Search Results (1,565)

Breeding programs often overlook the use of root traits. Therefore, we investigated the relevance of sorghum root traits in explaining its adaptation to combined drought and heat stress (CDHS). Six (i.e., three pre-release lines + three checks) sorghum genotypes were established at two low-altitude (i.e., <600 masl) locations with a long-term history of averagely very high temperatures in the beginning of the summer season, under two management (i.e., CDHS and well-watered (WW)) regimes. At each location, the genotypes were laid out in the field using a randomized complete block design (RCBD) replicated two times. Root trait data, namely root diameter (RD), number of roots (NR), number of root tips (NRT), total root length (TRL), root depth (RDP), root width (RW), width–depth ratio (WDR), root network area (RNA), root solidity (RS), lower root area (LRA), root perimeter (RP), root volume (RV), surface area (SA), root holes (RH) and root angle (RA) were gathered using the RhizoVision Explorer software during the pre- and post-flowering stage of growth. RSA traits differentially showed significant (p < 0.05) correlations with grain yield (GY) at pre- and post-flowering growth stages and under CDHS and WW conditions also revealing genotypic variation estimates exceeding 50% for all the traits. Regression models varied between pre-flowering (p = 0.013, R² = 47.15%, R² Predicted = 29.32%) and post-flowering (p = 0.000, R² = 85.64%, R² Predicted = 73.30%) growth stages, indicating post-flowering as the optimal stage to relate root traits to yield performance. RD contributed most to the regression model at post-flowering, explaining 51.79% of the 85.64% total variation. The Smith–Hazel index identified ICSV111IN and ASAREACA12-3-1 as superior pre-release lines, suitable for commercialization as new varieties. The study demonstrated that root traits (in particular, RD, RW, and RP) are linked to crop performance under CDHS conditions and should be incorporated in breeding programs. This approach may accelerate genetic gains not only in sorghum breeding programs, but for other crops, while offering a nature-based breeding strategy for stress adaptation in crops. Full article

Optimization problems often feature local optima with a significant difference in the basin of attraction (BoA), making evolutionary computation methods prone to discarding solutions located in less-attractive BoAs, thereby posing challenges to the search for optima in these BoAs. To enhance the ability to find these optima, various niching methods have been proposed to restrict the competition scope of individuals to their specific neighborhoods. However, redundant searches in more-attractive BoAs as well as necessary searches in less-attractive BoAs can only be promoted simultaneously by these methods. To address this issue, we propose a general framework for niching methods named niching-driven divide-and-conquer hill exploration (NDDCHE). Through gradually learning BoAs from the search results of a niching method and dividing the problem into subproblems with a much smaller number of optima, NDDCHE aims to bring a more balanced distribution of searches in the BoAs of optima found so far, and thus enhance the niching method’s ability to find optima in less-attractive BoAs. Through experiments where niching methods with different categories of niching techniques are integrated with NDDCHE and tested on problems with significant differences in the size of the BoA, the effectiveness and the generalization ability of NDDCHE are proven. Full article

This study aims to characterize the emissions of ammonia (NH₃) and methane (CH₄) from naturally ventilated dairy barns located in cold regions during the winter season, thereby providing a scientific basis for optimizing dairy barn environmental management. The target barn was selected at a commercial dairy farm in Ulanchab, Inner Mongolia, China. Environmental factors, including temperature, humidity, wind speed, and concentrations of NH₃, CH₄, and CO₂, were monitored both inside and outside the barn. The ventilation rate and emission rate were calculated using the CO₂ mass balance method. Additionally, NH₃ and CH₄ emission prediction models were developed using the adaptive neural fuzzy inference system (ANFIS). Correlation analyses were conducted to clarify the intrinsic links between environmental factors and NH₃ and CH₄ emissions, as well as the degree of influence of each factor on gas emissions. The ANFIS model with a Gaussian membership function (gaussmf) achieved the highest performance in predicting NH₃ emissions (R² = 0.9270), while the model with a trapezoidal membership function (trapmf) was most accurate for CH₄ emissions (R² = 0.8977). The improved ANFIS model outperformed common models, such as multilayer perceptron (MLP) and radial basis function (RBF). This study revealed the significant effects of environmental factors on NH₃ and CH₄ emissions from dairy barns in cold regions and provided reliable data support and intelligent prediction methods for realizing the precise control of gas emissions. Full article

