Search Results (8,682)

This paper presents the design, simulation, and experimental evaluation of a low-cost, fixed-step MPPT algorithm based on the incremental conductance technique for operation in a low-power photovoltaic (PV) system with a full-bridge DC-AC converter. The performance of the MPPT algorithm was improved by selecting an appropriate fixed perturbation step size and frequency, ensuring efficient power tracking. The implementation was further optimized by restructuring the conventional algorithm and adapting the DC-AC converter control parameters, which enhanced overall performance and optimized coupling for AC loads. The simulation was performed in Simulink/Matlab with a 560 Wp PV system and a resistive load, under variable irradiation conditions. The perturbation step size was set to 1%, and the perturbation frequency ranged between 2 Hz and 15 Hz, with the converter output at 60 Hz. Experimentally, it was validated at an irradiance of 1000 W/m² and an ambient temperature of 45 °C. The algorithm achieved simulation efficiencies of up to 98.93% and an average experimental efficiency of 96.76%. The response time improved by 86% with a perturbation frequency of 15 Hz. This developed MPPT algorithm demonstrates its reliability, accuracy, and feasibility for implementation. Full article

The scarcity of high-quality labeled data poses a challenge to the application of deep learning (DL) in landslide identification from remote sensing (RS) images. Semi-supervised learning (SSL) has emerged as a promising approach to address the issue of low accuracy caused by the limited availability of high-quality labels. Nevertheless, the application of SSL approaches developed for natural images to landslide identification encounters several challenges. This study focuses on two specific challenges: inadequate information extraction from limited unlabeled RS landslide images and the generation of low-quality pseudo-labels. To tackle these challenges, we propose a novel and generic DL framework called hybrid perturbation mean match (HPM-Match). The framework combines dual-branch input perturbation (DIP) and independent triple-stream perturbation (ITP) techniques to enhance model accuracy with limited labels. The DIP generation approach is designed to maximize the utilization of manually pre-defined perturbation spaces while minimizing the introduction of erroneous information during the weak-to-strong consistency learning (WSCL) process. Moreover, the ITP structure unifies input, feature, and model perturbations, thereby broadening the perturbation space and enabling knowledge extraction from unlabeled landslide images across various perspectives. Experimental results demonstrate that HPM-Match has substantial improvements in IoU, with maximum increases of 26.68%, 7.05%, and 12.96% over supervised learning across three datasets with the same label ratio and reduces the number of labels by up to about 70%. Furthermore, HPM-Match strikes a better balance between precision and recall, identifying more landslides than other state-of-the-art (SOTA) SSL approaches. Full article

We propose the adaptive t-distribution spiral search Dung Beetle Optimization (TSDBO) Algorithm to address the limitations of the vanilla Dung Beetle Optimization Algorithm (DBO), such as vulnerability to local optima, weak convergence speed, and poor convergence accuracy. Specifically, we introduced an improved Tent chaotic mapping-based population initialization method to enhance the distribution quality of the initial population in the search space. Additionally, we employed a dynamic spiral search strategy during the reproduction phase and an adaptive t-distribution perturbation strategy during the foraging phase to enhance global search efficiency and the capability of escaping local optima. Experimental results demonstrate that TSDBO exhibits significant improvements in all aspects compared to other modified algorithms across 12 benchmark tests. Furthermore, we validated the practicality and reliability of TSDBO in robotic path planning applications, where it shortened the shortest path by 5.5–7.2% on a 10 × 10 grid and by 11.9–14.6% on a 20 × 20 grid. Full article

Deep learning models have been widely applied to synthetic aperture radar (SAR) target recognition, offering end-to-end feature extraction that significantly enhances recognition performance. However, recent studies show that optical image recognition models are widely vulnerable to adversarial examples, which fool the models by adding imperceptible perturbation to the input. Although the targeted adversarial attack (TAA) has been realized in the white box setup with full access to the SAR model’s knowledge, it is less practical in real-world scenarios where white box access to the target model is not allowed. To the best of our knowledge, our work is the first to explore transferable TAA on SAR models. Since contrastive learning (CL) is commonly applied to enhance a model’s generalization, we utilize it to improve the generalization of adversarial examples generated on a source model to unseen target models in the black box scenario. Thus, we propose the contrastive learning-based targeted adversarial attack, termed CL-TAA. Extensive experiments demonstrated that our proposed CL-TAA can significantly improve the transferability of adversarial examples to fool the SAR models in the black box scenario. Full article

