Investigations on Hard Bycatch Reduction Devices for Selective Trawling

In the context of Indian fisheries there is a paucity of information on bycatch, in general, and bycatch reduction technologies, in particular. In this study, a detailed investigation on trawl bycatch and bycatch reduction measures is attempted with a view to evolve optimized BRDs for improving selectivity of commercial shrimp trawls. The objectives of the study included design and development of hard bycatch reduction devices (BRDs), comparative evaluation of hard bycatch reduction devices, for selective trawling, bycatch characterisation of the trawl landings, off Central Kerala; and investigations on status of the existing trawling systems operated off Central Kerala.

Electrochemical Reduction and Voltammetric Determination of Metronidazole Benzoate at Modified Carbon Paste Electrode

A metalloporphyrin incorporated carbon paste sensor has been developed for the determination of metronidazole benzoate (MTZB). Zn(II) complex of 5,10,15,20-tetrakis (3-methoxy-4-hydroxy phenyl) porphyrin (TMHPP) was used as the active material. The MTZB gave a well-defined reduction peak at - 0.713V in 0.1 mol l -1 phosphate buffer solution of pH around 7. Compared with bare carbon paste electrode (CPE), the TMHPP Zn(II) modified electrode significantly enhanced the reduction peak current of MTZB as well as lowered its reduction potential. Under optimum conditions the reduction peak current was proportional to MTZB concentration over the range 1×10-3 mol1-1 to 1×10-5mol1-1. The detection limit was found to be 4.36×10-6mol1-1 . This sensor has been successfully applied for the determination of MTZB in pharmaceutical formulations and urine samples.

Catalytic activity of lanthanum oxide for the reduction of cyclohexanone

Lanthanum oxide, La2O3 has been found to be an effective catalyst for the liquid phase reduction of cyclohexanone. The catalytic activities of La2O3 activated at 300, 500 and 800·C and its mixed oxides with alumina for the reduction of cyclohexanone with 2-propanol have been determined and the data parallel that of the electron donating properties of the catalysts. The electron donating properties of the catalysts have been determined from the adsorption of electron acceptors of different electron affinities on the surface of these oxides.

Invertase immobilised on montmorillonite: reusability enhancement and reduction in leaching

Invertase was immobilised on microporous montmorillonite K-10 via adsorption and covalent binding. The immobilised enzymes were tested for sucrose hydrolysis activity in a batch reactor. Km for immobilised systems was greater than free enzyme. The immobilised forms could be reused for 15 continuous cycles without any loss in activity. After 25 cycles, 85% initial activity was retained. A study on leaching of enzymes showed that 100% enzyme was retained even after 15 cycles of reuse. Leaching increased with reaction temperature. Covalent binding resisted leaching even at temperatures of 70 °C.

Applications of Fractal Based Structures in Radar Cross Section Reduction

This thesis presents the Radar Cross Section measurements of different geometric structures such as flat plate,cylinder, corner reflector and circular cone loaded with fractal based metallo dielectric structures.Use of different fractal geometris,metallizations of different shapes as well as the frequency tanability is investigated for TE and TM polarization of the incident electromagnetic field.Application of fractal based metallo-dielectric structures results in RCS reduction over a wide range of frequency bands.RCS enhancement of dihedral corner is observed at certain acute and obtuse corner angles.The experimental results are validated using electromagnetic simulation softwares.

Reduction of radar cross section of targets using periodic strip grating technique

The- classic: experiment of Heinrich Hertz verified the theoretical predict him of Maxwell that kxnfli radio and light waves are physical phenomena governed by the same physical laws. This has started a.rnnJ era of interest in interaction of electromagnetic energy with matter. The scattering of electromagnetic waves from a target is cleverly utilized im1 RADAR. This electronic system used tx> detect and locate objects under unfavourable conditions or obscuration that would render the unaided eye useless. It also provides a means for measuring precisely the range, or distance of an object and the speed of a moving object. when an obstacle is illuminated by electromagnetic waves, energy is dispersed in all directions. The dispersed energy depends on the size, shape and composition of the obstacle and frequency and nature of the incident wave. This distribution of energy’ is known as ‘scattering’ and the obstacle as ‘scatterer’ or 'target'.

