Search Results (11,009)

Background/Objectives: Possessing red and white ecotypes, and utilized in traditional Guyanese medicine, Doliocarpus dentatus’ red ecotype is preferred locally for its purported superior therapeutic efficacy. Although therapeutic metabolites were detected in D. dentatus previously, phytohormones remain largely unexplored, until now. Cytokinins, phytohormones responsible for plant cell division, growth and differentiation, are gaining traction for their therapeutic potential in human health. This study screened and quantified endogenous cytokinins and correlated detected cytokinins with selected secondary metabolites. Methods: Liquid chromatography–mass spectrometry was used to acquire phytohormone and metabolite data. Bioinformatics tools were used to assess untargeted metabolomics datasets via statistical and pathway analyses, and chemical groupings of putative metabolites. Results: In total, 20 of the 35 phytohormones were detected and quantified in both ecotypes, with the red ecotype displaying higher free base and glucoside cytokinin concentrations and exhibited 6.2 times the total CK content when compared to the white ecotype. Pathway analysis revealed flavonoid and monoterpenoid biosynthesis in red and white ecotypes, respectively. Positive correlations between specific cytokinins and alkaloids, and between trans-Zeatin and isopentenyladenosine riboside with phenolic compounds were observed. Conclusions: These results suggest that the red ecotype’s elevated cytokinin levels coupled with flavonoid biosynthesis enrichment support its preference in Guyanese traditional medicine. Full article

In line with the goals of “peak carbon emissions and carbon neutrality”, this study aims to develop a market-coordinated operation mechanism to promote renewable energy adoption and consumption, addressing the challenges of integrating medium- and long-term trading with spot markets in power systems with high renewable energy penetration. A three-stage joint operation framework is proposed. First, a medium- and long-term trading game model is established, considering multiple energy types to optimize the benefits of market participants. Second, machine learning algorithms are employed to predict renewable energy output, and a contract decomposition mechanism is developed to ensure a smooth transition from medium- and long-term contracts to real-time market operations. Finally, a day-ahead market-clearing strategy and an incentive-compatible settlement mechanism, incorporating the constraints from contract decomposition, are proposed to link the two markets effectively. Simulation results demonstrate that the proposed mechanism effectively enhances resource allocation and stabilizes market operations, leading to significant revenue improvements across various generation units and increased renewable energy utilization. Specifically, thermal power units achieve a 19.12% increase in revenue, while wind and photovoltaic units show more substantial gains of 38.76% and 47.52%, respectively. Concurrently, the mechanism drives a 10.61% increase in renewable energy absorption capacity and yields a 13.47% improvement in Tradable Green Certificate (TGC) utilization efficiency, confirming its overall effectiveness. This research shows that coordinated optimization between medium- and long-term/spot markets, combined with a well-designed settlement mechanism, significantly strengthens the market competitiveness of renewable energy, providing theoretical support for the market-based operation of the new power system. Full article

The integration of a real-time image processing system using multiple webcams with a variable rate spraying system mounted on the back of an unmanned tractor presents an effective solution to the labor shortage in agriculture. This research aims to design and fabricate a low-cost, variable-rate, remote-controlled sprayer specifically for use in sugarcane fields. The primary method involves the modification of a 15-horsepower tractor, which will be equipped with a remote-control system to manage both the driving and steering functions. A foldable remote-controlled spraying arm is installed at the rear of the unmanned tractor. The system operates by using a webcam mounted on the spraying arm to capture high-angle images above the sugarcane canopy. These images are recorded and processed, and the data is relayed to the spraying control system. As a result, chemicals can be sprayed on the sugarcane accurately and efficiently based on the insights gained from image processing. Tests were conducted at various nozzle heights of 0.25 m, 0.5 m, and 0.75 m. The average system efficiency was found to be 85.30% at a pressure of 1 bar, with a chemical spraying rate of 36 L per hour and a working capacity of 0.975 hectares per hour. The energy consumption recorded was 0.161 kWh, while fuel consumption was measured at 6.807 L per hour. In conclusion, the development of the remote-controlled variable rate sprayer mounted on an unmanned tractor enables immediate and precise chemical application through remote control. This results in high-precision spraying and uniform distribution, ultimately leading to cost savings, particularly by allowing for adjustments in nozzle height from a minimum of 0.25 m to a maximum of 0.75 m from the target. Full article

