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Ye Mengde, a well-known Chinese poet and poetry critic in the Song Dynasty, advocated the concept of implicit naturalness in his work, Shilin Shihua (Shilin’s Remarks on Poetry) . He emphasized that the use of allusions in poetic composition should not be far-fetched and demanded that words and sentences be refined and hammered. Based on Ye’s insights, Yang Wanli, another famous Chinese poet in the Southern Song Dynasty, further developed the theory. In Chengzhai Shihua (Chengzhai’s Remarks on Poetry) , he proposed that the essence of poetry lies in “poems being concluded while the taste remains eternal,” paid attention to the skillful integration of idioms and allusions, advocated the importance of creating “stunning verses” in writing poetry, and practiced what he preached by actively engaging in poetic composition. He shattered the traditional conventions of the late Jiangxi School of Poetry, created the unique “Chengzhai Style” characterized by freshness and liveliness, and injected a vibrant “poetic taste” into poetry, enhancing the role of poetic composition theory in guiding poetry creation practices.

In the context of the rapid development of blockchain technology, smart contracts have also been widely used in the Internet of Things, finance, healthcare, and other fields. There has been an explosion in the number of smart contracts, and at the same time, the security of smart contracts has received widespread attention because of the financial losses caused by smart contract vulnerabilities. Existing analysis tools can detect many smart contract security vulnerabilities, but because they rely too heavily on hard rules defined by experts when detecting smart contract vulnerabilities, the time to perform the detection increases significantly as the complexity of the smart contract increases. In the present study, we propose a novel hybrid deep learning model named CBGRU that strategically combines different word embedding (Word2Vec, FastText) with different deep learning methods (LSTM, GRU, BiLSTM, CNN, BiGRU). The model extracts features through different deep learning models and combine these features for smart contract vulnerability detection. On the currently publicly available dataset SmartBugs Dataset-Wild, we demonstrate that the CBGRU hybrid model has great smart contract vulnerability detection performance through a series of experiments. By comparing the performance of the proposed model with that of past studies, the CBGRU model has better smart contract vulnerability detection performance.

Diabetes and sarcopenia are emerging as serious public health issues. Sarcopenia, an age-related disorder characterized by loss of skeletal muscle mass and function, is recognized as a new complication in elderly patients with type 2 diabetes mellitus (T2DM). Type 2 diabetes is characterized by insulin resistance, chronic inflammation, accumulation of advanced glycation products and increased oxidative stress, which can negatively affect skeletal muscle mass, strength and function leading to sarcopenia. There is a mutual interrelationship between T2DM and sarcopenia in light of pathophysiology mechanism and long-term outcome. T2DM will accelerate the decline of muscle mass and function, which will in turn lead to glucose metabolism disorders, reduced physical activity and the risk of diabetes. However, the specific mechanism involved has not been thoroughly studied. Therefore, this review aims to explore the pathophysiology and therapeutic strategy related to sarcopenia and diabetes and provide insight for future investigations, which is of great significance for improving the quality of life in the elderly with diabetes and concurrently reducing the incidence of related complications.

Blockchain presents a chance to address the security and privacy issues of the Internet of Things; however, blockchain itself has certain security issues. How to accurately identify smart contract vulnerabilities is one of the key issues at hand. Most existing methods require large-scale data support to avoid overfitting; machine learning (ML) models trained on small-scale vulnerability data are often difficult to produce satisfactory results in smart contract vulnerability prediction. However, in the real world, collecting contractual vulnerability data requires huge human and time costs. To alleviate these problems, this paper proposed an ensemble learning (EL)-based contract vulnerability prediction method, which is based on seven different neural networks using contract vulnerability data for contract-level vulnerability detection. Seven neural network (NN) models were first pretrained using an information graph (IG) consisting of source datasets, which then were integrated into an ensemble model called Smart Contract Vulnerability Detection method based on Information Graph and Ensemble Learning (SCVDIE). The effectiveness of the SCVDIE model was verified using a target dataset composed of IG, and then its performances were compared with static tools and seven independent data-driven methods. The verification and comparison results show that the proposed SCVDIE method has higher accuracy and robustness than other data-driven methods in the target task of predicting smart contract vulnerabilities.

