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BackgroundEpidemiological and experimental evidence suggests that chronic inflammation plays an important role in the onset and progression of sarcopenia. However, there is inconsistent data on the inflammatory cytokines involved in the pathogenesis of sarcopenia. Therefore, we performed a two-sample Mendelian randomization (MR) analysis to explore the causal relationship between circulating cytokines and sarcopenia-related traits.MethodsThe MR analysis utilized genetic data from genome-wide association study that included genetic variations in 41 circulating cytokines and genetic variant data for appendicular lean mass (ALM), hand grip strength, and usual walking pace. Causal associations were primarily explored using the inverse variance-weighted (IVW) method, supplemented by MR-Egger, simple mode, weighted median, and weighted mode analyses. Additionally, sensitivity analyses were also performed to ensure the reliability and stability of the results.ResultsThree cytokines [hepatocyte growth factor (HGF), interferon gamma-induced protein 10 (IP-10), and macrophage colony-stimulating factor (M-CSF)] were positively associated with ALM (β: 0.0221, 95% confidence interval (CI): 0.0071, 0.0372, P= 0.0039 for HGF; β: 0.0096, 95%CI: 4e-04, 0.0189, P= 0.0419 for IP-10; and β: 0.0100, 95%CI: 0.0035, 0.0165, P= 0.0025 for M-CSF). Conversely, higher levels of interleukin-7 (IL-7), monocyte chemotactic protein 3 (MCP-3), and regulated on activation, normal T cell expressed and secreted (RANTES) were associated with decreased hand grip strength (β: -0.0071, 95%CI: -0.0127, -0.0014, P= 0.0140 for IL-7; β: -0.0064, 95%CI: -0.0123, -6e-04, P= 0.0313 for MCP-3; and β: -0.0082, 95%CI: -0.0164, -1e-04, P= 0.0480 for RANTES). Similarly, interleukin 1 receptor antagonist (IL-1RA) was negatively correlated with usual walking pace (β: -0.0104, 95%CI: -0.0195, -0.0013, P= 0.0254). Sensitivity analysis confirmed the robustness of these findings.ConclusionsOur study provides additional insights into the pivotal role of specific inflammatory cytokines in the pathogenesis of sarcopenia. Further research is required to determine whether these cytokines can be used as targets for the prevention and treatment of sarcopenia.

ObjectivesThis study aimed to construct prediction models based on computerized tomography (CT) signs, histogram and morphology features for the diagnosis of micropapillary or solid (MIP/SOL) components of stage IA lung adenocarcinoma (LUAC) and to evaluate the models’ performance.MethodsThis clinical retrospective study included image data of 376 patients with stage IA LUAC based on postoperative pathology, admitted to Putian First Hospital from January 2019 to June 2023. According to the presence of MIP/SOL components in postoperative pathology, patients were divided into MIP/SOL+ and MIP/SOL- groups. Cases with tumors ≤ 3 cm and ≤ 2 cm were separately analyzed. Each subgroup of patients was then randomly divided into a training set and a test set in a ratio of 7:3. The training set was used to build the prediction model, and the test set was used for internal validation.ResultsFor tumors ≤ 3 cm, ground-glass opacity (GGO) [odds ratio (OR) = 0.244; 95% confidence interval (CI): 0.103–0.569; p = 0.001], entropy (OR = 1.748; 95% CI: 1.213–2.577; p = 0.004), average CT value (OR = 1.002; 95% CI: 1.000–1.004; p = 0.002), and kurtosis (OR = 1.240; 95% CI: 1.023–1.513; p = 0.030) were independent predictors of MIP/SOL components of stage IA LUAC. The area under the ROC curve (AUC) of the nomogram prediction model for predicting MIP/SOL components was 0.816 (95% CI: 0.756–0.877) in the training set and 0.789 (95% CI: 0.689–0.889) in the test set. In contrast, for tumors ≤ 2 cm, kurtosis was no longer an independent predictor. The nomogram prediction model had an AUC of 0.811 (95% CI: 0.731–0.891) in the training set and 0.833 (95% CI: 0.733–0.932) in the test set.ConclusionFor tumors ≤ 3 cm and ≤ 2 cm, GGO, average CT value, and entropy were the same independent influencing factors in predicting MIP/SOL components of stage IA LUAC. The nomogram prediction models have potential diagnostic value for identifying MIP/SOL components of early-stage LUAC.

