Dielectric studies of Liquid Crystal Nanocomposites and Nanomaterial systems.

Liquid crystals (LCs) have revolutionized the display and communication technologies. Doping of LCs with inorganic nanoparticles such as carbon nanotubes, gold nanoparticles and ferroelectric nanoparticles have garnered the interest of research community as they aid in improving the electro-optic performance. In this thesis, we examine a hybrid nanocomposite comprising of 5CB liquid crystal and block copolymer functionalized barium titanate ferroelectric nanoparticles. This hybrid system exhibits a giant soft-memory effect. Here, spontaneous polarization of ferroelectric nanoparticles couples synergistically with the radially aligned BCP chains to create nanoscopic domains that can be rotated electromechanically and locked in space even after the removal of the applied electric field. The resulting non-volatile memory is several times larger than the non-functionalized sample and provides an insight into the role of non-covalent polymer functionalization. We also present the latest results from the dielectric and spectroscopic study of field assisted alignment of gold nanorods.

CD81 interacting proteins

Fertilization is a multistep and complex process culminating in the merge of gamete membranes, cytoplasmic unity and fusion of genome. CD81 is a tetraspanin protein that participates in sperm-oocyte interaction, being present at the oocyte surface. CD81 has also been implicated in other biological processes, however its specific function and molecular mechanisms of action remain to be elucidated. The interaction between CD81 and its binding partner proteins may underlie the CD81 involvement in a variety of cellular processes and modulate CD81/interactors specific functions. Interestingly, in a Yeast two Hybrid system previously performed in our lab, CD81 has emerged as a putative interactor of the Amyloid Precursor Protein (APP). In the work here described, bioinformatics analyses of CD81 interacting proteins were performed and the retrieved information used to construct a protein-protein interaction network, as well as to perform Gene Ontology enrichment analyses. CD81 expression was further evaluated in CHO, GC-1 and SH-SY5Y cell lines, and in human sperm cells. Additionally, its subcellular localization was analyzed in sperm cells and in the neuronal-like SH-SY5Y cell line. Subsequently, coimmunoprecipitation assays were performed in CHO and SH-SY5Y cells to attempt to prove the physical interaction between CD81 and APP. A functional interaction between these two proteins was accessed thought the analyses of the effects of CD81 overexpression on APP levels. A co-localization analysis of CD81 and some interactors proteins retrieved from the bioinformatics analyses, such as APP, AKT1 and cytoskeleton-related proteins, was also performed in sperm cells and in SH-SY5Y cells. The effects of CD81 in cytoskeleton remodeling was evaluated in SH-SY5Y cells through monitoring the effects of CD81 overexpression in actin and tubulin levels, and analyzing the colocalization between overexpressed CD81 and F-actin. Our results showed that CD81 is expressed in all cell lines tested, and also provided the first evidence of the presence of CD81 in human sperm cells. CD81 immunoreactivity was predominantly detected in the sperm head, including the acrosome membrane, and in the midpiece, where it co-localized with APP, as well as in the post-acrosomal region. Furthermore, CD81 co-localizes with APP in the plasma membrane and in cellular projections in SH-SY5Y cells, where CD81 overexpression has an influence on APP levels, also visible in CHO cells. The analysis of CD81 interacting proteins such as AKT1 and cytoskeletonrelated proteins showed that CD81 is involved in a variety of pathways that may underlie cytoskeleton remodeling events, related to processes such as sperm motility, cell migration and neuritogenesis. These results deepen our understanding on the functions of CD81 and some of its interactors in sperm and neuronal cells.

Asymptotic preserving and time diminishing schemes for rarefied gas dynamic

In this work, we introduce a new class of numerical schemes for rarefied gas dynamic problems described by collisional kinetic equations. The idea consists in reformulating the problem using a micro-macro decomposition and successively in solving the microscopic part by using asymptotic preserving Monte Carlo methods. We consider two types of decompositions, the first leading to the Euler system of gas dynamics while the second to the Navier-Stokes equations for the macroscopic part. In addition, the particle method which solves the microscopic part is designed in such a way that the global scheme becomes computationally less expensive as the solution approaches the equilibrium state as opposite to standard methods for kinetic equations which computational cost increases with the number of interactions. At the same time, the statistical error due to the particle part of the solution decreases as the system approach the equilibrium state. This causes the method to degenerate to the sole solution of the macroscopic hydrodynamic equations (Euler or Navier-Stokes) in the limit of infinite number of collisions. In a last part, we will show the behaviors of this new approach in comparisons to standard Monte Carlo techniques for solving the kinetic equation by testing it on different problems which typically arise in rarefied gas dynamic simulations.