This paper presents a robust fitted mesh finite difference method for solving a dynamical system of two parameter convection–reaction–diffusion delay differential equations defined on the interval $[0,2]$. The method incorporates a piecewise uniform Shishkin mesh to accurately resolve the solution behavior caused by small perturbation parameters and delay terms. The proposed numerical scheme is proven to be parameter-robust and achieves almost first-order convergence. Numerical illustrations are provided to showcase the method’s effectiveness, highlighting its capability to address boundary and interior layers with improved accuracy. The results, supported by symmetrical considerations in the figures, enhance the precision and serve as validation for the theoretical results. Full article

Elevated glucose levels at the fetal–maternal interface are associated with placental trophoblast dysfunction and increased incidence of pregnancy complications. Trophoblast cells predominantly utilize glucose as an energy source, metabolizing it through glycolysis in the cytoplasm and oxidative respiration in the mitochondria to produce ATP. The TGFβ1/SMAD2 signaling pathway and the transcription factors PPARγ, HIF1α, and AMPK are key regulators of cell metabolism and are known to play critical roles in extravillous trophoblast cell differentiation and function. While HIF1α promotes glycolysis over mitochondrial respiration, PPARγ and AMPK encourage the opposite. However, the interplay between TGFβ1 and these energy-sensing regulators in trophoblast cell glucose metabolism remains unclear. This study aimed to investigate whether and how TGFβ1 regulates energy metabolism in trophoblast cells exposed to normal and high glucose conditions. The trophoblast JEG-3 cells were incubated in normal (5 mM) and high (25 mM) glucose conditions for 24 h in the absence and the presence of TGFβ1. The protein expression levels of phosphor (p)-SMAD2, GLUT1/3, HIF1α, PPARγ, p-AMPK, and specific OXPHOS protein subunits were determined by western blotting, and ATP and lactate production by bioluminescent assay kits. JEG-3 cells exposed to 25 mM glucose decreased ATP production but did not affect lactate production. These changes led to a reduction in the expression levels of GLUT1/3, mitochondrial respiratory chain proteins, and PPARγ, coinciding with an increase in HIF1α expression. Conversely, TGFβ1 treatment at 25 mM glucose reduced HIF1α expression while enhancing the expression levels of GLUT1/3, PPARγ, p-AMPK, and mitochondrial respiratory chain proteins, thereby rejuvenating ATP production. Our findings reveal that high glucose conditions disrupt cellular glucose metabolism in trophoblast cells by perturbing mitochondrial oxidative respiration and decreasing ATP production. Treatment with TGFβ1 appears to counteract this trend, probably by enhancing both glycolytic and mitochondrial metabolism, suggesting a potential regulatory role of TGFβ1 in placental trophoblast cell glucose metabolism. Full article

To address the challenges of slow convergence speed, poor convergence precision, and getting stuck in local optima for unmanned aerial vehicle (UAV) three-dimensional path planning, this paper proposes a path planning method based on an Improved Human Evolution Optimization Algorithm (IHEOA). First, a mathematical model is used to construct a three-dimensional terrain environment, and a multi-constraint path cost model is established, framing path planning as a multidimensional function optimization problem. Second, recognizing the sensitivity of population diversity to Logistic Chaotic Mapping in a traditional Human Evolution Optimization Algorithm (HEOA), an opposition-based learning strategy is employed to uniformly initialize the population distribution, thereby enhancing the algorithm’s global optimization capability. Additionally, a guidance factor strategy is introduced into the leader role during the development stage, providing clear directionality for the search process, which increases the probability of selecting optimal paths and accelerates the convergence speed. Furthermore, in the loser update strategy, an adaptive t-distribution perturbation strategy is utilized for its small mutation amplitude, which enhances the local search capability and robustness of the algorithm. Evaluations using 12 standard test functions demonstrate that these improvement strategies effectively enhance convergence precision and algorithm stability, with the IHEOA, which integrates multiple strategies, performing particularly well. Experimental comparative research on three different terrain environments and five traditional algorithms shows that the IHEOA not only exhibits excellent performance in terms of convergence speed and precision but also generates superior paths while demonstrating exceptional global optimization capability and robustness in complex environments. These results validate the significant advantages of the proposed improved algorithm in effectively addressing UAV path planning challenges. Full article