With the advent of big data, the swift advancement of diverse algorithmic technologies has enhanced the transaction efficiency of the e-commerce business. Nevertheless, it is crucial to acknowledge that e-commerce platforms might employ algorithmic technology to enforce differential pricing for various consumers with the aim of maximizing profits, thus infringing upon the lawful rights and interests of consumers. This paper focuses on the algorithmic price discrimination commonly observed on e-commerce platforms. To effectively regulate this behavior, the paper utilizes evolutionary game theory (EGT) to analyze the strategies employed by e-commerce platforms, consumers, and market regulators to achieve stability. This research employs a real-life situation and utilizes parametric simulation to visualize and analyze the process and outcomes of the three-party evolutionary game. The results demonstrate the credibility and feasibility of the article’s findings. Based on our research, we have reached the following findings: During the process of evolution, the strategic decisions made by the game participants from the three parties will mutually impact each other, and various elements exert varying degrees of influence on the strategic choices made by the game participants from each party. Collaborative governance can enable consumers and market regulators to regulate the discriminatory pricing behavior of e-commerce platforms effectively. This article offers valuable insights into the governance of violations in the e-commerce sector based on robust data and model research.

There exists many types of power equipments in power system operation and inspection scenarios. Among them, different types of equipments often have common representations, thus these various types of power equipments with similar characteristics would bring certain challenges to power equipment defect recognition. The traditional multi-label image recognition is usually with low recognition accuracy. The main reason is that the relationship between each label in the image is ignored. Furthermore, graph convolutional neural networks rely on the modeling ability of graphs to further improve the accuracy of recognition. To this end, we propose a multi-label image recognition model for electric power equipment inspection based on multi-scale dynamic graph convolutional network. In our model, multi-scale image features are extracted through a multi-scale feature extraction network firstly, and then the label relevance of a specific image is adaptively learned through combining the dynamic graph convolutional network. Finally, a self-built dataset of electrical equipment defects is used to conduct experimental results for comparison and validation. According to the analysis of our experimental results, our proposed model shows a better performance. The average accuracy of our model can reach 88.1%, which increases by 0.8% and 31.8% compared with the original model and the baseline model, respectively, showing the effectiveness and superiority of the proposed method.

Improved tillage management in fragrant rice cropping systems can enhance soil organic carbon (SOC) and the content of 2-Acetyl-1-Pyrroline (2-AP), a crucial volatile compound contributing to the aroma of fragrant rice. Despite this, the interplay between 2-AP content in fragrant rice and SOC metabolism, alongside the influences exerted by soil microbial functions, remains poorly understood. This study introduces a comprehensive 6-year field experiment which aims to correlate SOC with rice grain 2-AP content by analyzing soil microbial KEGG functions, such as carbon and amino acid metabolism, using metagenomic sequencing. The experiment assessed three tillage practices, conventional tillage (CT), reduced tillage (RT), and no tillage (NT), with soil samples collected on three dates in 2022. The results indicated that NT significantly (p < 0.05) enhanced SOC content and modified carbon metabolism by upregulating the Calvin cycle (K01601) and reducing hemicellulose degradation (K01710). Additionally, NT notably increased the soil levels of alkaline amino acids, such as histidine and ornithine, which were 165.17% and 1218.42% higher, respectively, than those in CT, possibly linked to an increase in soil pH. Furthermore, the 2-AP content in fragrant rice under NT was significantly higher by 52.02% and 13.90% compared to under RT and CT, respectively. NT also upregulated K00250 (alanine, aspartate, and glutamate metabolism) and K00290 (valine, leucine, and isoleucine biosynthesis), leading to significantly higher levels of 2-AP biosynthesis-related amino acids proline and glutamate in fragrant rice grain. This study links SOC and 2-AP biosynthesis via soil microbial functions, presenting a novel strategy for improving the quality of fragrant rice through soil management practices.

Charging batteries in underwater scenarios is generally very expensive and impractical, and solar cell (SC)-based underwater simultaneous lightwave information and power transfer (SLIPT) systems are a powerful solution. However, silicon SC receiver devices have limited bandwidth and are prone to deep signal-to-noise (SNR) degradation during underwater light fading effects. For these problems, this manuscript proposes a negative-biased SC optical receiver scheme to increase the $-3$ dB bandwidth of silicon SC from 440 kHz to 780 kHz. For the deep fading of SNR caused by various degradation effects in the water environment, a low peak average power ratio (PAPR) discrete Fourier transform spread orthogonal frequency division multiplexing (DFT-S-OFDM) modulation scheme is employed to counteract the deep fading phenomenon in the system. Achieved a communication rate of 15.2 Mbps in a 60 cm underwater environment with a fading factor of 0.403 and a bit error rate (BER) of $1.59\times 10^{-3}$ under perturbation. Also, the performance of DFT-S-OFDM and orthogonal frequency division multiplexing (OFDM) modulation systems in water environments with different turbidity (absorption characteristics and scattering) and presence of disturbances are compared separately, and the DFT-S-OFDM system is more robust. Finally, we complete the energy harvesting during the communication process, and the experiments show that the total battery power efficiency of the energy harvesting system can be increased by 1.87 times under the white light-emitting diode (LED) continuous irradiation for three hours.

