) ammonium hydroxide) (PSBMA). The resultant PVA/PSBMA-PA hydrogel demonstrated notable swelling resistance, a property attributable to the incorporation of non-covalent interactions (electrostatic interactions and hydrogen bonding) through the addition of phytic acid (PA). The hydrogel exhibited high stretchability (maximum tensile strength up to 304 kPa), high conductivity (5.8 mS/cm), and anti-swelling (only 1.8% swelling occurred after 14 days of immersion in artificial seawater). Assembled as a sensor, it exhibited high strain sensitivity (0.77), a low detection limit (1%), and stable electrical properties after multiple tensile cycles. The utilization of PVA/PSBMA-PA hydrogel as a wearable sensor shows promise for detecting human joint movements, including those of the fingers, wrists, elbows, and knees. Due to the excellent resistance to swelling, the PVA/PSBMA-PA-based sensors are also suitable for underwater applications, enabling the detection of underwater mannequin motion. This study proposes an uncomplicated and pragmatic methodology for producing hydrogel sensors suitable for use within subaquatic environments, thereby concomitantly broadening the scope of applications for wearable electronic devices. Full article