Search Results (1,686)

Continuous and accurate biomass measurement is a critical enabler for control, decision making, and optimization in modern plant production systems. It supports the development of plant growth models for advanced control strategies like model predictive control, and enables responsive, data-driven, and plant state-dependent cultivation. Traditional biomass measurement methods, such as destructive sampling, are time-consuming and unsuitable for high-frequency monitoring. In contrast, image-based estimation using computer vision and deep learning requires frequent retraining and is sensitive to changes in lighting or plant morphology. This work introduces a low-cost, load-cell-based biomass monitoring system tailored for vertical farming applications. The system operates at the level of individual growing trays, offering a valuable middle ground between impractical plant-level sensing and overly coarse rack-level measurements. Tray-level data allow localized control actions, such as adjusting light spectrum and intensity per tray, thereby enhancing the utility of controllable LED systems. This granularity supports layer-specific optimization and anomaly detection, which are not feasible with rack-level feedback. The biomass sensor is easily scalable and can be retrofitted, addressing common challenges such as mechanical noise and thermal drift. It offers a practical and robust solution for biomass monitoring in dynamic, growing environments, enabling finer control and smarter decision making in both commercial and research-oriented vertical farming systems. The developed sensor was tested and validated against manual harvest data, demonstrating high agreement with actual plant biomass and confirming its suitability for integration into vertical farming systems. Full article

To address the limitations of the existing cotton seedling growth prediction methods in field environments, specifically, poor representation of spatiotemporal features and low visual fidelity in texture rendering, this paper proposes an algorithm for the prediction of cotton seedling growth from images based on FCA-STNet. The model leverages historical sequences of cotton seedling RGB images to generate an image of the predicted growth at time t + 1 and extracts 37 phenotypic traits from the predicted image. A novel STNet structure is designed to enhance the representation of spatiotemporal dependencies, while an Adaptive Fine-Grained Channel Attention (FCA) module is integrated to capture both global and local feature information. This attention mechanism focuses on individual cotton plants and their textural characteristics, effectively reducing the interference from common field-related challenges such as insufficient lighting, leaf fluttering, and wind disturbances. The experimental results demonstrate that the predicted images achieved an MSE of 0.0086, MAE of 0.0321, SSIM of 0.8339, and PSNR of 20.7011 on the test set, representing improvements of 2.27%, 0.31%, 4.73%, and 11.20%, respectively, over the baseline STNet. The method outperforms several mainstream spatiotemporal prediction models. Furthermore, the majority of the predicted phenotypic traits exhibited correlations with actual measurements with coefficients above 0.8, indicating high prediction accuracy. The proposed FCA-STNet model enables visually realistic prediction of cotton seedling growth in open-field conditions, offering a new perspective for research in growth prediction. Full article

In an era of exponential data growth, ensuring high data quality has become essential for effective, evidence-based decision making. This study presents a structured and comparative review of the field by integrating data classifications, quality dimensions, assessment methodologies, and modern software tools. Unlike earlier reviews that focus narrowly on individual aspects, this work synthesizes foundational concepts with formal frameworks, including the Findable, Accessible, Interoperable, and Reusable (FAIR) principles and the ISO/IEC 25000 series on software and data quality. It further examines well-established assessment models, such as Total Data Quality Management (TDQM), Data Warehouse Quality (DWQ), and High-Quality Data Management (HDQM), and critically evaluates commercial platforms in terms of functionality, AI integration, and adaptability. A key contribution lies in the development of conceptual mappings that link data quality dimensions with FAIR indicators and maturity levels, offering a practical reference model. The findings also identify gaps in current tools and approaches, particularly around cost-awareness, explainability, and process adaptability. By bridging theory and practice, the study contributes to the academic literature while offering actionable insights for building scalable, standards-aligned, and context-sensitive data quality management strategies. Full article

