Optimised synthesis of ZnO‐nano‐fertiliser through green chemistry: boosted growth dynamics of economically important L. esculentum

Mounting‐up economic losses to annual crops yield due to micronutrient deficiency, fertiliser inefficiency and increasing microbial invasions (e.g. Xanthomonas cempestri attack on tomatoes) are needed to be solved via nano‐biotechnology. So keeping this in view, the authors’ current study presents the new horizon in the field of nano‐fertiliser with highly nutritive and preservative effect of green fabricated zinc oxide‐nanostructures (ZnO‐NSs) during Lycopersicum esculentum (tomato) growth dynamics. ZnO‐NS prepared via green chemistry possesses highly homogenous crystalline structures well‐characterised through ultraviolet and visible spectroscopy, Fourier transform infrared spectroscopy, X‐ray diffraction and scanning electron microscope. The ZnO‐NS average size was found as small as 18 nm having a crystallite size of 5 nm. L. esculentum were grown in different concentrations of ZnO‐NS to examine the different morphological parameters includes time of seed germination, germination percentage, the number of plant leaves, the height of the plant, average number of branches, days count for flowering and fruiting time period along with fruit quantity. Promising results clearly predict that bio‐fabricated ZnO‐NS at optimum concentration resulted as growth booster and dramatically triggered the plant yield.Inspec keywords: zinc compounds, II‐VI semiconductors, wide band gap semiconductors, nanostructured materials, nanofabrication, ultraviolet spectra, visible spectra, Fourier transform infrared spectra, X‐ray diffraction, scanning electron microscopy, crystallites, biomedical materials, nanomedicineOther keywords: ZnO‐nanofertiliser, green chemistry, boosted growth dynamics, L. esculentum, mounting‐up economic losses, micronutrient deficiency, fertiliser inefficiency, microbial invasions, Xanthomonas cempestri, nanobiotechnology, zinc oxide‐nanostructures, Lycopersicum esculentum, high‐homogenous crystalline structures, ultraviolet spectroscopy, visible spectroscopy, Fourier transform infrared spectroscopy, X‐ray diffraction, scanning electron microscopy, crystallite size, morphological parameters, seed germination, germination percentage, plant leaves, ZnO     

Intelligent Cloud Based Load Balancing System Empowered with Fuzzy Logic

Cloud computing is seeking attention as a new computing paradigm to handle operations more efficiently and cost-effectively. Cloud computing uses dynamic resource provisioning and de-provisioning in a virtualized environment. The load on the cloud data centers is growing day by day due to the rapid growth in cloud computing demand. Elasticity in cloud computing is one of the fundamental properties, and elastic load balancing automatically distributes incoming load to multiple virtual machines. This work is aimed to introduce efficient resource provisioning and de-provisioning for better load balancing. In this article, a model is proposed in which the fuzzy logic approach is used for load balancing to avoid underload and overload of resources. A Simulator in Matlab is used to test the effectiveness and correctness of the proposed model. The simulation results have shown that our proposed intelligent cloud-based load balancing system empowered with fuzzy logic is better than previously published approaches.     

Analytical Model to Quantify Crude Oil Spill Volume in Sandy Layered Aquifers

Crude oil spills have far-reaching consequences not only from the point of view of economic considerations but also from the point of view of environmental pollution. This is particularly important in oil-producing countries like Saudi Arabia. In an attempt to study this phenomenon further, an analytical and experimental study was undertaken. In this study analytical estimation of crude oil spills in layered sandy soils was performed by vertically integrating the oil content function. This function exhibits discontinuities at the interfaces of different soil layers. The influence of layering on the estimation process was examined by comparing the analytical results with and without the layering. An experimental setup was used to assess the predictions of the analytical model formulated. The experimental results, for homogeneous soils, were compared with predictions of previous analytical and empirical models. This study shows that analytical as well as empirical models consistently underestimate the actual spill volume in soils. Furthermore, analytical predictions were shown to depend on how close the system was to hydrostatic equilibrium. It also shows that soil layering is a very significant factor that should be incorporated in quantifying the extent of aquifer contamination.     