Timely detection of pathogen spores is fundamental to ensuring early intervention and reducing the spread of corn diseases, like northern corn leaf blight, corn head smut, and corn rust. Traditional spore detection methods struggle to identify spore-level targets within complex backgrounds. To improve the recognition accuracy of various maize disease spores, this study introduced the YOLOv8s-SPM model by incorporating the space-to-depth and convolution (SPD-Conv) layers, the Partial Self-Attention (PSA) mechanism, and Minimum Point Distance Intersection over Union (MPDIoU) loss function. First, we combined SPD-Conv layers into the Backbone of the YOLOv8s to enhance recognition performance on small targets and low-resolution images. To improve computational efficiency, the PSA mechanism was incorporated within the Neck layer of the network. Finally, MPDIoU loss function was applied to refine the localization performance of bounding boxes. The results revealed that the YOLOv8s-SPM model achieved 98.9% accuracy on the mixed spore dataset. Relative to the baseline YOLOv8s, the YOLOv8s-SPM model yielded a 1.4% gain in accuracy. The improved model significantly improved spore detection accuracy and demonstrated superior performance in recognizing diverse spore types under complex background conditions. It met the demands for high-precision spore detection and filled a gap in intelligent spore recognition for maize, offering an effective starting point and practical path for future research in this field. Full article

Background: Point-of-care ultrasound (POCUS) is gaining recognition as a valuable diagnostic tool in various fields of medicine, including pediatrics. Its application at the point of care enables real-time clinical decision-making, which is particularly advantageous in pediatric settings. Although global interest in POCUS is growing, many European countries—including Poland—still lack formal training programs for POCUS at both the undergraduate and postgraduate levels. Nevertheless, the number of pediatricians incorporating POCUS into their daily clinical practice in Poland is increasing. However, the extent of its use and perceived value among pediatricians remains largely unknown. This study aimed to evaluate the current level of POCUS utilization in pediatric care in Poland, focusing on pediatricians’ self-assessed competencies, perceptions of its clinical utility, and key barriers to its implementation in daily practice. Methods: This cross-sectional study was conducted between July and August 2024 using an anonymous online survey distributed to pediatricians throughout Poland via national professional networks, with a response rate of 7.3%. Categorical variables were analyzed using the chi-square test of independence to assess the associations between key variables. Quantitative data were analyzed using descriptive statistics, and qualitative data from open-ended responses were subjected to a thematic analysis. Results: A total of 210 pediatricians responded. Among them, 149 (71%) reported access to ultrasound equipment at their workplace, and 89 (42.4%) reported having participated in some form of POCUS training. Only 46 respondents (21.9%) reported frequently using POCUS in their clinical routine. The self-assessed POCUS competence was rated as low or very low by 136 respondents (64.8%). While POCUS was generally perceived as a helpful tool in facilitating and accelerating clinical decisions, the main barriers to implementation were a lack of formal training and limited institutional support. Conclusions: Although POCUS is perceived as clinically valuable by the surveyed pediatricians in Poland, its routine use remains limited due to training and systemic barriers. Future efforts should prioritize the development of a validated, competency-based training framework and the implementation of a larger, representative national study to guide the structured integration of POCUS into pediatric care. Full article

We investigate whether a belief in trend continuation, often associated with the so-called “hot-hand effect,” can be endogenously triggered by personal performance feedback in a controlled financial experiment. Participants allocated funds across assets with randomly generated prices, under conditions of known probabilities and varying levels of risk. In a two-stage setup, participants were first exposed to random price sequences to learn the task and potentially develop perceptions of personal success. They then faced additional price paths under incentivized conditions. Our findings show that participants initially increased purchases following gains—consistent with a feedback-driven belief in momentum—but this pattern faded over time. When facing sustained losses, loss aversion dominated decision-making, overriding early optimism. These results highlight how cognitive heuristics and emotional biases interact dynamically, suggesting that belief in trend continuation is context-sensitive and constrained by the reluctance to realize losses. Full article

