Effects of Essential Oils and Plant Extracts on Hatching, Migration and Mortality of Meloidogyne incognita

The nematicidal activity of the essential oil/pure components and plant extracts of naturally grown aromatic plant species against hatching, migration and mortality of the root knot nematode Meloidogyne incognita was investigated. The pure components carvacrol, thymol, and linalool at 1, 2 and 4 mg liter-1 concentrations were the most toxic against M. incognita second-stage juveniles (J2s) followed by terpineol and menthone. Hatching was completely inhibited at low concentrations (2, 4 mg liter-1) of carvacrol, thymol, and linalool. Clove extracts (1 mg liter-1) of Allium sativum significantly reduced hatching activity to below 8%, followed by flower extracts of Foeniculum vulgare which reduced hatching to below 25%. These extracts were also toxic against J2s of M. incognita (LC50 43) followed by leaf extracts of Pinus pinea, Origanum syriacum, Mentha microcorphylla, Eucalyptus spp. and Citrus sinensis with an estimated LC50 of 44, 50, 65, 66 and 121 ppm respectively. Flower extracts of F. vulgare had the highest effect on J2 mortality in sand (86%). The highest concentration of essential oils (6%) was detected in leaf extracts of Origanium syriacum. Over 30 major components were identified in all the plant extracts tested

URBAN WATERFRONT REVITALIZATION THROUGH LANDSCAPE DESIGN ENHANCING SOCIAL-CULTURAL CONDITIONS OF EL-MINA, TRIPOLI

The concept of waterfront revitalization has been a prominent notion in the field of urban planning and is considered an effective strategy to enhance deteriorated sites and inspire the identity and authenticity of communities. Nevertheless, there is no definite prescription for success when it comes to linking waterfront landscape design with improving the social and cultural site conditions in a deteriorating historical area. This paper examines the revitalization of the waterfront through greenway design in El-Mina, Tripoli, Lebanon in order to put emphasis on the local culture of the city and create socio-cultural opportunities for the area of study and better improve the quality of life of its residents. The research employs a mixed method approach, where both qualitative and quantitative methods are applied. The qualitative approach is based on theoretical study and the analysis of three case studies and the quantitative part of the study consists of both a survey and simulations to understand the various struggles and problems that the residents of the zone of study face and to find the optimum landscape design proposal that links the waterfront to the existing urban fabric along with sustainable environmental solutions to enhance the surrounding heritage context through the design of a Cultural Park that is applied on a lot by the city’s waterfront. A set of design recommendations regarding the connectivity of landscape waterfront spaces to heritage spaces were then issued and developed based on the synthesis of the simulation results and urban analysis

Landscape transformations at the dawn of agriculture in southern Syria (10.7–9.9 ka cal. BP): plant-specific responses to the impact of human activities and climate change

In southwest Asia, the accelerated impact of human activities on the landscape has often been linked to the development of fully agricultural societies during the middle and late Pre-Pottery Neolithic B (PPNB) period (around 10.2–7.9 ka cal. BP). This work contributes to the debate on the environmental impact of the so-called Neolitisation process by identifying the climatic and anthropogenic factors that contributed to change local and regional vegetation at the time when domesticated plants appeared and developed in southern Syria (around 10.7–9.9 ka cal. BP). In this work a multidisciplinary analysis of plant microremains (pollen and phytoliths) and macroremains (wood charcoal) is carried out along with stable carbon isotope discrimination of wood charcoals in an early PPNB site (Tell Qarassa North, west of the Jabal al-Arab area). Prior to 10.5 ka cal. BP, the results indicate a dynamic equilibrium in the local and regional vegetation, which comprised woodland-steppe, Mediterranean evergreen oak-woodlands, wetland vegetation and coniferous forests. Around 10.5–9.9 ka cal. BP, the elements that regulated the vegetation system changed, resulting in reduced proportions of arboreal cover and the spread of cold-tolerant and wetlands species. Our data show that reinforcing interaction between the elements of the anthropogenic (e.g. herding, fire-related activities) and climatic systems (e.g. temperature, rainfall) contributed to the transformation of early Holocene vegetation during the emergence of fully agricultural societies in southern Syria

Parameter self-tuning schemes for the two phase test sample sparse representation classifier

International audienceSparse Representation Classifier (SRC) and its variants were considered as powerful classifiers in the domains of computer vision and pattern recognition. However, classifying test samples is computationally expensive due to the l1norm minimization problem that should be solved in order to get the sparse code. Therefore, these classifiers could not be the right choice for scenarios requiring fast classification. In order to overcome the expensive computational cost of SRC, a two-phase coding classifier based on classic Regularized Least Square was proposed. This classifier is more efficient than SRC. A significant limitation of this classifier is the fact that the number of the samples that should be handed over to the next coding phase should be specified a priori. This paper overcomes this main limitation and proposes five data-driven schemes allowing an automatic estimation of the optimal size of the local samples. These schemes handle the three cases that are encountered in any learning system: supervised, unsupervised, and semi-supervised. Experiments are conducted on five image datasets. These experiments show that the introduced learning schemes can improve the performance of the two-phase linear coding classifier adopting ad-hoc choices for the number of local samples