The Impact of Rehabilitation Approaches in the Sustainability of the Management of Small Tanks in Sri Lanka

Small tanks, the ancient man-made rain water storage systems, support the pheasant life and agriculture of the dry zone of Sri Lanka. Many small tanks were abandoned with time due to various reasons. Such tanks, rehabilitated in the recent past, were found to be less sustainable and most of these rehabilitation approaches have failed. The objective of this research is to assess the impact of the rehabilitation approaches in the management of small tanks in the Kurunegala District of Sri Lanka with respect to eight small tanks. A Sustainability index was developed using seven indicators representing the ability and commitment of the villagers to maintain these tanks. The sustainability index of the eight tanks varied between 79.2 and 47.2 out of a total score of 100. The conclusion is that, the approaches used for tank rehabilitation have a significant effect on the sustainability of the management of these small tanks.

A Novel Design Approach for Mechatronic Systems Based On Multidisciplinary Design Optimization

In this paper, a novel approach for the multidisciplinary design optimization (MDO) of complex mechatronic systems. This approach, which is a part of a global project aiming to include the MDO aspect inside an innovative design process. As a first step, the paper considers the MDO as a redesign approach which is limited to the parametric optimization. After defining and introducing the different keywords, the proposed method which is based on the V-Model which is commonly used in mechatronics.

Describing Learning Features of Reusable Resources: A Proposal

One of the main advantages of the LO paradigm is to allow the availability of good quality, shareable learning material through the Web. The effectiveness of the retrieval process requires a formal description of the resources (metadata) that closely fits the user-s search criteria; in spite of the huge international efforts in this field, educational metadata schemata often fail to fulfil this requirement. This work aims to improve the situation, by the definition of a metadata model capturing specific didactic features of shareable learning resources. It classifies LOs into “teacher-oriented" and “student-oriented" categories, in order to describe the role a LO is to play when it is integrated into the educational process. This article describes the model and a first experimental validation process that has been carried out in a controlled environment.

Carrageenan Properties Extracted From Eucheuma cottonii, Indonesia

The effect of extraction solvent upon properties of carrageenan from Eucheuma cottonii was studied. The distilled water and KOH solution (concentration 0.1- 0.5N) were used as the solvent. Extraction process was carried out in water bath equipped by stirrer with constant speed of 275 rpm with a constant ratio of seaweed weight to solvent volume ( 1:50 g/mL) at 86oC for 45 minutes. The extract was then precipitated in 3 volume of 90% ethanol, oven dried at 60oC. Based on experimental data, alkali significantly influenced yield and properties of extracted carrageenan. The extracted carrageenan was found to have essentially identical FTIR spectra to the reference samples of kappa-carrageenan. Increasing the KOH concentration led to carrageenan containing less sulfate content and intrinsic viscosity. The gel strength increased along with the increasing of KOH concentration. The decreasing of intrinsic viscosity value indicates that a polymer degradation occurs during alkali extraction.

Health Care Ethics in Vulnerable Populations: Clinical Research through the Patient's Eyes

Chronic conditions carry with them strong emotions and often lead to charged relationships between patients and their health providers and, by extension, patients and health researchers. Persons are both autonomous and relational and a purely cognitive model of autonomy neglects the social and relational basis of chronic illness. Ensuring genuine informed consent in research requires a thorough understanding of how participants perceive a study and their reasons for participation. Surveys may not capture the complexities of reasoning that underlies study participation. Contradictory reasons for participation, for instance an initial claim of altruism as rationale and a subsequent claim of personal benefit (therapeutic misconception), affect the quality of informed consent. Individuals apply principles through the filter of personal values and lived experience. Authentic autonomy, and hence authentic consent to research, occurs within the context of patients- unique life narratives and illness experiences.

