Search Results (2,433)

This paper proposes a cascaded state estimation framework based on proprioception for robots. A generalized-momentum-based Kalman filter (GMKF) estimates the ground reaction forces at the feet through joint torques, which are then input into an error-state Kalman filter (ESKF) to obtain the robot’s prior state estimate. The system’s dynamic equations on the Lie group are parameterized using canonical coordinates of the first kind, and variations in the tangent space are mapped to the Lie algebra via the inverse of the right trivialization. The resulting parameterized system state equations, combined with the prior estimates and a sliding window, are formulated as a moving horizon estimation (MHE) problem, which is ultimately solved using a parallel real-time iteration (Para-RTI) technique. The proposed framework operates on manifolds, providing a tightly coupled estimation with higher accuracy and real-time performance, and is better suited to handle the impact noise during foot–ground contact in legged robots. Experiments were conducted on the BQR3 robot, and comparisons with measurements from a Vicon motion capture system validate the superiority and effectiveness of the proposed method. Full article

Proton-exchange membrane fuel cells (PEMFCs) stand out for their exceptional efficiency, high power density, and zero emissions, as they produce merely heat and water as byproducts. Appropriate and robust thermal management is the key to ensuring the maximum efficiency of the fuel cell (FC) as its optimum operating temperature is 70~80 °C. The current study was designed for the parametric evaluation of coolant channels (CCs) based on the multi-pass serpentine flow field (MPSFF) to investigate the relationship between channel geometry and thermal performance in PEM fuel cells, offering novel insights into optimal design configurations for improved thermal management. Six 3D computational models of PEMFCs with varying numbers of coolant channels were created and evaluated using COMSOL 6.2. The acquired results suggested that longer channel lengths with more serpentine turns cause the maximum number of hot spots around turns and offer a maximal pressure drop, whereas increasing the number of channels results in a uniform thermal distribution and leads to a minimal pressure drop. The findings indicate that systematic variations in geometrical configurations of MPSFFs can significantly enhance thermal uniformity and minimize the pressure drop, offering valuable insights for improving the efficiency of PEMFCs. Full article

Background/Objectives: Triple negative breast cancer (TNBC) is an aggressive, molecularly heterogeneous subtype of breast cancer, accounting for approximately 10–15% of all cases. While reproductive and metabolic factors contribute to breast cancer development, growing concerns about environmental exposures, alongside biological and socio-cultural influences, underscore the need for targeted prevention strategies across diverse populations. Despite increasing evidence linking biological, socioeconomic, and environmental factors to TNBC outcomes, the molecular mechanisms underlying these relationships remain poorly understood. Micro-RNAs (miRNAs), which regulate gene expression and play critical roles in cancer development, have emerged as potential mediators between environmental exposures and TNBC progression. The goal of this research is to identify environmental risk factors that directly relate to TNBC stages and enhance understanding of the mechanisms underlying how miRNAs link environmental exposures to TNBC stages. Methods: In this study, we analyzed 434 Formalin-Fixed, Paraffin-Embedded (FFPE) tumor samples from 434 women diagnosed with TNBC between 2009 and 2019, encompassing diverse cancer stages (184 cases from early stage and 250 cases from advanced stage), racial backgrounds, and socioeconomic statuses. The sequencing data were linked with the Louisiana Tumor Registry data and the Environmental Justice index. Results: A total of 348 unique miRNAs were identified as differentially expressed across environmental risk factors statistically associated with TNBC stage, adjusting for plate effects. An UpSet plot revealed 44 miRNAs commonly differentially expressed across TNBC stages and multiple environmental exposures. At least one differentially expressed (DE) miRNA was shared between the TNBC stage and each environmental factor, with many associated with receptor-negative and aggressive breast cancer subtypes. Conclusions: These findings highlight potential biological pathways through which exposures may drive the TNBC progression and contribute to disparities in outcomes. Full article

Gray mold disease, caused by Botrytis cinerea, severely impacts grape production worldwide. Although proanthocyanidins (PAs) contribute to fungal pathogen resistance, their role in grape defense against B. cinerea remains unclear. Here, we demonstrate that VvMYBPA1, a key transcriptional regulator of PA biosynthesis, negatively modulates B. cinerea resistance in grape berries. While infection suppressed endogenous VvMYBPA1, its agroinfiltration-mediated transient overexpression in berries elevated susceptibility, paralleling reduced β-1,3-glucanase (BGL) and polyphenol oxidase (PPO) activities. Additionally, VvMYBPA1 overexpression elevated VvRBOHs’ expression and reduced peroxidase (POD) activity, resulting in excessive hydrogen peroxide (H₂O₂) accumulation and more cell death. Our results reveal that VvMYBPA1 negatively regulates B. cinerea resistance by disrupting antioxidant enzyme activity and ROS homeostasis, providing new insights into the interplay between PA biosynthesis and fungal defense mechanisms. Full article

