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19 pages, 1363 KiB  
Article
Non-Structural Carbohydrate Concentration Increases and Relative Growth Decreases with Tree Size in the Long-Lived Agathis australis (D.Don) Lindl.
by Julia Kaplick, Benjamin M. Cranston and Cate Macinnis-Ng
Forests 2025, 16(8), 1270; https://doi.org/10.3390/f16081270 (registering DOI) - 3 Aug 2025
Abstract
The southern conifer Agathis australis (D.Don) Lindl. is a large and long-lived species endemic to Aotearoa New Zealand. It is threatened due to past logging activities, pathogen attack and potentially climate change, with increasing severity and frequency of drought and heatwaves across its [...] Read more.
The southern conifer Agathis australis (D.Don) Lindl. is a large and long-lived species endemic to Aotearoa New Zealand. It is threatened due to past logging activities, pathogen attack and potentially climate change, with increasing severity and frequency of drought and heatwaves across its distribution. Like many large tree species, little is known about the carbon dynamics of this ecologically and culturally significant species. We explored seasonal variations in non-structural carbohydrates (NSCs) and growth in trees ranging from 20 to 175 cm diameter at breast height (DBH). NSCs were seasonally stable with no measurable pattern across seasons. However, we found growth rates standardised to basal area and sapwood area (growth efficiency) declined with tree age and stem NSC concentrations (including total NSCs, sugars and starch) all increased as trees aged. Total NSC concentrations were 0.3%–0.6% dry mass for small trees and 0.8%–1.8% dry mass for larger trees, with strong relationships between DBH and total NSC, sugar and starch in stems but not roots. Cumulative growth efficiency across the two-year study period declined as tree size increased. Furthermore, there was an inverse relationship between growth efficiency across the two-year study period and NSC concentrations of stems. This relationship was driven by differences in carbon dynamics in trees of different sizes, with trees progressing to a more conservative carbon strategy as they aged. Simultaneously declining growth efficiency and increasing NSC concentrations as trees age could be evidence for active NSC accumulation to buffer against carbon starvation in larger trees. Our study provides new insights into changing carbon dynamics as trees age and may be evidence for active carbon accumulation in older trees. This may provide the key for understanding the role of carbon processes in tree longevity. Full article
(This article belongs to the Section Forest Ecophysiology and Biology)
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13 pages, 2384 KiB  
Article
Legacy and Luxury Effects: Dual Drivers of Tree Diversity Dynamics in Beijing’s Urbanizing Residential Areas (2006–2021)
by Xi Li, Jicun Bao, Yue Li, Jijie Wang, Wenchao Yan and Wen Zhang
Forests 2025, 16(8), 1269; https://doi.org/10.3390/f16081269 (registering DOI) - 3 Aug 2025
Abstract
Numerous studies have demonstrated that in residential areas of Western cities, both luxury and legacy effects significantly shape tree species diversity dynamics. However, the specific mechanisms driving these diversity patterns in China, where urbanization has progressed at an unprecedented pace, remain poorly understood. [...] Read more.
