Miracle-dz/UniFAR-FTUs huggingface dataset:
EFFECT OF EMOTIONAL INTELLIGENCE ON CUSTOMER RETENTION AMONGST BANK EMPLOYEES

Customer satisfaction is the ultimate goal of the any organization. It includes service industry and banking is the one such industry where customer retention is very much dependent on front executives. In spite of a number of research studies being conducted in the field of consumer behavior, the research involving customer retention especially in the service industry is still evolving. A quantitative research was used to study the relationship between the employees’ emotional intelligence, their service performance and customer retention in banks of Indore. The study explored the effect of EI and gender on customer retention orientation of bank employees. The previous studies support that the service performance of the employee and their capability to retain the customer is dependent on various component of emotional intelligence dimensions namely social awareness, self regulation and self awareness. But the present study does not support the same.
A Discrete Event Simulation of the Joint Space Cell

The Australian Defence Force (ADF) has formed a joint space cell (JSC), to develop a set of information products to provide space situational awareness (SSA) for the Australian Defence Organisation. The JSC responds to information requests that can arrive from any agency or group within Defence. The ADF is considering different resource allocation options to enhance the capabilities offered by the JSC. To this end, we are investigating the characteristics of the relationship between the quantity of allocated resources and the rate of output through the following analytical questions: 1. What are the expected waiting times of customers requesting SSA products, given the current production process and operator allocation? 2. How many operators could be efficiently utilised within the JSC, given the existing processes for producing SSA products? 3. Is it possible to achieve significant reductions to customer waiting times through the automation of a subset of SSA production processes? This paper reports on problem formulation, modelling and simulation approach, data acquisition and interim results. We have taken a queueing systems approach to model the operational mechanics of the JSC. We treat the operation of the JSC as a manufacturing process for information products. The JSC produces a portfolio of products and operates with a single queue and multiple servers. We have used the JSC’s email traffic to characterise one of the arrival parameters of the queue. The service time that each customer experiences depends upon the product that is requested. The results from the email analysis indicate that the exponential distribution may be used to model the daily arrival of emails into the JSC. The simulation output indicates a combination of deterministic and stochastic behaviour which is consistent with the use of the triangular distribution for generating service times. Preliminary results indicate that DES is a reasonable approach for modelling the JSC and will provide a capability to aid decision making.
Chapter 13 Upscaling the Biogeochemical Role of Arbuscular Mycorrhizal Fungi on Metal Mobility

In this chapter we introduce a concept relating the biogeochemical role of microorganisms explicitly to cross-scale effects in the frame of an approach we termed objective scale integrated biogeochemistry. By objective, we mean that the scale of analyses is that of the environmental objects involved in the cycling, and is not assumed a priori based on human (institutional) interests. By integrated, we refer to the consideration of multi-element fluxes (of nutrients and toxic elements) through the biotic and abiotic compartments controling productivity of the system. After a critical analysis of the knowledge on arbuscular mycorrhizal fungi (AMF) relevant for estimating their biogeochemical role in the mobility of metals, we propose a methodology for characterizing this role and underline the practical limitations linked to current state of knowledge concerning AMF. These limitations are due to basic issues such as estimating the dimension of a physiological individual, the size in space of a population of AMF, and the species diversity of AMF in the hyphal compartment. We then focus on an example of myco-phyto-remediation using selected plants and commercial inocula. A multi-scale (pot, lysimeter, field plot) experimental approach for studying the biogeochemical role of AMF in metal mobility was developed, tackling scale-specific issues. The main findings of the operational research program are summarized.
The effect of the sulphate concentration in the nutrient solution on nitrate content and some components of tomato fruits ( Lycopersicon esculentum Mill . )

The experiment with tomato of Cunero F1 cultivar was carried out in winter-summer growing cycle in 2001. The aim of the study was to evaluate the effect of the elevated sulphate content in the nutrient solution on nitrate and ammonium nitrogen levels as well as on the content of some components in fruits of tomato grown on the mineral wool. The elevated content of sulphates in the nutrient solution caused significant increase of nitrate accumulation in the top part of plant and in the second leaf under the heading place, no effect, however, was observed on the content of ammonium nitrogen form as well as on activity of nitrate reductase in these parts of plants. Fruits of tomato, grown on the nutrient solution enriched with sulphates contained more nitrate, more ascorbic acid and the higher amount of free amino acids in comparison with the control treatment. The effect of elevated sulphate level in the nutrient solution on the constituents analysed in the fruits, depended on their maturity stage.
Social skills and their relationship to drug use among 15 – 16-year-old students in Estonia : An analysis based on the ESPAD data Research report

