Search Results (369)

Vegetative restoration in degraded landscapes requires scalable deployment strategies capable of adapting to heterogeneous terrain conditions. Conventional aerial seeding methods typically operate in open-loop mode, distributing seeds uniformly without considering terrain suitability. This study presents a modular, vision-guided unmanned aerial vehicle (UAV) system for real-time adaptive seed deployment based on the closed-loop integration of onboard perception and actuation under embedded computational constraints. The proposed system combines RGB-based terrain classification, embedded processing, and altitude-adaptive seed dispensing within a unified perception–decision–actuation framework, enabling selective and context-aware seed deployment during flight. Terrain suitability is evaluated onboard using three convolutional neural network (CNN) models and a color-based baseline to distinguish sowable and non-sowable areas. A confidence-based decision strategy with temporal filtering improves reliability, while an altitude-adaptive control mechanism regulates seed distribution across varying flight heights. Field experiments conducted in semi-arid environments demonstrate classification accuracy above 85% with inference latency below 100 ms on a Jetson Nano platform. Additional offline evaluation under varying altitude, speed, illumination, and terrain conditions confirms the robustness of the perception module. The results demonstrate the feasibility of integrating real-time perception with adaptive actuation, enabling UAVs to transition from passive sensing platforms to active agents for environmental intervention. The proposed system provides a practical and scalable approach for precision vegetative restoration in heterogeneous environments. Full article

The spread of unverified health claims about drugs, dietary supplements, and alternative remedies on social media poses a growing public health concern. In this study, we present a retrieval-augmented generation (RAG) pipeline that uses large language models (LLMs) grounded in biomedical evidence from PubMed, openFDA adverse event reports, and NIH/NCCIH dietary supplement fact sheets to detect and classify health product misinformation. A total of 3493 health-related posts were collected from Reddit (948 posts across 12 subreddits) and YouTube (2545 video descriptions and comments), from which 8250 structured claims were extracted using Claude Haiku. Each claim was matched to biomedical evidence from three authoritative sources, achieving 79.4% evidence coverage, and classified into one of five veracity categories: supported (7.0%), unsupported (59.9%), exaggerated (22.4%), contradicted (2.0%), or dangerous (8.6%), together with an associated risk tier. Overall, 13.5% of claims were assigned high or critical risk. Cross-platform analysis showed that YouTube contained higher proportions of dangerous (11.3% vs. 2.9%) and exaggerated (27.0% vs. 12.4%) claims than Reddit. Compared with keyword-based and zero-shot transformer baselines, the LLM+RAG pipeline produced a more balanced and fine-grained classification of unsupported, exaggerated, contradicted, and dangerous claims. The most frequently implicated products were ashwagandha, kratom, black seed oil, turmeric, and ivermectin, with disease cure claims showing the highest dangerous classification rate (30.1%). These model-assigned results suggest that evidence-grounded LLM pipelines can support health misinformation surveillance, while also highlighting the need for expert validation and broader cross-platform evaluation. Full article

The transition of coal regions under the European Green Deal and Just Transition Fund creates a need for quantitative, transparent monitoring of ecological recovery on post-mining land. This study presents an autonomous UAV-based methodology for high-resolution monitoring of vegetation dynamics on a reclaimed coal waste heap in Upper Silesia, Poland. A DJI Mavic 3 Multispectral platform with RTK positioning conducted approximately biweekly flights from August 2024 to October 2025 over three study plots acquiring RGB and multispectral imagery at approximately 4 cm/pixel. Photogrammetric processing in DJI Terra produced radiometrically corrected orthomosaics and NDVI maps, which were analyzed using an automated QGIS workflow for reprojection, clipping, NDVI-based classification, and quantification of vegetation area across three different reclamation variants. The results indicate that intensive soil conditioning through the application of compost derived from bio-waste achieved a maximum vegetation cover of 94.4%. This treatment consistently maintained the highest level of cover during periods of environmental stress and significantly surpassed both seeding-only treatments and those combining seeding with irrigation. Baseline vegetation cover below 6% confirmed the necessity of active reclamation. This workflow provides rapid and reproducible metrics that are suitable for adaptive management and regulatory reporting. It also offers a scalable template for monitoring coal waste heaps across Europe undergoing SDG-aligned reclamation. Full article