Numerous studies have demonstrated that in residential areas of Western cities, both luxury and legacy effects significantly shape tree species diversity dynamics. However, the specific mechanisms driving these diversity patterns in China, where urbanization has progressed at an unprecedented pace, remain poorly understood. In this study we selected 20 residential settlements and 7 key socio-economic properties to investigate the change trend of tree diversity (2006–2021) and its socio-economic driving factors in Beijing. Our results demonstrate significant increases in total, native, and exotic tree species richness between 2006 and 2021 (p < 0.05), with average increases of 36%, 26%, and 55%, respectively. Total and exotic tree Shannon-Wiener indices, as well as exotic tree Simpson’s index, were also significantly higher in 2021 (p < 0.05). Housing prices was the dominant driver shaping total and exotic tree diversity, showing significant positive correlations with both metrics. In contrast, native tree diversity exhibited a strong positive association with neighborhood age. Our findings highlight two dominant mechanisms: legacy effect, where older neighborhoods preserve native diversity through historical planting practices, and luxury effect, where affluent communities drive exotic species proliferation through ornamental landscaping initiatives. These findings elucidate the dual dynamics of legacy conservation and luxury-driven cultivation in urban forest development, revealing how historical contingencies and contemporary socioeconomic forces jointly shape tree diversity patterns in urban ecosystems. Full article
(This article belongs to the Section Urban Forestry)
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18 pages, 2864 KiB  
Article
Physiological and Chemical Response of Urochloa brizantha to Edaphic and Microclimatic Variations Along an Altitudinal Gradient in the Amazon
by Hipolito Murga-Orrillo, Luis Alberto Arévalo López, Marco Antonio Mathios-Flores, Jorge Cáceres Coral, Melissa Rojas García, Jorge Saavedra-Ramírez, Adriana Carolina Alvarez-Cardenas, Christopher Iván Paredes Sánchez, Aldi Alida Guerra-Teixeira and Nilton Luis Murga Valderrama
Agronomy 2025, 15(8), 1870; https://doi.org/10.3390/agronomy15081870 (registering DOI) - 1 Aug 2025
Viewed by 74
Abstract
Urochloa brizantha (Brizantha) is cultivated under varying altitudinal and management conditions. Twelve full-sun (monoculture) plots and twelve shaded (silvopastoral) plots were established, proportionally distributed at 170, 503, 661, and 1110 masl. Evaluations were conducted 15, 30, 45, 60, and 75 days [...] Read more.
Urochloa brizantha (Brizantha) is cultivated under varying altitudinal and management conditions. Twelve full-sun (monoculture) plots and twelve shaded (silvopastoral) plots were established, proportionally distributed at 170, 503, 661, and 1110 masl. Evaluations were conducted 15, 30, 45, 60, and 75 days after establishment. The conservation and integration of trees in silvopastoral systems reflected a clear anthropogenic influence, evidenced by the preference for species of the Fabaceae family, likely due to their multipurpose nature. Although the altitudinal gradient did not show direct effects on soil properties, intermediate altitudes revealed a significant role of CaCO3 in enhancing soil fertility. These edaphic conditions at mid-altitudes favored the leaf area development of Brizantha, particularly during the early growth stages, as indicated by significantly larger values (p < 0.05). However, at the harvest stage, no significant differences were observed in physiological or productive traits, nor in foliar chemical components, underscoring the species’ high hardiness and broad adaptation to both soil and altitude conditions. In Brizantha, a significant reduction (p < 0.05) in stomatal size and density was observed under shade in silvopastoral areas, where solar radiation and air temperature decreased, while relative humidity increased. Nonetheless, these microclimatic variations did not lead to significant changes in foliar chemistry, growth variables, or biomass production, suggesting a high degree of adaptive plasticity to microclimatic fluctuations. Foliar ash content exhibited an increasing trend with altitude, indicating greater efficiency of Brizantha in absorbing calcium, phosphorus, and potassium at higher altitudes, possibly linked to more favorable edaphoclimatic conditions for nutrient uptake. Finally, forage quality declined with plant age, as evidenced by reductions in protein, ash, and In Vitro Dry Matter Digestibility (IVDMD), alongside increases in fiber, Neutral Detergent Fiber (NDF), and Acid Detergent Fiber (ADF). These findings support the recommendation of cutting intervals between 30 and 45 days, during which Brizantha displays a more favorable nutritional profile, higher digestibility, and consequently, greater value for animal feeding. Full article
(This article belongs to the Section Agricultural Biosystem and Biological Engineering)
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21 pages, 5062 KiB  
Article
Forest Management Effects on Breeding Bird Communities in Apennine Beech Stands
by Guglielmo Londi, Francesco Parisi, Elia Vangi, Giovanni D’Amico and Davide Travaglini
Ecologies 2025, 6(3), 54; https://doi.org/10.3390/ecologies6030054 (registering DOI) - 1 Aug 2025
Viewed by 31
Abstract
Beech forests in the Italian peninsula are actively managed and they also support a high level of biodiversity. Hence, biodiversity conservation can be synergistic with timber production and carbon sequestration, enhancing the overall economic benefits of forest management. This study aimed to evaluate [...] Read more.