Improvement of Virtual Screening Predictions using Computational Intelligence Methods

Virtual Screening (VS) methods can considerably aid clinical research, predicting how ligands interact with drug targets. However, the accuracy of most VS methods is constrained by limitations in the scoring function that describes biomolecular interactions, and even nowadays these uncertainties are not completely understood. In order to improve accuracy of scoring functions used in most VS methods we propose a hybrid novel approach where neural networks (NNET) and support vector machines (SVM) methods are trained with databases of known active (drugs) and inactive compounds, this information being exploited afterwards to improve VS predictions.

Optical Enhancement of Exoskeleton-Based Estimation of Glenohumeral Angles

In Robot-Assisted Rehabilitation (RAR) the accurate estimation of the patient limb joint angles is critical for assessing therapy efficacy. In RAR, the use of classic motion capture systems (MOCAPs) (e.g., optical and electromagnetic) to estimate the Glenohumeral (GH) joint angles is hindered by the exoskeleton body, which causes occlusions and magnetic disturbances. Moreover, the exoskeleton posture does not accurately reflect limb posture, as their kinematic models differ. To address the said limitations in posture estimation, we propose installing the cameras of an optical marker-based MOCAP in the rehabilitation exoskeleton. Then, the GH joint angles are estimated by combining the estimated marker poses and exoskeleton Forward Kinematics. Such hybrid system prevents problems related to marker occlusions, reduced camera detection volume, and imprecise joint angle estimation due to the kinematic mismatch of the patient and exoskeleton models. This paper presents the formulation, simulation, and accuracy quantification of the proposed method with simulated human movements. In addition, a sensitivity analysis of the method accuracy to marker position estimation errors, due to system calibration errors and marker drifts, has been carried out. The results show that, even with significant errors in the marker position estimation, method accuracy is adequate for RAR.

CHARACTERIZATION OF POSTHARVEST PHYSIOLOGY ATTRIBUTES OF SIX COMMERCIALLY GROWN TOMATO VARIETIES IN KENYA

Tomato ( Lycopersicon esculentum Mill) is the leading vegetable in terms of production in Kenya. The Kenyan local market has a wide variety of tomato cultivars with a wide range of morphological and sensorial characteristics. However, information on the nutritional and postharvest quality of these varieties is lacking. The aim of this research was to investigate and identify tomato varieties of superior postharvest quality and recommend them to small and medium scale farmers. In this study, six tomato varieties were grown in a greenhouse and analyzed at three maturity stages (mature green, turning and red ripe). The tomatoes were analyzed at specific days after harvest and storage at room temperature (25o C). Percentage weight loss, color, respiration and ethylene production rates were analyzed to assess the postharvest quality of the tomatoes. The color was measured using a Minolta Chromameter while the respiration rate and ethylene production rates were determined using the static system approach. Color, weight loss, respiration and ethylene production rates were positively affected by storage time when harvested at the three maturity stages. The percentage weight loss of the tomato fruits was higher in the determinate varieties, and at the turning stage of maturity (3.8 %). Minor color changes were observed after storage of the tomatoes harvested at red stage for six days. Both rates of respiration and ethylene production were low, with the respiration rate ranging between 56-10 ml CO2 Kg-1h-1. The Chonto F1 variety had the highest rate of ethylene production (5.4 μL C2H4 Kg-1h-1) on the 4th day of storage after harvest at the red ripe stage. Overall, the indeterminate tomato varieties displayed better postharvest quality that can prolong the fruits shelf life for marketing. In turn, the turning stage of maturity proved to be a better stage to harvest tomatoes as the color development was more uniform.

Service Orientation in Demand-Supply Chains: Towards an Integrated Framework

Part 5: Service Orientation in Collaborative Networks

Flexible endoscopic treatment of Zenker's diverticulum: thirteen years' experience in Spain

