The 21st century is characterised by a rapid technologically advancing environment, where organisations constantly change to gain and maintain a competitive advantage. The use of call centres in organisations has been in line with this technological advancement. South African organisations have not been an exception as they also have experienced the growth in call centres in the past few decades. Some of the benefits the business gains from the use of call centres include cutting costs and increased profits. However benefits to individuals working in call centres is not that obvious. Call centres have been labelled with such nicknames as electronic Sweatshops as they are characterised by poor working conditions, as individuals have to perform extremely routine tasks under extreme surveillance and harsh performance management systems. Such an environment has implications for the well being of individuals working in them. Previous research has indicated a relationship between organisational climate and employee well being. While this environment is viewed as toxic individuals continuously enter into it and some prosper than other, which brings into question whether the amount of control an individual perceives to have over his/her environment has any effect on their perception of the organisational climate. The main aim here is that work can be redesigned to benefit both the organisation and employees’ needs in the workplace. Therefore the aim of the current study is to investigate the organisational climate apparent in call centres, also looking at the relationship this construct has with Job Satisfaction, Life satisfaction and self esteem for call centre agents. The current research will also look at whether Locus of Control has an effect on this relationship. The findings of this research prove invaluable in its attempt to create awareness of the nature of work activities and quality of work life of call centre agents. The sample consisted of 97 call centre agents working in Johannesburg, South Africa. The results indicate there is a strong positive relationship between organisational climate including its subscales and Job Satisfaction, a weak and positive relationship between organisational climate and its subscale and Life satisfaction, while there was no observed relationship between organisational climate and self esteem. Furthermore the results indicated that Locus of Control does not moderate the relationship between organisational climate and job satisfaction, life satisfaction and self esteem.

Bakgrund: Anestesisjuksköterskans främsta uppgift vid traumalarm är att säkra fria luftvägar. Hur anestesisjuksköterskor hanterar dessa situationer är individuellt och vårdandet ställer krav på teamarbete, tydlig kommunikation, ledarskap och möjlighet till debriefing. Tidigare forskning visar att dessa element är viktiga för att skydda patientsäkerheten under traumalarm. Syfte: Att beskriva anestesisjuksköterskors upplevelser av traumalarm på akutmottagning och hur de hanterar dessa situationer. Metod: Kvalitativ ansats med beskrivande design. Ändamålsenligt urval har använts för att inkludera tio anestesisjuksköterskor från två sjukhus i Mellansverige. Semistrukturerade intervjuer har genomförts. Data har analyserats med kvalitativ innehållsanalys. Huvudresultat: I resultatet framkom tre huvudkategorier; Trygghet i yrkesprofessionen, Teamarbetes betydelse och Debriefing och beskrivna under åtta subkategorier; Att känna sig mentalt förberedd, Att känna trygghet i yrkesrollen, Att behålla lugnet, Att ha ett koncept att följa, Att ha ett tydligt ledarskap, Att ha en tydlig kommunikation, Att få prata med kollegor och att få återkoppling på sin insats, vilka speglade anestesisjuksköterskors upplevelser av traumalarm på akutmottagning samt hur de hanterade dessa situationer. Slutsats: Anestesisjuksköterskorna beskrev en mental trygghet i yrkesrollen baserad på tidigare erfarenheter. Teamarbete och tydlig kommunikation var viktigt för samarbetet vid traumalarm. Otydligt ledarskap och brist på struktur bidrog till minskat samarbete. Det framkom även att debriefing med kollegor var betydelsefullt. Formell debriefing och mer tid för återkoppling och traumateamövningar efterfrågades. / Background: The main task of nurse anaesthetists during trauma calls is to secure the patients airway. How they experience and handle these situations are individual and depend on teamwork, communication, leadership and the possibility for debriefing. Previous studies have shown that these elements are of importance to ensure patient safety during trauma calls. Aim: To describe nurse anaesthetist’s experiences of trauma calls on the emergency room and how they cope with these situations. Method: Qualitative approach and descriptive design was used. Ten nurse anaesthetists from two hospitals in the middle part of Sweden were included. Data were collected through semi-structured interviews and analysed with qualitative content analysis. Findings: Based on the data collected three main categories emerged; Confident in the profession, The role of teamwork and Debriefing. These categories were described in eight subcategories; To be mentally prepared, To feel confident in the profession, To keep calm, To have a concept to follow, To have clear leadership, To have direct communication, To talk with colleagues and To get feedback on their efforts. These reflected nurse anaesthetist’s experiences of trauma calls in the emergency room and how they manage these situations. Conclusion: The nurse anaesthetists described mental confidence in the profession based on previous experience. Teamwork and communication was of importance for the cooperation in the emergency room, while unclear leadership and lack of structure contributed to less collaboration. It also emerged that debriefing with colleagues were of importance. Formal debriefing and more time for feedback were demanded.

