Abstract: Our goal is development of an algorithm capable of
predicting the directional trend of the Standard and Poor’s 500 index
(S&P 500). Extensive research has been published attempting to
predict different financial markets using historical data testing on an
in-sample and trend basis, with many authors employing excessively
complex mathematical techniques. In reviewing and evaluating these
in-sample methodologies, it became evident that this approach was
unable to achieve sufficiently reliable prediction performance for
commercial exploitation. For these reasons, we moved to an out-ofsample
strategy based on linear regression analysis of an extensive
set of financial data correlated with historical closing prices of the
S&P 500. We are pleased to report a directional trend accuracy of
greater than 55% for tomorrow (t+1) in predicting the S&P 500.
Abstract: The management of Helicobacter pylori (H. pylori)
eradication is still a matter of discussion, full effectiveness is rarely
achieved, and it has many adverse effects. The use of probiotics may
be associated with better eradication rates and possibly prevention of
adverse events due to antibiotic therapy. The present clinical study
was undertaken to evaluate the efficacy of a specially designed
fermented milk product, containing Bifidobacterium lactis B420, on
the eradication of H. pylori infection in a prospective, randomized,
double-blind, controlled study in humans. Four test fermented milks
(FM) were specially designed in which counts of viable cells in all
products were 10^10 Log CFU. 100 mL-1 for Bifidobacterium lactis -
Bifidobacterium species 420. 190 subjects infected with H. pylori,
with previous diagnosis of functional dyspepsia according to Rome
III criteria entered the study. Bifidobacterium lactis B420,
administered twice a day for 90 days was not able to eradicate H.
pylori in Brazilian patients with functional dyspepsia.
Abstract: Artificial intelligence applications are commonly used
in industry in many fields in parallel with the developments in the
computer technology. In this study, a fire room was prepared for the
resistance of wooden construction elements and with the mechanism
here, the experiments of polished materials were carried out. By
utilizing from the experimental data, an artificial neural network
(ANN) was modelled in order to evaluate the final cross sections of
the wooden samples remaining from the fire. In modelling,
experimental data obtained from the fire room were used. In the
developed system, the first weight of samples (ws-gr), preliminary
cross-section (pcs-mm2), fire time (ft-minute), and fire temperature
(t-oC) as input parameters and final cross-section (fcs-mm2) as output
parameter were taken. When the results obtained from ANN and
experimental data are compared after making statistical analyses, the
data of two groups are determined to be coherent and seen to have no
meaning difference between them. As a result, it is seen that ANN
can be safely used in determining cross sections of wooden materials
after fire and it prevents many disadvantages.
Abstract: Sustainability and eco-friendly requirement of
engineering materials are sort for in recent times, thus giving rise to
the development of bio-composites. However, the natural fibres to
matrix interface interactions remain a key issue in getting the desired
mechanical properties from such composites. Treatment of natural
fibres is essential in improving matrix to filler adhesion, hence
improving its mechanical properties. In this study, investigations
were carried out to determine the effect of sodium hydroxide
treatment on the tensile, flexural, impact and hardness properties of
crushed and uncrushed Luffa cylindrica fibre reinforced recycled low
density polyethylene composites. The LC (Luffa cylindrica) fibres
were treated with 0%, 2%, 4%, 6%, 8% and 10% wt. sodium
hydroxide (NaOH) concentrations for a period of 24 hours under
room temperature conditions. A formulation ratio of 80/20 g (matrix
to reinforcement) was maintained for all developed samples. Analysis
of the results showed that the uncrushed luffa fibre samples gave
better mechanical properties compared with the crushed luffa fibre
samples. The uncrushed luffa fibre composites had a maximum
tensile and flexural strength of 7.65 MPa and 17.08 Mpa respectively
corresponding to a young modulus and flexural modulus of 21.08
MPa and 232.22 MPa for the 8% and 4% wt. NaOH concentration
respectively. Results obtained in the research showed that NaOH
treatment with the 8% NaOH concentration improved the mechanical
properties of the LC fibre reinforced composites when compared with
other NaOH treatment concentration values.