The precise identification of priority areas for conservation based on connectivity can significantly enhance protection efficacy and mitigate biodiversity loss in fragmented landscapes. Priority area selection efforts are typically conducted in landscapes with a limited number of patches or simplified to focus on large patches, while landscapes with numerous patches are rarely explored. In this paper, we used a forest in Kanas, Xinjiang, China, as a case study to explore priority patches for conservation according to their contribution to maintaining overall landscape connectivity, as well as to assess how structural factors influence patch importance in connectivity, based on graph theory. We found that the rank of patches varied with patch importance indices (which can be used to calculate the contribution of individual patches to maintaining overall landscape). Dispersal distances were selected, as they placed different emphasis on the size and topological location of patches, and different types of links (binary or probabilistic connection) were used. One critical and seven important connected patches were identified as priority patches for conservation after taking multiple connectivity indices and dispersal distances into comprehensive consideration. In addition, neighboring patch density was the dominant factor that influenced patch importance for species with 50 and 100 m dispersal distances, while patch size contributed most for species with 200 m and longer dispersal distances; therefore, we suggested that neighboring patch density and patch size could be used to support efforts to identify priority patches. Overall, our results provide a unique perspective and a more simplified process for the selection of priority protected sites in patch-rich landscapes, allowing us to highlight which action is suitable for optimizing landscape connectivity and biodiversity conservation. Full article

This paper presents some of the most significant findings in the design of a hydraulic servomechanism for flight controls, which were primarily achieved by the first author during his activity in an aviation institute. These results are grouped into four main topics. The first one outlines a classical theory, from the 1950s–1970s, of the analysis of nonlinear automatic systems and namely the issue of absolute stability. The uninformed public may be misled by the adjective “absolute”. This is not a “maximalist” solution of stability but rather highlights in the system of equations a nonlinear function that describes, for the case of hydraulic servomechanisms, the flow-control dependence in the distributor spool. This function is odd, and it is therefore located in quadrants 1 and 3. The decision regarding stability is made within the so-called Lurie problem and is materialized by a matrix inequality, called the Lefschetz condition, which must be satisfied by the parameters of the electrohydraulic servomechanism and also by the components of the control feedback vector. Another approach starts from a classical theorem of V. M. Popov, extended in a stochastic framework by T. Morozan and I. Ursu, which ends with the description of the local and global spool valve flow-control characteristics that ensure stability in the large with respect to bounded perturbations for the mechano-hydraulic servomechanism. We add that a conjecture regarding the more pronounced flexibility of mathematical models in relation to mathematical instruments (theories) was used. Furthermore, the second topic concerns, the importance of the impedance characteristic of the mechano-hydraulic servomechanism in preventing flutter of the flight controls is emphasized. Impedance, also called dynamic stiffness, is defined as the ratio, in a dynamic regime, between the output exerted force (at the actuator rod of the servomechanism) and the displacement induced by this force under the assumption of a blocked input. It is demonstrated in the paper that there are two forms of the impedance function: one that favors the appearance of flutter and another that allows for flutter damping. It is interesting to note that these theoretical considerations were established in the institute’s reports some time before their introduction in the Aviation Regulation AvP.970. However, it was precisely the absence of the impedance criterion in the regulation at the appropriate time that ultimately led, by chance or not, to a disaster: the crash of a prototype due to tailplane flutter. A third topic shows how an important problem in the theory of automatic systems of the 1970s–1980s, namely the robust synthesis of the servomechanism, is formulated, applied and solved in the case of an electrohydraulic servomechanism. In general, the solution of a robust servomechanism problem consists of two distinct components: a servo-compensator, in fact an internal model of the exogenous dynamics, and a stabilizing compensator. These components are adapted in the case of an electrohydraulic servomechanism. In addition to the classical case mentioned above, a synthesis problem of an anti-windup (anti-saturation) compensator is formulated and solved. The fourth topic, and the last one presented in detail, is the synthesis of a fuzzy supervised neurocontrol (FSNC) for the position tracking of an electrohydraulic servomechanism, with experimental validation, in the laboratory, of this control law. The neurocontrol module is designed using a single-layered perceptron architecture. Neurocontrol is in principle optimal, but it is not free from saturation. To this end, in order to counteract saturation, a Mamdani-type fuzzy logic was developed, which takes control when neurocontrol has saturated. It returns to neurocontrol when it returns to normal, respectively, when saturation is eliminated. What distinguishes this FSNC law is its simplicity and efficiency and especially the fact that against quite a few opponents in the field, it still works very well on quite complicated physical systems. Finally, a brief section reviews some recent works by the authors, in which current approaches to hydraulic servomechanisms are presented: the backstepping control synthesis technique, input delay treated with Lyapunov–Krasovskii functionals, and critical stability treated with Lyapunov–Malkin theory. Full article

