Search Results (46,277)

The use of biodegradable mulch films (BDM) in agriculture has raised concerns about the potential impact of the microplastics (MPs) they release over time, after the BDM’s useful life. The effects of BDM MPs have been explored through a diversity of assays, with still poorly understood and frequently contrasting results. Furthermore, the impact on plants as the MPs evolve in size and as a function of residence time in the soil remains largely unexplored. Through a controlled in vitro lettuce culture, this study explores the effect of BDM MPs size, using fractions 5 to <0.2 mm and pre-incubation times of 0 to 8 weeks, on plant development. Short incubation times, of 1 and 2 weeks, and freshly adding the BDM MPs inhibited plantlet growth, with smaller MPs inducing stronger effects. In contrast, longer MPs incubation, of 8 weeks, promoted plantlet development, enhancing leaf and particularly root elongation while reducing lateral root branching. The effects on roots were more pronounced, as the MPs size decreased. Germination and photosynthetic pigments were unaffected by any treatment. Overall, BDM MPs’ impact on plants was mainly driven by particle size and incubation time in the medium prior to seeding, with adverse effects on plant development observed at short incubation times that were no longer present when incubation was extended. These findings highlight the need to unravel the dynamic and temporal nature of the BDM MPs’ interaction with plants. Full article

Precise timing of rice harvesting is critical for ensuring grain yield and quality. Traditional manual evaluation methods are highly subjective and time-consuming, highlighting the critical demand for rapid, non-destructive approaches to estimate rice maturity. This study focused on cold-region japonica rice grown in Heilongjiang Province, aiming to develop and validate dual-scale (pot and field) maturity estimation models. For model development, canopy spectral data were collected using two complementary acquisition tools: a ground-based active sensor (CGMD402) and UAV-borne multispectral imagery. Four modeling algorithms—Linear Regression (LR), Decision Tree (DT), Random Forest (RF), and Support Vector Machine (SVM)—were utilized, with input variables comprising single spectral indices (Normalized Difference Vegetation Index, NDVI; Ratio Vegetation Index, RVI) and composite spectral indices (Normalized Difference Maturity Ratio Vegetation Index, NDMRVI; Normalized Difference Pigment Ratio Vegetation Index, NDPRVI). At the pot scale, composite spectral indices showed stronger correlations with rice maturity than single indices. Among the four algorithms, the DT model with combined NDVI + RVI input yielded the optimal comprehensive performance, with a coefficient of determination (R²) of 0.957, a root mean square error (RMSE) of 0.064, and a relative error (RE) of 4.8% in the test set. At the field scale, NDVI and RVI both exhibited strong negative correlations with maturity (Spearman’s correlation coefficients of −0.76 and −0.79, respectively). While the RF model performed best in the training set (R² = 0.752), it was prone to overfitting; in contrast, Multiple Linear Regression (MLR, Ridge Regression) with NDVI + RVI combination demonstrated greater stability in the test set (R² = 0.515, RMSE = 0.116). Notably, composite spectral indices consistently outperformed single indices across all modeling algorithms, but their accuracy was comparable to the optimal single index combination model. To tackle the challenge of scaling models from pot to field conditions, this research developed a “modeling–validation–evaluation–scaling” framework and a four-indicator combined judgment criterion (ΔR²–ΔRMSE–ΔRE–SF). Quantitative analysis showed that the optimal pot-scale model suffered significant accuracy loss during cross-scale transfer: ΔR² = 0.447, ΔRMSE = 0.120, ΔRE = 22.84%, and Scale Transfer Factor (SF) = 2.875. A “regional calibration + residual correction” scheme was proposed, which is expected to reduce the transferred RMSE to below 0.12 and SF to 1.8–2.0. Overall, this research offers a reliable technical method for large-scale, non-destructive monitoring of rice maturity, which can facilitate data-driven precision harvesting decisions. Full article