基于正则互表示的无监督特征选择方法

针对高维数据含有的冗余特征影响机器学习训练效率和泛化能力的问题,为提升模式识别准确率、降低计算复杂度,提出了一种基于正则互表示（RMR）性质的无监督特征选择方法。首先,利用特征之间的相关性,建立由Frobenius范数约束的无监督特征选择数学模型;然后,设计分治-岭回归优化算法对模型进行快速优化;最后,根据模型最优解综合评估每个特征的重要性,选出原始数据中具有代表性的特征子集。在聚类准确率指标上,RMR方法与Laplacian方法相比提升了7个百分点,与非负判别特征选择（NDFS）方法相比提升了7个百分点,与正则自表示（RSR）方法相比提升了6个百分点,与自表示特征选择（SR_FS）方法相比提升了3个百分点;在数据冗余率指标上,RMR方法与Laplacian方法相比降低了10个百分点,与NDFS方法相比降低了7个百分点,与RSR方法相比降低了3个百分点,与SR_FS方法相比降低了2个百分点。实验结果表明,RMR方法能够有效地选出重要特征,降低数据冗余率,提升样本聚类准确率。     

TC-Net: A Modest & Lightweight Emotion Recognition System Using Temporal Convolution Network

Speech signals play an essential role in communication and provide an efficient way to exchange information between humans and machines. Speech Emotion Recognition (SER) is one of the critical sources for human evaluation, which is applicable in many real-world applications such as healthcare, call centers, robotics, safety, and virtual reality. This work developed a novel TCN-based emotion recognition system using speech signals through a spatial-temporal convolution network to recognize the speaker’s emotional state. The authors designed a Temporal Convolutional Network (TCN) core block to recognize long-term dependencies in speech signals and then feed these temporal cues to a dense network to fuse the spatial features and recognize global information for final classification. The proposed network extracts valid sequential cues automatically from speech signals, which performed better than state-of-the-art (SOTA) and traditional machine learning algorithms. Results of the proposed method show a high recognition rate compared with SOTA methods. The final unweighted accuracy of 80.84%, and 92.31%, for interactive emotional dyadic motion captures (IEMOCAP) and berlin emotional dataset (EMO-DB), indicate the robustness and efficiency of the designed model.     

Recent developments in the organization goals conformance using ontology

Organizational goals serve as the most important achievement target in every organization. Even though some researchers have developed the concept of the organization goals, but structuring the organization goals model is always questionable by the way it is being used. In this paper, we propose ontology to develop a unified model for the organization goals structure. We review the recent literature on the organization modelling and ontology development as an effort to evaluate the organization goals using a metrics for the achievement of the organization goals. We suggest that the metrics is important to identify the relevant organization data in relation to the organization goals conformance. In order to achieve this purpose, we investigate various associated concepts and organize the literature based on the organization goals, organization ontology and metrics model. We observe our proposed models are important for domain experts and entrepreneurs to evaluate the relevant organization data and to assist them in decision making. In summary, the contribution of this survey may serve as a first step in understanding the evaluation of the organization data for the achievement of the organization goals.     

Annealing Induced Enhancement in Magnetic Properties of Co0.5Zn0.5Fe2O4 Nanoparticles

In this paper, cobalt zinc ferrite (Co_0.5Zn_0.5Fe₂O₄) nanoparticles (NPs) have been prepared using chemical co-precipitation method. In order to investigate the annealing induced effects on their various physical properties, the prepared samples have been annealed at 500 °C, 650 °C and 1000 °C and then compared with as-prepared sample. X-ray diffraction (XRD) patterns of as-prepared and annealed samples at various temperatures exhibit single phase spinel structure. Enhancement in crystallinity and crystallite size is observed with the increase in annealing temperature. The annealing has also greatly influence the morphology and grain size of prepared NPs. The Co_0.5Zn_0.5Fe₂O₄ NPs have shown remarkable enhancement in magnetic moment with increase in annealing temperature. The bandgap energies of Co_0.5Zn_0.5Fe₂O₄ NPs have been measured via UV Spectrometer and observed to decrease with annealing temperature. FTIR spectra of the samples reveal the presence of both high frequency and low-frequency bands due to tetrahedral and octahedral sites, which corroborate well with the XRD results. The observed characteristics of cobalt zinc ferrite NPs as a function of annealing temperature are the rising contender for many data storage and nanodevice applications. Finally, the genotoxicity of prepared nanoferrites has been checked via comet assay.     

A Methodology for Developing Maintenance Policy for Automated Manufacturing Plants

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