The Impact of the Service Quality on Customer Satisfaction: A Case Study of Colombo Stock Exchange, Sri Lanka

This study was undertaken with the objective of finding out the relationship between quality of service and customers satisfaction Colombo Stock Exchange.  For this study,   Quality of services is evaluated by reliability, functionality, responsiveness service design and assurances, and also reviewed with help of the GAP analyze this was established by Zeithaml, Parasuramn and Berry.   Customer satisfaction is appraised by service facility and accessories, convenience and supporting service, total customer value, total customer cost. The present study is initiated on” relationship between service quality and customer satisfaction” with the samples of 3oo customers of Colombo Stock Exchange.  The study found that the correlation value between service quality and customer satisfaction is 0.797. It is significant at 0.01 levels. There is positive linear relationship between the service quality and customer satisfactions. According to the Regression analysis, 63% service quality impact on customer satisfaction. Finally, service quality influences on customer satisfaction. The study further points out that keen attention should be paid on to polish service quality.  Because, service quality are inter related with customer satisfaction. Key words; Customer Satisfaction, Service Quality, Colombo Stock Exchang

Occupational Stress and its Impact on the Succession of Entrepreneurs in the Jaffna District

Occupational stress is ubiquitous at work places and has recently gained numerous researches because of the impact it has on the succession of entrepreneurs. Unlike developing and newly industrialized countries, most advanced countries are becoming more familiar with the phenomenon and how to manage it. Systematic quasi – random sampling technique selected every second customer to enter to reload center and mobile service providing companies on a day of the survey. The sample correlation coefficient between response variable and the predictor variables is 0.924. For this model the amount of variation in the response variable is 85.4% which is explained by predictor variables.   The stress which has an impact on success of the organization has to be managed into, an optimum level, in order to sustain the en entrepreneurship that is what the stress management became potential to career success. Keywords: Occupational stress and succession of entrepreneur

M.S. Environmental Biology Capstone Project

Chapter 1: Coexistence begins with respect: human impacts on brown bears (Ursus arctos) Chapter 2: Grizzly bear foraging patterns in relation to human disturbances in Sweden Chapter 3: Activity budgets and social relationships of bull Asian elephants (Elephas maximus) at Denver Zoo Chapter 4: Loris trade not so slow: conservation and welfare of slow lorise

