Search Results (719)

To address seed contamination, narrow-row mechanized cutting difficulties, and potential damage to maternal plants in muddy paddy fields during hybrid rice seed production, a walk-behind self-propelled hybrid rice male parent pulverizing and cutting machine was designed. The machine primarily consists of three key structures: a key cutting device, a gravity-free crop dividing device, and a crawler walking mechanism. The cutting device features an innovative mechanism where main-shaft rotation drives flail blades into inertial autorotation, while a stopper bar physically constrains their maximum swing amplitude to guarantee a 500 mm working width. Crucially, the gravity-free crop dividing device safely pushes aside adjacent maternal plants to effectively prevent accidental mechanical injury. A flexible plant model and a kinematic model were established using DEM software EDEM 2024. A three-factor, three-level orthogonal experiment indicated that the primary order of influence on the male parent cutting rate is forward speed > flail-blade rotational speed > blade arrangement. The optimal simulation parameters were a 0.4 m/s forward speed, a 1700 r/min blade rotational speed, and a straight–curved blade arrangement, yielding a simulated cutting rate of 97.60%. Furthermore, field tests demonstrated that under these optimal parameters, influenced by complex paddy conditions and natural plant lodging, the actual average cutting rate was 91.39%. The machine exhibited excellent passability and pulverizing performance, thoroughly satisfying the requirements of agronomic and agricultural machinery integration. Full article

Antenna selection is investigated as a means of reducing radio-frequency (RF) chain power in massive multiple-input multiple-output (MIMO) base stations under realistic channel state information (CSI) impairments. The study is motivated by the mismatch between conventional selection objectives and multi-cell operation with estimation error, pilot contamination, spatial correlation and inter-cell interference. APCS-Boost-R is introduced as the primary contribution. An interference-whitened D-optimal seed is combined with projected rank-one exchanges and a calibrated surrogate that incorporates a user-side interference-plus-noise report and a closed-form estimation-error correction. APCS-Boost-RG is retained as an optional graph neural network (GNN) refinement in which residual exchanges are ranked after the algebraic solution has been formed, while feasibility and non-degradation of the calibrated surrogate are verified deterministically. In a three-cell urban macro configuration derived from Third Generation Partnership Project (3GPP) TR 38.901 with 64 antennas, 16 active RF chains and eight users per cell, APCS-Boost-R achieves 19.364 bit/s/Hz over 200 paired realizations. Improvements of 2.58 percent over APCS-Boost, 6.76 percent over greedy search and 10.16 percent over a genetic algorithm are obtained. APCS-Boost-RG adds 0.019 bit/s/Hz but is treated as an optional refinement because it requires a second-stage neighborhood evaluation and offline model maintenance. In the archived common timing record, APCS-Boost-R requires 20.376 ms per three-cell realization, compared with 12.728 ms for APCS-Boost, 57.775 ms for norm-initialized greedy search and 41.302 ms for the genetic algorithm, while APCS-Boost-RG requires 24.0 ms versus 20.4 ms for APCS-Boost-R in the separate archived learned-stage record. Separate reconstructions on the documented reproducibility host require $55.3\pm 14.5$ ms for APCS-Boost-R and $592.2\pm 181.9$ ms for a complete APCS-Boost-RG rebuild. Additional paired examinations confirm robustness across stronger search budgets, report imperfections, regularized precoding, coordination, near-field sensitivity, hardware perturbations, and configurations ranging from 32 to 128 antennas and one to seven cells. Full article

