scholarly journals Insight and loyalty: Building a data-driven loyalty programme for Cambodian retailers

2021 ◽  
Author(s):  
◽  
Lytor Seng
Keyword(s):  
Technical Assistance ◽  
Early Stage ◽  
Low Cost ◽  
Relevant Literature ◽  
Business Case ◽  
Data Driven ◽  
Smart Devices ◽  
Success Factor ◽  
Second Phase ◽  
Loyalty Programme

<p>The difficulties in making effective decisions and the abundant amount of data have driven many businesses to adopt data-driven processes using enterprise applications like business intelligence. Although these applications have been around over the last decade and the benefits of using them are evident in several countries, Cambodia still falls behind in adopting such technology. This thesis, therefore, aims to explore opportunities for a data tool, specifically tailored to meet the needs of retail firms in Cambodia.  In order to achieve the objective, the study employs a two-phase approach. In the first phase, a qualitative method through in-depth interview was undertaken. Six managers from different retail businesses were interviewed in the areas of: data utilisation, perception and investment towards data technologies, and relevant future plans. Findings reveal that the incorporation of data in decision-making was limited. Although managers did embrace the use of data and acknowledge its importance, the costly nature of the technologies held them back from major investments. The findings imply that although there are opportunities for data-related tools for enterprises, certain components are necessary for their success. Managers tended to look for technology that produced final results with little or no technical assistance from their side. The ability to gather data outside their consumer base is also emphasised. The need for a low-cost application is an important implication.  The first phase of the research led to the decision to create a data-driven loyalty programme due to its double benefits for firms (loyalty and data), its low cost, and the ability to capture data from a large base of consumers. To understand consumers’ usage and attitude towards loyalty programmes, the second phase of the research was carried out using a quantitative method. One survey was distributed and completed by 187 respondents, the majority of whom were teenagers and young adults, a potential segment for the loyalty programme. Data was cleaned and analysed using descriptive analysis. Findings from consumers revealed interesting insights into how loyalty programmes are perceived in relation to shopping behaviours. Consumers were open to a new loyalty programme and embraced the idea of combining all the cards into one application. Flexibility was found to be the most important factor driving the participation of loyalty programmes. That covered the ability to set up their own plan for reward redemption and to receive personalised communication. Technology was another important success factor, specifically mobile technology that allows consumers to manage their profile through touch on their smart devices.  A business case for the loyalty programme has been developed based on the findings from both phases, relevant literature, and discussions with others. The target segment, who are the urban, young, and middle- and high-income class, was studied. The potential market for the programme was assessed by looking at the size, need and trends of the segment. Competition in the country and the ASEAN is also evaluated. The programme adopts a multi-sided platform model and the closed-loop mechanics. Furthermore, the details of how the programme is designed and managed are discussed. At this early stage, many other features still need to be further studied, including the technical development, detailed financial forecast and planning, and team management.</p>

scholarly journals Insight and loyalty: Building a data-driven loyalty programme for Cambodian retailers

2021 ◽  
Author(s):  
◽  
Lytor Seng
Keyword(s):  
Technical Assistance ◽  
Early Stage ◽  
Low Cost ◽  
Relevant Literature ◽  
Business Case ◽  
Data Driven ◽  
Smart Devices ◽  
Success Factor ◽  
Second Phase ◽  
Loyalty Programme

<p>The difficulties in making effective decisions and the abundant amount of data have driven many businesses to adopt data-driven processes using enterprise applications like business intelligence. Although these applications have been around over the last decade and the benefits of using them are evident in several countries, Cambodia still falls behind in adopting such technology. This thesis, therefore, aims to explore opportunities for a data tool, specifically tailored to meet the needs of retail firms in Cambodia.  In order to achieve the objective, the study employs a two-phase approach. In the first phase, a qualitative method through in-depth interview was undertaken. Six managers from different retail businesses were interviewed in the areas of: data utilisation, perception and investment towards data technologies, and relevant future plans. Findings reveal that the incorporation of data in decision-making was limited. Although managers did embrace the use of data and acknowledge its importance, the costly nature of the technologies held them back from major investments. The findings imply that although there are opportunities for data-related tools for enterprises, certain components are necessary for their success. Managers tended to look for technology that produced final results with little or no technical assistance from their side. The ability to gather data outside their consumer base is also emphasised. The need for a low-cost application is an important implication.  The first phase of the research led to the decision to create a data-driven loyalty programme due to its double benefits for firms (loyalty and data), its low cost, and the ability to capture data from a large base of consumers. To understand consumers’ usage and attitude towards loyalty programmes, the second phase of the research was carried out using a quantitative method. One survey was distributed and completed by 187 respondents, the majority of whom were teenagers and young adults, a potential segment for the loyalty programme. Data was cleaned and analysed using descriptive analysis. Findings from consumers revealed interesting insights into how loyalty programmes are perceived in relation to shopping behaviours. Consumers were open to a new loyalty programme and embraced the idea of combining all the cards into one application. Flexibility was found to be the most important factor driving the participation of loyalty programmes. That covered the ability to set up their own plan for reward redemption and to receive personalised communication. Technology was another important success factor, specifically mobile technology that allows consumers to manage their profile through touch on their smart devices.  A business case for the loyalty programme has been developed based on the findings from both phases, relevant literature, and discussions with others. The target segment, who are the urban, young, and middle- and high-income class, was studied. The potential market for the programme was assessed by looking at the size, need and trends of the segment. Competition in the country and the ASEAN is also evaluated. The programme adopts a multi-sided platform model and the closed-loop mechanics. Furthermore, the details of how the programme is designed and managed are discussed. At this early stage, many other features still need to be further studied, including the technical development, detailed financial forecast and planning, and team management.</p>