We investigated the spatial structure and colony itinerancy of Audouin’s gull (Ichthyaetus audouinii) adult breeders across multiple breeding sites in the central Mediterranean Sea during 25 years of fieldwork. Using cluster analysis of marked individuals from different years and sites, we identified five spatial breeding units of increasing hierarchical scale—Breeding Sites, Colonies, Districts, Regions and Marine Sectors—which reflect biologically meaningful boundaries beyond simple geographic proximity. To determine the most appropriate scale for monitoring local populations, we applied multievent capture–recapture models and examined variation in survival and site fidelity across these units. Audouin’s gulls frequently change their location at the Breeding Site and Colony levels from one year to another, without apparent survival costs. In contrast, dispersal beyond Districts boundaries was found to be rare and associated with reduced survival rates, indicating that breeding Districts represent the most relevant biological unit for identifying local populations. The survival disadvantage observed in individuals leaving their District likely reflects increased extrinsic mortality in unfamiliar environments and the selective dispersal of lower-quality individuals. Within breeding Districts, birds may benefit from local knowledge and social information, supporting demographic stability and higher fitness. Our findings highlight the value of adopting a District-based framework for long-term monitoring and conservation of this endangered species. At this scale, demographic trends such as population growth or decline emerge more clearly than when assessed at the level of singular colonies. This approach can enhance our understanding of population dynamics in other mobile species and support more effective conservation strategies aligned with natural population structure. Full article

Land cover, topography, precipitation, and socio-economic factors exert both direct and indirect influences on urban land surface temperatures. Within the broader context of global climate change, these influences are magnified by the escalating intensity of the urban heat island effect. However, the interplay and underlying mechanisms of natural and socio-economic determinants of land surface temperatures remain inadequately explored, particularly in the context of cold-region cities located in the northern temperate zone of China. This study focuses on Changchun City, employing multispectral remote sensing imagery to derive and spatially map the distribution of land surface temperatures and topographic attributes. Through comprehensive analysis, the research identifies the principal drivers of temperature variations and delineates their seasonal dynamics. The findings indicate that population density, night-time light intensity, land use, GDP (Gross Domestic Product), relief, and elevation exhibit positive correlations with land surface temperature, whereas slope demonstrates a negative correlation. Among natural factors, the correlations of slope, relief, and elevation with land surface temperature are comparatively weak, with determination coefficients (R²) consistently below 0.15. In contrast, socio-economic factors exert a more pronounced influence, ranked as follows: population density (R² = 0.4316) > GDP (R² = 0.2493) > night-time light intensity (R² = 0.1626). The overall hierarchy of the impact of individual factors on the temperature model, from strongest to weakest, is as follows: population, night-time light intensity, land use, GDP, slope, relief, and elevation. In examining Changchun and analogous cold-region cities within the northern temperate zone, the research underscores that socio-economic factors substantially outweigh natural determinants in shaping urban land surface temperatures. Notably, human activities catalyzed by population growth emerge as the most influential factor, profoundly reshaping the urban thermal landscape. These activities not only directly escalate anthropogenic heat emissions, but also alter land cover compositions, thereby undermining natural cooling mechanisms and exacerbating the urban heat island phenomenon. Full article

Objectives: This study aimed to evaluate the effects of AMCOP^® elastodontic appliances on cephalometric parameters of skeletal divergence and upper airway dimensions in growing patients, comparing treated individuals with an untreated control group. Methods: A total of 60 subjects (30 treated with AMCOP^® devices and 30 controls) were selected, with mean ages of 8.67 ± 1.3 and 9.19 ± 0.8 years, respectively. The AMCOP^® appliances, designed for mixed dentition, were worn for 1 h during the day and throughout the night for 6–8 months. Cephalometric analyses were conducted at the beginning (T0) and end (T1) of treatment. Statistical analyses were performed using multivariable linear regression models to assess changes in skeletal and airway parameters, with significance set at p < 0.05. Results: Significant reductions were observed in Ans-Snp^Go-Gn (p = 0.0351), SN^Go-Gn (p = 0.0091), and FMA (p < 0.001) in the treated group compared to controls, indicating improved mandibular rotation. Upper airway spaces (SPAS, MAS, IAS) increased significantly, suggesting enhanced airway patency. Regression models confirmed the positive impact of AMCOP^® therapy on skeletal and airway outcomes, particularly in subjects with pronounced vertical discrepancies. Conclusions: AMCOP^® elastodontic devices effectively promote anterior mandibular rotation and reduce mandibular plane inclination in hyperdivergent patients, contributing to balanced craniofacial growth. The expansion of pharyngeal spaces indicates potential respiratory benefits. Future research is needed to confirm long-term stability and address variability in treatment response. Full article

