Improvement of Lipase Catalytic Properties by Immobilization in Hybrid Matrices

Lipases are enzymes particularly amenable for immobilization by entrapment methods, as they can work equally well in aqueous or non-conventional media and long-time stability of enzyme activity and enantioselectivity is needed to elaborate more efficient bioprocesses. The improvement of Pseudomonas fluorescens (Amano AK) lipase characteristics was investigated by optimizing the immobilization procedure in hybrid organic-inorganic matrices using ionic liquids as additives. Ionic liquids containing a more hydrophobic alkyl group in the cationic moiety are beneficial for the activity of immobilized lipase. Silanes with alkyl- or aryl nonhydrolizable groups used as precursors in combination with tetramethoxysilane could generate composites with higher enantioselectivity compared to the native enzyme in acylation reactions of secondary alcohols. The optimal effect on both activity and enantioselectivity was achieved for the composite made from octyltrimethoxysilane and tetramethoxysilane at 1:1 molar ratio (60% increase of total activity following immobilization and enantiomeric ratio of 30). Ionic liquids also demonstrated valuable properties as reaction media for the studied reactions, comparable with the usual organic solvent, hexane.

Methane and Other Hydrocarbon Gas Emissions Resulting from Flaring in Kuwait Oilfields

Air pollution is a major environmental health problem, affecting developed and developing countries around the world. Increasing amounts of potentially harmful gases and particulate matter are being emitted into the atmosphere on a global scale, resulting in damage to human health and the environment. Petroleum-related air pollutants can have a wide variety of adverse environmental impacts. In the crude oil production sectors, there is a strong need for a thorough knowledge of gaseous emissions resulting from the flaring of associated gas of known composition on daily basis through combustion activities under several operating conditions. This can help in the control of gaseous emission from flares and thus in the protection of their immediate and distant surrounding against environmental degradation. The impacts of methane and non-methane hydrocarbons emissions from flaring activities at oil production facilities at Kuwait Oilfields have been assessed through a screening study using records of flaring operations taken at the gas and oil production sites, and by analyzing available meteorological and air quality data measured at stations located near anthropogenic sources. In the present study the Industrial Source Complex (ISCST3) Dispersion Model is used to calculate the ground level concentrations of methane and nonmethane hydrocarbons emitted due to flaring in all over Kuwait Oilfields. The simulation of real hourly air quality in and around oil production facilities in the State of Kuwait for the year 2006, inserting the respective source emission data into the ISCST3 software indicates that the levels of non-methane hydrocarbons from the flaring activities exceed the allowable ambient air standard set by Kuwait EPA. So, there is a strong need to address this acute problem to minimize the impact of methane and non-methane hydrocarbons released from flaring activities over the urban area of Kuwait.

Effective Traffic Lights Recognition Method for Real Time Driving Assistance Systemin the Daytime

This paper presents an effective traffic lights recognition method at the daytime. First, Potential Traffic Lights Detector (PTLD) use whole color source of YCbCr channel image and make each binary image of green and red traffic lights. After PTLD step, Shape Filter (SF) use to remove noise such as traffic sign, street tree, vehicle, and building. At this time, noise removal properties consist of information of blobs of binary image; length, area, area of boundary box, etc. Finally, after an intermediate association step witch goal is to define relevant candidates region from the previously detected traffic lights, Adaptive Multi-class Classifier (AMC) is executed. The classification method uses Haar-like feature and Adaboost algorithm. For simulation, we are implemented through Intel Core CPU with 2.80 GHz and 4 GB RAM and tested in the urban and rural roads. Through the test, we are compared with our method and standard object-recognition learning processes and proved that it reached up to 94 % of detection rate which is better than the results achieved with cascade classifiers. Computation time of our proposed method is 15 ms.

