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Dengue virus (DENV) is a mosquito-borne virus with a significant human health concern. With 390 million infections annually and 96 million showing clinical symptoms, severe dengue can lead to life-threatening conditions like dengue hemorrhagic fever (DHF) and dengue shock syndrome (DSS). The only FDA-approved vaccine, Dengvaxia, has limitations due to antibody-dependent enhancement (ADE), necessitating careful administration. The recent pre-approval of TAK-003 by WHO in 2024 highlights ongoing efforts to improve vaccine options. This review explores recent advancements in dengue vaccine development, emphasizing potential utility of mRNA-based vaccines. By examining current clinical trial data and innovations, we aim to identify promising strategies to address the limitations of existing vaccines and enhance global dengue prevention efforts.

Artificial intelligence (AI) in complex networks has made revolutionary breakthroughs in this century, and AI-driven methods are being increasingly integrated into different scientific research [...]

Artificial intelligence is at the intersection of innovation and escalating energy demands. This paper addresses the AI energy paradox through an integrated sociotechnical framework that combines technological architectures, organizational practices, and adaptive governance. Comprehensive case analyses reveal critical leverage points where targeted interventions boost performance while significantly reducing energy consumption. Our findings challenge conventional views of inherent efficiency–performance trade-offs, showing that these limitations largely stem from outdated design choices. We propose a balanced strategy: deploy mid-scale models for routine, high-efficiency tasks (e.g., dataset processing and rapid document summarization) and reserve high-capacity models with advanced reasoning for complex challenges. By aligning optimized hardware architectures with strategic policy measures, our approach offers considerable economic, operational, and environmental benefits. Furthermore, our analysis highlights an urgent need for innovative, energy-conscious AI development strategies. This roadmap empowers researchers, practitioners, and policymakers to harness AI’s transformative potential while ensuring ethical and sustainable development for current and future generations.

Given the gradual intensification of the current network security situation, malicious attack traffic is flooding the entire network environment, and the current malicious traffic detection model is insufficient in detection efficiency and detection performance. This paper proposes a data processing method that divides the flow data into data flow segments so that the model can improve the throughput per unit time to meet its detection efficiency. For this kind of data, a malicious traffic detection model with a hierarchical attention mechanism is also proposed and named HAGRU (Hierarchical Attention Gated Recurrent Unit). By fusing the feature information of the three hierarchies, the detection ability of the model is improved. An attention mechanism is introduced to focus on malicious flows in the data flow segment, which can reasonably utilize limited computing resources. Finally, compare the proposed model with the current state of the method on the datasets. The experimental results show that: the novel model performs well in different evaluation indicators (detection rate, false-positive rate, F -score), and it can improve the performance of category recognition with fewer samples when the data is unbalanced. At the same time, the training of the novel model on larger datasets will enhance the generalization ability and reduce the false alarm rate. The proposed model not only improves the performance of malicious traffic detection but also provides a new research method for improving the efficiency of model detection.

According to previous observational researches and clinical trials, the gut microbiota is related to prostate diseases. However, the potential association between gut microbiota and prostate disorders is still uncertain. We first identified groups of gut microbiota based on the phylum, class, order, family, and genus levels from consortium MiBioGen. And we acquired prostate diseases statistics from the FINNGEN study and PRACTICAL consortium. Next, two-sample Mendelian randomization was used to investigate the potential associations between three prevalent prostate disease and gut microbiota. In addition, we performed a reverse MR analysis and Benjamini-Hochberg (BH) test for further research. We investigated the connection between 196 gut microbiota and three prevalent prostate diseases. We identified 42 nominally significant associations and 2 robust causative links. Upon correction for multiple comparisons using the Benjamini–Hochberg procedure, our analysis revealed a positive correlation between the risk of prostatitis and the presence of the taxonomic order Gastranaerophilales. Conversely, the risk of prostate cancer exhibited an inverse correlation with the presence of the taxonomic class Alphaproteobacteria. Our study revealed the potential association between gut microbiota and prostate diseases. The results may be useful in providing new insights for further mechanistic and clinical studies of prostate diseases.

Classification of faults in mechanical components using machine learning is a hot topic in the field of science and engineering. Generally, every real-world running mechanical system exhibits personalized vibration behaviors that can be measured with acceleration sensors. However, faulty samples of such systems are difficult to obtain. Therefore, machine learning methods, such as support vector machine (SVM), neural network (NNs), etc., fail to obtain agreeable fault detection results through smart sensors. A personalized diagnosis fault method is proposed to activate the smart sensor networks using finite element method (FEM) simulations. The method includes three steps. Firstly, the cosine similarity updated FEM models with faults are constructed to obtain simulation signals (fault samples). Secondly, every simulation signal is separated into sub-signals to solve the time-domain indexes to generate the faulty training samples. Finally, the measured signals of unknown samples (testing samples) are inserted into the trained SVM to classify faults. The personalized diagnosis method is applied to detect bearing faults of a public bearing dataset. The classification accuracy ratios of six types of faults are 90% and 92.5%, 87.5% and 87.5%, 85%, and 82.5%, respectively. It confirms that the present personalized diagnosis method is effectiveness to detect faults in the absence of fault samples.