Beech forests in the Italian peninsula are actively managed and they also support a high level of biodiversity. Hence, biodiversity conservation can be synergistic with timber production and carbon sequestration, enhancing the overall economic benefits of forest management. This study aimed to evaluate the effect of forest management regimes on bird communities in the Italian Peninsula during 2022 through audio recordings. We studied the structure, composition, and specialization of the breeding bird community in four managed beech stands (three even-aged beech stands aged 20, 60, and 100 years old, managed by a uniform shelterwood system; one uneven-aged stand, managed by a single-tree selection system) and one uneven-aged, unmanaged beech stand in the northern Apennines (Tuscany region, Italy). Between April and June 2022, data were collected through four 1-hour audio recording sessions per site, analyzing 5 min sequences. The unmanaged stand hosted a richer (a higher number of species, p < 0.001) and more specialized (a higher number of cavity-nesting species, p < 0.001; higher Woodland Bird Community Index (WBCI) values, p < 0.001; and eight characteristic species, including at least four highly specialized ones) bird community, compared to all the managed forests; moreover, the latter were homogeneous (similar to each other). Our study suggests that the unmanaged beech forests should be a priority option for conservation, while in terms of the managed beech forests, greater attention should be paid to defining the thresholds for snags, deadwood, and large trees to be retained to enhance their biodiversity value. Studies in additional sites, conducted over more years and including multi-taxon communities, are recommended for a deeper understanding and generalizable results. Full article
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29 pages, 1289 KiB  
Article
An Analysis of Hybrid Management Strategies for Addressing Passenger Injuries and Equipment Failures in the Taipei Metro System: Enhancing Operational Quality and Resilience
by Sung-Neng Peng, Chien-Yi Huang, Hwa-Dong Liu and Ping-Jui Lin
Mathematics 2025, 13(15), 2470; https://doi.org/10.3390/math13152470 - 31 Jul 2025
Viewed by 195
Abstract
This study is the first to systematically integrate supervised machine learning (decision tree) and association rule mining techniques to analyze accident data from the Taipei Metro system, conducting a large-scale data-driven investigation into both passenger injury and train malfunction events. The research demonstrates [...] Read more.
This study is the first to systematically integrate supervised machine learning (decision tree) and association rule mining techniques to analyze accident data from the Taipei Metro system, conducting a large-scale data-driven investigation into both passenger injury and train malfunction events. The research demonstrates strong novelty and practical contributions. In the passenger injury analysis, a dataset of 3331 cases was examined, from which two highly explanatory rules were extracted: (i) elderly passengers (aged > 61) involved in station incidents are more likely to suffer moderate to severe injuries; and (ii) younger passengers (aged ≤ 61) involved in escalator incidents during off-peak hours are also at higher risk of severe injury. This is the first study to quantitatively reveal the interactive effect of age and time of use on injury severity. In the train malfunction analysis, 1157 incidents with delays exceeding five minutes were analyzed. The study identified high-risk condition combinations—such as those involving rolling stock, power supply, communication, and signaling systems—associated with specific seasons and time periods (e.g., a lift value of 4.0 for power system failures during clear mornings from 06:00–12:00, and 3.27 for communication failures during summer evenings from 18:00–24:00). These findings were further cross-validated with maintenance records to uncover underlying causes, including brake system failures, cable aging, and automatic train operation (ATO) module malfunctions. Targeted preventive maintenance recommendations were proposed. Additionally, the study highlighted existing gaps in the completeness and consistency of maintenance records, recommending improvements in documentation standards and data auditing mechanisms. Overall, this research presents a new paradigm for intelligent metro system maintenance and safety prediction, offering substantial potential for broader adoption and practical application. Full article
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14 pages, 2651 KiB  
Article
Conifer Growth Patterns in Primary Succession Locations at Mount St. Helens
by Alicia Rose, Cody Blackketter, Marisa D. Fisher, Carri J. LeRoy and Dylan G. Fischer
Forests 2025, 16(8), 1245; https://doi.org/10.3390/f16081245 - 30 Jul 2025
Viewed by 207
Abstract
The 1980 eruption of Mount St. Helens (WA, USA) presented a unique opportunity to observe primary succession in a post-eruption landscape previously dominated by conifer forests. The eruption scoured soil and biological communities adjacent to the mountain, and species of conifers have generally [...] Read more.