Candida albicans is a clinically important fungal pathogen capable of causing both superficial and systemic infections, particularly in immunocompromised individuals. A key factor contributing to its pathogenicity is its ability to form biofilms, structured microbial communities that confer significant resistance to conventional antifungal therapies. Addressing this challenge, we explored the antivirulence potential of acridine derivatives, a class of heterocyclic aromatic compounds known for their diverse biological activities, including antimicrobial, antitumor, and antiparasitic properties. In this study, a series of acridine derivatives was screened against C. albicans biofilms, revealing notable inhibitory activity and highlighting their potential as scaffolds for the development of novel antifungal agents. Among the tested compounds, acridine-4-carboxylic acid demonstrated the most promising activity, significantly inhibiting the biofilm formation at 10 µg/mL without affecting planktonic cell growth, and with a minimum inhibitory concentration (MIC) of 60 µg/mL. Furthermore, it attenuated filamentation and cell aggregation in a fluconazole-resistant C. albicans strain. Toxicity assessments using Caenorhabditis elegans and plant models supported its low-toxicity profile. These findings highlight the potential of acridine-based scaffolds, particularly acridine-4-carboxylic acid, as lead structures for the development of therapeutics targeting both fungal growth and biofilm formation in Candida albicans infections. Full article

Background/Objectives: Overt hypothyroidism during pregnancy has been linked to adverse outcomes, including preterm birth, low birth weight, and impaired fetal neurocognitive development. This study aimed to evaluate pregnancy complications in women with overt hypothyroidism (TSH ≥ 10) through a cross-sectional study. Methods: Data from 259,897 live-birth pregnancies (2013–2022) from Clalit Health Services (CHS) were analyzed. The study included all CHS-insured women aged ≥ 18 years with available TSH results during pregnancy. Overt hypothyroidism was defined as a mean TSH ≥ 10 mIU/L, while the euthyroid reference group had TSH levels < 4 mIU/L and no history of hypothyroidism or levothyroxine use. Cases of overt hypothyroidism were matched with 15 controls using propensity score-based matching. Covariates included maternal age, ethnicity, socioeconomic status, IVF use, recurrent pregnancy loss, and smoking. Pregnancy complications were compared between groups using descriptive statistics and univariate analysis. A quasi-Poisson regression model was used to assess complication risk in overt hypothyroidism versus matched controls. Results: The final analysis included 9125 euthyroid and 611 overt hypothyroid pregnancies, with comparable baseline characteristics between groups. No significant differences were found in maternal age, ethnicity, socioeconomic scores, IVF rates, recurrent pregnancy loss, diabetes, smoking, gestational age at delivery, or rates of preterm birth, pre-eclampsia, gestational diabetes, cesarean section, and intrauterine growth restriction. Overall, overt hypothyroidism was not associated with increased complications. Sensitivity analyses using maximum TSH levels during pregnancy showed a slightly elevated risk for pregnancy complications (IRR 1.1, CI 1.04–1.18; p = 0.002). Conclusions: Overt hypothyroidism was not associated with an increased risk of adverse pregnancy outcomes when adjusted for confounding factors, suggesting that treatment decisions should be made on an individual basis. Full article