Introduction: Flexible endoscopic treatment is one of the alternative approaches for the management of Zenker's diverticum. The present paper shows our short-term and long-term results with flexible endoscopic cricopharyngeal myotomy/septotomy. Patients and methods: A retrospective analysis of our experience in patients with Zenker's diverticulum treated using a flexible endoscope, assisted by a flexible diverticuloscope, between 2002 and 2015. Myotomy/septotomy was performed with a needle-knife papillotome under deep sedation or general anesthesia. Results: Among the 64 patients treated, two died within 10 days of surgery from causes not directly related to the procedure, and one presented with pharyngo-esophageal perforation, which recovered with conservative management at 47 days after admission. Four additional patients were lost to short-term follow-up. Among the 57 remaining patients, 52 had complete relief of dysphagia after 6 weeks. Eleven of these had recurrent symptoms on the mid and the long term. Eight were retreated with the same flexible endoscopic technique, one with a hybrid endoscopic approach, one with classical open surgery and one refused retreatment. After a mean follow-up of 2 years and a half, 33 of 37 patients reported absent or minimal dysphagia, controllable with punctual dietary restrictions. Conclusions: Flexible endoscopic treatment for Zenker's diverticulum is effective and safe. It represents an option on an equal footing to rigid endoscopy and classical open surgery and may also be used when the latter two are technically impracticable or contraindicated.

Simple approximate MAP inference for Dirichlet processes mixtures

The Dirichlet process mixture model (DPMM) is a ubiquitous, flexible Bayesian nonparametric statistical model. However, full probabilistic inference in this model is analytically intractable, so that computationally intensive techniques such as Gibbs sampling are required. As a result, DPMM-based methods, which have considerable potential, are restricted to applications in which computational resources and time for inference is plentiful. For example, they would not be practical for digital signal processing on embedded hardware, where computational resources are at a serious premium. Here, we develop a simplified yet statistically rigorous approximate maximum a-posteriori (MAP) inference algorithm for DPMMs. This algorithm is as simple as DP-means clustering, solves the MAP problem as well as Gibbs sampling, while requiring only a fraction of the computational effort. (For freely available code that implements the MAP-DP algorithm for Gaussian mixtures see http://www.maxlittle.net/.) Unlike related small variance asymptotics (SVA), our method is non-degenerate and so inherits the “rich get richer” property of the Dirichlet process. It also retains a non-degenerate closed-form likelihood which enables out-of-sample calculations and the use of standard tools such as cross-validation. We illustrate the benefits of our algorithm on a range of examples and contrast it to variational, SVA and sampling approaches from both a computational complexity perspective as well as in terms of clustering performance. We demonstrate the wide applicabiity of our approach by presenting an approximate MAP inference method for the infinite hidden Markov model whose performance contrasts favorably with a recently proposed hybrid SVA approach. Similarly, we show how our algorithm can applied to a semiparametric mixed-effects regression model where the random effects distribution is modelled using an infinite mixture model, as used in longitudinal progression modelling in population health science. Finally, we propose directions for future research on approximate MAP inference in Bayesian nonparametrics.

Using Research Methods in Human Computer Interaction to Design Technology for Resilience

Research in human computer interaction (HCI) covers both technological and human behavioural concerns. As a consequence, the contributions made in HCI research tend to be aware to either engineering or the social sciences. In HCI the purpose of practical research contributions is to reveal unknown insights about human behaviour and its relationship to technology. Practical research methods normally used in HCI include formal experiments, field experiments, field studies, interviews, focus groups, surveys, usability tests, case studies, diary studies, ethnography, contextual inquiry, experience sampling, and automated data collection. In this paper, we report on our experience using the evaluation methods focus groups, surveys and interviews and how we adopted these methods to develop artefacts: either interface’s design or information and technological systems. Four projects are examples of the different methods application to gather information about user’s wants, habits, practices, concerns and preferences. The goal was to build an understanding of the attitudes and satisfaction of the people who might interact with a technological artefact or information system. Conversely, we intended to design for information systems and technological applications, to promote resilience in organisations (a set of routines that allow to recover from obstacles) and user’s experiences. Organisations can here also be viewed within a system approach, which means that the system perturbations even failures could be characterized and improved. The term resilience has been applied to everything from the real estate, to the economy, sports, events, business, psychology, and more. In this study, we highlight that resilience is also made up of a number of different skills and abilities (self-awareness, creating meaning from other experiences, self-efficacy, optimism, and building strong relationships) that are a few foundational ingredients, which people should use along with the process of enhancing an organisation’s resilience. Resilience enhances knowledge of resources available to people confronting existing problems.