Deep learning models have obvious advantages in detecting oil spills, but the training of deep learning models heavily depends on a large number of samples of high quality. However, due to the accidental nature, unpredictability, and urgency of oil spill incidents, it is difficult to obtain a large number of labeled samples in real oil spill monitoring scenarios. Surprisingly, few-shot learning can achieve excellent classification performance with only a small number of labeled samples. In this context, a new cross-domain few-shot SAR oil spill detection network is proposed in this paper. Significantly, the network is embedded with a hybrid attention feature extraction block, which consists of a coordinate attention module to perceive the channel information and spatial location information, as well as a global self-attention transformer module capturing the global dependencies and a multi-scale self-attention module depicting the local detailed features, thereby achieving deep mining and accurate characterization of image features. In addition, to address the problem that it is difficult to distinguish between the suspected oil film in seawater and real oil film using few-shot due to the small difference in features, this paper proposes a double loss function category determination block, which consists of two parts: a well-designed category-perception loss function and a traditional cross-entropy loss function. The category-perception loss function optimizes the spatial distribution of sample features by shortening the distance between similar samples while expanding the distance between different samples. By combining the category-perception loss function with the cross-entropy loss function, the network’s performance in discriminating between real and suspected oil films is thus maximized. The experimental results effectively demonstrate that this study provides an effective solution for high-precision oil spill detection under few-shot conditions, which is conducive to the rapid identification of oil spill accidents. Full article

Ecological security patterns (ESPs) are fundamental to safeguarding regional ecological integrity and enhancing human well-being. Consequently, research on conservation and restoration in critical regions is vital for ensuring ecological security and optimizing territorial ecological spatial configurations. Focusing on the Henan section of the Yellow River Basin, this study established the regional ESP and conservation–restoration framework through an integrated approach: (1) assessing four key ecosystem services—soil conservation, water retention, carbon sequestration, and habitat quality; (2) identifying ecological sources based on ecosystem service importance classification; (3) calculating a comprehensive resistance surface using the entropy weight method, incorporating key factors (land cover type, NDVI, topographic relief, and slope); (4) delineating ecological corridors and nodes using Linkage Mapper and the minimum cumulative resistance (MCR) theory; and (5) integrating ecological functional zoning to synthesize the final spatial conservation and restoration strategy. Key findings reveal: (1) 20 ecological sources, totaling 8947 km² (20.9% of the study area), and 43 ecological corridors, spanning 778.24 km, were delineated within the basin. Nineteen ecological barriers (predominantly located in farmland, bare land, construction land, and low-coverage grassland) and twenty-one ecological pinch points (primarily clustered in forestland, grassland, water bodies, and wetlands) were identified. Collectively, these elements form the Henan section’s Ecological Security Pattern (ESP), integrating source areas, a corridor network, and key regional nodes for ecological conservation and restoration. (2) Building upon the ESP and the ecological baseline, and informed by ecological functional zoning, we identified a spatial framework for conservation and restoration characterized by “one axis, two cores, and multiple zones”. Tailored conservation and restoration strategies were subsequently proposed. This study provides critical data support for reconciling ecological security and economic development in the Henan Yellow River Basin, offering a scientific foundation and practical guidance for regional territorial spatial ecological restoration planning and implementation. Full article