Attribute reduction and missing value imputing with ANN: prediction of learning disabilities

Learning disability (LD) is a neurological condition that affects a child’s brain and impairs his ability to carry out one or many specific tasks. LD affects about 10% of children enrolled in schools. There is no cure for learning disabilities and they are lifelong. The problems of children with specific learning disabilities have been a cause of concern to parents and teachers for some time. Just as there are many different types of LDs, there are a variety of tests that may be done to pinpoint the problem The information gained from an evaluation is crucial for finding out how the parents and the school authorities can provide the best possible learning environment for child. This paper proposes a new approach in artificial neural network (ANN) for identifying LD in children at early stages so as to solve the problems faced by them and to get the benefits to the students, their parents and school authorities. In this study, we propose a closest fit algorithm data preprocessing with ANN classification to handle missing attribute values. This algorithm imputes the missing values in the preprocessing stage. Ignoring of missing attribute values is a common trend in all classifying algorithms. But, in this paper, we use an algorithm in a systematic approach for classification, which gives a satisfactory result in the prediction of LD. It acts as a tool for predicting the LD accurately, and good information of the child is made available to the concerned

Performance Assessment of Sandwich Structures with Debonds and Dents

A sandwich construction is a special form of the laminated composite consisting of light weight core, sandwiched between two stiff thin face sheets. Due to high stiffness to weight ratio, sandwich construction is widely adopted in aerospace industries. As a process dependent bonded structure, the most severe defects associated with sandwich construction are debond (skin core bond failure) and dent (locally deformed skin associated with core crushing). Reasons for debond may be attributed to initial manufacturing flaws or in service loads and dent can be caused by tool drops or impacts by foreign objects. This paper presents an evaluation on the performance of honeycomb sandwich cantilever beam with the presence of debond or dent, using layered finite element models. Dent is idealized by accounting core crushing in the core thickness along with the eccentricity of the skin. Debond is idealized using multilaminate modeling at debond location with contact element between the laminates. Vibration and buckling behavior of metallic honeycomb sandwich beam with and without damage are carried out. Buckling load factor, natural frequency, mode shape and modal strain energy are evaluated using finite element package ANSYS 13.0. Study shows that debond affect the performance of the structure more severely than dent. Reduction in the fundamental frequencies due to the presence of dent or debond is not significant for the case considered. But the debond reduces the buckling load factor significantly. Dent of size 8-20% of core thickness shows 13% reduction in buckling load capacity of the sandwich column. But debond of the same size reduced the buckling load capacity by about 90%. This underscores the importance of detecting these damages in the initiation level itself to avoid catastrophic failures. Influence of the damages on fundamental frequencies, mode shape and modal strain energy are examined. Effectiveness of these parameters as a damage detection tool for sandwich structure is also assessed

Learning analysis by reduction from positive data

Analysis by reduction is a linguistically motivated method for checking correctness of a sentence. It can be modelled by restarting automata. In this paper we propose a method for learning restarting automata which are strictly locally testable (SLT-R-automata). The method is based on the concept of identification in the limit from positive examples only. Also we characterize the class of languages accepted by SLT-R-automata with respect to the Chomsky hierarchy.

Zur Druck-Querzug-Festigkeit von Stahlbeton und stahlfaserverstärktem Stahlbeton in scheibenförmigen Bauteilen

Am Fachgebiet Massivbau (Institut für Konstruktiven Ingenieurbau – IKI) des Fachbereichs Bauingenieurwesen der Universität Kassel wurden Bauteilversuche an zweiaxial auf Druck-Zug belasteten, faserfreien und faserverstärkten Stahlbetonscheiben durchgeführt. Dabei wurden die Auswirkungen der Querzugbeanspruchung und der Rissbildung auf die Druckfestigkeit, auf die Stauchung bei Erreichen der Höchstlast sowie auf die Drucksteifigkeit des stabstahl- und faserbewehrten Betons an insgesamt 56 faserfreien und faserverstärkten Beton- und Stahlbetonscheiben untersucht. Auf der Grundlage der experimentell erhaltenen Ergebnisse wird ein Vorschlag zur Abminderung der Druckfestigkeit des gerissenen faserfreien und faserbewehrten Stahlbetons in Abhängigkeit der aufgebrachten Zugdehnung formuliert. Die Ergebnisse werden den in DIN 1045-1 [D4], Eurocode 2 [E3, E4], CEB-FIP Model Code 1990 [C1] und ACI Standard 318-05 [A1] angegebenen Bemessungsregeln für die Druckstrebenfestigkeit des gerissenen Stahlbetons gegenübergestellt und mit den Untersuchungen anderer Wissenschaftler verglichen. Die bekannten Widersprüche zwischen den Versuchsergebnissen, den vorgeschlagenen Modellen und den Regelwerken aus U.S.A., Kanada und Europa können dabei weitgehend aufgeklärt werden. Für nichtlineare Verfahren der Schnittgrößenermittlung und für Verformungsberechnungen wird ein Materialmodell des gerissenen faserfreien und faserbewehrten Stahlbetons abgeleitet. Hierzu wird die für einaxiale Beanspruchungszustände gültige Spannungs-Dehnungs-Linie nach Bild 22 der DIN 1045-1 auf den Fall der zweiaxialen Druck-Zug-Beanspruchung erweitert.