Gasoline and Diesel Production via Fischer- Tropsch Synthesis over Cobalt Based Catalyst

Performance of a cobalt doped sol-gel derived silica (Co/SiO2) catalyst for Fischer–Tropsch synthesis (FTS) in slurryphase reactor was studied using paraffin wax as initial liquid media. The reactive mixed gas, hydrogen (H2) and carbon monoxide (CO) in a molar ratio of 2:1, was flowed at 50 ml/min. Braunauer-Emmett- Teller (BET) surface area and X-ray diffraction (XRD) techniques were employed to characterize both the specific surface area and crystallinity of the catalyst, respectively. The reduction behavior of Co/SiO2 catalyst was investigated using the Temperature Programmmed Reduction (TPR) method. Operating temperatures were varied from 493 to 533K to find the optimum conditions to maximize liquid fuels production, gasoline and diesel.

Distributed e-Learning System with Client-Server and P2P Hybrid Architecture

We have developed a distributed asynchronous Web based training system. In order to improve the scalability and robustness of this system, all contents and a function are realized on mobile agents. These agents are distributed to computers, and they can use a Peer to Peer network that modified Content-Addressable Network. In this system, all computers offer the function and exercise by themselves. However, the system that all computers do the same behavior is not realistic. In this paper, as a solution of this issue, we present an e-Learning system that is composed of computers of different participation types. Enabling the computer of different participation types will improve the convenience of the system.

Biodegradation of Carbazole By a Promising Gram-Negative Bacterium

In the present work we report a gram negative bacterial isolate, from soil of a dye industry, with promising biorefining and bioremediation potential. This isolate (GBS.5) could utilize carbazole (nitrogen containing polycyclic aromatic hydrocarbon) as the sole source of nitrogen and carbon and utilize almost 98% of 3mM carbazole in 100 hours. The specific activity of our GBS.5 isolate for carbazole degradation at 30°C and pH 7.0 was found to be 11.36 μmol/min/g dry cell weight as compared to 10.4 μmol/min/g dry cell weight, the highest reported specific activity till date. The presence of car genes (the genes involved in denitrogenation of carbazole) was confirmed through PCR amplification.

Effect of Fermentation Time on Xanthan Gum Production from Sugar Beet Molasses

Xanthan gum is a microbial polysaccharide of great commercial significance. The purpose of this study was to select the optimum fermentation time for xanthan gum production by Xanthomonas campestris (NRRL-B-1459) using 10% sugar beet molasses as a carbon source. The pre-heating of sugar beet molasses and the supplementation of the medium were investigated in order to improve xanthan gum production. Maximum xanthan gum production in fermentation media (9.02 g/l) was observed after 4 days shaking incubation at 25°C and 240 rpm agitation speed. A solution of 10% sucrose was used as a control medium. Results indicated that the optimum period for xanthan gum production in this condition was 4 days.

Person Identification using Gait by Combined Features of Width and Shape of the Binary Silhouette

Current image-based individual human recognition methods, such as fingerprints, face, or iris biometric modalities generally require a cooperative subject, views from certain aspects, and physical contact or close proximity. These methods cannot reliably recognize non-cooperating individuals at a distance in the real world under changing environmental conditions. Gait, which concerns recognizing individuals by the way they walk, is a relatively new biometric without these disadvantages. The inherent gait characteristic of an individual makes it irreplaceable and useful in visual surveillance. In this paper, an efficient gait recognition system for human identification by extracting two features namely width vector of the binary silhouette and the MPEG-7-based region-based shape descriptors is proposed. In the proposed method, foreground objects i.e., human and other moving objects are extracted by estimating background information by a Gaussian Mixture Model (GMM) and subsequently, median filtering operation is performed for removing noises in the background subtracted image. A moving target classification algorithm is used to separate human being (i.e., pedestrian) from other foreground objects (viz., vehicles). Shape and boundary information is used in the moving target classification algorithm. Subsequently, width vector of the outer contour of binary silhouette and the MPEG-7 Angular Radial Transform coefficients are taken as the feature vector. Next, the Principal Component Analysis (PCA) is applied to the selected feature vector to reduce its dimensionality. These extracted feature vectors are used to train an Hidden Markov Model (HMM) for identification of some individuals. The proposed system is evaluated using some gait sequences and the experimental results show the efficacy of the proposed algorithm.