Considering a computational service as a set of processes, several issues can impact its performance, such as failures and shutdowns. Many strategies can be used to reduce this impact as the assignment of one service in several machines or the distribution of machines in different locations by respecting some constraints such as process dependency and capacity. The Machine Reassignment Problem is a hard problem that consists of a set of machines with associated resources and processes already assigned to these machines. The goal is to obtain a redistribution of the processes according to some optimization criteria, satisfying a set of constraints. In this work, we propose an efficient collaborative local search algorithm to solve the Machine Reassignment Problem. We pay special attention to designing an easily understandable algorithm that requires less computational resources than other more sophisticated well-known approaches in the literature. We show that our approach is effective using the ROADEF competition instances as a benchmark and that can obtain high-quality solutions. Full article

Integrated energy systems (IESs) serve as pivotal platforms for realizing the reform of energy structures. The rational planning of their equipment can significantly enhance operational economic efficiency, environmental friendliness, and system stability. Moreover, the inherent randomness and intermittency of renewable energy generation, coupled with the peak and valley characteristics of load demand, lead to fluctuations in the output of multi-energy coupling devices within the IES, posing a serious threat to its operational stability. To address these challenges, this paper focuses on the economic and stable operation of the IES, aiming to minimize the configuration costs of hybrid energy storage systems, system voltage deviations, and net load fluctuations. A multi-objective optimization planning model for an electric–hydrogen hybrid energy storage system is established. This model, applied to the IEEE-33 standard test system, utilizes the Multi-Objective Artificial Hummingbird Algorithm (MOAHA) to optimize the capacity and location of the electric–hydrogen hybrid energy storage system. The Multi-Objective Artificial Hummingbird Algorithm (MOAHA) is adopted due to its faster convergence and superior ability to maintain solution diversity compared to classical algorithms such as NSGA-II and MOEA/D, making it well-suited for solving complex non-convex planning problems. The simulation results demonstrate that the proposed optimization planning method effectively improves the voltage distribution and net load level of the IES distribution network, while the complementary characteristics of the electric–hydrogen hybrid energy storage system enhance the operational flexibility of the IES. Full article

Age-related macular degeneration (AMD) is the main cause of blindness in Western nations. AMD models addressing specific pathological pathways are desired. Through this study, a best-practice protocol for polarized porcine single-eye retinal pigment epithelium (RPE) preparation for AMD-relevant models of RPE barrier and polarity is established. Single-eye porcine primary RPE cells (from one eye for one well) were prepared in 12-well plates including Transwell inserts. Different coatings (laminin (Lam), Poly-ᴅ-Lysine (PDL), fibronectin (Fn) and collagens) and varying serum contents (1%, 5% and 10%) were investigated to determine optimal culture parameters for this model. Success rates of cultures, cell number (trypan-blue exclusion assay), morphology/morphometry (light and fluorescence microscopy), protein secretion/expression (ELISA, Western blot), gene expression (qPCR), transepithelial electric resistance (TEER) and polar location of bestrophin 1 (BEST1) by cryosectioning (IHC-Fr) were assessed. Cells seeded on Lam exhibited the highest level of epithelial cells and confluence properties. Fn resulted in the highest cell number growth. Lam and Fn exhibited the highest culture success rates. TEER values and vascular endothelial growth factor secretion were highest when Lam was used. For the first time, polar (Transwell) porcine single-eye RPE morphometry parameters were determined. RPE on Lam showed bigger cells with a higher variety of cell shapes. CIV displayed the lowest claudin 19 expression. The highest basolateral expression of BEST1 was achieved with Lam coating. The higher the serum, the better the cell number increase and confluence success. A reduction in serum on Lam showed positive results for RPE morphology, while morphometry remained stable. A five percent serum on Lam showed the highest culture success rate and best barrier properties. RPE65 expression was reduced by using 10% serum. Altogether, the most suitable coating of Transwell inserts was Lam, and a reduction in serum to 5% is recommended, as well as a cultivation time of 28 days. A protocol for the use of polar porcine single-eye cultures with validated parameters was established and is provided herein. Full article

