Saved Queries

by Anna Błaszczyk-Pośpiech, Sylwia Kiryk, Natalia Nawrot, Julia Kensy, Jan Kiryk, Agnieszka Kotela, Magdalena Wawrzyńska, Maria Szymonowicz, Jacek Matys and Maciej Dobrzyński

Materials 2026, 19(5), 950; https://doi.org/10.3390/ma19050950 (registering DOI) - 28 Feb 2026

Abstract

Objective: This systematic review aimed to evaluate material-based modifications of resorbable root canal filling materials for primary teeth, assessing how compositional changes—including bioactive additives, antimicrobial agents, and alternative base matrices—influence antimicrobial performance. Methods: A systematic search of PubMed, Scopus, Web of Science (WoS), and Embase was performed in October 2025. Search terms included (primary teeth OR deciduous teeth) AND (root canal filling materials OR root canal filling OR canal obturation) AND (antibacterial agents OR antibacterial OR antimicrobial). Study selection adhered to PRISMA 2020 standards and was systematically organized through the PICO framework. From 199 identified records, 18 studies met the eligibility criteria. Results: Most studies evaluated modified zinc oxide-based materials. Additives such as propolis, Morinda citrifolia extract, Aloe vera, and olive oil enhanced antimicrobial activity or improved clinical and radiographic outcomes compared with conventional zinc oxide–eugenol. Triclosan-containing formulations consistently demonstrated strong antibacterial effects. In contrast, chlorhexidine yielded variable results, with some calcium hydroxide–based pastes showing superior performance in its absence. Antibiotic-enriched materials exhibited high antimicrobial efficacy; however, several studies raised concerns regarding the potential development of bacterial resistance. Conclusion: Most of the introduced modifications of resorbable root canal filling materials for primary teeth enhance antimicrobial activity and their physicochemical properties in vitro. Clinical evidence is limited and heterogeneous, and therefore, its superiority over conventional materials cannot be definitively determined. Further long-term, randomized clinical trials on large patient groups, evaluating the same modifications, are needed to confirm the effects observed in laboratory studies. Full article

(This article belongs to the Special Issue Recent Research in Restorative Dental Materials (2nd Edition))

18 pages, 3855 KB

Open AccessArticle

Airports in SUMP: Multi-Criteria Sustainability Assessment

by Marcin Jacek Kłos, Grzegorz Sierpiński, Grażyna Rosa, Leszek Mindur and Maciej Mindur

Sustainability 2026, 18(5), 2369; https://doi.org/10.3390/su18052369 (registering DOI) - 28 Feb 2026

Abstract

Modern urban transport systems face the critical challenge of fully integrating regional and international hubs into local mobility strategies. This article addresses the role of airports in shaping sustainable urban mobility, with a specific focus on their inclusion in Sustainable Urban Mobility Plans (SUMPs). Despite airports being major generators of passenger and freight traffic, they are often treated as isolated “transport islands” in spatial planning. The primary objective of this research is to develop and validate an original method for assessing the integration and transport accessibility of airports using the AirportSustainIndex. The methodology is based on a mathematical Weighted Sum Model (WSM), integrating twelve technical, economic, and environmental criteria, including travel times and costs for public vs. private transport, frequency of rail and bus connections, availability of electric vehicle infrastructure, and tariff integration. The analysis is supported by Geographic Information Systems (GIS) tools and OpenStreetMap data, allowing for a precise reflection of real-world network accessibility. The study covers two significant aviation hubs in Poland: Katowice Airport in Pyrzowice and Poznań-Ławica Airport. The results reveal a paradox: Katowice Airport, despite its significant distance from the agglomeration center (approx. 36 km), achieved a markedly higher sustainability index (0.554) than Poznań-Ławica Airport (0.301), which is located close to the city center (approx. 7 km). Key factors determining this outcome include the high frequency of metropolitan bus lines (“M” lines), the implementation of new rail infrastructure, and a coherent parking policy for low-emission vehicles. The article demonstrates that physical distance from the center is not the primary barrier to building sustainable mobility, provided that high intermodality and integration within the SUMP framework are ensured. The presented research tool is universal and can be applied by policymakers and urban planners to optimize airport-city connectivity, a necessary condition for achieving EU climate goals in the transport sector. Full article