Communication is a behavioral strategy used by species in the competition for survival and reproduction. Animals produce signals that are sent to receivers and occasionally transmit information that can be beneficial in a variety of contexts including avoiding predators, locating food resources, and maintaining the pair bond between partners. Researchers are interested in if auditory signals are variable and if the variability is biologically meaningful to receivers. Some species, such as siamang, sing duets as their form of acoustic communication and these duets have been studied for a variety of purposes such as investigating their structure, variability, and functionality. Siamang are a species of gibbon that combine a finite number of call units into structurally complex sequences in rule-governed ways, hereby conveying different contextual situations. Some wild gibbon species have demonstrated variation in the temporal structure of their duets and the distinctiveness in acoustic timing is unique enough for researchers to distinguish among individuals and groups based on their duet contributions. The process of coordinating and producing a uniquely timed duet could function in relation to the formation and maintenance of the pair bond. This dissertation analyzed the temporal variation and call order of wild siamang (Symphalangus syndactylus) duets that are comprised of four distinct sequences comprised of several note types. Previous research has provided detailed descriptions of captive siamang duets that include category descriptions of four call types: grunting, introduction, interlude, and great call. While these descriptions provide a general understanding of the structure of siamang duets, they can be confusing and difficult to study empirically due to their arbitrary nature. For instance, the siamang great call has been described as occurring in over 30 variants and in stable, or typical, and unstable, or atypical forms. The stable or typical form of the great call has been described to occur only after the male’s first bitonal scream. This previous research did not measure the timing of sequences through entire duets or conduct individual note analyses. Also, the most detailed investigations of siamang duets were conducted with captive animals and therefore wild siamang duets have not been quantitatively analyzed. This dissertation research measures the temporal variation of wild siamang duet call types, and the call order of individual notes in the US-II phrase. The findings will provide a more objective description of the siamang duet as well as determine if it has a more stereotyped or flexible nature. Also, in the wake of globalization, duets and other behaviors (such as travel) could be sensitive to anthropogenic disturbances. So, I am interested in how wild siamang might be impacted by human presence. The three main objectives of this dissertation are: 1) document the temporal variation in the call types of wild siamang; 2) document variation in the call order of wild siamang US-II phrases; and 3) document how wild siamang travel is impacted by anthropogenic noise. The study was conducted at 4 remote field sites in northern Sumatra, Indonesia from August 2017 through May 2018. With the assistance of a research team, I recorded acoustic and behavioral data from 10 siamang groups and two individual females. I collected acoustic data using a combination of autonomous recording units and handheld recording devices while following the study subjects on foot. I marked GPS points during behavioral scans and conducted experimentally induced noise playback experiments. I analyzed the audio data with the software program Raven Pro 1.6 and spectrograms were produced with a 512-point (11.6 ms) Hann window (3 dB bandwidth = 135 Hz), with 50% overlap and a 512-point DFT, yielding time and frequency measurement precision of 2.9 ms and 43.1 Hz. I conducted statistical testing in R Studio Version 1.3.959 in conjunction with the statistics lab at Southern Illinois University. In Chapter 3, I performed multilevel regression analysis in the form of a Generalized Linear Mixed Model (GLMM) in order to investigate temporal variation the time spent singing call types (grunting, introduction, interlude, and great call) accounting for differences between songs, groups, and field sites. I used a Bayesian hierarchical model to avoid calculating estimates using data with repeated and imbalanced sampling, and explicitly model variation within individuals, between songs, between groups, and field sites. In Chapter 4, I used the Levenshtein Distance (LD) method for investigating structural variation in the call order of US-II phrases in the siamang duet. The LD method is a quantitative technique for measuring the similarity of sequences and to quantify the differences between strings of data. In Chapter 5, I used the Friedman test to compare the means of distances traveled in 5-minute intervals in the following conditions: pre-playback, cicada (control), traffic, jackhammer, and music. I used post hoc tests with