Accurate Detection and Quantization of Leaf- Diseases through Soft Computing

2018 ◽  
Vol 1 (1) ◽  
pp. 236-247
Author(s):  
Divya Srivastava ◽  
Rajitha B. ◽  
Suneeta Agarwal
Keyword(s):  
Machine Learning ◽  
Image Processing ◽  
Agricultural Production ◽  
Bacterial Blight ◽  
Early Stage ◽  
Second Phase ◽  
Computationally Efficient ◽  
Stage Of Disease ◽  
Accurate Detection ◽  
Two Phases

Diseases in leaves can cause the significant reduction in both quality and quantity of agricultural production. If early and accurate detection of disease/diseases in leaves can be automated, then the proper remedy can be taken timely. A simple and computationally efficient approach is presented in this paper for disease/diseases detection on leaves. Only detecting the disease is not beneficial without knowing the stage of disease thus the paper also determine the stage of disease/diseases by quantizing the affected of the leaves by using digital image processing and machine learning. Though there exists a variety of diseases on leaves, but the bacterial and fungal spots (Early Scorch, Late Scorch, and Leaf Spot) are the most prominent diseases found on leaves. Keeping this in mind the paper deals with the detection of Bacterial Blight and Fungal Spot both at an early stage (Early Scorch) and late stage (Late Scorch) on the variety of leaves. The proposed approach is divided into two phases, in the first phase, it identifies one or more disease/diseases existing on leaves. In the second phase, amount of area affected by the disease/diseases is calculated. The experimental results obtained showed 97% accuracy using the proposed approach.

Identification of Coronary Artery Disease using Artificial Neural Network and Case Based Reasoning

2020 ◽  
Vol 13 ◽  
Author(s):  
Varun Sapra ◽  
M.L Saini ◽  
Luxmi Verma
Keyword(s):  
Neural Network ◽  
Coronary Artery Disease ◽  
Coronary Artery ◽  
Early Stage ◽  
Second Phase ◽  
Case Based Reasoning ◽  
Data Set ◽  
Artery Disease ◽  
Severity Of The Disease ◽  
Case Based

Background: Cardiovascular diseases are increasing at an alarming rate with very high rate of mortality. Coronary artery disease is one of the type of cardiovascular disease, which is not easily diagnosed in its early stage. Prevention of Coronary Artery Disease is possible only if it is diagnosed, at early stage and proper medication is done. Objective: An effective diagnosis model is important not only for the early diagnosis but also to check the severity of the disease. Method: In this paper, a hybrid approach is followed, with the integration of deep learning (multi-layer perceptron) with Case based reasoning to design analytical framework. This paper suggests two phases of the study, one in which the patient is diagnosed for Coronary artery disease and in second phase, if the patient is suffering from the disease then employing Case based reasoning to diagnose the severity of the disease. In the first phase, multilayer perceptron is implemented on reduced dataset and with time-based learning for stochastic gradient descent respectively. Results: The classification accuracy is increase by 4.18 % with reduced data set using deep neural network with time based learning. In second phase, if the patient is diagnosed as positive for Coronary artery disease, then it triggers the Case based reasoning system to retrieve from the case base, the most similar case to predict the severity for that patient. The CBR model achieved 97.3% accuracy. Conclusion: The model can be very useful for medical practitioners as a supporting decision system and thus can save the patients from unnecessary medical expenses on costly tests and can improve the quality and effectiveness of medical treatment.

scholarly journals Automation of absolute protein-ligand binding free energy calculations for docking refinement and compound evaluation

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Germano Heinzelmann ◽  
Michael K. Gilson
Keyword(s):  
Free Energy ◽  
Early Stage ◽  
Binding Free Energy ◽  
Low Cost ◽  
Free Energy Calculations ◽  
Free Energies ◽  
Energy Calculations ◽  
Drug Candidates ◽  
Binding Free Energy Calculations ◽  
Binding Free Energies

