Search Results (4,695)

The Agricultural Park Integrated Energy System (APIES) is a key platform for integrating distributed renewable energy (DRE) with agricultural production. However, its economic operation and the stability of crop growth environments are severely challenged by bidirectional uncertainties from external meteorology. These include the inherent variability of wind-solar generation and critical agricultural loads, such as supplementary lighting and temperature control, a challenge that existing models with static environmental parameters fail to address. To solve this, a bi-level optimization scheduling model for APIES considering meteorological uncertainty is proposed. The upper layer minimizes operation costs by quantifying uncertainties via triangular fuzzy chance constraints, with core constraints on DRE output, energy storage charging-discharging, and load shifting, solved by YALMIP-Gurobi linear programming. The lower layer maximizes crop growth environment satisfaction using a dynamic weight adaptive mechanism and NSGA-II multi-objective algorithm. The two layers iterate alternately for coordination. Using a small agricultural park in Xinjiang, China, as a case study, the results indicate that the proposed two-layer optimal scheduling model reduces costs by 10.8% compared to the traditional single-layer optimization model, and improves environmental satisfaction by 4.3% compared to the fixed-weight two-layer optimization model.: Full article

Titanium alloys are widely used for biomedical implants, but their performance is limited by wear, corrosion, and susceptibility to bacterial colonisation. To overcome these drawbacks, multilayer Ti–Cu oxide coatings were deposited on Ti6Al4V substrates using direct current magnetron sputtering. Two multilayer architectures (6 × 2 and 12 × 2 TiO₂/CuO bilayers) were fabricated and evaluated for their structural, mechanical, electrochemical, and biological properties. SEM/EDS and XRD confirmed well-adhered crystalline coatings consisting of rutile/anatase TiO₂ and monoclinic CuO with uniform elemental distribution. The coatings increased surface roughness, improved adhesion, and enhanced hardness by up to ~180% compared to uncoated Ti6Al4V alloy. Compared to the bare substrate, electrochemical testing in simulated body fluid showed higher corrosion resistance of both coated samples, but particularly for the 12 × 2 multilayers. Both architectures provided sustained Cu²⁺ release over seven days without a burst effect. In vitro biological testing showed that both multilayer coatings achieved over 96% inhibition of Gram-positive bacteria such as Staphylococcus aureus and Bacillus subtilis, while exhibiting moderate antibacterial effects against Gram-negative strains (Escherichia coli, Pseudomonas aeruginosa). Despite the presence of copper, MG-63 osteoblast-like cells demonstrated sustained viability and successful extracellular matrix mineralisation, indicating excellent cytocompatibility of the coatings with bone-forming cells. These results demonstrate that multilayer Ti–Cu oxide coatings can effectively balance antibacterial performance, corrosion resistance, mechanical strength, and support bone cell integration, making them a promising strategy for the surface modification of titanium-based biomedical implants. Full article

Sodium hypochlorite (NaClO) is widely used to purify oocysts in laboratories. While previous studies have extensively examined its effects on oocyst viability, pathogenicity, and sporulation rate, the impact of NaClO treatment on proteomic profiles remains uncharacterized. Transmission electron microscopy was used in the present study to characterize structural changes in unsporulated oocyst walls of Eimeria tenella treated with NaClO. The results indicated that NaClO treatment destroyed the bilayer wall of unsporulated oocysts, stripping away the outer wall and making the inner layer thicker. Label-free quantitative proteomics was employed to identify differentially expressed proteins (DEPs) in NaClO-treated (Et-T) and untreated (Et-C) unsporulated oocysts. Among 2422 identified proteins, 1345 were differentially expressed, with 1210 upregulated and 134 downregulated in Et-T vs. Et-C. Functional analysis revealed that upregulated proteins are predominantly associated with oocyst wall biosynthesis and cellular stress responses, whereas downregulated proteins are involved in outer wall assembly and structural integrity. Notably, 12 proteins—including 9 hypothetical proteins, acid phosphatase, adenylate cyclase, and microneme protein 2—were exclusively detected in the Et-C, indicating their potential essentiality in outer wall formation. These findings reveal the structure and protein composition of the oocyst wall of E. tenella, supporting research on its biosynthesis and environmental resilience. Full article

