Abstract:

Background: Knee osteoarthritis (KOA) alters the performance of daily activities, such as stair negotiation, by compromising lateral stability and neuromuscular control. This pilot study evaluated the feasibility of a 10-week Dynamic Neuromuscular Stabilization (DNS) program and explored preliminary biomechanical changes during stair ascent and descent in middle-aged women with KOA. Methods: Twenty-six participants were randomly assigned to a DNS group (n = 13) or a control group (n = 13). The DNS group completed a 10-week intervention (twice weekly). Feasibility was assessed via recruitment, retention, and adherence. Primary outcomes were mediolateral (ML) center of pressure (COP) parameters, while secondary outcomes included anteroposterior (AP) COP parameters and lower limb range of motion (ROM). Effect sizes (η²_p) were estimated using 3D motion analysis and force plates. Results: The intervention showed high potential feasibility, with 100% recruitment and retention rates and 98.5% compliance. No adverse events occurred. Large effect sizes were observed for reduced ML COP velocity (ascent: η²_p = 0.79; descent: η²_p = 0.62) and RMS (descent: η²_p = 0.16). Secondary outcomes, including AP COP parameters and joint ROM (increased sagittal flexion and decreased coronal instability), also demonstrated large effect sizes. Conclusions: This pilot study suggests that progressive DNS training is a safe and potentially feasible intervention for patients with KOA. The preliminary effect sizes observed in COP control and lower kinetic chain mechanics provide promising evidence that may serve as foundational data for designing future large-scale clinical trials to definitively verify efficacy.

Abstract: Background/Objectives: Niclosamide, an established anthelmintic drug, has shown promise in overcoming various types of drug-resistant cancers. However, despite its potent anti-proliferative effects, niclosamide suffers from low aqueous solubility, which can certainly limit oral bioavailability. To address these limitations and improve its physicochemical and pharmacokinetic properties, cocrystallization was employed as a strategic approach. Methods: In this study, we developed a niclosamide–nafamostat pharmaceutical cocrystal using a conventional solvent evaporation technique. The newly formed cocrystal was characterized using X-ray powder diffraction (XRPD), Fourier-transform infrared spectroscopy (FTIR), nuclear magnetic resonance (NMR), and differential scanning calorimetry (DSC), which collectively indicated that hydrogen bonds between the drug and co-former were the primary stabilizing interaction. Results: The NNC cocrystal was structurally confirmed by NMR, indicating the presence of hydrogen bonding, which was further supported by FTIR and XRD analyses. The antiproliferative activity of the cocrystal was evaluated across multiple cancer cell lines, where it exhibited approximately tenfold higher cytotoxicity compared to the parent compounds. Additionally, antiviral efficacy against SARS-CoV-2–infected cells demonstrated a potency of 0.17 µM, representing more than a twentyfold improvement over the standard drug. In vivo studies revealed that the cocrystal achieved a twofold enhancement in tumor growth suppression and a tenfold reduction in tumor burden relative to the control treatment, with high statistical significance (p < 0.0001). Conclusion: This cocrystallization approach a promising strategy to enhance drug solubility, bioavailability, and therapeutic efficacy, potentially enabling optimized dosing for future clinical applications.

Abstract: Severe cutaneous adverse reactions (SCARs) are rare, life-threatening drug hypersensitivity syndromes. Although pharmacovigilance can identify drugs disproportionately reported with SCARs, it does not reveal which local chemistries recur among them. To address this, we assessed whether drugs with disproportionate SCAR reporting in the FDA Adverse Event Reporting System (FAERS) share interpretable chemical motifs. We screened FAERS data from 2004Q1 to 2024Q3, identified 5523 drugs with available Simplified Molecular-Input Line-Entry System (SMILES) representations, and constructed a signal-enriched dataset of 1676 compounds with nominally significant broad-SCAR associations after excluding predefined therapeutic/supportive confounders. Compounds were assigned to positive-signal [natural logarithm of reporting odds ratio (lnROR) > 0, n = 1219] or nonpositive-signal (lnROR ≤ 0, n = 457) classes and encoded with 9753 explicitly mappable atom-centered local substructure descriptors. A light gradient boosting machine classifier evaluated using repeated nested cross-validation (six-fold outer × 50 repeats) achieved moderate but stable internal discrimination (mean area under the receiver operating characteristic curve = 0.7041 ± 0.0337). SHapley Additive exPlanations (SHAP) analysis revealed a clear fragment-level contrast: allylamine-like, ethanolamine, and diaminopropane-related motifs were associated with higher positive-signal class probability, whereas phenol and pyrimidine motifs were associated with corresponding lower probability. The phenol fragment (Oc1ccccc1) was the most influential feature overall (mean |SHAP| = 0.1727), followed by an allylamine-like fragment (0.1031). These findings suggest that broad-SCAR concern detected in the FAERS is not chemically random within the selected dataset. Overall, the proposed framework should be viewed not as a direct predictor of absolute clinical SCAR risk but as an interpretable structure-guided safety-screening approach to prioritize compounds and motif families for further SCAR-focused evaluation.

