On the basis of fully analysing the many problems of manual scheduling operations in traditional cigarette storage and transportation enterprises, the industry's internal and external transportation resources are integrated through standardised, digital, and intelligent transportation management mechanisms. At the same time, a vehicle loading standardisation rule system is set based on the product characteristics of cigarette logistics and transportation market standards. For the first time, a digital model of cigarette product loading for freight vehicles with cost efficiency and low-carbon logistics is creatively constructed. Finally, a genetic algorithm based loading problem simulation is implemented through Python programming. Simulation results show that the proposed intelligent loading algorithm can automatically generate optimal loading plans, sharply reduce manual scheduling effort, shorten order-response time, and improve the turnover efficiency of both vehicles and goods, thereby enhancing overall operational performance and internal management of cigarette logistics enterprises.
Ransomware has evolved from a financial crime into a systemic threat to critical infrastructure (CI), disrupting healthcare, transport, and government services with kinetic consequences. While current cybersecurity discourses prioritise data protection (confidentiality) and organisational compliance, they often overlook the cascading physical and social harms inherent in tightly coupled digital systems. This paper critiques existing technocentric and regulatory frameworks - such as GDPR and NIS2 - arguing they are ill-equipped to address the 'human cost' of service interruptions. Adopting a socio-legal perspective and utilising Normal Accident Theory, the study analyses the limitations of current governance models in protecting public safety against 'infrastructural violence'. It contributes to the literature by proposing a Human-Centred Critical Infrastructure Cybersecurity Framework. This framework redefines ransomware as a public safety issue, advocating for governance structures that prioritise human reliance, ethical responsibility, and social resilience over mere technical recovery.
Calibration transfer between different sampling dates was the primary issue in the practical application of fixed-point monitoring by spectral analysis. Here, we proposed a new method to resolve the calibration transfer across sampling dates. We used a reference sample to calibrate the working state of the spectrometer, thereby eliminating the influence factors that require model transfer. The reference sample were made by the ash mixture of the calibration samples. After calibrating the spectra of calibration samples by the reference sample, a new spectral model is reconstructed, and fast spectral analysis can be performed on new samples collected at different sampling dates without the traditional model transfer. Compared to existing model transfers, our calibration method is a purely computational process, achieving rapid analysis of new sample spectra without the need for model transfer.
Offshore wind farm inspection includes various tasks and vessels, so task assignment and path planning form a complex combinatorial optimisation problem. Efficient solutions are essential to improve efficiency and reduce costs. This paper applies the task matching multiple travelling salesmen problem (TM-MTSP) to offshore wind farm inspection and proposes a novel greedy dynamic elite artificial bee colony algorithm (GDE-ABC) for scheduling multiple inspection vessels. The objective is to minimise the maximum inspection time. A greedy strategy with task constraints is used to generate initial solutions. The algorithm balances exploration and exploitation by using dynamic retention probability-based neighbourhood search and tournament selection. Simulation results show that the proposed GDE-ABC is effective. Compared with traditional algorithms, it significantly shortens the maximum inspection time, improves operation and maintenance efficiency, and reduces related costs.
As a popular swarm intelligence optimisation approach, firefly algorithm (FA) has exhibited excellent search capabilities in various optimisation problems. However, FA still has some limitations. The search efficiency is sensitive to the step size factor, and the single search pattern results in slow convergence rate. To tackle these issues, this paper proposes a multi-stage adaptive FA with enhanced search (MSAFAES). First, a new adaptive parameter method is designed, in which the entire search is divided into two stages. Different parameters strategies are adopted for different search stages. Then, three types of search patterns are employed in the search process. To validate the performance of MSAFAES, 10 well-known benchmark problems are tested. Computational results demonstrate the effectiveness of MSAFAES when compared with three other FA variants.