Characterisation of chitosan and its films for tissue engineering

Chitosan is a renewable polymer produced from a waste product of the seafood industry. It has been seen as useful for a range of applications due to its inherent properties. It is antifungal, antibacterial, biodegradable, biocompatible and has low immunogenicity which makes it attractive specifically for biomedical applications. Examples of chitosan’s possible applications include bioadhesive films, stem cell growth substrates and drug delivery agents. Chitosan is produced from the N-deacetylation of chitin. Chitin is the second most abundant polysaccharide in the world (by volume after cellulose) and is synthesized by many organisms which results in it being readily available, inexpensive and abundant. Its natural occurrence includes the shells of arthropods such as shrimps, crabs and the cell walls of yeasts. Unfortunately, due to its natural origin and the variation in processing conditions, chitosan is plagued by batch-to-batch variations which affect its widespread utilization. It thus requires appropriate characterization to allow exploitation of its inherent properties. The molecular structure of chitosan includes varying proportions of Dglucosamine and N-acetyl- D-glucosamine monomer units. The degree of acetylation (DA) is defined as the fraction of N-acetyl- D-glucosamine and the distribution of DAs is defined as its variation between polymer chains in a given sample. Although it has been well documented that a distribution of DAs exists (not all chitosan chains have the same DA), this is often overlooked. Therefore common characterization of chitosan is often incomplete and only takes into account one of the average DAs and neglects the distributions of DAs. The complexity and importance of the distribution of DAs had been revealed recently through a coupling of size-exclusion chromatography (SEC) with 1H NMR spectroscopy; however, it had not been measured before the work in this PhD. To allow an accurate characterization of polymers in solution, a true solution must be obtained. Unfortunately, the dissolution of chitosan is often overlooked. Utilizing capillary electrophoresis in the critical conditions (CECC), solution- and solid-state NMR spectroscopy, the dissolution of chitosan was probed. Obtaining a true chitosan solution has been proven to be challenging even with commonly used aqueous solvents. Aqueous AcOH which is most commonly used was seen to dissolve chitosan inefficiently compared to aqueous HCl. However, significant deacetylation was seen in chitosan dissolved in aqueous HCl and kept at high temperatures for prolonged periods of time. The standard for polymer size analysis, SEC, was shown to detect aggregation of the chitosan chains in the SEC eluent. Furthermore, comparisons of the average DA obtained with solution-state compared to solidstate NMR spectroscopy gave evidence of a clear bias in the characterization due to incomplete dissolution. This is extremely significant as chitosan is often characterized with solution-state NMR spectroscopy. The dissolution was concluded to be complex and a compromise is necessary in allowing a more complete dissolution and minimal deacetylation. However, for routinely measured average DA values, measurements should be undertaken in the solid state. To allow a more comprehensive characterization of chitosan composition, methods were developed in this PhD using free solution capillary electrophoresis in the critical conditions (CE-CC). CE-CC is a separation method and therefore is able to yield distributions. Complex polymers can have distributions of various parameters including composition, branching, end groups and molar mass. For chitosan, CE-CC separates by composition (or degree of acetylation). Capillary electrophoresis has been proven to be a robust technique for the separation and characterization of both natural and synthetic polymers. A method was developed to calculate dispersities from distributions obtained with CE-CC analogous to the calculation of dispersity from molar mass distributions determined by size-exclusion chromatography (SEC). Using a ratio of moments, the dispersity of electrophoretic mobility and composition distributions were obtained. The dispersity values represented either the heterogeneity of branching or composition of the complex polymers. This resulted in further characterization of complex polymers based on their composition or architecture. The dispersity values allowed the quantification and numerical representation of the heterogeneity. This allowed comparisons and trends to be quantified between samples. In the further analysis of chitosan, improvements in the separation were sought. This included changing the counter-ion of the buffer during the CE separation from sodium to lithium. Lithium showed trends of greater selectivity and combining these results with previous work in reducing the adsorption (lower pH and higher temperature) allowed a more accurate separation. The dispersity was then calculated for a larger range of chitosan samples and both distributions of electrophoretic mobilities and composition distributions were obtained. A trend was seen in which the dispersity first increased with the average DA and then began to reduce. Using the correlation between composition and mobility allowed composition distributions to be obtained for chitosan for the first time. This was the first determination of composition distributions and of their corresponding dispersity values for a statistical copolymer. The results identified chitosan samples with very similar measured average DA values having significantly different dispersity values. These results confirm the inaccuracy of characterizing chitosan by only through its average DA. Finally, to improve the use of chitosan for tissue engineering, regenerative medicine and other biomedical applications it was important to ensure low immunogenicity especially in the application of implantation. Poly(ethylene glycol), PEG, and the peptide RGDS was grafted onto the surface of chitosan and the chemical reaction was monitored using CE. The robustness of CE allows samples to be injected without sample preparation and allows it to be used effectively in the analysis of chemical reactions. The films were then characterized by thermogravimetric analysis (TGA) and differential scanning calorimetry (DSC) and the grafting of PEG onto the surface of the chitosan film was validated. Previous cell attachment studies showed that the proliferation of cells occurred in specific regions. This was likely due to an inherent heterogeneity of the chitosan films which could be caused by incomplete dissolution and aggregation seen in the dissolution studies. To probe this heterogeneity of the chitosan films and powder, advanced solid-state NMR spectroscopy measurements were undertaken. The analysis compared the mobile and rigid fractions of chitosan. The results suggested similar behavior in both fractions, however, gave evidence of possible orientation of the acetyl group away from the backbone. The permeability of the films to small molecules was also tested and confirmed. In summary, the dissolution of chitosan was seen to be complex and currently used methods were either deemed inefficient in the dissolution or conversely caused degradation. A new method was developed to numerically represent the heterogeneity of composition or branching and this was tested with various complex polymer samples including chitosan. Further development of this method allowed composition distributions of a statistical copolymer (chitosan) to be obtained for the first time and the heterogeneity of composition to be obtained. New low immunogenicity films were produced by the grafting of poly(ethylene glycol) onto the surface of chitosan films and the grafting process was monitored by CE. The grafting was validated and the permeability and heterogeneity of chitosan films were also probed. Future work should involve probing the dissolution of chitosan with ionic liquids, applying the calculation of dispersity of both distributions of electrophoretic mobility and composition distributions to a broader range of polyelectrolytes, further improving the selectivity of the separation of chitosan and testing the biocompatibility of PEG grafted chitosan films. The methods developed in this thesis will enable chitosan to reach its potential for various applications ranging from tissue regeneration, through bioplastics to drug delivery

An Examine the Relationship between Participative Management Style and Student satisfaction