The management of Pig Slurry (PS) and Wine Vinasse (WV) poses major environmental and economic challenges, Anaerobic co-digestion (AcoD) offers a promising approach, producing both renewable energy and nutrient-rich digestates with agronomic potential. This study evaluated digestates obtained from the AcoD of a 50:50 mixture of pig slurry and wine vinasse under Temperature-Phased Anaerobic Digestion (TPAD) conditions. The acidogenic reactor reached stability at a hydraulic retention time (HRT) of 5 days, achieving 51.34 ± 3.08% of tCOD removal and approximately 0.5 L of daily green hydrogen production, whereas the methanogenic stage reached stability at an HRT of 10 days with 89.14 ± 2.33% tCOD removal and recording daily biomethane production of up to 1 L. Digestates were tested in germination assays using Lepidium sativum (garden cress), Lactuca sativa (lettuce), and Raphanus sativus (radish) seeds to assess phytotoxicity, and pathogen analyses were conducted to confirm sanitary safety (contains 0.8 × 10³ MPN/gTS E. coli). Results showed that agronomic performance was primarily influenced by dilution level, at 10D–15D% dilutions, germination and root growth remained stable, with Germination Index (GI) values above 80%. In contrast, concentrations above 25D% led to marked inhibition, with GI values below 50%. These findings demonstrate that the TPAD system operates effectively when treating pig slurry and winery vinasse, producing digestates that are safe and effective organic amendments. Moreover, given their compliance with sanitary standards, these digestates can be classified as Class A biosolids suitable for agricultural application, provided that adequate dilution is ensured. Full article

The proliferation of Internet of Things (IoT) devices and embedded processors has recently spurred rapid advances in hardware-level security. This paper systematically reviews developments in securing microcontroller units (MCUs) and constrained embedded platforms from 2020 to 2026, a period marked by the finalization of NIST’s post-quantum cryptography standards and accelerated commercial deployment of hardware security primitives. Through analysis of the peer-reviewed literature, industry implementations, and standardization efforts, we survey five critical areas: post-quantum cryptography (PQC) implementations on resource-constrained hardware, physically unclonable functions (PUFs) for device authentication, hardware Roots of Trust and secure boot mechanisms, side-channel attack mitigations, and Trusted Execution Environments (TEEs) for microcontroller-class devices. For each domain, we analyze technical mechanisms, deployment constraints (power, memory, cost), security guarantees, and commercial maturity. Our review distinguishes itself through its integration perspective, examining how these primitives must be composed to secure real-world embedded systems, and its emphasis on post-standardization PQC developments. We highlight critical gaps including PQC memory overhead challenges, ML-resistant PUF designs, and TEE developer friction, while documenting commercial progress such as PSA Level 3 certified components and 500+ million PUF-enabled devices deployed. This synthesis provides practitioners with practical guidance for securing the next generation of IoT and embedded systems. Full article

Knowledge graphs represent real-world facts as structured triplets and underpin a wide range of applications, including question answering, recommendation, and retrieval-augmented generation. Automatically extracting such triplets from unstructured text is essential for scalable knowledge base construction. Traditional extraction methods require task-specific training data and struggle to generalize across domains. Large language models (LLMs) offer an alternative through in-context learning, enabling flexible extraction without fine-tuning. However, LLMs frequently hallucinate—generating plausible triplets unsupported by the source text. The root cause is the lack of provenance: existing methods produce triplets without explicit links to their textual origins, making faithfulness unverifiable. This paper presents Anchor-Extraction-Verification-Supplement (AEVS), a framework that grounds every triplet element to the source text. AEVS operates in three stages: (1) anchor discovery identifies entities, relation phrases, and attribute values with precise positions, forming a constrained extraction vocabulary; (2) grounded extraction generates triplets linked to discovered anchors; and (3) restoration-based verification validates triplets through hierarchical matching, with a coverage-aware supplement ensuring comprehensive extraction. Experiments on WebNLG, REBEL, and Wiki-NRE demonstrate consistent improvements over both trained models and LLM-based baselines. Ablation studies confirm that anchor-based constraints are the primary mechanism for hallucination reduction. Dedicated analyses of anchor discovery quality, computational cost (2.83–4.28 LLM calls per sample), and hallucination rates (0.23–20.23% across model–dataset configurations) provide insights into the framework’s practical applicability and limitations. . Full article