AIM – to investigate the relationship between levels of social skills and drug use among 15–16-yearold students in Estonia. METHODS – A total of 2,460 Estonian schoolchildren, born in 1995, participated in the ESPAD study in 2011. Individual social skills levels were measured with questions on prosocial and antisocial behaviours assessing how children perform within their social milieu. The relationship between social skills levels and drug use was estimated using chi-squared tests and logistic regression analysis. Odds ratios (OR) were adjusted for gender, parents’ education and financial well-being, and 95% confidence intervals (CI) were used to estimate risks of drug use by social skills level. RESULTS – Students with lower social skills were at greater risk of starting smoking and smoking daily (50% risk increase). Students with low social skills had a higher risk of lifetime use of cannabis (OR=1.4; 95%CI 1.1–1.9), sedatives/tranquillisers without a prescription (OR=2.3; 1.4–3.9), and inhalants (OR=1.9; 1.2–3.0). The number of students with lower social skills was significantly higher among boys than girls: 35% vs 19%. CONCLUSIONS – Students’ social skill levels were related to their licit and illicit drug use. A low level of social skills can increase adolescents’ vulnerability to drug use. As boys’ social skills levels appeared much lower and their drug use higher than that of girls, we suggest that gender-related risk prevention programmes of social skills training could be beneficial in preventing drug use.
Meta-Heuristic Approaches for a New Modeling of Single Machine Scheduling Problem

The aim of this paper is to propose a new model for a single machine-scheduling problem. According to just in time (JIT) approach, production managers should consider more than one criterion in scheduling problems. However, three criteria, including minimizing the number of tardy jobs, total weighted earliness and total weighted tardiness, are considered in this proposed model. To solve the model, firstly, branch and bound (BBA) method is applied, because it can solve the small size problems. Furthermore, the results obtained from this algorithm, are good measurement to test effectiveness of proposed meta-heuristic algorithms. In the literature, this problem is classified in the NP-hard class. Hence, two well-known metaheuristic methods, including genetic algorithm (GA), simulated annealing (SA), are applied to tackle large scale problems. Finally, heuristic solutions were compared with the global optimum which is obtained by (BBA) method. Computational results showed that both the heuristic algorithms yield good quality solutions using reasonable computation time.
The Effect of Coherent Waving Motion on Turbulence Structure in Flexible Vegetated Open Channel Flows

Most aquatic plants are highly flexible and the waving motions of plants are usually observed. The downstream advection of coherent vortices causes the coherent waving of aquatic vegetation, so-called the ‘‘Monami’’. The strong oscillations in canopy geometry have the effects of decreasing the amount of vertical turbulent momentum and mass transport into the aquatic canopies. Therefore, hydrodynamic characteristics of vegetated canopy open-channel flows have been investigated intensively, as well as in terrestrial canopy flows. However, the effects of the plant waving motion on turbulence structure have not been revealed as yet. In the present study, the illuminated flow images were taken by CMOS camera (1024×1024pixels) and the simultaneous measurements of water velocity and vegetation motion were successfully conducted by the use of both PIV and PTV techniques. As the results, we revealed the effect of waving motion on turbulence structure in flexible vegetated open-channel flows. The former part of the present study examines the coherent-vortex formulation zones on the basis of the Adrian et al.(2000) ’s detection method. The latter part focused on the relation between coherent structure and the occurrence of Monami phenomena.
Predicting Blood Glucose Levels of Diabetes Patients

Time series data is used for modelling, description, forecasting, and control in many fields from engineering to statistics. Time series forecasting is one of the domains of time series analysis, which requires regression. Along with the recent developments in deep learning techniques, the advancement in the technologies personal health care devices are making it possible to apply deep learning methods on the vast amounts of electronic health data. We aim to provide reliable blood glucose level prediction for diabetes patients so that the negative effects of the disease can be minimized. Currently, recurrent neural networks (RNNs), and in particular the long-short term memory unit (LSTM), are the state-of-the-art in timeseries forecasting. Alternatively, in this work we employ convolutional neural networks (CNNs) with multiple layers to predict future blood glucose level of a diabetes type 2 patient. Besides our CNN model, we also investigate whether our static insulin sensitivity calculation model’s results have a correlation with basal rate of the patient. We use the static insulin sensitivity data with our prediction model, in order to find out whether it contributes for a better prediction or not. Our experimental results demonstrate that calculated static insulin sensitivity values do not have any correlation with the basal rate. Our convolutional neural network model forecasts multivariate timeseries with multiple outputs including the blood glucose level with a 1.0729 mean absolute error for the prediction horizon of 15 minutes.
ON SOUND AND VIBRATION DECEMBER 15-18 , 1997 ADELAIDE , SOUTH AUSTRALIA THE EFFECTIVENESS OF SPARSE RANDOM ARRAYS FOR UNDERWATER ACOUSTIC IMAGING