In modern world, many different management styles are implemented by managers to enhance individual and collective efficiency of stakeholders.  The study surveys the efficacy of participative management style that to what extent it is effective in the modern educational requirements. The study was conducted on one hundred seven students. The data were collected through a questionnaire regarding the type of management educational managers/administrators practice in their organizations.  Participative management style millions of public sector students could be given harmonious educational environment which is friendly, accommodating and helpful in their academic career and could bring back the golden days of public sector schools. Hence, the study suggests that public sector school managers should be offered management courses frequently so that by implementing participative management style quality education could be ensured. Key Words: Management, educational managers, collective efficacy, participative management

Bruk av kunstig intelligens til medisinsk beslutningstøtte: Sammenligning av Dynamic Ensemble Selection med klassiske ML-algoritmer i kreftprediksjon

Sammendrag I en verden med teknologiske fremskritt som stadig påvirker ulike næringer, har maskinlæring (ML) vist seg å være en banebrytende teknologi som kan revolusjonere ulike sektorer. Helsesektoren, som i stor grad står overfor kritiske og komplekse utfordringer, er en sektor som kan dra stor nytte av ML. Maskinlæring er en gren av datavitenskapen som bruker algoritmer og statistiske modeller til å forbedre datamaskiners ytelse, og er en fundamental byggestein i utviklingen av kunstig intelligens. Denne oppgaven tar for seg problemstillingen om å analysere prediksjon av kreftpasienter ved å anvende ulike ML-algoritmer. I denne sammenhengen ble Dynamic Ensemble Selection (DES) undersøkt for å evaluere om det kan gi bedre resultater for prediksjon av kreftpasienter enn kjente klassiske ML-algoritmer. Flere ML-teknikker ble brukt til å utføre prediksjonstester og øke forståelsen av algoritmene. Videre ble en MCDA-analyse benyttet for å sammenligne resultatene med den nåværende beslutningsprosessen, som tar hensyn til kliniske og etiske retningslinjer samt pasientens behov og interesse. Studien vil gi innsikt i hvilken grad DES og de klassiske ML-algoritmene kan bidra til å forbedre dagens situasjon om å støtte medisinsk beslutningstaking i kreftbehandling. Datasettene som ble brukt til å trene de prediktive modellene inneholdt omfattende klinisk informasjon om pasienter behandlet ved Oslo universitetssykehus (OUS). Datasettene inkluderte en gruppe på 192 pasienter som gjennomgikk behandling for kolorektal kreft i tidsrommet 2013-2017, samt en annen gruppe på 197 pasienter som ble behandlet for hode- og halskreft i perioden 2007-2013. Åtte klassifiseringsalgoritmer ble trent på disse datasettene med kliniske egenskaper for generell overlevelse (OS), progresjonsfri overlevelse (PFS) og sykdomsfri overlevelse (DFS). Resultatene ble validert ved å måle nøyaktighet, F1-score for positiv og negativ, Matthews korrelasjonskoeffisient (MCC) og ROC AUC. Videre ble modellen for hode- og halskreft testet på et eksternt datasett bestående av 99 behandlede pasienter ved MAASTRO-klinikken i Nederland. Funnene fra oppgaven tyder på at det er flere muligheter å dra nytte av i forhold til å anvende ulike ML-algoritmer. De klassiske algoritmene presterer generelt bedre enn DES med hensyn til nøyaktighet, prediksjonsytelse, og antall feilaktig klassifisering. I følge MCDA-analysen blir også de klassiske algoritmene sett på som den beste løsningen i kombinasjon av den eksisterende beslutningsprosessen. Den nye løsningen skal ikke være en erstatning, men bli sett på som et mulig beslutningsstøtteverktøy. Det er viktig å merke seg at ulike algoritmer og teknikker vil respondere forskjellig og gi ulike svar på forskjellige typer data og problemer. Dermed er denne anbefalingen gitt for de datasettene og algoritmene som denne oppgaven har basert seg på. For videre forskning anbefales det å samle et større og mer dagsaktuelt datasettet, som kan bidra til å optimalisere prognosen og overlevelsesraten for kreftpasienter. Dette kan gi mer presise og pålitelige prediksjoner om hvilken behandling som vil gi best resultat for den enkelte pasient. Resultatene fra denne oppgaven kan danne grunnlag for utvikling av modeller som kan identifisere optimal kreftbehandling for en pasient og brukes som beslutningsstøtteverktøy av helsepersonell ved behandling av nye kreftpasienter.Abstract In a world of technological advancements that continue to impact various industries, machine learning (ML) has proven to be a ground-breaking technology that can revolutionise various sectors. The health sector, which largely faces critical and complex challenges, is a sector that can greatly benefits from ML. Machine learning is a part of computer science that deals with using algorithms and statistical models to learn and improve computer performance based on feedbacks and experiences from previous data and is a fundamental in the development of artificial intelligence. The master thesis deals with the problem of analyzing the prediction of cancer patients using different ML algorithms. In this context, several ML techniques are used to perform prediction tests and increase the understanding of the algorithms. Furthermore, an MCDA-analysis is used to compare the results with the current solution, which is based on clinical and ethical guidelines as well as the patients' needs and interests. The aim is to investigate whether Dynamic Ensemble Selection (DES) gives better results for predicting cancer patients than existing models, like random forest and logistic regression. The study will provide insight into the extent to which the DES algorithms and the classical algorithms can contribute to improving the current situation of supporting medical decision-making in cancer treatment. The datasets used for training the predictive models consisted of clinical information from 192 patients who were treated for colorectal cancer in the period 2013 to 2017, and 197 patients who were treated for head and neck cancer in period 2007 to 2013 at Oslo University Hospital, OUS. Eight classification algorithms were trained on these datasets with clinical characteristics of overall survival (OS), progression-free survival (PFS), and disease-free survival (DFS). The results were validated by measuring accuracy, F1-score for positive and negative, Matthew's correlation coefficient (MCC) and ROC AUC. Furthermore, an external data set consisting of 99 patients who received treatment at the MAASTRO clinic in the Netherlands was used to test head and neck cancer models. The findings from the thesis indicate that several opportunities can benefit from the use of different ML algorithms. The classical algorithms generally outperform DES when it comes to accuracy, prediction performance, and number of misclassifications. According to MCDA-analysis, the classic algorithms are also seen as the best solution in combination with the current situation. The new solution should not be a replacement but be seen as a possible decision-support tool. It is also important to note that different algorithms and techniques will respond differently and give another output to different type of data and problems. This recommendation is therefore given for the datasets and algorithms on which this task is based on. A challenge with the datasets that are used in this thesis is that they were limited and contained little information. For further research, a larger and more up-to-date data set should be collected, which can help to optimize cancer patients' prognosis and survival rate This can provide more precise and reliable predications about which treatment will give the best result for the individual patient. The results from this thesis can form the basis for the development of models that can identify optimal cancer treatment for a patient and be used as a decision-support tool by healthcare professionals when treating new cancer patients