The Pyrenean capercaillie (Tetrao urogallus aquitanicus) is a forest obligate grouse that has experienced a marked population decline in recent decades owing to the lack of optimal habitats. However, the effect of forest structure on potential predators and habitat competitors has not been well-studied. We conducted a camera-trapping study at three conservation areas in Huesca province (northeastern Spain), which were classified as ‘optimal’, ‘favorable’, and ‘unfavorable’ based on habitat suitability for the capercaillie. This study was conducted for 3417 days at a total of 130 camera locations in autumn–winter and spring–summer, capturing 8757 valid photos. In total, 36 different species were recorded. The most frequently detected species were Southern chamois (Rupicapra pyrenaica pyrenaica; 32.6%), roe deer (Capreolus capreolus; 18%), wild boar (Sus scrofa; 9.6%), red squirrel (Sciurus vulgaris; 6.1%), mustelids (5.6%), and red fox (Vulpes vulpes; 4.8%). Capercaillies were photographed in the optimal and favorable habitat areas. Nest predators, such as mustelids and red fox, were more frequently detected in the favorable area during autumn–winter and in the optimal area in spring–summer, while corvids were more frequently detected in the unfavorable habitat area during both periods. No clear pattern was found for wild boar (nest predator and habitat competitor) or cervids (competitors). As capercaillie coexist with a wide range of predators and competitors, and habitat structure may not always explain species relative abundance, factors such as disturbance and food resources should be also taken into account when aiming to develop targeted management for the benefit of the capercaillie. Full article

For the local oscillation phenomenon of the APF algorithm in the face of static U-shaped obstacles, the path cusp phenomenon caused by the vehicle corner and path curvature constraints is not taken into account, as well as the low path safety caused by ignoring the vehicle volume constraints. Therefore, an APF-Dijkstra path planning fusion algorithm based on steering model and volume constraints is proposed to improve it. First, perform an expansion treatment on the obstacles in the map, optimize the search direction of the Dijkstra algorithm and its planned global path, ensuring that the distance between the path and the expanded grid is no less than 1 m, and use the path points as temporary target points for the APF algorithm. Secondly, a Gaussian function is introduced to optimize the potential energy function of the APF algorithm, and the U-shaped obstacle is ellipticized, and a virtual target point is used to provide the gravitational force. Again, the three-point arc method based on the steering model is used to determine the location of the predicted points and to smooth the paths in real time while constraining the steering angle. Finally, a 4.5 m × 2.5 m vehicle rectangle is used instead of the traditional mass points to make the algorithm volumetrically constrained. Meanwhile, a model for detecting vehicle collisions is established to cover the rectangle boundary with 14 envelope circles, and the combined force of the computed mass points is transformed into the combined force of the computed envelope circles to further improve path safety. The algorithm is validated by simulation experiments, and the results show that the fusion algorithm can avoid static U-shaped obstacles and dynamic obstacles well; the curvature change rate of the obstacle avoidance path is 0.248, 0.162, and 0.169, and the curvature standard deviation is 0.16, which verifies the smoothness of the fusion algorithm. Meanwhile, the distances between the obstacles and the center of the rear axle of the vehicle are all higher than 1.60 m, which verifies the safety of the fusion algorithm. Full article