The large-scale integration of wind power generation, as an important sustainable energy, into the power grid relies on the support of the power system, and accurate wind power prediction is the key to ensuring the continuous and stable operation of the power system. Therefore, this paper proposes a hybrid wind power prediction model that integrates Successive Variational Mode Decomposition (SVMD) with KANCNN-BiLSTM. To address data volatility, the original wind power sequence is decomposed into seven modal components using SVMD. Subsequently, for enhanced capability in capturing nonlinear relationships, a KAN linear layer is integrated into a convolutional neural network, constructing the KANCNN-BiLSTM model for component prediction. Simultaneously, model hyperparameters are optimized via the Optuna framework to further improve predictive performance. Additionally, SHAP theory is applied to interpret the contribution of each component to the prediction results, thereby enhancing the transparency of the decomposition–integration process. Experimental results indicate that the proposed interpretable SVMD-KANCNN-BiLSTM wind power prediction model achieves a prediction accuracy of 0.998959, outperforms all comparison models across multiple evaluation metrics, and indicates superior predictive capability; additionally, the global interpretability analysis reveals that all IMF components positively contribute to the model’s predictions. The establishment of this model provides an interpretable new approach for realizing wind power prediction. Full article

Silver nanoparticles (AgNPs) are promising agents for nanomedicine but their interactions with lipid membranes, which are a key interfaces for drug delivery, require a deeper understanding. This study investigates the influence of fructose-capped AgNPs on the physicochemical properties of SOPC-based liposomal bilayers, with potential implications for drug delivery and photothermal therapy. We employed a multitechnique approach, including infrared (IR) spectroscopy, differential scanning calorimetry (DSC), thermally induced shape fluctuation analysis, and laser irradiation at 343, 515, and 1030 nm. Our results show that AgNPs incorporated into the bilayer cause measurable perturbations: DSC reveals a decrease in the main phase transition enthalpy (from 0.280 to 0.234 J/g) and temperature (from 2.80 to 3.41 °C), while shape fluctuation analysis indicates a reduction in bending modulus (from 1.18 × 10−19 J to 0.93 × 10−19 J), confirming increased membrane fluidity. FTIR confirms interactions of fructose-capped nanoparticles and the lipid’s carbonyl and phosphate groups. Furthermore, the AgNPs-liposomes exhibit a strong, wavelength-dependent photothermal response with a temperature increase of ≈22 °C under 515 nm laser irradiation, compared to only 3–5 °C at 1030 nm. We conclude that fructose-capped AgNPs moderately fluidify lipid bilayers while enabling efficient, controllable photothermal capability, making them excellent candidates for the eventual design of advanced liposomal systems for combined therapy and diagnostics. Full article

Small target detection in remote sensing images faces challenges due to complex backgrounds, weak features, and large scale differences. This paper proposes an improved YOLOv5-based network, termed ClearSight-RS, with the full name “Clear and Accurate Small-target Insight for Remote Sensing”. As the name implies, the network is dedicated to achieving clear feature perception and accurate target localization for small targets in remote sensing images. The improvements focus on three aspects: integrating an improved Dynamic Snake Convolution (DSConv) module into the backbone network to strengthen the extraction of small target boundaries and geometric features, as well as the expression of weak textures; embedding a Bi-Level Routing Attention (BRA) module in the Neck part to enhance target focusing and suppress background interference; and optimizing the detection head by retaining only shallow high-resolution feature layers for prediction, reducing feature loss and redundant computations. Experimental results show that, based on the VEDAI dataset, ClearSight-RS achieves the highest mAP for all 8 vehicle categories; based on the NWPU VHR-10 dataset, its overall mAP reaches 93.8%, significantly outperforming algorithms such as Faster RCNN and YOLOv5l; based on the DOTA dataset, the capability of the proposed BRA module in suppressing background interference and capturing small target features is demonstrated. The network balances accuracy and efficiency, performing prominently in detecting vehicles and multi-category small targets in complex backgrounds, verifying its effectiveness. Full article