Model of Small Holders Dairy Cattle Waste Pollution at Several Natural Physical and Economic Social Conditions: A Case Study in the Province of Central Java

Most of dairy farmings in Indonesia are managed in the forms of smallholder farmings that give more attention to productivity rather than ecological concerns. The purpose of this study was to make models of dairy cattle animal waste pollution on clean water quality and degree of smell at several regions with different natural physical and economic social conditions. This study also investigated the waste management that match with the characteristics, behavior, and the attitude of the farmers in each region.  System approach was used as the method of this study through model simulation with program of Powersim 2.5c. Model compiled based on to component situation of livestock, condition of environment and situation of farmer. Determination of regional sample of sub-province or town until chosen countryside conducted with multistage sampling without randomization (purposive sampling).  Based on the result of research can be concluded that (1) low natural physical region and low economic social condition to have endangering pollution level of environment, (2) the waste management for the region with high natural physical and high economic social of bio gas and composting accompanied by removing to excess of animal waste for regional with low natural physical and low economic social. (Animal Production 6(2): 68-75 (2004) Key Words: model, dairy  cattle, waste pollution

Hybrid Approach Combining Machine Learning and a Rule-Based Expert System for Text Categorization

This paper discusses a novel hybrid approach for text categorization that combines a machine learning algorithm, which provides a base model trained with a labeled corpus, with a rule-based expert system, which is used to improve the results provided by the previous classifier, by filtering false positives and dealing with false negatives. The main advantage is that the system can be easily fine-tuned by adding specific rules for those noisy or conflicting categories that have not been successfully trained. We also describe an implementation based on k-Nearest Neighbor and a simple rule language to express lists of positive, negative and relevant (multiword) terms appearing in the input text. The system is evaluated in several scenarios, including the popular Reuters-21578 news corpus for comparison to other approaches, and categorization using IPTC metadata, EUROVOC thesaurus and others. Results show that this approach achieves a precision that is comparable to top ranked methods, with the added value that it does not require a demanding human expert workload to train

Developing a hybrid expert system program to aid in the design of plastic injection molding process

Experts in injection molding often refer to previous solutions to find a mold design similar to the current mold and use previous successful molding process parameters with intuitive adjustment and modification as a start for the new molding application. This approach saves a substantial amount of time and cost in experimental based corrective actions which are required in order to reach optimum molding conditions. A Case-Based Reasoning (CBR) System can perform the same task by retrieving a similar case which is applied to the new case from the case library and uses the modification rules to adapt a solution to the new case. Therefore, a CBR System can simulate human e~pertise in injection molding process design. This research is aimed at developing an interactive Hybrid Expert System to reduce expert dependency needed on the production floor. The Hybrid Expert System (HES) is comprised of CBR, flow analysis, post-processor and trouble shooting systems. The HES can provide the first set of operating parameters in order to achieve moldability condition and producing moldings free of stress cracks and warpage. In this work C++ programming language is used to implement the expert system. The Case-Based Reasoning sub-system is constructed to derive the optimum magnitude of process parameters in the cavity. Toward this end the Flow Analysis sub-system is employed to calculate the pressure drop and temperature difference in the feed system to determine the required magnitude of parameters at the nozzle. The Post-Processor is implemented to convert the molding parameters to machine setting parameters. The parameters designed by HES are implemented using the injection molding machine. In the presence of any molding defect, a trouble shooting subsystem can determine which combination of process parameters must be changed iii during the process to deal with possible variations. Constraints in relation to the application of this HES are as follows. - flow length (L) constraint: 40 mm < L < I 00 mm, - flow thickness (Th) constraint: -flow type: - material types: I mm < Th < 4 mm, unidirectional flow, High Impact Polystyrene (HIPS) and Acrylic. In order to test the HES, experiments were conducted and satisfactory results were obtained.

A hybrid approach for resolving the electromagnetic fields inside a waveguide loaded with a lossy medium

In this work a novel hybrid approach is presented that uses a combination of both time domain and frequency domain solution strategies to predict the power distribution within a lossy medium loaded within a waveguide. The problem of determining the electromagnetic fields evolving within the waveguide and the lossy medium is decoupled into two components, one for computing the fields in the waveguide including a coarse representation of the medium (the exterior problem) and one for a detailed resolution of the lossy medium (the interior problem). A previously documented cell-centred Maxwell’s equations numerical solver can be used to resolve the exterior problem accurately in the time domain. Thereafter the discrete Fourier transform can be applied to the computed field data around the interface of the medium to estimate the frequency domain boundary condition in-formation that is needed for closure of the interior problem. Since only the electric fields are required to compute the power distribution generated within the lossy medium, the interior problem can be resolved efficiently using the Helmholtz equation. A consistent cell-centred finite-volume method is then used to discretise this equation on a fine mesh and the underlying large, sparse, complex matrix system is solved for the required electric field using the iterative Krylov subspace based GMRES iterative solver. It will be shown that the hybrid solution methodology works well when a single frequency is considered in the evaluation of the Helmholtz equation in a single mode waveguide. A restriction of the scheme is that the material needs to be sufficiently lossy, so that any penetrating waves in the material are absorbed.