Abstract: Waxapple (Syzygium samarngense Merr.et Perry) is an
important tropical fruit in Taiwan. The famous producing area is
located on the coast in Pingtung County. Land subsidence and climate
change will tend to soil alkalization more seriously. This study was to
evaluate the effects of NaCl in waxapple seedlings. NaCl salinity
reduced waxapple shoot growth; it may due to reducing relative water
content in leaf and new shoot. Leaf Cl and Na concentration were
increased but K, Ca, and Mg content had no significant difference after
irrigated with NaCl for six weeks. In roots, Na and Cl content increase
significantly with 90 mM NaCl treatment, but K, Ca, and Mg content
was reduced. 30-90mM Nacl treatment do not effect K/Na, Ca/Na and
Mg/Na ratio, but decrease significantly in 90mM treatment in roots.
The leaf and root electrolyte leakage were significantly affected by 90
mM NaCl treatment. Suggesting 90mM was optimum concentration
for sieve out other tolerance waxapples verities.
Abstract: The planning of geological survey works is an
iterative process which involves planner, geologist, civil engineer and
other stakeholders, who perform different roles and have different
points of view. Traditionally, the team used paper maps or CAD
drawings to present the proposal which is not an efficient way to
present and share idea on the site investigation proposal such as
sitting of borehole location or seismic survey lines. This paper
focuses on how a GIS approach can be utilised to develop a webbased
system to support decision making process in the planning of
geological survey works and also to plan site activities carried out by
Singapore Geological Office (SGO). The authors design a framework
of building an interactive web-based GIS system, and develop a
prototype, which enables the users to obtain rapidly existing
geological information and also to plan interactively borehole
locations and seismic survey lines via a web browser. This prototype
system is used daily by SGO and has shown to be effective in
increasing efficiency and productivity as the time taken in the
planning of geological survey works is shortened. The prototype
system has been developed using the ESRI ArcGIS API 3.7 for Flex
which is based on the ArcGIS 10.2.1 platform.
Abstract: This paper describes the tradeoffs and the design from
scratch of a self-contained, easy-to-use health dashboard software
system that provides customizable data tracking for patients in smart
homes. The system is made up of different software modules and
comprises a front-end and a back-end component. Built with HTML,
CSS, and JavaScript, the front-end allows adding users, logging into
the system, selecting metrics, and specifying health goals. The backend
consists of a NoSQL Mongo database, a Python script, and a
SimpleHTTPServer written in Python. The database stores user
profiles and health data in JSON format. The Python script makes use
of the PyMongo driver library to query the database and displays
formatted data as a daily snapshot of user health metrics against
target goals. Any number of standard and custom metrics can be
added to the system, and corresponding health data can be fed
automatically, via sensor APIs or manually, as text or picture data
files. A real-time METAR request API permits correlating weather
data with patient health, and an advanced query system is
implemented to allow trend analysis of selected health metrics over
custom time intervals. Available on the GitHub repository system,
the project is free to use for academic purposes of learning and
experimenting, or practical purposes by building on it.
Abstract: Non-linear FEM calculations are indispensable when
important technical information like operating performance of a
rubber component is desired. For example rubber bumpers built into
air-spring structures may undergo large deformations under load,
which in itself shows non-linear behavior. The changing contact
range between the parts and the incompressibility of the rubber
increases this non-linear behavior further. The material
characterization of an elastomeric component is also a demanding
engineering task.
The shape optimization problem of rubber parts led to the study of
FEM based calculation processes. This type of problems was posed
and investigated by several authors. In this paper the time demand of
certain calculation methods are studied and the possibilities of time
reduction is presented.
Abstract: Composite material based on Fe3Si micro-particles
and Mn-Zn nano-ferrite was prepared using powder metallurgy
technology. The sol-gel followed by autocombustion process was
used for synthesis of Mn0.8Zn0.2Fe2O4 ferrite. 3 wt.% of mechanically
milled ferrite was mixed with Fe3Si powder alloy. Mixed micro-nano
powder system was homogenized by the Resonant Acoustic Mixing
using ResodynLabRAM Mixer. This non-invasive homogenization
technique was used to preserve spherical morphology of Fe3Si
powder particles. Uniaxial cold pressing in the closed die at pressure
600 MPa was applied to obtain a compact sample. Microwave
sintering of green compact was realized at 800°C, 20 minutes, in air.
