Transportation organizations face pressing issues due to the aging of road and bridge infrastructure, rising traffic demand, and tight resources. Manual inspection and reactive repair, which are frequently ineffective, expensive, and unreliable, are significant components of traditional maintenance. Therefore, a change to proactive, data-driven management is crucial. The Digital Twin, Building Information Modeling (BIM), Geographic Information System (GIS), and Internet of Things (IoT) are all integrated in this paper's proposed unified maintenance architecture. Data collection, semantic integration, analytical modules, and decision optimization make up the architecture's four tiers. Three functional modules in this system, automated defect detection, time-series performance prediction, and network-level risk assessment, cooperate to convert unprocessed data into insights that can be used. After that, a portfolio of potential treatments is assembled and assessed using economic and optimization analysis. The system is flexible and scalable, making it appropriate for both regional networks and single bridges. It gives agencies a workable option to transition from reactive repairs to preventive stewardship by integrating transparency and traceability. Throughout the whole life cycle of infrastructure assets, this proactive strategy lowers emergency interventions, improves safety, and increases cost-effectiveness.

The pathological diagnosis of gastric cancer relies on the precise differentiation of its fine-grained subtypes, a process often challenged by the lack of annotated data. This study proposes a novel dual-attention meta-learning framework, aiming to address the problem of fine-grained image classification under fee-shot conditions in the medical field, and is particularly suitable for histopathological images of gastric cancer. This study proposes a meta-learning method combined with a dual-attention mechanism to enhance the recognition of imperceptible inter-class differences in pathological images. The channel attention module automatically selects key features by analyzing the information density of the feature map, while the spatial attention module precisely locates the morphological structure in the image. Supported by the meta-learning framework, this model can quickly adapt to new disease classification tasks with a limited number of samples. Experimental results on a self-constructed pathological dataset containing multiple fine-grained subtypes of gastric cancer show that the proposed mean is significantly superior to several benchmark models, demonstrating obvious performance advantages. This work provides a new technical approach for developing computer-aided diagnostic systems in the absence of annotated data.

The orbital change of the Hot Jupiter WASP-4b constitutes a difficult enigma for the theory of exoplanetary dynamics which are normally explained by tidal interactions with the host star; but here we consider an alternative explanation attributing the perturbation to the gravitational influence from an unseen third body. To test this hypothesis, a model was built and analyzed on the basis of the entire set of mass ephemerides with new modeling possibilities from the radial velocity (RV) datasets compiled by HARPS over several years. CORALIE and HIRES measurements together with a combination of Hubble Space Telescope observations and archival Kepler Transit Timing Variations results enable us to infer the existence of a hitherto unknown massive companion, having a 6.5 MJup mass, on a ~7.58 au orbit around WASP-4A, a B8V star slightly more luminous than the Sun. We establish a connection between such a massive companion and the observed orbital decay of WASP-4b; it is revealed through CORALIE and HIRES time-series that massive companions gravitationally perturb such systems, altering the expected period and causing apparent changes in their orbital timing, without tidal effects being involved.

Recent studies have found a negative correlation between atmospheric pressure and the muon flux measured by detectors. This study aims to present a correction method to reflect pressure-related variation. The different muon flux in diverse atmospheric backgrounds are used in the study to test the linear relationship between the changes in pressure at the detector site and the deviations in muon flux. The results show that as the pressure increases, the muon flux collected by multiple detectors decrease. These findings suggest that there is a need to find a correction method to reflect pressure-related variations, which contribute to researching the energy spectrum of cosmic rays, particularly in the regime of ultra–high energies.

The Elephant Trunk demonstrates unique advantages in complex environments thanks to its flexibility, high degree of freedom and versatile transport characteristics. In the industrial world, it is known as nature's universal robotic arm. However, the ability to achieve three-dimensional smooth movement and stiffness adjustment similar to that of the elephant trunk puts forward higher requirements on the structural design, drive integration and functional adaptability of the robot. Here I proposed and implemented a bionic elephant trunk robotic system that incorporates a modular architecture, soft actuators, and adjustable stiffness mechanisms, with 3D motion achieved by three 120 degrees soft bellows actuators working in concert, and thus possessing continuous deformation and spatial localization capabilities. In addition, in order to cope with the demand of rigid-flexible switching for different grasping tasks, the system introduces a variable stiffness mechanism based on skeleton-particle coupling, which is capable of state adjustment and structural toughening under external loads. In the end-operation environment, the robot integrates soft suction cups with adsorption functions. This effectively improves the stability of non-regular target attachment and grasping. The system is modeled using SOLIDWORKS software and the soft actuator is constructed using 3D printing and silicone injection moulding. Experimental results show that the robot exhibits excellent adaptability, load capacity, and locomotion in multi-scenario operations. It shows its wide potential in the direction of complex environment manipulation such as service robots.

This paper investigates the local regularity of weak solutions to a class of inhomogeneous generalized Laplace equations where the right-hand side depends on the weak solutions themselves. The core innovation lies in handling the strong coupling between the inhomogeneous term of the equation and the weak solution. We first establish the local boundedness of the solution as a key a priori estimate for the analysis. On this basis, by constructing ingenious test functions and applying delicate iterative techniques, we overcome the additional nonlinear interference caused by the inhomogeneous term, and finally prove the Harnack inequality satisfied by the weak solution in the interior domain. The results of this study provide a theoretical tool for analyzing the more refined properties of related nonlinear problems.

