Impact of different mulching treatments on weed flora and productivity of maize (Zea mays L.) and sunflower (Helianthus annuus L.)

The concerns on weed control through herbicides are increasing due to their negative impacts on environment and human health. Therefore, alternative weed management methods are inevitable for sustainable crop production and lowering the negative consequences of herbicides. Mulching is an environment-friendly weed management approach capable of substituting herbicides to significant extent. Therefore, this study evaluated the role of different mulching treatments on suppressing weed flora in maize (Zea mays L.) and sunflower (Helianthus annuus L.) crops. Furthermore, the impact of different mulching treatments on the productivity of both crops was also investigated. Three mulch treatments, i.e., plastic mulch (PLM), sorghum mulch (SM) and paper mulch (PM) along with two controls, i.e., weed-free (WF) and weedy-check (WC) were included in the study. Different mulch treatments significantly altered weed flora in both crops. The PLM and PM resulted in the highest suppression (43–47%) of weed flora compared to WC treatment in both crops. The highest and the lowest weed diversity was recorded for WC and WF treatments, respectively. Different allometric traits, i.e., leaf area index, crop growth rate and root length of both crops were significantly improved by PLM as compared to the WC. Overall, maize crop recorded higher density of individual and total weeds compared to sunflower with WC treatment. The density of individual and total weeds was significantly lowered by PLM compared to WC treatment in both crops. Similarly, higher growth and yield-related traits of both crops were noted with PLM compared to the rest of the mulching treatments. Results of the current study warrant that PLM could suppress weed flora and improve the productivity of both crops. However, PLM alone could not provide 100% control over weed flora; therefore, it should be combined with other weed management approaches for successful weed control in both crops

Variance Ranking for Multi-Classed Imbalanced Datasets: A Case Study of One-Versus-All

Imbalanced classes in multi-classed datasets is one of the most salient hindrances to the accuracy and dependable results of predictive modeling. In predictions, there are always majority and minority classes, and in most cases it is difficult to capture the members of item belonging to the minority classes. This anomaly is traceable to the designs of the predictive algorithms because most algorithms do not factor in the unequal numbers of classes into their designs and implementations. The accuracy of most modeling processes is subjective to the ever-present consequences of the imbalanced classes. This paper employs the variance ranking technique to deal with the real-world class imbalance problem. We augmented this technique using one-versus-all re-coding of the multi-classed datasets. The proof-of-concept experimentation shows that our technique performs better when compared with the previous work done on capturing small class members in multi-classed datasets

Interactive regulation of root exudation and rhizosphere denitrification by plant metabolite content and soil properties

Aims Root exudates are known to shape microbial activities in the rhizosphere and to be of fundamental importance for plant-soil-microbe-carbon–nitrogen interactions. However, it remains unclear how and to what extent the amount and composition of root exudation affects rhizosphere denitrification. Methods In this study root exudation patterns and rhizosphere denitrification enzyme activity of three different grass species grown on two agricultural soils under two different soil water contents were investigated under controlled conditions. Results We found that root exudation of primary metabolites largely depends on plant species, soil type, soil moisture and root exudation medium. In dependence of soil properties and soil moisture levels, plants largely controlled amount and quality of root exudation. Exudates affected denitrification activity and plant–microbe competition for nitrate. Specifically, exudation of organic acids stimulated denitrifying activity while the sugar lyxose exhibited an inhibitory effect. Conclusion We show that interactive effects of physicochemical soil properties and species-specific effects of plant metabolism on root exudation act as a dominant control of rhizosphere denitrification, thereby explaining more than half of its variance

Reinforcement learning based resource management for 6G-enabled mIoT with hypergraph interference model

For the future 6G-enabled massive Internet of Things (mIoT), how to effectively manage spectrum resources to support huge data traffic under the large-scale overlapping caused by the dense deployment of massive devices is the imperative challenge. In this paper, a novel hypergraph interference model is designed, and two reinforcement learning (RL)-based resource management algorithms in the 6G-enabled mIoT are proposed to enhance the network throughput and avoid overlapping interference. Then, based on the hypergraph interference model, the resource management problem of execution network throughput maximization is theoretically formulated under large-scale overlapping interference scenarios. To handle this problem, we convert it into a Markov decision process (MDP) model and then deal with this MDP model through the advantage actor-critic (A2C)-based resource management algorithm and asynchronous advantage actor-critic (A3C)-based resource management algorithm, which aim to maximize network throughput of the spectrum resource allocation among massive devices. The simulation results verify that the proposed algorithms can not only avoid large-scale overlapping interference but also improve the network throughput