This paper considers the problem of estimating the quantitative parameters of a two-phase fluid flow in a well based on the dynamic physical flow model. This is a challenging problem in the oil and gas industry, where the knowledge of multiphase production rates plays an important role during reservoir characterization, production optimization and reservoir management. As the direct measurement of these rates is not easily available, they can be inferred from conventional sensors (e.g., pressure gauges) in combination with a dynamic multiphase flow model. The methodology proposed in this work uses inverse modeling concepts to estimate flow rates that are not measured directly. The mismatch between the available data and model prediction is numerically minimized, leading to the optimal set of dynamic flow variables characterizing the flow. Two different scenarios are considered: firstly, when the well has only a flow meter located at the wellhead (minimum amount of available information), and when the well has distributed pressure sensors in addition to the topside flow meter (maximum amount of information). The feasibility of the proposed concept is assessed via several simulation-based case studies. Full article

This study aims to enhance the accuracy and interpretability of flood susceptibility mapping (FSM) in Seoul, South Korea, by integrating automated machine learning (AutoML) with explainable artificial intelligence (XAI) techniques. Ten topographic and environmental conditioning factors were selected as model inputs. We first employed the Tree-based Pipeline Optimization Tool (TPOT), an evolutionary AutoML algorithm, to construct baseline ensemble models using Gradient Boosting (GB), Random Forest (RF), and XGBoost (XGB). These models were further fine-tuned using Bayesian optimization via Optuna. To interpret the model outcomes, SHAP (SHapley Additive exPlanations) was applied to analyze both the global and local contributions of each factor. The SHAP analysis revealed that lower elevation, slope, and stream distance, as well as higher stream density and built-up areas, were the most influential factors contributing to flood susceptibility. Moreover, interactions between these factors, such as built-up areas located on gentle slopes near streams, further intensified flood risk. The susceptibility maps were reclassified into five categories (very low to very high), and the GB model identified that approximately 15.047% of the study area falls under very-high-flood-risk zones. Among the models, the GB classifier achieved the highest performance, followed by XGB and RF. The proposed framework, which integrates TPOT, Optuna, and SHAP within an XAI pipeline, not only improves predictive capability but also offers transparent insights into feature behavior and model logic. These findings support more robust and interpretable flood risk assessments for effective disaster management in urban areas. Full article

Evaluating the development process of mosquito species under the influence of temperature is essential for understanding their ecology and geographical distribution, as well as assessing their potential as vectors of pathogens. Aedes (Protomacleaya) terrens, a species recognized for its susceptibility and competence in transmitting the chikungunya virus, serves as a relevant model for research in this context. This study aimed to analyze the influence of temperature on egg hatching and the development cycle of this species to expand knowledge on its biology and implications for public health. During the experiment, 800 eggs were used, collected through 10 ovitraps in a forest remnant located in Uruaçu, Goiás, Brazil. The total number of eggs was divided into four groups, exposed to constant temperatures of 15 ± 2 °C, 20 ± 2 °C, 25 ± 2 °C, and 30 ± 2 °C. After hatching, first-instar larvae were individually separated and monitored daily under controlled conditions until adult emergence. The highest hatching rate occurred at 25 °C, showing an optimal point around 27 °C. Throughout development, temperature significantly reduced the duration of each stage, with the fastest complete cycle at 30 °C, a difference of approximately 10–12 days when compared to 20 °C and approximately 47 days when compared to 25 °C. These results offer valuable insights into the temperature sensitivity of Ae. terrens across its developmental stages, suggesting that each stage has its own optimal temperature. Thus, small variations in responses to environmental conditions and differentiation between sexes may become more pronounced throughout development. In this sense, temperature can affect not only the development and survival of dipterans but also the capacity for virus transmission, as the pathogen influences the reproduction rate and longevity of the vectors. Full article