Indole-3-acetic acid (IAA) is the main natural auxin and a key regulator of plant growth. However, most commercial auxins are synthetically produced from non-renewable resources. Here, we present a minimal synthetic biology platform for microbial IAA production that also serves as a teaching model for genetic circuit design and bioprocess development. We developed codon-optimized versions of the iaaM and iaaH genes, which encode tryptophan 2-monooxygenase and indole-3-acetamide hydrolase, and assembled them into a compact expression cassette in Escherichia coli TOP10. Correct expression of both enzymes was confirmed by SDS-PAGE. The engineered strain was cultivated in a low-cost medium made from avocado seed hydrolysate, an agro-industrial waste, supplemented with tryptophan as a precursor. IAA was quantified using the Salkowski colorimetric assay and further validated by HPLC, reaching approximately 303–313 µg/mL at 48 h, with the medium costing approximately fivefold cheaper locally than traditional LB. The supernatants containing biosynthetic IAA induced root formation in 100% of tobacco leaf explants, outperforming the commercial standard at the same concentration and confirming biological activity. Since this workflow follows the Design–Build–Test–Learn (DBTL) cycle, Design (pathway selection and codon optimization), Build (plasmid assembly), Test (protein expression, metabolite quantification, plant bioassays), and Learn (medium and process optimization), it provides a sustainable production method and an accessible educational platform for synthetic biology. Full article

Mobile robot navigation remains challenging when fast convergence, collision avoidance and deployability must be satisfied simultaneously. The original Q-learning with Artificial Potential Field (QAPF) paradigm is extended in this paper with three coordinated mechanisms that together yield a reported-horizon convergence reduction of approximately four orders of magnitude (from $\sim 3\times {10}^6$ episodes to $\sim 200$ to 230 episodes under the present protocol) and an internal-ablation collision-rate reduction of approximately one order of magnitude ($6.2\%$ to $0.3\%$), and that open a new capability frontier covering dynamic obstacles, multi-robot coordination, energy-aware velocity modulation and embedded-deployable inference timing. The first mechanism is a potential-based reward-shaping schedule whose unclipped fixed-weight form follows the policy-invariant shaping theorem, while the implemented clipped and time-varying form is used as an empirically stable approximation. Under the present experimental protocol, the reported convergence horizon is reduced from the $\sim 3\times {10}^6$ episodes reported for the original QAPF formulation to approximately 200 to 230 episodes; this comparison is protocol-dependent and is not claimed as a controlled one-to-one runtime speedup. The second mechanism is a discrete Control Barrier Function (CBF)-inspired action filter (thediscrete filter described in this paper is inspired by the continuous-time CBF literature, but does not carry a forward-invariance proof; it is used as an empirical safety mechanism rather than as a formal Control Barrier Function in the formal continuous-time sense) with per episode visit memory by which the held-out collision rate is reduced from $6.2\%$ for QAPF alone to $0.3\%$ while $93.8\%$ task completion is maintained, where this collision-rate comparison is internal to the QAPF ablation because the prior QAPF reference does not report a comparable held-out collision metric. The third mechanism is a set of extensions to dynamic obstacles, two-robot cooperative navigation under a centralized scheme (with an explicit $\mathcal{O}(N^2)$ scaling-cost analysis and three decentralization strategies for fleets beyond the small-N regime), curriculum learning and energy-aware velocity modulation. Disturbance robustness tests, empirical timeout/stagnation detection for unreachable-goal cases, i7 reference inference timing with projected embedded-device latencies, multi-axis generalization over obstacle density and grid size, scalability analysis for centralized multi-robot coordination and a scope comparison against A* and RRT* are added by the revised evaluation. Across 30 independent seeds on held-out static maps, $94.5\pm 2.1\%$ success is achieved by adaptive QAPF while $93.8\pm 2.3\%$ success with $0.3\pm 0.4\%$ collisions is achieved by QAPF+CBF. Under a separate finite robustness suite, $85.0\pm 4.1\%$ success is retained by QAPF+CBF in the combined disturbance regime. The timing study indicates that the 20 Hz real-time threshold is comfortably exceeded by all methods on the measured i7 reference platform and by all projected embedded-device equivalents. The results show that a lightweight and safety-oriented navigation policy for grid-based mobile-robot settings can be provided by APF-guided tabular reinforcement learning when it is paired with a discrete safety filter and a clarified energy and robustness analysis. Full article

