Search Results (31,197)

Low-count positron emission tomography (PET) is inherently affected by Poisson-dominated noise, which degrades image contrast, structural delineation, and quantitative reliability. This study systematically evaluated residual learning-based deep neural networks to investigate the influence of residual block depth on PET image restoration performance under low-count conditions. We employed a physically controlled striatum phantom, fabricated using 3D printing technology, to ensure reproducible acquisition conditions and controlled physical variability. PET images were acquired using a clinical PET/computed tomography (CT) system with list-mode acquisition. Low-count images reconstructed from short-duration acquisition were paired with high-count reference images reconstructed from extended acquisitions. We compared conventional filtering techniques, including median, Wiener, and modified median Wiener filters, with residual network (ResNet)-based models incorporating 8, 16, and 32 residual blocks. Image quality was quantitatively assessed using contrast-to-noise ratio (CNR), coefficient of variation (COV), line profile analysis, universal quality index (UQI), and perceptual image patch similarity (LPIPS). The results demonstrated that ResNet-based restorations substantially outperformed conventional filtering techniques in contrast recovery, signal stability, and structural preservation. The ResNet-16 model achieved the most balanced performance, yielding the highest CNR (9.02) and lowest COV (0.105), while also demonstrating superior structural and perceptual similarity, as indicated by UQI (0.9224) and LPIPS (0.0174), relative to the high-count reference images. Deeper network configurations exhibited diminishing returns and reduced structural consistencies. These findings indicate that an intermediate residual block depth is optimal for low-count PET image restoration and highlight the importance of architectural optimization in deep learning-based PET image enhancement with phantom-based evaluation frameworks. Full article

Owing to its high theoretical sodium-storage capacity of approximately 1000 mAh g⁻¹ and cost-efficient characteristics, Fe₂VO₄ has emerged as a highly attractive anode material for sodium-ion batteries (SIBs). In this work, MXene-incorporated Fe₂VO₄ composites were successfully synthesized. Comprehensive electrochemical characterization demonstrates that MXene incorporation significantly enhances the electronic conductivity and sodium-ion diffusion kinetics of Fe₂VO₄, while effectively mitigating volume expansion during cycling. The synthetic substantially improves its cycling stability and rate capability. When the MXene loading ratio is optimized at 5 wt%, the composite exhibits outstanding cyclic durability, with a remarkable reversible specific capacity of 323.3 mAh g⁻¹ maintained after 200 cycles at a current density of 0.1 A g⁻¹. Furthermore, the composite demonstrates outstanding rate performance, with a specific capacity of 164.5 mAh g⁻¹ achieved at a current density of 2 A g⁻¹. The synergistic integration of Fe₂VO₄ and MXene not only constructs a three-dimensional electrically conductive framework for efficient charge transport but also reinforces strong structural stability against cycling-induced degradation. This work proposes a versatile engineering strategy that can be adapted for other conversion-type electrode materials in the context of advanced energy storage technologies. Full article

In real-world scenarios, multivariate time series data typically presents a variety of complex characteristics simultaneously, including long-term trends, multiple seasonality, sudden event disturbances and random noise. Owing to remarkable discrepancies among different variables in dimensions, periodic stability and other aspects, and the gradual evolution of these periodic characteristics over time, models are confronted with numerous challenges in handling non-stationarity, multi-scale dynamic variations and heterogeneous fusion of variables. To tackle these problems, this paper proposes a time–frequency parallel fusion framework—TFDG-Net (Time–Frequency Dual-Branch Gated Fusion Network). This framework models the prior information in the frequency domain and the temporal query network in the time domain in parallel, and introduces a channel-wise gating mechanism to achieve more flexible adaptive fusion after data inverse normalization. Such a design enables the model to operate collaboratively on the original physical scale, which not only improves the long-term prediction capability for periodically stable variables, but also effectively suppresses the interference of noise and event-driven factors, thus significantly enhancing prediction accuracy and the robustness of the training process. In multiple long-term prediction benchmark tests covering fields such as energy and finance, compared with various mainstream models, TFDG-Net reduces the mean squared error and mean absolute error by an average of 12.0% and 7.8% respectively. Full article