AbstractAbsolute binding free energy calculations with explicit solvent molecular simulations can provide estimates of protein-ligand affinities, and thus reduce the time and costs needed to find new drug candidates. However, these calculations can be complex to implement and perform. Here, we introduce the software BAT.py, a Python tool that invokes the AMBER simulation package to automate the calculation of binding free energies for a protein with a series of ligands. The software supports the attach-pull-release (APR) and double decoupling (DD) binding free energy methods, as well as the simultaneous decoupling-recoupling (SDR) method, a variant of double decoupling that avoids numerical artifacts associated with charged ligands. We report encouraging initial test applications of this software both to re-rank docked poses and to estimate overall binding free energies. We also show that it is practical to carry out these calculations cheaply by using graphical processing units in common machines that can be built for this purpose. The combination of automation and low cost positions this procedure to be applied in a relatively high-throughput mode and thus stands to enable new applications in early-stage drug discovery.

scholarly journals Process Engineering of the Acetone-Ethanol-Butanol (ABE) Fermentation in a Linear and Feedback Loop Cascade of Continuous Stirred Tank Reactors: Experiments, Modeling and Optimization

Fuels ◽  
2021 ◽  
Vol 2 (2) ◽  
pp. 108-129
Author(s):  
Katja Karstens ◽  
Sergej Trippel ◽  
Peter Götz
Keyword(s):  
Feedback Loop ◽  
Early Stage ◽  
Formation Rate ◽  
Abe Fermentation ◽  
Operating Conditions ◽  
Stirred Tank ◽  
Second Phase ◽  
Stirred Tank Reactors ◽  
Continuous Stirred Tank ◽  
Continuous Stirred Tank Reactors

The production of butanol, acetone and ethanol by Clostridium acetobutylicum is a biphasic fermentation process. In the first phase the carbohydrate substrate is metabolized to acetic and butyric acid, in the following second phase the product spectrum is shifted towards the economically interesting solvents. Here we present a cascade of six continuous stirred tank reactors (CCSTR), which allows performing the time dependent metabolic phases of an acetone-butanol-ethanol (ABE) batch fermentation in a spatial domain. Experimental data of steady states under four operating conditions—with variations of the pH in the first bioreactor between 4.3 and 5.6 as well as the total dilution rate between 0.042 h−1 and 0.092 h−1—were used to optimize and validate a corresponding mathematical model. Beyond a residence time distribution representation and substrate, biomass and product kinetics this model also includes the differentiation of cells between the metabolic states. Model simulations predict a final product concentration of 8.2 g butanol L−1 and a productivity of 0.75 g butanol L−1 h−1 in the CCSTR operated at pHbr1 of 4.3 and D = 0.092 h−1, while 31% of the cells are differentiated to the solventogenic state. Aiming at an enrichment of solvent-producing cells, a feedback loop was introduced into the cascade, sending cells from a later state of the process (bioreactor 4) back to an early stage of the process (bioreactor 2). In agreement with the experimental observations, the model accurately predicted an increase in butanol formation rate in bioreactor stages 2 and 3, resulting in an overall butanol productivity of 0.76 g L−1 h−1 for the feedback loop cascade. The here presented CCSTR and the validated model will serve to investigate further ABE fermentation strategies for a controlled metabolic switch.

scholarly journals Real-time GNSS precise point positioning for low-cost smart devices

GPS Solutions ◽  
2021 ◽  
Vol 25 (2) ◽  
Author(s):  
Liang Wang ◽  
Zishen Li ◽  
Ningbo Wang ◽  
Zhiyu Wang
Keyword(s):  
Carrier Phase ◽  
Measurement Errors ◽  
Precise Point Positioning ◽  
Low Cost ◽  
Smart Devices ◽  
Dual Frequency ◽  
Static Mode ◽  
Phase Measurements ◽  
Positioning Errors ◽  
Point Positioning

AbstractGlobal Navigation Satellite System raw measurements from Android smart devices make accurate positioning possible with advanced techniques, e.g., precise point positioning (PPP). To achieve the sub-meter-level positioning accuracy with low-cost smart devices, the PPP algorithm developed for geodetic receivers is adapted and an approach named Smart-PPP is proposed in this contribution. In Smart-PPP, the uncombined PPP model is applied for the unified processing of single- and dual-frequency measurements from tracked satellites. The receiver clock terms are parameterized independently for the code and carrier phase measurements of each tracking signal for handling the inconsistency between the code and carrier phases measured by smart devices. The ionospheric pseudo-observations are adopted to provide absolute constraints on the estimation of slant ionospheric delays and to strengthen the uncombined PPP model. A modified stochastic model is employed to weight code and carrier phase measurements by considering the high correlation between the measurement errors and the signal strengths for smart devices. Additionally, an application software based on the Android platform is developed for realizing Smart-PPP in smart devices. The positioning performance of Smart-PPP is validated in both static and kinematic cases. Results show that the positioning errors of Smart-PPP solutions can converge to below 1.0 m within a few minutes in static mode and the converged solutions can achieve an accuracy of about 0.2 m of root mean square (RMS) both for the east, north and up components. For the kinematic test, the RMS values of Smart-PPP positioning errors are 0.65, 0.54 and 1.09 m in the east, north and up components, respectively. Static and kinematic tests both show that the Smart-PPP solutions outperform the internal results provided by the experimental smart devices.

