Anti-inflammatory new coumarin from the Ammi majus L

Investigation of the aerial parts of the Egyptian medicinal plant Ammi majus L. led to isolation of new coumarin, 6-hydroxy-7-methoxy-4 methyl coumarin (2) and 6-hydroxy-7-methoxy coumarin (3); this is the first time they have been isolated from this plant. The structures of the compounds (2 &3) were elucidated by spectroscopic data interpretation and showed anti-inflammatory and anti-viral activity

Recent Foraminifera From the Firth of Clyde

Seventy-four samples were collected from the central part of the Firth of Clyde; sixty-three of these were collected with a Van Veen Grab, the remainder with a 10 cm sq. tray from intertidal sand flats. The sediment was analysed and divided into seven categories using the Wentworth scales gravel, sandy gravel, gravelly sand, sand, muddy sand, sandy mud and mud. Sixty-five of the stations yielded Foraminifera, belonging to fifty species, of which thirteen were predominant, constituting 76% of the total population. Living individuals were rare except in the shallow water, but this may have reflected the method of sampling. The distribution of the dead specimens was examined by cluster analysis, using Jaccard's Coefficient. This indicated the presence of eight thanatotopes which were principally controlled by type of sediment and depth of water. Four thanatotopes are characteristic of shallow water; one of these is from intertidal sand flats, the second from sands and gravels of about 1 m depth of water, the third from sandy sediments of average depth 14 m (range 5 - 45 m), the fourth from muddy sands of 12 - 16 m depth. The remaining four thanatotopes were from deeper water, average depth 44m, with muddy sediments. Diversity is greatest in shallow water sands and gravelly sands (1 - 45 m), and the distribution of living species in the shallow water can be correlated with the shallow water thanatotopes. The dominant species of the areas is Egerella scabra. The species recorded are similar to those found in other places around the British Isles

Performance Evaluation of Different Universal Steganalysis Techniques in JPG Files

Steganalysis is the art of detecting the presence of hidden data in files. In the last few years, there have been a lot of methods provided for steganalysis. Each method gives a good result depending on the hiding method. This paper aims at the evaluation of five universal steganalysis techniques which are “Wavelet based steganalysis”, “Feature Based Steganalysis”, “Moments of characteristic function using wavelet decomposition based steganalysis”, “Empirical Transition Matrix in DCT Domain based steganalysis”, and “Statistical Moment using jpeg2D array and 2D characteristic function”. A large Dataset of Images -1000 images- are subjected to three types of steganographic techniques which are “Outguess”, “F5” and “Model Based” with the embedding rate of 0.05, 0.1, and 0.2. It was followed by extracting the steganalysis feature used by each steganalysis technique for the stego images as well as the cover image. Then half of the images are devoted to train the classifier. The Support vector machine with a linear kernel is used in this study. The trained classifier is then used to test the other half of images, and the reading is reported The “Empirical Transition Matrix in DCT Domain based steganalysis” achieves the highest values among all the properties measured and it becomes the first choice for the universal steganalysis technique

Potential Influences of Graphic Design, And Critical Thinking on Publishing Scientific Products and Performance of Academic Services

Graphic design is a creative process that includes art and technology to convey thoughts, particularly if it is accompanied with creative skills based on strong academic knowledge. It can be used to reflect ideas, trends, and tendencies and this helps touching their reality. This research is mainly aiming at studying how critical presentation of scientific findings, data and applications with graphic and creative designs using an expressive visual language would help enhancing data dissemination and simplifying difficult scientific data and phenomenon making them more convenient for a wide range of audiences and better understood by various levels of background and professionality

Stabilisation of clay subgrade soils using ground granulated blastfurnace slag

Roads constructed on expansive clays may be adversely affected by the behaviour of the clay. Expansive clays suffer volume change due to changes in moisture content which causes heaving, cracking and the break up of the road pavement. Stabilisation of these types of soil is necessary to suppress swelling and increase the strength of the soil and thus partially decrease the thickness of road pavement layers. The use of by-product materials for stabilisation has environmental and economic benefits. Ground granulated blastfurnace slag (GGBS), a by-product material in Egypt, and lime are used in the current work to stabilise samples of a clay soil similar to a typical Egyptian clay soil. This test soil comprises 80% River Aire soil and 20% calcium montmorillonite. The main objectives of this research were to investigate the effect of GGBS, with and without lime, on the engineering behaviour (plasticity characteristics, compaction, unconfined compressive strength (UCS) and swelling potential) of the test soil and to identify the reaction products of the stabilised materials to determine the mechanisms by which changes in engineering properties are obtained. In order to achieve these objectives, extensive laboratory investigations were carried out. Various mixes (up to 10% GGBS by dry weight of the test soil and up to 30% replacement by hydrated lime) were prepared and cured under two representative conditions {20°C with 90-100% relative humidity (CCI) and 35° C with 50-60% relative humidity (CC2)} for up to 12 months. Compaction and plasticity were measured soon after mixing, the swelling potential and UCS were measured after longer curing periods. Four analytical techniques {X ray diffraction, scanning electron microscopy, differential thermal analysis and nuclear magnetic resonance (NMR)} were used to identify the reaction products of the clay fraction of the test soil mixed with various amount of GGBS and lime. This pure clay test soil was used to ease identification of the reaction products. The investigations showed that generally the engineering properties (UCS, swelling, plasticity) improved with the addition of GGBS and with increasing curing period and temperature. The addition of lime resulted in a dramatic improvement within the test ranges covered in the programme. The maximum dry density, MDD, decreased and the optimum moisture content, OMC, increased with increasing GGBS and lime content. The major changes in the UCS and swelling behaviour are due to the formation of new cementitious materials. The analytical investigation confirmed two major reactions when GGBS and lime were added to the pure clay soil, hydration of GGBS activated by lime to produce calcium aluminosilicate hydrate gel (C-A-S-H) and hydrotalcite type phase, and the clay-lime reaction to produce calcium silicate hydrate (C-S-H), (C-A-H) and (C-A-S-H). The NMR test results revealed that the aluminosilicate chain length (EL), the aluminium: silicate (Al/Si) ratio and the amount of Si in the formed C-S-H significantly increased with an increase in the curing temperature and period, which indicates a more stable and well crystalline C-S-H. The results indicate that the use of GGBS alone, or preferably with lime, could have a significant effect on the behaviour of potentially swelling clays. Recommendations for further studies include a study of the effect of cyclic loading on the test soil. Also, site trials should be carried out to assess the suitability of using these materials in the field