Noise Reduction Using Low Weight and Constant Weight Coding Techniques

Signalling off-chip requires significant current. As a result, a chip's power-supply current changes drastically during certain output-bus transitions. These current fluctuations cause a voltage drop between the chip and circuit board due to the parasitic inductance of the power-supply package leads. Digital designers often go to great lengths to reduce this "transmitted" noise. Cray, for instance, carefully balances output signals using a technique called differential signalling to guarantee a chip has constant output current. Transmitted-noise reduction costs Cray a factor of two in output pins and wires. Coding achieves similar results at smaller costs.

Using Recurrent Networks for Dimensionality Reduction

This report explores how recurrent neural networks can be exploited for learning high-dimensional mappings. Since recurrent networks are as powerful as Turing machines, an interesting question is how recurrent networks can be used to simplify the problem of learning from examples. The main problem with learning high-dimensional functions is the curse of dimensionality which roughly states that the number of examples needed to learn a function increases exponentially with input dimension. This thesis proposes a way of avoiding this problem by using a recurrent network to decompose a high-dimensional function into many lower dimensional functions connected in a feedback loop.

Parallel Coupled Micro-Macro Actuators

This thesis presents a new actuator system consisting of a micro-actuator and a macro-actuator coupled in parallel via a compliant transmission. The system is called the Parallel Coupled Micro-Macro Actuator, or PaCMMA. In this system, the micro-actuator is capable of high bandwidth force control due to its low mass and direct-drive connection to the output shaft. The compliant transmission of the macro-actuator reduces the impedance (stiffness) at the output shaft and increases the dynamic range of force. Performance improvement over single actuator systems was expected in force control, impedance control, force distortion and reduction of transient impact forces. A set of quantitative measures is proposed and the actuator system is evaluated against them: Force Control Bandwidth, Position Bandwidth, Dynamic Range, Impact Force, Impedance ("Backdriveability'"), Force Distortion and Force Performance Space. Several theoretical performance limits are derived from the saturation limits of the system. A control law is proposed and control system performance is compared to the theoretical limits. A prototype testbed was built using permanenent magnet motors and an experimental comparison was performed between this actuator concept and two single actuator systems. The following performance was observed: Force bandwidth of 56Hz, Torque Dynamic Range of 800:1, Peak Torque of 1040mNm, Minimum Torque of 1.3mNm. Peak Impact Force was reduced by an order of magnitude. Distortion at small amplitudes was reduced substantially. Backdriven impedance was reduced by 2-3 orders of magnitude. This actuator system shows promise for manipulator design as well as psychophysical tests of human performance.

Understanding Subsystems in Biology through Dimensionality Reduction, Graph Partitioning and Analytical Modeling

Biological systems exhibit rich and complex behavior through the orchestrated interplay of a large array of components. It is hypothesized that separable subsystems with some degree of functional autonomy exist; deciphering their independent behavior and functionality would greatly facilitate understanding the system as a whole. Discovering and analyzing such subsystems are hence pivotal problems in the quest to gain a quantitative understanding of complex biological systems. In this work, using approaches from machine learning, physics and graph theory, methods for the identification and analysis of such subsystems were developed. A novel methodology, based on a recent machine learning algorithm known as non-negative matrix factorization (NMF), was developed to discover such subsystems in a set of large-scale gene expression data. This set of subsystems was then used to predict functional relationships between genes, and this approach was shown to score significantly higher than conventional methods when benchmarking them against existing databases. Moreover, a mathematical treatment was developed to treat simple network subsystems based only on their topology (independent of particular parameter values). Application to a problem of experimental interest demonstrated the need for extentions to the conventional model to fully explain the experimental data. Finally, the notion of a subsystem was evaluated from a topological perspective. A number of different protein networks were examined to analyze their topological properties with respect to separability, seeking to find separable subsystems. These networks were shown to exhibit separability in a nonintuitive fashion, while the separable subsystems were of strong biological significance. It was demonstrated that the separability property found was not due to incomplete or biased data, but is likely to reflect biological structure.