The resilience concept is well established in engineering, but the quantitative studies of vessel impact resilience for bridge structures remain limited. This paper presents an integrated framework for assessing vessel impact resilience under combined rebar corrosion and vessel collision effects. First, a corroded reinforced concrete bridge is considered for nonlinear static analysis to quantify initial corrosion damage and for nonlinear dynamic analysis to evaluate post-impact function loss. Then, recovery for each damage state is modeled by using both negative exponential and triangular recovery functions to estimate restoration times and to obtain a vessel impact resilience index. The results show that increasing corrosion severity markedly reduces resilience capacity. Furthermore, resilience indices obtained from the negative exponential function generally exceed those from the triangular function, and this improvement becomes more significant at lower resilience levels. Resilience indices calculated by using negative exponential and triangular recovery functions show negligible differences when the concrete bridge is in the uncorroded initial state and the vessel impact velocity is below 1.5 m/s. However, as reinforcement corrosion increases, the maximum discrepancy between these two recovery functions also increases, reaching a value of 67% at a corrosion level of 15.0%. From the numerical results obtained from a case study, it is important to select an appropriate recovery model when assessing vessel impact resilience. For rapid initial restoration followed by slower long-term recovery, the negative exponential model yields greater resilience gains compared to the triangular model. The proposed method thus provides an effective tool for engineers and decision makers to evaluate and improve the vessel impact resilience of aging bridges under the combined corrosion and impact effects. This proposes a quantitative metric for resilience-based condition assessment and maintenance planning. Full article

This paper proposes an intelligent power adaptation framework for Free-Space Optics (FSO) communication systems operating under different weather conditions exploiting a deep learning (DL) analysis of received eye diagram images. The system incorporates two Convolutional Neural Network (CNN) architectures, LeNet and Wide Residual Network (Wide ResNet) algorithms to perform regression tasks that predict received signal quality metrics such as the Quality Factor (Q-factor) and Bit Error Rate (BER) from the received eye diagram. These models are evaluated using Mean Squared Error (MSE) and the coefficient of determination (${\mathrm{R}}^2$ score) to assess prediction accuracy. Additionally, a custom CNN-based classifier is trained to determine whether the BER reading from the eye diagram exceeds a critical threshold of ${10}^{-4}$; this classifier achieves an overall accuracy of 99%, correctly detecting 194/195 “acceptable” and 4/5 “unacceptable” instances. Based on the predicted signal quality, the framework activates a dual-amplifier configuration comprising a pre-channel amplifier with a maximum gain of 25 dB and a post-channel amplifier with a maximum gain of 10 dB. The total gain of the amplifiers is adjusted to support the operation of the FSO system under all-weather conditions. The FSO system uses a 15 dBm laser source at 1550 nm. The DL models are tested on both internal and external datasets to validate their generalization capability. The results show that the regression models achieve strong predictive performance, and the classifier reliably detects degraded signal conditions, enabling the real-time gain control of the amplifiers to achieve the quality of transmission. The proposed solution supports robust FSO communication under challenging atmospheric conditions including dry snow, making it suitable for deployment in regions like Northern Europe, Canada, and Northern Japan. Full article

Direct seeding has gained prominence as a labor-efficient and environmentally sustainable alternative to conventional transplanting in rice cultivation. In direct seeding systems, early-stage management is crucial for stable seedling establishment, with sowing uniformity measured by seed counts being a critical indicator of success. However, conventional manual seed counting methods are time-consuming, prone to human error, and impractical for large-scale or repetitive tasks, necessitating advanced automated solutions. Recent advances in computer vision technologies and precision agriculture tools, offer the potential to automate seed counting tasks. Nevertheless, challenges such as domain discrepancies and limited labeled data restrict robust real-world deployment. To address these issues, we propose a density estimation-based seed counting framework integrating semi-supervised learning and background augmentation. This framework includes a cost-effective data acquisition system enabling diverse domain data collection through indoor background augmentation, combined with semi-supervised learning to utilize augmented data effectively while minimizing labeling costs. The experimental results on field data from unknown domains show that our approach reduces seed counting errors by up to 58.5% compared to conventional methods, highlighting its potential as a scalable and effective solution for agricultural applications in real-world environments. Full article