Use of E-Learning by University Students in Malaysian Higher Educational Institutions: A Case in Universiti Teknologi Malaysia

This paper examines university students' intention to utilize e-learning. In this paper, we apply and use the theory of a technology acceptance model. We employ the structural equation modeling approach with a SmartPLS software to investigate students' adoption process. Findings indicate that the content of e-learning and self-efficacy has a positive impact and substantially associated with perceived usefulness and student satisfaction, which impact university students' intention to utilize e-learning. Although e-learning has gained acceptance in universities around the world, the study of the intention to use e-learning is still largely unexplored in Malaysia. The developed model is employed to explain the university student's intention to utilize e-learning. The study concludes that university students in Malaysia have positive perceptions toward e-learning and intend to practice it for educational purposes

Gene Expression-Based Cancer Classification for Handling the Class Imbalance Problem and Curse of Dimensionality

Cancer is a leading cause of death globally. The majority of cancer cases are only diagnosed in the late stages of cancer due to the use of conventional methods. This reduces the chance of survival for cancer patients. Therefore, early detection consequently followed by early diagnoses are important tasks in cancer research. Gene expression microarray technology has been applied to detect and diagnose most types of cancers in their early stages and has gained encouraging results. In this paper, we address the problem of classifying cancer based on gene expression for handling the class imbalance problem and the curse of dimensionality. The oversampling technique is utilized to overcome this problem by adding synthetic samples. Another common issue related to the gene expression dataset addressed in this paper is the curse of dimensionality. This problem is addressed by applying chi-square and information gain feature selection techniques. After applying these techniques individually, we proposed a method to select the most significant genes by combining those two techniques (CHiS and IG). We investigated the effect of these techniques individually and in combination. Four benchmarking biomedical datasets (Leukemia-subtypes, Leukemia-ALLAML, Colon, and CuMiDa) were used. The experimental results reveal that the oversampling techniques improve the results in most cases. Additionally, the performance of the proposed feature selection technique outperforms individual techniques in nearly all cases. In addition, this study provides an empirical study for evaluating several oversampling techniques along with ensemble-based learning. The experimental results also reveal that SVM-SMOTE, along with the random forests classifier, achieved the highest results, with a reporting accuracy of 100%. The obtained results surpass the findings in the existing literature as well

Intelligent traffic engineering in software-defined vehicular networking based on multi-path routing

© 2013 IEEE. This paper addresses traffic engineering (TE) issues in software-defined vehicular networking (SDVN). A brief analysis of the features of SDVN, which improves the efficiency of TE in SDVN, is presented. The feasibility of using multi-path routing with TE is substantiated. A procedure and an example of the formation of multiple routes based on a modified wave routing algorithm are given. Considering the features of the SDVN technology, a modified TE method is proposed, which reduces both the time complexity of forming multiple paths and the path reconfiguration time. The dynamic path reconfiguration algorithm is presented

Red Beetroot Extract Abrogates Chlorpyrifos-Induced Cortical Damage in Rats

Organophosphorus insecticides including chlorpyrifos (CPF) are mainly used for agriculture, household, and military purposes; their application is associated with various adverse reactions in animals and humans. This study was conducted to evaluate the potential neuroprotective effect of red beetroot methanolic extract (RBR) against CPF-induced cortical damage. Twenty-eight adult male Wistar albino rats were divided into 4 groups (n=7 in each group): the control group was administered physiological saline (0.9% NaCl), the CPF group was administered CPF (10 mg/kg), the RBR group was administered RBR (300 mg/kg), and the RBR+CPF group was treated with RBR (300 mg/kg) 1 hr before CPF (10 mg/kg) supplementation. All groups were treated for 28 days. Rats exposed to CPF exhibited a significant decrease in cortical acetylcholinesterase activity and brain-derived neurotrophic factor and a decrease in glial fibrillary acidic protein. CPF intoxication increased lipid peroxidation, inducible nitric oxide synthase expression, and nitric oxide production. This was accompanied by a decrease in glutathione content and in the activities of glutathione peroxidase, glutathione reductase, superoxide dismutase, and catalase in the cortical tissue. Additionally, CPF enhanced inflammatory response, indicated by increased levels and expression of interleukin-1β and tumor necrosis factor-α. CPF triggered neuronal apoptosis by upregulating Bax and caspase-3 and downregulating Bcl-2. However, RBR reversed the induced neuronal alterations following CPF intoxication. Our findings suggest that RBR can minimize and prevent CPF neurotoxicity through its antioxidant, anti-inflammatory, and antiapoptotic activities