An Improved Hierarchical Genetic Algorithm for Sheet Cutting Scheduling with Process Constraints

For the first time, an improved hierarchical genetic algorithm for sheet cutting problem which involves n cutting patterns for m non-identical parallel machines with process constraints has been proposed in the integrated cutting stock model. The objective of the cutting scheduling problem is minimizing the weighted completed time. A mathematical model for this problem is presented, an improved hierarchical genetic algorithm (ant colony—hierarchical genetic algorithm) is developed for better solution, and a hierarchical coding method is used based on the characteristics of the problem. Furthermore, to speed up convergence rates and resolve local convergence issues, a kind of adaptive crossover probability and mutation probability is used in this algorithm. The computational result and comparison prove that the presented approach is quite effective for the considered problem

Visualize and Learn Sorting Algorithms in Data Structure Subject in a Game-based Learning

The Data Structure subject is an essential Computer Science subject. Sorting algorithms are important topics in Data Structure where students are expected to learn how various sorting algorithms work and their time complexities. Some sorting algorithms may easily cause confusions to novice students, as they usually find it challenging to understand and memorize these algorithms. There is a need to find a means of technology enhanced learning to improve the learning process of students. Game based learning is a pedagogy where students learn through game playing. This mode of learning could effectively engage students to focus on the learning topics more efficiently. The study uses a sorting algorithm serious game to allow students to learn four types of sorting algorithms: Bubble sort, Selection sort, Insertion sort and Quick sort. The students would carry out self-directed learning lecture materials in the serious game, followed by refreshing their learning using a visualizer, and lastly reinforce their learning through playing a sorting serious game. Two groups of students participate in the experiment, a control group and an experiment group. The experiment group that sues the sorting algorithm games achieves better results, compared to the control group who learns without the serious game. Game-based learning provides a positive learning experience to the students that could improve the learning effectiveness. Coupled with technology such as VR headsets as a future upgrade, it would be a niche factor that would create an immersive learning experience to engage the students and enhance their learning in a virtual environment

Multi-Loop Integral Control-Based Heart Rate Regulation for Fast Tracking and Faulty-Tolerant Control Performance in Treadmill Exercises

In order to offer a reliable, fast, and offset-free tracking performance for the regulation of heart rate (HR) during treadmill exercise, a two-input single-output (2ISO) control system by simultaneously manipulating both treadmill speed and gradient is proposed. The decentralized integral controllability (DIC) analysis is extended to nonlinear and non-square processes especially for a 2ISO process, namely multi-loop integral controllability (MIC). The proposed multi-loop integral control-based HR regulation by manipulating treadmill speed and gradient is then validated through a comparative treadmill experiment that compares the system performance of the proposed 2ISO MIC control loop with that of single-input single-output (SISO) loops, speed/gradient-to-HR. The experimental validation presents that by simultaneously using two control inputs, the automated system can achieve the fastest HR tracking performance and stay close to the reference HR during steady state, while comparing with two SISO structures, and offer the fault-tolerant ability if the gains of the two multi-loop integral controllers are well tuned. It has a vital implication for the applications of exercise rehabilitation and fitness in relation to the automated control system

FEDRKG: A Privacy-Preserving Federated Recommendation Framework via Knowledge Graph Enhancement

Federated Learning (FL) has emerged as a promising approach for preserving data privacy in recommendation systems by training models locally. Recently, Graph Neural Networks (GNN) have gained popularity in recommendation tasks due to their ability to capture highorder interactions between users and items. However, privacy concerns prevent the global sharing of the entire user-item graph. To address this limitation, some methods create pseudo-interacted items or users in the graph to compensate for missing information for each client. Unfortunately, these methods introduce random noise and raise privacy concerns. In this paper, we propose FedRKG, a novel federated recommendation system, where a global knowledge graph (KG) is constructed and maintained on the server using publicly available item information, enabling higher-order user-item interactions. On the client side, a relation-aware GNN model leverages diverse KG relationships. To protect local interaction items and obscure gradients, we employ pseudo-labeling and Local Differential Privacy (LDP). Extensive experiments conducted on three real-world datasets demonstrate the competitive performance of our approach compared to centralized algorithms while ensuring privacy preservation. Moreover, FedRKG achieves an average accuracy improvement of 4% compared to existing federated learning baselines

