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Predicting and extending the remaining life of cutting tools during machining processes is crucial for sustainable manufacturing. Traditional prognosis methods often struggle to adapt to diverse working conditions throughout the machining process lifecycle. This paper introduced a comprehensive framework that effectively addressed the challenges by integrating multi-source data and using deep learning techniques. The system integrated power and vibration data collected from LGMazak VTC-16A and IRON MAN QM200 machines with the following innovations: (1) A standardized data fusion method was developed to integrate multi-source data sources, the hybrid graph convolutional network (GCN) with attention mechanisms was employed to improve the prognosis accuracy of cutting tool remaining life, best accuracy of 98.56% and average accuracy of 97.71% were achieved. (2) The optimization of laser shock peening (LSP) remanufacturing parameters using the bees algorithm showed good performance, a fitness value of 0.95 was achieved with convergence within 15 iterations. (3) Monitoring of the LSP remanufacturing process was designed based on sound and vibration data for optimal remanufacturing performance. (4) The remanufacturing approach in extending the remaining life of cutting tool was validated through FEA analysis and experimental testing, cutting tool life was extended by 29.32% to achieve a sustainable manufacturing process. • Hybrid GCN processed multi-source data for tool life prediction. • Bees algorithm optimized LSP parameters to enhance remanufacturing process. • Continuous monitoring with sound and vibration enhanced optimal LSP performance. • FEA analysis and experiments validated significant improvements in tool life. [ABSTRACT FROM AUTHOR]

The fire risk of historical districts is a huge challenge faced by all countries in the world when protecting historical and cultural heritage. As the historic districts are transformed into commercial districts nowadays, increasing tourists and businessmen start to visit and move into these districts, it is more urgent to deal with the fire risks with the growing demand of fire and electricity. Accurate assessment of the fire resilience and targeted implementation of resilience improvement actions are considered to be an effective way to solve this problem. However, the existing research has not fully considered the complex effects of space structure, tourists' behavior and the interaction between them on fire resilience. Therefore, this study constructed a fire resilience assessment framework combining space structure and tourists' behavior, and considering the spatial-behavior interaction. The results revealed that the space structure of commercial historical districts and the behavior of tourists show different characteristics and regulars in our study area, such as the categories of shops using open fires, the location of open fires and the periodicity of tourists ' behavior over time. By taking an experimental verification in Ciqikou Historic district, Chongqing, China, this assessment framework showed a flexible scale of application with strong operability and versatility, which also evidenced that space structure and tourists' behavior must be taken into account simultaneously to improve the fire resilience of historical districts. At last, we proposed targeted strategies to improve the fire resilience from the aspects of space structure and tourists' behavior, respectively. [ABSTRACT FROM AUTHOR]

• A deep neural network enhanced by transfer learning is designed for joint failure load prediction. • A fruit fly optimisation algorithm is developed to achieve the best joint parameters. • Case studies are conducted to demonstrate the effectiveness of the approach. Adhesively bonded joints have been extensively employed in the aeronautical and automotive industries to join thin-layer materials for developing lightweight components. To strengthen the structural integrity of joints, it is critical to estimate and improve joint failure loads effectually. To accomplish the aforementioned purpose, this paper presents a novel deep neural network (DNN) model-enabled approach, and a single lap joint (SLJ) design is used to support research development and validation. The approach is innovative in the following aspects: (i) the DNN model is reinforced with a transfer learning (TL) mechanism to realise an adaptive prediction on a new SLJ design, and the requirement to re-create new training samples and re-train the DNN model from scratch for the design can be alleviated; (ii) a fruit fly optimisation (FFO) algorithm featured with the parallel computing capability is incorporated into the approach to efficiently optimise joint parameters based on joint failure load predictions. Case studies were developed to validate the effectiveness of the approach. Experimental results demonstrate that, with this approach, the number of datasets and the computational time required to re-train the DNN model for a new SLJ design were significantly reduced by 92.00% and 99.57% respectively, and the joint failure load was substantially increased by 9.96%. [ABSTRACT FROM AUTHOR]

Hydroxamic acid derivatives are potential histone deacetylase inhibitors, and several hydroxamic acid-based histone deacetylase inhibitors have already been used clinically as potent anticancer agents, so hydroxamic acid derivatives are useful scaffolds for the development of novel anticancer agents. Hybridization of hydroxamic acid moiety with other anticancer pharmacophores can overcome drug resistance and improve the specificity, so rational design of hydroxamic acid hybrids may provide valuable therapeutic interventions for the treatment of cancers. The purpose of the present review article is to update the current developments in hydroxamic acid hybrids with an emphasis on anticancer activity, structure-activity relationships, and mechanisms of action. This review covers the recent advances of hydroxamic acid hybrids with potential anticancer activity covering articles published between 2015 and 2020. Image 1 • The recent advances of hydroxamic acid hybrids with potential anticancer activity were summarized. • These hybrids were potential dual-/multi-targeted inhibitors. • These hybrids exhibited significant in vitro and in vivo potency. • The SAR was discussed. [ABSTRACT FROM AUTHOR]