The oxygen evolution reaction (OER), a key half-reaction in electrochemical water splitting, is limited by sluggish multi-electron transfer kinetics, starting extensive research into efficient, low-cost nanoscale electrocatalysts, particularly those based on nickel, cobalt, and iron, as well as mixed-metal, hybrid, and heteroatom-doped carbon-based metal-free systems, as presented here. Ni- and Co-based electrocatalysts show high efficiency for alkaline OER due to optimized nanostructures, surface modifications, heterostructure design, and multi-metal doping, which enhance activity, stability, and electronic properties. Their performance relies on precise atomic-level control of structure and synergistic interactions, enabling them to approach or rival noble-metal catalysts. Iron-based electrocatalysts are also promising due to their abundance, low cost, and flexible redox chemistry, forming active iron oxyhydroxide species during operation; however, their low conductivity requires structural and electronic optimization. Beyond Fe, Ni, and Co, copper-based compounds, zeolitic imidazolate framework-derived structures, and manganese phosphide–cerium oxide composites offer enhanced oxygen vacancies, tunable structures, and strong interfacial synergy. Furthermore, heteroatom-doped carbon materials incorporating nitrogen, phosphorus, or sulfur improve catalytic activity by modifying electronic structure, creating active sites, and enhancing charge transfer. Overall, careful control of composition, structure, and electronic properties enables the development of efficient, durable, and scalable noble-metal-free catalysts for OER. Full article

In the automotive industry, the most critical factor affecting dimensional stability during the forming of Advanced High-Strength Steels (AHSSs) is the springback phenomenon. This study systematically investigates the springback behavior of four distinct dual-phase steel grades (DP450, DP600, DP800, and DP1000) in U-shaped body-in-white (BIW) structures across 180 distinct scenarios. The experimental design varied sheet thicknesses (1.2, 1.6, 2 mm), die clearance angles (5°, 10°, 15°), and bending radii (R6, R8, R10, R12, R14). Numerical simulations using Autoform R8 were validated against Atos 3D optical scanning data, achieving values exceeding 0.90 for all grades. Quantitative validation metrics showed exceptional fidelity for lower-strength grades with error margins below 1.1%, while the maximum deviation was limited to 3.1% for the ultra-high-strength DP1000 grade. The findings demonstrate that while increasing material strength substantially intensifies springback, the strategic augmentation of sheet thickness and optimization of die radius effectively mitigate these deviations, thereby enhancing process stability. Full article

Cocoa pod husk (CPH), a major agro-industrial residue, contains valuable bioactive compounds whose recovery can support sustainable waste valorization. This study evaluated the influence of increasing ethanol concentrations on the ultrasound-assisted extraction (UAE) of bioactive compounds from CPH and their antioxidant, antihypertensive, and antihyperglycemic activity. Dried and milled CPH was extracted using ethanol–water mixtures (0–100% ethanol) under fixed ultrasonic conditions. Cocoa pod husk powder characterization and the resulting extracts were analyzed in terms of chemical composition (lignocellulosic compounds, proximate and elemental composition, and bromatological composition), antioxidant capacity, and in vivo antihypertensive and antihyperglycemic effects in Wistar rats. The results showed that solvent polarity strongly modulated extraction efficiency: absolute ethanol yielded the highest phenolic (171.43 mg GAE/g) and flavonoid (132.05 mg QE/g) content, whereas hydroalcoholic mixtures, particularly 50:50, enhanced overall antioxidant performance, especially in FRAP. The chemical analysis results showed the selective recovery of compounds such as quercetin, hesperidin, and theobromine, and FTIR-PCA results revealed distinct solvent-dependent chemical profiles. In vivo assays indicated modest blood pressure stabilization and a more pronounced antihyperglycemic effect after chronic administration. Overall, UAE proved an effective, rapid, and solvent-efficient method for CPH valorization, highlighting its potential for producing natural antioxidants applicable to food, nutraceutical, and cosmetic formulations. Full article