Background: Adolescents’ subjective well-being (SWB) is a key indicator of quality of life. While its development during schooling has been widely studied, few studies have examined changes in SWB after leaving school due to the need for longitudinal data. This study investigates changes in SWB among adolescents in Germany over the two years before and after leaving school, focusing on school type, socioeconomic position, gender, and family structure. Methods: We use data from the ninth-grade cohort of the German National Educational Panel Study, first surveyed in 2010 and followed annually. Growth modeling (specifically, a multilevel discontinuity model) is applied to analyze SWB trajectories and potential moderation by background characteristics. The final sample includes 19,767 observations from 6599 individuals. Results: SWB increases notably after leaving school and remains stable before and after the transition. The increase is smaller for adolescents completing higher secondary education, living in nuclear families, or identifying as male. These groups report higher SWB prior to the transition, so post-school changes reduce group differences. Conclusion: The findings suggest that schools may lack adequate resources to support adolescents in mastering key developmental challenges. While school is a critical environment, it may also impose pressures that are associated with lower well-being. Full article

Mycotoxins are toxic secondary metabolites produced by various fungi that contaminate livestock feed, posing serious threats to animal health, productivity, and food safety. Although historical research has often examined individual mycotoxins in isolation, real-world conditions typically involve the simultaneous presence of multiple mycotoxins, resulting in additive or synergistic toxic effects that are often more severe than those observed with single toxin exposures. This review comprehensively synthesizes recent findings on multi-mycotoxin contamination in livestock feed, highlighting their physiological effects, mechanisms of action, and implications for regulatory frameworks. Multi-mycotoxin interactions exacerbate oxidative stress, immune suppression, impaired reproduction, and organ damage across species, leading to reduced growth performance, decreased milk and egg production, compromised carcass and wool quality, and increased mortality rates. A major concern is that current international regulatory standards mainly address individual mycotoxins, overlooking the compounded risks of co-occurrence. Global surveillance studies consistently reveal high prevalence rates of mycotoxin mixtures in feedstuffs, especially combinations involving DON, ZEN, AFB₁, FB₁, and OTA. Understanding these interactions and their underlying cellular mechanisms is critical for improving risk assessment models, formulating integrated mitigation strategies, and safeguarding both livestock productivity and human food security. Full article

The interconnections among water, energy, and food (WEF) systems are growing increasingly complex, making it essential to understand their evolutionary mechanisms and coordination barriers to enhance regional resilience and sustainability. In this study, we investigated the WEF system in Northeast China by constructing a comprehensive indicator system encompassing resource endowment and utilization efficiency. The coupling coordination degree (CCD) of the WEF system was quantitatively assessed from 2001 to 2022. An obstacle degree model was employed to identify key constraints, while grey relational analysis was used to evaluate the driving influence of individual indicators. Furthermore, a co-evolution model based on logistic growth and competition–cooperation dynamics was developed to simulate system interactions. The results reveal the following: (1) the regional WEF-CCD increased from 0.627 in 2001 to 0.769 in 2022, reaching the intermediate coordination level, with the CCDs of the food, water, and energy subsystems rising from 0.39 to 0.62, 0.38 to 0.60, and 0.40 to 0.55, respectively, highlighting that the food subsystem had the most stable and significant improvement; (2) Jilin Province attained the highest WEF-CCD, 0.850, in 2022, while that for Heilongjiang remained the lowest, at 0.715, indicating substantial interprovincial disparities; (3) key indicators, such as food self-sufficiency rate, electricity generation, and ecological water use, functioned as both core constraints and major drivers of system performance; (4) co-evolution modeling revealed that the food subsystem exhibited the fastest growth, followed by water and energy (${\alpha }_3$ >${ \alpha }_1$ > ${ \alpha }_2$ > 0), with mutual promotion between water and energy subsystems and inhibitory effects from the food subsystem, ultimately converging toward a stable equilibrium state; and (5) interprovincial co-evolution modeling indicated that Jilin leads in WEF system development, followed by Liaoning and Heilongjiang, with predominantly cooperative interactions among provinces driving convergence toward a stable and coordinated equilibrium despite structural asymmetries. This study proposes a transferable, multi-method analytical framework for evaluating WEF coordination, offering practical insights into bottlenecks, key drivers, and co-evolutionary dynamics for sustainable resource governance. Full article