Air pollution threatens human health and ecosystems, making timely forecasting essential. The spatiotemporal dynamics of pollutants, shaped by various factors, challenge traditional methods. Therefore, spatiotemporal graph-based deep learning has gained attention for its ability to capture spatial and temporal dependencies within monitoring networks. However, many existing models, despite their high predictive accuracy, face computational complexity and scalability challenges. This study introduces clustered and lightweight spatio-temporal graph convolutional network with gated recurrent unit (ClusLite-STGCN-GRU), a hybrid model that integrates spatial clustering based on pollutant time series for graph construction, Copula-based dependency analysis for selecting relevant pollutants to predict PM_2.5, and graph convolution combined with gated recurrent units to extract spatiotemporal features. Unlike conventional approaches that require learning or dynamically updating adjacency matrices, ClusLite-STGCN-GRU employs a fixed, simple cluster-based structure. Experimental results on Tehran air quality data demonstrate that the proposed model not only achieves competitive predictive performance compared to more complex models, but also significantly reduces computational cost—by up to 66% in training time, 83% in memory usage, and 84% in number of floating-point operations—making it suitable for real-time applications and offering a practical balance between accuracy, interpretability, and efficiency. Full article

The chaotic dynamics observed in mathematical models of cancer can correspond to the unpredictable tumor growth and treatment responses seen in clinical settings. Suppressing this chaos is a significant challenge in theoretical oncology. This paper investigates and compares four distinct control strategies designed to stabilize a chaotic three-dimensional tumor-immune interaction model. The objective is to steer the system from its chaotic attractor to a target unstable periodic orbit, representing a transition to a more regular and predictable dynamic. The strategies, all based on the external force control paradigm, include continuous control, a simple state-dependent intermittent control, an improved intermittent control with a minimum activation duration to suppress chattering, and an adaptive intermittent control with a time-varying feedback gain. The performance of each strategy is quantitatively evaluated based on tracking accuracy and the required control effort. Full article

With the growing severity of environmental pollution, people are paying increasing attention to their health. However, naturally occurring wood with health benefits and applications in human healthcare is still scarce. Natural wood exhibits a limited negative oxygen ion release capacity, and this release has a short duration, failing to meet practical application requirements. This study innovatively developed a humidity-responsive, healthy wood material with a high negative oxygen ion release capacity based on fast-growing poplar. Through vacuum cyclic impregnation technology, hexagonal stone powder was infused into the pores of poplar wood, endowing it with the ability to continuously release negative oxygen ions. The healthy wood demonstrated a static average negative oxygen ion release rate of 537 ions/cm³ (peaking at 617 ions/cm³) and a dynamic average release rate of 3,170 ions/cm³ (peaking at 10,590 ions/cm³). The results showed that the particle size of hexagonal stone powder in suspension was influenced by the dispersants and dispersion processes. The composite dispersion process demonstrated optimal performance when using 0.5 wt% silane coupling agent γ-(methacryloxy)propyltrimethoxysilane (KH570), achieving the smallest particle size of 8.93 μm. The healthy wood demonstrated excellent impregnation performance, with a weight gain exceeding 14.61% and a liquid absorption rate surpassing 165.18%. The optimal impregnation cycle for vacuum circulation technology was determined to be six cycles, regardless of the type of dispersant. Compared with poplar wood, the hygroscopic swelling rate of healthy wood was lower, especially in PEG-treated samples, where the tangential, radial, longitudinal, and volumetric swelling rates decreased by 70.93%, 71.67%, 69.41%, and 71.35%, respectively. Combining hexagonal stone powder with fast-growing poplar wood can effectively enhance the release of negative oxygen ions. The static average release of negative oxygen ions from healthy wood is 1.44 times that of untreated hexagonal stone powder, and the dynamic release reaches 2 to 3 times the concentration of negative oxygen ions specified by national fresh air standards. The water-responsive mechanism revealed that negative oxygen ion release surged when ambient humidity exceeded 70%. This work proposes a sustainable and effective method to prepare healthy wood with permanent negative oxygen ion release capability. It demonstrates great potential for improving indoor air quality and enhancing human health. Full article

The global cement industry remains a significant contributor to carbon dioxide (CO₂) emissions, prompting substantial research efforts toward sustainable construction materials. Lithium slag (LS), a by-product of lithium extraction, has attracted attention as a supplementary cementitious material (SCM). This review synthesizes experimental findings on LS replacement levels, fresh-state behavior, mechanical performance (compressive, tensile, and flexural strengths), time-dependent deformation (shrinkage and creep), and durability (sulfate, acid, abrasion, and thermal) of LS-modified concretes. Statistical analysis identifies an optimal LS dosage of 20–30% (average 24%) for maximizing compressive strength and long-term durability, with 40% as a practical upper limit for tensile and flexural performance. Fresh-state tests show that workability losses at high LS content can be mitigated via superplasticizers. Drying shrinkage and creep strains decrease in a dose-dependent manner with up to 30% LS. High-volume (40%) LS blends achieve up to an 18% gain in 180-day compressive strength and >30% reduction in permeability metrics. Under elevated temperatures, 20% LS mixes retain up to 50% more residual strength than controls. In advanced systems—autoclaved aerated concrete (AAC), one-part geopolymers, and recycled aggregate composites—LS further enhances both microstructural densification and durability. In particular, LS emerges as a versatile SCM that optimizes mechanical and durability performance, supports material circularity, and reduces the carbon footprint. Full article