(This article belongs to the Special Issue Smart Mobility for Sustainable Development)

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22 pages, 533 KB

Open AccessArticle

Bonding Without Bridging: Social Capital, Integration, and Well-Being Among Filipina Marriage Migrants in South Korea

by Asterio T. Miranda, Juneth Lourdes F. Miranda and Eungi Kim

Int. J. Environ. Res. Public Health 2026, 23(3), 305; https://doi.org/10.3390/ijerph23030305 (registering DOI) - 28 Feb 2026

Abstract

This study examined whether strong ethnic community participation facilitates social integration or reinforces social separation among Filipina marriage migrants in the Daegu–Gyeongbuk region of South Korea. A mixed-methods design combined survey data collected between 2018 and 2019 with a media discourse analysis covering 2020 to 2025. Survey results indicate extensive ethnic network participation, with 94.5% of respondents involved in religious or Filipino community organizations, yet persistent integration challenges. Language barriers were reported by 54.8% of respondents and cultural misunderstandings by 40%, suggesting strong bonding social capital alongside limited bridging social capital even after prolonged residence. Drawing on Putnam’s social capital theory, 328 news articles on Filipino–Korean relations were screened, of which only 10 directly addressed marriage migrants. None examined the routine experiences identified in the survey, reflecting discursive erasure shaped by polarized narratives of victimization or exceptional success. The temporal separation between the datasets enables an assessment of whether documented integration patterns are acknowledged in public discourse. The findings raise concerns about policy approaches that prioritize ethnic community centers without providing sustained opportunities for intercultural interaction, particularly given that many respondents entered marriage through religious matching programs that embedded them within ethnic networks, with potential health implications. Full article

(This article belongs to the Special Issue Health Equity and Social Determinants of Maternal, Child, and Minority Health)

15 pages, 3945 KB

Open AccessArticle

Frailty and Socioeconomic Development in the European Region—Associations with Mortality in Middle-Aged and Older Adults

by Rónán O’Caoimh, Aoife Wall and Mark R. O’Donovan

Int. J. Environ. Res. Public Health 2026, 23(3), 307; https://doi.org/10.3390/ijerph23030307 (registering DOI) - 28 Feb 2026

Abstract

The Sociodemographic Index (SDI) captures a country’s or region’s relative socioeconomic development and has been linked to age-related disease burden and life expectancy. Frailty is a multidimensional geriatric syndrome associated with adverse health outcomes and mortality. This study examined the relationship between country-level SDI, frailty prevalence, and mortality across Europe. We conducted a secondary analysis of community-dwelling adults aged 50 years and older from 12 countries participating in the Survey of Health, Ageing and Retirement in Europe (SHARE). Frailty status and SDI were assessed at Wave 2 (2007), with mortality follow-up at Wave 4 (2011). Countries were categorised into lower- and higher-SDI groups using the median as a cut-off. Frailty was measured using a 70-item frailty index (FI ≥ 0.25) and a modified Fried frailty phenotype (FP ≥ 3 criteria). Frailty prevalence varied substantially by country and assessment method, ranging from 7 to 40% using the FI and 4–21% using the FP. Prevalence was lowest in Switzerland and highest in Poland and was strongly correlated with national SDI scores (r ≥ 0.8). After adjustment for age and sex, lower SDI was independently associated with higher odds of frailty using both frailty measures. Although mortality was lower in higher-SDI countries, this association was not statistically significant after adjusting for age, sex, and frailty. Lower social development was strongly associated with frailty prevalence but did not independently predict mortality, highlighting frailty as a potential pathway linking social context to later-life health outcomes in Europe. Full article

