Search Results (5,221)

Accurate and automated detection of diseases is crucial for sustainable strawberry production. However, the challenges posed by small size, mutual occlusion, and high intra-class variance of symptoms in complex agricultural environments make this difficult. Mainstream deep learning detectors often do not perform well under these demanding conditions. We propose a novel detection framework designed for superior accuracy and robustness to address this critical gap. Our framework introduces four key innovations: First, we propose a novel attention-driven detection head featuring our Parallel Pyramid Attention (PPA) module. Inspired by pyramid attention principles, our module’s unique parallel multi-branch architecture is designed to overcome the limitations of serial processing. It simultaneously integrates global, local, and serial features to generate a fine-grained attention map, significantly improving the model’s focus on targets of varying scales. Second, we enhance the core feature fusion blocks by integrating Monte Carlo Attention (MCAttn), effectively empowering the model to recognize targets across diverse scales. Third, to improve the feature representation capacity of the backbone without increasing the parametric overhead, we replace standard convolutions with Frequency-Dynamic Convolutions (FDConv). This approach constructs highly diverse kernels in the frequency domain. Finally, we employ the Scale-Decoupled Loss function to optimize training dynamics. By adaptively re-weighting the localization and scale losses based on target size, we stabilize the training process and improve the Precision of bounding box regression for small objects. Extensive experiments on a challenging dataset related to strawberry diseases demonstrate that our proposed model achieves a mean Average Precision (MAP) of 81.1%. This represents an improvement of 2.1% over the strong YOLOv12-n baseline, highlighting its practical value as an effective tool for intelligent disease protection. Full article

Crops of cocoa, avocado, cassava, yam, and maize are of utmost importance to the economy of the Colombian Caribbean, as they serve as the primary source of income for many families. However, establishing these crops requires the replacement of natural ecosystems, with limited understanding of how these areas contribute to biodiversity conservation. This study analyzed the diversity of dung beetles in both transitory and permanent crops within a landscape in San Jacinto, Bolívar, to assess their contribution to the conservation of diversity within this insect group. Dung beetle communities were sampled in permanent crops of avocado and cocoa, transitory crops (cassava, yam, and maize), and a forest fragment. The forest fragment exhibited high levels of species richness, abundance, and diversity regardless of the sampling period; these values were only matched by those of the permanent cocoa crop, and only during the rainy season. Our findings highlight the necessity of preserving forest fragments for biodiversity conservation, while also indicating that certain permanent crops may contribute to this effort. Full article

High-Temperature Proton-Exchange Membrane Fuel Cells (HT-PEMFCs) represent a promising clean energy technology and are valued for their fuel flexibility and simplified balance of plant. Their commercialization, however, is critically hindered by the prohibitive cost and resource scarcity of platinum-group metal (PGM) catalysts. The challenge is amplified in the phosphoric acid (PA) electrolyte of HT-PEMFCs, where the severe anion poisoning of PGM active sites necessitates impractically high catalyst loadings. This review addresses the urgent need for cost-effective alternatives by providing a comprehensive assessment of recent advancements in non-precious metal (NPM) catalysts for the oxygen reduction reaction (ORR) in HT-PEMFCs. It systematically explores synthesis strategies and structure–performance relationships for emerging catalyst classes, including transition metal compounds, metal–nitrogen–carbon (M-N-C) materials, and metal-free heteroatom-doped carbons. A significant focus is placed on M-N-C catalysts, particularly those with atomically dispersed Fe-Nx active sites, which have emerged as the most viable replacements for platinum due to their high intrinsic activity and notable tolerance to phosphate poisoning. This review critically analyzes key challenges that impede practical application, such as the trade-off between catalyst activity and stability, mass transport limitations in thick electrodes, and long-term degradation in the harsh PA environment. Finally, it outlines future research directions, emphasizing the need for a synergistic approach that integrates computational modeling with advanced operando characterization to guide the rational design of durable, high-performance catalysts and electrode architectures, thereby accelerating the path to commercial viability for HT-PEMFC technology. Full article