Density of the powders and composite was measured by
Hepycnometry. Impulse excitation method was used to measure
elastic properties of sintered composite. Mechanical properties were
evaluated by measurement of transverse rupture strength (TRS) and
Vickers hardness (HV). Resistivity was measured by 4 point probe
method. Ferrite phase distribution in volume of the composite was
documented by metallographic analysis.
It has been found that nano-ferrite particle distributed among
micro- particles of Fe3Si powder alloy led to high relative density
(~93%) and suitable mechanical properties (TRS >100 MPa, HV
~1GPa, E-modulus ~140 GPa) of the composite. High electric
resistivity (R~6.7 ohm.cm) of prepared composite indicate their
potential application as soft magnetic material at medium and high
frequencies.
Abstract: Microbial fuel cells (MFCs) represent a promising
technology for simultaneous bioelectricity generation and wastewater
treatment. Catalysts are significant portions of the cost of microbial
fuel cell cathodes. Many materials have been tested as aqueous
cathodes, but air-cathodes are needed to avoid energy demands for
water aeration. The sluggish oxygen reduction reaction (ORR) rate at
air cathode necessitates efficient electrocatalyst such as carbon
supported platinum catalyst (Pt/C) which is very costly. Manganese
oxide (MnO2) was a representative metal oxide which has been
studied as a promising alternative electrocatalyst for ORR and has
been tested in air-cathode MFCs. However the single MnO2 has poor
electric conductivity and low stability. In the present work, the MnO2
catalyst has been modified by doping Pt nanoparticle. The goal of the
work was to improve the performance of the MFC with minimum Pt
loading. MnO2 and Pt nanoparticles were prepared by hydrothermal
and sol gel methods, respectively. Wet impregnation method was
used to synthesize Pt/MnO2 catalyst. The catalysts were further used
as cathode catalysts in air-cathode cubic MFCs, in which anaerobic
sludge was inoculated as biocatalysts and palm oil mill effluent
(POME) was used as the substrate in the anode chamber. The asprepared
Pt/MnO2 was characterized comprehensively through field
emission scanning electron microscope (FESEM), X-Ray diffraction
(XRD), X-ray photoelectron spectroscopy (XPS), and cyclic
voltammetry (CV) where its surface morphology, crystallinity,
oxidation state and electrochemical activity were examined,
respectively. XPS revealed Mn (IV) oxidation state and Pt (0)
nanoparticle metal, indicating the presence of MnO2 and Pt.
Morphology of Pt/MnO2 observed from FESEM shows that the
doping of Pt did not cause change in needle-like shape of MnO2
which provides large contacting surface area. The electrochemical
active area of the Pt/MnO2 catalysts has been increased from 276 to
617 m2/g with the increase in Pt loading from 0.2 to 0.8 wt%. The
CV results in O2 saturated neutral Na2SO4 solution showed that
MnO2 and Pt/MnO2 catalysts could catalyze ORR with different
catalytic activities. MFC with Pt/MnO2 (0.4 wt% Pt) as air cathode
catalyst generates a maximum power density of 165 mW/m3, which
is higher than that of MFC with MnO2 catalyst (95 mW/m3). The
open circuit voltage (OCV) of the MFC operated with MnO2 cathode
gradually decreased during 14 days of operation, whereas the MFC
with Pt/MnO2 cathode remained almost constant throughout the
operation suggesting the higher stability of the Pt/MnO2 catalyst.
Therefore, Pt/MnO2 with 0.4 wt% Pt successfully demonstrated as an
efficient and low cost electrocatalyst for ORR in air cathode MFC with higher electrochemical activity, stability and hence enhanced
performance.
