Search Results (584)

With the rapid advancement of Internet of Things (IoT) technology, intrusion detection systems (IDSs) have become pivotal in ensuring network security. However, the data produced by IoT devices is typically sensitive and tends to display non-independent and identically distributed (Non-IID) properties. These factors impose significant limitations on the application of traditional centralized learning. In response to these issues, this study introduces a novel IDS framework grounded in federated learning and knowledge distillation (KD), termed FD-IDS. The proposed FD-IDS aims to tackle issues related to safeguarding data privacy and distributed heterogeneity. FD-IDS employs mutual information for feature selection to enhance training efficiency. For Non-IID data scenarios, the system combines a proximal term with KD. The proximal term restricts the deviation between local and global models, while KD utilizes the global model to steer the training process of local models. Together, these mechanisms effectively alleviate the problem of model drift. Experiments conducted on both the Edge-IIoT and N-BaIoT datasets demonstrate that FD-IDS achieves promising detection performance across multiple evaluation metrics. Full article

This study presents a computational approach to optimize the stiffness distribution of large deformable elastic braces (LDEBs), which possess a high elastic deformation capacity and are designed to enhance the seismic performance of building structures. An optimization problem was formulated to minimize the seismic response of two-story buildings modeled as multi-degree-of-freedom systems, in which both the building frame and the LDEBs were represented by spring elements. Seismic responses under earthquake excitations were evaluated through time-history analyses. Particle swarm optimization (PSO) was employed to determine the optimal stiffness ratios of LDEBs that minimize the maximum story drift. Extensive round-robin analyses were conducted to verify the validity of the PSO results, generating response surfaces that mapped the maximum story drift against the LDEBs’ stiffness under three different earthquake records. The analysis revealed that the optimal solutions obtained from the PSO coincided with the global minimum identified in the round-robin response surfaces. These results confirm the effectiveness of the proposed optimization framework and demonstrate the potential of LDEBs for enhancing seismic resilience in structural designs. Full article

To solve the problems of carbon fiber (CF) electrodes, including poor frequency response and large potential drift, CFs were subjected to a roughening pretreatment process combining thermal oxidation and electrochemical anodic oxidation and then modified with Ag nanoparticles (AgNPs) using electroplating to prepare a CF electric field sensor. The surface morphology of the as-prepared AgNP-CF electric field sensor was characterized via optical microscopy, scanning electron microscopy, XPS, and energy-dispersive spectroscopy, and its impedance, polarization drift, self-noise, and temperature drift values were determined. Results show that the surface modification of the AgNP-CF electric field sensor is uniform, and its specific surface area is considerably increased. The electrode potential drift, characteristic impedance, self-noise, and temperature drift are 52.1 µV/24 h, 3.6 Ω, 2.993 nV/√Hz@1 Hz, and less than 70 µV/°C, respectively. Additionally, the AgNP-CF electric field sensor demonstrates low polarization and high stability. In field and simulated ocean tests, the AgNP-CF electrode exhibits excellent performance in the field and underwater environments, which renders it promising for the measurement of the ocean and geoelectric fields owing to its advantages, such as low noise and high stability. Full article

Problem: Agriculture in India faces pressing challenges related to water scarcity, excessive pesticide use, and inefficient energy consumption, impacting both economic sustainability and environmental health. Methodology: This study integrates Life Cycle Assessment (LCA), Data Envelopment Analysis (DEA), Intelligent Management Models (IMMs), and Multi-Criteria Decision Analysis (MCDA) to assess the economic and environmental benefits of UAV-based spraying in Indian agriculture. Data were collected from UAV service providers and field trials in Punjab, Haryana, and Rajasthan. Results: UAV spraying achieved a 70% reduction in water use, 40% reduction in pesticide consumption, and a 50% reduction in CO₂ emissions compared to conventional spraying. DEA results showed higher efficiency scores for UAVs, while IMM optimization achieved 95% pesticide coverage and reduced drift by 80%. Implications: MCDA ranked government subsidies as the most effective policy intervention. These findings support UAV spraying as a viable, scalable solution for climate-smart agriculture in India, offering both productivity and sustainability gains. Full article