To address the issue of inefficient interfacial diffusion between virgin asphalt and the aged asphalt in Reclaimed Asphalt Pavement (RAP), this study investigates how a rejuvenator improves the interfacial blending behavior and restores the functional properties of aged asphalt. Molecular dynamics (MD) simulations were employed to construct aged asphalt–rejuvenator models with varying rejuvenator contents and to establish a bilayer dynamic model of the virgin-aged asphalt–rejuvenator diffusion system. The kinetic characteristics of the diffusion process were analyzed based on system density and relative concentration profiles, while the mean square displacement (MSD) and diffusion coefficients were calculated to elucidate the diffusion mechanism. The accuracy of the MD simulation results was validated using Fourier Transform Infrared Spectroscopy (FTIR) and Differential Scanning Calorimetry (DSC), and the regulatory mechanism of the rejuvenator on the interfacial diffusion between virgin and aged asphalt was revealed at the microscopic scale. The results demonstrated that the addition of the rejuvenator effectively promotes the blending and diffusion at the virgin-aged asphalt interface. Specifically, a 6% rejuvenator significantly improved the diffusion efficiency at elevated temperatures, optimized system density toward virgin asphalt properties, and achieved the most uniform molecular distribution, thereby facilitating balanced intermolecular interactions. Meanwhile, the regenerant effectively restored the aromatic fraction content, reduced polar functional groups such as sulfoxide, and significantly lowered the glass transition temperature (Tg), thereby enhancing the low-temperature crack resistance and overall mechanical performance of RAP. Full article

Given the urgent demand for flexible peak-shaving in power systems and underutilized distributed photovoltaic (PV) regulation potential, this paper proposes a distributed PV peak-shaving control strategy based on the temporal coupling self-organizing map (TC-SOM) neural network and a bi-level model. First, the SOM algorithm is improved for efficient feature extraction and accurate clustering of distributed PV data, realizing rational PV cluster division. On this basis, a bi-level peak-shaving model for distributed PV is constructed, forming a hierarchical peak-shaving mechanism from node demand to PV clusters to individual PVs to ensure inter- and intra-cluster coordination. This hierarchical structure embodies symmetric response logic, enabling balanced interaction between upper-layer node demand guidance and lower-layer PV execution, as well as inter-cluster coordination. Simulations on the IEEE-33 node system confirm its effectiveness: it significantly smooths the load curve, reduces peak–valley differences, and optimizes the flexible utilization of distributed PV through coordinated control, aggregation management, and curtailment regulation, providing strong support for precise PV cluster regulation and stable operation of high-proportion PV-integrated power grids. Full article

In this work, a broadband polarization-sensitive metamaterial absorber (MA) based on a plasmonic bilayer nanostructure array is designed and numerically investigated. The proposed polarization-sensitive MA exhibits a high average absorption of 91.9% for TM-polarized light from 500 nm to 2000 nm and as low as 11.6% for TE-polarized light. Furthermore, a plasmonic germanium photodetector (PD) functionalized by the proposed polarization-sensitive MA is constructed and the potential application of distinguishing TM- and TE-polarized light is explored in simulation. The calculated results demonstrate that the photo-current difference generated by the PD under TM- and TE-polarized light excitation can reach 11.9 μA. The proposed broadband polarization-sensitive MA can be used in many fields, including polarization detection and other polarization-sensitive optoelectronic devices. Full article