Deploying post-quantum cryptography on highly constrained devices remains challenging due to the large key sizes and substantial storage and memory-traffic demands of leading lattice-based schemes. Although constructions such as Kyber, Dilithium, and NTRU offer strong resistance against quantum adversaries, their multi-kilobyte public keys and intensive memory access patterns limit practical adoption in microcontrollers, smart cards, and low-power edge environments. This work proposes a hybrid key-encapsulation mechanism that integrates a compact, seed-generated Module-LWE structure with a quantum-secure hash-based authentication layer. The design employs a small public seed to instantiate lattice matrices on demand via a lightweight pseudorandom generator and incorporates a Merkle-tree commitment to represent compressed auxiliary error information. Additional design considerations—including sparsity-aware secret keys, SIMD-friendly polynomial operations, and cache-efficient decryption paths—are intended to reduce runtime memory usage and computational overhead. The security of the proposed construction is analysed under both Module-LWE and hash-based one-way assumptions, with further consideration of constant-time execution and cache-line alignment to mitigate side-channel risks. This hybrid approach outlines a design pathway toward post-quantum key-encapsulation mechanisms suitable for deployment on memory-limited and energy-constrained platforms. Full article

This study evaluated whether three-dimensional alginate–gelatin hydrogels (AGHs) crosslinked with calcium chloride (CaCl₂) enhance the osteo-odontogenic differentiation of odontoblast-like cells in vitro. Two seeding configurations were compared: inter-hydrogel (INT) surface seeding and intra-hydrogel (INTR) encapsulation. Here, the MDPC-23 cells were cultured in AGHs crosslinked with 70 or 100 mM CaCl₂ and assessed for proliferation, cytoskeletal morphology, alkaline phosphatase (ALPase) activity, osteo-odontogenic gene expression, and mineralized nodule formation. After 7 days, cell proliferation was significantly greater in the alginate–gelatin hydrogel (AGH) groups than in the control group. Cells in the intra alginate–gelatin hydrogel 100 (INTR-AGH100) remained predominantly rounded, whereas those in the inter alginate–gelatin hydrogel 100 (INT-AGH100) formed irregular clusters on the hydrogel surface. ALPase activity was highest in INTR-AGH100 at the early stage of culture. Both INT-AGH100 and INTR-AGH100 showed significantly increased expression of DSPP, DMP-1, BSP, OCN, OPN, and Runx-2, together with enhanced mineralized nodule formation. Although no significant differences were detected between the two seeding strategies in all assays, distinct morphological patterns were observed, and the INTR configuration showed relatively greater early differentiation-related activity. These findings suggest that 100 mM CaCl₂-crosslinked AGHs provide a favorable three-dimensional microenvironment under the present experimental conditions and represent a promising in vitro scaffold platform to support future studies of scaffold-guided dentin regeneration. Full article

The application of autonomous robotics in food production is still in its infancy but bears significant potential, especially in the sector of precision farming. Here we present the results of a survey conducted during the years 2022–2023 with local farmers in Northern Germany to identify the challenges that are being faced by both conventional and organic farming practices due to climate change and increased food production regulations in the EU region. Additionally, a pilot study with a medium-sized (Demeter) farm is presented, identifying the real needs and problems organic farmers face during their food production chain from seeding, weeding, and maintaining the equipment till bringing in the harvest. The results indicate a strong demand for modular, autonomous, and digitized solutions to address key challenges in agricultural production. To address these challenges we propose a novel mechanical robotic platform design that has been developed in accordance with the Fab City principles of open and local production resulting in a low-cost open-source solution. Full article