Compact toroid (CT) injection, with its characteristics of high plasma density and extremely high injection velocity, is considered a highly promising method for core fueling in fusion reactors. Previous studies have lacked investigation into the transport process of CT within drift tubes. To investigate the dynamic processes of CT in drift tubes, this study developed a compressible magnetohydrodynamics (MHD) solver and a magnetic diffusion solver based on the OpenFOAM platform. They were integrated into a multi-region coupling framework to create a multi-region coupled MHD solver, mhdMRF, for simulating the dynamic behavior of CT in drift tubes and its interaction with finite-resistivity walls. The solver demonstrated excellent performance in simulations of the Orszag–Tang MHD vortex problem, the Brio–Wu shock tube problem, analytical verification of magnetic diffusion, and validation of internal coupling boundary conditions. Additionally, this work innovatively explored the effects of the geometric structure at the end of the inner electrode and finite-resistivity walls on the transport processes of CT. The results indicate that optimizing the geometric structure at the end of the inner electrode can significantly enhance the confinement performance and stability of CT transport. The resistivity of the wall profoundly influences the magnetic field structure and density distribution of CT. Full article

To address the technical problems of broad droplet size spectrum, insufficient atomization uniformity, and spray drift in plant protection unmanned aerial vehicle (UAV) applications, this study developed a novel two-stage aerial electrostatic spraying device based on the coupled mechanisms of hydraulic atomization and electrostatic induction, and, through the integration of three-dimensional numerical simulation and additive manufacturing technology, a new two-stage inductive charging device was designed on the basis of the traditional hydrodynamic nozzle structure, and a synergistic optimization study of the charging effect and atomization characteristics was carried out systematically. With the help of a charge ratio detection system and Malvern laser particle sizer, spray pressure (0.25–0.35 MPa), charging voltage (0–16 kV), and spray height (100–1000 mm) were selected as the key parameters, and the interaction mechanism of each parameter on the droplet charge ratio (C/m) and the particle size distribution (Dv50) was analyzed through the Box–Behnken response surface experimental design. The experimental data showed that when the charge voltage was increased to 12 kV, the droplet charge-to-mass ratio reached a peak value of 1.62 mC/kg (p < 0.01), which was 83.6% higher than that of the base condition; the concentration of the particle size distribution of the charged droplets was significantly improved; charged droplets exhibited a 23.6% reduction in Dv50 (p < 0.05) within the 0–200 mm core atomization zone below the nozzle, with the coefficient of variation of volume median diameter decreasing from 28.4% to 16.7%. This study confirms that the two-stage induction structure can effectively break through the charge saturation threshold of traditional electrostatic spraying, which provides a theoretical basis and technical support for the optimal design of electrostatic spraying systems for plant protection UAVs. This technology holds broad application prospects in agricultural settings such as orchards and farmlands. It can significantly enhance the targeted deposition efficiency of pesticides, reducing drift losses and chemical usage, thereby enabling agricultural enterprises to achieve practical economic benefits, including reduced operational costs, improved pest control efficacy, and minimized environmental pollution, while generating environmental benefits. Full article

We investigate the bridge problem for stochastic processes, that is, we analyze the statistical properties of trajectories constrained to begin and terminate at a fixed position within a time interval $\tau$. Our primary focus is the time-reversal symmetry of these trajectories: under which conditions do the statistical properties remain invariant under the transformation $t\to \tau -t$? To address this question, we compare the stochastic differential equation describing the bridge, derived equivalently via Doob’s transform or stochastic optimal control, with the corresponding equation for the time-reversed bridge. We aim to provide a concise overview of these well-established derivation techniques and subsequently obtain a local condition for the time-reversal asymmetry that is specifically valid for the bridge. We are specifically interested in cases in which detailed balance is not satisfied and aim to eventually quantify the bridge asymmetry and understand how to use it to derive useful information about the underlying out-of-equilibrium dynamics. To this end, we derived a necessary condition for time-reversal symmetry, expressed in terms of the current velocity of the original stochastic process and a quantity linked to detailed balance. As expected, this formulation demonstrates that the bridge is symmetric when detailed balance holds, a sufficient condition that was already known. However, it also suggests that a bridge can exhibit symmetry even when the underlying process violates detailed balance. While we did not identify a specific instance of complete symmetry under broken detailed balance, we present an example of partial symmetry. In this case, some, but not all, components of the bridge display time-reversal symmetry. This example is drawn from a minimal non-equilibrium model, namely Brownian Gyrators, that are linear stochastic processes. We examined non-equilibrium systems driven by a "mechanical” force, specifically those in which the linear drift cannot be expressed as the gradient of a potential. While Gaussian processes like Brownian Gyrators offer valuable insights, it is known that they can be overly simplistic, even in their time-reversal properties. Therefore, we transformed the model into polar coordinates, obtaining a non-Gaussian process representing the squared modulus of the original process. Despite this increased complexity and the violation of detailed balance in the full process, we demonstrate through exact calculations that the bridge of the squared modulus in the isotropic case, constrained to start and end at the origin, exhibits perfect time-reversal symmetry. Full article