Abstract Background This study investigates whether three-dimensional (3D) printing-assisted revision total hip/knee arthroplasty could improve its clinical and radiological outcomes and assess the depth and breadth of research conducted on 3D printing-assisted revision total hip and knee arthroplasty. Methods A literature search was carried out on PubMed, Web of Science, EMBASE, and the Cochrane Library. Only studies that investigated 3D printing-assisted revision total hip and knee arthroplasty were included. The author, publication year, study design, number of patients, patients’ age, the time of follow-up, surgery category, Coleman score, clinical outcomes measured, clinical outcomes conclusion, radiological outcomes measured, and radiological outcomes conclusion were extracted and analyzed. Results Ten articles were included in our review. Three articles investigated the outcome of revision total knee arthroplasty, and seven investigated the outcome of revision total hip arthroplasty. Two papers compared a 3D printing group with a control group, and the other eight reported 3D printing treatment outcomes alone. Nine articles investigated the clinical outcomes of total hip/knee arthroplasty, and eight studied the radiological outcomes of total hip/knee arthroplasty. Conclusion 3D printing is being introduced in revision total hip and knee arthroplasty. Current literature suggests satisfactory clinical and radiological outcomes could be obtained with the assistance of 3D printing. Further long-term follow-up studies are required, particularly focusing on cost-benefit analysis, resource availability, and, importantly, the durability and biomechanics of customized prostheses using 3D printing compared to traditional techniques.

LED displays can be used to realize the dual functions of display and communication simultaneously. However, existing LED display-based visible light communication (VLC) systems suffer due to their short transmission distance and are not practical. A long-distance, real-time display-camera communication (DCC) system is proposed in this paper. First, a LED-DCC point clustering scheme is proposed to increase the transmission distance by clustering multiple adjacent LED display points for improving the quality of the VLC signal captured by an image sensor. Then, a lightweight, back-forth, fast image processing algorithm is proposed to reduce the introduced additional computational complexity caused by point clustering and enhance the reliability of information extraction from the real-time captured images/video frames. The experimental system was implemented with a 2.2-inch 16 × 16 point LED display and the CMOS camera on the smartphone. Experimental results show that the proposed system can achieve a maximum data transmission distance of 7 m under a bit error rate (BER) of 0.5, which is about 9 times that of the previous LED-DCC system, and can achieve a data transmission distance of 175 cm under the 7% forward error correction (FEC) limit, which is about 12 times that of the previous LED-DCC system. Additionally, the decoding latency for extracting information from each video frame is only 13.26 ms, which guarantees real-time data reception.

Robustness is a key factor for real-time positioning and navigation, especially for high-speed vehicles. While visible light positioning (VLP) based on LED illumination and image sensors is widely studied, most of the VLP systems still suffer from the high positioning latency and the image blurs caused by high-speed movements. In this paper, a robust VLP system for high-speed vehicles is proposed based on a deep learning and data-driven approach. The proposed system can significantly increase the success rate of decoding VLP-LED user identifications (UID) from blurred images and reduce the computational latency for detecting and extracting VLP-LED stripe image regions from captured images. Experimental results show that the success rate of UID decoding using the proposed BN-CNN model could be higher than 98% when that of the traditional Zbar-based decoder falls to 0, while the computational time for positioning is decreased to 9.19 ms and the supported moving speed of our scheme can achieve 38.5 km/h. Therefore, the proposed VLP system can enhance the robustness against high-speed movement and guarantee the real-time response for positioning and navigation for high-speed vehicles.

Accurate detection of network-based attacks is crucial to prevent security breaches of information systems. The recent application of deep learning approaches for network intrusion detection has shown promising. However, the challenges remain on how to deal with imbalance data and small samples as well as reducing false alarm rate (FAR). To address these issues, this work has proposed a multiple-layer representation learning model for accurate end-to-end network intrusion detection by combining deep convolutional neural networks (CNN) with gcForest. The contributions of this work lie in 1) a new data encoding scheme based on P-Zigzag to encode network traffic data into two-dimensional gray-scale images for representation learning without loss of original information; 2) The combination of gcForest and CNN allows accurate detection on imbalanced data and small scale data with fewer hyperparamters comparing to most existing deep learning models, which increase computational efficiency. The proposed approach is based on a multiple-layer approach consisting of a coarse layer and a fine layer, in which the coarse layer with the improved CNN model (GoogLeNetNP) focuses on identification of N abnormal classes and a normal class. While in the fine layer, an improved model based on gcForest (caXGBoost) further classifies the abnormal classes into N-1 subclasses. This ensures fine-grained detection of various attacks. The proposed framework has been compared with the existing deep learning models using three real datasets (a new dataset NBC, a combination of UNSW-NB15 and CICIDS2017 consisting of 101 classes). The experimental results show that our proposed method outperforms other single deep learning methods (i.e., AlexNet, VGG19, GoogleNet, InceptionV3, ResNet18) in terms of accuracy, detection rate, and FAR, which demonstrates its effectiveness in detecting fine-grained attacks and handling imbalanced datasets with high-precision and low FAR.

