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Eucalyptus globulus Labill. is one of the most widely planted hardwood species worldwide. This species occupies a quarter of the country's forested area in Portugal, so there is a growing concern about its post-fire dispersal. Although it is generally recognised that fire promotes E. globulus natural regeneration and that precipitation and topography influence recruitment, little is known about the role of post-fire conditions on the establishment of the species following off-season fires. We examine how post-fire conditions affect E. globulus natural regeneration and invasive potential. Sapling establishment was assessed in plantations and under old and large isolated eucalyptus trees (seed-trees) following two off-season fire events (2017 June and October fires). Abiotic and biotic local factors affected differently sapling establishment in plantations and under seed-trees. In plantations, sapling cover was more influenced by tree-related traits (age and height), whereas the conditions beneath seed-trees mitigated the impact of harsh conditions on saplings. In both cases, post-fire climatic conditions constrained natural regeneration, with less impact under seed-trees, reinforcing their importance in establishment success. Thus, isolated seed-trees should be considered while managing the species’ unplanned spread.

Understanding forest dynamics at the stand level is crucial for sustainable management. Landsat time series have been shown to be effective for identification of drastic changes, such as natural disturbances or clear-cuts, but detecting subtle changes requires further research. Time series of six Landsat-derived vegetation indexes (VIs) were analyzed with the BFAST (Breaks for Additive Season and Trend) algorithm aiming to characterize the changes resulting from harvesting practices of different intensities (clear-cutting, cutting with seed-trees, and thinning) in a Mediterranean forest area of Spain. To assess the contribution of airborne laser scanner (ALS) data and the potential implications of it being after or before the detected changes, two scenarios were defined (based on the year in which ALS data were acquired (2010), and thereby detecting changes from 2005 to 2010 (before ALS data) and from 2011 to 2016 (after ALS data). Pixels identified as change by BFAST were attributed with change in VI intensity and ALS-derived statistics (99th height percentile and forest canopy cover) for classification with random forests, and derivation of change maps. Fusion techniques were applied to leverage the potential of each individual VI change map and to reduce mapping errors. The Tasseled Cap Brightness (TCB) and Normalized Burn Ratio (NBR) indexes provided the most accurate results, the latter being more precise for thinning detection. Our results demonstrate the suitability of Landsat time series and ALS data to characterize forest stand changes caused by harvesting practices of different intensity, with improved accuracy when ALS data is acquired after the change occurs. Clear-cuttings were more readily detectable compared to cutting with seed-trees and thinning, detection of which required fusion approaches. This methodology could be implemented to produce annual cartography of harvesting practices, enabling more accurate statistics and spatially explicit identification of forest operations.

Commitments to Forest and Landscape Restoration are rapidly growing and being implemented globally to tackle the climate and biodiversity crises. Restoration initiatives largely based on tree planting necessitate an increased supply of high-quality and suitably adapted tree planting material. We evaluated the native tree seed supply systems in Burkina Faso, Cameroon, Ghana, and Kenya, four countries with large commitments to increase tree cover. We applied an established indicator framework to assess the adequacy of any current tree seed system to meet national needs. The study aimed to analyse (i) how well-established the native tree seed supply systems are, (ii) how public and non-public actors differ regarding the perception of existing seed systems, and (iii) the main barriers to strengthening current seed systems. Our findings identified significant gaps in the native tree seed supply systems of the four countries, arising particularly from shortfalls in the enabling environment. We found a lack of involvement of local community members in the seed systems, with a crucial need for strengthening policy, capacity building and investment in seed systems. We propose a multi-stakeholder approach and the application of online tools to improve seed systems to meet the demand for high-quality native tree seeds.

Optimal seed-tree selection during natural regeneration of shade-intolerant species requires ensuring an ample and uniform seed supply from residual trees with the smallest possible seed-tree density. Here, we propose a novel approach for seed-tree selection using the genetic algorithm. Data are derived from a 3-hectare even-aged stand of Pinus canariensis C.Sm. ex DC, comprising 364 mature trees and 103 seed-traps. Seeds were collected in 2007 and 2008. After constructing a seed-dispersal model for each seed-crop year, we employ the multi-objective non-dominated sorting genetic algorithm to identify the smallest seed-tree set that maximizes post-treatment seed supply and its spatial homogeneity. Optimal solutions range from a maximum of 68.4% to a minimum of 38.1% reduction in stand density, resulting in a 59.5% to 28% reduction in post-felling seed supply. The coefficient of variation of among-site seed-flux varies from 28% to 59.5%. Proposing a treatment involving the removal of 240 trees (65.9% stand-density reduction) and leaving 40 seed-trees per hectare, our findings provide insights into balancing the conflicting objectives of sufficient post-treatment seed supply at a minimum seed-tree density. This approach marks a departure from traditional practices, as the decision about which trees to cut is historically left to the discretion of field managers.

The Tree–Seed Algorithm (TSA) is a symmetry-driven metaheuristic algorithm that shows potential for complex optimization problems, but it suffers from local optimum entrapment and slow convergence. To address these limitations, we propose the ADTSA algorithm. First, ADTSA adopts a symmetry-driven dual-layer framework for seed generation, which promotes effective information exchange between subpopulations and accelerates convergence speed. In later iterations, ADTSA enhances the population’s exploitation ability through a population fusion mechanism, further improving the convergence speed. Moreover, we propose a historical optimal solution archiving and replacement mechanism, along with a t-distribution perturbation mechanism, to enhance the algorithm’s ability to escape local optima. ADTSA also strengthens population diversity and avoids local optima through convex lens symmetric reverse generation based on the optimal solution. With these mechanisms, ADTSA converges more effectively to the global optimum during the evolutionary process. Tests on the IEEE CEC 2014 benchmark functions showed that ADTSA outperformed several top-performing algorithms, such as LSHADE, JADE, LSHADE-RSP, and the latest TSA variants, and it also excelled in comparison with other optimization algorithms, including GWO, PSO, BOA, GA, and RSA, underscoring its robust performance across diverse testing scenarios. The proposed ADTSA’s applicability in solving complex constrained problems was also validated, with the results showing that ADTSA achieved the best solutions for these complex problems.