The Normalized Difference Vegetation Index (NDVI) derived from polar-orbiting satellites is widely used for vegetation monitoring; however, its temporal continuity is often limited by cloud contamination and fixed revisit cycles. To address this limitation, this study investigates the feasibility of using geostationary satellite observations to enhance the spatial completeness of Sentinel-2 NDVI at its standard revisit intervals through cloud gap-filling applications. Geostationary Ocean Color Imager II (GOCI-II) data (250 m) was used as input, while Sentinel-2 Multispectral Instrument (MSI) NDVI (10 m) served as the reference dataset. To enable cross-sensor integration, a data-driven transformation framework was developed to convert GOCI-II NDVI into MSI-like NDVI while preserving dominant spatial variation patterns rather than pursuing strict pixel-level super-resolution. The transformed NDVI was assessed through spatial comparisons and statistical metrics, including correlation coefficient, mean absolute error, root mean square error (RMSE), normalized RMSE, and structural similarity index measure. Results show that geostationary-derived NDVI captures broad spatial organization and field-scale variability observed in MSI NDVI. Building on this cross-scale consistency, cloud gap-filling experiments demonstrate that temporally adjacent transformed NDVI scenes maintain consistent variation patterns, supporting their complementary use for compensating cloud-induced gaps. Although reduced contrast and magnitude-dependent biases remain, primarily due to the large spatial resolution difference and sub-pixel heterogeneity, an intermediate-resolution (80 m) sensitivity analysis indicates improved stability when the resolution gap is reduced. Overall, these findings highlight the practical potential of integrating geostationary and polar-orbiting observations to improve NDVI spatial continuity in cloud-prone regions. Full article

Aboveground biomass (AGB) is a key indicator of vegetation productivity and terrestrial carbon stocks; therefore, robust AGB estimation is critical for assessing ecosystem services and carbon cycle research. Previous studies have largely focused on forest and cropland ecosystems. In contrast, roadside vegetation along highways and other linear transport corridors remains comparatively underexplored despite its potentially important role as a carbon sink. Here, we integrate field-measured AGB samples with GF-2 high-resolution satellite imagery to evaluate the suitability of multiple remote-sensing predictors and machine-learning algorithms for estimating AGB in highway roadside vegetation. Six remote-sensing variables were used as predictors, including four vegetation indices (Normalized Difference Vegetation Index (NDVI), Perpendicular Vegetation Index (PVI), Enhanced Vegetation Index (EVI), and Modified Soil-Adjusted Vegetation Index (MSAVI) and two-band ratios (B342 and B12/34). Five regression models—multiple linear regression (MLR), partial least squares regression (PLSR), random forest (RF), support vector regression (SVR), and extreme gradient boosting (XGBoost)—were developed and systematically compared under both single-variable and multi-variable scenarios. Model performance was evaluated using five-fold cross-validation, with the coefficient of determination (R²) and the root mean square error (RMSE) as metrics of evaluation. The results indicate that the RF model under the multi-variable scenario achieved the best overall performance, with a training R² of 0.83 and a testing RMSE of 0.84 kg·m⁻², substantially outperforming the other linear and non-linear models. The optimal RF model was further applied to GF-2 imagery to produce a spatially explicit AGB map for a 32 km highway segment and a 30 m roadside buffer on both sides, yielding an estimated total aboveground biomass of 566.97 t for the corridor. These findings demonstrate that combining high-resolution remote sensing with machine-learning approaches can effectively improve AGB estimation for linear roadside vegetation systems, providing technical support for ecological monitoring, roadside greening management, and carbon accounting for transport infrastructure. Full article