The TransT object tracking algorithm, built on Transformer architecture, effectively integrates deep feature extraction with attention mechanisms, thereby enhancing the stability and accuracy of the algorithm. However, this algorithm exhibits insufficient tracking accuracy and boundary box drift when dealing with similar background clutter, which directly affects the subsequent tracking process. To overcome this problem, this paper constructs a semantic enhancement model, which utilizes multi-layer feature representations extracted from deep networks, and correlates and fuses shallow features with deep features by using cross-attention. At the same time, in order to adapt to the changes in the surrounding environment of the object and establish good discrimination with similar objects, this paper proposes a dynamic mask strategy to optimize the attention allocation mechanism and finally employs an object template update mechanism to improve the adaptability of the model by comparing the spatio-temporal information of successive frames to update the object template in time, further enhancing its tracking performance in complex scenes. Experimental comparison results demonstrate that the algorithm proposed in this paper can effectively handle similar background clutter, leading to a significant improvement in the overall performance of the tracking model. Full article

Aiming at the problem of appearance drift and susceptibility to noise interference in visual place recognition (VPR), we propose DD–DPFE: a Dynamic Difference and Dual-Path Feature Enhancement method. Embedding differential attention mechanisms in the DINOv2 model to mitigate the effects of process interference and adding serial-parallel adapters allows efficient model parameter migration and task adaptation. Our method constructs a two-way feature enhancement module with global–local branching synergy. The global branch employs a dynamic fusion mechanism with a multi-layer Transformer encoder to strengthen the structured spatial representation to cope with appearance changes, while the local branch suppresses the over-response of redundant noise through an adaptive weighting mechanism and fuses the contextual information from the multi-scale feature aggregation module to enhance the robustness of the scene. The experimental results show that the model architecture proposed in this paper is an obvious improvement in different environmental tests. This is most obvious in the simulation test of a night scene, verifying that the proposed method can effectively enhance the discriminative power of the system and its anti-jamming ability in complex scenes. Full article

Portfolio optimisation is a crucial decision-making task. Traditionally static, this problem is more realistically addressed as dynamic, reflecting frequent trading within financial markets. The dynamic nature of the portfolio optimisation problem makes it susceptible to rapid market changes or financial contagions, which may cause drifts in historical data. While reinforcement learning (RL) offers a framework that allows for the formulation of portfolio optimisation as a dynamic problem, existing RL approaches lack the ability to adapt to rapid market changes, such as pandemics, and fail to capture the resulting concept drift. This study introduces a recurrent proximal policy optimisation (PPO) algorithm, leveraging recurrent neural networks (RNNs), specifically the long short-term memory network (LSTM) for pattern recognition. Initial results conclusively demonstrate the recurrent PPO’s efficacy in generating quality portfolios. However, its performance declined during the COVID-19 pandemic, highlighting susceptibility to rapid market changes. To address this, an incremental recurrent PPO is developed, leveraging incremental learning to adapt to concept drift triggered by the pandemic. This enhanced algorithm not only learns from ongoing market data but also consistently identifies optimal portfolios despite significant market volatility, offering a robust tool for real-time financial decision-making. Full article