Bonferroni correction to analyze the pairwise comparisons of the five experimental conditions. Overall, the results suggest support of previous studies on the variability of the gibbon duet and animal travel in response to noise. In Chapter 3, I found some temporal variation in call types to exist at all levels of the modeling but only statistically significant when comparing the field sites of Ketambe and Kutapanjang. Relating to the functionality of total call duration, I did not find significant differences in the behavioral categories before or after any of the calls regardless of their total duration. In Chapter 4, I found US-II phrases are not as stereotyped as previous descriptions have suggested. The mean LD distances were higher than those compared by other duetting primates. Also, the results from one-way ANOVA and Games-Howell Post Hoc Testing showed significant mean differences between groups. In Chapter 5, I found siamang are impacted by noise as they traveled further after the anthropogenic noise playback experiments compared to pre-playback travel. However, the pairwise comparisons were not statistically significant and therefore I could not determine which noise condition caused the change in travel distance. Collectively, the results of this dissertation suggest that there is considerable variability in both wild siamang call types and call order in a previously described stereotyped phrase, and that wild siamang travel away is impacted by introduced anthropogenic noise. What this contributes to the field of vocalization studies is further support that precise measurement of song parameters gives a better understanding of song structure than somewhat arbitrary written descriptions. It is important to have descriptions of notes produced in songs, but to understand how those notes relate to each other, an individual note analysis and rigorous statistical methodology is necessary. The variation found in this dissertation could be due to the unique siamang response to their partner’s timing in the process of forming the pair bond. But as the siamang duet is thought to have multiple functions, the temporal variation could also be transmitting other information such as individual and group identity while functioning in territorial defense. In addition, anthropogenic noise caused the animals to change their travel behavior and this suggests it could be harmful if faced on a regular basis. Anthropogenic noise can be detrimental if it occurs in home ranges of species that are territorial and facing shrinking habitats due to continued human encroachment. In order to understand if these findings are biologically meaningful and to have firmer conclusions, future long-term studies will need a larger data set over longer periods of time. It is also important to have a larger data set and compare more siamang groups that live in various noise levels and measure variables such as caloric intake, daily travel, copulations, and number of offspring.

The main purpose of exception handling mechanisms is to improve software robustness by handling exceptions when they occur. However, empirical evidence indicates that improper implementation of exception handling code can be a source of faults in software systems. There is still limited empirical knowledge about the relationship between exception handling code and defects. In this dissertation, we present three case studies investigating defect densities of exception handling code. The results show that in every system under study, the defect density of exception handling code was significantly higher than the defect density of overall source code and normal code. The ability to predict the location of faults can assist in directing quality enhancement efforts to modules that are likely to have faults. This information can be used to guide test plans, narrow the test space, and improve software quality. We hypothesize that complicated exception handling structure is a predictive factor that is associated with defects. To the best of our knowledge, no study has addressed the relationship between the attributes of exception handling method call structures and defect occurrence, nor has prior work addressed fault prediction. We extract exception-based software metrics from the structural attributes of exception handling call graphs. To find out whether there are patterns of relationship between exception-based software metrics and fault-proneness, we propose a defect prediction model using exception handling call structures. We apply the J48 algorithm, which is the Java implementation of the C4.5 algorithm, to build exception defect prediction models. In two out of three systems under study, the results reveal that there are logical patterns of relationships between most class level exception metrics and fault-proneness. The accuracy of our prediction models is comparable to the results of defect prediction model studies in the literature. It was observed that our approach has somewhat worse predictive accuracy when a system has low average defects per class.

