Search Results (701)

Potato production in the Andean highlands demands strategies that reduce dependence on synthetic inputs without sacrificing yield. We evaluated two microbial bioinputs—Bacillus subtilis and Trichoderma viride—applied once pre-plant to seed tubers, under three organo-mineral fertilization regimes (0%, 50%, and 100% of the recommended NPK rate) in two cultivars (INIA 303-Canchán and Yungay) in field conditions in Ayacucho, Peru, using a randomized complete block, split-plot design (three replicates). Agronomic traits (plant height, root dry weight, stems per plant, tubers per plant, and plot-level yield) were analyzed with robust two-way ANOVA and multivariate methods. Combining microbial inoculation with 50% NPK sustained growth responses comparable to 100% NPK for key traits: in Yungay with T. viride, plant height at 50% NPK (≈96.15 ± 1.71 cm) was not different from 100% NPK (≈98.87 ± 1.70 cm), and root dry weight at 50% NPK (≈28.50 ± 0.28 g) matched or exceeded 100% NPK (≈16.97–22.62 g depending on cultivar–treatment). Notably, T. viride increased root biomass even without mineral fertilizer (≈27.62 ± 0.29 g in Yungay), while B. subtilis enhanced canopy vigor and stem number at full NPK (≈4.5 ± 0.29 stems). Yungay out-yielded INIA 303-Canchán overall (≈57.5 ± 2.5 kg vs. ≈42.7 ± 2.5 kg per plot). The highest yields occurred with B. subtilis + 100% NPK (≈62.88 ± 6.07 kg per plot), followed by B. subtilis + 50% NPK (≈51.7 ± 6.07 kg per plot). Plant height was the strongest correlate of yield (Spearman ρ ≈ 0.60), underscoring its value as a proxy for productivity. Overall, a single pre-plant inoculation with B. subtilis or T. viride can halve mineral fertilizer inputs while maintaining growth and sustaining high, cultivar-dependent yields in highland potato systems. Full article

Cotton production faces sustainability challenges due to the lack of effective sustainable defoliants for mechanical harvesting, which constrains the expansion of organic cotton (currently 0.5% of global production). In this framework, this study evaluated pelargonic acid, a rapidly biodegradable compound, as a sustainable defoliant alternative, comparing it with the synthetic pyraflufen-ethyl and a water placebo. A two-year field trial (2023–2024) in Sicily, southern Italy, tested three application rates per treatment in a randomized complete block design. Parameters assessed included defoliation efficacy, root diameter, boll number per plant, average boll weight, raw yield, lint yield, and seed yield. Results indicated significant “Year × Treatment” interaction effects on all parameters. Pelargonic acid applied at 16 L ha⁻¹ achieved the highest boll number per plant in 2024, significantly exceeding pyraflufen-ethyl at its label-recommended rate, with treatments at 12 L ha⁻¹ also producing larger root diameters than the synthetic defoliant. Pelargonic acid at 18 L ha⁻¹ in 2023 achieved complete defoliation, matching the efficacy of pyraflufen-ethyl, while the lowest pelargonic rate (12 L ha⁻¹) produced >90% leaf drop across both years. These findings position pelargonic acid as a rapidly degradable alternative to synthetic defoliants, directly addressing a key bottleneck in sustainable cotton production. Full article

The widening of entrance lanes at urban intersections improves the capacity. However, limited by length, vehicles queuing in different functional lanes often interfere with each other, causing wasted green time. This study analyses turning demand, lane division, and signal timing at short-lane intersections, identifying four types of blockages: left-turn queues overflow blocking straight-ahead, straight-ahead blocking left-turn, right-turn queues overflow blocking straight-ahead, and straight-ahead blocking right-turn. Then, various strategies, including signal timing adjustment, phase sequence, and variable lane functions, are considered. The lane capacity matching rate is calculated, and a model for matching the capacity of different functional lanes at signal-controlled intersections is established. The results show that the matching effect of left-turn is significant, with an improvement of 8.0%, followed by a 7.0% increase in right-turn. The corresponding lane delays are also improved, which demonstrates the effectiveness of the model. Full article