Abstract: In this paper, we propose an automatic verification
technology of software patches for user virtual environments on IaaS
Cloud to decrease verification costs of patches. In these days, IaaS
services have been spread and many users can customize virtual
machines on IaaS Cloud like their own private servers. Regarding to
software patches of OS or middleware installed on virtual machines,
users need to adopt and verify these patches by themselves. This task
increases operation costs of users. Our proposed method replicates
user virtual environments, extracts verification test cases for user
virtual environments from test case DB, distributes patches to virtual
machines on replicated environments and conducts those test cases
automatically on replicated environments. We have implemented the
proposed method on OpenStack using Jenkins and confirmed the
feasibility. Using the implementation, we confirmed the effectiveness
of test case creation efforts by our proposed idea of 2-tier abstraction
of software functions and test cases. We also evaluated the automatic
verification performance of environment replications, test cases
extractions and test cases conductions.
Abstract: Newly synthesized Polypropylene-g-Polyethylene
glycol polymer was first time used for a compartment-less enzymatic
fuel cell. Working electrodes based on Polypropylene-g-Polyethylene
glycol were operated as unmediated and mediated system (with
ferrocene and gold/cobalt oxide nanoparticles). Glucose oxidase and
bilirubin oxidase was selected as anodic and cathodic enzyme,
respectively. Glucose was used as fuel in a single-compartment and
membrane-less cell. Maximum power density was obtained as 0.65
nW cm-2, 65 nW cm-2 and 23500 nW cm-2 from the unmediated,
ferrocene and gold/cobalt oxide modified polymeric film,
respectively. Power density was calculated to be ~16000 nW cm-2 for
undiluted wastewater sample with gold/cobalt oxide nanoparticles
including system.
Abstract: This study was developed to compare the behavior
and the ability of polymer foam composites towards sound absorption
test of Shorea leprosula wood (SL) of acid hydrolysis treatment with
particle size
Abstract: The web’s increased popularity has included a huge
amount of information, due to which automated web page
classification systems are essential to improve search engines’
performance. Web pages have many features like HTML or XML
tags, hyperlinks, URLs and text contents which can be considered
during an automated classification process. It is known that Webpage
classification is enhanced by hyperlinks as it reflects Web page
linkages. The aim of this study is to reduce the number of features to
be used to improve the accuracy of the classification of web pages. In
this paper, a novel feature selection method using an improved
Particle Swarm Optimization (PSO) using principle of evolution is
proposed. The extracted features were tested on the WebKB dataset
using a parallel Neural Network to reduce the computational cost.
Abstract: The main aim of this research was to investigate a
prototype bamboo shading device. There were two objectives to this
study: first, to investigate the effects of non-chemical treatments on
bamboo shading devices damaged by powder-post beetles and fungi,
and second to develop a prototype bamboo shading device. This
study of the effects of non-chemical treatments on bamboo shading
devices damage by powder-post beetles in the laboratory showed
that, among seven treatments tested, wood vinegar treatment can
protect powder-post beetles better than the original method by up to
92.91%. It was also found that wood vinegar treatment shows the
best performance in fungi protection and works better than the
original method by up to 40%. A second experiment was carried out
by constructing four bamboo shading devices and installing them on
a building for 28 days. All aspects of shading device were
investigated in terms of their beauty, durability, and ease of
construction and assembly. The final prototype was developed from
the lessons learned from the test results. In conclusion, this study
showed the effectiveness of some natural preservatives against insect
and fungi damage, and it also illustrated the characteristics of a
prototype bamboo shading device that can be constructed by rural
workers within one week.
Abstract: This work reports the potential of using Palm Kernel
(PK) ash and shell as a partial substitute for Portland Cement (PC)
and coarse aggregate in the development of mortar and concrete. PK
ash and shell are agro-waste materials from palm oil mills, the
disposal of PK ash and shell is an environmental problem of concern.
The PK ash has pozzolanic properties that enables it as a partial
replacement for cement and also plays an important role in the
strength and durability of concrete, its use in concrete will alleviate
the increasing challenges of scarcity and high cost of cement. In order
to investigate the PC replacement potential of PK ash, three types of
PK ash were produced at varying temperature (350-750C) and they
were used to replace up to 50% PC. The PK shell was used to replace
up to 100% coarse aggregate in order to study its aggregate
replacement potential. The testing programme included material
characterisation, the determination of compressive strength, tensile
splitting strength and chemical durability in aggressive sulfatebearing
exposure conditions. The 90 day compressive results showed
a significant strength gain (up to 26.2 N/mm2). The Portland cement
and conventional coarse aggregate has significantly higher influence
in the strength gain compared to the equivalent PK ash and PK shell.