This paper deals with the design and the robustness analysis of a model predictive control (MPC) for the position tracking of primary flight movables driven by electro-mechanical actuators. This study is, in particular, focused on a rotary electro-mechanical actuator (EMA) by UMBRAGROUP, employing a patented mechanical transmission based on a differential ball-screw mechanism characterized by a huge gear ratio. To obtain a baseline reference, conventional PID regulators were initially optimized by using multi-objective cost functions based on tracking accuracy, load disturbance rejection, and power consumption. The position regulator was then replaced by an MPC regulator, designed to balance performance, computational resources, and safety constraints. A nonlinear physics-based simulation model of the EMA, entirely developed in the Matlab–Simulink environment and validated with experiments, was used to compare the two control strategies. The simulation results in both the time and frequency domains highlight that the MPC solution provides faster and more accurate position tracking, improved dynamic stiffness, and reduced power absorption. Finally, the robustness against model uncertainties of the MPC was addressed by imposing random and combined deviations of model parameters from the nominal values (via Monte Carlo analysis). The results demonstrate that the implementation of MPC control laws could enhance the stability and the reliability of EMAs, thus supporting their application for safety-critical flight control functions. Full article

The heat exchanger in a hydropower unit plays a critical role in ensuring the stability of the unit and improving operational efficiency. This paper conducted a global flow-field/heat-transfer numerical analysis of multi-tube heat exchangers in hydropower units (with 98 tubes) and applied it to optimization research under actual operating conditions. Using a three-dimensional two-phase flow model, this work systematically analyzes the effects of different sand content and particle size on heat-transfer performance, revealing the impact of particle-flow and fluid-flow nonuniformity on heat-exchange efficiency. This research fills the gap in existing studies regarding the analysis of the impact of complex operating conditions on hydropower unit radiators. To address the issues of nonuniform flow fields and poor flow mixing in existing heat exchangers, an improved inlet/outlet structural-optimization plan is proposed. The original cylindrical inlet/outlet is replaced with a square structure, and its area is increased. The optimized structure improves flow uniformity, reduces flow losses, enhances heat-transfer performance by 7.7%, and achieves a significant reduction of 0.53 K in oil temperature. The findings of this study provide theoretical and engineering guidance for the design and optimization of heat exchangers in hydropower units and are of high value for practical applications. Full article

The diagnosis of swine influenza A virus (swIAV) has to involve laboratory detection, as the clinical signs are not pathognomonic. Nasal swabs (NSs) have been the preferred sample material for swIAV PCR diagnostics, but oral fluid (OF) is a convenient alternative material. In this study, NSs and OFs from 35 Polish swine herds were collected and tested with real-time RT-PCR in order to assess swIAV circulation patterns in Poland and improve protocols for efficient, non-invasive and cost-effective swIAV surveillance in pig farms. The study showed that the swIAV RNA was detected in 65.7% of the tested farms. In total, 21.2% of NS pools and 48.6% of OF samples were positive for swIAV. The Ct values in NS pools and OFs were similar (p > 0.05), but a significant reduction (p < 0.05) in swIAV prevalence in NSs was observed in nursery pigs from farms applying swIAV vaccinations. Successful subtyping was achieved more effectively with OFs compared to NSs, and the H1avN2 was most prevalent subtype detected. The results emphasized that OF can be useful for monitoring swIAV and subtyping. However, OFs cannot replace NSs, which were more useful in the assessment of the effect of swIAV vaccinations in nursery pigs. Full article

The construction industry increasingly integrates technological advancements to enhance efficiency and meet technical, environmental, and economic requirements. Self-compacting mortars are gaining popularity due to their superior fluidity, optimized compaction, and improved mechanical properties. This study explores the potential of statistical mix design methodology to optimize self-compacting mortars’ fresh properties and strength development by replacing up to 20% of cement with pozzolana, limestone, and marble powder. A self-compacting mortar repository was used to develop robust models predicting slump flow, compressive strength at 28 days, water absorption, and capillary absorption. Results indicate that marble powder mixtures exhibit superior slump flow, up to 9% higher than other formulations. Compressive strengths range from 50 MPa to 70 MPa. Pozzolana and marble-based mortars show 15% and 12% strength reductions compared to the limestone-based mix, respectively. Water absorption increases slightly for mortars with marble (+2%) or pozzolana (+3%). The mortar containing marble powder has the lowest sorptivity coefficient due to its high specific surface area. The statistical analysis was conducted using a mixture design approach based on a second-order polynomial regression model. ANOVA results for the studied responses indicate that the calculated F-values exceed the critical thresholds, with p-values below 0.05 and R-squared values above 0.83, confirming the robustness and predictive reliability of the developed models. Life cycle assessment reveals that cement production accounts for over 80% of the environmental impact. Partial replacement with pozzolana, limestone, and marble powder reduces up to 19% of greenhouse gas emissions and 17.22% in non-renewable energy consumption, demonstrating the environmental benefits of optimized formulations. Full article