The rational delineation of open-pit mining areas constitutes the core foundation for achieving safe, efficient, economical, and sustainable mining operations. The quality of this decision-making directly impacts the economic benefits experienced throughout the mine’s entire lifecycle and the efficiency of resource recovery. Traditional open-pit mining area delineation relies on an experience-driven manual process that is inefficient and incapable of real-time dynamic data optimization. Thus, there is an urgent need to establish an intelligent decision-making model integrating multi-source data and multi-objective optimization. To this end, this study proposes an intelligent mining area division algorithm. First, a geological complexity quantification model is constructed, incorporating innovative adaptive discretisation resolution technology to achieve precise quantification of coal seam distribution. Second, based on the quantified stripping-to-mining ratio within grids, a block-growing algorithm generates block grids, ensuring uniformity of the stripping-to-mining ratio within each block. Subsequently, a matrix of primary directional variations in the stripping-to-mining ratio is constructed to determine the principal orientation for merging blocks into mining areas. Finally, intelligent open-pit mining area delineation is achieved by comprehensively considering factors such as the principal direction of mining areas, geological conditions, boundary shapes, and economic scale. Practical validation was conducted using the Shitoumei No. 1 Open-Pit Coal Mine in Xinjiang as a case study in engineering. Engineering practice demonstrates that adopting this methodology transforms mining area delineation from an experience-driven to a data-driven approach, significantly enhancing delineation efficiency. Manual simulation of a single scheme previously required approximately 15 days. Applying the methodology proposed herein reduces this to just 0.5 days per scheme, representing a 96% increase in efficiency. Design costs were reduced by approximately CNY 190,000 per iteration. Crucially, the intelligently recommended scheme matched the original design, validating the algorithm’s reliability. This research provides crucial support for theoretical and technological innovation in intelligent open-pit coal mining design, offering technical underpinnings for the sustainable development of open-pit coal mines. Full article

Ship detection in synthetic aperture radar (SAR) remote sensing imagery is of great significance in military and civilian applications. However, two factors limit detection performance: (1) a high prevalence of small-scale ship targets with limited information content and (2) interference affecting ship detection from speckle noise and land–sea clutter. To address these challenges, we propose a novel end-to-end (E2E) transformer-based SAR ship detection framework, called Flow-Aligned Nested Transformer for SAR Small Ship Detection (FANT-Det). Specifically, in the feature extraction stage, we introduce a Nested Swin Transformer Block (NSTB). The NSTB employs a two-level local self-attention mechanism to enhance fine-grained target representation, thereby enriching features of small ships. For multi-scale feature fusion, we design a Flow-Aligned Depthwise Efficient Channel Attention Network (FADEN). FADEN achieves precise alignment of features across different resolutions via semantic flow and filters background clutter through lightweight channel attention, further enhancing small-target feature quality. Moreover, we propose an Adaptive Multi-scale Contrastive Denoising (AM-CDN) training paradigm. AM-CDN constructs adaptive perturbation thresholds jointly determined by a target scale factor and a clutter factor, generating contrastive denoising samples that better match the physical characteristics of SAR ships. Finally, extensive experiments on three widely used open SAR ship datasets demonstrate that the proposed method achieves superior detection performance, outperforming current state-of-the-art (SOTA) benchmarks. Full article

Unmanned aerial vehicles (UAVs) have become a tool for solving numerous practical tasks. UAV sensors provide images and videos for on-line or off-line data processing for object localization, classification, and tracking due to the use of trained convolutional neural networks (CNNs) and artificial intelligence. However, quality of images acquired by UAV-based sensors is not always perfect due to many factors. One of them could be noise arising because of several reasons. Its presence, especially if noise is intensive, can make significantly worse the performance characteristics of CNN-based techniques of object localization and classification. We analyze such degradation for a set of eleven modern CNNs for additive white Gaussian noise model and study when (for what noise intensity and for what CNN) the performance reduction becomes essential and, thus, special means to improve it become desired. Representatives of two most popular families, namely the block matching 3-dimensional (BM3D) filter and DRUNet denoiser, are employed to enhance images under condition of a priori known noise properties. It is shown that, due to preliminary denoising, the CNN performance characteristics can be significantly improved up to almost the same level as for the noise-free images without CNN retraining. Performance is analyzed using several criteria typical for image denoising, object localization and classification. Examples of object localization and classification are presented demonstrating possible object missing due to noise. Computational efficiency is also taken into account. Using a large set of test data, it is demonstrated that: (1) the best results are usually provided for SSD Mobilenet V2 and VGG16 networks; (2) the performance characteristics for cases of applying BM3D filter and DRUNet denoiser are similar but the use of DRUNet is preferable since it provides slightly better results. Full article