Abstract: Gastrotrichs (Phylum Gastrotricha) are widespread and species‑rich components of benthic and periphytic communities, where they are thought to contribute substantially to food‑web functioning by linking the microbial loop to higher trophic levels through their feeding on detritus, bacteria, microalgae, and fungi and serve as prey for larger animals. Despite the well‑recognized role as primary consumers, their position as potential prey remains largely unresolved, with documented predators so far restricted to carnivorous protists. Here, we report the first documented case of metazoan predation on a freshwater gastrotrich, in which a cyclopoid copepod actively captures and partially consumes a chaetonotid species. The interaction was first detected under minimally disturbed conditions and subsequently replicated in controlled experimental settings. Predation was documented through in vivo video recordings and supported by species-level identification of both predator and prey. These findings expand the currently recognized trophic interactions involving freshwater gastrotrichs and provide new insight into their ecological role within aquatic food webs.

Abstract: This paper revisits and extends the machine learning framework for U.S. recession prediction introduced by Yazdani2020 by incorporating post-pandemic macroeconomic dynamics, an expanded predictor set and machine learning models. Using monthly data from January 1959 through December 2024, recession forecasting is formulated as an imbalanced binary classification problem. We use downsampling for static models and class-weighted loss functions for neural networks and evaluate model performance using classification metrics robust to rare events. We further examine structural stability across four economic regimes and assess economic value through a dynamic stock–bond allocation strategy. We observe that ensemble tree methods, particularly gradient boosting (XGBoost, LightGBM) and random forests, consistently deliver the strongest discrimination, with out-of-sample AUC above 0.99 and PR-AUC above 0.96. The Transformer achieves probability calibration, and Deep sequence models exhibit high discrimination, while performance deteriorates across model classes in the 2020–2024 regime, especially for linear specifications. We also examine risk-adjusted returns of models. Overall, ensemble trees and Transformers show high predictive power and emerge as complementary tools in macroeconomic recession forecasting.

Abstract: Oral squamous cell carcinoma (OSCC) remains a major contributor to global head and neck cancer morbidity and mortality. This review examines the epidemiological aspects of OSCC and the biological mechanisms through which established exposures (e.g., tobacco, alcohol, betel/areca nut, socioeconomic and selected viral infections etc.) and emerging determinants (oral microbiome dysbiosis, host genetics/epigenetics, and immune dysfunction) converge to initiate and promote malignant transformation. We emphasize that OSCC risk is probabilistic and multifactorial: incidence rises markedly with age and cumulative exposures, yet the majority of individuals risk exposed to these risk factors will not develop disease. Mechanistically, carcinogen-driven DNA damages intersects with dysbiosis characterized by enrichment of periodontal pathobionts (notably Porphyromonas gingivalis and Fusobacterium nucleatum), which can sustain chronic inflammation, increase local generation of acetaldehyde and nitrosamines, and promote immune evasion via expansion of immunosuppressive cell populations and checkpoint signaling. We summarize recurrent molecular and genetic alterations in OSCC and highlight progress in early detection, including adjunctive visualization, optical and vibrational spectroscopy, and liquid-biopsy approaches using salivary and blood-based biomarkers. Finally, we discuss prevention opportunities spanning risk-factor modification, historical cultural practices, oral hygiene to mitigate dysbiosis (pH modulation and probiotics), and dietary/nutritional strategies. Integrating exposure history with microbial and molecular profiling may enable risk-stratified screening and prevention paradigms for OSCC.