Federated, semi-supervised novelty detection is well suited for intrusion detection on resource-constrained Internet of Things (IoT) nodes: each device learns a model of benign traffic, shares only summary statistics, and does not transmit raw traffic samples. A previously published cross-layer federated detector, Baseline K-Means, showed that periodically merging worker statistics through a coordinator raises the detection rate, but it also exhibited a systematic side effect: after every merge, the precision decays, and the false-positive rate (FPR) climbs because the coordinator recomputes its threshold from streaming distances filtered by the closest observed anomaly, so tightens after every merge, flagging progressively more benign traffic; the threshold was also hand-tuned. We present AF-BKM, an Adaptive Federated Baseline K-Means that repairs the federated mechanism with two label-free, statistics-only enhancements, denoted as E1 and E2: (i) an adaptive decision threshold read from the benign Mahalanobis-distance distribution, requiring no manual percentile search and no attack labels (E1), and (ii) a robust, benignly anchored aggregation that blends worker means under quality weighting and outlier-worker filtering and recalibrates the threshold on a trusted benign anchor to a stable, anchor-referenced false-positive level, which a target-FPR rule can make operator-selectable instead of tightening it toward the nearest anomaly (E2). With MinMax scaling fit only on benign baseline data and non-IID federated streams on NSL-KDD, UNSW-NB15 and the N-BaIoT corpus of real traffic from commercial IoT devices, AF-BKM removes the merge-induced precision decay (the first-to-last-epoch precision change improves from $-0.134$ to $-0.002$ on NSL-KDD, from $-0.121$ to $-0.014$ on UNSW-NB15, and from $-0.170$ to $-0.009$ on N-BaIoT) and reduces the mean FPR by 30–$64\%$, depending on the dataset; all central improvements are significant across 10 seeds (Wilcoxon $p=0.002$, large effect sizes). AF-BKM preserves recall on NSL-KDD and N-BaIoT and, on the harder UNSW-NB15, exposes an explicit precision–recall trade-off through a benign target-FPR knob. In fp32, the deployed model serializes to $5.5$–52 KB, a packet is classified in 11–27 µs on a desktop CPU, and each merge round uploads a $d+3$-value summary (160–472 B) $94.6$–$98.3\%$ smaller than the same summary extended with the covariance upper triangle. A robustness study covering selected faulty-worker updates, contamination of the commissioning anchor, and detector-level white-box evasion reports the measured degradation patterns: fabricated threshold candidates have no direct path to the threshold, although a fabricated mean still reaches it indirectly through the blended centroid, and the anchor-referenced false-positive level remains stable under percent-level anchor contamination, while recall sensitivity is dataset-dependent and the evasion budget tracks the benign–attack margin of each dataset. We frame the contribution with a focused taxonomy that identifies merge-induced precision decay under non-IID workers as an open gap. Code is released for reproducibility. Full article

Certified seed purity is a prerequisite for sustaining the agronomic performance and commercial value of Khao Dawk Mali 105 (KDML-105), Thailand’s premium aromatic rice; however, conventional inspection methods are destructive, labour-intensive, and poorly suited to high-throughput operations. This study developed a non-destructive purity inspection system integrating five-band MSI acquired using a MicaSense RedEdge-MX sensor with three machine learning classifiers: Extra Trees (ET), Random Forest (RF), and Support Vector Machine (SVM). The system was evaluated systematically across four illumination levels (360.90–13,188.46 lx) to discriminate KDML-105 from three morphologically similar contaminating varieties: Chainat-1, RD-6, and RD-15. Two-way ANOVA confirmed that classifier type was the dominant performance determinant (η² = 0.847). Under optimal illumination (L4, 13,188.46 lx), ET achieved the highest accuracy (88.8%), recall (86.6%), and F1-score (88.4%) with a training time of 0.098 s. External validation confirmed model generalisability: KDML-105 seeds were identified with 90.6% accuracy and 95.6% recall, while Chainat-1 and RD-6 yielded accuracies of 85.1% and 90.6%, respectively; RD-15 remained challenging (62.9%; 67.4%) owing to spectral proximity to KDML-105. These findings establish MSI combined with ET classification as a viable, cost-effective approach for automated seed purity screening in certified rice production. Full article