A precision bandgap reference (BGR) is an essential building block in modern analog and mixed-signal systems, as it provides stable and predictable current and voltage references required for reliable operation across process, voltage, and temperature variations. However, one of the key challenges in conventional BGR circuits is their sensitivity to resistance variations, which directly impacts the accuracy of bias currents. Even small changes in resistance can lead to significant current mismatch between the core branches of the circuit, thereby degrading output stability and limiting the precision of the overall system. This degradation is particularly problematic in high-performance applications such as data converters, oscillators, and low-power biasing networks, where robust current matching is critical. To address this limitation, this work proposes a resistance-compensated BGR architecture that incorporates an auxiliary trimming network and a compensation branch. The trimming network senses variations in resistance and generates a control bias proportional to the deviation, while the compensation branch injects a corrective current into the output stage. By dynamically balancing the mismatch introduced by resistor spread, the proposed architecture effectively restores current stability across process corners. This method achieves reduction in the current variation across resistance corners from 21% to 3% in worst-case corners (±3%). This approach offers enhancement of current mismatches in analog systems in which robust current is essential. Full article

Pedestrian localization is a fundamental sensing task for intelligent outdoor systems. To overcome the limitations of accuracy and efficiency in conventional binocular approaches, this study introduces a trinocular stereo vision framework that integrates template matching with geometric constraint optimization. The system employs a trinocular camera configuration arranged in an equilateral triangle, which enables complementary perspectives beyond a standard horizontal baseline. Based on this setup, an initial depth estimate is obtained through multi-scale template matching on the primary binocular pair. The additional vertical viewpoint is then incorporated by enforcing three-view geometric consistency, yielding refined and more reliable depth estimates. We evaluate the method on a custom outdoor trinocular dataset. Experimental results demonstrate that the proposed approach achieves a mean absolute error of 0.435 m with an average processing time of 3.13 ms per target. This performance surpasses both the binocular Semi-Global Block Matching (0.536 m) and RAFT-Stereo (0.623 m for the standard model and 0.621 m for the real-time model without fine-tuning). When combined with the YOLOv8-s detector, the system can localize pedestrians in 7.52 ms per frame, maintaining real-time operation (>30 Hz) for up to nine individuals, with a total end-to-end latency of approximately 32.56 ms. Full article

The irregular and highly non-uniform spatial distribution inherent to dynamic three-dimensional (3D) point clouds (DPCs) severely hampers the extraction of reliable temporal context, rendering inter-frame compression a formidable challenge. Inspired by two-dimensional (2D) image and video compression methods, existing approaches attempt to model the temporal dependence of DPCs through a motion estimation/motion compensation (ME/MC) framework. However, these approaches represent only preliminary applications of this framework; point consistency between adjacent frames is insufficiently explored, and temporal correlation requires further investigation. To address this limitation, we propose a hierarchical ME/MC framework that adaptively selects the granularity of the estimated motion field, thereby ensuring a fine-grained inter-frame prediction process. To further enhance motion estimation accuracy, we introduce a dual-attention-based KNN block-matching (DA-KBM) network. This network employs a bidirectional attention mechanism to more precisely measure the correlation between points, using closely correlated points to predict inter-frame motion vectors and thereby improve inter-frame prediction accuracy. Experimental results show that the proposed DPC compression method achieves a significant improvement (gain of 70%) in the BD-Rate metric on the 8iFVBv2 dataset. compared with the standardized Video-based Point Cloud Compression (V-PCC) v13 method, and a 16% gain over the state-of-the-art deep learning-based inter-mode method. Full article