Blind Non-Minimum Phase Channel Identification Using 3rd and 4th Order Cumulants

In this paper we propose a family of algorithms based on 3rd and 4th order cumulants for blind single-input single-output (SISO) Non-Minimum Phase (NMP) Finite Impulse Response (FIR) channel estimation driven by non-Gaussian signal. The input signal represents the signal used in 10GBASE-T (or IEEE 802.3an-2006) as a Tomlinson-Harashima Precoded (THP) version of random Pulse-Amplitude Modulation with 16 discrete levels (PAM-16). The proposed algorithms are tested using three non-minimum phase channel for different Signal-to-Noise Ratios (SNR) and for different data input length. Numerical simulation results are presented to illustrate the performance of the proposed algorithms.

Optimization Based Obstacle Avoidance

Based on a non-linear single track model which describes the dynamics of vehicle, an optimal path planning strategy is developed. Real time optimization is used to generate reference control values to allow leading the vehicle alongside a calculated lane which is optimal for different objectives such as energy consumption, run time, safety or comfort characteristics. Strict mathematic formulation of the autonomous driving allows taking decision on undefined situation such as lane change or obstacle avoidance. Based on position of the vehicle, lane situation and obstacle position, the optimization problem is reformulated in real-time to avoid the obstacle and any car crash.

Thermo-mechanical Behavior of Pressure Tube of Indian PHWR at 20 bar Pressure

In a nuclear reactor Loss of Coolant accident (LOCA) considers wide range of postulated damage or rupture of pipe in the heat transport piping system. In the case of LOCA with/without failure of emergency core cooling system in a Pressurised Heavy water Reactor, the Pressure Tube (PT) temperature could rise significantly due to fuel heat up and gross mismatch of the heat generation and heat removal in the affected channel. The extent and nature of deformation is important from reactor safety point of view. Experimental set-ups have been designed and fabricated to simulate ballooning (radial deformation) of PT for 220 MWe IPHWRs. Experiments have been conducted by covering the CT by ceramic fibers and then by submerging CT in water of voided PTs. In both the experiments, it is observed that ballooning initiates at a temperature around 665´┐¢C and complete contact between PT and Caldaria Tube (CT) occurs at around 700´┐¢C approximately. The strain rate is found to be 0.116% per second. The structural integrity of PT is retained (no breach) for all the experiments. The PT heatup is found to be arrested after the contact between PT and CT, thus establishing moderator acting as an efficient heat sink for IPHWRs.

Closing the Achievement Gap Within Reading and Mathematics Classrooms by Fostering Hispanic Students- Educational Resilience

While many studies have conducted the achievement gap between groups of students in school districts, few studies have utilized resilience research to investigate achievement gaps within classrooms. This paper aims to summarize and discuss some recent studies Waxman, Padr├│n, and their colleagues conducted, in which they examined learning environment differences between resilient and nonresilient students in reading and mathematics classrooms. The classes consist of predominantly Hispanic elementary school students from low-income families. These studies all incorporated learning environment questionnaires and systematic observation methods. Significant differences were found between resilient and nonresilient students on their classroom learning environments and classroom behaviors. The observation results indicate that the amount and quality of teacher and student academic interaction are two of the most influential variables that promote student outcomes. This paper concludes by suggesting the following teacher practices to promote resiliency in schools: (a) using feedback from classroom observation and learning environment measures, (b) employing explicit teaching practices; and (c) understanding students on a social and personal level.

Does Practice Reflect Theory? An Exploratory Study of a Successful Knowledge Management System

To investigate the correspondence of theory and practice, a successfully implemented Knowledge Management System (KMS) is explored through the lens of Alavi and Leidner-s proposed KMS framework for the analysis of an information system in knowledge management (Framework-AISKM). The applied KMS system was designed to manage curricular knowledge in a distributed university environment. The motivation for the KMS is discussed along with the types of knowledge necessary in an academic setting. Elements of the KMS involved in all phases of capturing and disseminating knowledge are described. As the KMS matures the resulting data stores form the precursor to and the potential for knowledge mining. The findings from this exploratory study indicate substantial correspondence between the successful KMS and the theory-based framework providing provisional confirmation for the framework while suggesting factors that contributed to the system-s success. Avenues for future work are described.