The 1980 eruption of Mount St. Helens (WA, USA) presented a unique opportunity to observe primary succession in a post-eruption landscape previously dominated by conifer forests. The eruption scoured soil and biological communities adjacent to the mountain, and species of conifers have generally been slow to colonize the nutrient-poor substrate surrounding the volcano. Further, different species of conifer establish and grow at different rates. The recent advancement of conifers in the post-eruption landscape has highlighted a research gap related to conifer growth patterns. We measured the height, age, and incremental growth of 472 trees representing three common conifers, Pseudotsuga menziesii, Abies procera, and Pinus contorta, on debris avalanche (80 sites) and pyroclastic flow (82 sites) disturbance zones of the 1980 eruption. We paired annual incremental growth with recent climate data. We found that height, age, and growth rates differ among species and sites. All species had higher growth rates on the debris avalanche deposit compared to the pyroclastic flow due to either climate or substrate. Climate influences were mixed, where one species increased growth with temperature, another declined, and another was unrelated. Nevertheless, more than 40 years after the eruption, we find rapid height growth in species with implications for future forests. Full article
(This article belongs to the Section Forest Ecology and Management)
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25 pages, 9676 KiB  
Article
A Comparative Analysis of SAR and Optical Remote Sensing for Sparse Forest Structure Parameters: A Simulation Study
by Zhihui Mao, Lei Deng, Xinyi Liu and Yueyang Wang
Forests 2025, 16(8), 1244; https://doi.org/10.3390/f16081244 - 29 Jul 2025
Viewed by 220
Abstract
Forest structure parameters are critical for understanding and managing forest ecosystems, yet sparse forests have received limited attention in previous studies. To address this research gap, this study systematically evaluates and compares the sensitivity of active Synthetic Aperture Radar (SAR) and passive optical [...] Read more.
Forest structure parameters are critical for understanding and managing forest ecosystems, yet sparse forests have received limited attention in previous studies. To address this research gap, this study systematically evaluates and compares the sensitivity of active Synthetic Aperture Radar (SAR) and passive optical remote sensing to key forest structure parameters in sparse forests, including Diameter at Breast Height (DBH), Tree Height (H), Crown Width (CW), and Leaf Area Index (LAI). Using the novel computer-graphics-based radiosity model applicable to porous individual thin objects, named Radiosity Applicable to Porous Individual Objects (RAPID), we simulated 38 distinct sparse forest scenarios to generate both SAR backscatter coefficients and optical reflectance across various wavelengths, polarization modes, and incidence/observation angles. Sensitivity was assessed using the coefficient of variation (CV). The results reveal that C-band SAR in HH polarization mode demonstrates the highest sensitivity to DBH (CV = −6.73%), H (CV = −52.68%), and LAI (CV = −63.39%), while optical data in the red band show the strongest response to CW (CV = 18.83%) variations. The study further identifies optimal acquisition configurations, with SAR data achieving maximum sensitivity at smaller incidence angles and optical reflectance performing best at forward observation angles. This study addresses a critical gap by presenting the first systematic comparison of the sensitivity of multi-band SAR and VIS/NIR data to key forest structural parameters across sparsity gradients, thereby clarifying their applicability for monitoring young and middle-aged sparse forests with high carbon sequestration potential. Full article
(This article belongs to the Section Forest Inventory, Modeling and Remote Sensing)
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15 pages, 787 KiB  
Article
Beyond Treatment Decisions: The Predictive Value of Comprehensive Geriatric Assessment in Older Cancer Patients
by Eleonora Bergo, Marina De Rui, Chiara Ceolin, Pamela Iannizzi, Chiara Curreri, Maria Devita, Camilla Ruffini, Benedetta Chiusole, Alessandra Feltrin, Giuseppe Sergi and Antonella Brunello
Cancers 2025, 17(15), 2489; https://doi.org/10.3390/cancers17152489 - 28 Jul 2025
Viewed by 151
Abstract
Background: Comprehensive Geriatric Assessment (CGA) is essential for evaluating older cancer patients, but significant gaps persist in both research and clinical practice. This study aimed (I) to identify the CGA elements that most influence anti-cancer treatment decisions in older patients and (II) [...] Read more.