The Effects of the Impact of Instructional Immediacy on Cognition and Learning in Online Classes

Current research has explored the impact of instructional immediacy, defined as those behaviors that help build close relationships or feelings of closeness, both on cognition and motivation in the traditional classroom and online classroom; however, online courses continue to suffer from higher dropout rates. Based on Albert Bandura-s Social Cognitive Theory, four primary relationships or interactions in an online course will be explored in light of how they can provide immediacy thereby reducing student attrition and improving cognitive learning. The four relationships are teacher-student, student-student, and student-content, and studentcomputer. Results of a study conducted with inservice teachers completing a 14-week online professional development technology course will be examined to demonstrate immediacy strategies that improve cognitive learning and reduce student attrition. Results of the study reveal that students can be motivated through various interactions and instructional immediacy behaviors which lead to higher completion rates, improved self-efficacy, and cognitive learning.

A Dynamic Composition of an Adaptive Course

The number of framework conceived for e-learning constantly increase, unfortunately the creators of learning materials and educational institutions engaged in e-formation adopt a “proprietor" approach, where the developed products (courses, activities, exercises, etc.) can be exploited only in the framework where they were conceived, their uses in the other learning environments requires a greedy adaptation in terms of time and effort. Each one proposes courses whose organization, contents, modes of interaction and presentations are unique for all learners, unfortunately the latter are heterogeneous and are not interested by the same information, but only by services or documents adapted to their needs. Currently the new tendency for the framework conceived for e-learning, is the interoperability of learning materials, several standards exist (DCMI (Dublin Core Metadata Initiative)[2], LOM (Learning Objects Meta data)[1], SCORM (Shareable Content Object Reference Model)[6][7][8], ARIADNE (Alliance of Remote Instructional Authoring and Distribution Networks for Europe)[9], CANCORE (Canadian Core Learning Resource Metadata Application Profiles)[3]), they converge all to the idea of learning objects. They are also interested in the adaptation of the learning materials according to the learners- profile. This article proposes an approach for the composition of courses adapted to the various profiles (knowledge, preferences, objectives) of learners, based on two ontologies (domain to teach and educational) and the learning objects.

Linear-Operator Formalism in the Analysis of Omega Planar Layered Waveguides

A complete spectral representation for the electromagnetic field of planar multilayered waveguides inhomogeneously filled with omega media is presented. The problem of guided electromagnetic propagation is reduced to an eigenvalue equation related to a 2 ´ 2 matrix differential operator. Using the concept of adjoint waveguide, general bi-orthogonality relations for the hybrid modes (either from the discrete or from the continuous spectrum) are derived. For the special case of homogeneous layers the linear operator formalism is reduced to a simple 2 ´ 2 coupling matrix eigenvalue problem. Finally, as an example of application, the surface and the radiation modes of a grounded omega slab waveguide are analyzed.

New Wavelet Indices to Assess Muscle Fatigue during Dynamic Contractions

The purpose of this study was to evaluate and compare new indices based on the discrete wavelet transform with another spectral parameters proposed in the literature as mean average voltage, median frequency and ratios between spectral moments applied to estimate acute exercise-induced changes in power output, i.e., to assess peripheral muscle fatigue during a dynamic fatiguing protocol. 15 trained subjects performed 5 sets consisting of 10 leg press, with 2 minutes rest between sets. Surface electromyography was recorded from vastus medialis (VM) muscle. Several surface electromyographic parameters were compared to detect peripheral muscle fatigue. These were: mean average voltage (MAV), median spectral frequency (Fmed), Dimitrov spectral index of muscle fatigue (FInsm5), as well as other five parameters obtained from the discrete wavelet transform (DWT) as ratios between different scales. The new wavelet indices achieved the best results in Pearson correlation coefficients with power output changes during acute dynamic contractions. Their regressions were significantly different from MAV and Fmed. On the other hand, they showed the highest robustness in presence of additive white gaussian noise for different signal to noise ratios (SNRs). Therefore, peripheral impairments assessed by sEMG wavelet indices may be a relevant factor involved in the loss of power output after dynamic high-loading fatiguing task.