Radio frequency identification (RFID) tags are now included in the plastic wrap used to protect seed cotton formed into cylindrical or “round” modules on modern cotton harvesters. In this paper, the development of a new work tool system for handling round modules with articulated wheel loaders or telehandlers is described. The work tool system reads the module-specific identification number from the RFID tags in the wrap and associates the module’s weight, seed cotton moisture content, GPS location, cotton ownership, and load information with the module serial number. Finite element analysis of critical components indicated that the system was capable of processing modules weighing 3178 kg (7000 lb.) Module weight was determined on the loader using measurements of the hydraulic pressure in the lift arm circuit. Seed cotton moisture content was measured using a custom-designed resistance-based probe. To help reduce the potential for lint bale contamination from module wrap plastic, the work tool system was designed to rotate modules so that the wrap can be cut within the manufacturer-recommended cut zone before the wrap is removed at the gin. The total cost for the system configured for fully automated data collection and module rotation control was $28,909. Full article

Supervised fine-tuning is often discussed in terms of data volume and target quality, but the format of the supervision itself can change the reasoning strategy a model expresses in its outputs. We study this effect on a six-category reasoning benchmark spanning symbol transformation, text decryption, bit manipulation, gravitational constant estimation, numeral conversion, and unit conversion (drawn from the NVIDIA Nemotron Model Reasoning Challenge). Using NVIDIA Nemotron-3-Nano-30B-A3B with matched LoRA training settings, we compare three symbol-supervision formats: verbose English rule descriptions, compact family tags, and compact formula notation. We hypothesize that supervision renderings bias token-level reasoning priors, and that these priors transfer across task boundaries in multi-task SFT. In the canonical strict-rescore inventory, the best compact tag and formula checkpoints are statistically equivalent in aggregate within a pre-specified ±4-point margin: K8A-800 reaches 72.3% strict-scored overall accuracy and K8B-700 reaches 71.2% (TOST p = 0.003). Compact tags nevertheless provide a cleaner behavioral profile: an earlier K8A-400 checkpoint reaches 66.4% overall, 98.7% gravity accuracy, and 36.9% bit accuracy without the same contamination signatures. In contrast, verbose English rule descriptions are associated with heuristic parroting, with up to 57% of symbol failures at audited verbose checkpoints collapsing to a single remove-operator template, while formula notation is associated with cross-category contamination: numeric-looking predictions appear more often in text decryption (higher at five of six matched training steps under the canonical seed; matched-step means 15.8 vs. 11.7 numeric predictions per 157 text rows), and gravity failures at a representative K8B formula checkpoint shift toward shortcut stubs and explicit g = 9.8/9.81 fallbacks. We further show that checkpoint selection and strict evaluation auditing materially change branch decisions. Across three training seeds, neither compact format shows a consistent aggregate advantage, while the contamination signatures are partly seed-specific: the gravity-shortcut severity difference persists but is not exclusive to the formula branch, and the numeric–text signature does not reproduce under reseeding. These results support treating supervision format as a first-class hyperparameter for multi-task reasoning SFT in large language models—at least in this benchmark-and-model setting—rather than a mere rendering detail. Because such symbolic and procedural reasoning tasks recur in domains including cybersecurity, mathematics, and code generation, the same formatting choices plausibly shape the policy that any later reinforcement-learning stage would inherit, which we flag as future work. Full article

In this study, biochars derived from agri-food wastes, including different avocado residues (peel- and seed-derived fractions), olive pits and cherry stones, were evaluated as low-cost and sustainable adsorbents for the removal of the emerging contaminants sulfamethoxazole (SMX), antipyrine (ANT) and trazodone (TRZ) from aqueous solutions. Despite their very low BET surface areas, the biochars exhibited notable adsorption capacities, indicating that adsorption performance was mainly governed by surface chemistry rather than textural properties. FTIR analysis confirmed the presence of abundant oxygen-containing functional groups on the biochar surfaces, enabling electrostatic interactions, hydrogen bonding and dipole–dipole interactions with the target contaminants. Adsorption kinetics for all systems were well described by the pseudo-first-order model, with equilibrium capacities closely matching experimental values, suggesting rapid and reversible surface interactions. Intraparticle diffusion analysis revealed that diffusion was not the sole rate-limiting step, with external mass transfer and surface interactions dominating the initial adsorption stage. Equilibrium isotherm analysis showed that SMX and ANT adsorption onto avocado-derived biochars followed the Langmuir model, whereas TRZ adsorption was better described by the Freundlich model. In contrast, olive pit biochar exhibited a markedly higher affinity for TRZ, achieving near-complete removal and fitting the Langmuir isotherm. Finally, high removal efficiencies were maintained when applying the selected biochar to real municipal wastewater, demonstrating robustness under complex matrix conditions. Overall, the results highlight the potential of these agri-food waste-derived biochars, including multiple avocado fractions, as sustainable and economically viable adsorbents for quaternary wastewater treatment. Full article