Background/Objectives: Chipping of the veneering part of the crown at the zirconia–porcelain interface remains a major complication of bilayered zirconia systems. This systematic review evaluated whether incorporating a lithium-disilicate (LD) liner or press-on or CAD-on interlayer between zirconia and veneer improves bond strength and fracture performance in vitro. Methods: Following PRISMA 2020, we searched PubMed, Scopus, and Web of Science up to 7 September 2025 for open-access English in vitro studies using LD-based interlayers at zirconia–veneer interfaces and reporting quantitative bond and/or fracture outcomes. Data included extracted materials, processing parameters, and mechanical results; due to heterogeneity, findings were synthesized descriptively and as the ratio-of-means (ROM). Results: Five in vitro studies from Korea, Thailand, and India met the criteria. LD interlayers increased microtensile bond strength from 18.83 to 44.20 MPa and from 19.6 to 47.7 MPa, and shear bond strength from 41.3 to 59.7 MPa, 11.40 ± 1.29 to 18.81 ± 1.76 MPa, and 21.5 to 60.2 MPa. Corresponding ROMs ranged from 1.46 to 2.80 (median 2.35), with thermocycled LD groups maintaining strengths >25 MPa. LD liners also raised crown fracture loads from ~2.18 to ~3.45 kN and characteristic strength from 3.42 to 5.64 kN, while chipping loads in implant crowns increased from ~0.34 to ~0.84 kN and global fracture from ~1.71 to ~1.93 kN. Conclusions: Across diverse zirconia–veneer configurations, LD interlayers consistently enhanced bond metrics and fracture/chipping resistance, supporting their use as a targeted interfacial strategy; however, clinical confirmation is still needed. Full article

This study investigates the development of sustainable PVC-based composites filled with surface-modified wood flour for potential use in modern, energy-efficient building systems. The aim was to enhance the mechanical performance, thermal stability, and interfacial compatibility of PVC plastisols by incorporating fine- and coarse-grained coniferous wood flour modified with silane and surfactants. Composites were formulated using emulsion PVC (Vinnolit E-2059), bis(2-ethylhexyl) adipate as a plasticizer, and MARK-17 MOK as a thermal stabilizer, and were gelled under pressure at 150 °C. Their physical, mechanical, structural, and thermal characteristics were evaluated using density and hardness measurements, SEM, thermomechanical analysis, DMA, and TGA. The results demonstrated that composites containing fine-grained, silane-treated wood flour (Lignocel C-120) exhibited the most advantageous balance of stiffness, elasticity, and thermal resistance, attributable to improved polymer–wood interfacial adhesion. The findings confirm the potential of modified wood flour as an effective bio-based filler enabling the design of durable, thermally stable coating and insulating materials with reduced environmental impact. The proposed composites may serve as protective, bonding, or insulating layers in sustainable construction, supporting the development of innovative, wood-based materials for low-carbon building applications. Full article

Creatinine (Crn) is a clinically relevant biomarker commonly used for the diagnosis and monitoring of kidney disease. In this work, we report the fabrication of reduced-graphene-oxide-based field-effect transistors (rGO FETs) for Crn detection. These devices were functionalized using a layer-by-layer (LbL) assembly, in which polyethyleneimine (PEI) and creatinine deiminase (CD) were alternately deposited. This LbL strategy allows for the effective incorporation of CD without compromising its structural or functional integrity, while also taking advantage of the local pH changes caused by creatinine hydrolysis. It also benefits from the use of a polyelectrolyte that can amplify the enzymatic signal. Furthermore, it enables scalable and efficient fabrication. These transistors also address the challenges of point-of-care implementation in single-use cartridges. It is worth noting that the devices showed a linear relationship between the Dirac-point shift and the logarithm of the creatinine concentration in the 20–500 µM range in diluted simulated urine. The sensor response improved with increasing numbers of PEI/CD bilayers. Furthermore, the functionalized FETs demonstrated rapid detection dynamics and good long-term stability. Present results confirm the potential of these devices as practical biosensors for sample analysis under real-world conditions, making them ideal for implementation in practical settings. Full article