The Amazon River Basin (ARB) experienced an extreme drought from summer 2023 to spring 2024, driven by complex interactions among multiple climatic and environmental factors. A detailed investigation into this drought is crucial in understanding the entire process of the drought. Here, we employ drought indices derived from the Gravity Recovery and Climate Experiment (GRACE), GRACE Follow-On (GFO), and Swarm missions to reconstruct the drought’s progression, combined with reanalysis datasets and extreme-climate indices to analyze atmospheric and hydrological mechanisms. Our findings reveal a six-month drought from September 2023, reaching a drought peak of −1.29 and a drought severity of −5.62, with its epicenter migrating systematically from the northwestern to southeastern basin, spatially mirroring the 2015–2016 extreme drought pattern. Reduced precipitation and abnormal warming were the direct causes, which were closely linked to the 2023 El Niño event. This event disrupted atmospheric vertical movements. These changes led to abnormally strong sinking motions over the basin, which interacted synergistically with anomalies in land cover types caused by deforestation, triggering this extreme drought. This study provides spatiotemporal drought diagnostics valuable for hydrological forecasting and climate adaptation planning. Full article

Mitochondrial dysfunction is implicated across a spectrum of neurological diseases, yet its causal role and mechanistic specificity remain unclear. This study employed a multi-modal integrative analysis of mitochondrial gene expression in Alzheimer’s Disease (AD), Amyotrophic Lateral Sclerosis (ALS), Multiple Sclerosis (MS), and Parkinson’s Disease (PD) to address these gaps. We combined machine learning for predictive modeling with genetic causal inference methods (Mendelian Randomization, colocalization, PheWAS), followed by drug enrichment analysis and molecular docking. Our machine learning models, particularly Support Vector Machine and Multi-layer Perceptron, effectively classified these conditions, with MS exhibiting the highest predictability (mean Accuracy: 0.758). Causal inference analyses identified specific gene–disease links; for instance, genetically predicted increased expression of PDK1 was causally associated with an elevated risk for both AD (OR = 1.041) and ALS (OR = 1.037), identifying pyruvate metabolism as a shared vulnerability. In contrast, genes like SLC25A38 emerged as highly predictive specifically for PD. We also observed evidence of potential brain–periphery interaction, such as a bidirectional causal relationship between red blood cell indices and MS risk. Finally, drug enrichment analysis highlighted Celecoxib, and subsequent molecular docking predicted a strong binding affinity to PDK1 (docking score S = −6.522 kcal/mol), generating hypotheses for potential metabolic modulation. Taken together, this study provides a computational hypothesis framework suggesting mitochondrial pathways and targets that warrant future biological validation. This study provides specific, genetically supported evidence for the causal role of mitochondrial pathways in neurological diseases and identifies tangible targets for future therapeutic development. Full article

Heavy metal pollution, particularly copper contamination, threatens the ecological environment and human survival. In response to this pressing environmental issue, the development of innovative remediation strategies has become imperative. Bioremediation technology is characterized by remarkable advantages, including its ecological friendliness, cost-effectiveness, and operational efficiency. In our previous research, Rossellomorea sp. ZC255 demonstrated substantial potential for environmental bioremediation applications. This study investigated the removal characteristics and underlying mechanism of strain ZC255 and revealed that the maximum removal capacity was 253.4 mg/g biomass under the optimal conditions (pH 7.0, 28 °C, and 2% inoculum). The assessment of the biosorption process followed pseudo-second-order kinetics, while the adsorption isotherm may fit well with both the Langmuir and Freundlich models. Cell surface alterations on the Cu(II)-treated biomass were observed through scanning electron microscopy (SEM). Cu(II) binding functional groups were determined via Fourier transform infrared spectroscopy (FTIR) analysis. Simultaneously, the genomic analysis of strain ZC255 identified multiple genes potentially involved in heavy metal resistance, transport, and metabolic processes. These studies highlight the significance of strain ZC255 in the context of environmental heavy metal bioremediation research and provide a basis for using strain ZC255 as a copper removal biosorbent. Full article