With global sustainable construction growth, fully recycled coarse aggregate concrete (RCAC)—eco-friendly for cutting construction waste and reducing natural aggregate over-exploitation—has poor durability in seasonally freezing saline-soil regions (e.g., Tumushuke, Xinjiang): freeze-thaw and salt ions (NaCl, Na₂SO₄) cause microcracking, faster performance decline, and shorter service life, limiting its use and requiring better salt freeze resistance. To address this, a field survey of Tumushuke’s saline soil was first conducted to determine local salt type and concentration, based on which a matching 12% NaCl + 4% Na₂SO₄ mixed salt solution was prepared. RCAC specimens modified with fly ash (FA), silica fume (SF), and polypropylene fiber (PPF) were then fabricated, cured under standard conditions (20 ± 2 °C, ≥95% relative humidity), and subjected to rapid freeze-thaw cycling in the salt solution. Multiple macro-performance and microstructural indicators (appearance, mass loss, relative dynamic elastic modulus (RDEM), porosity, microcracks, and corrosion products) were measured post-cycling. Results showed the mixed salt solution significantly exacerbated RCAC’s freeze-thaw damage, with degradation severity linked to cycle count and admixture dosage. The RCAC modified with 20% FA and 0.9% PPF exhibited optimal salt freeze resistance: after 125 cycles, its RDEM retention reached 75.98% (6.60% higher than the control), mass loss was only 0.28% (67.80% lower than the control), and its durability threshold (RDEM > 60%) extended to 200 cycles. Mechanistic analysis revealed two synergistic effects for improved performance: (1) FA optimized pore structure by filling capillaries, reducing space for pore water freezing and salt penetration; (2) PPF enhanced crack resistance by bridging microcracks, suppressing crack initiation/propagation from freeze-thaw expansion and salt crystallization. A “pore optimization–ion blocking–fiber crack resistance” triple synergistic protection model was proposed, which clarifies admixture-modified RCAC’s salt freeze damage mechanism and provides theoretical/technical guidance for its application in extreme seasonally freezing saline-soil environments. Full article

Imaging through dynamic scattering media remains a fundamental challenge because of severe information loss and the ill-posed nature of the inversion problem. Conventional methods often struggle to strike a balance between reconstruction fidelity and efficiency in evolving environments. In this study, we present DynaFlowNet, a framework that leverages conditional flow matching theory to establish a continuous, invertible mapping from speckle patterns to target images via deterministic ordinary differential equation (ODE) integration. Central to this is the novel temporal–conditional residual attention block (TCResAttnBlock), which is designed to model spatiotemporal scattering dynamics. DynaFlowNet achieves real-time performance at 134.77 frames per second (FPS), which is 117 times faster than diffusion-based models, while maintaining state-of-the-art reconstruction quality (28.46 dB peak signal-to-noise ratio (PSNR), 0.9112 structural similarity index (SSIM), and 0.8832 Pearson correlation coefficient (PCC)). In addition, the proposed framework demonstrates exceptional geometric generalization, with only a 1.05 dB PSNR degradation across unseen geometries, significantly outperforming existing methods. This study establishes a new paradigm for real-time high-fidelity imaging using dynamic scattering media, with direct implications for biomedical imaging, remote sensing, and underwater exploration. Full article

Performance analysis in elite football still faces significant challenges: traditional descriptive statistics often fail to capture tactical adaptability, and African teams remain underrepresented in the scientific literature despite achieving historic breakthroughs. The FIFA World Cup Qatar 2022 marked a turning point, with Morocco becoming the first African nation to reach the semi-finals. This study systematically analyzed the tactical, physical, and structural performance of the Moroccan national team across seven matches using official FIFA post-match reports. A three-level methodological framework was adopted: (i) descriptive analysis of key performance indicators (KPIs); (ii) visual profiling through radar charts, heatmaps, and passing networks; and (iii) exploratory modelling using principal component analysis (PCA) and clustering. Results revealed consistent defensive organization, low ball possession (<40% in five matches), and effective counter-attacking transitions, with pressing peaks against Spain (288 actions) and France (299 actions). PCA explained 76% of the variance, identifying two principal axes (physical intensity vs. technical mastery; verticality vs. build-up play) and clustering distinguished three match types: low-block defensive games, transition-oriented games, and open matches. These findings highlight Morocco’s tactical adaptability and sustained physical commitment. The study demonstrates how AI-enhanced analytics and multidimensional data visualization can uncover latent performance patterns and support evidence-based decision-making. Practical implications include actionable insights for performance analysts and coaching staff, particularly as Morocco prepares for the 2025 Africa Cup of Nations and the FIFA World Cups in 2026 and 2030. This integrative approach can serve as a model for federations seeking data-driven performance optimization in elite football. Full article