This study focused on the generation and characterization of sludge produced by electrocoagulation (EC) combined with Moringa oleifera seed extract (MOSE) to remove water hardness. First, an experimental data set was generated and used as the baseline data for mathematical modeling to identify the effects of different parameters on Ca²⁺ and Mg²⁺ ion hardness removal. Then, using the generated data set, operational conditions were optimized using neural networks integrated with a genetic algorithm, resulting in the selection of Fe electrodes, 12.5 mL of MOSE per 100 mL of water, a current density (j) of 49.16 mA cm⁻², and a reaction time of 5.3 min, considering Ca²⁺ ions as the sample contaminant. Additionally, machine learning analysis identified contaminant type, reaction time, and cathode material as the most influential variables affecting sludge formation, with optimal conditions identified for both Ca²⁺ and Mg²⁺ ion systems. For all the mathematical models, experimental validation was performed. The MOSE extract was characterized for the presence of proteins, polyphenols, flavonoids, and polysaccharides, which provide functional groups that promote aggregation and floc development. Sludge characterization by FT-IR, TGA, and TEM revealed the formation of organic–inorganic hybrid matrices composed of biomolecules interacting with electrochemically generated Fe³⁺ and Al³⁺ species, as well as Ca²⁺ and Mg²⁺ ions. These results highlight the role of plant-derived biomolecules in modulating the sludge structure and composition, providing insight into the mechanisms of sludge formation and the implications for handling and valorization of EC-based water treatment systems. Full article

Aerial radiation surveys produce sparse trajectories that must be reconstructed into contamination maps. Conventional aerial interpolators—inverse distance weighting (IDW) and ordinary kriging—treat observations as local ground samples, ignoring that each measurement integrates radiation over an extended footprint A(x, y) = (C * K)(x, y). The resulting double-blurring imposes a second smoothing on already-convolved values, causing systematic underprediction regardless of measurement density. We cast reconstruction as inverse deconvolution. A physics-aware encoder–decoder receives five channels (sparse measurements, IDW baseline, land–water scalar prior, measurement mask, water mask) and learns to invert K under a forward-consistency loss. The network is pretrained on synthetic data and deployed without fine-tuning. At a 50% random within-system holdout over the 2213-point Ukedo benchmark trajectory, 25 runs achieve a mean root-mean-square error (RMSE) of 705.4 ± 102.8 counts per second (CPS) versus 916.8 ± 34.2 (IDW) and 832.4 ± 31.3 (Kriging), with directional improvement over IDW in 25/25 runs. In a three-model ensemble diagnostic, among held-out points exceeding T = 6000 CPS (n = 64 at split seed 10, near the IDW ceiling), the U-Net recovers approximately 80% while IDW and kriging both fall to approximately 0%. The operational value lies in high-intensity hotspot recovery. These gains apply to dense-trajectory, within-coverage reconstruction; under large-gap extrapolation beyond the observed trajectory, the advantage over conventional interpolation is drastically reduced, and spatially independent validation remains an open challenge. Full article