Bradycardia and tachycardia reflect abnormalities of the heart that can lead to severe harm to the cardiovascular system. The pulsatile signal is a useful tool to detect these two kinds of arrhythmias. In this study, we present a pulsatile synthesis-by-analysis (PSA) modeling based method to detect bradycardia and tachycardia. A new PSA modeling method was proposed to quantitively describe the changes of pulsatile waves, and we obtained twelve parameters for constructing a feature vector from the PSA model of each wave, by which we trained classifiers of probabilistic neural network (PNN) and random forest (RF). Our experiments were performed on the Fantasia and 2015 PhysioNet/CinC Challenge databases. Some pathological and physiological changes were extracted from the average models of the subjects in different groups. The two-sample ks-test results show that all the parameters between different groups are all markedly different (h = 1, p

Development of computer science has led to the blooming of artificial intelligence (AI), and neural networks are the core of AI research. Although mainstream neural networks have done well in the fields of image processing and speech recognition, they do not perform well in models aimed at understanding contextual information. In our opinion, the reason for this is that the essence of building a neural network through parameter training is to fit the data to the statistical law through parameter training. Since the neural network built using this approach does not possess memory ability, it cannot reflect the relationship between data with respect to the causality. Biological memory is fundamentally different from the current mainstream digital memory in terms of the storage method. The information stored in digital memory is converted to binary code and written in separate storage units. This physical isolation destroys the correlation of information. Therefore, the information stored in digital memory does not have the recall or association functions of biological memory which can present causality. In this paper, we present the results of our preliminary effort at constructing an associative memory system based on a spiking neural network. We broke the neural network building process into two phases: the Structure Formation Phase and the Parameter Training Phase. The Structure Formation Phase applies a learning method based on Hebb's rule to provoke neurons in the memory layer growing new synapses to connect to neighbor neurons as a response to the specific input spiking sequences fed to the neural network. The aim of this phase is to train the neural network to memorize the specific input spiking sequences. During the Parameter Training Phase, STDP and reinforcement learning are employed to optimize the weight of synapses and thus to find a way to let the neural network recall the memorized specific input spiking sequences. The results show that our memory neural network could memorize different targets and could recall the images it had memorized.

Abstract The Tibetan Plateau is home to numerous glaciers that are important for freshwater supply and climate regulation. These glaciers, which are highly sensitive to climatic variations, serve as vital indicators of climate change. Understanding glacier-fed hydrological systems is essential for predicting water availability and formulating climate adaptation strategies. This study investigated the dynamic fluctuations in the water level of the Blue Moon Lake Valley (BMLV), supplied by meltwater from Baishui River Glacier No. 1 on Yulong Snow Mountain. We focused on the lake pulse phenomenon—subtle yet significant water level fluctuations that have often been overlooked in prior research. By employing fast Fourier transform (FFT), multivariate regression (MVR), and random forest (RF) models, we examined the interactions among glacier melt dynamics, climatic variables, and hydrological responses. Our analysis indicates that the rate of change (ROC) in the water level fluctuates between − 0.006 and 0.01 m/min, with a median ROC of − 7.24E−06 m/min, highlighting the significant variability influenced by glacier melt and precipitation. The maximum cumulative sum (CS) value of 0.09 m suggests a net increase in the water level, predominantly due to increased precipitation, decreased evaporation, and increased glacier melting. We demonstrate that temperature critically influences glacier melt rates and water level variations, alongside solar radiation, rainfall, atmospheric pressure, and wind speed. The ROC of Baishui River Glacier No. 1 melt ranges from − 0.0016 to 0.0015 m/min, reflecting substantial variation with significant downstream implications for water availability during dry seasons. The mean interval between consecutive glacier melt peaks is approximately 2.87 h, with a strong positive linear trend R2 = 0.99, indicating frequent melt events. Conversely, water level peaks occur approximately every 6.5 h, with a strong positive trend R2 = 0.99, indicating a slower recurrence rate. The transit time for meltwater from Baishui River Glacier No. 1 to BMLV is estimated at approximately 4.16 h. Additionally, we quantify the water flux from BMLV across various timescales, highlighting the substantial contribution of glacial meltwater. This novel study systematically examines the hydrological dynamics of BMLV. This study has the potential to reveal broader implications for water resource management, ecosystem dynamics, and climate change adaptation in regions dependent on glacier-fed lakes.