Surgical suturing involves a sequence of tribological contacts that begins with needle penetration, continues through suture pull-through, and culminates in knot tightening and locking. Uncontrolled friction at these interfaces can cause tissue trauma, coating delamination, or knot slippage. This narrative review synthesises the biomechanics and tribology of the needle–tissue, suture–tissue, and suture–suture interfaces. PubMed, Scopus and Web of Science were searched for studies published from January 2000 to July 2026, and studies were selected for mechanical relevance and synthesised by tribological interface. Needle insertion force is a combined response of tissue deformation, cutting and shaft friction, with attention to needle geometry, trajectory, velocity, tissue properties and force sensing. Suture drag is examined in relation to material, diameter, surface roughness, monofilament or multifilament construction, hydration, loading and pulling speed. Surface-engineering approaches, including lubricants, silicone and polymeric treatments, antimicrobial coatings, dopamine-, chitosan- and graphene-based modifications, and tissue-integrating sutures, are evaluated for friction reduction, durability, handling and knot performance. The review also discusses the transition from low resistance during passage to sufficient suture–suture friction after tightening, together with experimental apparatus, biological tissue models, finite-element methods and robotic force feedback. Evidence shows that friction is stage-dependent and should be controlled rather than uniformly minimised. Future progress will depend on standardised wet-tissue testing, clearer separation of force components, repeated-passage evaluation of coatings, tissue-specific needle–suture selection, and stronger links between mechanical measurements, tissue response and adaptive robotic control.
Effective thermal control is essential for lithium-ion batteries used in electric vehicles and energy storage systems, as uncontrolled heat generation accelerates degradation, reduces performance, and increases safety risk. Conventional temperature-prediction studies often report high accuracy using random data splitting; however, such evaluation may not adequately represent performance on unseen battery cells. This study aims to develop an accurate, stable, and interpretable machine-learning framework for predicting lithium-ion battery temperature during discharge cycles under unseen-battery conditions. The NASA lithium-ion battery ageing dataset was used to train and evaluate Linear Regression, Random Forest, and Light Gradient Boosting Machine models. Five input features were considered: measured voltage, measured current, power, cumulative discharged energy, and voltage drop, with measured temperature as the target. Battery-wise validation was performed using cells B0005, B0006, B0007, and B0018, where each battery was used once as an independent test set. Model performance was assessed using root mean square error, mean absolute error, and coefficient of determination. Uncertainty analysis, residual diagnostics, and SHapley Additive exPlanations were used to examine prediction stability, error behaviour, and feature influence. Light Gradient Boosting Machine achieved the strongest overall generalization, with a mean root mean square error of 1.070 °C and a mean coefficient of determination of 0.910. Random Forest showed closely comparable performance and achieved the lowest mean absolute error of 0.893 °C, while Linear Regression showed weaker predictive performance. SHAP analysis identified power, cumulative discharged energy, and measured voltage as the dominant predictors. The proposed framework provides a reliable and interpretable approach for battery temperature prediction and supports data-driven thermal monitoring in battery management systems. Future work should validate the model using broader datasets, real-time sensor data, ageing indicators, and physics-informed learning under dynamic operating conditions.
Oxana Nurzhanova, Irina Khrustaleva, Vyacheslav Shkodyrev, Olga Zharkevich, Adilkhan Bakenov, Karshiga Zakirov, Artur Safaryan
Optimizing equipment and transport utilization is a critical task in process planning. Traditional approaches are fragmented and lack a comprehensive hierarchy, leading to downtime and reduced efficiency. The disruption of interfunctional links requires the creation of a unified asset planning system across all operational and calendar horizons. A multi-criteria vector optimization model for production assets with a rigid hierarchy is proposed. It is based on the decomposition of five time levels (year, quarter, month, day, shift), where the results of the upper level constrain the lower ones. The model was tested in a machine-building shop for 2026 (two-shift operation, 83 product groups, 450 operations). The operation of 17 machines (turning, milling, boring, grinding) and three electric pallet trucks was optimized. Redistributing batches based on Pareto-optimal solutions increased annual equipment efficiency by 15.11% and reduced load variability on milling machines (by 6.78%) and grinding machines (by 8.65%). Vehicle capacity utilization increased by 37.06%, while downtime decreased by 28.86%. Quarterly, equipment load variability and downtime decreased by 34.68% and 54.24%, respectively, and at the shift level, by 36.59% and 14.28% (for vehicles). Overall, the overall system load variability decreased by 15.9%, and total downtime by 19.31%. The results confirm the practical value of the end-to-end hierarchical model. Its modular architecture ensures scalability and adaptability to failures or inventory changes. The model eliminates the gaps between production, logistics, and warehousing, reducing production cycle times, work-in-process inventory, and order delays.
Electric-vehicle battery enclosures must reduce structural mass while preserving deformation clearance, sealing integrity, electrical isolation, and post-impact safety. These requirements are difficult under bottom strike and side-pole collision because both load cases create localized intrusion near cells and modules, where only limited deformation space is available. This Mini Review synthesizes recent lightweight enclosure research through a scenario- and mechanism-based protection framework, rather than a conventional material-by-material catalogue, with specific emphasis on localized bottom strike and side-pole collision. The review links external impact loads to enclosure response, cell-level safety indicators, and post-crash thermal or electrical functions. Aluminum alloys, composite and hybrid structures, foam-filled members, sandwich panels, honeycomb cores, auxetic lattices, and bio-inspired absorbers are compared in terms of intrusion control, energy absorption, manufacturability, and coupled mechanical-thermal performance. The synthesis indicates that no single material family solves the design problem alone. Future enclosure design should combine local puncture resistance, progressive crush, load spreading, cell deformation limits, thermal propagation mitigation, repairability, and standards-based validation. Therefore, battery-enclosure lightweighting should be assessed through a staged protection chain: external localized loading, enclosure deformation, cell-level abuse indicators, and post-impact thermal/electrical safety.
Gear surface defects vary in scale and often exhibit subtle local features that are obscured by complex metallic backgrounds. However, achieving high detection accuracy and robustness with computationally lightweight models remains challenging in complex industrial inspection settings. We propose GSDD-Net, a lightweight framework based on the YOLO architecture for detecting gear surface defects. The proposed framework incorporates four task-oriented components: (1) ADown combines average and max pooling for downsampling to reduce model complexity while preserving the edge and texture details of subtle defects. (2) CSP-EDLAN is integrated into the backbone and neck networks. It uses cross-stage partial connections, multi-gradient path aggregation, and DualConv units to improve gradient propagation, cross-channel interactions, and multiscale feature fusion. (3) C2TSSA uses token statistical self-attention to capture global contextual dependencies with linear computational complexity, improving the representation of subtle defects against complex backgrounds. (4) LQEHead uses localization quality estimation to extract stable localization features from predicted bounding-box distributions and classification confidence scores. GSDD-Net achieves an mAP50 of 73.0% and an mAP50–95 of 38.0% on the real-world GID dataset. On a public gear defect dataset, it achieves 97.8% and 69.6%, respectively. Compared with the baseline model, GSDD-Net increases mAP50 and mAP50–95 on the GID dataset by 3.1 and 2.1 percentage points, respectively. GSDD-Net reduces the parameter count from 2.6 million to 1.9 million and the computational cost from 6.3 to 5.9 GFLOPs. It also achieves an inference speed of 105.7 FPS. These results indicate that GSDD-Net balances detection accuracy and computational efficiency under the evaluated conditions, supporting its potential for industrial gear surface defect detection.
