Root-mediated effects in carrot resistance to the carrot fly,Psila rosae

Field experiments on two different soil types in Ireland assessed the extent and mechanisms of resistance toPsila rosae (F.), the carrot fly, with emphasis on the role of the carrot root. Ten carrot cultivars gave consistent results in terms of resistant and susceptible cultivars. Nonpreference oviposition was confirmed as a mechanism, and the use of egg traps, providing differential exposure of the main root, showed this was regulated by root factors, probably chemical constituents. Independent main root resistance to the larva was also confirmed, and this effect was established as consistent with a chemically mediated nonpreference. Antibiosis by the root was demonstrated. Such effects in three different modes indicate that main root properties are crucial in carrot resistance toPsila and suggest a pervasive influence of root chemicals on such resistance.     

Photo‐induced hydrogen on iron hexagonal mesoporous silica (Fe‐HMS) photo‐catalyst

The iron hexagonal mesoporous silica (Fe‐HMS)‐n photocatalyst, where n is the molar ratio Si/Fe in the precursor gel (=50), has been successfully synthesized at an ambient temperature using hexadecylamine as template agent. The material was characterized by X‐ray diffraction, N₂ adsorption measurement Brunauer, Emmet, Taller (BET) and Barrett–Joyner–Halenda theory, UV–Vis spectroscopy, high‐resolution transmission electron microscopy and electron spin resonance. The electrical conductivity follows an Arrhenius‐type law with activation energy of 0.04 eV. Fe₂O₃ is uniformly dispersed on the HMS surface; it works synergistically to make Fe‐HMS photoelectrochemically active. The flat band potential (?0.75 V_SCE) is higher than the potential of hydrogen generation (?0.50 V_SCE at pH~7), and the material shows high efficiency for hydrogen evolution upon visible light. The photoactivity in Na₂SO₄ and Na₂SO₃ (0.1 M) solution was found to be 136 and 175 µmol g^‐1 min^‐1, respectively under full light (29 mW cm^‐2). The tendency to saturation, observed only in SO₃^2‐ electrolyte, is ascribed to the competitive reduction of the end product, namely S₂O₆^2‐ with the water reduction. Copyright © 2011 John Wiley & Sons, Ltd.     

Heat Recovery of a Diammonium Phosphates Drying Unit

Solid product drying is a widely used unit operation in the chemical industry. It is highly energy intensive, requiring hot, dry air streams at high temperatures to dry a wide range of products. Exhaust air from a dryer is usually vented to the atmosphere with little or no heat recovery. In the present work, a study was performed to analyze the performance of an absorption heat transformer (AHT) applied to the evaluation of thermal rejections of drying unit of diammonium phosphates (DAP). First, a basic AHT system is described and the operating sequence is explained. Next, an application of the AHT system to an industrial company is analyzed. A numerical simulation of the system using Aspen Plus software was performed to determine the effect of different parameters on the AHT system performance and the results are presented in graphical form.     

Simulated annealing algorithm for combined economic and emission power dispatch using max/max price penalty factor

A new recurrent neural model for crack growth process of aluminium alloy is developed in this work. It is shown that a recurrent neural network with the feedback loops at the output layer is constructed to model the dynamic relationship between the crack growth and cyclic stress excitations of aluminium alloy. The output feedback loops in the neural model play the role of capturing the fine changes of crack growth dynamics. The Extreme Learning Machine is then used to uniformly randomly assign the input weights in a proper range and globally optimize both the output weights and feedback parameters, to ensure that the dynamics of crack growth under variable-amplitude loading can be accurately modeled. The simulation results with the averaged experimental data of the 2024-T351 aluminium alloy show that the excellent modeling and prediction performance of the recurrent neural model can be achieved for fatigue crack growth of aluminium alloys.     

Ab initio Insight of the Electronic,Structural, Mechanical and Optical Properties of X $$_3$$ 3 P $$_2$$ 2 (X= Mg,Ca) from GGA and Hybrid Functional (HSE06)

Journal of Superconductivity and Novel Magnetism - In this paper, the structural, mechanical and optoelectronic properties of X $$_3$$ P $$_2$$ (X= Mg, Ca) have been investigated by using the...     

Transfer Learning for Chest X-rays Diagnosis Using Dipper ThroatedAlgorithm

Most children and elderly people worldwide die from pneumonia, which is a contagious illness that causes lung ulcers. For diagnosing pneumonia from chest X-ray images, many deep learning models have been put forth. The goal of this research is to develop an effective and strong approach for detecting and categorizing pneumonia cases. By varying the deep learning approach, three pre-trained models, GoogLeNet, ResNet18, and DenseNet121, are employed in this research to extract the main features of pneumonia and normal cases. In addition, the binary dipper throated optimization (DTO) algorithm is utilized to select the most significant features, which are then fed to the K-nearest neighbor (KNN) classifier for getting the final classification decision. To guarantee the best performance of KNN, its main parameter (K) is optimized using the continuous DTO algorithm. To test the proposed approach, six evaluation metrics were employed namely, positive and negative predictive values, accuracy, specificity, sensitivity, and F1-score. Moreover, the proposed approach is compared with other traditional approaches, and the findings confirmed the superiority of the proposed approach in terms of all the evaluation metrics. The minimum accuracy achieved by the proposed approach is (98.5%), and the maximum accuracy is (99.8%) when different test cases are included in the evaluation experiments.     

Multi-source shared nearest neighbours for multi-modal image clustering

Shared Nearest Neighbours (SNN) techniques are well known to overcome several shortcomings of traditional clustering approaches, notably high dimensionality and metric limitations. However, previous methods were limited to a single information source whereas such methods appear to be very well suited for heterogeneous data, typically in multi-modal contexts. In this paper, we propose a new technique to accelerate the calculation of shared neighbours and we introduce a new multi-source shared neighbours scheme applied to multi-modal image clustering. We first extend existing SNN-based similarity measures to the case of multiple sources and we introduce an original automatic source selection step when building candidate clusters. The key point is that each resulting cluster is built with its own optimal subset of modalities which improves the robustness to noisy or outlier information sources. We experiment our method in the scope of multi-modal search result clustering, visual search mining and subspace clustering. Experimental results on both synthetic and real data involving different information sources and several datasets show the effectiveness of our method.     

Deterioration of a Tunisian coastal aquifer due to agricultural activities and possible approaches for better water management

The purpose of this study is to provide a better understanding of the impact of agricultural activities on groundwater quality in a semi‐arid coastal environment (Korba, Tunisia). Impacts have been assessed through groundwater samples that were analysed for a range of chemical and biological parameters. Results indicate that groundwater overexploitation for irrigation and fertiliser use have affected the quality of the groundwater not only by direct contamination but also seawater intrusion. The implementation of appropriate regulatory and economic instruments to foster sustainable practices in the agricultural sector have been analysed within the framework of the EC (European Community) FP6 (funding program 6) INECO (Institutional and Economic Instruments for Sustainable Water Management in the Mediterranean Region) Project. Through stakeholder involvement processes, the project analysed issues relating to the regulation and control of groundwater abstractions, the promotion of wastewater reuse, efficiency improvements in irrigation water use, as well as instruments to strengthen the socioeconomic and institutional environment towards better managing and conserving available groundwater resources.