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With the continuous development of Internet of Things (IoT) technology, there is a constant emergency of new IoT applications with low latency, high dynamics, and large bandwidth requirements.This has led to the widespread aggregation of massive devices and information at the network edge, promoting the emergence and deep development of fog computing architecture.However, with the widespread and in-depth application of fog computing architecture, the distributed network security architecture deployed to ensure its security is facing critical challenges brought by fog computing itself, such as the limitations of fog computing node computing and network communication resources, and the high dynamics of fog computing applications, which limit the edge deployment of complex network intrusion detection algorithms.To effectively solve the above problems, a personalized lightweight distributed network intrusion detection system (PLD-NIDS) was proposed based on the fog computing architecture.A large-scale complex network flow intrusion detection model was trained based on the convolutional neural network architecture, and furthermore the network traffic type distribution of each fog computing node was collected.The personalized model distillation algorithm and the weighted first-order Taylor approximation pruning algorithm were proposed to quickly compress the complex model, breaking through the limitation of traditional model compression algorithms that can only provide single compressed models for edge node deployment due to the high compression calculation overhead when facing a large number of personalized nodes.According to experimental results, the proposed PLD-NIDS architecture can achieve fast personalized compression of edge intrusion detection models.Compared with traditional model pruning algorithms, the proposed architecture achieves a good balance between computational loss and model accuracy.In terms of model accuracy, the proposed weighted first-order Taylor approximation pruning algorithm can achieve about 4% model compression ratio improvement under the same 0.2% model accuracy loss condition compared with the traditional first-order Taylor approximation pruning algorithm.

In the domain of data storage applications such as radar, communication, and electronic countermeasure, in order to meet the requirements of writing large capacity SATA Solid State Disk Array (SSD) continuously, steadily and high-speedily, a RAID0 storage array system based on FPGA is designed. This system takes FPGA as the data processing core, which realizes functions such as data receiving, data buffering, data transmission, and data storage. According to the garbage collection characteristic of SATA SSD, logic function modules are designed and optimized. The repeatable tests show that sixteen SATA SSD has the storage capacity up to 32 TB, writing bandwidth up to 5.5 GB/s, and the ability to work steadily for a long time in the industrial environment.

This study aimed to propose a portable and intelligent rehabilitation evaluation system for digital stroke-patient rehabilitation assessment. Specifically, the study designed and developed a fusion device capable of emitting red, green, and infrared lights simultaneously for photoplethysmography (PPG) acquisition. Leveraging the different penetration depths and tissue reflection characteristics of these light wavelengths, the device can provide richer and more comprehensive physiological information. Furthermore, a Multi-Channel Convolutional Neural Network–Long Short-Term Memory–Attention (MCNN-LSTM-Attention) evaluation model was developed. This model, constructed based on multiple convolutional channels, facilitates the feature extraction and fusion of collected multi-modality data. Additionally, it incorporated an attention mechanism module capable of dynamically adjusting the importance weights of input information, thereby enhancing the accuracy of rehabilitation assessment. To validate the effectiveness of the proposed system, sixteen volunteers were recruited for clinical data collection and validation, comprising eight stroke patients and eight healthy subjects. Experimental results demonstrated the system's promising performance metrics (accuracy: 0.9125, precision: 0.8980, recall: 0.8970, F1 score: 0.8949, and loss function: 0.1261). This rehabilitation evaluation system holds the potential for stroke diagnosis and identification, laying a solid foundation for wearable-based stroke risk assessment and stroke rehabilitation assistance. [ABSTRACT FROM AUTHOR]

Objective To investigate the association between the osteogenic differentiation in the soft tissue lump and the clinicopathological characteristics of osteosarcoma patients. Methods We retrospectively reviewed the clinical data of conventional osteosarcoma patients with soft tissue lumps, including Enneking stages, chemotherapy sensitivity, overall survival and post-metastatic survival time. The ossification level in soft tissue lumps was assessed by imaging and the proportion of osteoid matrix was assessed by pathological examination. Results A total of 189 cases were included in this study. In patients with Enneking IIIB, non-osteoblastic, partially osteoblastic and osteoblastic types accounted for 30.2%, 9.6% and 6.3%, respectively. Non-osteoblastic osteosarcoma patients had a higher rate of initial metastasis (P < 0.05); Chemotherapy efficiency of non-osteoblastic, partially osteoblastic and osteoblastic types were 60.5%, 59.6% and 31.3%, respectively. The osteoblastic osteosarcoma held the worst rate of chemotherapy sensitivity (P < 0.05). The overall survival of non-osteoblastic osteosarcoma was shorter than those of partially osteoblastic and osteoblastic types (P < 0.05). Post-metastatic survival time of osteoblastic osteosarcoma was longer than that of non-osteoblastic osteosarcoma (P=0.078). Conclusion For conventional osteosarcoma, the osteogenesis level in soft tissue lumps is related to the surgical stage, chemotherapy sensitivity and prognosis of tumors, which may provide guidance for the individual decision regarding chemotherapy and surgery timing on patients.

Rainbow trapping is a significant strategy for capturing waves of different frequencies at different spatial positions. It offers significant potential for multi-frequency on-chip nanophotonic devices in the era of big data and has attracted much interest recently, and topological rainbow trapping that is immune to disorder has been observed in optics and acoustics. Reported here is the topological rainbow trapping of elastic waves using gradient valley phononic crystal plates (PCPs) with a hexagonal lattice, based on valley edge states that are localized at the boundary between two PCPs with opposite valley topological phases. The gradient valley PCPs are achieved by gradually adjusting the height of the scatterers or the thickness of the PCPs. The topological rainbow trapping of elastic waves is shown by applying vertical vibration to the studied gradient PCPs, which are simple in structure and easily fabricated, providing a good solution for manipulating and trapping elastic waves. [ABSTRACT FROM AUTHOR]

An optical thermometry strategy based on Mn2+‐doped dual‐wavelength emission phosphor has been reported. Samples with different doping content were synthesized through a high‐temperature solid‐phase method under an air atmosphere. The electronic structure of Li4Zn(PO4)2 was calculated using density functional theory, revealing it to be a direct band gap material with an energy gap of 4.708 eV. Moreover, the emitting bands of Mn2+ at 530 and 640 nm can be simultaneously observed when using 417 nm as the exciting wavelength. This is due to the occupation of Mn2+ at the Zn2+ site and the interstitial site. Further analysis was conducted on the temperature‐dependent emission characteristics of the sample in the range 293–483 K. Mn2+ has different responses to temperature at different doping sites in Li4Zn(PO4)2. Based on the calculations using the fluorescence intensity ratio technique, the maximum relative sensitivity at a temperature of 483 K was determined to be 1.69% K−1, while the absolute sensitivity was found to be 0.12% K−1. The results showed that the Li4Zn(PO4)2:Mn2+ phosphor has potential application in optical thermometry. [ABSTRACT FROM AUTHOR]