Naked oat (Avena nuda L.) is an important dual-purpose crop for grain and forage in cold regions; however, its high fatty acid content renders seeds prone to deterioration during storage. This study aimed to investigate the protective effects of zinc oxide nanoparticles (ZnO NPs) on artificially aged naked oat seeds and elucidate the underlying molecular mechanisms. Non-aged seeds (Naged) were subjected to artificial aging at 45 °C and 100% relative humidity for 24 h (Aged), followed by priming with 30 mg L⁻¹ ZnO NPs for 6 h (Daged). Antioxidant enzyme activities were determined spectrophotometrically, and transcriptome sequencing was performed on an Illumina platform to identify differentially expressed genes (DEGs) and enriched pathways. We found that ZnO NPs increased catalase (CAT), peroxidase (POD) and superoxide dismutase (SOD) activities by 3–4-fold, restored germination rate from 75% to 98%, and enhanced seed vigor index. A total of 21,403 DEGs were detected, with 15,841 stably expressed in response to nano-priming. Reactive oxygen species (ROS) burst rapidly induced up-regulation of AP2/EREBP transcription factor family members, which subsequently activated antioxidant enzyme genes to maintain cellular redox homeostasis. Metabolic pathway analysis demonstrated that the phenylpropanoid pathway was reprogrammed, characterized by down-regulated lignin biosynthesis and up-regulated flavonoid production, thereby enhancing ROS scavenging capacity. Additionally, the pentose phosphate pathway was activated to provide additional NADPH for antioxidant defense, and up-regulated ADP-glucose pyrophosphorylase (AGPase) facilitated starch accumulation. Notably, the 40S ribosomal protein S13 exhibited the highest connectivity in protein–protein interaction networks, was up-regulated 2.1-fold, and was enriched in post-translational modification processes. These findings suggest that nano-priming with ZnO NPs represents a promising biotechnological strategy for enhancing seed vigor and storability in naked oat, with potential applications in sustainable agriculture and the seed industry. Full article

This paper presents an engineering framework for smart grid virtual power plant (VPP) day-ahead scheduling using fractional calculus-enhanced particle swarm optimization, targeting practical deployment in energy management systems. A fractional calculus-enhanced particle swarm optimization algorithm was developed and validated for day-ahead scheduling in virtual power plants using authentic market data and rigorous statistical analysis. The algorithm incorporates Grünwald–Letnikov fractional derivatives with adaptive memory into particle velocity updates, enabling trajectory-aware search that leverages historical exploration patterns. A factorial experiment across 500 independent test cases (50 dates × 10 trials) with controlled random seeds demonstrated that fractional particle swarm optimization increased mean daily profit by $205, representing a 4.1% improvement over standard particle swarm optimization. Wilcoxon signed-rank tests confirmed statistical significance (p < 0.0001, Cohen’s d = 1.08), with superior performance observed in 89.4% of cases. The factorial design identified fractional calculus as the primary performance driver, while advanced scenario generation provided no significant additional benefit. Sensitivity analysis indicated that wind generation variability was the primary predictor of performance variance, with profit difference standard deviations ranging from $34 to $325 depending on meteorological conditions, supporting the use of adaptive computational strategies. Computation required approximately two minutes per optimization on standard hardware. These findings establish fractional calculus as a credible enhancement for operational energy systems and demonstrate that the quality of optimization algorithms outweighs the complexity of forecast uncertainty modeling. The results extend fractional calculus applications from benchmark functions to practical infrastructure scheduling, with projected annual value exceeding $74,000 for a 50-megawatt system. The three-stage optimization architecture is designed for integration with standard energy management systems and SCADA platforms, offering a deployable pathway for smart grid operators. Full article