(This article belongs to the Special Issue Rehabilitation Approaches to Reduce Frailty and Promote Healthy Aging)

28 pages, 954 KB

Open AccessArticle

Proactive Proctoring: A Critical Analysis of Machine Learning Architectures and Custom Temporal Data Sets for Moodle Fraud Detection

by Andrei-Nicolae Vacariu, Marian Bucos, Marius Otesteanu and Bogdan Dragulescu

Appl. Sci. 2026, 16(5), 2381; https://doi.org/10.3390/app16052381 (registering DOI) - 28 Feb 2026

Abstract

This paper examines the use of Machine Learning (ML) approaches in maintaining academic integrity using the information provided in the Moodle system logs. The paper focuses on data set construction, handling the issue of class imbalance, and the assessment of the performance of different ML models in uncovering academic fraud. Twelve different data sets were created by using the concept of temporal windows (e.g., one-day and three-day windows) during the feature extraction stage from the Moodle system logs. The manual labeling of the data sets was done based on a predefined set of rules that outline the fraudulent activities. The issue of class imbalance was treated using eleven different resampling approaches, such as SMOTE, ADASYN, Tomek Links, and NearMiss. We evaluated six classification algorithms, thus resulting in a total of 792 experiments based on the interactions between the data sets, resampling methods, and classification algorithms. The results from the experiment show that the Random Forest and AdaBoost models performed the best in the experiment. Furthermore, we observed a trade-off between fraud detection rates and model precision based on the temporal windows and resampling methods. The shortest temporal windows and hybrid undersampling approaches resulted in the maximum recall value in this study and could identify the greatest number of at-risk students. On the other hand, the longest temporal windows and hybrid oversampling approaches with data cleaning resulted in the best results in terms of F1-Score and Cohen’s Kappa statistics. The results provide conclusive evidence that the models can identify fraud; however, they should be used as predictive models for the improvement of proctoring approaches, such as random selection for verification or seating arrangement strategies, instead of judgment models. Full article

(This article belongs to the Section Computing and Artificial Intelligence)

11 pages, 19603 KB

Open AccessArticle

First Record of Leiurus nigellus (Scorpiones: Buthidae) in Northern Saudi Arabia: Molecular and Morphological Insights from Ha’il Region, King Salman Bin Abdulaziz Royal Natural Reserve

by Abdulaziz M. Al-Amri, Mohammad A. Abdulhakeem, Abdulaziz R. Alqahtani, Ahmed M. Al-Malki and Wael M. Shohdi

Diversity 2026, 18(3), 149; https://doi.org/10.3390/d18030149 (registering DOI) - 28 Feb 2026

Abstract

This study documents the first confirmed record of the Buthid scorpion Leiurus nigellus from Jabal Arnan in the Ha’il region, located within the King Salman Bin Abdulaziz Royal Natural Reserve (KSRNR) in the northwestern Kingdom of Saudi Arabia (KSA). This species was originally described by Abu Afifeh, Aloufi & Al-Saraireh (2023). This locality extends the known distribution range of L. nigellus by over 200 km southeast of the type locality in Al-Ula, Al Madinah province. A total of six specimens of L. nigellus were collected during fieldwork conducted between June 2024 and April 2025, including two adult males, one adult female, and three juveniles. The objective of this study was to confirm the taxonomic identity of Leiurus nigellus from a newly discovered locality using morphological examination and mitochondrial DNA analysis and documentation of its known geographic distribution. Adult specimens (one male and one female) were examined using comparative morphometric analysis following standard scorpion taxonomic protocols, confirming diagnostic traits consistent with the original species description. Meanwhile, habitat assessments indicated adaptation to semi-arid rocky and gravel substrates. Molecular analysis was conducted on one adult male using targeted mitochondrial 16S rRNA gene sequencing (Sanger method). Phylogenetic relationships were inferred using neighbor-joining and maximum-parsimony analyses, placing L. nigellus within the Arabian Leiurus clade with bootstrap-supported affinity to Arabian congeners and limited intraspecific divergence. The generated 16S rRNA sequence represents the first molecular record for L. nigellus and has been deposited in GenBank. Sexual dimorphism was evident in morphometric traits, but these differences reflect normal biological variation rather than taxonomic differentiation. The discovery of L. nigellus in northern Saudi Arabia emphasizes the importance of continued faunistic and genetic surveys in underexplored regions, both to refine species distributions and to inform conservation management of specialized desert arachnofauna. Full article