With the rapid growth of residential electric vehicles, synchronized charging during peak periods can induce severe load ramping and exceed distribution network capacity limits. To mitigate these issues, governments have promoted a unified build-and-operate community model that enables centralized coordination of community charging and ensures real-time responsiveness to grid dispatch signals. Targeting this emerging operational paradigm, a dual-dimensional compensation mechanism for real-time electric vehicle (EV) demand response is proposed. The mechanism integrates two types of compensation: power regulation compensation, which rewards users for providing controllable power flexibility, and state-of-charge (SoC) loss compensation, which offsets energy deficits resulting from demand response actions. This dual-layer design enhances user willingness and long-term engagement in community-level coordination. Based on the proposed mechanism, a bi-level optimization framework is developed to realize efficient real-time regulation: the upper level maximizes the active response capacity under budget constraints, while the lower level minimizes the aggregator’s total compensation cost subject to user response behavior. Simulation results demonstrate that, compared with conventional fair-share curtailment and single-compensation approaches, the proposed mechanism effectively increases active user participation and reduces incentive expenditures. The study highlights the mechanism’s potential for practical deployment in unified build-and-operate communities and discusses limitations and future research directions. Full article

Azobenzene-based hologram recording materials are well known for their rewritable and polarization-selective properties that enable polarization-multiplexed recording and high-density optical storage. High diffraction efficiency, longer retention time, and shorter response time are desirable for rewritable recording materials, but they always require a trade-off relationship. In this study, we show that by simply coating the Azobenzene-based film with multiple layers of a suitable material, these parameters can be improved simultaneously without compromise. Bilayer films and triple layer films were prepared by depositing a DNA–surfactant complex-based layer above and below the azobenzene-based poly(CACzE-MMA) copolymer layer. The hologram recording performance was evaluated in terms of the diffraction efficiency, photoresponse time, and retention behavior of the recorded gratings. Compared with monolayer copolymer films, the multilayer DNA–surfactant complex-based copolymer films exhibited enhanced diffraction efficiency and faster photoresponse. In particular, the bilayer and trilayer structures showed a marked improvement in retention time, indicating suppressed relaxation of refractive index modulation. This enhancement is attributed to molecular confinement at the DNA–surfactant complex and copolymer interfaces generated by the layered architecture. These results demonstrate that a DNA–surfactant complex-based layering approach is an effective strategy for improving hologram stability and highlight the potential of DNA–surfactant complex-derived matrices as effective alternatives to poly(methyl methacrylate) (PMMA) in holographic applications. Full article

Natural Language Processing (NLP) is a valuable technology and business topic as it helps turn data into useful information with the spread of digital information. Nevertheless, there are some difficulties in its use, including the language’s complexity and the data quality. To address these challenges, in this study, the researchers first performed a series of ablation experiments on 14 models derived from various variations in Deep Learning (DL) methods, including A Lite BERT (ALBERT) together with Convolutional Neural Networks (CNNs), Long Short-Term Memory (LSTM), Bidirectional LSTM (BiLSTM), Max Pooling layer, and attention mechanism. Subsequently, they proposed an ALBERT-cascaded CNN hybrid model as an effective method to overcome the related challenges by evaluating the performance results obtained from these models. In the proposed model, a transformer architecture with parallel processing capability for both word and subword tokenization is used in addition to creating contextualized word embeddings. Local and global feature extraction was also performed using two 1-D CNN blocks before classification to improve the model performance. The model was optimized using an advanced hyperparameter optimization tool called OPTUNA. The findings of the experiment conducted with the proposed model were obtained based on Amazon Fashion 2023 data under 5-fold cross-validation conditions. The experimental results demonstrate that the proposed hybrid model exhibits good performance with average scores of 0.9308 (accuracy), 0.9296 (F1 score), 0.9412 (precision), 0.9182 (recall), and 0.9797 (AUC) in the validation dataset, and scores of 0.9313, 0.9305, 0.9414, 0.9199, and 0.9800 in the test dataset. In addition, comparisons of the model with models in studies using similar datasets support the experimental results and reveal that it can be used as a competitive approach for solving the problems encountered in the NLP field. Full article