The infection of plants by Heterodera glycines, commonly known as soybean cyst nematode (SCN), is a serious agricultural problem of worldwide extent. Meanwhile, it provides an excellent experimental model to study basic aspects of how cells function, in particular, during biotic challenge. Heterodera glycines challenges plant cells by initiating, developing and sustaining an interaction that results in the formation of a nurse cell from which the nematode derives nourishment. The presented experiments examine (1) how a cell can be de-differentiated and reprogrammed to perform a much different biological role and (2) how a cell’s immune responses can be engaged or suppressed to accomplish that goal. The observation of alpha soluble N-ethylmaleimide-sensitive factor attachment protein (alpha-SNAP) expression, its location within the rhg1 locus and known involvement in the vesicular transport machinery relating to defense made it a strong candidate for further functional analysis. Functional studies demonstrated that overexpression of alpha-SNAP in the susceptible G. max[Williams 82/PI 518671] genotype that lacks its expression results in the partial suppression of H. glycines infection. This indicated that the vesicles could be delivering cargo to the site of infection to engage a defense response. High levels of expression of a cell wall modifying gene called xyloglucan endotransglycosylase also occur during defense. XTHs associate with vesicles, act in the apoplast outside of the cell, and have a well-known function in cell wall restructuring. These observations indicated that alterations in the cell wall composition of nurse cells could be important for the successful defense response. Overexpression of a G. max xyloglucan endotransglycosylase (Gm-XTH) in the susceptible G. max[Williams 82/PI 518671] genotype resulted in a significant negative effect on H. glycines as well as R. reniformis parasitism. The results, including preliminary experiments on components of the vesicle transport system, identify a potent mechanism employed by plants to defend themselves from two types of plant-parasitic nematodes.

In consideration of TRC Call to Action 63.3 that asks teachers to facilitate cultural understanding, mutual respect, and empathy between First Nations and non-Indigenous students, my thesis sought to find out if a collective, collaborative, story-making activity with four Grade 5-6 students of different cultural backgrounds, including one First Nations student, could further the objectives of Call 63.3. The results of my research suggest that a collective and collaborative story-making activity does, on its own, further two of these reconciliation objectives, mutual respect, and empathy. The third objective, cultural understanding, could probably not been achieved without the intervention of a knowledgeable Indigenous adult, in my case, Annie, (a pseudonym) who was consulted by the story-makers during the scripted “mentor” part of the 12-part hero/ine’s journey story-making process. Using primarily a Posthumanist framework that also integrated some arts-based research/research-creation and critical discourse theoretical orientations for my analysis, I found that an extended focus on a single-story task by four students, not only brought them into a closer relationship with each other, thus facilitating mutual respect and empathy, it also permitted them to imagine a common vision of education. The education world they imagined, in which an educational reconciliation might be realized, was informed, in part, by Indigenous ways of knowing and teaching.

