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This paper investigates the failure mechanism of the rear drive shaft in a modified pickup truck which had operated for about 3000 km. The investigation included macroscopic and microscopic evaluation to document the morphologies of the fracture surface, measurement of the material composition, metallographic preparation and examination, mechanical testing, and finite element modelling and calculations. The results obtained suggest that rotation-bending fatigue was the primary cause of the drive shaft failure. The crack initiation is located in the root of the machined threads on the drive shaft surface and expanded along the side of the machining line surface. The main cause of fatigue cracks is attributable to a high stress concentration owing to a large unilateral bending impact under overload. Meanwhile, the bidirectional torsional force also produces a higher stress concentration and thus accelerates the fatigue crack to expand radially along the surface. Furthermore, the hardness of the central section of the drive shaft was marginally below standard. This deficiency results in harm to the bearings and other mechanical components, as well as expediting the enlargement of cracks. Finite element analysis revealed significant contact stress between the bearing and drive shaft, with stress levels exceeding the fatigue limit stress of the parent material. This highlights the need for reevaluation of the heat treatment process and vehicle loading quality to enhance the drive shaft's longevity. [ABSTRACT FROM AUTHOR]

Achieving precise stopping of electric locomotives is crucial for the realization of intelligent and unmanned auxiliary transportation systems. Presently, human drivers play a central role in ensuring accurate stopping, presenting obstacles to automation and cargo location precision, especially within the coal mining sector. This article centers on achieving the precise stopping of electric locomotives under various conditions through the utilization of permanent magnet synchronous motor-driven locomotives. This approach introduces a novel stopping control method that integrates a fuzzy proportional–integral–derivative (F-PID) controller with a vector control model for permanent magnet synchronous motors (PMSM). Subsequently, we develop the F-PID controller using the PMSM technique, incorporating new fuzzy rules for each subsystem to enhance control accuracy and efficiency. Finally, extensive simulations and real-world experiments are conducted on an electric locomotive stopping test bed to validate the effectiveness of the proposed control method. The results show that the method consistently achieves precise stopping under diverse working conditions, with an error of less than 0.3 m, confirming its robustness and reliability. [ABSTRACT FROM AUTHOR]

Currently, the main solution for braking systems for underground electric trackless rubber-tired vehicles (UETRVs) is traditional hydraulic braking systems, which have the disadvantages of hydraulic pressure crawling, the risk of oil leakage and a high maintenance cost. An electro-mechanical-braking (EMB) system, as a type of novel brake-by-wire (BBW) system, can eliminate the above shortcomings and play a significant role in enhancing the intelligence level of the braking system in order to meet the motion control requirements of unmanned UETRVs. Among these requirements, the accurate control of clamping force is a key technology in controlling performance and the practical implementation of EMB systems. In order to achieve an adaptive clamping force control performance of an EMB system, an optimized fuzzy proportional-integral-differential (PID) controller is proposed, where the improved fuzzy algorithm is utilized to adaptively adjust the gain parameters of classic PID. In order to compensate for the deficiency of single-close-loop control and adjusting the brake gap automatically, a cascaded three-closed-loop control architecture with force/position switch technology is established, where a contact point detection method utilizing motor rotor angle displacement is proposed via experiments. The results of the simulation and experiments indicate that the clamping force response of the proposed multi-close-loop Variable Universe Fuzzy–PID (VUF-PID) controller is faster than the multi-closed-loop Fuzzy–PID and cascaded three-close-loop PID controllers. In addition, the chattering of braking force can be suppressed by 17%. This EMB system may rapidly and automatically finish the operation of the overall braking process, including gap elimination, clamping force tracking and gap recovery, which can obviously enhance the precision of the longitudinal motion control of UETRVs. It can thus serve as a BBW actuator of mine autonomous driving electric vehicles, especially in the stage of braking control. [ABSTRACT FROM AUTHOR]

Photodynamic therapy (PDT) shows promise in tumor treatment, particularly when combined with nanotechnology. This study examines the impact of deep learning, particularly the Cellpose algorithm, on the comprehension of cancer cell responses to PDT. The Cellpose algorithm enables robust morphological analysis of cancer cells, while logistic growth modelling predicts cellular behavior post-PDT. Rigorous model validation ensures the accuracy of the findings. Cellpose demonstrates significant morphological changes after PDT, affecting cellular proliferation and survival. The reliability of the findings is confirmed by model validation. This deep learning tool enhances our understanding of cancer cell dynamics after PDT. Advanced analytical techniques, such as morphological analysis and growth modeling, provide insights into the effects of PDT on hepatocellular carcinoma (HCC) cells, which could potentially improve cancer treatment efficacy. In summary, the research examines the role of deep learning in optimizing PDT parameters to personalize oncology treatment and improve efficacy. [ABSTRACT FROM AUTHOR]

It is urgent for automated electric transportation vehicles in coal mines to have the ability of self-adaptive tracking target constant deceleration to ensure stable and safe braking effects in long underground roadways. However, the current braking control system of underground electric trackless rubber-tired vehicles (UETRVs) still adopts multi-level constant braking torque control, which cannot achieve target deceleration closed-loop control. To overcome the disadvantages of lower safety and comfort, and the non-precise stopping distance, this article describes the architecture and working principle of constant deceleration braking systems with an electro-mechanical braking actuator. Then, a deceleration closed-loop control algorithm based on fuzzy neural network PID is proposed and simulated in Matlab/Simulink. Finally, an actual brake control unit (BCU) is built and tested in a real industrial field setting. The test illustrates the feasibility of this constant deceleration control algorithm, which can achieve constant decelerations within a very short time and maintain a constant value of − 2.5   m / s 2 within a deviation of ± 0.1   m / s 2 , compared with the deviation of 0.11   m / s 2 of fuzzy PID and the deviation of 0.13   m / s 2 of classic PID. This BCU can provide electric and automated mine vehicles with active and smooth deceleration performance, which improves the level of electrification and automation for mine transport machinery. [ABSTRACT FROM AUTHOR]

Continental shale in China is a key exploration target with regard to unconventional hydrocarbons. Systematic research on the mineral composition, organic geochemistry, and hydrocarbon mobility has been performed in the Da'anzhai (J1dn) lacustrine shale oil reservoirs, clarifying the factors controlling shale oil accumulation. The results suggest that J1dn consists of three sub-members, with an organic-rich interval developed within the second sub-member. Three types of lithological associations have developed within the organic-rich interval: Type 1 represents the interbedding relationship between shale and shell limestone and Type 2 represents shale with intercalated limestone, followed by Type 3. The brittleness index of the J1dn continental organic-rich shale is relatively low. The porosity of the Da'anzhai lacustrine shale ranges between 0.5% and 10.5% (average value of 5.89%). The porosity is predominantly due to inorganic pores, with a small amount being attributable to nanoscale microfractures and organic pores. The average porosity of the shell limestone is only 1.2%, but fractures at the micron and centimeter scales are well developed. The organic matter in the J1dn continental shale is mainly II1–II2, with maturity in the oil generation stage. The average oil saturation of the J1dn continental shale is 3.15%, with most samples having oil saturation of less than 4%. The J1dn continental shale has great exploration potential with regard to shale oil. Type 1 shale oil is affected by multiscale fractures, including bedding fractures, and has the best mobility. The high hydrocarbon generation capacity of lacustrine shale, coupled with the multiscale fractures within shell limestone and shale, is the principal controlling factor for hydrocarbon enrichment. Based on exploration practices, the Type 1 shale association may represent the optimal interval for future shale oil exploration in the Da'anzhai Member. [ABSTRACT FROM AUTHOR]