Background: The attached gingiva (AG) is the portion of the gingiva firmly bound to the underlying alveolar bone and root cementum, rendering it immobile during functioning. Its dense connective tissue attachment contributes to resistance against mechanical challenges, stabilization of the gingival margin, and dissipation of forces transmitted from the alveolar mucosa. Histologically, AG is characterized by a keratinized epithelium supported by dense collagen fiber bundles, which provide structural integrity to the dento–gingival unit. Clinically, the buccal and lingual width of AG is estimated by subtracting sulcus depth from the total width of keratinized tissue. Although periodontal health may be maintained with minimal AG under optimal plaque control, substantial evidence supports its role in preserving gingival architecture and resisting mechanical trauma. Practical Application: From a clinical perspective, an adequate width of attached gingiva has traditionally been considered necessary to protect the periodontium; however, clinical situations may exist in which its dimension is reduced or absent. Available evidence suggests that a minimal width of approximately 1 mm of attached gingiva may be sufficient to maintain periodontal health under conditions of effective plaque control and absence of inflammation. Nevertheless, when only this minimal dimension is present, the attachment is predominantly derived from the junctional epithelium, which may offer limited mechanical protection to the dento–gingival unit. Within the limits of current evidence, a keratinized tissue width of approximately 3 mm appears to represent a functional threshold associated with increased connective tissue fiber density and enhanced resistance to mechanical trauma. Methods: A narrative review of classical and contemporary literature was conducted to evaluate the morphology, histology, function, and clinical relevance of the attached gingiva. Results: Evidence indicates that when AG width is minimal, reliance on junctional epithelial attachment alone provides limited resistance to mechanical challenges. In contrast, a greater width of AG incorporating connective tissue fiber attachment is associated with improved gingival margin stability, enhanced mechanical protection, and periodontal tissue resilience. Based on this synthesis, a tissue-based clinical categorization of AG is proposed. Conclusions: This review integrates current biological and clinical concepts regarding the functional dimensions of attached gingiva. The proposed categorization offers a practical framework to support clinical decision-making and to identify conditions in which surgical augmentation may be indicated for the management of mucogingival deficiencies. Full article

Armillaria solidipes, the causal agent of Armillaria root rot, poses a severe and persistent threat to poplar forest plantations. This study evaluated the biocontrol efficacy of the endophytic bacterium Bacillus velezensis BY6 against this pathogen and elucidated its multimodal mechanisms of action. BY6 application significantly reduced disease severity by 37.19% at 30 days post-treatment. 16S rRNA (V3–V4) microbiome analysis revealed that BY6 reshaped both the rhizosphere and phyllosphere bacterial communities, consistently enriching beneficial taxa, including Pantoea ananatis and members of Acidobacteria, while suppressing opportunistic groups. Concurrently, BY6 activated systemic defenses in poplar, evidenced by enhanced activities of key enzymes PAL and POD, and the upregulated expression of SA/JA pathway marker genes (PR1, JAZ, and COI1), coupled with the downregulation of the auxin transporter gene AUX1. These data indicate that the biocontrol efficacy of B. velezensis BY6 was mediated by a dual mechanism: the modulation of both rhizospheric and phyllospheric bacterial communities, direct elicitation of systemic defense pathways in poplar, which synergistically enhanced resistance against A. solidipes. Full article