The increasing global interest in fish consumption leads to a greater generation of fish waste. Fish waste, rich in nutrients such as protein, bioactive compounds, and vitamins, is attracting growing attention for its potential applications in food. In this study, gelatin hydrolysate obtained from fish skin waste was utilized as a stabilizer and/or emulsifier in ice cream production. It was found that gelatin hydrolysate significantly increased the protein content of the ice cream samples. The K and n values in different ice cream compositions varied between 0.009 and 1.012 Pa.sⁿ and 0.356 and 0.863, respectively. The consistency coefficients of samples D1 (sahlep and mono-diglyceride) and D3 (sahlep and gelatin hydrolysate) were almost the same, indicating that the mono-diglyceride was replaced by an equivalent amount of gelatin hydrolysate. All the ice cream mixtures tested showed non-Newtonian, pseudoplastic flow, as indicated by their n values being less than 1. All mixtures demonstrated greater elasticity than viscosity, as their storage modulus (G′) was higher than their loss modulus (G″). In the third interval of 3-ITT, all ice cream mixtures displayed thixotropic behavior, indicating that their viscoelastic properties could be restored after a sudden deformation. The overrun levels of the samples ranged from 9.55% to 21.74%; the use of gelatin hydrolysate resulted in a statistically significant increase (p < 0.05). The highest hardness and stickiness values in the samples were determined in the specific sample containing equal amounts of emulsifier, stabilizer, and gelatin hydrolysate. Furthermore, gelatin hydrolysate prolonged the first dripping time and melting rate of the samples. Full article

Creativity is the primary driver of our cultural evolution. The astonishing potential of artificial intelligence (AI) and its possible application in the creative process poses an urgent and dramatic challenge for humanity; how can we maximize the benefits of AI while minimizing the associated risks? In this article, we identify all forms of human–AI collaboration in this realm as cyber-creativity. We introduce the following two forward-looking scenarios: a utopian vision for cyber-creativity, in which AI serves to enhance and not replace human creativity, and a dystopian view associated with the pre-emption of all human creative agency caused by the rise of AI. In our view, the scientific community is called to bring its contribution, however small, to help humanity make steps towards the utopian scenario, while avoiding the dystopian one. Here, we present a decalogue of research challenges identified for this purpose, touching upon the following dimensions: (1) the theoretical framework for cyber-creativity; (2) sociocultural perspectives; (3) the cyber-creative process; (4) the creative agent; (5) the co-creative team; (6) cyber-creative products; (7) cyber-creative domains; (8) cyber-creative education; (9) ethical aspects; and (10) the dark side of cyber-creativity. For each dimension, a brief review of the state-of-the-art is provided, followed by the identification of a main research challenge, then specified into a list of research questions. Whereas there is no claim that this decalogue of research challenges represents an exhaustive classification, which would be an impossible objective, it still should serve as a valid starting point for future (but urgent) research endeavors, with the ambition to provide a significant contribution to the understanding, development, and alignment of AI to human values the realm of creativity. Full article

Based on an established appropriate substitution level for corn distillers dried grains with solubles (DDGSs) replacing energy-supplying components in the basal diet for pregnant sows, the substitution method was employed to determine the available energy values of corn DDGSs. In Exp. 1, forty pregnant sows (gestation day = 50 ± 5 d; body weight = 220 ± 24.9 kg; parity, 4 to 6) were randomly allocated into five treatments. The control group was fed a corn–soybean basal diet, while test diets contained 20%, 30%, 40%, 50% corn DDGSs replacing the energy-supplying portion of the basal diet. In Exp. 2, the available energy of five corn DDGS sources was determined using the substitution method at a 30% substitution level. Twelve pregnant sows (gestation day = 50 ± 5 d; body weight = 225.4 ± 29.2 kg; parity, 4 to 6) were arranged in a 6 × 3 Youden square design. Dietary treatments consisted of the corn–soybean basic diet and five test diets incorporating different corn DDGS types. Increasing the substitution level of corn DDGSs displayed both linear and quadratic effects (p < 0.05) on the apparent total tract digestibility (ATTD) of dry matter (DM), organic matter (OM), acid detergent fiber (ADF), ether extract (EE) and gross energy (GE) in diets. The ATTD of neutral detergent fiber (NDF), digestible energy (DE) and metabolizable energy (ME) was quadratically affected by different substitution levels (p < 0.05), with the highest value achieved at the 30% substitution level. As the substitution level of corn DDGSs increased, the ATTD of ADF in corn DDGSs decreased. In contrast, the ATTD of crude protein (CP) increased either linearly or quadratically (p < 0.05), and a significant quadratic effect was observed for the ATTD of EE in corn DDGSs (p < 0.05). Although the GE, DE, and ME of corn DDGSs were not significantly influenced by the substitution level, the 30% corn DDGSs resulted in the lowest coefficients of variation (CV). Therefore, a 30% substitution level of corn DDGSs is recommended for energy-supplying components in the basal diet of pregnant sows using the substitution method. The ranges of DE, ME and net energy (NE) of five corn DDGSs samples were 15.58–18.07, 12.17–16.42 and 8.76–15.88 MJ/kg DM, respectively. The absolute value of relative error (|RE|) between the predicted available energy values obtained from the prediction equations established in Exp. 2 and the determined values were below 5%, except for ME for corn DDGSs N3 (5.81%). Full article