Peanut planting presents unique challenges due to the large, fragile, and irregular seed and the sensitivity of seed metering systems to operating conditions. Field experiments were conducted between 2022 and 2025 in Georgia to evaluate how planting speed, row-unit downforce, vacuum setting, and planter platform influence peanut stand establishment, final within-row plant distribution, and yield in single-row planting systems. Trials included speed × downforce evaluations using an electric seed meter and planter-platform × speed × planter-specific vacuum comparisons involving ground-driven, hydraulic-driven, and electric-driven seed meters. Achieved population was determined from post-emergence stand counts, plant distribution was evaluated using emerged-plant position classification relative to theoretical plant spacing, and yield was measured at harvest. Across site years, achieved population patterns were consistently associated with planting speed and vacuum setting, whereas downforce effects were minor and inconsistent within site years. Higher achieved populations were generally obtained at 5 km h⁻¹ and at higher planter-specific vacuum settings, especially for the ground-driven planter. Hydraulic- and electric-driven planter platforms were less sensitive to changes in speed and vacuum and more often maintained acceptable stands at 8 km h⁻¹. Despite large differences in achieved population and plant distribution, peanut yield was often not significantly reduced until stand loss became severe, indicating substantial yield compensation. Spacing uniformity remained poor across all treatments, with skips and long skips common regardless of planter platform. These results indicate that peanut planting performance in current single-row systems is constrained primarily by seed singulation rather than downforce, and that hydraulic- and electric-driven planter platforms improve operational flexibility more consistently than yield. Full article

Background/Objectives: Poly(ADP-ribose) polymerase 1 (PARP1) is an important therapeutic target in DNA repair-deficient cancers, but discovery of new inhibitors remains constrained by scaffold convergence, tolerability limits, and acquired resistance. This study aimed to develop an interpretable, reliability-stratified cheminformatics workflow for PARP1 potency prioritization and structure-based follow-up. Methods: A curated ChEMBL dataset of 3339 PARP1 inhibitors was encoded using RDKit 2D descriptors and Avalon fingerprints (1143 initial features), then reduced to 132 informative variables by Random Forest-based feature selection. Five regression models were optimized, including a stacked ensemble. Model interpretation was performed using permutation feature importance and SHAP. External near-domain corroboration was assessed using a stringent PubChem similarity expansion (Tanimoto > 0.90) around sub-10 nM seed compounds, followed by comparison with retrievable experimental PARP1 activity values. Top scaffold-diverse candidates were further evaluated by complementary docking against PARP1 (PDB: 4R6E) using AutoDock Vina and cavity-guided docking through the SwissDock platform. Results: The stacked ensemble achieved the best held-out performance (test R² = 0.723; RMSE = 0.610 pIC₅₀ units), with 83.7% of test predictions within ≤0.75 pIC₅₀ units and only 2.7% exceeding 1.5 pIC₅₀ units. PubChem similarity expansion retrieved approximately 32,450 analogs, of which 3349 were predicted to have IC₅₀ ≤ 10 nM. Among 366 compounds with retrievable experimental PARP1 activity values, predicted versus experimental pIC₅₀ showed a positive association (R² = 0.124; Pearson r = 0.479), with RMSE = 0.491 and MAE = 0.330 pIC₅₀ units. Three ligands—CID 168873053, CID 175154210, and CID 172894737—showed the strongest complementary docking support and pocket-consistent poses relative to niraparib. Conclusions: This workflow provides a transparent and practically useful framework for near-domain PARP1 inhibitor prioritization. The combined QSAR, explainability, external corroboration, and docking strategy supports shortlist generation for experimental follow-up. Full article

Glioblastoma (GBM) is one of the most aggressive and therapy-resistant primary brain tumors, mainly due to its pronounced intratumoral heterogeneity and highly invasive phenotype. Patient-derived three-dimensional (3D) culture models, including tumor spheroids, represent valuable tools to preserve the cellular complexity, phenotypic plasticity, and microenvironmental features of GBM ex vivo. However, standardized and reproducible protocols for the generation and maintenance of GBM spheroids from surgical specimens are still limited. Here, we describe a detailed and robust protocol for the isolation, 3D cultures, and expansion of primary GBM cells obtained from patient biopsies, leading to the formation of stable and morphologically consistent spheroids. The protocol provides step-by-step instructions for tissue dissociation, cell seeding under low-adhesion conditions, optimization of culture density, and long-term spheroid maintenance. In addition, we include guidelines for the morpho-phenotypical characterization of the resulting 3D structures. This methodological workflow offers a reproducible platform for modeling GBM in vitro, enabling the study of tumor biology and supporting translational applications such as drug screening, biomarker validation, and patient-specific therapeutic testing in a 3D context. Full article