Background: Lumbar spondylosis is a frequent cause of chronic low back pain, often associated with radiculopathy. Although imaging evaluation is widely used, it does not always reflect the degree of functional impairment of the nerve roots. Electrophysiological assessments, such as nerve conduction studies (NCS) and surface electromyography (sEMG), can provide additional information on neuromuscular function under conservative treatment. Methods: This quasi-experimental study included 60 patients with lumbar spondylosis and 25 healthy subjects, who underwent clinical, imaging, and electrophysiological assessments. NCS and sEMG parameters were assessed in the patient group before and six months after rehabilitation treatment. The control group was assessed only once, at baseline. We analyzed the nerve conduction velocity of the tibial and peroneal nerves and the sEMG activity of the tibialis anterior muscle bilaterally. Statistical analysis used nonparametric tests, Spearman’s coefficient, and Hodges–Lehmann estimates. Results: Compared to the control group, patients presented increased residual latencies and reduced CMAP amplitude and motor conduction velocity values (p < 0.001). After rehabilitation treatment, significant improvements in NCS parameters were observed, with decreased latencies and increased CMAP amplitude and motor conduction velocity bilaterally (p < 0.001). Also, sEMG amplitude and recruitment pattern scores increased significantly at the 6-month follow-up (p ≤ 0.004). Correlations between electrophysiological parameters and the severity of imaging changes were limited, with modest associations for left tibial latencies (ρ = 0.401–0.467; p < 0.050). Conclusions: In patients with lumbar spondylosis, rehabilitation treatment was associated with functional improvements in nerve conduction velocity parameters and muscle activity. Full article

Silicon (Si) serves as a beneficial element that enhances plant resistance to both abiotic and biotic stresses. Although its positive effects have been widely investigated, the molecular mechanisms by which silicon improves stress tolerance in rice (Oryza sativa L.) remain unclear. Here, we show that Si displayed an optimal improved effect at concentrations of 2–4 mM in hydroponic system, and Si enhanced rice tolerance to drought and blast disease by maintaining reactive oxygen species (ROS) homeostasis and reducing root cell damage. In addition, Si at 4 mM upregulated the ABA biosynthesis gene OsNCED3, stress- and ABA-responsive genes OsDREB2A and OsLEA5, as well as the catalase gene OsCatB, while suppressing the drought-responsive negative regulator OsWRKY5, thereby enhancing drought tolerance through an ABA-dependent signaling pathway. Si at 4 mM enhanced resistance to rice blast by activating defense-related genes OsPBZ1, OsPR10a, OsPR5 and OsWRKY45 while simultaneously boosting ROS-scavenging capacity. Collectively, our results demonstrate that Si enhances rice tolerance to drought and blast disease through the coordinated modulation of ABA signaling, ROS homeostasis, and stress-related gene expression. Full article

The growing need for sustainable and lightweight building materials has accelerated research on alternatives to conventional concretes, out of which Lightweight Expanded Clay Aggregate (LECA) concrete has emerged as a promising solution. However, the high porosity and nonlinear mechanical behavior of LECA concrete complicate the accurate prediction of compressive strength through conventional empirical models. The main focus of the paper is on identifying a comprehensive machine learning-based framework for modeling and predicting the 28-day compressive strength of LECA-based lightweight concrete. The dataset was created and preprocessed by using statistical normalization and correlation analysis. In this study, five supervised machine learning models—Multiple Linear Regression (MLR), Support Vector Regression (SVR), Random Forest (RF), eXtreme Gradient Boosting (XGBoost), and Categorical Boosting (CatBoost)—were developed and fine-tuned using a grid-search strategy combined with ten-fold cross-validation. The quality of the prediction made by each model was evaluated by means of standard performance indicators, such as the coefficient of determination (R²), root mean square error (RMSE), mean absolute error (MAE), and mean absolute percentage error (MAPE). After the evaluation, the models were subsequently compared and ranked according to the Gray Relational Analysis (GRA) method. The comparative assessment shows that CatBoost demonstrated the most reliable performance, achieving an R² of 0.907, RMSE of 3.41 MPa, MAE of 2.47 MPa, and MAPE of 10.05%, outperforming the remaining algorithms. To interpret the significance of features, SHAP (Shapley Additive exPlanations) analysis was applied, which identified water and LECA content as the dominant factors influencing compressive strength, followed by the cement and fine aggregate proportions. The findings reveal that the ensemble-based gradient boosting model is capable of capturing intricate nonlinear interactions, as observed in the heterogeneous matrix of LECA concrete. Full article