Herein, a bi-functional composite photocatalyst was synthesized by integrating carbon nanotubes (CNTs) and graphitic carbon nitride (GCN) via a facile electrostatic self-assembly strategy. The resulting CNTs/GCN composite served dual roles as both a solid emulsifier and a photocatalyst, enabling highly efficient photocatalytic benzyl alcohol oxidation within a Pickering emulsion system. The relationship between emulsion droplet size and solid emulsifier dosage was investigated and optimized. The enhanced photocatalytic function was supported by an improved photocurrent response and reduced charge-transfer resistance, attributed to superior charge separation efficiency. Consequently, the benzyl alcohol conversion efficiency achieved in the Pickering emulsion system (58.9%) was three-fold of that observed in a traditional oil–water non-emulsion system (19.0%). Key active species were identified as photoholes, and an interfacial reaction mechanism was proposed. This work provides a new approach for extending photocatalytic applications in aqueous environments to diverse organic conversion reactions through the construction of multifunctional photocatalysts. Full article

In recent years, most proposed digital image encryption algorithms have primarily focused on encrypting raw pixel data, often neglecting the integration with image compression techniques. Image compression algorithms, such as JPEG, are widely utilized in internet applications, highlighting the need for encryption methods that are compatible with compression processes. This study introduces an innovative color image encryption algorithm integrated with JPEG compression, designed to enhance the security of images susceptible to attacks or tampering during prolonged transmission. The research addresses critical challenges in achieving an optimal balance between encryption security and compression efficiency. The proposed encryption algorithm is structured around three key compression phases: Discrete Cosine Transform (DCT), quantization, and entropy coding. At each stage, the algorithm incorporates advanced techniques such as block segmentation, block replacement, DC coefficient confusion, non-zero AC coefficient transformation, and RSV (Run/Size and Value) pair recombination. Extensive simulations and security analyses demonstrate that the proposed algorithm exhibits strong robustness against noise interference and data loss, effectively meeting stringent security performance requirements. Full article

Biothiols, including cysteine (Cys), homocysteine (Hcy), and glutathione (GSH), are crucial for physiological regulation and their imbalance poses severe health risks. Herein, we developed a pH-responsive liquid crystal (LC)-based sensing platform for detection of biothiols by doping 4-n-pentylbiphenyl-4-carboxylic acid (PBA) into 4-n-pentyl-4-cyanobiphenyl (5CB). Urease catalyzed urea hydrolysis to produce OH⁻, triggering the deprotonation of PBA, thereby inducing a vertical alignment of LC molecules at the interface corresponding to dark optical appearances. Heavy metal ions (e.g., Hg²⁺) could inhibit urease activity, under which condition LC presents bright optical images and LC molecules maintain a state of tilted arrangement. However, biothiols competitively bind to Hg²⁺, the activity of urease is maintained which enables the occurrence of urea hydrolysis. This case triggers LC molecules to align in a vertical orientation, resulting in bright optical images. This pH-driven reorientation of LCs provides a visual readout (bright-to-dark transition) correlated with biothiol concentration. The detection limits of Cys/Hcy and GSH for the PBA-doped LC platform are 0.1 μM and 0.5 μM, respectively. Overall, this study provides a simple, label-free and low-cost strategy that has a broad application prospect for the detection of biothiols. Full article

The Shahejie Formation in Nanpu Sag is a crucial region for deep-layer hydrocarbon exploration in the Bohai Bay Basin. To address the impact of faults on sublacustrine fan formation and spatial distribution within the study area, this study integrated well logging, laboratory analysis, and 3D seismic data to systematically analyze sedimentary characteristics of sandbodies from the first member of the Shahejie Formation (Es₁) sublacustrine fans, clarifying their planar and cross-sectional distributions. Further research indicates that Gaoliu Fault activity during Es₁ deposition played a significant role in fan development through two mechanisms: (1) vertical displacement between hanging wall and footwall reshaped local paleogeomorphology; (2) tectonic stresses generated by fault movement affected slope stability, triggering gravitational mass transport processes that remobilized fan delta sediments into the central depression zone as sublacustrine fans through slumping and collapse mechanisms. Core observations reveal soft-sediment deformation features, including slump structures, flame structures, and shale rip-up clasts. Seismic profiles show lens-shaped geometries with thick centers thinning laterally, exhibiting lateral pinch-out terminations. Inverse fault-step architectures formed by underlying faults control sandbody distribution patterns, restricting primary deposition locations for sublacustrine fan development. The study demonstrates that sublacustrine fans in the study area are formed by gravity flow processes. A new model was established, illustrating the combined control of the Gaoliu Fault and reverse stepover faults on fan development. These findings provide valuable insights for gravity flow exploration and reservoir prediction in the Nanpu Sag, offering important implications for hydrocarbon exploration in similar lacustrine rift basins. Full article