Greenhouse cultivation enables year-round vegetable production and high yields through precise environmental regulation. Yet, the same stable microclimate that promotes crop growth also favors the proliferation of pests and diseases. This review synthesizes current knowledge on how greenhouse climate variables govern pest and disease epidemiology in tomato, cucumber, and sweet pepper. Only greenhouse-based studies were included to ensure direct relevance to protected horticulture. Microclimatic stability determines infection probability, vector behavior, and host susceptibility. Warm, humid conditions promote fungal and bacterial pathogens, whereas dry, high vapor pressure deficit (VPD) environments favor mites and thrips and enhance virus transmission. Species-specific traits further modulate vulnerability. Tomato is dominated by virus–bacterium complexes and foliar/stem fungal diseases, cucumber by phytopathogenic fungi favored by high relative humidity (RH) and soilborne pathogens, and sweet pepper by virus–vector systems and long-cycle fungal infections. Temperature exerts the strongest influence, while RH and VPD jointly regulate surface moisture and vector activity. Light intensity and spectral composition also affect pest orientation and fungal sporulation. Integrating environmental sensing, biological control, and adaptive climate regulation offers a pathway toward preventive, climate-smart Integrated Pest Management (IPM). The review highlights the emerging role of climate-informed decision-support systems (DSSs) and the need for greenhouse-specific datasets to improve pest and disease forecasting. Full article

The blending process in raw material yards is essential for maintaining precise material proportions in downstream production, directly influencing product quality and energy efficiency in industries such as steel and coal processing. However, stringent operational constraints, including silo capacity limits, discharge rates, equipment movement delays, and a strict no-empty-silo requirement, result in a strongly coupled, high-dimensional combinatorial scheduling problem. In this paper, we develop a mixed-integer nonlinear programming (MINLP) model to capture the complex dynamics of silo weight and equipment operations. The primary scientific contribution of this work lies in the theoretical discovery of a structural decoupling property within the complex MINLP. We analytically prove that by fixing the replenishment sequence, the intractable global problem can be rigorously decomposed into two subproblems: a linear programming (LP) problem for silo-filling cart scheduling and a shortest-path problem solvable via dynamic programming (DP) for reclaimer scheduling. Leveraging this decomposition, a two-stage metaheuristic algorithm is proposed, combining greedy initialization with multi-round simulated annealing enhanced by local search. Experimental validation using real industrial data demonstrates that the proposed method consistently outperforms the greedy algorithm. Crucially, while the commercial solver Gurobi struggles to converge within a practical 1800 s time limit, our approach yields comparable solution quality in mere seconds. Furthermore, robustness analysis under a 20% demand surge confirms the algorithm’s adaptive capability, maintaining the silo weight stability through re-optimization. This research provides a robust, computationally efficient solution for the blending process in raw material yards. Full article

Epiphytic lichens are highly sensitive components of forest ecosystems, increasingly threatened by habitat disturbance and climate change. While habitat protection remains central to lichen conservation, translocation has emerged as a promising tool to address population decline, although its global effectiveness remains poorly evaluated. This scoping review, conducted under PRISMA-ScR guidelines, analyzes 30 taxa across 12 countries to evaluate current methodologies and outcomes. The reviewed literature is largely characterized by small-scale, method-oriented interventions, with a strong predominance of thallus fragment translocation over diaspore-based approaches. Success is most often evaluated through short-term survival and persistence of transplanted material, whereas indicators of long-term population self-maintenance and reproductive viability are rarely considered. Major limitations emerge from technical constraints, including early sample loss due to inadequate fixation, as well as from mismatches between donor requirements and recipient-site microhabitat conditions. Although high initial survival is frequently reported, evidence for long-term population stability, secondary colonization, and genetic resilience remains scarce. Overall, translocation may support short-term establishment under favorable environmental conditions, mainly at local scales, but its reliability as a long-term conservation strategy requires further validation. This review identifies a critical gap in long-term monitoring and highlights the need for research priorities that enhance the effectiveness, conceptual clarity, and technical precision of future translocation efforts to ensure the persistence of epiphytic lichen populations within changing forest landscapes. Full article