The chemical durability results demonstrated that after a prolonged
period of exposure, significant strength losses in all the concretes
were observed. This phenomenon is explained, due to lower change
in concrete morphology and inhibition of reaction species and the
final disruption of the aggregate cement paste matrix.
Abstract: The organizations in the knowledge economy era have
recognized the importance of building knowledge assets for
sustainable growth and development. In comparison to other
industries, Information Technology (IT) enterprises, holds an edge in
developing an effective Knowledge Management (KM) programmethanks
to their in-house technological abilities. This paper tries to
study the various knowledge based incentive programmes and its
effect on Knowledge Sharing and Learning in the context of the
Indian IT sector. A conceptual model is developed linking KM
Incentives, Knowledge Sharing and Learning. A questionnaire study
is conducted to collect primary data from the knowledge workers of
the IT organizations located in India. The data was analysed using
Structural Equation Modeling using Partial Least Square method. The
results show a strong influence of knowledge management incentives
on knowledge sharing and an indirect influence on learning.
Abstract: The development of the United Arab Emirates (UAE)
into a regional trade, tourism, finance and logistics hub has
transformed its real estate markets. However, speculative activity and
price volatility remain concerns. UAE residential market values
(MV) are exposed to fluctuations in capital flows and migration
which, in turn, are affected by geopolitical uncertainty, oil price
volatility and global investment market sentiment. Internally, a
complex interplay between administrative boundaries, land tenure,
building quality and evolving location characteristics fragments UAE
residential property markets. In short, the UAE Residential Valuation
System (UAE-RVS) confronts multiple challenges to collect, filter
and analyze relevant information in complex and dynamic spatial and
capital markets. A robust (RVS) can mitigate the risk of unhelpful
volatility, speculative excess or investment mistakes. The research
outlines the institutional, ontological, dynamic and epistemological
issues at play. We highlight the importance of system capabilities,
valuation standard salience and stakeholders trust.
Abstract: In this study, we have focused our attention on
combining of molecular imprinting into nanofilms and QCM
nanosensor approaches and producing QCM nanosensor for anti-
CCP, chosen as model protein, using anti-CCP imprinted nanofilms.
The nonimprinted nanosensor was also prepared to evaluate the
selectivity of the imprinted nanosensor. Anti-CCP imprinted QCM
nanosensor was tested for real time detection of anti-CCP from
aqueous solution. The kinetic and affinity studies were determined by
using anti-CCP solutions with different concentrations. The
responses related with mass shifts (%m) and frequency shifts (%f)
were used to evaluate adsorption properties. To show the selectivity
of the anti-CCP imprinted QCM nanosensor, competitive adsorption
of anti-CCP and IgM was investigated. The results indicate that anti-
CCP imprinted QCM nanosensor has higher adsorption capabilities
for anti-CCP than for IgM, due to selective cavities in the polymer
structure.
Abstract: Human motion capture has become one of the major
area of interest in the field of computer vision. Some of the major
application areas that have been rapidly evolving include the
advanced human interfaces, virtual reality and security/surveillance
systems. This study provides a brief overview of the techniques and
applications used for the markerless human motion capture, which
deals with analyzing the human motion in the form of mathematical
formulations. The major contribution of this research is that it
classifies the computer vision based techniques of human motion
capture based on the taxonomy, and then breaks its down into four
systematically different categories of tracking, initialization, pose
estimation and recognition. The detailed descriptions and the
relationships descriptions are given for the techniques of tracking and
pose estimation. The subcategories of each process are further
described. Various hypotheses have been used by the researchers in
this domain are surveyed and the evolution of these techniques have
been explained. It has been concluded in the survey that most
researchers have focused on using the mathematical body models for
the markerless motion capture.