Abstract: Background/Objectives: Immediate provisionalization in the esthetic zone is a well-documented but technique-sensitive procedure, and the choice of provisional connection geometry (indexed vs. non-indexed) remains debated. The aim of this ret-rospective single-arm cohort clinical study was to evaluate the clinical performance of a digitally planned, guide-delivered provisionalization protocol using prefabricated provisional crowns connected to 5-degree Morse taper implants without an antirota-tional index, with emphasis on emergence profile shaping and peri-implant tissue sta-bility at one year; Methods: Twenty consecutive single-implant cases (19 female, 1 male; mean age 38.1 ± 12.7 years; 18 anterior and 2 premolar sites) were treated between January 2024 and February 2026. All implants were placed with primary inser-tion torque ≥ 30 N·cm (mean 34.75 ± 2.55 N·cm) and immediately restored with a digitally designed, non-antirotational provisional crown. Primary outcome was provision-al retention without major intervention; secondary outcomes included biologic com-plications, papilla score, marginal bone change at T0–T3 and T3–T4, and buccal con-tour change (T0 vs T2 intraoral scan superimposition). Wilson 95% confidence inter-vals, Fisher’s exact test, and Mann–Whitney U test were used (α = 0.05); Results: Pro-visional retention without major intervention was 75.0% (15/20; 95% CI 53.1–88.8). Biologic complications were uncommon (bleeding on probing, suppuration, midfacial recession, and chairside adjustment, each 5.0%). Mean total marginal bone loss at one year was 0.37 ± 0.20 mm; mean buccal contour gain was 1.41 ± 0.48 mm. A complete papilla was preserved in 70.0% of cases; Conclusions: Digitally planned, guide-delivered provisionalization on a non-antirotational 5-degree Morse taper interface appears clinically feasible for emergence profile shaping in the esthetic zone, with stable peri-implant tissues at one year.

Abstract: Maritime ports—now deeply digitalized andinterdependent—face escalating cyber risk amid hybridgeopolitical pressures, complex vendor ecosystems, andwidening social dependence on uninterrupted trade flows.Situated at the intersection of the Belt and Road Initiative andthe Trans-Caspian International Transport Route, the CaspianBasin exemplifies both the promise of data-driven logistics andthe vulnerability of fragmented cybersecurity governance. Thisstudy extends the Strategic Data Alignment Framework(SDAF), originally designed to align corporate strategy withdata governance, into a cybersecurity governance model forcritical maritime infrastructure under hybrid threat conditions.Using comparative policy analysis and benchmarking againstcontemporary global standards (e.g., NIS2-style obligations,maritime cyber guidelines, and digital trade principles), thestudy identifies systemic weaknesses in harmonization,institutional capacity, supply-chain assurance, and resilienceplanning. It reconceptualizes cyber-resilience as a strategicresource and proposes a five-step roadmap combining regionalthreat-intelligence sharing, vendor risk controls, standardsalignment, AI-enabled detection, and stress-tested recovery.The findings underscore urgent needs for coordinated action tosafeguard digital corridors and the societies they serve.

Abstract: Breast cancer constitutes one of the oncological diagnoses with the greatest impact on women’s lives, with consequences that extend beyond active treatment into a survi-vorship period marked by profound transformations in identity, relationships, and well-being. This study aimed to explore the impacts that breast cancer produces on the everyday lives of survivor women in the municipality of Villarrica, La Araucanía Region. A qualitative methodology with a phenomenological orientation was employed, based on discourse analysis of three focus groups with breast cancer survivor women. The analysis identified five categories: impact on everyday life and work, management of uncertainty and fear, transformation of self-care and life priorities, support networks and community, and barriers to accessing the healthcare system. The findings demon-strate the coexistence of posttraumatic growth and persistent psychological distress, together with structural inequities that limit access to comprehensive care during the survivorship period. It is concluded that cancer survivorship demands public policy responses that are continuous, multilevel, and integrative of a gender perspective, ar-ticulating individual, family, and community interventions from primary healthcare.

Abstract: This paper presents a comprehensive cross-era analysis of the algorithmic evolution of Large Language Models (LLMs) through four developmental epochs: Before Transformer (pre-2017), Transformer (post-2017), Instruction-tuned \& Open-source LLMs, and Multimodal Agents (2024-2025). A novel innovation pathway framework is introduced that traces causal relationships between architectural breakthroughs and emergent capabilities, addressing critical research gaps in three dimensions: (1) Cross-paradigm synthesis connecting statistical foundations to modern multimodal systems, (2) Causal innovation mapping demonstrating how architectural choices propagate through model generations, and (3) Cross-domain capability analysis quantifying transfer between representation learning, knowledge acquisition, behavioral alignment, and multimodal integration. This analysis reveals that LLM progression represents fundamental paradigm shifts rather than incremental improvements, with transformer architectures, human feedback mechanisms, and open-source ecosystems collectively enabling the transition from specialized NLP tools to general reasoning systems. We provide empirical evidence through case studies of capability emergence, quantify innovation impacts using performance metrics, and examine safety implications through recent jailbreak analysis and refusal mechanism studies. The contributions include: (a) a unified lifecycle synthesis with original analytical framework, (b) innovation trajectory mapping with causal pathway analysis, and (c) validated evolutionary principles for forecasting next-generation AI capabilities.