This paper investigates the near-field source localization of multiple narrowband signals in the presence of unknown nonuniform noise with an arbitrary diagonal covariance matrix. From a covariance-fitting perspective, we reformulate the near-field localization problem as a joint symmetric matrix factorization and the estimation of nonuniform noise variances. This reformulation explicitly accounts for noise heterogeneity in the covariance structure, thereby avoiding noise mismodeling and enabling robust near-field localization for nonuniform noise. To solve the intractable symmetric matrix factorization problem, we develop a computationally efficient iterative algorithm based on the block majorization–minimization principle. The proposed algorithm has light per-iteration complexity and admits a closed-form iteration update. Furthermore, we also derive the Cramér–Rao bound (CRB) for near-field localization under nonuniform noise. Extensive numerical experiments demonstrate that the proposed approach outperforms the existing state-of-the-art near-field localization methods and closely matches the CRB while maintaining strong robustness against severe nonuniform noise. Full article

Well-killing operations in water-sensitive hydrophilic formations are often complicated by extended well clean-up periods and, in some cases, failure to restore the well’s production potential post-kill. Typical development targets exhibiting these properties include the Neocomian and Jurassic deposits of fields in Western Siberia and Western Kazakhstan. This paper proposes a well-killing method incorporating simultaneous near-wellbore treatment. In cases where heavy oil components (asphaltenes, resins, or paraffins) are deposited in the near-wellbore zone, their removal with a solvent results in post-operation flow rates that exceed pre-restoration levels. For wells not affected by asphaltene, resin, and paraffin deposits, killing is performed using a blocking pill of invert emulsion stabilized with an emulsifier and hydrophobic nanosilica. During filtration into the formation, this emulsion does not break but rather reforms according to the pore throat sizes. Flow rates in such wells typically match pre-restoration levels. The described engineering solution proves less effective when the well fluid water cut exceeds 60%. For wells exhibiting premature water breakthrough that have not yet produced their estimated oil volume, the water source is identified, and water shutoff operations are conducted. This involves polymer-gel systems crosslinked with resorcinol and paraform, reinforced with inorganic components such as chrysotile microdispersions, micro- and nanodispersions of shungite mineral, and gas black. Oscillation testing identified the optimal additive concentration range of 0.6–0.7 wt%, resulting in a complex modulus increase of up to 25.7%. The most effective polymer-inorganic composite developed by us, incorporating gas black, demonstrates high water shutoff capability (residual resistance factor ranges from 12.5 to 65.0 units within the permeability interval of 151.7 to 10.5 mD). Furthermore, the developed composites exhibit the ability to selectively reduce water permeability disproportionately more than oil permeability. Filtration tests confirmed that the residual permeability to oil after placing the blocking composition with graphene is 6.75 times higher than that to water. Consequently, such treatments reduce the well water cut. Field trials confirmed the effectiveness of the developed polymer-inorganic composite systems. Full article

A systematic literature review is fundamental to building a robust research foundation, informing experimental methodology, and ensuring the quality of future scientific output. However, manual extraction of targeted information from scientific publications is often laborious and prone to error, especially when researchers require rapid access to relevant findings without specialized hardware. This paper introduces an automated workflow for information extraction from scientific publications in the engineering domain. The proposed workflow consists of two primary stages: data preparation and information extraction. During data preparation, PDF files are converted to plain text and segmented into logical sections using a rule-based block detection and classification algorithm for keeping semantics. Information extraction is then performed by applying regular expressions both on keys and values in the same sentence to identify and extract relevant process and material data from the segmented text. The approach was evaluated on a dataset of 18 open-access scientific publications from various journals and conference proceedings in the AM domain. The results of the automated extraction were compared with manual extraction and with a modern large language model (LLM)-based approach. The findings demonstrate that the proposed workflow can accurately and efficiently extract relevant process and material data, achieving competitive performance relative to the LLM-based method. The workflow offers a significant reduction in time and potential errors associated with manual extraction, with automated processing averaging 15 s per document compared to one hour for manual extraction, and achieving a 76% match rate. This efficiency enables researchers to rapidly and effectively extract data. The methodology is readily transferable to other scientific fields where systematic literature reviews and structured data extraction are required. Full article