Malicious malware targeting Android systems has alarmingly increased due to the quick spread of Android devices. For these devices to be secure and to protect the private data of users, Android virus detection is essential. The selection of features, model performance, and efficiency are issues with existing Android malware detection techniques. To overcome these drawbacks, we suggest a unique method for identifying malicious Android data that combines Tree Seed Optimization with Support Vector Machines (TSO-SVM).TSO is a nature-inspired optimization technique that looks for the best feature subsets by simulating the tree's seed dispersal process. The efficiency and effectiveness of SVM-based classification are increased by our method's use of TSO to choose the most instructive features from the Android malware dataset. To normalize the features of the Android application dataset before training, we use a data-cleaning method known as Z-Score normalization. Our Android malware detection solution uses Independent Component Analysis (ICA) as a feature reduction method. Our test results show how well the TSO-SVM technique works at detecting Malicious Android data. In terms of accuracy, precision, recall, and F1-Score for malicious detection, the suggested model achieves 97.12%, 96.35%, 97.88%, and 96.84%, respectively. The proposed technique successfully solves the problem of suboptimal classification accuracy in the presence of dynamic and changing malware threats. The results of this work highlight the potential of TSO techniques for enhancing the security of Android-based devices and present a promising direction for further investigation in the area of mobile security.

Vast areas of hardwood and mixedwood forests of eastern North America have been high-graded in the past and need silvicultural treatments to increase their value and productivity. To rehabilitate a high-graded temperate mixedwood stand, in Quebec, Canada, we used a split–split-split plot design with three replicates to assess different seed-tree and strip cutting systems, in combination with scarification and planting. The experiment consisted of three regeneration cuts in main plots: 10 seed-trees/ha, 40 seed-trees/ha and a 40-m wide strip clearcut (0 seed-tree/ha) with 60 seed-trees/ha in leave strips, thereby resulting in four levels of tree retention, and all included understory brushing. We applied two types of scarification (patch scarification or disk-trenching) to subplots, two regeneration modes (natural regeneration or planting with white spruce [Picea glauca]) to sub-subplots and two mechanical release treatments (softwood or mixedwood production) to planted sub–sub-subplots. Density of seed-trees did not affect the natural regeneration dynamics after 5 years, but disk-trenching was more efficient for the establishment of yellow birch (Betula alleghaniensis) and sugar maple (Acer saccharum). Few seed-trees of desirable white spruce were present and most died standing, confirming the importance of supplemental planting. Height growth of planted seedlings was 15% higher in the 0 and 10 (26–27 cm/year) than in the 40 and 60 (23 cm/year) seed-trees/ha treatments, and release doubled mean height growth (33.1 vs. 16.6 cm/year). Despite intensive site preparation, pre-established beaked hazel (Corylus cornuta) and mountain maple (Acer spicatum) were present at high densities in the regeneration stratum. Controlling this recalcitrant layer might be the greatest challenge for rehabilitating degraded stands of the mixedwood forest, especially since the use of herbicides is prohibited on Quebec’s public lands.

This experiment investigated effects of phenological stages and ensiling length on chemical composition of Megathyrsus maximus ensiled with Moringa oleifera seeds at different proportions. The grass was harvested at 8 (vegetative stage) and 11 (reproductive stage) weeks after planting and were ensiled with Moringa oleifera seeds at different proportions (100:0, 75:25 and 50:50) for 30, 60, 90 and 120 days. Data collected were analyzed using the 2×3×4 factorial design. Result showed that the CP were significantly lower (P < 0.05) in sole M. maximus silage at both phenological stages for 120 days (70.38 and 63.99 g kg-1) and also in sole M. maximus silage at reproductive stage ensiled for 90 days (68.82 g kg-1). The value of NDF for all proportions decreased with increasing length of ensiling with lowest value at 120 days. The highest tannin content was recorded for 50% M. oleifera seed silage at reproductive stage ensiled for 30 days (4.15 g kg-1). It can be concluded from this study that silages from both phenological stages containing M. oleifera seeds improved chemical composition as the ensiling length prolonged.

INTRODUCTION: The method of minimizing the number of random variables or attributes from the enormous data set is the reduction of dimensionality. The space available for storing the database is therefore minimized by decreasing the scale of the features.OBJECTIVES: The PCA algorithm is used to achieve dimensional reduction by deep learning to recover image characteristics. This approach is designed to reduce the dimensionality of such datasets, improve interpretability while minimizing the loss of information.METHODS: The dimensionality reduction of the method by using optimized PCA algorithm. The input data set can be reducing the dimension by using PCA algorithm. The tree seed optimization algorithm (TSO) can be utilized to select the optimal data’s in PCA algorithms. After completing the TSO-PCA the new data set are created by the reduced dimensions.RESULTS: The input data and images are used to reduce the dimension based on the TSO-PCA algorithms. The simulations for obtaining the results were carried out using python. The results of the feature dimensionality reduction on DIABETES dataset and Indian pines dataset.CONCLUSION: The best data for the data collection, the TSO algorithm is used and the PCA algorithm is used to minimize the dimensions. The suggested method is better than the existing method compared to the linear, kernel, random basic function, and polynomial for evaluating the outcome and discussion. In order to improve accuracy in future work, we will continue research and try to find more advanced techniques for this problem.