Abstract: Background/Objectives: One of the ongoing clinical constraints is limiting microbial growth on facial and dental prostheses, justifying the need for material surface enhancements for reducing the associated microbial complications. This study aimed to investigate a clinically applicable and reproducible coating technique to overcome microbial clinical challenges. Methods: Ag nanoparticles (NPs) were applied to three types of facial materials through spray, spin, and dip coating techniques. Surface characterization, elemental composition, and chemical bond formation were assessed by Scanning Electron Microscopy (SEM), Energy-dispersive X-ray Spectroscopy (EDS), and Fourier Transform Infrared (FTIR) spectroscopy, respectively. Subsequent optimization of spray numbers was performed. Antimicrobial performance was examined by agar diffusion, direct contact, and adhesion (time-dependent) assays, with different layers, against Pseudomonas aeruginosa. Results: Spray coating exhibited superior coating uniformity compared with others. 15 sprays was determined as optimal number for a single layer coating. EDS confirmed Ag NP presence, FTIR revealed no chemical alteration of specimens. Disk diffusion tests showed no inhibition zones. Adhesion and direct contact tests displayed antibacterial activity, the effect of which was stronger for the latter. Time-dependent adhesion test of 1-layer coating of acrylic and silicone had a consistent decrease in bacterial amount, whilst zirconia had only a strong initial activity. In general, the 3-layer coating did not showcase an increased antimicrobial activity, suggesting that the increase in layering negatively impacts surface effectiveness. Conclusions: spray coating of Ag NPs can provide a promising, clinically-applicable, large-scale manufacturing strategy for improving dental and facial material antibacterial qualities without altering the inherent prosthetic properties.

Abstract: Introduction: Online forums use as a source for mental health support has surged among individuals worldwide. Social media has become popular for sharing personal experiences and seeking information and support. Studies have analyzed posts on social media forums, focusing on frequency of engagement by users, why individuals engage, and how they engage on these web-based platforms. However, key questions about how mental illness is experienced, discussed and emotionally expressed from the user's perspective is needed to add important insight. Objective: This study aims to investigate how mental health related disorders and conditions are discussed, experienced, and emotionally framed in online discourse, specifically focusing on how mental health symptoms and distress language across mental health dialogues are expressed and examining the text-based communication beyond prevalence-based analyses and simplified sentiment analysis through symptom and experience-centered approach to uncover patterns in how symptoms are articulated and emotions expressed, and how distress is framed across multiple mental health conditions, by systematically analyzing digital textual data associated with various mental illnesses. Methodology: A retrospective observational design was conducted. The dataset used in this study was scrapped from YouTube between 11/2/2025 to 11/30/2025 by using a predefined keyword resulting in a total sample of 646 279 comments. The data was prepared and preprocessed using standard NLP procedures. Descriptive analysis of Disorder Representation, Symptom Expression Analysis, Emotional Tone and Distress Analysis, Cross-Disorder Statistical Comparisons and Crisis-Oriented Language Analysis was conducted. Kruskal–Wallis test a non-parametric analysis revealed no statistically significant differences in emotional proportion scores across mental health condition categories. Pearson’s chi-squared test indicted a robust and statistically significant association between mental health disorder type and symptom category. Result: Among the mental health conditions discussed online, content related to anxiety made up the largest count of the dataset (n = 125,001; 19.3%), followed by depression (n = 100,281; 15.5%) mental breakdown (n = 93,836; 14.5%), and PTSD (n = 110,935; 14.2%). However, Obsessive-Compulsive Disorder exhibited a robust engagement (Eng = 158.5); and panic attack–related posts showed higher levels of engagement (Eng = 189). Mental health conditions such as panic attacks (0.1050), anxiety (0.0532), depression (0.0490), and mental illness (0.0497) demonstrated intense emotions. For the category Anxiety Terms, the most negative terminology was recorded with the most negative sentiment score (−62,667). Stigmatizing revealed a net negative sentiment (−7,787) while Self-Disclosure also revealed a net negative sentiment (−2,344). Empathy showed the highest positive sentiment score (50,432), followed by Supportive (24,867) and Advocacy (4,897) categories.No statistically significant differences in emotional proportion scores across mental health disorder categories were revealed (X2 (6) = 0.118, p=1.000). However, a robust and statistically significant association between mental health disorder type and symptom category was identified (X2(18) =11, 623, p<0.001), suggesting that each mental health condition presents different symptom profiles across cognitive, emotional, and somatic dimensions. Conclusion: In conclusion, this study makes several important contributions to mental health research and practice in understanding mental illness as a lived experience rather than solely a diagnostic category. This finding also provides empirical support for conceptualizing OCD as a cognitive-based disorder, where distress is often expressed through intrusive thought patterns and not solely emotional states.