Carbon monoxide (CO) is a toxic pollutant emitted by municipal solid waste incineration (MSWI), which has a strong correlation with dioxins. In terms of the sustainable development of an ecological environment, CO emission concentration is strictly controlled by the environmental departments of various countries in the world. The construction of its prediction model is conducive to pollution reduction control. The MSWI process is affected by multi-factors such as MSW component fluctuation, equipment wear and maintenance, and seasonal change, and has complex nonlinear and time-varying characteristics, which makes it difficult for the CO prediction model based on offline historical data to adapt to the above changes. In addition, the continuous emission monitoring system (CEMS) used for conventional pollutant detection has unavoidable misalignment and failure problems. In this article, a novel prediction model of CO emission from the MSWI process based on semi-supervised concept drift (CD) detection in kernel feature space is proposed. Firstly, the CO emission deep prediction model and the kernel feature space detection model are constructed based on offline batched historical data, and the historical data set for the real-time construction of the pseudo-labeling model is obtained. Secondly, the drift detection for the CO emission prediction model is carried out based on real-time data by using unsupervised kernel principal component analysis (KPCA) in terms of feature space. If CD occurs, the pseudo-label model is constructed, the pseudo-truth value is obtained, and the drift sample is confirmed and selected based on the Page–Hinkley (PH) test. If no CD occurs, the CO emission concentration is predicted based on the historical prediction model. Then, the updated data set of the CO emission prediction model and kernel feature space detection is obtained by combining historical samples and drift samples. Finally, the offline history model is updated with a new data set when the preset conditions are met. Based on the real data set of an MSWI power plant in Beijing, the validity of the proposed method is verified. Full article

Dengue, an acute infectious disease caused by the dengue virus, remains a major public health problem in the 21st century. This study investigated the global dengue burden, identified high-risk regions, evaluated the long-term incidence trends, and can inform evidence-based control strategies. Using GBD 2021 data, we analysed the dengue incidence from 1992 to 2021 using age–period–cohort models. We determined the net drift (overall annual percentage change), local drift (annual percentage change for each age group), longitudinal age curves (expected longitudinal age-specific rates), and periods’ (cohorts’) relative risks. In 2021, the global age-standardised incidence rate reached 752.04/100,000 (95% UI: 196.33–1363.35), a 47.26% increase since 1992. High-risk regions included eastern sub-Saharan Africa, Southeast Asia, South Asia, and Latin America and the Caribbean. Southeast Asia experienced the largest rise (65.43%), with a net drift of 2.47% (1992–2021). While individuals aged 5–39 years bore the highest burden, those over 80 faced an elevated risk. Dengue remains a critical public health threat, disproportionately affecting younger populations but increasingly endangering older adults. Targeted interventions in high-risk regions and age groups, coupled with precision public health strategies, are essential to enhance prevention and control efforts. Full article

Background/Objectives. While mind wandering has often been linked to negative outcomes, some research suggests it has potential benefits for creativity, particularly through incubation. However, two critical gaps remain: limited understanding of mind wandering’s effects on creative performance and lack of comparative research examining its impact on both divergent and convergent thinking. The study addressed these gaps by comparing the effects of two types of mind wandering (i.e., with and without awareness) on both types of creative thinking, using repeated and novel problems post-incubation to isolate effects. Methods. Eighty-five participants completed divergent (Unusual Uses Task, UUT) and convergent (Compound Remote Associate Task, CRA) thinking tasks, interspersed with a 0-back incubation task. Thought probes measured mind wandering frequency and awareness. Performance was assessed for fluency and originality (UUT) and accuracy (CRA), with problems categorised by difficulty. Results. Results revealed no significant effects of mind wandering on divergent thinking, though incubation improved fluency, particularly for repeated items. For convergent thinking, mind wandering with awareness enhanced performance on low-difficulty repeated items, while mind wandering without awareness hindered novel moderate-difficulty items. Divergent and convergent performance showed no correlation, suggesting distinct cognitive demands. Conclusions. The findings provide evidence that mind wandering’s impact on creativity is limited and context-dependent, with conscious reflection during incubation more beneficial than uncontrolled drifting. Differences in task demands and difficulty levels further modulate these effects. Future research should explore naturalistic settings and use of incubation tasks that do not compete for cognitive resources with the core task to better understand incubation and mind wandering’s roles in creativity. Full article