Background: Comprehensive Geriatric Assessment (CGA) is essential for evaluating older cancer patients, but significant gaps persist in both research and clinical practice. This study aimed (I) to identify the CGA elements that most influence anti-cancer treatment decisions in older patients and (II) to explore the predictive value of CGA components for mortality. Methods: This observational study included older patients with newly diagnosed, histologically confirmed solid or hematological cancers, recruited consecutively from 2003 to 2023. Participants were followed for four years. The data collected included CGA measures of functional (Activities of Daily Living-ADL), cognitive (Mini-Mental State Examination-MMSE), and emotional (Geriatric Depression Scale-GDS) domains. Patients were categorized into frail, vulnerable, or fit groups based on Balducci’s criteria. Statistical analyses included decision tree modeling and Cox regression to identify predictors of mortality. Results: A total of 7022 patients (3222 females) were included, with a mean age of 78.3 ± 12.9 years. The key CGA factors influencing treatment decisions were ADL (first step), cohabitation status (second step), and age (last step). After four years, 21.9% patients had died. Higher GDS scores (OR 1.04, 95% CI 1.01–1.07, p = 0.04) were independently associated with survival in men and living with family members (OR 1.67, 95% CI 1.35–2.07, p < 0.001) in women. Younger patients (<77 years) showed both MMSE and GDS as significant risk factors for mortality. Conclusions: Functional capacity, cohabitation status, and GDS scores are crucial for guiding treatment decisions and predicting mortality in older cancer patients, emphasizing the need for a multidimensional geriatric assessment. Full article
(This article belongs to the Section Clinical Research of Cancer)
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13 pages, 1058 KiB  
Article
A Machine Learning-Based Guide for Repeated Laboratory Testing in Pediatric Emergency Departments
by Adi Shuchami, Teddy Lazebnik, Shai Ashkenazi, Avner Herman Cohen, Yael Reichenberg and Vered Shkalim Zemer
Diagnostics 2025, 15(15), 1885; https://doi.org/10.3390/diagnostics15151885 - 28 Jul 2025
Viewed by 298
Abstract
Background/Objectives: Laboratory tests conducted in community settings are occasionally repeated within hours of presentation to pediatric emergency departments (PEDs). Reducing unnecessary repetitions can ease child discomfort and alleviate the healthcare burden without compromising the diagnostic process or quality of care. The aim [...] Read more.
Background/Objectives: Laboratory tests conducted in community settings are occasionally repeated within hours of presentation to pediatric emergency departments (PEDs). Reducing unnecessary repetitions can ease child discomfort and alleviate the healthcare burden without compromising the diagnostic process or quality of care. The aim of this study was to develop a decision tree (DT) model to guide physicians in minimizing unnecessary repeat blood tests in PEDs. The minimal decision tree (MDT) algorithm was selected for its interpretability and capacity to generate optimally pruned classification trees. Methods: Children aged 3 months to 18 years with community-based complete blood count (CBC), electrolyte (ELE), and C-reactive protein (CRP) measurements obtained between 2016 and 2023 were included. Repeat tests performed in the pediatric emergency department within 12 h were evaluated by comparing paired measurements, with tests considered justified when values transitioned from normal to abnormal ranges or changed by ≥20%. Additionally, sensitivity analyses were conducted for absolute change thresholds of 10% and 30% and for repeat intervals of 6, 18, and 24 h. Results: Among 7813 children visits in this study, 6044, 1941, and 2771 underwent repeated CBC, ELE, and CRP tests, respectively. The mean ages of patients undergoing CRP, ELE, and CBC testing were 6.33 ± 5.38, 7.91 ± 5.71, and 5.08 ± 5.28 years, respectively. The majority were of middle socio-economic class, with 66.61–71.24% living in urban areas. Pain was the predominant presented complaint (83.69–85.99%), and in most cases (83.69–85.99%), the examination was conducted by a pediatrician. The DT model was developed and evaluated on training and validation cohorts, and it demonstrated high accuracy in predicting the need for repeat CBC and ELE tests but not CRP. Performance of the DT model significantly exceeded that of the logistic regression model. Conclusions: The data-driven guide derived from the DT model provides clinicians with a practical, interpretable tool to minimize unnecessary repeat laboratory testing, thereby enhancing patient care and optimizing healthcare resource utilization. Full article
(This article belongs to the Special Issue Artificial Intelligence for Health and Medicine)
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28 pages, 5172 KiB  
Article
Machine Learning-Assisted Sustainable Mix Design of Waste Glass Powder Concrete with Strength–Cost–CO2 Emissions Trade-Offs
by Yuzhuo Zhang, Jiale Peng, Zi Wang, Meng Xi, Jinlong Liu and Lei Xu
Buildings 2025, 15(15), 2640; https://doi.org/10.3390/buildings15152640 - 26 Jul 2025
Viewed by 457
Abstract
Glass powder, a non-degradable waste material, offers significant potential to reduce cement consumption and carbon emissions in concrete production. However, existing mix design methods for glass powder concrete (GPC) fail to systematically balance economic efficiency, environmental sustainability, and mechanical performance. To address this [...] Read more.