Noise profoundly affects the quality of electromagnetic data, and selecting the appropriate hyperparameters for machine learning models poses a significant challenge. Consequently, the current machine learning denoising techniques fall short in delivering precise processing of Wide Field Electromagnetic Method (WFEM) data. To eliminate the noise, this paper presents an electromagnetic data denoising approach based on the improved dung beetle optimized (IDBO) gated recurrent unit (GRU) and its application. Firstly, Spatial Pyramid Matching (SPM) chaotic mapping, variable spiral strategy, Levy flight mechanism, and adaptive T-distribution variation perturbation strategy were utilized to enhance the DBO algorithm. Subsequently, the mean square error is employed as the fitness of the IDBO algorithm to achieve the hyperparameter optimization of the GRU algorithm. Finally, the IDBO-GRU method is applied to the denoising processing of WFEM data. Experiments demonstrate that the optimization capacity of the IDBO algorithm is conspicuously superior to other intelligent optimization algorithms, and the IDBO-GRU algorithm surpasses the probabilistic neural network (PNN) and the GRU algorithm in the denoising accuracy of WFEM data. Moreover, the time domain of the processed WFEM data is more in line with periodic signal characteristics, its overall data quality is significantly enhanced, and the electric field curve is more stable. Therefore, the IDBO-GRU is more adept at processing the time domain sequence, and the application results also validate that the proposed method can offer technical support for electromagnetic inversion interpretation. Full article

Mechanical stress is one of the factors influencing the initiation of pitting corrosion and deterioration of the protective properties of the passive layer on stainless steel. The tests carried out on AISI 304L stainless steel showed that, in the 3.5% NaCl environment for samples loaded in the elastic and plastic range, no pitting corrosion initiation was observed. Only mechanical damage of the passive layer occurred. Galvanodynamic electrochemical impedance spectroscopy (g-DEIS) was used as the measuring technique. This technique ensures the monitoring of corrosion processes at zero external current (I_DC = 0) and no potential perturbation of the system. It also allows one to perform many measurements, so that short-term changes such as cracking of the layer and its repassivation are possible to monitor. Full article

Background/Objectives: Intrahepatic cholangiocarcinoma (iCCA) is a malignant liver tumor with a rising global incidence and poor prognosis, largely due to late-stage diagnosis and limited effective treatment options. Standard chemotherapy regimens, including cisplatin and gemcitabine, often fail because of the development of multidrug resistance (MDR), leaving patients with few alternative therapies. Doxycycline, a tetracycline antibiotic, has demonstrated antitumor effects across various cancers, influencing cancer cell viability, apoptosis, and stemness. Based on these properties, we investigated the potential of doxycycline to overcome gemcitabine resistance in iCCA. Methods: We evaluated the efficacy of doxycycline in two MDR iCCA cell lines, MT-CHC01R1.5 and 82.3, assessing cell cycle perturbation, apoptosis induction, and stem cell compartment impairment. We assessed the in vivo efficacy of combining doxycycline and gemcitabine in mouse xenograft models. Results: Treatment with doxycycline in both cell lines resulted in a significant reduction in cell viability (IC₅₀ ~15 µg/mL) and induction of apoptosis. Doxycycline also diminished the cancer stem cell population, as indicated by reduced cholangiosphere formation. In vivo studies showed that while neither doxycycline nor gemcitabine alone significantly reduced tumor growth, their combination led to marked decreases in tumor volume and weight at the study endpoint. Additionally, metabolic analysis revealed that doxycycline reduced glucose uptake in tumors, both as a monotherapy and more effectively in combination with gemcitabine. Conclusions: These findings suggest that doxycycline, especially in combination with gemcitabine, can restore chemotherapy sensitivity in MDR iCCA, providing a promising new strategy for improving outcomes in this challenging disease. Full article

The remote sensing-based inversion of sound speed profile (SSP) enables the acquisition of high-spatial-resolution SSP without in situ measurements. The spatial division of the inversion grid is crucial for the accuracy of results, determining both the number of samples and the consistency of inversion relationships. The result of our research is the introduction of a physics-inspired self-organizing map (PISOM) that facilitates SSP inversion by clustering samples according to the physical perturbation law. The linear physical relationship between sea surface parameters and the SSP drives dimensionality reduction for the SOM, resulting in the clustering of samples exhibiting similar disturbance laws. Subsequently, samples within each cluster are generalized to construct the topology of the solution space for SSP reconstruction. The PISOM method significantly improves accuracy compared with the SOM method without clustering. The PISOM has an SSP reconstruction error of less than 2 m/s in 25% of cases, while the SOM method has none. The transmission loss calculation also shows promising results, with an error of only 0.5 dB at 30 km, 5.5 dB smaller than that of the SOM method. A physical interpretation of the neural network processing confirms that physics-inspired clustering can bring better precision gains than the previous spatial grid. Full article