Automatic Clustering of Gene Ontology by Genetic Algorithm

Nowadays, Gene Ontology has been used widely by many researchers for biological data mining and information retrieval, integration of biological databases, finding genes, and incorporating knowledge in the Gene Ontology for gene clustering. However, the increase in size of the Gene Ontology has caused problems in maintaining and processing them. One way to obtain their accessibility is by clustering them into fragmented groups. Clustering the Gene Ontology is a difficult combinatorial problem and can be modeled as a graph partitioning problem. Additionally, deciding the number k of clusters to use is not easily perceived and is a hard algorithmic problem. Therefore, an approach for solving the automatic clustering of the Gene Ontology is proposed by incorporating cohesion-and-coupling metric into a hybrid algorithm consisting of a genetic algorithm and a split-and-merge algorithm. Experimental results and an example of modularized Gene Ontology in RDF/XML format are given to illustrate the effectiveness of the algorithm.

Improved Plasmonic Demultiplexer Based on Tapered and Rectangular Slot MIM Waveguide

In this paper, we have proposed two novel plasmonic demultiplexing structures based on metal-insulator-metal surfaces which, beside their compact size, have a very good transmission spectrum. The impact of the key internal parameters on the transmission spectrum is numerically analyzed by using the twodimensional (2D) finite difference time domain (FDTD) method. The proposed structures could be used to develop ultra-compact photonic wavelength demultiplexing devices for large-scale photonic integration.

Comparative Study of Decision Trees and Rough Sets Theory as Knowledge ExtractionTools for Design and Control of Industrial Processes

General requirements for knowledge representation in the form of logic rules, applicable to design and control of industrial processes, are formulated. Characteristic behavior of decision trees (DTs) and rough sets theory (RST) in rules extraction from recorded data is discussed and illustrated with simple examples. The significance of the models- drawbacks was evaluated, using simulated and industrial data sets. It is concluded that performance of DTs may be considerably poorer in several important aspects, compared to RST, particularly when not only a characterization of a problem is required, but also detailed and precise rules are needed, according to actual, specific problems to be solved.

Volatile Organochlorine Compounds Emitted by Temperate Coniferous Forests

Chlorine is one of the most abundant elements in nature, which undergoes a complex biogeochemical cycle. Chlorine bound in some substances is partly responsible for atmospheric ozone depletion and contamination of some ecosystems. As due to international regulations anthropogenic burden of volatile organochlorines (VOCls) in atmosphere decreases, natural sources (plants, soil, abiotic formation) are expected to dominate VOCl production in the near future. Examples of plant VOCl production are methyl chloride, and bromide emission from (sub)tropical ferns, chloroform, 1,1,1-trichloroethane and tetrachloromethane emission from temperate forest fern and moss. Temperate forests are found to emit in addition to the previous compounds tetrachloroethene, and brominated volatile compounds. VOCls can be taken up and further metabolized in plants. The aim of this work is to identify and quantitatively analyze the formed VOCls in temperate forest ecosystems by a cryofocusing/GC-ECD detection method, hence filling a gap of knowledge in the biogeochemical cycle of chlorine.

Autonomously Determining the Parameters for SVDD with RBF Kernel from a One-Class Training Set

The one-class support vector machine “support vector data description” (SVDD) is an ideal approach for anomaly or outlier detection. However, for the applicability of SVDD in real-world applications, the ease of use is crucial. The results of SVDD are massively determined by the choice of the regularisation parameter C and the kernel parameter  of the widely used RBF kernel. While for two-class SVMs the parameters can be tuned using cross-validation based on the confusion matrix, for a one-class SVM this is not possible, because only true positives and false negatives can occur during training. This paper proposes an approach to find the optimal set of parameters for SVDD solely based on a training set from one class and without any user parameterisation. Results on artificial and real data sets are presented, underpinning the usefulness of the approach.