Glass powder, a non-degradable waste material, offers significant potential to reduce cement consumption and carbon emissions in concrete production. However, existing mix design methods for glass powder concrete (GPC) fail to systematically balance economic efficiency, environmental sustainability, and mechanical performance. To address this gap, this study proposes an AI-assisted framework integrating machine learning (ML) and Multi-Objective Optimization (MOO) to achieve a sustainable GPC design. A robust database of 1154 experimental records was developed, focusing on five key predictors: cement content, water-to-binder ratio, aggregate composition, glass powder content, and curing age. Seven ML models were optimized via Bayesian tuning, with the Ensemble Tree model achieving superior accuracy (R2 = 0.959 on test data). SHapley Additive exPlanations (SHAP) analysis further elucidated the contribution mechanisms and underlying interactions of material components on GPC compressive strength. Subsequently, a MOO framework minimized unit cost and CO2 emissions while meeting compressive strength targets (15–70 MPa), solved using the NSGA-II algorithm for Pareto solutions and TOPSIS for decision-making. The Pareto-optimal solutions provide actionable guidelines for engineers to align GPC design with circular economy principles and low-carbon policies. This work advances sustainable construction practices by bridging AI-driven innovation with building materials, directly supporting global goals for waste valorization and carbon neutrality. Full article
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10 pages, 586 KiB  
Article
Possession of Injectable Epinephrine Among Children with Parent-Reported Food Allergies in Saudi Arabia
by Amer Khojah, Ameera Bukhari, Ibrahim Alibrahim, Maria AlSulami, Turki Alotaibi, Ruba Alotaibi, Elaf Bahareth, Inam Abulreish, Sumayyah Alsuruji, Raghad Rajab, Loie Goronfolah, Mohammad Binhussein, Adeeb Bulkhi, Saddiq Habiballah and Imad Khojah
J. Clin. Med. 2025, 14(15), 5274; https://doi.org/10.3390/jcm14155274 - 25 Jul 2025
Viewed by 243
Abstract
Background/Objectives: A food allergy (FA) is an immune-mediated hypersensitivity reaction to specific food. FA reactions vary from mild to life-threatening anaphylaxis. Despite the effectiveness of epinephrine auto-injectors (EAIs), barriers such as lack of knowledge, limited access, and fear of needles hinder their [...] Read more.
Background/Objectives: A food allergy (FA) is an immune-mediated hypersensitivity reaction to specific food. FA reactions vary from mild to life-threatening anaphylaxis. Despite the effectiveness of epinephrine auto-injectors (EAIs), barriers such as lack of knowledge, limited access, and fear of needles hinder their use. This study explores EAI possession among children with parent-reported food allergies in Saudi Arabia. Methods: A cross-sectional study conducted from October 2023 to February 2024 included 296 parents of children with reported food allergies under the age of 18. Data were collected through a validated self-administered questionnaire. Results: Among 2102 respondents, 296 (14.1%) reported having a child with a food allergy. Most respondents were female (70%), with asthma being the most common comorbidity (26%). Common allergens included eggs, tree nuts, peanuts, milk, and sesame. Only 23.3% of children had an EAI. Higher EAI possession was associated with parental education, maternal allergy history, and access to specialist care. Conclusions: EAI possession among Saudi children with food allergies is suboptimal. Targeted educational interventions, increased access to allergists, and comprehensive management plans are essential to improve preparedness for anaphylaxis. Full article
(This article belongs to the Special Issue Allergic Diseases Across the Lifespan: From Infancy to Old Age)
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12 pages, 462 KiB  
Article
AI-Based Classification of Mild Cognitive Impairment and Cognitively Normal Patients
by Rafail Christodoulou, Giorgos Christofi, Rafael Pitsillos, Reina Ibrahim, Platon Papageorgiou, Sokratis G. Papageorgiou, Evros Vassiliou and Michalis F. Georgiou
J. Clin. Med. 2025, 14(15), 5261; https://doi.org/10.3390/jcm14155261 - 25 Jul 2025
Viewed by 366
Abstract
Background: Mild Cognitive Impairment (MCI) represents an intermediate stage between normal cognitive aging and Alzheimer’s Disease (AD). Early and accurate identification of MCI is crucial for implementing interventions that may delay or prevent further cognitive decline. This study aims to develop a [...] Read more.