Treatment of Oily Wastewater by Fibrous Coalescer Process: Stage Coalescer and Model Prediction

The coalescer process is one of the methods for oily water treatment by increasing the oil droplet size in order to enhance the separating velocity and thus effective separation. However, the presence of surfactants in an oily emulsion can limit the obtained mechanisms due to the small oil size related with stabilized emulsion. In this regard, the purpose of this research is to improve the efficiency of the coalescer process for treating the stabilized emulsion. The effects of bed types, bed height, liquid flow rate and stage coalescer (step-bed) on the treatment efficiencies in term of COD values were studied. Note that the treatment efficiency obtained experimentally was estimated by using the COD values and oil droplet size distribution. The study has shown that the plastic media has more effective to attach with oil particles than the stainless one due to their hydrophobic properties. Furthermore, the suitable bed height (3.5 cm) and step bed (3.5 cm with 2 steps) were necessary in order to well obtain the coalescer performance. The application of step bed coalescer process in reactor has provided the higher treatment efficiencies in term of COD removal than those obtained with classical process. The proposed model for predicting the area under curve and thus treatment efficiency, based on the single collector efficiency (ηT) and the attachment efficiency (α), provides relatively a good coincidence between the experimental and predicted values of treatment efficiencies in this study.

Defect Detection of Tiles Using 2D-Wavelet Transform and Statistical Features

In this article, a method has been offered to classify normal and defective tiles using wavelet transform and artificial neural networks. The proposed algorithm calculates max and min medians as well as the standard deviation and average of detail images obtained from wavelet filters, then comes by feature vectors and attempts to classify the given tile using a Perceptron neural network with a single hidden layer. In this study along with the proposal of using median of optimum points as the basic feature and its comparison with the rest of the statistical features in the wavelet field, the relational advantages of Haar wavelet is investigated. This method has been experimented on a number of various tile designs and in average, it has been valid for over 90% of the cases. Amongst the other advantages, high speed and low calculating load are prominent.

An Intelligent Fuzzy-Neural Diagnostic System for Osteoporosis Risk Assessment

In this article, we propose an Intelligent Medical Diagnostic System (IMDS) accessible through common web-based interface, to on-line perform initial screening for osteoporosis. The fundamental approaches which construct the proposed system are mainly based on the fuzzy-neural theory, which can exhibit superiority over other conventional technologies in many fields. In diagnosis process, users simply answer a series of directed questions to the system, and then they will immediately receive a list of results which represents the risk degrees of osteoporosis. According to clinical testing results, it is shown that the proposed system can provide the general public or even health care providers with a convenient, reliable, inexpensive approach to osteoporosis risk assessment.

Structural Parsing of Natural Language Text in Tamil Using Phrase Structure Hybrid Language Model

Parsing is important in Linguistics and Natural Language Processing to understand the syntax and semantics of a natural language grammar. Parsing natural language text is challenging because of the problems like ambiguity and inefficiency. Also the interpretation of natural language text depends on context based techniques. A probabilistic component is essential to resolve ambiguity in both syntax and semantics thereby increasing accuracy and efficiency of the parser. Tamil language has some inherent features which are more challenging. In order to obtain the solutions, lexicalized and statistical approach is to be applied in the parsing with the aid of a language model. Statistical models mainly focus on semantics of the language which are suitable for large vocabulary tasks where as structural methods focus on syntax which models small vocabulary tasks. A statistical language model based on Trigram for Tamil language with medium vocabulary of 5000 words has been built. Though statistical parsing gives better performance through tri-gram probabilities and large vocabulary size, it has some disadvantages like focus on semantics rather than syntax, lack of support in free ordering of words and long term relationship. To overcome the disadvantages a structural component is to be incorporated in statistical language models which leads to the implementation of hybrid language models. This paper has attempted to build phrase structured hybrid language model which resolves above mentioned disadvantages. In the development of hybrid language model, new part of speech tag set for Tamil language has been developed with more than 500 tags which have the wider coverage. A phrase structured Treebank has been developed with 326 Tamil sentences which covers more than 5000 words. A hybrid language model has been trained with the phrase structured Treebank using immediate head parsing technique. Lexicalized and statistical parser which employs this hybrid language model and immediate head parsing technique gives better results than pure grammar and trigram based model.