Robot-assisted screw removal can greatly facilitate the disassembly and remanufacturing automation of end-of-life products to realise circular economies. However, it is challenging to determine the exact positions of disassembling screws in practical remanufacturing environments. To tackle the issue, in this research, a novel approach designed based on reinforcement deep learning (DRL)-based optimisation processes is herein presented. First, to identify the search directions for the exact positions of disassembling screws, an analytical model was established to quantify geometrical relationships between a robotic screwdriver and the screws for removal. Secondly, a Markov model was built to represent key parameters related to robot configuration and the evolving relationships in the analytical model. Furthermore, a proximal policy optimisation (PPO) algorithm, which is a high-performing DRL algorithm, was developed to determine the optimal values of key parameters in the analytical model and the Markov model. Finally, experiments were conducted to identify optimal parameters by applying this approach and to benchmark its effectiveness. Experimental outcomes demonstrated that high success rates in screw positioning were achieved using this approach, and it outperformed the comparative approaches/optimisation algorithms in terms of screw positioning accuracy and efficiency. • A robot-based unscrewing approach was designed using deep reinforcement learning. • Proximal policy optimisation was used to determine the optimal values in the approach. • Experiments demonstrated high success rates in screw positioning were achieved. [ABSTRACT FROM AUTHOR]

Laser shock peening, an advanced technology for severe surface plasticity peening, encounters challenges such as shallow hardened layers and surface spalling when dealing with difficult-to-machine materials. In this study, we introduced a high-frequency electropulsing-assisted laser shock peening (HFEP-LSP) technique that coupled laser shock peening with high-frequency electric pulses to achieve a significant and deeper plastic deformation layer. In the HFEP-LSP technique, we first considered the dual "skin effect", which coupled the skin effect of high-frequency electric pulses with the "skin effect" of the mechanical effect induced by the laser shock wave. An integrated experimental platform comprising an electric pulse generator, laser shock peening equipment, and a control system was built. A >1.6 mm deep compressive residual stress layer was obtained, and the depth of the plastic deformation layer increased by 83.3 %. Furthermore, we elucidated the dual "skin effect"-induced complex heterostructure and β m phase transition. A comprehensive analysis revealed the factors contributing to the deeper strengthening layer induced by HFEP-LSP, including the compressive residual stress and plastic deformation layers. In addition, the effects of laser shock peening and HFEP-LSP on the mechanical properties were investigated. Compared to the annealed samples, the ultimate tensile strength and elongation of the HFEP-LSP-treated samples were increased by 12.3 % and 57.1 %, respectively, with a fatigue life improvement of 176.4 %. The mechanism of synergistic improvement in strength and ductility was demonstrated. [Display omitted] • High-frequency electropulsing-assisted laser shock peening (HFEP-LSP) was proposed. • HFEP-LSP-induced dual skin effect was found for the first time. • Dual skin effect induced heterogenous structure and metastable β m phase. • Formation mechanism of ultra-deep compressive residual stress layer was elucidated. • Synergistic improvement in strength-ductility mechanism was explained. [ABSTRACT FROM AUTHOR]

Cities are the primary carbon emitters in China and play a critical role in achieving national and regional emission-reduction targets. However, because existing CO 2 emission datasets are mostly limited to national and provincial levels and have significant time lags, there are few studies on urban emission patterns and drivers below the annual level. In this study, utilizing near-real-time daily CO 2 emission data from Carbon Monitor Cities-China, we analyzed the seasonality and peak patterns of city-level emissions and conducted a decomposition of emission drivers by sector based on sociometric and natural factors. Our results indicated that most cities in China exhibited seasonal emissions, with peak emissions occurring during winter. However, heavily industrialized cities tend to produce high year-round emissions. The residential and power sectors are crucial for reducing emissions in winter-peaked cities, whereas the industrial and power sectors are the primary energy consumers in non-winter-peaked cities. The results of the decomposition analysis show that the emission structure of the service industry and the impact of temperature on power generation are the primary drivers of CO 2 emissions during winter. The above analysis offers recommendations for developing emission-reduction policies for various types of cities. Service-based cities should concentrate their efforts on reducing emissions from tertiary industries, whereas energy-dependent cities should continue to focus on controlling emissions from secondary industries such as industry and power. In addition, both service-based and energy-dependent cities must address the significant impact of heating demands on emissions. • Chinese cities show a seasonal pattern of carbon emissions peaking in winter. • Heavy industrial cities tend to show higher emissions throughout the year. • Residential, power, and industry are the key emissions reduction sectors. • The temperature factor is important for energy-saving and emissions-reducing. • The emissions structure of the service industry accelerates emissions in winter. [ABSTRACT FROM AUTHOR]