Adsorption and activation of C–H bonds by photocatalysts are crucial for the efficient conversion of C–H bonds to produce high-value chemicals. Nevertheless, the delivery of surface-active oxygen species for C–H bond oxygenation inevitably needs to overcome obstacles due to the separated active centers, which suppresses the catalytic efficiency. Herein, Ni dopants are introduced into a monolayer Bi 2 WO 6 to create cascaded active units consisting of unsaturated W atoms and Bi/O frustrated Lewis pairs. Experimental characterizations and density functional theory calculations reveal that these special sites can establish an efficient and controllable C–H bond oxidation process. The activated oxygen species on unsaturated W are readily transferred to the Bi/O sites for C–H bond oxygenation. The catalyst with a Ni mass fraction of 1.8% exhibits excellent toluene conversion rates and high selectivity towards benzaldehyde. This study presents a fascinating strategy for toluene oxidation through the design of efficient cascaded active units. By introducing Ni dopants into monolayer Bi 2 WO 6 , Bi/O frustrated Lewis pairs, unsaturated W atoms, and Ni active units are formed. The authors show that these units facilitate oxygen species transfer and enhance photocatalytic toluene oxidation.

Since the coronavirus disease 2019 (COVID-19) pandemic, the value of mRNA vaccine has been widely recognized worldwide. Messenger RNA (mRNA) therapy platform provides a promising alternative to DNA delivery in non-viral gene therapy. Lipid nanoparticles (LNPs), as effective mRNA delivery carriers, have been highly valued by the pharmaceutical industry, and many LNPs have entered clinical trials. We developed an ideal lipid nanoformulation, named LNP3, composed of 1,2-dioleoyl-3-trimethylammonium-propane (DOTAP), 1,2-dioleoyl-sn-glycero-3-phosphoethanolamine (DOPE) and cholesterol, and observed its release efficiency, sustained release, organ specific targeting and thermal stability. In vitro studies showed that the transfection efficiency of LNP3 was higher than that of LNPs composed of DOTAP-DOPE and DOTAP-cholesterol. The positive to negative charge ratio of LNPs is a determinant of mRNA transfer efficiency in different cell lines. We noted that the buffer affected the packaging of mRNA LNPs and identified sodium potassium magnesium calcium and glucose solution (SPMCG) as a favorable buffer formulation. LNP3 suspension can be lyophilized into a thermally stable formulation to maintain activity after rehydration both in vitro and in vivo. Finally, LNP3 showed sustained release and organ specific targeting. We have developed an ideal lipid nanoformulation composed of DOTAP, DOPE and cholesterol for effective mRNA delivery.

Modeling solar systems necessitates the effective identification of unknown and variable photovoltaic parameters. To efficiently convert solar energy into electricity, these parameters must be precise. The research introduces the multi-strategy learning boosted colony predation algorithm (MLCPA) for optimizing photovoltaic parameters and boosting the efficiency of solar power conversion. In MLCPA, opposition-based learning can be used to investigate each individual’s opposing position, thereby accelerating convergence and preserving population diversity. Level-based learning categorizes individuals according to their fitness levels and treats them differently, allowing for a more optimal balance between variation and intensity during optimization. On a variety of benchmark functions, the MLCPA’s performance is compared to the performance of the best algorithms currently in use. On a variety of benchmark functions, the MLCPA’s performance is compared to that of existing methods. MLCPA is then used to estimate the parameters of the single, double, and photovoltaic modules. Last but not least, the stability of the proposed MLCPA algorithm is evaluated on the datasheets of many manufacturers at varying temperatures and irradiance levels. Statistics have demonstrated that the MLCPA is more precise and dependable in predicting photovoltaic mode critical parameters, making it a viable tool for solar system parameter identification issues.