(This article belongs to the Topic Arthropod Biodiversity: Ecological and Functional Aspects, 2nd Edition)

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18 pages, 1714 KB

Open AccessArticle

A Novel Transformer Architecture for Scalable Perovskite Thin-Film Detection

by Mengke Li, Hongling Li, Yuyu Shi and Yanfang Meng

Micromachines 2026, 17(3), 314; https://doi.org/10.3390/mi17030314 (registering DOI) - 28 Feb 2026

Abstract

The further development of scalable fabrication for perovskite solar cells has been considerably constrained by strong process variability and the lack of a reliable real-time predictive mechanism during the thin-film formation process. Existing machine learning-based methods are incapable of capturing the inherent multi-stage kinetic characteristics and uncertainties of the perovskite crystallization process, as they rely on deterministic point prediction models and flatten time-series signals into static features, which necessitates more advanced modeling strategies. To address these challenges, an in situ process monitoring and predictive modeling framework based on a lightweight probabilistic Transformer is proposed for the scalable preparation of perovskite thin films. The strategically designed inputs, consisting of time-resolved photoluminescence (PL) and diffuse reflectance imaging signals acquired during the vacuum quenching process, enable the model to directly learn the conditional probability distribution of the final device performance metrics. Rather than producing a single predicted value, this method enables the explicit quantification of prediction uncertainty, providing statistical support for uncertainty-aware process assessment. Leveraging its advantages over feed-forward neural networks and traditional tree-based machine learning methods, the proposed Transformer architecture effectively captures the staged and non-stationary kinetic features of thin-film formation. Consequently, it exhibits higher robustness and superior uncertainty calibration capability during the early-stage prediction phase. The results demonstrate that the probabilistic Transformer-based modeling paradigm provides a viable pathway toward uncertainty-aware, data-driven process evaluation in perovskite manufacturing. This framework extends its application beyond perovskite photovoltaic device fabrication, providing a generalizable modeling strategy for real-time predictive assessment in the preparation of other complex materials governed by irreversible stochastic dynamics. Full article

(This article belongs to the Special Issue Advanced Thin-Films: Design, Fabrication and Applications, Third Edition)

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13 pages, 438 KB

Open AccessSystematic Review

Assessment of Language Impairments Towards Identifying Markers for Early Diagnosis of Pathological Cognitive Decline

by Claudia Espinoza and Diana Martella

Behav. Sci. 2026, 16(3), 345; https://doi.org/10.3390/bs16030345 (registering DOI) - 28 Feb 2026

Abstract

A major challenge in research on cognitive decline and dementia is the identification of at-risk populations in the preclinical phase. In this context, there is growing interest in language markers as early indicators of cognitive impairment. Objectives: This study aims to identify early linguistic markers that may facilitate the detection of individuals at risk of cognitive decline and dementia during the preclinical stage. Additionally, it seeks to evaluate the effectiveness of various assessment techniques and instruments for detecting such language impairments. Methods: A systematic review was conducted in accordance with the PRISMA guidelines, encompassing studies published between 2014 and 2025. A total of 109 articles were included in the qualitative synthesis. Results: The findings indicate that syntactic–structural features—particularly complexity, discourse coherence, and global organization—together with acoustic parameters such as pause duration, exhibit a higher accuracy and predictive value for the early diagnosis of cognitive decline and its progression to dementia. Furthermore, narrative-based tasks analyzed through automated methods demonstrate significant advantages for the assessment of language impairments. Conclusions: The analysis of language markers—particularly through the examination of syntactic complexity, acoustic features, and automated narrative assessments—represents a promising and effective approach for the early identification of cognitive impairment and the prediction of subsequent dementia onset. Full article