Background/Objectives: Peripheral artery disease (PAD) impacts more than 200 million individuals globally and leads to mortality and morbidity secondary to progressive limb dysfunction and amputation. However, clinical management of PAD remains suboptimal, in part because of the lack of standardized biomarkers to predict patient outcomes. Growth differentiation factor 15 (GDF15) is a stress-responsive cytokine that has been studied extensively in cardiovascular disease, but its investigation in PAD remains limited. This study aimed to use explainable statistical and machine learning methods to assess the prognostic value of GDF15 for limb outcomes in patients with PAD. Methods: This prognostic investigation was carried out using a prospectively enrolled cohort comprising 454 patients diagnosed with PAD. At baseline, plasma GDF15 levels were measured using a validated multiplex immunoassay. Participants were monitored over a two-year period to assess the occurrence of major adverse limb events (MALE), a composite outcome encompassing major lower extremity amputation, need for open/endovascular revascularization, or acute limb ischemia. An Extreme Gradient Boosting (XGBoost) model was trained to predict 2-year MALE using 10-fold cross-validation, incorporating GDF15 levels along with baseline variables. Model performance was primarily evaluated using the area under the receiver operating characteristic curve (AUROC). Secondary model evaluation metrics were accuracy, sensitivity, specificity, negative predictive value (NPV), and positive predictive value (PPV). Prediction histogram plots were generated to assess the ability of the model to discriminate between patients who develop vs. do not develop 2-year MALE. For model interpretability, SHapley Additive exPlanations (SHAP) analysis was performed to evaluate the relative contribution of each predictor to model outputs. Results: The mean age of the cohort was 71 (SD 10) years, with 31% (n = 139) being female. Over the two-year follow-up period, 157 patients (34.6%) experienced MALE. The XGBoost model incorporating plasma GDF15 levels and demographic/clinical features achieved excellent performance for predicting 2-year MALE in PAD patients: AUROC 0.84, accuracy 83.5%, sensitivity 83.6%, specificity 83.7%, PPV 87.3%, and NPV 86.2%. The prediction probability histogram for the XGBoost model demonstrated clear separation for patients who developed vs. did not develop 2-year MALE, indicating strong discrimination ability. SHAP analysis showed that GDF15 was the strongest predictive feature for 2-year MALE, followed by age, smoking status, and other cardiovascular comorbidities, highlighting its clinical relevance. Conclusions: Using explainable statistical and machine learning methods, we demonstrated that plasma GDF15 levels have important prognostic value for 2-year MALE in patients with PAD. By integrating clinical variables with GDF15 levels, our machine learning model can support early identification of PAD patients at elevated risk for adverse limb events, facilitating timely referral to vascular specialists and aiding in decisions regarding the aggressiveness of medical/surgical treatment. This precision medicine approach based on a biomarker-guided prognostication algorithm offers a promising strategy for improving limb outcomes in individuals with PAD. Full article