-It is intended to produce an underwater acoustic imaging system with three-dimensional images of resolution around 1 mm at 1 m range. For this purpose, sparse random arrays have considerable cost and feasibility advantages overfilled arrays. There is no degradation of beamwidth, while the average distant sidelobe level (ADSL) can be reduced to a level expected to be satisfactory for imaging surjaces such as sea mines, although unsatisfactory for imaging continua such as human tissue. We have simulated the imaging of point targets by random arrays of N elements. The simulation uses exact path lengths in the near field. Good range resolution is obtained through either a short toneburst or a cross-correlated chirped signal. It is confirmed that, as the array is made more sparse, the ADSL rises. For broadband signals, the ADSL is found to be markedly lower than the monofrequency value. The use of partially random arrays, constructed out of identical subarrays for ease of manufacture but oriented randomly, leads to an appreciable degradation in ADSL. For example, a system of 100 square subarrays with four subarray orientations resulted in an ADSL penalty of 5 dB.
ECOLOGY AND SPECIES ASSOCIATION OF GRASS SPECIES IN RESPONSE TO ALTITUDINAL GRADIENT IN THE POTOHAR REGION

An investigation was carried out to evaluate impact of altitudinal gradient on species composition and distributional pattern of individual grass species in the Potohar region. Vegetation was sampled by quadrat method, which were laid along a transect line at different selected sites. All ecological parameters like species composition, pair-wise association and distributional pattern, and community structure significantly changed with altitudinal gradient. Species with broad distributional range can be related to high degree of tolerance to a variety of environmental stresses. Species growing at low altitudes were different in their structure and contribution towards community structure. High altitude species are generally with restricted distributional range. Species richness was the maximum at moderate elevation because of better growth conditions. Chrysopogon serrulatus Trin. , Cymbopogon jwarancusa (Jones) Schult. and Cynodon dactylon (L.) Pers. dominated the Potohar region, all showed significant association among themselves. Association usually existed between dominant species that shared similar resources. Species colonizing moister habitats, saline patches, drier hills. mountain slopes, low temperature ranges and sandy clayey soils, all have strong associations. Domination of species was similar up to 1200 m a.s. l. , but species composition changed significantly along increasing elevation.
Do ethnic minority candidates mobilise ethnic minority voters ? Evidence from the 2010 General Election ( Draft )

This paper explores the effect of candidate ethnicity and religion on turnout, voter registration and attitudes to political engagement among ethnic minorities in the UK, using data from the recent Ethnic Minority British Election Study (EMBES). Ethnic minorities and Muslims are no more likely to register or vote when a coethnic or Muslim candidate is standing. Nevertheless, ethnic minorities are less likely to believe that parties are unlikely to change things for black and Asian people in constituencies where a coethnic candidate stood, and Muslims report higher duty to vote, satisfaction when voting, political interest and knowledge in constituencies where a Muslim candidate stood. Although ethnic or religious minority candidates at the Parliamentary level may not be an effective way of mobilising ethnic minority voters, more positive attitudes when a coethnic or Muslim candidate stands adds to the normative case for descriptive representation in the UK.
Exploring children ’ s perceptions and experiences of outdoor spaces

All people employ their senses in order to locate themselves in their environment; however the perception of space is not common to everyone and differs related to various factors. Considering these differences in perception and the factors affecting it, it should be stated that children’s perceptions and experiences of spaces are different from adults’. Unless the places are created by them to play, children perceive and experience environments that are created by adults. Thus, spaces that children experience are generally based on adult’s logic of design configuration. As children are the active participants of their home and school environments, the present study focuses on a pilot study which is conducted at a primary school with the 5 th year students. Therefore, the aim is to discover children’s landmark recognition and preferences of outdoor spaces in terms of their homeschool routes, playing areas and reveal the underlying syntactic structures of the spaces as well. Considering the main structure of the research which is based on the comparison between children’s responses to a questionnaire and syntactic properties of the significant places, the methodology is organised as two phases including the individual interviews with students and syntactic analyses of the spaces identified and preferred by children. The first phase of the methodology is based on the examination of children’s perceptions and uses of outdoor spaces with the help of a questionnaire where students answer the questions and draw their route from home to school. In this phase, students are also asked to comment on the photographs taken from the predetermined city landmarks. For the second phase, spaces identified by children are analysed due to the syntactic properties based on various calculations. The results of the research emphasise that the edges of the city and especially the city walls seem to affect the spatial perceptions of children. Although children mostly prefer to stay in the close vicinity to their home-school environment, they can identify significant city landmarks. The outcomes of the research also indicate that there’s a difference between the female and male students considering the preferences of playing areas.
Parasites-Iflavirus association and emergence of three master variants of DWV affecting Apis mellifera intermissa in Tunisian apiaries