(This article belongs to the Special Issue Novel Approaches to Intervention in Aphasia)

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25 pages, 12080 KB

Open AccessArticle

An Experimental Investigation of Constitutive Models for Steel Fiber-Reinforced Concrete Tunnel Linings Subjected to Freeze–Thaw Cycles

by Li-Ming Wu, Feng Gao, Hu-Xin-Tong Huang, Wen-Jie Luo and Guang-Na Liu

Buildings 2026, 16(5), 957; https://doi.org/10.3390/buildings16050957 (registering DOI) - 28 Feb 2026

Abstract

To investigate the mechanical properties of steel fiber-reinforced concrete under freeze-thaw cycles and the accuracy of its finite element simulation, a constitutive model and its functional expressions for steel fiber-reinforced concrete under tension and compression before and after freeze-thaw cycles were developed. This was based on the stress-strain curve characteristics obtained from experiments, combined with the Hognestad model, the Guo Zhenhai model, and the tensile-compressive model. Finite element simulations were conducted using ABAQUS to model the evolution of the mechanical properties of the lining structure during freeze-thaw processes, revealing the damage characteristics and failure modes of the lining mechanical properties induced by freeze-thaw cycles. The results indicated that after experiencing freeze-thaw cycles, the peak strength of the specimens decreased from 43.3 GPa to 35.3 GPa. Validation through scaled model tests confirmed that the established constitutive model and the corresponding finite element method accurately reflect the cumulative process of freeze-thaw damage, with the numerical simulation results showing good agreement with the experimental data. This study verifies the feasibility of accurately simulating the structural performance of steel fiber-reinforced concrete by developing a freeze-thaw constitutive model, thereby providing a theoretical basis and analytical method for the design and durability assessment of tunnel linings in cold regions. Full article

(This article belongs to the Section Building Structures)

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10 pages, 386 KB

Open AccessArticle

Potential Predictors of Pain and Stiffness Response Following Genicular Artery Embolization for Knee Osteoarthritis

by Tarub S. Mabud, Seon-Hi Shin, Anthony Chong, Mukundan Attur, Erin Alaia, Shu Liu, Elizabeth Morris, Jonathan Samuels, William Macaulay and Bedros Taslakian

J. Clin. Med. 2026, 15(5), 1876; https://doi.org/10.3390/jcm15051876 (registering DOI) - 28 Feb 2026

Abstract

Background/Objectives: Patient-level predictors of treatment response after genicular artery embolization (GAE) for knee osteoarthritis (OA) are poorly understood. We evaluated clinical, serum, and imaging biomarkers for their ability to predict achievement of the minimally clinically important difference (MCID) for WOMAC pain and stiffness subscales following GAE. Methods: Data from a prospective single-arm clinical trial of 25 patients who underwent GAE for symptomatic knee OA was retrospectively analyzed. Candidate predictors included sex, age, BMI, contralateral Kellgren–Lawrence (KL) scores, and baseline values for serum IL-1Ra, serum VEGF, and total bone marrow edema scores on MRI using the MOAKS methodology. The primary outcomes were the frequency of achieving the MCID in WOMAC pain and WOMAC stiffness at 1, 3, and 12 months, modeled as an ordinal outcome (0–3). Ordinal logistic regression models were constructed. Variance inflation factors (VIFs) were assessed to detect multicollinearity, and leave-one-out cross-validation was performed to evaluate model robustness. Results: All candidate predictors were successfully incorporated into regression models, with no evidence of multicollinearity by VIF analysis. Lower contralateral KL scores (OR: 0.087 [0.012–0.618], p = 0.0146) and higher BMI (OR: 1.383 [1.001–1.910], p = 0.049) were significantly associated with achievement of the MCID for WOMAC pain, although significance for BMI was borderline. Lower baseline serum IL-1Ra levels (OR: 0.122 [0.018–0.816], p = 0.030) were significantly associated with achievement of the MCID for WOMAC stiffness. The remaining clinical, serum, and imaging biomarkers were not significantly associated with MCID achievement. Conclusions: In this exploratory analysis, specific baseline clinical and serum factors were associated with achievement of clinically meaningful improvements in pain and stiffness. Analysis of larger cohorts will help clarify ideal demographic-, biomarker- and imaging-based patient selection strategies that can improve prediction of treatment response and guide clinical decision-making in GAE for knee OA. Full article