This study reports the synthesis and evaluation of diclofenac-derived organotin(IV) complexes as photostabilizing additives for poly(vinyl chloride) (PVC). Diclofenac was selected as a ligand due to its aromatic structure and heteroatom-rich framework, enabling the formation of stable tin-based complexes with potential UV-absorbing and radical-scavenging properties. The synthesized di- and tri-organotin complexes were incorporated into PVC films at 0.5 wt.% and exposed to UV irradiation (365 nm) for up to 300 h to assess their stabilizing efficiency. Photodegradation was monitored by tracking changes in carbonyl, polyene, and hydroxyl indices, as well as weight loss and surface deterioration. Compared with blank PVC and ligand-containing films, the organotin-modified samples exhibited significantly slower growth of degradation indices, reduced mass loss, and improved surface integrity after irradiation. Among the evaluated additives, the tributyltin complex demonstrated the highest photostabilizing performance, showing superior retention of chlorine content and lower surface roughness parameters. Overall, the results indicate that diclofenac-based organotin(IV) complexes are effective photostabilizers for PVC, with the tributyltin derivative emerging as the most promising candidate for enhancing the durability of PVC materials under UV exposure. Full article

Material extrusion (MEX) additive manufacturing can produce material-dependent variations in dimensional fidelity, internal structure, and deposition stability, even under identical processing conditions. In this study, a comprehensive experimental investigation is conducted on MEX-printed specimens manufactured from a broad set of PLA-based composite materials to quantify these variations and assess their mutual interdependence. Dimensional behavior, internal structural characteristics, and process behavior were systematically investigated using complementary geometric, physical, and deposition-related descriptors. All properties were determined from replicated specimens to ensure statistical robustness, and the resulting datasets were examined using both conventional metrics and multivariate 3D correlation approaches. Compact PLA-based formulations exhibit consistent internal packing, characterized by relative density (RD) values of approximately 0.40–0.46, porosity (ϕ) levels around 55–60%, reduced (≤0.15%) density variability (CV), and small (−0.4–0.0%) volumetric deviations (ΔV). These features reflect stable extrusion and predictable dimensional response. In contrast, foamed, fiber-reinforced, and organic-filled composites display reduced internal packing (RD < 0.40), increased ϕ (>60%), elevated CV (0.27–0.58%), and systematically larger positive ΔV (up to +1.4%), indicating a higher sensitivity to process-induced heterogeneity. Multivariate correlations further reveal that volumetric dimensional distortion is jointly governed by internal packing efficiency and extrusion stability. Overall, the results demonstrate that dimensional accuracy in MEX of PLA-based composites arises from coupled structure–process interactions rather than isolated material or process parameters. The experimental framework proposed here provides quantitative guidance for material selection and process optimization aimed at enhancing geometric fidelity in composite filament fabrication. Full article

Dental tissue regeneration, particularly alveolar bone and gingival repair, remains a major challenge in regenerative medicine. 3D bioprinting offers patient-specific and anatomically precise constructs, representing an advanced alternative to conventional grafting. In this study, nanohydroxyapatite (nHA), chitosan (CS), and collagen (CoL) were combined to fabricate and characterize 3D bioprinted dental grafts. SEM revealed a highly porous, interconnected architecture favorable for cell infiltration and nutrient exchange. EDS confirmed Ca/P ratios of 2.06 for nHA/CoL and 1.83 for nHA/CS/CoL, both of which are above the stoichiometric 1.67, indicating the presence of additional mineral phases and ion substitutions. FTIR and XRD verified characteristic functional groups and crystalline phases, including B-type HA with carbonate substitution. Mechanical testing showed that pure nHA exhibited the lowest compressive strength, whereas CoL incorporation improved stiffness. The nHA/CS/CoL composite achieved the highest compressive strength, elastic modulus, and toughness, demonstrating superior mechanical resilience. DSC analysis indicated endothermic peaks at 106.49 °C and 351.91 °C, with enthalpy values (264.91 J/g and 15.09 J/g) surpassing those of nHA alone. TGA revealed ~28.8% weight loss across three degradation stages, confirming enhanced thermal stability. In vitro cytocompatibility testing using L929 fibroblasts validated the biocompatibility of the composites. Collectively, the synergy between bioceramics and biopolymers markedly improved both mechanical and thermal performance. These findings position the nHA/CS/CoL scaffold as a promising candidate for clinical applications in dental tissue regeneration. Unlike conventional grafting materials, this study introduces a synergistically optimized nHA/CS/CoL bio-ink formulation specifically designed for extrusion-based 3D bioprinting of patient-specific dental constructs. The core innovation lies in the precise integration of nHA within a dual-polymer matrix (CS/CoL), which bridges the gap between mechanical resilience and biological signaling, achieving a compressive strength that mimics native alveolar bone while maintaining high cytocompatibility. Full article