During the training process of federated learning, the model parameters uploaded by clients or the changes brought by parameter updates may still leak the relevant data information of the corresponding clients. As a mainstream privacy-preserving technology at present, differential privacy usually adopts the operation of adding uniform noise to parameters. However, this method often leads to slower convergence speed of model training, unsatisfactory model accuracy, and increased communication overhead. To address these issues, this paper proposes a dynamic differential privacy federated learning method guided by gradient-increment mask based on sensitivity. Starting from the update status of model parameters, this method establishes a sensitivity scoring mechanism according to the amplitude of parameter updates and gradient information, which is used to measure the degree of privacy leakage risk loss of different parameters in the training process. On this basis, a gradient-increment mask mechanism is added to selectively protect the parameters with high sensitivity scores, so as to reduce the unnecessary disturbance of noise distribution on low-sensitivity parameters. Furthermore, this paper constructs a dynamic privacy budget scheduling strategy guided by sensitivity, which flexibly adjusts the noise intensity according to the parameter sensitivity distribution to achieve refined and rational allocation as well as efficient utilization of privacy budget. Based on the analysis of these aspects, to resolve the contradiction between global noise injection and selective protection in differential privacy, this paper proposes a sensitivity-guided privacy budget allocation algorithm. This algorithm identifies the key parameters for model training through the mask consensus mechanism and allocates stronger noise protection to these parameters to meet the requirements of dynamic differential privacy. Theoretical analysis shows that this scheme satisfies the guarantee requirements of differential privacy. Experimental results indicate that under the same privacy budget, compared with the conventional full-coverage differential privacy federated learning, the model accuracy trained by this scheme in the same number of rounds is 4%-6% higher on several benchmark datasets, which basically proves the feasibility of the scheme proposed in this paper.

Translation in the medical field is an important research direction for large language models. Medical translation texts are usually very professional and have different meanings for the same word. We have very high requirements for the trustworthiness of translation models. But the existing methods are not good at making professional term accuracy and semantic consistency unified. Medical experts can hardly trust the decisions made by the models. For this reason, this study puts forward an interpretable framework with two-stage LoRA fine-tuning based on LLaMA-3-Chinese-8B-Instruct-v3—TSX-MedTrans. The first stage is term-level fine-tuning to make the representation of professional terms better. The second stage is sentence-level fine-tuning to make the context semantic coherence better. This framework also adds an interpretability module. It is used to show the attention distribution visually, so as to check the model's decision-making mechanism. The experimental results show that TSX-MedTrans improves the translation quality a lot: after term-level fine-tuning, BLEU rises from 29.624 to 34.728, and COMET rises from 0.836 to 0.891; after sentence-level fine-tuning, BLEU rises from 34.583 to 38.232, and COMET rises from 0.846 to 0.876.

With the acceleration of the global energy transition process and the wide popularization of smart homes, household electricity consumption data is showing high-frequency and multi-dimensional characteristics. Hourly electricity consumption prediction has gradually become a key technical link to support the load dispatching of smart grids and help users achieve energy-saving management. Machine learning algorithms, with their powerful nonlinear fitting capabilities and efficient feature learning abilities, provide effective solutions for temporal power consumption prediction. This paper proposes the HLOA-CNN-BIGRU regression algorithm. Firstly, correlation analysis and violin plot analysis are carried out. Then, through comparative experiments of multiple machine learning algorithms, it is found that the KELM-LSTM-Transformer algorithm shows significant advantages in all indicators: Its MSE is 0.002, which is much lower than the minimum value of 0.006 in other algorithms. The RMSE is 0.047, significantly lower than the minimum value of 0.075 in other algorithms. The MAE is 0.038, which is significantly better than the minimum value of 0.056 in other algorithms. The MAPE is 2.28%, which is lower than the minimum value of 3.49% in other algorithms. R² is 99.4%, which is higher than the maximum value of 98.7% in other algorithms. Among traditional algorithms, ExtraTrees has a relatively superior overall performance, with R² reaching 98.7%, RMSE 0.075, and MAPE 3.58%, outperforming algorithms such as AdBoost and Random Forest. The overall performance of GBDT is relatively weak, with R² being only 96.8%, and MSE, RMSE, MAE, and MAPE are all the highest among all traditional algorithms. The R² of CatBoost is 97.8%, and its various error indicators are also higher than those of algorithms such as ExtraTrees and AdBoost. Overall, the KELM-LSTM-Transformer algorithm comprehensively outperforms other compared machine learning algorithms in terms of prediction accuracy and fitting effect, with stronger performance, providing important technical support for the efficient operation of smart grids and the refined management of household energy.

To address low accuracy and poor robustness in tomato maturity detection under complex agricultural environments (light variations, occlusion, multi-scale targets), this study proposes a lightweight multi-scale differential fusion network Tomato Net-MSF. Its core innovation is scale-customized feature enhancement modules: (1) Small-scale KSFA module (multi-core selection + spatial-spectral attention) suppresses background and improves small-target recognition; (2) Medium-scale C3k2l module (2*2 simplified convolution + dual-bottleneck stacking) enhances fusion stability; (3) Large-scale LSConv module (global perception + dynamic aggregation) refines target contours. Experiments on Laboro Tomato dataset show: mAP50 0.75 (8% higher than baseline YOLOv11), F1 0.72 (6% higher), and 50FPS inference speed (20ms/frame) on RK3588NPU (5-fold acceleration). The model optimizes multi-scale detection balance, integrating high precision, robustness and edge deployment capability, providing an efficient solution for real-time tomato maturity detection in complex scenarios.