Cultural Identity - A (Re)construction?

The study explored the question of who am I? As a (re)construction of cultural identity by delving into globalization, communication, and social change in Malta during a historical moment when Malta became a European Union Member State. Three objectives guided this qualitative study. Firstly the study reviewed European Union (EU) policies that regulate broadcasting and their implementation in Member States, whilst meeting the challenges of globalization and new media technology. Secondly the research investigated the changes of the media landscape via organizational structures, programs and television (TV) content. Finally the study explored the impact of these transformations taking place in the way Maltese live as they (re)construct their cultural identity. Despite the choices available to the Maltese audience, old local traditions and new foreign customs coexist as informants continue to (re)construct their cultural identity and define who they are.

Metabolic Analysis of Fibroblast Conditioned Media and Comparison with Theoretical Modeling

Understanding the consumption and production of various metabolites of fibroblast conditioned media is needed for its proper and optimized use in expansion of pluripotent stem cells. For this purpose, we have used the HPLC method to analyse the consumption of glucose and the production of lactate over time by mouse embryonic fibroblasts. The experimental data have also been compared with mathematical model fits. 0.025 moles of lactate was produced after 72 hrs while the glucose concentration decreased from 0.017 moles to 0.011 moles. The mathematical model was able to predict the trends of glucose consumption and lactate production.

Development of Workplace Environmental Monitoring Systems Using Ubiquitous Sensor Network

In this study, workplace environmental monitoring systems were established using USN(Ubiquitous Sensor Networks) and LabVIEW. Although existing direct sampling methods enable finding accurate values as of the time points of measurement, those methods are disadvantageous in that continuous management and supervision are difficult and costs for are high when those methods are used. Therefore, the efficiency and reliability of workplace management by supervisors are relatively low when those methods are used. In this study, systems were established so that information on workplace environmental factors such as temperatures, humidity and noises is measured and transmitted to the PC in real time to enable supervisors to monitor workplaces through LabVIEW on the PC. When any accidents have occurred in workplaces, supervisors can immediately respond through the monitoring system and this system enables integrated workplace management and the prevention of safety accidents. By introducing these monitoring systems, safety accidents due to harmful environmental factors in workplaces can be prevented and these monitoring systems will be also helpful in finding out the correlation between safety accidents and occupational diseases by comparing and linking databases established by this monitoring system with existing statistical data.

Effects of Proactive Coping on Workplace Adaptation After Transition from College to Workplace

Proactive coping directed at an upcoming as opposed to an ongoing stressor, is a new focus in positive psychology. The present study explored the proactive coping-s effect on the workplace adaptation after transition from college to workplace. In order to demonstrate the influence process between them, we constructed the model of proactive coping style effecting the actual positive coping efforts and outcomes by mediating proactive competence during one year after the transition. Participants (n = 100) started to work right after graduating from college completed all the four time-s surveys --one month before (Time 0), one month after (Time 1), three months after (Time 2), and one year after (Time 3) the transition. Time 0 survey included the measurement of proactive coping style and competence. Time 1, 2, 3 surveys included the measurement of the challenge cognitive appraisal, problem solving coping strategy, and subjective workplace adaptation. The result indicated that proactive coping style effected newcomers- actual coping efforts and outcomes by mediating proactive coping competence. The result also showed that proactive coping competence directly promoted Time1-s actual positive coping efforts and outcomes, and indirectly promoted Time 2-s and Time 3-s.