(This article belongs to the Special Issue New Insights into Clinical Application of Embolization Techniques)

18 pages, 3325 KB

Open AccessArticle

Residue Estimation of Selected Herbicides for Weed Control in Greek Oregano Cultivation

by Elissavet Gavriil, Chris Anagnostopoulos, Konstantinos Liapis, Ilias Eleftherohorinos and Garifalia Economou

Agronomy 2026, 16(5), 545; https://doi.org/10.3390/agronomy16050545 (registering DOI) - 28 Feb 2026

Abstract

Greek oregano (Origanum vulgare ssp. hirtum) is an important aromatic and medicinal crop grown in Greece, often on marginal lands. Effective weed management is essential for sustainable production, but the use of herbicides raises concerns about potential pesticide residues. Therefore, this study was conducted to evaluate the residue levels of metribuzin + pendimethalin applied and incorporated pre-planting, as well metribuzin + cycloxydim and glyphosate applied post-emergence in oregano crop grown over a three-year period in the Agrinio location in Greece. Herbicide residue analysis in the edible part of the oregano plants was performed using two validated protocols, i.e., QuEChERS and QuPPe coupled with LC-MS/MS. The analytical methods demonstrated high sensitivity, with limits of quantification (LOQ) at 0.01 mg/kg and recovery rates ranging from 71% to 102%. These results indicated that the application of the above herbicides in oregano crop grown under Greek field conditions resulted in no detectable residues above the established LOQs, strongly supporting the potential safe use of these herbicides in oregano crop and their possible use for regulatory assessments and consumer safety assurance. Full article

(This article belongs to the Special Issue Integrated Management and Sustainability of Herbicide Resistance in Crops)

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31 pages, 1166 KB

Open AccessReview

Post-Diagnosis Adherence to the Mediterranean Diet and Cancer Recurrence and Fatigue Outcomes in Cancer Survivors, with Emphasis on Colorectal Cancer: A Systematic Review and Meta-Analysis

by Dimitris Papamichael, Kyriacos Felekkis and Eleni P. Andreou

Nutrients 2026, 18(5), 807; https://doi.org/10.3390/nu18050807 (registering DOI) - 28 Feb 2026

Abstract

Background: Cancer survivors face heightened risks of recurrence and persistent cancer-related fatigue (CRF), both of which impair quality of life. The Mediterranean Diet (MD), characterized by its antioxidant and anti-inflammatory profile, has been proposed as a potentially beneficial dietary pattern. This systematic review and meta-analysis evaluated the association between post-diagnosis MD adherence and outcomes of cancer recurrence and CRF among adult survivors, with particular attention to colorectal cancer. Methods: Systematic searches of PubMed, CINAHL, and the Cochrane Library (January 1995–May 2024) identified prospective cohort studies and randomized controlled trials (RCTs) reporting post-diagnosis MD adherence. Primary outcomes were cancer recurrence and CRF. Random-effects models were applied for recurrence analyses due to anticipated heterogeneity, while a fixed-effects model was used for CRF given the limited number of trials. Heterogeneity was assessed using the I² statistic. Risk of bias was evaluated using the Cochrane RoB 2 tool (RCTs) and ROBINS-I (cohort studies). This review was registered in PROSPERO (CRD420251248086). Results: Eight studies met inclusion criteria: six prospective cohort studies assessed recurrence (n = 6697), and two RCTs assessed CRF (n = 76). For recurrence, higher post-diagnosis MD adherence was associated with a lower hazard of recurrence or cancer-specific mortality (HR = 0.83; 95% CI: 0.70–0.99; I² = 49%). For CRF, the pooled effect from two independent RCTs showed no statistically significant overall effect (MD = 0.29; 95% CI: −0.58 to 1.16). Both outcomes were limited by small study numbers and methodological heterogeneity. Conclusions: Higher adherence to the Mediterranean Diet after cancer diagnosis was associated with recurrence-related outcomes in observational studies, while evidence for CRF remains exploratory and statistically imprecise. Larger, adequately powered randomized trials are needed to clarify the role of the Mediterranean Diet in survivorship care. Full article