This study determined the environmental condition of the benthos of Milford Haven Waterway, an area that is arguably the most vulnerable in the UK to anthropogenic activities, including the potential effects of a major oil spill in 1996, using historical data on the macrobenthos more than a decade later in 2008, 2010 and 2013. These data show a gradual decline in numerous univariate diversity measures from the outer (marine) to inner (estuarine) stations. Taxonomic distinctness generally falls within the expected range, and most stations have above-average values compared with other monitoring stations around the UK. The W-statistics for Abundance/Biomass Comparison (ABC) plots are usually strongly positive and never negative. There was a sequential change in community composition from the outer to inner stations, which was strongly related to salinity, and, to a lesser extent, sediment granulometry. None of the species regarded as indicators of organic pollution were prominent in the macrobenthic community of Milford Haven Waterway. On this basis, although there are some slight indications of environmental perturbation at particular sites in certain years, it can be concluded that the benthic communities of Milford Haven Waterway are in a healthy state. This study provides a baseline against which the potential effects of any future environmental accidents and/or the increased industrial development can be assessed. Full article

Thermal condition affects the development and growth of ectotherms. The stenothermic honeybee brood, particularly the prepupae, are sensitive to low rearing temperature. The fat body plays important roles in energy reserve and metabolism during the honeybee brood development. To date, the fat body metabolic changes in prepupae responding to cold stress have not been completely understood. In this study, the ultra-performance liquid chromatography coupled with tandem mass spectrometry (UPLC-MS/MS)-based non-target metabolome was analyzed between the cold-treated (CT, 20 °C, 36 h) and control (CK, 35 °C) fat body in prepupal honeybees. The fat body metabolomic data showed that the levels of 1860 and 254 metabolites were significantly increased and decreased, respectively, in cold-stressed prepupae. These altered metabolites, glutamine, glutamic acid, pyroglutamic acid, and oxidized glutathione, were significantly enriched into glutamine metabolism and glutathione metabolism pathways. Furthermore, the expression levels of glutamine metabolism-related genes, glutaminase (GLS), glutamate dehydrogenase (GDH), and gamma-glutamyl transferase (GGT-1 and GGT-7), were significantly decreased in cold-exposed prepupae compared with the control groups. Meanwhile, the oxidized glutathione (GSSG), but not the reduced glutathione (GSH) content, was increased in the cold-exposed group compared with controls. Collectively, our data revealed the fat body metabolomic changes in larva-to-pupa transition when exposed to cold stress. Our data provided new insights into stenothermic honeybee sensitivity to cold, characterized by perturbation of glutamine metabolism and oxidative stress. Full article

The existing learning resource recommendation systems suffer from data sparsity and missing data labels, leading to the insufficient mining of the correlation between users and courses. To address these issues, we propose a learning resource recommendation method based on graph contrastive learning, which uses graph contrastive learning to construct an auxiliary recommendation task combined with a main recommendation task, achieving the joint recommendation of learning resources. Firstly, the interaction bipartite graph between the user and the course is input into a lightweight graph convolutional network, and the embedded representation of each node in the graph is obtained after compilation. Then, for the input user–course interaction bipartite graph, noise vectors are randomly added to each node in the embedding space to perturb the embedding of graph encoder node, forming a perturbation embedding representation of the node to enhance the data. Subsequently, the graph contrastive learning method is used to construct auxiliary recommendation tasks. Finally, the main task of recommendation supervision and the constructed auxiliary task of graph contrastive learning are jointly learned to alleviate data sparsity. The experimental results show that the proposed method in this paper has improved the Recall@5 by 5.7% and 11.2% and the NDCG@5 by 0.1% and 6.4%, respectively, on the MOOCCube and Amazon-Book datasets compared with the node enhancement methods. Therefore, the proposed method can significantly improve the mining level of users and courses by using a graph comparison method in the auxiliary recommendation task and has better noise immunity and robustness. Full article

For the structural design of cable domes, the determination of prestress force distribution, the section of the structural components, and initial configuration are prerequisites for the subsequent detailed design of cable and strut sizes. To solve this problem, this paper elucidates the basic theory of the Minor Perturbation Method, introduces this theory into the field of force finding design for cable dome structures, and develops a new design method whose core is the comparison between the combined stress of each component conforming to mechanical characteristics of cable-strut structure and control stress, and meeting the convergence condition by adjusting the prestress level and cross-section of components. A corresponding design flow chart is established and programmed with finite element analysis software. Through the case studies of two different kinds of cable dome, it is proven that the proposed method and software program can simply, quickly, and effectively design the cable domes with an economic cross-section. Full article