As a social insect living in large groups, the honey bee Apis mellifera is vulnerable to parasites and pathogens, which have become a major threat for apiculture and pollination services worldwide. The co-occurrence of parasites, mainly Varroa destructor, with viral pathogens is suspected to constitute a primary cause in colony mortality and population losses. In our study, we investigated the presence and possible association of five honey bee pathogens occurring in Tunisian apiaries. This included the endoparasitic microsporidia Nosema ceranae and Nosema apis, the ectoparasitic mite V. destructor and two honey bee RNA virus; deformed wing virus (DWV or DWV genotype A) and Varroa destructor virus-1 (VDV-1), otherwise known as DWV genotype B. There was no statistical association between the five biotic stressors. Interestingly, from the most Varroa-infested colony sample (A27.H4, 45%); we detected a co-infection with three pathogens, DWV, N. apis and N. ceranae. Using a haplotype network analysis, we revealed a high diversity of the Tunisian DWV genotypes A and B from Apis mellifera intermissa with twelve haplotypes showing similarities with various European, Asian, and South American genotypes. We detected double infection by DWV genotypes A and B in the Bizerte region. These co-occurring genotypes were suggested that such co-infection gives rise to recombinant virus with potential enhanced virulence. Here, we support the presence of DWV-VDV-1 recombinant in A. m. intermissa. Overall and despite the lack of a clear epidemiological link between honey bee pathogens and colony health, their interactions may lead to disease and colony losses.
Theses 11-19-2018 Detection of Autism using Magnetic Resonance Imaging data and Graph Convolutional Neural Networks

Autism or Autism Spectrum Disorder (ASD) is a development disability which generally begins during childhood and may last throughout the lifetime of an individual. It is generally associated with difficulty in communication and social interaction along with repetitive behavior. One out of every 59 children in the United States is diagnosed with ASD [11] and almost 1% of the world population has ASD [12]. ASD can be difficult to diagnose as there is no definite medical test to diagnose this disorder. The aim of this thesis is to extract features from resting state functional Magnetic Resonance Imaging (rsfMRI) data as well as some personal information provided about each subject to train variations of a Graph Convolutional Neural Network to detect if a subject is Autistic or Neurotypical. The time series information as well as the connectivity information of specific parts of the brain are the features used for analysis. The thesis converts fMRI data into a graphical representation where the vertex represents a part of the brain and the edge represents the connectivity between two parts of the brain. New adjacency matrix filters were added to the Graph CNN model and the model was altered to add a time dimension.
Data-Driven Fault Detection and Isolation of Industrial Fluid Transfer Systems

One of the key promises of Industry 4.0 is to reshape the various industrial sectors through the integration of advanced analytics and IoT into its day to day operations. A major component of Industry 4.0 is predictive maintenance, which allows real-time remote condition monitoring of critical industrial equipment, leading to reduced downtimes and optimal asset management. However, the application of analytical methods into existing industrial ecosystems is largely a non-standard problem and varies from case to case. In this thesis we provide an end-toend solution for fault detection and isolation (FDI) in an industrial fluid transfer system, with the primary objective of finding leaks in the reservoirs using advanced signal processing and machine learning techniques. Optimization remains a driving force throughout the implementation, keeping in mind the environmental constraints and the limited hardware and sensing capabilities of the devices involved. We extract the relevant features from the available data-set and use it to train the predictive models. The algorithms consist of a single strong learner and ensemble of weak learners to provide comparative analysis for determining the best possible option. We employ Bayesian statistics to further improve the predictive outcomes. For the next part, we identify the optimal parameters for our use case through various experiments involving the different phases of our implementation life cycle. Finally, we design a prototype of our solution in the industrial setup to demonstrate the feasibility of the design concept. Our results show a prediction accuracy of over 99% for the reservoirs, while at the same time being fast, lightweight and unobtrusive to the existing test-bed. On top of that, our overall implementation and optimization techniques provide flexibility in terms of architectural and hardware changes, making it robust and future-proof.
Preventing Over-offering Behavior in Capacity Markets

Capacity markets provides additional revenue stream for the power suppliers. In a capacity-energy combined market environment, suppliers have incentives to deliberately over-offering their capacities in the capacity market while bid very high price in the energy and ancillary markets to avoid operation. This paper analyzes the risks and profits of this capacity-over-offering behavior, and develops a method for computing non-operable penalty level which can prevent the capacity-over-offering behavior. It is found that the effective penalty level is highly correlated with the stochastic characteristics of supplier’s profit streams and supplier’s risk attitudes. Two types of suppliers are suggested as the potential cheaters in the analysis. The methodology and the results are potentially useful for the operation and mitigation in a capacity-energy market environment. Key-Words: Capacity Market; Over-offering; Risk Analysis; Non-operable Penalty; Prospect Theory; MonteCarlo Simulation