(This article belongs to the Section Nutritional Epidemiology)

20 pages, 4514 KB

Open AccessArticle

Hybrid Physical–Machine Learning Soil Moisture Modeling at Orchard Scale in Irrigated Citrus Orchards Using Sentinel 1 and 2 and Agroclimatic Data

by Héctor Izquierdo-Sanz and Enrique Moltó

Agronomy 2026, 16(5), 541; https://doi.org/10.3390/agronomy16050541 (registering DOI) - 28 Feb 2026

Abstract

Accurate orchard-scale soil moisture information is a key requirement for efficient irrigation management in perennial crops such as citrus orchards, particularly in Mediterranean environments characterized by water scarcity and strong spatial and temporal variability in soil moisture, canopy structure, and irrigation scheduling. This study proposes a hybrid physical–machine learning methodology for soil moisture estimation that integrates in situ capacitance sensor measurements, Sentinel-1 SAR observations, Sentinel-2 optical imagery, and ERA5-Land agroclimatic variables. Physically based soil moisture estimates were first obtained through the inversion of Sentinel-1 backscatter using integral equation scattering models, a physically based soil dielectric model, and a simplified vegetation attenuation scheme. These physically derived estimates were subsequently incorporated as predictors within supervised machine learning models, together with multi-source remote sensing and meteorological variables. Several algorithms were evaluated, including regularized linear models, support vector regression, random forests, and gradient boosting methods. Model performance was assessed using a strict interannual validation strategy based on independent-year predictions to ensure robust generalization. Within this methodology, tree-based ensemble models achieved the highest and most consistent performance at the orchard scale, with coefficients of determination ranging from 0.55 to 0.76 and root mean square errors typically between 0.7 and 1.1% volumetric soil moisture in the best-performing cases. Benchmarking against a physical-only baseline demonstrated that the hybrid methodology consistently reduced prediction errors and improved temporal robustness under independent-year validation. Overall, the results demonstrate that hybrid physical–machine learning approaches provide a robust and scalable solution for orchard-scale soil moisture monitoring in irrigated citrus orchards using operational data streams, supporting advanced irrigation management and precision agriculture applications in Mediterranean perennial cropping systems. Full article

(This article belongs to the Special Issue Advances in Remote Sensing Agronomic Application for Mapping and Modeling Soil Properties)

14 pages, 578 KB

Open AccessArticle

Comparative Effect of Different Nanoparticles with Different Concentrations on Fracture Toughness and Elastic Modulus of Restorative Dental Composite Resin

by Mohamed Ahmed Helal, Emad Amin Azmy, Amal Al-Faraj, Faris A. Alshahrani, Firas K. Alqarawi, Hamad S. AlRumaih, Mohammed M. Gad and Mostafa I. Fayad

Dent. J. 2026, 14(3), 134; https://doi.org/10.3390/dj14030134 (registering DOI) - 28 Feb 2026