Early Prediction of Employee Turnover Using Machine Learning Algorithms

Employee turnover is a serious challenge for organizations and companies. Thus, the prediction of employee turnover is a vital issue in all organizations and companies. The present work proposes prediction models for predicting the turnover intentions of workers during the recruitment process. The proposed models are based on k-nearest neighbors (KNN) and random forests (RF) machine learning algorithms. The models use the dataset of employee turnover created by IBM. The used dataset includes the most essential features, which are considered during the recruitment process of the employee and may lead to turnover. These features are salary, age, distance from home, marital status, and gender. The KNN-based model exhibited better performance in terms of accuracy, precision, F-score, specificity (SP), and false-positive rate (FPR) in comparison to the RF-based model. The models predict the average probability percentage of turnover intentions of the workers. Therefore, the models can be used to aid the human resource managers to make precautionary decisions; whether the candidate employee is likely to stay or leave the job, depending on the given relevant information about the candidate employee

Generating design-sensitive occupant-related schedules for building performance simulations

Despite the benefits of occupant behavior (OB) models in simulating the effect of design factors on OB, there are challenges associated with their use in the building simulation industry due to extensive time and computational requirements. To this end, we present a novel method to incorporate these models in building performance simulations (BPS) as design-sensitive schedules. Over 2,900 design alternatives of an office were generated by varying orientation, window to wall ratio (WWR), the optical characteristics of windows and blinds, as well as indoor surfaces’ reflectance. By using daylight simulations and stochastic OB modeling, unique light use schedules were generated for each design alternative. A decision tree was then developed to be used by building designers to select light use schedules based on design parameters. These findings are relevant for building energy codes as they provide an approach to incorporate design-sensitive operational schedules for use as BPS inputs by practitioners. These design-sensitive schedules are expected to be superior to default ones currently specified in codes and standards, which ignore the effect of design factors on OB, and ultimately on energy consumption

Usability and comfort in Canadian offices: Interview of 170 university employees

Increasing building automation to improve energy efficiency introduces a risk of reducing occupants' perceived control and overall comfort. To this end, this paper presents a field study that used contextual techniques to explore the relationship between occupants' perceived control and comfort, as well as their preferences for building automation. A total of 170 occupants in 23 Canadian university campus buildings were interviewed in their offices using semi-structured interviews. All interviews entailed verbally administering a survey while photographs were systematically used to identify the context of occupants' interactions with building controls. Findings revealed that occupants' perception of comfort was moderately correlated to their perception of control over their indoor environment. Occupants also showed an overwhelming preference for more control opportunities in their offices (e.g. operable windows and dimmable lighting controls). Conducting interviews in offices yielded many interesting anecdotes and enabled the researcher to identify contextual issues related to building controls' accessibility, which may have been unnoticed otherwise. The findings of this research contribute to a broader debate within the research community about the appropriate level of building automation to optimize energy efficiency and occupant comfort

Key Performance Indicators Detection Based Data Mining

One of the most prosperous domains that Data mining accomplished a great progress is Food Security and safety. Some of Data mining techniques studies applied several machine learning algorithms to enhance and traceability of food supply chain safety procedures and some of them applying machine learning methodologies with several feature selection methods for detecting and predicting the most significant key performance indicators affect food safety. In this research we proposed an adaptive data mining model applying nine machine learning algorithms (Naive Bayes, Bayes Net Key -Nearest Neighbor (KNN), Multilayer Perceptron (MLP), Random Forest (RF), Support Vector Machine (SVM), J48, Hoeffding tree, Logistic Model Tree) with feature selection wrapper methods (forward and backward techniques) for detecting food deterioration’s key performance indicators. In conclusion the proposed model applied effectively and successfully detected the most significant indicators for meat safety and quality with the aim of helping farmers and suppliers for being sure of delivering safety meat for consumer and diminishing the cost of monitoring meat safety

Risk Assessment Approaches in Banking Sector –A Survey

Prediction analysis is a method that makes predictions based on the data currently available. Bank loans come with a lot of risks to both the bank and the borrowers. One of the most exciting and important areas of research is data mining, which aims to extract information from vast amounts of accumulated data sets. The loan process is one of the key processes for the banking industry, and this paper examines various prior studies that used data mining techniques to extract all served entities and attributes necessary for analytical purposes, categorize these attributes, and forecast the future of their business using historical data, using a model, banks\u27 business, and strategic goals