The remarkable success of autoregressive Large Language Models (LLMs) is predicated on the causal attention mechanism, which enforces a static and rigid form of informational asymmetry by permitting each token to attend only to its predecessors. While effective for sequential generation, this hard-coded unidirectional constraint fails to capture the more complex, dynamic, and nonlinear dependencies inherent in sophisticated reasoning, logical inference, and discourse. In this paper, we challenge this paradigm by introducing Dynamic Asymmetric Attention (DAA), a novel mechanism that replaces the static causal mask with a learnable context-aware guidance module. DAA dynamically generates a continuous-valued attention bias for each query–key pair, effectively learning a “soft” information flow policy that guides rather than merely restricts the model’s focus. Trained end-to-end, our DAA-augmented models demonstrate significant performance gains on a suite of benchmarks, including improvements in perplexity on language modeling and notable accuracy boosts on complex reasoning tasks such as code generation (HumanEval) and mathematical problem-solving (GSM8k). Crucially, DAA provides a new lens for model interpretability. By visualizing the learned asymmetric attention patterns, it is possible to uncover the implicit information flow graphs that the model constructs during inference. These visualizations reveal how the model dynamically prioritizes evidence and forges directed logical links in chain-of-thought reasoning, making its decision-making process more transparent. Our work demonstrates that transitioning from a static hard-wired asymmetry to a learned and dynamic one not only enhances model performance but also paves the way for a new class of more capable and profoundly more explainable LLMs. Full article

True slime mould assemblages respond acutely to microhabitat structure, which may constitute potential indicators of forest dynamics; however, large-scale syntheses integrating habitat scale and substrate specificity remain exceedingly scarce. By collating 3085 occurrence records into eight ecologically coherent habitats and ten substrate guilds, we quantified richness, entropy, turnover and indicator strength via rarefaction, Chao1/ACE, Shannon–Simpson indices, $\beta$-diversity partitioning, NMDS, PERMANOVA and ${\mathit{IndVal}}_g$ analysis. Broadleaved deciduous forests accounted for 37.9% of observations and hosted the most taxa, while lignicolous samples in both deciduous and bog–mire contexts dominated species counts; open grasslands were compositionally depauperate. Species replacement, not nestedness, structured assemblages (${\beta }_{\mathrm{SIM}}/{\beta }_{\mathrm{SOR}}\approx 0.82$), and habitat plus substrate explained two-thirds of variance. Indicator analysis isolated six habitat-diagnostic genera (notably Cribraria, Hemitrichia and Licea) and, at species resolution, highlighted Diderma niveum, Fuligo septica and Ceratiomyxa fruticulosa as high-fidelity bioindicators of montane grassland, bog–mire and broadleaved forest conditions, respectively. Taken together, our findings lay the groundwork for employing true slime moulds to identify habitat types and assess their ecological condition, while underscoring the conservation value of dead wood retention and structural heterogeneity. The benchmarked indicator set we provide enables rapid assessments and establishes a temporal baseline for tracking climate- and management-driven change in Central European Eumycetozoa diversity. Full article