Abstract

Background/Objective: Resin-based composite (RBC) gained wide popularity in dentistry due to its excellent biocompatibility, superior aesthetics, and good bonding to enamel and dentine. However, they have several shortcomings, including mechanical insufficiency and shrinkage tendency. Many researchers have utilized nanoparticles (NPs) as a reinforcing filler for RBCs. This article focused on assessing the impact of three different nanoparticles, ZrO₂, TiO_2, and SiO_2, with concentrations of 3 wt% and 7 wt%, on the elastic modulus (E) and fracture toughness (K_IC) of one commercial light-activated dental resin composite. Methods: 140 rectangular specimens were constructed according to ISO 4049 with dimensions (25 × 2 × 5 ± 0.03 mm) and (25 × 2 × 2 ± 0.03 mm) for fracture toughness and elastic modulus, respectively. Specimens were categorized into four main groups based on nanofiller types. Control: plain without filler (CC) and three modified ones with ZrO₂ (ZC), TiO₂ (TC), and SiO₂ (SC). Furthermore, modified groups were divided into two subgroups according to nanofiller concentration, 3 and 7 wt% (ZC3, ZC7, TC3, TC7, SC3, and SC7), n = 10. Mechanical testing for fracture toughness was completed using a single-edge notched beam, while a three-point bending test was used for elastic modulus. Analysis of data was based on two-way ANOVA and Bonferroni post hoc (α = 0.05). Results: ZrO₂ provided the most substantial improvement in both E and K_IC, with the optimal performance observed at 3 wt% for stiffness and 7 wt% for toughness. TiO₂ groups also enhanced these properties at both concentrations; however, the gains were less pronounced compared to ZrO₂. SiO₂ improved mechanical performance at 3 wt%, but a higher loading of 7 wt% resulted in reduced values. Conclusions: Resin-based composite modified with 3 wt% of NPs tends to possess higher fracture toughness and modulus of elasticity. Fracture toughness enhancement was concentration-dependent with ZrO₂ NPs, where the best result was obtained with 7 wt%. Nanoparticle-reinforced composite, particularly ZrO₂, may be suitable for prosthodontic applications. Full article

(This article belongs to the Section Dental Materials)

21 pages, 2201 KB

Open AccessArticle

SCBI-EfficientNetV2: A Lightweight Attention-Based Network for Regression Prediction of Nitrogen Content in Maize Leaves

by Cuimin Sun, Biao He, Liuxue Huang, Ji Liu, Qiulian Chen and Xi Qin

Agronomy 2026, 16(5), 544; https://doi.org/10.3390/agronomy16050544 (registering DOI) - 28 Feb 2026

Abstract

Accurate assessment of nitrogen content in maize leaves is crucial for scientific fertilization and environmental protection in agricultural production. Traditional nutrient diagnosis methods are inefficient, costly, and destructive, while machine learning approaches based on handcrafted features rely heavily on manual design, leading to limited generalization ability and suboptimal prediction accuracy. To address these issues, this paper proposes a convolutional neural network model named SCBI-EfficientNetV2, which adopts EfficientNetV2-S as the backbone to overcome the limitations of manual feature engineering through automatic feature extraction. Furthermore, a Spatial and Channel Synergistic Attention (SCSA) module is introduced to enhance the modeling of critical regions and informative channels, and a Bidirectional Feature Pyramid Network (BiFPN) is incorporated to achieve effective multi-scale feature fusion, thereby improving the representation of hierarchical structural features in maize leaves. Experimental results show that SCBI-EfficientNetV2 achieves a coefficient of determination (R²) of 0.9417 on the test set, representing a 5.25% improvement over the baseline model and outperforming five classical deep learning approaches. In addition, the proposed model maintains high prediction accuracy with relatively low computational cost, demonstrating good adaptability for edge deployment. This study provides a feasible solution for non-destructive intelligent diagnosis of maize nutrition and offers technical support for precision fertilization and sustainable agricultural development. Full article

(This article belongs to the Special Issue Crop Nutrition Diagnosis and Efficient Production)

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