Magnetorheological (MR) semi-active suspensions offer clear advantages in improving ride comfort and handling stability, yet their engineering applications are often hindered by strong nonlinear hysteresis of the damper, the randomness of road excitations, and the reliance on manual tuning of controller parameters. To address these issues, this paper proposes an integrated framework of “experimental modeling–semi-active implementation–adaptive control.” First, characteristic tests of the MR damper are conducted, based on which a current-dependent Bouc–Wen forward model is established. Tianji’s Horse Racing Optimization (THRO) is then employed for parameter identification to reproduce the hysteresis behavior accurately. Second, a back propagation (BP) neural network-based inverse current model is developed to achieve rapid mapping from “desired damping force” to “driving current,” enabling semi-active actuation. Furthermore, a radial basis function (RBF) neural network is embedded into the active disturbance rejection control (ADRC) structure to estimate the system Jacobian online and to tune key extended state observer (ESO) gains in real time, forming the proposed RBF-ADRC strategy and thereby enhancing disturbance observation and compensation capability. Simulation results under pulse-road and Class-C random-road excitations show that, compared with the passive suspension, the proposed method reduces the root mean square error values of sprung-mass acceleration, suspension dynamic deflection, and tire dynamic load by 25.14%, 18.71%, and 11.61%, respectively, while also outperforming skyhook control and fixed-gain ADRC. Frequency-domain results further show stronger attenuation in the low-frequency band relevant to body vibration. Under pulse excitation, RBF-ADRC yields smaller peak and trough body accelerations and faster post-impact recovery. Under ±30% sprung-mass variations, it achieves the best worst-case and fluctuation-range robustness among the compared strategies and remains close to offline retuning. These results demonstrate that the proposed method improves both control performance and robustness while reducing the need for repeated manual calibration. Full article

The discovery of novel anti-infective agents is a continuous challenge in medicinal chemistry, particularly due to the rise in resistant fungal and viral strains. Within this context, the biphenyl subunit has been identified as a highly versatile privileged structure capable of interacting with diverse protein targets via hydrophobic and π-interactions. The purpose of this study is to review the pharmacological potential of biphenyl-based compounds, focusing on their application as anti-infective agents. We comprehensively analyzed recent literature and rational design strategies concerning biphenyl derivatives, examining structure-activity relationships, molecular docking insights, and structural optimizations aimed at enhancing both pharmacodynamics and pharmacokinetics. The reviewed studies demonstrate that incorporating biphenyl moieties yields compounds with potent antifungal and antiviral activities. Specifically, optimized biphenyl derivatives exhibit strong inhibitory effects against resistant Candida strains and crucial viral targets, including mutant variants of the HIV-1 reverse transcriptase and protease enzymes. Furthermore, strategic modifications, such as scaffold hopping and the introduction of specific substituents, successfully mitigated cytotoxicity and improved metabolic stability against cytochrome P450 enzymes. Biphenyl serves as a robust and adaptable scaffold for drug design. Its rational structural optimization provides a viable pathway to overcome drug resistance and develop effective, metabolically stable anti-infective therapeutics. Full article

Unmanned Aerial Vehicle (UAV) trajectory planning in complex three-dimensional environments with threats remains a challenging optimization problem requiring efficient algorithms and threat detection capabilities. This study proposes the Conservative Enhanced Dwarf Mongoose Optimization Algorithm (CEDMOA), which introduces four key innovations to the original DMOA: hybrid population initialization, adaptive vocalization parameters, elite-guided learning strategy, and intelligent restart mechanisms. This work proposed the integration of CEDMOA with a novel vision-based threat detection system using YOLO object detection technology, enabling the identification and incorporation of threats into the optimization process. CEDMOA was comprehensively evaluated on the CEC2022 benchmark test suite, demonstrating superior performance compared to other state-of-the-art algorithms in solution quality and convergence stability. The results show the approach successfully generates an optimal collision-free flight trajectory in complex environments in UAV trajectory planning with both static and dynamic threats. Combining metaheuristic optimization with computer vision technology provides a robust framework for autonomous navigation that adapts to changing threat conditions. Experimental results validate the effectiveness of both the enhanced algorithm and the vision-based threat integration approach for practical UAV operations. Full article