To address the environmental risks associated with large-scale stockpiling of red mud (RM) and coal gangue (CG) and the demand for their high-value utilization, this study proposes a ternary concrete system incorporating RM, fly ash (FA), and CG aggregate. The effects of RM content, FA content, CG aggregate replacement rate, and water-to-binder ratio on workability, mechanical properties, and frost resistance durability were systematically investigated through orthogonal experiments, with the underlying micro-mechanisms revealed by scanning electron microscopy (SEM) and energy dispersive spectroscopy (EDS). The results indicate that workability is predominantly governed by the water-to-binder ratio, while the micro-aggregate effect of FA significantly enhances fluidity. Mechanical properties are most significantly influenced by RM content; under a 20% CG aggregate replacement rate and a 0.45 water-to-binder ratio, an optimal compressive strength was achieved with a low content combination of RM and FA. Frost resistance deteriorated markedly with increasing RM and FA content, with the high-content group approaching the failure threshold after only 25 freeze–thaw cycles, occurring 50 and 125 cycles earlier than the medium- and low-content groups, respectively. Macro-micro results indicate a synergistic threshold at 20% red mud and 45% fly ash, yielding a compressive strength of 24.96 MPa. This value exceeds the 24.87 MPa of the 10% red mud + 45% fly ash group and the 21.90 MPa of the 10% red mud + 55% fly ash group. Microstructurally, this group also exhibits superior C-S-H gel uniformity and narrower crack widths compared to the others. Excessive incorporation of red mud and fly ash leads to agglomeration of unhydrated particles and increased porosity, aligning with the observed macroscopic strength degradation. This research identifies and quantifies the synergistic threshold governing RFCTSWC performance evolution, providing theoretical support for engineering applications of solid waste concrete. Full article

This study presents an innovative unmanned aerial vehicle (UAV)-based intelligent detection method utilizing an improved Faster Region-based Convolutional Neural Network (Faster R-CNN) architecture to address the inefficiency and inaccuracy inherent in manual wheat spike counting. We systematically collected a high-resolution image dataset (2000 images, 4096 × 3072 pixels) covering key growth stages (heading, grain filling, and maturity) of winter wheat (Triticum aestivum L.) during 2022–2023 using a DJI M300 RTK equipped with multispectral sensors. The dataset encompasses diverse field scenarios under five fertilization treatments (organic-only, organic–inorganic 7:3 and 3:7 ratios, inorganic-only, and no fertilizer) and two irrigation regimes (full and deficit irrigation), ensuring representativeness and generalizability. For model development, we replaced conventional VGG16 with ResNet-50 as the backbone network, incorporating residual connections and channel attention mechanisms to achieve 92.1% mean average precision (mAP) while reducing parameters from 135 M to 77 M (43% decrease). The GFLOPS of the improved model has been reduced from 1.9 to 1.7, an decrease of 10.53%, and the computational efficiency of the model has been improved. Performance tests demonstrated a 15% reduction in missed detection rate compared to YOLOv8 in dense canopies, with spike count regression analysis yielding R² = 0.88 (p < 0.05) against manual measurements and yield prediction errors below 10% for optimal treatments. To validate robustness, we established a dedicated 500-image test set (25% of total data) spanning density gradients (30–80 spikes/m²) and varying illumination conditions, maintaining >85% accuracy even under cloudy weather. Furthermore, by integrating spike recognition with agronomic parameters (e.g., grain weight), we developed a comprehensive yield estimation model achieving 93.5% accuracy under optimal water–fertilizer management (70% ETc irrigation with 3:7 organic–inorganic ratio). This work systematically addresses key technical challenges in automated spike detection through standardized data acquisition, lightweight model design, and field validation, offering significant practical value for smart agriculture development. Full article

As bisphenol A (BPA) has gradually become restricted in production scenarios, the ecological risk level of its main replacement chemicals, i.e., bisphenol S (BPS) and bisphenol F (BPF), should be noted. To overcome the limitations of toxicity data, two kinds of in silico toxicology models (quantitative structure–activity relationship (QSAR) and interspecies correlation estimation (ICE) models) were used to predict enough toxicity data for multiple species. The accuracy of the coupled in silico toxicology models was verified by comparing experimental and predicted data results. Reliable predicted no-effect concentrations (PNECs) of 8.04, 35.2, and 34.2 μg/L were derived for BPA, BPS, and BPF, respectively, using species sensitivity distribution (SSD). Accordingly, the ecological risk quotient (RQ) values of BPA, BPS, and BPF for aquatic organisms were assessed in 32 major Chinese surface waters; they ranged from nearly 0 to 1.86, but were <0.1 in most cases, which indicated that the overall ecological risk level of BPA and its alternatives was low. However, in some cases, the ecological risks posed by BPA alternatives have reached equivalent levels to those posed by BPA (e.g., Liuxi River, Taihu Lake, and Pearl River), which requires further attention. This study provides evidence that the application of coupled in silico toxicology models can effectively predict toxicity data for new chemicals, avoiding time-consuming and laborious animal experiments. The main findings of this study can support environmental risk assessment and management for new chemicals that lack toxicity data. Full article