Modeling the Value of Strategic Actions in the Superior Colliculus

In learning models of strategic game play, an agent constructs a valuation (action value) over possible future choices as a function of past actions and rewards. Choices are then stochastic functions of these action values. Our goal is to uncover a neural signal that correlates with the action value posited by behavioral learning models. We measured activity from neurons in the superior colliculus (SC), a midbrain region involved in planning saccadic eye movements, while monkeys performed two saccade tasks. In the strategic task, monkeys competed against a computer in a saccade version of the mixed-strategy game ”matching-pennies”. In the instructed task, saccades were elicited through explicit instruction rather than free choices. In both tasks neuronal activity and behavior were shaped by past actions and rewards with more recent events exerting a larger influence. Further, SC activity predicted upcoming choices during the strategic task and upcoming reaction times during the instructed task. Finally, we found that neuronal activity in both tasks correlated with an established learning model, the Experience Weighted Attraction model of action valuation (Camerer and Ho, 1999). Collectively, our results provide evidence that action values hypothesized by learning models are represented in the motor planning regions of the brain in a manner that could be used to select strategic actions

Medication waste disposal practices among patients attending selected out patient departments in a tertiary care institution: a cross sectional survey

Background: Medication waste has major implications on human and animal health, environmental safety and the health economy. Low and middle income countries have paid less attention to proper medication waste disposal at household and community level. This is the first baseline assessment on medication waste disposal practices among the general public in Sri Lanka.Methods: This was a descriptive, cross-sectional survey, conducted via face to face interviews using a structured questionnaire among selected outpatient clinics at the National Hospital of Sri Lanka. A non-probability sampling technique was used to achieve a representative sample from each clinic. The data collectors were trained prior to administering the questionnaire. Data was presented as descriptive statistics using percentages. Chi-square test was used to find associations.Results: From the total number of participants (n=200) majority were females 135 (67.5%). Majority of the participants (78%) stated that they have unused medicines at home. Among them, tablet form was the commonest (78%) followed by topical preparations (49%). Commonest reason for having unused medicines at home were self-discontinuation as illness resolved (57.5%). There was a significant difference between the knowledge and practices when disposing tablet form (<0.001), syrups (0.002), topical preparations (0.04) and sharps (<0.001). Majority (23%) discarded sharp to rubbish bins. Rubbish bin was the commonest mode of disposal for all dosage forms as well as devices.Conclusions: In this sample majority had unused medicines at home which was compatible with the pattern seen in other countries and need proper attention