Abstract: To investigated the effect of exogenous selenium on selenium enrichment and antioxidant activity of germinated chestnut. We treated ‘Zaofeng’ chestnuts with 0, 20, 40, 60 and 80 mg/L concentration of Na₂SeO₃, and the analyses focused on total Se, SeCys₂, MeSecys, Se^IV, SeMet, Se^VI, γ -aminobutyric acid (GABA), antioxidant enzyme (PAL, SOD, GPX, CAT) activity, non-enzymatic antioxidant substances (total polyphenols and flavonoids) content and antioxidant capacity (DPPH, ABTS) during germination. The results indicated that low concentration of selenium (20-40 mg/L) significantly promoted the organic transformation of selenium, increased antioxidant enzyme activity and phenol accumulation, and enhanced antioxidant capacity of chestnut. High concentration of selenium (80 mg/L) induced oxidative stress, inhibited enzyme activity and reduces antioxidant capacity of chestnut. During the germination of chestnut, selenite was absorbed by embryo and subsequently transformed into organic Se in vivo, ultimately being stored in the form of SeCys₂. The selenium enrichment rate decreased significantly with the increase of Na₂SeO₃ treatment concentration. Furthermore, the treatment with 40 mg/L Na₂SeO₃ led to a significant increase in GABA content of germinated chestnut. Overall, 20-40 mg/L Na₂SeO₃ were identified as the suitable concentration for germinated chestnut. This study provides a theoretical basis for developing functional food of selenium-enriched germinated chestnut.

Abstract: Self-regulated transportation networks belong to the class of continuous network models and are widely used not only in biological applications, such as vascular systems, neural networks or tissues regeneration but also in urban infrastructure and in communication technologies. Their well-established tree structure prevents the formation of loops, which limits their ability to capture an important feature observed in real systems: when a disruption or damage occurs, the network should be able to reorganize to restore transport pathways. In this work, we propose alternative modeling strategies to incorporate this capability. These approaches allow the network to adapt to perturbations by modifying its structure and, in some cases, by creating alternative routes that compensate for damaged regions. Numerical results illustrate how the modified models can reproduce self-repair mechanisms that are not captured by standard formulations.

Abstract:

To investigate the effects of long-term biochar application on different forms of potassium (K) content in maize rhizosphere soil and maize growth, two biochar application rates (B0: 0 t ha⁻¹ yr⁻¹, B1: 2.625 t ha⁻¹ yr⁻¹) and two K fertilizer application rates (K0: 0 kg ha⁻¹ yr⁻¹, K1: 60 kg ha⁻¹ yr⁻¹) to create four treatments (B0K0, B0K1, B1K0, B1K1). In this long-term field trial, we investigated various forms of K in the maize rhizosphere soil, together with soil physicochemical properties and maize growth indicators. Results indicate that biochar significantly increased microbial biomass carbon (MBC), cation exchange capacity (CEC), and electrical conductivity (EC) in the rhizosphere soil, while also improving rhizosphere soil pH. Compared with the treatment without biochar, biochar application significantly increased the content of water-soluble potassium (WSK), exchangeable potassium (EK), and non-exchangeable potassium (NEK) in the rhizosphere soil by 18.57% (2021) and 11.18% (2022), 13.49% (2021), and 11.43% (2022), 14.65% (2021), and 17.06% (2022), respectively. Maize roots were more developed, and plant height, stem diameter, and leaf area index were significantly increased. With above-ground dry weight and K uptake significantly increasing by 13.87% (2021) and 12.04% (2022), and 41.84% (2021) and 43.87% (2022), respectively. Compared with B0K0, the B1K1 treatment—which combined biochar with K fertilizer—exhibited the highest K content in all forms within the rhizosphere soil, along with the greatest maize aboveground dry weight and K uptake. This study demonstrates biochar’s potential in meeting crop root K demands, laying the foundation for its application in enhancing soil K fertility.

