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.
BackgroundEffective 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.ObjectiveThis 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.MethodsThe 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.ResultsLight 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.ConclusionThe 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.
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.
Wide belt sowing is an innovative method to improve production. In order to ensure uniform for wheat wide belt sowing, this paper proposed a conical seed separator plate and optimized its critical structural parameters. Firstly, a mathematical model of conical seed separator plate was established, with cone radius, cone angle and installation angle as variables. Secondly, a simulation model based discrete element method (DEM) was developed to evaluate wheat sowing uniformity using lateral variation coefficient (LVC), and single factor simulation experiment with three variables were conducted to determine the optimal range of structural parameters. Finally, optimal structural parameters were confirmed through orthogonal simulation experiment, and validated through indoor and field experiments. In field experiments, the emergence rate was monitored at different sowing depths after the wheat was sown. The results of single factor experiment showed that the optimal LVC was obtained when cone radius ranged from 70 to 80 mm, cone angle ranged from 40° to 60° and installation angle ranged from 30° to 40°. The results of orthogonal simulation experiment showed that the primary and secondary order of the influence of each factor on LVC was installation angle, cone angle and cone radius. The corresponding parameter values after optimization were 74.82 mm for cone radius, 49.61° for cone angle and 34.18° for installation angle, with a regression predicted LVC of 12.36%. To validate the optimized parameters, simulation validation, indoor experiment and field experiments by self-made field in situ multifunctional test bench with no-till wide belt sowing and rotary tillage wide belt sowing were conducted, obtaining LVC of 12.28%, 14.73%, 16.28% and 16.89%, respectively. For emergence rate, the sowing depth of 4 cm was highest. Rotary tillage promoted emergence than no-till. These findings can provide guidance for optimizing the structural parameters of seed separator plate in wide belt seeders.
ObjectiveThis study develops an adaptive multi-agent traffic-management framework that responds to real-time urban traffic conditions using Internet of Vehicles (IoV) traffic-state information and artificial intelligence. Remote-sensing information is discussed as a contextual extension of the broader framework rather than as an experimentally validated input in the present evaluation.MethodsA Sustainable Multi-Agent Traffic Management Strategy (SMATMS) is implemented using a Galactic Swarm Optimized Deep Q-Network (GSO-DQN). The empirical evaluation uses traffic-state variables available in the Urban Traffic Light Control Dataset and considers two- and six-intersection multi-agent reinforcement-learning scenarios. GSO is positioned as an outer optimization layer that supports exploration of learning/control parameter configurations, while DQN performs sequential signal-control decisions.ResultsThe reported aggregate comparisons show favorable observed mean performance for GSO-DQN relative to MADDPG and MARDDPG in reward, delay, queue length, travel time, and pedestrian waiting across the two evaluated scenarios. Because the archived results are aggregate summaries rather than a repeated-seed inferential dataset, the revised manuscript treats these differences descriptively and does not claim formal statistical significance.ConclusionThe framework provides an adaptive AI-based approach for coordinated traffic-signal control and supports traffic-efficiency-related sustainability objectives in the evaluated multi-intersection settings. Claims concerning metropolitan-scale scalability, direct emission reduction, and operational remote-sensing fusion are explicitly limited and identified as priorities for future validation.
