Potable Water Pollution Crime in Jordanian Penal Law

Most legislations in world countries enacted laws and regulations to protect the national environments of such countries, including Jordan. The Jordanian environmental law develops constantly to cope with the developments place in most industrial and technological fields. Jordanian Penal Law incriminates some acts committed against the environment including potable water pollution Crime as stipulated by the Jordanian legislator in Article No. 458 of Jordanian Penal Code.Such research aims to shed the light on such crime regarding describing its elements, the due penalty defined for such crime, distinguishing between the crime – subject matter of the research – and other crimes mentioned in penal law and environment protection law No. 6 of the year 2017 and defining its relative matters. Keywords: Environment, Water Pollution, Environment Protection, Penal Law

Using Machine Learning for Land Suitability Classification

Artificial intelligence and machine learning methods can be used to automate the land suitability classification. Multiple Classifier System (MCS) or ensemble methods are rapidly growing and receiving a lot of attention and proved to be more accurate and robust than an excellent single classifier in many fields. In this study a dataset based land suitability classification is addressed. It is done using a newly proposed ensemble classifier generation technique referred to as RotBoost, which is constructed by combining Rotation Forest and AdaBoost, and it is known to be the first time that RotBoost has been applied for suitability classification. The experiments conducted with the study area, Shavur plain, lies in the northern of Khuzestan province, southwest of Iran. It should be noted that suitability classes for the input data were calculated according to FAO method. This provides positive evidence for the utility of machine learning methods in land suitability classification especially MCS methods. The results demonstrate that RotBoost can generate ensemble classifiers with significantly higher prediction accuracy than either Rotation Forest or AdaBoost, which is about 99% and 88.5%, using two different performance evaluation measures

Bioactivity of essential oil from Satureja hortensis (Laminaceae) against three stored-product insect species

Human health problems and environmental hazards caused by the indiscriminate use of chemical pesticides during the past three decades, led scientists to look for less persistent and biodegradable alternatives. Essential oils from aromatic plants are recognized as proper alternatives. In this experiment, toxicity of Satureja hortensis essential oil that was isolated via hydrodistillation was investigated against 1 to 7 day-old adults of the red flour beetle, Tribolium castaneum (Herbst), 12 to 14 day-old larvae of the Mediterranean flour moth, Ephestia kuehniella (Zell.) and Indianmeal moth, Plodia interpunctella (Hübner). Repellency of this oil on all the three pest species adults was also studied. After 48 h of exposure, the LC50 value for T. castaneum was 192.35 μl/L. LC50 values were calculated as 80.9 μl/L and 139.8 μl/L after 9 h for E. kuhniella and P. interpunctella, respectively. S. hortensis oil showed more contact toxicity against P. interpunctella (LC50 = 0.19 μl/cm2) than E. kuehniella (LC50 = 0.27 μl/cm2). Repellency of this oil on all the insect species was high. Relationship between exposure time and oil concentration on mortality of all species indicated that mortality was increased by increasing the oil concentration and exposure time.Key words: Tribolium castaneum, Ephestia kuehniell, Plodia interpunctella, repellency, fumigant toxicity, contact toxicity

In search of a combined brucellosis and tuberculosis vaccine for cattle

Bovine brucellosis is caused by Brucella abortus. The bacterial pathogen causes economic losses because it induces abortion in cattle. Vaccination of calves with live B. abortus strain 19 induces a certain level of protection but induces persistent antibodies against cell envelope lipopolysaccharide that make it difficult to Distinguish Infected from Vaccinated Animals (DIVA). Live vaccine B. abortus strain RB51 was developed to eliminate such interfering antibodies and therefore, facilitate the differentiation of infected from vaccinated animals and help in the eradication of the disease. Vaccination with strain RB51 induces levels of protection similar to strain 19 but neither of the two vaccines give complete protection. We have been working to enhance protection induced by strain RB51 vaccine. Protective Brucella antigens can be over-expressed in strain RB51 by introducing a plasmid containing the leuB gene and the genes encoding such antigens. To avoid the expression of antibiotic resistance genes, we produced a leuB deficient strain RB51 and introduced a plasmid containing the leuB gene and the genes to be over-expressed. This new strain maintains the plasmid and has induced significantly high protection levels in mice. In addition, it allowed the construction of an RB51 vaccine strain able to express Mycobacterium bovis protective antigens so that the vaccine could protect against brucellosis and tuberculosis simultaneously

Gland segmentation in gastric histology images: detection of intestinal metaplasia

Gastric cancer is one of the most frequent causes of cancer-related deaths worldwide. Gastric intestinal metaplasia (IM) of the mucosa of the stomach has been found to increase the risk of gastric cancer and is considered as one of the precancerous lesions. Therefore, early detection of IM may have a valuable role in histopathological risk assessment regarding the possibility of progression to cancer. Accurate segmentation and analysis of gastric glands from the histological images plays an important role in the diagnostic confirmation of IM. Thus, in this paper, we propose a framework for segmentation of gastric glands and detection of IM. More specifically, we propose the GAGL-Net for the segmentation of glands. Then, based on two features of the extracted glands we classify the tissues into normal and IM cases. The results showed that the proposed gland segmentation approach achieves an F1 score equal to 0.914. Furthermore, the proposed methodology shows great potential for the IM detection achieving an accuracy score equal to 96.6%. To evaluate the efficiency of the proposed methodology we used a publicly available dataset and we created the GAGL dataset consisting of 59 Whole Slide Images (WSI) including both IM and normal cases