Wildlife baiting sites represent localized disturbance hotspots within natural habitats and may substantially alter plant diversity. We investigated these effects in the Mátra Mountains (Hungary) by surveying 20 baiting sites across two regions and two elevational zones. Vegetation was sampled using 1 × 1 m quadrats positioned along 20 m transects. Results revealed a highly significant (p < 0.001) degradation gradient centered around bait stations. Management intensity appeared to override natural environmental gradients, as invasive species cover differed significantly between regions (p < 0.001), with higher values in Western Mátra (1.88%) than in Eastern Mátra (1.15%). Weed cover showed a similar, although non-significant trend (15.66% vs. 7.84%). Landscape-level analysis identified the surrounding road network as a key dispersal corridor, with road density positively correlated with weed cover. Sites equipped with automatic baiting systems recorded significantly higher weed cover (16.15%) than traditional sites (7.36%). The frequent occurrence of crop species (Zea mays L., Triticum aestivum L.) together with invasive plants (Ambrosia artemisiifolia L., Xanthium spinosum L.) suggests that agricultural forage serves as a major pathway for the introduction of non-native seeds. As management intensity was more strongly associated with these patterns than environmental gradients, anthropogenic factors appear to be the primary drivers of localized changes in plant community composition and biodiversity. Full article

Vineyards and wineries generate seasonal, wet and compositionally variable side-streams whose safe use is constrained by rapid spoilage, contaminants, fragmented regulation and scale. This EU-focused structured narrative review synthesised 90 scientific and official sources and proposes an integrated decision framework rather than another catalogue of valorisation routes. The framework applies a non-compensatory sequence: characterise and stabilise the batch, test route-specific hazards, assign evidence-level and technology readiness, verify legal eligibility, define a safe fallback, and only then compare material flows, environmental burdens and risk-adjusted economics. Current implementation evidence is strongest for controlled composting, conventional wastewater treatment, anaerobic digestion and selected grape-seed oil, polyphenol and heat-integrated biochar operations; clinical, plant-protection and several novel-extract claims remain product- and context-specific. Illustrative ENPV cases show that high-value extraction can offer greater upside but lower robustness than compost/biochar when moisture, transport, rejection and price uncertainty are included. The framework provides an auditable basis for pilot design, regional cooperation and data collection, while explicitly requiring industrial and multi-season validation before investment or product approval. Full article

Durum wheat is highly susceptible to Fusarium head blight (FHB), a severe fungal disease caused primarily by Fusarium graminearum. FHB affects durum wheat production by causing significant yield losses and grain contamination with mycotoxins such as deoxynivalenol (DON) and zearalenone (ZEA). Given the limitations of chemical fungicides, finding sustainable biological control agents is essential. This study evaluated the antagonistic and biocontrol capabilities of Trichoderma harzianum ITEM 3636 against F. graminearum through in vitro and greenhouse experiments. In vitro dual and sandwich culture assays demonstrated that T. harzianum significantly inhibits pathogen mycelial growth through direct interaction and the emission of volatile compounds. In a competition test on rice kernels, co-inoculation with ITEM 3636 significantly reduced pathogen biomass, leading to maximum reductions of 96.5% for DON and 98% for ZEA. Furthermore, greenhouse trials on a commercial durum wheat cultivar revealed that T. harzianum ITEM 3636 significantly decreased FHB severity by 40% and reduced DON contamination by up to 32% only when a combined seed-coating and spike-spraying application was performed. Additionally, ITEM 3636 exhibited biostimulant-like effects on yield parameters, causing an increase in kernel weight of 33% in the greenhouse assay. These findings highlight T. harzianum ITEM 3636 as a promising and ecological alternative to synthetic fungicides for managing FHB, safeguarding crop production, and ensuring food safety. Full article

Marine oil spills represent a critical environmental threat. Petroleum contamination systematically accumulates in the world’s oceans, driving severe and long-term damage to the biodiversity of vulnerable coastal ecosystems. As its primary objective, this study assesses the ocean’s intrinsic genetic capacity to degrade aliphatic and aromatic hydrocarbons. Using the Ocean Gene Atlas v2.0 (OGA2) database, bacterial metabolic pathways were profiled via a four-marker framework: PF00487 (AlkB) and PF03433 (LadA) for medium and long-chain alkanes, alongside PF00355 and PF00848 domains for aromatic ring activation. The analyses revealed that while salinity levels between 34–36 PSU sustain baseline abundances, temperature acts as a primary selective filter, segregating microbial communities into distinct thermal niches. Medium-chain aliphatic potential (PF00487) is ubiquitous, reaching maximum values in surface polar waters near 0 °C before declining with depth. Conversely, long-chain machinery (PF03433) is restricted to warm surface hotspots. Aromatic-degrading potential (PF00355/PF00848) displayed high thermal resilience, narrowing vertically except for a mesopelagic cluster in the Arabian Sea. Global taxonomic analysis confirmed the dominance of Pseudomonadota (59%), which was mainly represented by the class Gammaproteobacteria (15%), with Alcanivorax (10%) as the most abundant genus. On the other hand, aromatic degraders persist as a low-abundance seed bank. In conclusion, the mere presence of specific genes does not automatically imply metabolic expression; actual in situ biodegradation remains strictly governed by transcriptional triggers and environmental factors. Full article