Cross-Industry Innovations – Systematic Identification and Adaption

Due to today-s fierce competition, companies have to be proactive creators of the future by effectively developing innovations. Especially radical innovations allow high profit margins – but they also entail high risks. One possibility to realize radical innovations and reduce the risk of failure is cross-industry innovation (CII). CII brings together problems and solution ideas from different industries. However, there is a lack of systematic ways towards CII. Bridging this gap, the present paper provides a systematic approach towards planned CII. Starting with the analysis of potentials, the definition of promising search strategies is crucial. Subsequently, identified solution ideas need to be assessed. For the most promising ones, the adaption process has to be systematically planned – regarding the risk affinity of a company. The introduced method is explained on a project from the furniture industry.

An Experimental Method for Measuring Clamping Force in Bolted Connections and Effect of Bolt Threads Lubrication on Its Value

In this paper, the details of an experimental method to measure the clamping force value at bolted connections due to application of wrenching torque to tighten the nut have been presented. A simplified bolted joint including a holed plate with a single bolt was considered to carry out the experiments. This method was designed based on Hooke-s law by measuring compressive axial strain of a steel bush placed between the nut and the plate. In the experimental procedure, the values of clamping force were calculated for seven different levels of applied torque, and this process was repeated three times for each level of the torque. Moreover, the effect of lubrication of threads on the clamping value was studied using the same method. In both conditions (dry and lubricated threads), relation between the torque and the clamping force have been displayed in graphs.

Effects of a Nectandra Membranacea Extract on Labeling of Blood Constituents with Technetium-99m and on the Morphology of Red Blood Cells

The aim of this in vitro study was to evaluate the possible interference of a Nectandra membranacea extract (i) on the labeling of blood cells (BC), (ii) on the labeling process of BC and plasma (P) proteins with technetium-99m (Tc-99m) and (iii) on the morphology of red blood cells (RBC). Blood samples were incubated with a Nectandra membranacea crude extract, stannous chloride, Tc- 99m (sodium pertechnetate) was added, and soluble (SF) and insoluble (IF) fractions were isolated. Morphometry studies were performed with blood samples incubated with Nectandra membranacea extract. The results show that the Nectandra membranacea extract does not promote significant alteration of the labeling of BC, IF-P and IF-BC. The Nectandra membranacea extract was able to alter the erythrocyte membrane morphology, but these morphological changes were not capable to interfere on the labeling of blood constituents with Tc-99m.

An Innovational Intermittent Algorithm in Networks-On-Chip (NOC)

Every day human life experiences new equipments more automatic and with more abilities. So the need for faster processors doesn-t seem to finish. Despite new architectures and higher frequencies, a single processor is not adequate for many applications. Parallel processing and networks are previous solutions for this problem. The new solution to put a network of resources on a chip is called NOC (network on a chip). The more usual topology for NOC is mesh topology. There are several routing algorithms suitable for this topology such as XY, fully adaptive, etc. In this paper we have suggested a new algorithm named Intermittent X, Y (IX/Y). We have developed the new algorithm in simulation environment to compare delay and power consumption with elders' algorithms.

An Anomaly Detection Approach to Detect Unexpected Faults in Recordings from Test Drives

In the automotive industry test drives are being conducted during the development of new vehicle models or as a part of quality assurance of series-production vehicles. The communication on the in-vehicle network, data from external sensors, or internal data from the electronic control units is recorded by automotive data loggers during the test drives. The recordings are used for fault analysis. Since the resulting data volume is tremendous, manually analysing each recording in great detail is not feasible. This paper proposes to use machine learning to support domainexperts by preventing them from contemplating irrelevant data and rather pointing them to the relevant parts in the recordings. The underlying idea is to learn the normal behaviour from available recordings, i.e. a training set, and then to autonomously detect unexpected deviations and report them as anomalies. The one-class support vector machine “support vector data description” is utilised to calculate distances of feature vectors. SVDDSUBSEQ is proposed as a novel approach, allowing to classify subsequences in multivariate time series data. The approach allows to detect unexpected faults without modelling effort as is shown with experimental results on recordings from test drives.