Aiming to address the problems of fewer related studies on autonomous navigation algorithms based on multi-sensor fusion in complex scenarios in pastures, lower degrees of fusion, and insufficient cruising accuracy of the operation path in complex outdoor environments, a multimodal autonomous navigation system is proposed based on a loosely coupled architecture of Cartographer–RTAB-Map (real-time appearance-based mapping). Through laser-vision inertial guidance multi-sensor data fusion, the system achieves high-precision mapping and robust path planning in complex scenes. First, comparing the mainstream laser SLAM algorithms (Hector/Gmapping/Cartographer) through simulation experiments, Cartographer is found to have a significant memory efficiency advantage in large-scale scenarios and is thus chosen as the front-end odometer. Secondly, a two-way position optimization mechanism is innovatively designed: (1) When building the map, Cartographer processes the laser with IMU and odometer data to generate mileage estimations, which provide positioning compensation for RTAB-Map. (2) RTAB-Map fuses the depth camera point cloud and laser data, corrects the global position through visual closed-loop detection, and then uses 2D localization to construct a bimodal environment representation containing a 2D raster map and a 3D point cloud, achieving a complete description of the simulated ranch environment and material morphology and constructing a framework for the navigation algorithm of the pushing robot based on the two types of fused data. During navigation, the combination of RTAB-Map’s global localization and AMCL’s local localization is used to generate a smoother and robust positional attitude by fusing IMU and odometer data through the EKF algorithm. Global path planning is performed using Dijkstra’s algorithm and combined with the TEB (Timed Elastic Band) algorithm for local path planning. Finally, experimental validation is performed in a laboratory-simulated pasture environment. The results indicate that when the RTAB-Map algorithm fuses with the multi-source odometry, its performance is significantly improved in the laboratory-simulated ranch scenario, the maximum absolute value of the error of the map measurement size is narrowed from 24.908 cm to 4.456 cm, the maximum absolute value of the relative error is reduced from 6.227% to 2.025%, and the absolute value of the error at each location is significantly reduced. At the same time, the introduction of multi-source mileage fusion can effectively avoid the phenomenon of large-scale offset or drift in the process of map construction. On this basis, the robot constructs a fusion map containing a simulated pasture environment and material patterns. In the navigation accuracy test experiments, our proposed method reduces the root mean square error (RMSE) coefficient by 1.7% and Std by 2.7% compared with that of RTAB-MAP. The RMSE is reduced by 26.7% and Std by 22.8% compared to that of the AMCL algorithm. On this basis, the robot successfully traverses the six preset points, and the measured X and Y directions and the overall position errors of the six points meet the requirements of the pasture-pushing task. The robot successfully returns to the starting point after completing the task of multi-point navigation, achieving autonomous navigation of the robot. Full article

It is very common for multiple experiments to be conducted under non-identical but similar conditions, perhaps because of implementation errors, natural variability in experimental material, or gradual drifting of experimental conditions. In the extremes of modelling, each dataset could be analysed independently or the differences ignored entirely and the datasets treated as replicates. In this paper, an alternative approach is proposed in which a common parametric family is assumed across all datasets, but which then links parameters in the separate datasets through prior models. It is assumed that knowledge exists about the relationship between the model parameters. For example, there may be some parameters which are expected to be equal, some which can be ordered, and others which follow more complex relationships. The proposed modelling approach is motivated by and illustrated using a collection of 18 autoradiography line-source experiments, which are then used to determine details of a blur function used in the analysis of electron microscope autoradiography images. Appropriate prior models are considered which contain prior parameters controlling the level of agreement with the assumption; at one extreme the analyses are independent, while at the other they are treated as replicates. The results show how the parameter estimates and goodness-of-fit depend on the level of agreement; in addition, a hyper-prior is placed on these parameters to for allow automatic analysis. Parameter estimation is performed using Markov chain Monte Carlo methods. As well as presenting a novel analysis of autoradiography data, the proposed method also provides a general framework for dealing with a wide variety of practical data analysis problems, showing potential for widespread use across the experimental sciences. Full article