Buckling Analysis of a Composite Honeycomb Reinforced Sandwich Embedded with Viscoelastic Damping Material

In this study, the buckling loads of a composite sandwich structure, which is reinforced by a honeycomb layer and filled with viscoelastic damping material, are analyzed. By applying von Karman anisotropic plate equations for large deflection, the governing equation of the composite sandwich structure is determined, and the deflection of the structure is further defined by a double triangular series. According to the dynamic equivalent effective stiffness obtained by the homogenous asymptotic method and Hill’s generalized self-consistent model based on the Halpin–Tsai model, limiting the dynamic load buckling of the composite honeycomb reinforced sandwich structure embedded with viscoelastic damping material under axial compression can be achieved. The factors that influence the composite sandwich’s buckling loads are discussed and compared, such as the load and geometry parameters, the thickness of the honeycomb reinforcement layer and the honeycomb’s width. Finally, the results obtained by the present method are validated by the existing literature

An Intelligent Approach to the Unit Nesting Problem of Coil Material

With the popularization of small batch production, the main cutting method for sheet metal parts has changed. Laser cutting has become the main production method for coil material cutting. Developing an irregular part nesting method for the continuous cutting of coil material is urgent. Based on the coil material cutting process, this paper proposes an intelligent approach for the unit nesting problem of coil material. Firstly, a unit nesting model of coil material was constructed. Secondly, an intelligent approach using an improved marine predator algorithm was used to solve this model. In solving the model, the minimum nesting unit was continuously updated by changing the position, angle, and quantity of the nesting parts. Thirdly, the geometric characteristics of this minimum nesting unit were extracted. Finally, the nesting units for production were obtained using a single row or opposite row of the minimum nesting unit. The computational results and comparison prove that the presented approach is feasible and effective in improving material utilization, reducing production costs, and meeting the requirements of the production site

Prioritizing exhausted T cell marker genes highlights immune subtypes in pan-cancer

Summary: Exhausted T (TEX) cells are main immunotherapy targets in cancer, but it lacks a general identification method to characterize TEX cell in disease. To assess the characterization of TEX cell, we extract signature of TEX cell from large cancer and chronic infection cohorts. Based on single-cell transcriptomes, a systematic T cell exhaustion prediction (TEXP) model is designed to define TEX cell in cancer and chronic infection. We then prioritize 42 marker genes, including HAVCR2, PDCD1, TOX, TIGIT and LAG3, which are associated with T cell exhaustion. TEXP could identify high TEX and low TEX subtypes in pan-cancer of TCGA. The high TEX subtypes are characterized by high immune score, immune cell infiltration, high expression of TEX marker genes and poor prognosis. In summary, TEXP and marker genes provide a resource for understanding the function of TEX cell, with implications for immune prediction and immunotherapy in chronic infection and cancer

Self-Stabilized Quasi-2D Perovskite with an Ion-Migration-Inhibition Ligand for Pure Green LEDs

Perovskite light-emitting diodes (PeLEDs) have recently achieved a great breakthrough in external quantum efficiency (EQE). However, the operational stability of pure primary color PeLEDs lags far behind because of serious ion migration. Herein, a self-stabilized quasi-2D perovskite is constructed with a strategically synthesized ion-migration-inhibition ligand (IMIligand) to realize highly stable and efficient pure green PeLEDs approaching the standard green light of Rec. 2020. The IMIligand takes the role to not only eliminate migration pathways and anchor halide ions to suppress the ion migration but to also further enhance the crystalline orientation and energy transfer in quasi-2D perovskites. Meanwhile, the self-stabilized quasi-2D perovskite overcomes the degradation of electrical performance caused by conventional exogenous passivation additives. Ultimately, the figure of merit of the pure green quasi-2D PeLEDs is at least double that of previous works. The devices achieve an EQE of 26.2% and operational stability of 920 min at initial luminance of 1000 cd m–2