Background: Mild Cognitive Impairment (MCI) represents an intermediate stage between normal cognitive aging and Alzheimer’s Disease (AD). Early and accurate identification of MCI is crucial for implementing interventions that may delay or prevent further cognitive decline. This study aims to develop a machine learning-based model for differentiating between Cognitively Normal (CN) individuals and MCI patients using data from the Alzheimer’s Disease Neuroimaging Initiative (ADNI). Methods: An ensemble classification approach was designed by integrating Extra Trees, Random Forest, and Light Gradient Boosting Machine (LightGBM) algorithms. Feature selection emphasized clinically relevant biomarkers, including Amyloid-β 42, phosphorylated tau, diastolic blood pressure, age, and gender. The dataset was split into training and held-out test sets. A probability thresholding strategy was employed to flag uncertain predictions for potential deferral, enhancing model reliability in borderline cases. Results: The final ensemble model achieved an accuracy of 83.2%, a recall of 80.2%, and a precision of 86.3% on the independent test set. The probability thresholding mechanism flagged 23.3% of cases as uncertain, allowing the system to abstain from low-confidence predictions. This strategy improved clinical interpretability and minimized the risk of misclassification in ambiguous cases. Conclusions: The proposed AI-driven ensemble model demonstrates strong performance in classifying MCI versus CN individuals using multimodal ADNI data. Incorporating a deferral mechanism through uncertainty estimation further enhances the model’s clinical utility. These findings support the integration of machine learning tools into early screening workflows for cognitive impairment. Full article
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14 pages, 8566 KiB  
Article
An Evaluation of Mercury Accumulation Dynamics in Tree Leaves Growing in a Contaminated Area as Part of the Ecosystem Services: A Case Study of Turda, Romania
by Marin Senila, Cerasel Varaticeanu, Simona Costiug and Otto Todor-Boer
Land 2025, 14(8), 1529; https://doi.org/10.3390/land14081529 - 24 Jul 2025
Viewed by 244
Abstract
Mercury (Hg) poses a significant threat to human health and ecosystems, garnering increased attention in environmental studies. This paper evaluates the dynamics of Hg accumulation in various common tree leaves, specifically white poplar, linden, and cherry plum, throughout their growing season. The findings [...] Read more.
Mercury (Hg) poses a significant threat to human health and ecosystems, garnering increased attention in environmental studies. This paper evaluates the dynamics of Hg accumulation in various common tree leaves, specifically white poplar, linden, and cherry plum, throughout their growing season. The findings offer valuable insights into air quality and the ability of urban vegetation to mitigate mercury pollution in urban areas. A case study was conducted in Turda, a town in northwestern Romania, where a former chlor-alkali plant operated throughout the last century. Although the plant ceased its electrolysis activities over 25 years ago, the surrounding soil remains contaminated with mercury (Hg) due to the significant amounts released during its operation. The results indicated that the Hg concentration varied between 2.4 and 7.3 mg kg−1 dry weight (dw), exceeding the intervention threshold for soil of 2.0 mg kg−1. Additionally, the Hg content in the leaf samples consistently increased over time, influenced by leaf age and tree species. The Hg content increased in the following order: cherry plum < white poplar < linden. On average, white poplar leaves accumulated 72 ng Hg g−1 dw, linden leaves 128 ng Hg g−1 dw, and cherry plum leaves 47 ng Hg g−1 dw during the six-month monitored period from April to September. The results obtained can be used to evaluate the potential of different tree species for mitigating atmospheric Hg contamination and to elaborate on the suitable management of fallen leaves in the autumn. Full article
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32 pages, 7115 KiB  
Article
Advancing Knowledge on Machine Learning Algorithms for Predicting Childhood Vaccination Defaulters in Ghana: A Comparative Performance Analysis
by Eliezer Ofori Odei-Lartey, Stephaney Gyaase, Dominic Asamoah, Thomas Gyan, Kwaku Poku Asante and Michael Asante
Appl. Sci. 2025, 15(15), 8198; https://doi.org/10.3390/app15158198 - 23 Jul 2025
Viewed by 301
Abstract
High rates of childhood vaccination defaulting remain a significant barrier to achieving full vaccination coverage in sub-Saharan Africa, contributing to preventable morbidity and mortality. This study evaluated the utility of machine learning algorithms for predicting childhood vaccination defaulters in Ghana, addressing the limitations [...] Read more.