Borehole electrical imaging logs provide important sensor-derived information for identifying fractures, bedding structures, and reservoir heterogeneity. However, strip-like missing regions commonly arise from uneven tool pad distribution, incomplete borehole coverage, and acquisition limitations, which can distort geological structures and reduce interpretation reliability. Because the true values inside real missing regions are unobservable, complete ground-truth labels are generally unavailable. This study therefore proposes a real missing-region-constrained self-supervised inpainting framework for borehole electrical imaging logs. The key idea is to use real missing masks to define safe intact regions and to generate artificial strip-like training masks only within those reliable regions, thereby avoiding supervision contamination from originally missing or adjacent unstable areas. A UNet-based inpainting model is developed, and two attention-enhanced variants, UNet-SE and UNet-CBAM, are evaluated together with Telea and Navier–Stokes baselines. In addition to the main comparison, mask-level ablation, model-level ablation, repeated-seed robustness analysis, and lightweight expert-assisted geological assessment are used to examine the reliability of the proposed strategy. The results show that learning-based methods consistently outperform conventional approaches for structurally complex strip-like gaps. UNet-CBAM achieves the best overall performance, with mask-MAE, mask-PSNR, and mask-SSIM values of 14.8, 21.2 dB, and 0.8, respectively. The safe-region-constrained strategy further reduces supervision contamination and improves reconstruction quality compared with random or less restrictive mask-generation strategies. These findings indicate that the proposed framework offers a practical self-supervised solution for improving the quality and interpretability of sensor-captured borehole imaging logs when complete labels are unavailable. Full article

Heavy metal (HM) contamination near mining operations in Kazakhstan poses a serious threat to the environment. However, data on the state of chernozem soils in this region is limited. This study assessed the bioaccumulation of HMs and translocation within the soil–sunflower (Helianthus annuus L.) system in a southern Calcic Chernozem in the Kostanay region (Northern Kazakhstan), which is located 50 km from the nearest mining facility. The content of seven HMs (Cd, Co, Cr, Cu, Ni, Pb, and Zn) and arsenic (As), as well as five macroelements (K, Ca, S, Mg, and P), was determined in 18 soil samples from the complete soil pedon (0–150 cm) and in eight anatomical parts of six sunflower plants at physiological maturity. Most metals exhibited a deficiency relative to upper continental crustal Clarke values (Clarke of Concentration (CC) < 1 for Cr, Cu, Ni, Pb, and Zn), with a moderate lithogenic anomaly for Cd (CC = 1.65–3.57) and a localized Co anomaly in the Bk horizon (56.26 mg kg⁻¹), indicating no pronounced HM contamination at the investigated agricultural site. Metal distribution exhibited strong organ specificity in sunflower plants. Cd, Cu, and Zn accumulated preferentially in the leaves, whereas Ni and Co were more concentrated in the seeds and stems, respectively. Only cadmium exceeded the threshold values for both BCF > 1 (1.01) and TF > 1 (1.47), confirming the status of sunflower as a cadmium accumulator. These results provide a preliminary reference dataset of the organ-specific distribution of heavy metals in H. annuus L. plants, which can serve as a local baseline for sunflower growth in uncontaminated southern Chernozems. This information can contribute to future environmental monitoring purposes in the region, acting as an exploratory benchmark. Full article