Figures of Merit of Low-Cost CuAl0.9Fe0.1O2 Thermoelectric Material Prepared at Different Solid State Reaction Sintering Temperatures

2015 ◽  
Vol 659 ◽  
pp. 185-189
Author(s):  
Aparporn Sakulkalavek ◽  
Rungnapa Thonglamul ◽  
Rachsak Sakdanuphab
Keyword(s):  
Figure Of Merit ◽  
Sintering Temperature ◽  
Low Cost ◽  
Uniaxial Pressure ◽  
Rhombohedral Structure ◽  
Second Phase ◽  
X Ray Diffraction ◽  
High Purity Grade ◽  
Average Grain Size ◽  
Xrd Patterns

In this study, we investigated a CuAl0.9Fe0.1O2 compound prepared at two different sintering temperatures in order to find out the effect of sintering temperature on the compound's figure of merit of thermoelectric properties. The thermoelectric CuAl0.9Fe0.1O2 compound was prepared from high purity grade Cu2O, Al2O3 and Fe2O3 powders. The mixture of these powders were ground and then pressed with uniaxial pressure into pellets. The pellets obtained were sintered in the air at 1423 K and 1473 K. X-ray diffraction (XRD) patterns showed a single phase of CuAl0.9Fe0.1O2 with rhombohedral structure, , along with a trace of CuO second phase. Moreover, the XRD peaks of the sample sintered at 1423 K indicated that more Fe3+ atoms replaced Al3+ atoms in this sample than they did in the sample sintered at 1473 K. The average grain size of the CuAl0.9Fe0.1O2 compound prepared increased with increasing sintering temperature, whereas its mean pore size and porosity decreased with increasing sintering temperature. The dispersed small pores markedly decreased the thermal conductivity of the compound, while the Fe3+ substitution of Al3+ increased its electrical conductivity. The highest figure of merit (ZT) found was 0.021 at 973 K in the CuAl0.9Fe0.1O2 sample sintered at 1423 K. Our findings show that this low-cost material with a reasonable figure of merit is a good candidate for thermoelectric applications at high-temperature.

INVITED POLICY ECONOMICS PAPER — ECONOMICS IN ASIA 1995–2020

2021 ◽  
pp. 1-15
Author(s):  
JOERGEN OERSTROEM MOELLER
Keyword(s):  
Market Economy ◽  
Value Chain ◽  
Global Economy ◽  
Consumer Preferences ◽  
Low Cost ◽  
Second Phase ◽  
Public And Private ◽  
The Us ◽  
Anglo American ◽  
Asian Economy

Over the last 25 years, Asia’s economic rise has been extraordinary. Its share of global gross domestic product (GDP) has risen from 5.8% to 22.9%. 1 The first phase of high economic growth — up to 1995 — saw Asia enter the global supply chain primarily with labor-intensive/low-cost manufacturing. Domestic consumption was a fairly low share of GDP; Asia was manufacturing mainly for consumption in the US and Europe. As such, it was primarily a rule-taker. In the second phase — from 1995 to 2020 — it gradually turned into an economic force joining the US and Europe in shaping the global economy, exercising significant influence upon the value chain, the cycles of the global economy, transport and logistics, the global capital markets and consumption patterns (consumer preferences and tastes). While not yet among the leading rule-makers, it had become difficult for policymakers (public and private) to make decisions without Asia’s consent. To form an opinion of today’s emerging third phase — post 2020 — the intriguing question is whether the Asian countries have adopted what may be termed Anglo-American economic thinking (basically, the primacy of the market). Or whether behind the curtain, the Asian economy works in its own way diverging from the American and British economic schools. Since demographics and sheer economic scale mean that Asia will dominate the global economy in the years to come, the nature of the Asian economy will be of crucial importance for the future global economy. The conclusion of this paper is that “Asia” in many respects differs — and fundamentally so — from market economy principles. How this prospect should be interpreted is also evolving, as circumstances change. Certainly, the repercussions of COVID-19 have not been the same in the US, Europe, East Asia and South Asia — and this may suggest that socio-political structures have a stronger impact on economic outcomes than economic theory teaches, thus calling into question the global validity of market economy principles.

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