Wounds are undeniably important gateways for pathogens to enter the body. In addition to their detrimental local effects, they can also cause adverse systemic effects. For this reason, developing methods for eradicating pathogens from wounds is a challenging medical issue. Polymers, particularly hydrogels, are one of the more essential materials for designing novel drug-delivery systems, thanks to the ease of tuning their structures. This work exploits this property by utilizing copolymerization, microwave modification, and drug-loading processes to obtain antibacterial gels. Synthesized xylitol-modified glycidyl methacrylate-co-ethyl methacrylate ([P(EMA)-co-(GMA)]-Xyl]) matrices were loaded with bacitracin, gentian violet, furazidine, and brilliant green, used as active pharmaceutical ingredients (APIs). The hydrophilic properties, API release mechanism, and antibacterial properties of the obtained hydrogels against Escherichia coli, Pseudomonas aeruginosa, and Staphylococcus epidermidis containing [P(EMA)-co-(GMA)]-Xyl] were studied. The hydrogels with the APIs efficiently inhibit bacteria growth with low doses of drugs, and our findings are statistically significant, confirmed with ANOVA analysis at p = 0.05. The results confirmed that the proposed system is hydrophilic and has extended the drug-release capabilities of APIs with a controlled burst effect based on [P(EMA)-co-(GMA)]-Xyl] content in the hydrogel. Hydrogels are characterized by the prolonged release of APIs in a very short time (a few minutes). Although the amount of released APIs is about 10%, it still exceeds the minimum inhibitory concentrations of drugs. Several kinetic models (first-order, second-order, Baker–Lonsdale, and Korsmeyer–Peppas) were applied to fit the API release data from the [P(EMA)-co-(GMA)]-Xyl-based hydrogel. The best fit of the Korsmeyer–Peppas kinetic model to the experimental data was determined, and it was confirmed that a diffusion-controlled release mechanism of the APIs from the studied hydrogels is dominant, which is desirable for applications requiring a consistent, controlled release of therapeutic agents. A statistical analysis of API release using Linear Mixed Model was performed, examining the relationship between % mass of API, sample (hydrogels and control), time, sample–time interaction, and variability between individuals. The model fits the data well, as evidenced by the determination coefficients close to 1. The analyzed interactions in the data are reliable and statistically significant (p < 0.001). The outcome of this study suggests that the presented acrylate-based gel is a promising candidate for developing wound dressings. Full article

(1) Background: As society progresses, increasing numbers of individuals are experiencing hair loss, which can be attributed to factors such as unhealthy diets, insufficient sleep, stress, and hormonal imbalances. Currently available pharmacological treatments for hair loss often cause undesirable side effects, highlighting the urgent need to explore safer and more effective agents to promote hair restoration. This study investigated the role of recombinant human type XVII collagen derived from the α1 chain (rhCOL17A1) in facilitating hair growth and restoration. (2) Methods: We analyzed the impact of rhCOL17A1 on the mRNA expression of several growth factors, as well as Bcl-2 and Bax, at the cellular level. Moreover, the effects of rhCOL17A1 on the expression of key proteins in the Wnt/β-catenin and Sonic Hedgehog (SHH)/GLI signaling pathways were examined by Western blotting (WB). At the organismal level, we established a model in C57BL/6 mice through chronic subcutaneous administration of 5% testosterone propionate. We subsequently assessed the effect of rhCOL17A1 on hair regrowth via histological analysis using hematoxylin and eosin (H&E) staining and immunofluorescence staining. (3) Results: rhCOL17A1 contributes to the resistance of hair follicle dermal papilla cells (HFDPCs) to apoptosis. rhCOL17A1 activates the Wnt/β-catenin and SHH/GLI signaling pathways, and increases the expression of type XVII collagen (COLXVII), thereby creating a favorable environment for hair growth. Furthermore, rhCOL17A1 exerts a significant growth-promoting effect at the animal level. (4) Conclusions: rhCOL17 promotes hair growth by activating the Wnt/β-catenin and SHH/GLI signaling pathways and upregulating COLXVII expression. Full article

Accurate forecasting of electricity production is crucial for the stability of the entire energy sector. However, predicting future renewable energy production and its value is difficult due to the complex processes that affect production using renewable energy sources. In this article, we examine the performance of basic deep learning models for electricity forecasting. We designed deep learning models, including recursive neural networks (RNNs), which are mainly based on long short-term memory (LSTM) networks; gated recurrent units (GRUs), convolutional neural networks (CNNs), temporal fusion transforms (TFTs), and combined architectures. In order to achieve this goal, we have created our benchmarks and used tools that automatically select network architectures and parameters. Data were obtained as part of the NCBR grant (the National Center for Research and Development, Poland). These data contain daily records of all the recorded parameters from individual solar and wind farms over the past three years. The experimental results indicate that the LSTM models significantly outperformed the other models in terms of forecasting. In this paper, multilayer deep neural network (DNN) architectures are described, and the results are provided for all the methods. This publication is based on the results obtained within the framework of the research and development project “POIR.01.01.01-00-0506/21”, realized in the years 2022–2023. The project was co-financed by the European Union under the Smart Growth Operational Programme 2014–2020. Full article