To address the insufficient integration between master production scheduling (MPS) and material management (MM) in China's tobacco industry, this study proposes a multi-objective hierarchical model for their collaborative optimisation. The model features a two-layer structure: the upper level optimises capacity utilisation and production costs, while the lower level minimises material waste and inventory levels. The NSGA-II algorithm was adopted to solve the model, and its effectiveness was verified through a case study based on actual enterprise operational data. Results demonstrate that the model significantly reduces raw-material waste by 19.3%, shortens the production cycle by 14 days, and improves Pareto-frontier efficiency by 32.7%. The study validates the model's effectiveness in complex, long-cycle production scenarios, offering a practical solution for refined production management in the tobacco industry.
This paper proposes a novel framework for analysing the dynamic effects of extreme snow and ice (S&I) conditions on energy flow within integrated energy systems (IES). First, an enhanced IES framework is developed to strengthen the coupling between electricity, heat, gas systems, energy hubs, and renewable energy sources. Second, an improved transmission line icing failure model is introduced, considering the variations in icing thickness and the breaking force. Monte Carlo simulations and numerical calculations are applied to assess the impacts of extreme S&I events on energy flow in IES. A 64-bus case study illustrates the significant operational differences and safety risks arising from such extreme weather conditions. The proposed IES framework provides a comprehensive view of complex energy interactions and underscores the need for resilient systems.
This study presents an integrated framework for optimising machine layout and production planning in dynamic cellular manufacturing systems under uncertainty. The framework addresses key challenges including machine deterioration and breakdowns, order rejection, and tardiness costs, which are often treated separately in traditional approaches. A multi-objective mathematical model is developed to maximise profit, increase the number of accepted orders, and balance machine workloads to reduce failures. The solution employs a three-step hierarchical approach: heuristic machine-to-cell assignment, deep reinforcement learning for real-time order acceptance and scheduling while considering machine deterioration, and heuristic layout refinement. Computational results show that the proposed method accepts 2.63% more orders with a 5.7% profit reduction, enhancing customer attraction and competitiveness. Workload balancing decreases machine repairs by 11.5%, improving system stability and reducing maintenance costs. Despite an average profit loss of 9.77% due to machine deterioration, the framework significantly improves efficiency and operational resilience in dynamic manufacturing environments.
The investigation focuses on the onset of convection in a horizontal layer with the inclusion of Oldroyd-B nanofluid. The non-dimensional governing equations is solved using the normal mode technique, resulting in an eigenvalue problem. Analytical expression for Rayleigh number is obtained. Critical Rayleigh number values are determined for specific parameter settings. The influence of dimensionless parameters such as the Lewis number (Le), Prandtl number (Pr), Modified particle density increment (N-b), modified diffusivity ratio (N-a), Taylor number (Ta), Nanoparticle Rayleigh number (Rn), and the relaxation times of the fluid lambda(1) and lambda(2) on the critical Rayleigh number is analysed. The results of the study indicate that the Taylor number (Ta) acts as a stabilising factor for the system, while the modified diffusivity ratio (N-a) and Nanoparticle Rayleigh number (Rn) function as destabilising factors. Moreover, the critical Rayleigh number exhibits a non-monotonic dependency on the coefficients lambda(1)and lambda(2).
To address severe mural image defects and the low-resolution and rough reconstruction details of mural reconstruction methods, which can lead to a reduction in mural artistry. This paper presents an artistic reconstruction method (RSSRGAN) for murals. This method adopts the architecture of generative adversarial networks and introduces an attention mechanism. First, channel separation is performed on the feature map obtained during preliminary feature extraction, and weight prediction is performed on the 64-dimensional features to construct the channel dependence between the mural feature maps to retain the high-frequency features lost by the murals in the LR space. Finally, mural textural features are retained to improve the artistic reconstruction effect. Compared with the four popular superresolution reconstruction baseline models, the proposed method achieves a peak signal-to-noise ratio (PSNR) increase of more than 0.58 and an increase in the structural similarity index (SSIM) of more than 0.025 on the mural dataset. Moreover, public dataset verification on the DIV2K dataset showed that the method achieved good reconstruction quality, in which the PSNR increased by more than 0.27 and the SSIM increased by more than 0.014. The RSSRGAN method has achieved significant improvements in mural image reconstruction and provides a new and effective method for artistic mural reconstruction.