It has been well established that exogenous melatonin (MT) improves storage quality of many agricultural products. However, contrasting results have been reported in the regulation of MT with respect to several postharvest parameters, e.g., germination/sprouting and lignification, indicating that roles of MT may vary with plant species or storage environment. Previous studies mainly focus on above-ground organs including fruits, leaves, seedlings and flowers without addressing underground organs such as the storage root (SR) of sweetpotato (Ipomoea batatas). This study showed that spraying 0.5 mM MT solution improved postharvest quality of sweetpotato SRs during 40 d of storage at 15 °C. First, MT treatment inhibited SR sprouting by differentially regulating the content of germination-related hormones, i.e., increasing the content of ABA and JA but decreasing GA content. Second, MT reduced weight loss and lignification by inhibiting respiration as reflected by decreased respiration rate and hexose kinase activity. Third, MT treatment increased soluble sugar content by elevating the activity and expression of sucrose synthase (Sus) since the activities and expressions of invertases (CWIN, CIN and VIN) were inhibited by MT. Simultaneously, inhibited respiration by MT also contributed to increased content of soluble sugar by reducing their expenditure via glycolysis. Additionally, MT increased starch content by inhibiting β-amylase activity and possibly also by increasing Sus activity, which provides a substrate for starch biosynthesis. Finally, MT upregulated the activities of SOD, POD and CAT, which may improve storage quality of SRs by inhibiting senescence and lignification. This study provides an alternative option to maintain the storage quality of sweetpotato. Full article

Continuous cultivation in solar greenhouses degrades black soil, leading to soil-borne diseases, nutrient imbalances, reduced porosity, and microbial dysbiosis, all of which collectively decrease crop productivity. Improving soil structure and microbial balance often requires costly amendments that are inconsistent in their effectiveness. This study evaluated two low-cost soil amendments—carbonized rice hull (CRH) and fermented rice hull (FRH)—using colored pepper as a model crop. Treatments included soil mixed with 30% CRH (T1), 30% FRH (T2), and untreated black soil (CK). Both amendments significantly improved soil physical properties. Compared with CK, soil porosity increased by 8.80% in T1 and 17.84% in T2, while water-holding capacity increased by 75.32% and 133.45%, respectively. Soil microbial richness, as indicated by Abundance-based Coverage Estimator (ACE) and Chao indices, followed the order T2 > T1 > CK. Plant physiological performance was also enhanced. Net photosynthetic rate increased by 7.18% (T1) and 15.33% (T2), plant height increased by 14.42% (T1) and 28.85% (T2), and root activity improved significantly. Fruit weight increased by 15.33% in T1 and 21.62% in T2. Both rice hull amendments improved soil quality and promoted crop growth, with FRH performing consistently better. These findings indicate that fermented rice hull is a promising, low-cost strategy for greenhouse soil remediation. Full article

Apple replant disease (ARD) is a soil-borne disease that severely restricts root development in orchards, impedes tree growth, and leads to reduced yields and decreased fruit quality, and thus significant economic losses. Previous studies identified Fusarium oxysporum as a major pathogenic agent. In this study, a T-DNA insertion mutant library of 13,000 F. oxysporum HS2 strains was utilized to screen for mutants with impaired pathogenicity. Nine mutants exhibiting reduced virulence were obtained, and the insertion sites of five mutants were successfully identified. Among them, we selected the HS2-29 strain, which exhibited the most significant decrease in conidial production, for further investigation. Its T-DNA was inserted into the FoPLT gene. RT-qPCR analysis revealed that the expression of the FoPLT gene rapidly increased during the early infection stage, followed by a decline and eventual stabilization. After the deletion of the FoPLT gene, the production of aerial hyphae, conidial yield, conidial length, and conidial diameter all significantly decreased. Stress tolerance assays indicated that FoPLT does not affect cell wall integrity in F. oxysporum. The deletion of the FoPLT gene significantly reduced the pathogenicity of F. oxysporum, and inoculating Malus robusta seedlings with the FoPLT knockout mutant led to significant increases in plant height, root length, fresh weight, and dry weight. These results suggest that the FoPLT gene plays a critical role in the pathogenicity of F. oxysporum. Full article