Background It is of great clinical significance to develop an accurate computer aided system to accurately diagnose the breast cancer. In this study, an enhanced machine learning framework is established to diagnose the breast cancer. The core of this framework is to adopt fruit fly optimization algorithm (FOA) enhanced by Levy flight (LF) strategy (LFOA) to optimize two key parameters of support vector machine (SVM) and build LFOA-based SVM (LFOA-SVM) for diagnosing the breast cancer. The high-level features abstracted from the volunteers are utilized to diagnose the breast cancer for the first time. Results In order to verify the effectiveness of the proposed method, 10-fold cross-validation method is used to make comparison among the proposed method, FOA-SVM (model based on original FOA), PSO-SVM (model based on original particle swarm optimization), GA-SVM (model based on genetic algorithm), random forest, back propagation neural network and SVM. The main novelty of LFOA-SVM lies in the combination of FOA with LF strategy that enhances the quality for FOA, thus improving the convergence rate of the FOA optimization process as well as the probability of escaping from local optimal solution. Conclusions The experimental results demonstrate that the proposed LFOA-SVM method can beat other counterparts in terms of various performance metrics. It can very well distinguish malignant breast cancer from benign ones and assist the doctor with clinical diagnosis.

The task of pose-guided person image generation has long been confronted with two major challenges: high-frequency texture details tend to blur and be lost during appearance transfer, while the semantic identity of the person is difficult to maintain consistently during pose changes. To address these issues, this paper proposes a diffusion-based generative framework that integrates frequency awareness and global semantic alignment. The framework consists of two core modules: a multi-level fractional-order Gabor frequency-aware network, which accurately extracts and reconstructs high-frequency texture features such as hair strands and fabric wrinkles, enhances image detail fidelity through fractional-order filtering and complex domain modeling; and a global semantic-pose alignment module that utilizes a cross-modal attention mechanism to establish a global mapping between pose features and appearance semantics, ensuring pose-driven semantic alignment and appearance consistency. The collaborative function of these two modules ensures that the generated results maintain structural integrity and natural textures even under complex pose variations and large-angle rotations. The experimental results on the DeepFashion and Market1501 datasets demonstrate that the proposed method outperforms existing state-of-the-art approaches in terms of SSIM, FID, and perceptual quality, validating the effectiveness of the model in enhancing texture fidelity and semantic consistency.

Image segmentation techniques play a vital role in aiding COVID-19 diagnosis. Multi-threshold image segmentation methods are favored for their computational simplicity and operational efficiency. Existing threshold selection techniques in multi-threshold image segmentation, such as Kapur based on exhaustive enumeration, often hamper efficiency and accuracy. The whale optimization algorithm (WOA) has shown promise in addressing this challenge, but issues persist, including poor stability, low efficiency, and accuracy in COVID-19 threshold image segmentation. To tackle these issues, we introduce a Latin hypercube sampling initialization-based multi-strategy enhanced WOA (CAGWOA). It incorporates a COS sampling initialization strategy (COSI), an adaptive global search approach (GS), and an all-dimensional neighborhood mechanism (ADN). COSI leverages probability density functions created from Latin hypercube sampling, ensuring even solution space coverage to improve the stability of the segmentation model. GS widens the exploration scope to combat stagnation during iterations and improve segmentation efficiency. ADN refines convergence accuracy around optimal individuals to improve segmentation accuracy. CAGWOA's performance is validated through experiments on various benchmark function test sets. Furthermore, we apply CAGWOA alongside similar methods in a multi-threshold image segmentation model for comparative experiments on lung X-ray images of infected patients. The results demonstrate CAGWOA's superiority, including better image detail preservation, clear segmentation boundaries, and adaptability across different threshold levels.