Mining activities are among the main sources of heavy metal contamination in the environment. To analyze heavy metal pollution of soils from coal mines in China, we assessed pollution and potential ecological risk, compared heavy metal concentrations between soils from coal mines and soils from metal mines and identified the relationship between heavy metals on the nationwide scale. The data of heavy metal concentrations for 50 coal mines and 35 metal mines were collected from the published literature. Coal mines referred in this paper are distributed in 18 provinces and 4 climatic zones in China. Methods including Index of geoaccumulation (Igeo), Nemerow pollution index (P), potential ecological risk index and other statistics (Pearson correlation method and ANOVA variance analysis) were utilized. Compared with soils influenced by metal mining, heavy metal concentrations in soils from coal mines were much lower. For heavy metals, higher Igeo for Cd, Pb and Ni was observed. Soils were contaminated or slightly contaminated when calculated based on Chinese soil guidelines (grade I and grade II) but slightly contaminated or severely contaminated when calculated based on province backgrounds. Most heavy metals (i.e., As, Cr, Cu, Ni and Zn) showed a low potential ecological risk, whereas Cd, Pb and Hg showed slightly higher ecological risk potential. Statistically significant and positive correlations were found in pairs of As/Cr, As/Ni, As/Pb, As/Hg, Ni/Cr and Ni/Cu (P < 0.01) and Cu/Pb (P < 0.05).

Debris flow hazard mapping (DFHM) played an important role in reducing the threat of debris flows. Conventional DFHM usually requires numerical simulations to obtain debris flow intensity, which is usually quite time-consuming. This paper is to introduce a combined application framework of numerical simulation and machine learning to improve the efficiency of DFHM. The FLO-2D model was employed to simulate debris flows with different recurrence intervals of 20 years, 50 years and 100 years, respectively. Corresponding maximum accumulation depth and flow velocity were collected as dependent variables. Rainfall with corresponding recurrence intervals, altitude, slope, plane curvature, profile curvature, topographic humidity index, normalized difference vegetation index and Manning’s coefficients were collected as independent variables. Then the 20-year, 50-year and 100-year datasets were prepared by combining the independent and dependent variables. The 20-year and 50-year datasets were used for training a machine learning model called gradient boosted decision tree (GBDT). The 100-year dataset was used for validation. The results showed that the predicted results are quite close to the simulated results, which verified the validity and rationality of the proposed method. In addition, the training and prediction process of machine learning models is more than 10 times faster than the running process of numerical model. This study proposed the potential use of machine learning models as alternatives to hydraulic simulations, which could provide a more efficient solution for large-scale DFHM.

Meta-analysis was conducted to examine the effect of supplemental curcumin intake on skeletal muscle injury status and to propose an optimal intervention program. In accordance with the procedures specified in the PRISMA statement for systematic reviews and meta-analyses of randomized controlled trials, the Review Manager 5.3 was used to analyze the results of creatine kinase (CK), muscle soreness, interleukin-6 (IL-6), and range of motion (ROM) as outcome indicators in the 349 subjects included in the 14 articles. The effect size of curcumin supplementation on muscle soreness, mean difference (MD) = -0.61; the relationship between curcumin supplementation and muscle soreness for time of measurement (I2 = 83.6%)、the relationship between curcumin supplementation and muscle soreness for period of intervention (I2 = 26.2%)、the relationship between whether one had been trained (I2 = 0%) and supplementation dose (I2 = 0%) were not heterogeneous for the relationship between curcumin supplementation and muscle soreness; The effect size on CK, MD = -137.32; the relationship between curcumin supplementation and CK (I2 = 79.7%)、intervention period (I2 = 91.9%)、whether or not trained (I2 = 90.7%)、and no heterogeneity in the relationship between curcumin supplementation and CK for the time of measurement (I2 = 0%); The effect size MD = 4.10 for the effect on ROM; The effect size for IL-6 was MD = -0.33. This meta-analysis highlights that curcumin supplementation significantly mitigates skeletal muscle damage, with notable improvements in CK levels, muscle soreness, IL-6 levels, and ROM. The results highlight the importance of curcumin dosage and timing, revealing that prolonged supplementation yields the best results, especially for untrained individuals or those less exposed to muscle-damaging exercise. For muscle soreness and ROM enhancement, a pre-emptive, low-dose regimen is beneficial, while immediate post-exercise supplementation is most effective at reducing CK and IL-6 levels.

Classical Chinese translation presents significant challenges: manual methods suffer from high costs and inconsistent quality, while both traditional machine translation and approaches relying solely on Large Language Models often fail to adequately capture intricate semantic nuances and cultural specificities. To overcome these limitations, this study proposes an LLM-driven multi-agent framework that decomposes translation into word-level interpretation, paragraph-level generation, and multi-dimensional review, integrating a specialized Key Word Interpretation Database, Retrieval-Augmented Generation, and iterative feedback. Experiments on The Records of the Grand Historian of China: The Hereditary Houses and the Biographies, Volume 7–10 show average improvements of 18.8–25.7% in BLEURT, BLEU-1, and METEOR over single-model baselines, with  12.7% reduction in score variance, indicating enhanced stability. Human evaluation confirms gains in fluency, adequacy, and cultural fidelity, particularly for weaker baselines. Ablation results reveal the indispensable roles of contextual coherence review, grammatical validation, and keyword interpretation, while efficiency analysis shows that compared with the framework without useful agents, the running time of the proposed method increases by 3.21 times, with the main contributing factor being the introduction of keyword interpretation. The framework excels in resolving polysemy, preserving cultural allusions, and improving semantic coherence. Beyond Classical Chinese, it offers a transferable blueprint for other historical or low-resource languages, supporting high-fidelity cultural heritage translation.