High rates of childhood vaccination defaulting remain a significant barrier to achieving full vaccination coverage in sub-Saharan Africa, contributing to preventable morbidity and mortality. This study evaluated the utility of machine learning algorithms for predicting childhood vaccination defaulters in Ghana, addressing the limitations of traditional statistical methods when handling complex, high-dimensional health data. Using a merged dataset from two malaria vaccine pilot surveys, we engineered novel temporal features, including vaccination timing windows and birth seasonality. Six algorithms, namely logistic regression, support vector machine, random forest, gradient boosting machine, extreme gradient boosting, and artificial neural networks, were compared. Models were trained and validated on both original and synthetically balanced and augmented data. The results showed higher performance across the ensemble tree classifiers. The random forest and extreme gradient boosting models reported the highest F1 scores (0.92) and AUCs (0.95) on augmented unseen data. The key predictors identified include timely receipt of birth and week six vaccines, the child’s age, household wealth index, and maternal education. The findings demonstrate that robust machine learning frameworks, combined with temporal and contextual feature engineering, can improve defaulter risk prediction accuracy. Integrating such models into routine immunization programs could enable data-driven targeting of high-risk groups, supporting policymakers in strategies to close vaccination coverage gaps. Full article
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18 pages, 3178 KiB  
Article
Biomass Estimation of Apple and Citrus Trees Using Terrestrial Laser Scanning and Drone-Mounted RGB Sensor
by Min-Ki Lee, Yong-Ju Lee, Dong-Yong Lee, Jee-Su Park and Chang-Bae Lee
Remote Sens. 2025, 17(15), 2554; https://doi.org/10.3390/rs17152554 - 23 Jul 2025
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Abstract
Developing accurate activity data on tree biomass using remote sensing tools such as LiDAR and drone-mounted sensors is essential for improving carbon accounting in the agricultural sector. However, direct biomass measurements of perennial fruit trees remain limited, especially for validating remote sensing estimates. [...] Read more.
Developing accurate activity data on tree biomass using remote sensing tools such as LiDAR and drone-mounted sensors is essential for improving carbon accounting in the agricultural sector. However, direct biomass measurements of perennial fruit trees remain limited, especially for validating remote sensing estimates. This study evaluates the potential of terrestrial laser scanning (TLS) and drone-mounted RGB sensors (Drone_RGB) for estimating biomass in two major perennial crops in South Korea: apple (‘Fuji’/M.9) and citrus (‘Miyagawa-wase’). Trees of different ages were destructively sampled for biomass measurement, while volume, height, and crown area data were collected via TLS and Drone_RGB. Regression analyses were performed, and the model accuracy was assessed using R2, RMSE, and bias. The TLS-derived volume showed strong predictive power for biomass (R2 = 0.704 for apple, 0.865 for citrus), while the crown area obtained using both sensors showed poor fit (R2 ≤ 0.7). Aboveground biomass was reasonably estimated (R2 = 0.725–0.865), but belowground biomass showed very low predictability (R2 < 0.02). Although limited in scale, this study provides empirical evidence to support the development of remote sensing-based biomass estimation methods and may contribute to improving national greenhouse gas inventories by refining emission/removal factors for perennial fruit crops. Full article
(This article belongs to the Special Issue Biomass Remote Sensing in Forest Landscapes II)
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