Social media platforms contain rich but noisy narratives of psychological distress, creating opportunities for early mental health risk detection. However, existing datasets capture heterogeneous constructs such as suicide risk severity, depression diagnosis, and DSM-5 symptom presence, and most prior models are trained and evaluated on a single corpus, limiting their clinical alignment and cross-dataset generalizability. In this study, we fine-tune a domain-specific long-document transformer, AIMH/Mental-Longformer-base-4096, for binary mental health risk detection (risk vs. no risk) using two clinically aligned Reddit datasets: the C-SSRS Reddit corpus and the eRisk 2025 depression dataset. To handle long user histories, we introduce an LLM-based summarization pipeline that compresses posts exceeding 2000 tokens while preserving mental health-relevant information. We also conduct a seven-configuration ablation study across combinations of three corpora (C-SSRS, eRisk, and ReDSM5) to examine how dataset semantics influence model performance. On a held-out C-SSRS + eRisk test set (n = 279), the proposed model achieves a mean balanced accuracy of 0.89 ± 0.01 across five random seeds, with a best run of 0.90 and a 5.74 percentage point improvement over the strongest baseline (TF-IDF + Random Forest). The model also shows strong cross-platform generalization, achieving BA = 0.78 on the depression-reddit-cleaned dataset (n = 7731) and BA = 0.85 (ROC-AUC = 0.92) on a Twitter suicidal-intention dataset (n = 9119) without additional fine-tuning. The ablation analysis shows that although a three-dataset configuration (C-SSRS + eRisk + ReDSM5) maximizes aggregate performance, the ReDSM5 labels encode symptom presence rather than clinical risk, creating a semantic mismatch. This finding highlights the importance of label compatibility when combining heterogeneous mental health corpora. Explainability analysis using Integrated Gradients and attention visualization shows that the model focuses on clinically meaningful expressions such as therapy references, diagnosis, and hopelessness rather than isolated keywords. These results demonstrate that clinically aligned long-context transformers can provide accurate and interpretable mental health risk detection from social media while emphasizing the critical role of dataset semantics in multi-corpus training. Full article

Background: Breast cancer brain metastases (BCBM) lack effective treatments, contributing to breast cancer-related morbidity and mortality. Integrating translational animal models and advanced non-invasive imaging can accelerate the development of urgently needed therapies. Method: In this study, we developed an intracarotid method mimicking BCBM and compared it to the stereotactic model in terms of animal welfare, tumour establishment, and blood–brain barrier (BBB) permeability. BCBM was established through intracarotid or stereotactic inoculation of BT474 and MDA-MB-231.Luc2 cells in NMRI nude mice. We utilised magnetic resonance imaging (MRI) and bioluminescence imaging (BLI) to monitor tumour growth and BBB permeability, supported by fluorescent immunohistochemistry for validation. Finally, light sheet microscopy (LSM) was employed to visualise tumour establishment in intact brains. Results: Both inoculation methods achieved a survival rate > 70%, with animals recovering within a week post-surgery. MRI and BLI effectively visualised tumour growth with stereotactic implantation, resulting in single tumours, while intracarotid inoculation led to micro-seeding of up to seven tumours in one brain. Tumour growth was rapid and homogenous in the stereotactic model, whereas the intracarotid model exhibited slower, heterogenous growth. Notably, BBB permeability was significantly higher in small tumours in the stereotactic model when compared to the intracarotid model (p = 0.003). Ex vivo analyses validated these findings with the identification of multiple metastasis in the intracarotid model and single tumours in the stereotactic model. Conclusions: We developed an animal model that closely mimics BCBM, highlighting extravasation and micro-seeding while maintaining animal welfare. Our established imaging protocols enable longitudinal evaluations of BBB permeability and treatment response, creating a translational platform for testing novel anti-cancer therapies. Full article