Powdery mildew, caused by Erysiphe quercicola, is one of the primary diseases responsible for the reduction in natural rubber production in China. This disease is a typical airborne pathogen, characterized by its ability to spread via air currents and rapidly escalate into an epidemic under favorable environmental conditions. Accurate prediction and determination of the prevention and control period represent both a critical challenge and key focus area in managing rubber-tree powdery mildew. This study investigates the effects of spore concentration, environmental factors, and infection time on the progression of powdery mildew in rubber trees. By employing six distinct machine learning model construction methods, with the disease index of powdery mildew in rubber trees as the response variable and spore concentration, temperature, humidity, and infection time as predictive variables, a preliminary predictive model for the disease index of rubber-tree powdery mildew was developed. Results from indoor inoculation experiments indicate that spore concentration directly influences disease progression and severity. Higher spore concentrations lead to faster disease development and increased severity. The optimal relative humidity for powdery mildew development in rubber trees is 80% RH. At varying temperatures, the influence of humidity on the disease index differs across spore concentration, exhibiting distinct trends. Each model effectively simulates the progression of powdery mildew in rubber trees, with predicted values closely aligning with observed data. Among the models, the Kernel Ridge Regression (KRR) model demonstrates the highest accuracy, the R² values for the training set and test set were 0.978 and 0.964, respectively, while the RMSE values were 4.037 and 4.926, respectively. This research provides a robust technical foundation for reducing the labor intensity of traditional prediction methods and offers valuable insights for forecasting airborne forest diseases. Full article

This paper presents the multi-triangular ring and self-routing protocol (MTRSRP), which is a new decentralized strategy designed to boost throughput and network efficiency in multiring scatternets. MTRSRP comprises two primary phases: leader election and scatternet formation, which collaborate to establish an effective multi-triangular ring topology. In the leader election phase, nodes exchange broadcast messages to gather neighbor information and elect coordinators through a competitive process. The scatternet formation phase determines the optimal number of rings based on the coordinator’s collected node information and predefined rules. The master nodes then send unicast connection requests to establish piconets within the scatternet, following a predefined role table. Intra- and inter-bridge nodes were activated to interconnect the piconets, creating a cohesive multi-triangular ring scatternet. Additionally, MTRSRP incorporates a self-routing addressing scheme within the triangular ring architecture, optimizing packet transmission paths and reducing overhead by utilizing master/slave relationships established during scatternet formation. Simulation results indicate that MTRSRP with dual-bridge connectivity outperforms the cluster-based on-demand routing protocol and Bluetooth low-energy mesh schemes in key network transmission performance metrics such as the transmission rate, packet delay, and delivery ratio. In summary, MTRSRP significantly enhances throughput, optimizes routing paths, and improves network efficiency in multi-ring scatternets through its multi-triangular ring topology and self-routing capabilities. Full article

With the support of the dual-carbon strategy and related policies, China’s offshore wind power has experienced rapid development. However, constrained by the inherent intermittency and volatility of wind power, large-scale expansion poses significant challenges to grid integration and exacerbates government fiscal burdens. To address these critical issues, this paper proposes a multi-market coupling trading model integrating energy storage-equipped offshore wind power into electricity–carbon–green certificate markets for large-scale grid networks. Firstly, a day-ahead electricity market optimization model that incorporates energy storage is established to maximize power revenue by coordinating offshore wind power generation, thermal power dispatch, and energy storage charging/discharging strategies. Subsequently, carbon market and green certificate market optimization models are developed to quantify Chinese Certified Emission Reduction (CCER) volume, carbon quotas, carbon emissions, market revenues, green certificate quantities, pricing mechanisms, and associated economic benefits. To validate the model’s effectiveness, a gradient ascent-optimized game-theoretic model and a double auction mechanism are introduced as benchmark comparisons. The simulation results demonstrate that the proposed model increases market revenues by 17.13% and 36.18%, respectively, compared to the two benchmark models. It not only improves wind power penetration and comprehensive profitability but also effectively alleviates government subsidy pressures through coordinated carbon–green certificate trading mechanisms. Full article

We developed an optimization method with value stream mapping (VSM), dynamic system simulation, and processing time prediction. First, VSM was used to quantitatively analyze the production process, identify value-added and non-value-added activities, and build a mathematical model to describe the flow of resources and waste at various stages. Then, a discrete event simulation (DES) was applied to simulate changes in the production process under different improvement conditions and to assess the effect of improved production efficiency using stochastic event modeling. As a result, we identified potential bottlenecks based on the flow of resources and waste sources throughout the production process and proposed improvement solutions for higher efficiency based on production simulations by predicting processing times for stability of production plans. Full article