Abstract: The high cost and complexity of Industry 4.0 laboratory infrastructure limit the adoption of Digital Twin concepts in engineering education. This paper proposes a low-cost Digital Twin framework for sustainable manufacturing education integrating SAP NetWeaver, Node-RED, and AI-based decision support. The framework adopts a layered architecture that connects PLC-based simulation, IoT middleware, enterprise resource planning systems, and intelligent decision-making components. Node-RED enables real-time data exchange, while SAP NetWeaver provides enterprise-level integration through OData services. An AI module supports decision-making for production and inventory management. The proposed framework is implemented as a functional prototype, demonstrating end-to-end integration without requiring physical manufacturing equipment. Competency-based mapping aligns the framework with Industry 4.0 engineering skills, supporting its use in academic environments. A sustainability assessment highlights reductions in infrastructure cost, energy consumption, and resource usage compared to traditional laboratory approaches. The results indicate that the framework provides a scalable and accessible solution for teaching Digital Twin concepts, contributing to sustainable engineering education in resource-constrained contexts.

Abstract: A covariant formulation of the Geometric Dilution of Precision (GDOP) matrix is presented in the framework of a Relativistic Positioning System (RPS). By including the receiver-emitter frequency ratios, the Frequency Geometric Dilution of Precision (FGDOP) scalar is computed in terms of observable quantities, the received frequencies and the angular separation between pairs of emitters in view. Some required concepts are first introduced: the FGDOP matrix and the Gram matrix associated to k light-like vectors. From the tensor form of the FGDOP matrix and its trace, a closed form of the FGDOP scalar is obtained, extending previous matrix calculations. Clarifying computations for symmetric emitter configurations are presented. The geometric interpretation of the GDOP scalar in terms of volumes and areas defined by the relative position of the emitters on the unit celestial sphere of the user is also recovered.

Abstract: ZPIF (Zero Pair Interaction Functional) is introduced as a quadratic spectral operator framework extending the classical explicit formula of the Riemann zeta function. Unlike the standard linear spectral decomposition, ZPIF incorporates second-order interactions between spectral modes within a Hilbert space formulation. The framework includes a rigorous operator definition, spectral expansion, trace-class regularization, and conditional convergence under truncation. A computational scheme based on numerical zeta zeros is also proposed. The novelty of ZPIF lies in introducing a quadratic spectral energy functional consistent with classical spectral heuristics without assuming unresolved conjectures. Numerical experiments demonstrate nonlinear growth behavior and quadratic interaction effects that are absent in classical linear formulations.

Abstract: This study evaluates the performance of LiDAR sensing and UAV photogrammetry for three-dimensional canopy reconstruction and structural parameter estimation in precision agriculture. Experiments were conducted in Sicily (Italy) on Moringa oleifera Lam. and Ficus macrophylla subsp. columnaris, representing contrasting canopy architectures. LiDAR and UAV data were used to generate canopy models and estimate canopy height, volume, and vegetation density. A voxel-based approach was applied to LiDAR point clouds to analyze internal canopy structure. LiDAR significantly outperformed UAV photogrammetry, achieving lower errors in canopy height estimation (RMSE = 0.19–0.21 m vs. 0.52–0.60 m) and canopy volume (3.5–4.2% vs. 13.7–16.1%). UAV photogrammetry provided reliable estimates of canopy surface but underestimated structural parameters in dense vegetation due to occlusion effects. Differences were more pronounced in Ficus macrophylla than in Moringa oleifera, highlighting the influence of canopy complexity. These findings demonstrate that LiDAR-derived structural metrics can improve canopy characterization and support precision agriculture applications such as biomass estimation, irrigation planning, and canopy management in Mediterranean cropping systems.

Abstract: This study investigates the effect of co-doping with nanographene on the properties of FTO layers produced by spray pyrolysis. The results show that co-doping with nanographene enhances phase separation processes within the bulk of the FTO layer. The inhomogeneities formed during phase separation are depleted of fluorine compared to the film bulk. Co-doping with nanographene also significantly modifies the optical properties of the FTO layers. Specifically, it alters the position and intensity of the peaks in the reflection spectra, indicating a change in the nature of the absorbing centers. Furthermore, the optical bandgap of the FTO layers decreases with an increasing degree of nanographene doping. Finally, co-doping with nanographene reduces the resistance of the FTO layers, which can be attributed to an increase in charge carrier mobility.