Floating solar technology has gained significant attention as part of the global expansion of renewable energy due to its potential for installation in underutilized water bodies. Several countries, including the Netherlands, have initiated efforts to extend this technology from inland freshwater applications to open offshore environments, particularly within offshore wind farm areas. This development is motivated by the synergistic benefits of increasing site energy density and leveraging the existing offshore grid infrastructure. The deployment of offshore floating photovoltaic (OFPV) systems involves assembling multiple modular units in a marine environment, introducing operational risks that may give rise to safety concerns. To mitigate these risks, weather windows must be considered prior to the task execution to ensure continuity between weather-sensitive activities, which can also lead to additional time delays and increased costs. Consequently, optimizing marine logistics becomes crucial to achieving the cost reductions necessary for making OFPV technology economically viable. This study employs a simulation-based approach to estimate the installation duration of a 5 MWp OFPV plant at a Dutch offshore wind farm site, started in different months and under three distinct risk management scenarios. Based on 20 years of hindcast wave data, the results reveal the impacts of campaign start months and risk management policies on installation duration. Across all the scenarios, the installation duration during the autumn and winter period is 160% longer than the one in the spring and summer period. The average installation durations, based on results from 12 campaign start months, are 70, 80, and 130 days for the three risk management policies analyzed. The result variation highlights the additional time required to mitigate operational risks arising from potential discontinuity between highly interdependent tasks (e.g., offshore platform assembly and mooring). Additionally, it is found that the weather-induced delays are mainly associated with the campaigns of pre-laying anchors and platform and mooring line installation compared with the other campaigns. In conclusion, this study presents a logistics modeling methodology for OFPV systems, demonstrated through a representative case study based on a state-of-the-art truss-type design. The primary contribution lies in providing a framework to quantify the performance of OFPV installation strategies at an early design stage. The findings of this case study further highlight that marine installation logistics are highly sensitive to local marine conditions and the chosen installation strategy, and should be integrated early in the OFPV design process to help reduce the levelized cost of electricity. Full article

Minimizing carbon dioxide emissions is crucial due to the generation of energy from fossil fuels. The significance of carbon capture and storage (CCS) technology, which is highly successful in mitigating carbon emissions, has increased. On the other hand, hydrogen is an important energy carrier for storing and transporting energy, and technologies that rely on hydrogen have become increasingly promising as the world moves toward a more environmentally friendly approach. Nevertheless, the integration of CCS technologies into power production processes is a significant challenge, requiring the enhancement of the combined power generation–CCS process. In recent years, there has been a growing interest in process intensification (PI), which aims to create smaller, cleaner, and more energy efficient processes. The goal of this research is to demonstrate the process intensification potential and to model and simulate a hybrid integrated–intensified adsorptive-membrane reactor process for simultaneous carbon capture and hydrogen production. A comprehensive, multi-scale, multi-phase, dynamic, computational fluid dynamics (CFD)-based process model is constructed, which quantifies the various underlying complex physicochemical phenomena occurring at the pellet and reactor levels. Model simulations are then performed to investigate the impact of dimensionless variables on overall system performance and gain a better understanding of this cyclic reaction/separation process. The results indicate that the hybrid system shows a steady-state cyclic behavior to ensure flexible operating time. A sustainability evaluation was conducted to illustrate the sustainability improvement in the proposed process compared to the traditional design. The results indicate that the integrated–intensified adsorptive-membrane reactor technology enhances sustainability by 35% to 138% for the chosen 21 indicators. The average enhancement in sustainability is almost 57%, signifying that the sustainability evaluation reveals significant benefits of the integrated–intensified adsorptive-membrane reactor process compared to HTSR + LTSR. Full article