Evaluating the impact of roads on the diversity pattern and density of trees to improve the conservation of species

Unveiling the resistance of native weed communities: insights for managing invasive weed species in disturbed environments

Weed communities influence the dynamics of ecosystems, particularly in disturbed environments where anthropogenic activities often result in higher pollution. Understanding the dynamics existing between native weed communities and invasive species in disturbed environments is crucial for effective management and normal ecosystem functioning. Recognising the potential resistance of native weed communities to invasion in disturbed environments can help identify suitable native plants for restoration operations. This review aims to investigate the adaptations exhibited by native and non-native weeds that may affect invasions within disturbed environments. Factors such as ecological characteristics, altered soil conditions, and adaptations of native weed communities that potentially confer a competitive advantage relative to non-native or invasive weeds in disturbed environments are analysed. Moreover, the roles of biotic interactions such as competition, mutualistic relationships, and allelopathy in shaping the invasion resistance of native weed communities are described. Emphasis is given to the consideration of the resistance of native weeds as a key factor in invasion dynamics that provides insights for conservation and restoration efforts in disturbed environments. Additionally, this review underscores the need for further research to unravel the underlying mechanisms and to devise targeted management strategies. These strategies aim to promote the resistance of native weed communities and mitigate the negative effects of invasive weed species in disturbed environments. By delving deeper into these insights, we can gain an understanding of the ecological dynamics within disturbed ecosystems and develop valuable insights for the management of invasive species, and to restore long-term ecosystem sustainability.

Automatic lung disease classification from the chest X-ray images using hybrid deep learning algorithm

The chest X-ray images provide vital information about the congestion cost-effectively. We propose a novel Hybrid Deep Learning Algorithm (HDLA) framework for automatic lung disease classification from chest X-ray images. The model consists of steps including pre-processing of chest X-ray images, automatic feature extraction, and detection. In a pre-processing step, our goal is to improve the quality of raw chest X-ray images using the combination of optimal filtering without data loss. The robust Convolutional Neural Network (CNN) is proposed using the pre-trained model for automatic lung feature extraction. We employed the 2D CNN model for the optimum feature extraction in minimum time and space requirements. The proposed 2D CNN model ensures robust feature learning with highly efficient 1D feature estimation from the input pre-processed image. As the extracted 1D features have suffered from significant scale variations, we optimized them using min-max scaling. We classify the CNN features using the different machine learning classifiers such as AdaBoost, Support Vector Machine (SVM), Random Forest (RM), Backpropagation Neural Network (BNN), and Deep Neural Network (DNN). The experimental results claim that the proposed model improves the overall accuracy by 3.1% and reduces the computational complexity by 16.91% compared to state-of-the-art methods.

Different leafy vegetable cropping systems regulate growth, photosynthesis, and PSII functioning in mono-cropped eggplant by altering chemical properties and upregulating the antioxidant system

Continuous cropping of eggplant threatened regional ecological sustainability by facilitating replanting problems under mono-cropping conditions. Therefore, alternative agronomic and management practices are required to improve crop productivity at low environmental cost for the development of sustainable agricultural systems in different regions. This study examined changes in soil chemical properties, eggplant photosynthesis, and antioxidant functioning in five different vegetable cropping systems over a 2-year period., 2017 and 2018. The results showed that welsh onion-eggplant (WOE), celery-eggplant (CE), non-heading Chinese cabbage-eggplant (NCCE), and leafy lettuce-eggplant (LLE) rotation systems significantly impacted growth, biomass accumulation, and yield than fallow-eggplant (FE). In addition, various leafy vegetable cropping systems, WOE, CE, NCCE, and LLT induced significant increases in soil organic matter (SOM), available nutrients (N, P, and K), and eggplant growth by affecting the photosynthesis and related gas exchange parameters with much evident effect due to CE and NCCE. Moreover, eggplant raised with different leafy vegetable rotation systems showed higher activity of antioxidant enzymes, resulting in lower accumulation of hydrogen peroxide and hence reduced oxidative damage to membranes. In addition, fresh and dry plant biomass was significantly increased due to crop rotation with leafy vegetables. Therefore, we concluded that leafy vegetable crop rotation is a beneficial management practice to improve the growth and yield of eggplant.