The first chapter of this thesis is centered around a simple problem: to do or not to do something. As in life, every decision has an unknown outcome and planning agents try to balance the trade offs of such decision based on some relevant information. After processing the relevant information a decision is reached. In this chapter, the problem is formalized and parameterized in two frameworks: In the first framework discrete decision models known as decision trees are studied, where we design an optimization algorithm to solve the misclassification cost problem in this family of representations; The second framework studies continuously differentiable models (such as logistic regression and Deep Neural Networks) where we propose a two-step optimization procedure of the misclassification cost problem, as well as characterizing the statistical estimation problem relative to the sample size used for training. We illustrate the methodology by developing a computerized scheme to administer (or not) a preventive intervention to patients arriving to the hospital with the objective of minimizing their risk of acquiring a Hospital Acquired Infection (HAI). The second chapter expands on the idea of the first one to a sequential setting. The problem is framed as a Markov Decision Process algorithm using a state aggregation strategy based on Decision Trees. These incremental state aggregations are solved using a Linear Programming (LP) approach to obtain a compact policy that converges to the optimal one asymptotically, as well as showing that the computational complexity of our algorithm depends on the tree structure of the optimal policy rather than the cardinality of the state space. We illustrate the advantages of our approach using the widely known cartpole balancing environment against a Deep Neural Network based approach showing that with a similar computational complexity our algorithm performs better in certain instances of MDP. In the last two chapters we deal with modeling Emergency Medical Service (EMS) optimization such that the demand for medical services is met with the best possible supply allocation in the face of uncertainty of the demand in space and time. In the third chapter we develop a short-term prediction model for call volume at a 911 call center. The rationale of the model is to use the recent call volume to update a historically calibrated model of the call volume that in periods when the call volume distribution drastically changes, can be arbitrarily distant from its expected value. The model is casted as a linear correction of the historical estimation, calculating both the mean and variance of the correction. We justify the formulation using a regime switching doubly stochastic process framework to illustrate the type of distribution changes our model captures. We also propose a staffing model to preemptively staff a call center using our volume prediction as input for the call center demand such that the waiting times of the customers are minimized. This formulation can be casted as a Second Order Cone Program (SOCP) or a Linear Program (LP) with integrality constraints. We illustrate the methodology to predict the call volume during the Covid-19 pandemic to a 911 call center in New York City. In the fourth chapter we modify a well known set covering formulation to perform ambulance scheduling such that the supply of ambulances matches the demand in space and time. We enhance this model using a high resolution simulation model to correct an unknown steady-state service rate of the system (dependent on many exogenous and endogenous factors such as the ambulance dispatch policy and time-varying traffic patterns) as a constraint in the scheduling formulation. We show that this formulation effectively makes the system faster by maximizing the minimum slack between supply and demand during a 24-hour period. We present an algorithm to iteratively solve the scheduling formulation while correcting the implied location and time dependent service rate of the ambulance system using the simulation generated ambulance waiting times of patients in the city. We illustrate our algorithm to schedule municipally managed ambulances in New York City as a case study.

Machine learning is a rapidly growing field within artificial intelligence that an increasing number of individuals and corporations are beginning to utilize. In recent times, the financial sector has also started to recognize the potential of these techniques and methods. Nasdaq Clearing is responsible for managing the clearing business for the clearinghouse's members, and the objective of this thesis has been to explore the possibilities of using machine learning to replicate a subpart of the SPAN model's margin call calculations, known as initial margin, in the commodities market. The purpose of replicating SPAN's initial margin calculations is to open up for possibilities to create transparency and understanding in how the input variables affect the output. In the long run, we hope to broaden the insights on how one can use machine learning within the margin call processes. Various machine learning algorithms, primarily focused on regression tasks but also a few classification ones, have been employed to replicate the initial margin size. The primary objective of the methodology was to determine the algorithm that demonstrated the best performance in obtaining values that were as close as possible to the actual initial margin values. The findings revealed that a model composed of a combination of classification and regression, with non-parametric algorithms such as Random Forest and KNN, performed the best in both cases. Our conclusion is that the developed model possesses the ability to effectively compute the size of the initial margin and thus accomplishes its objective. / Maskininlärning är ett snabbt växande område inom artificiell intelligens som allt fler individer och företag börjar använda. Finanssektorn har nu också börjat undersöka hur dessa tekniker och metoder kan skapa värde. Nasdaq Clearing hanterar clearingverksamheten för clearinghusets medlemmar och syftet med denna uppsats har varit att undersöka möjligheterna att använda maskininlärning för att replikera en del av SPAN-modellens beräkningar av marginkravet som kallas Initial Marginal. Syftet med att replikera SPANs initiala marginberäkningar är att öppna upp för möjligheter att skapa transparens och förståelse för hur inputvariablernapåverkar outputen. På sikt hoppas vi kunna bredda insikterna hur maskininlärningslösningar skulle kunna användas inom "Margin Call"- processen. De metoder som användes för att replikera storleken på Initial Margin var olika maskininlärningsalgoritmer, främst fokuserade på regressionsuppgifter men några klassificeringsalgoritmer användes också. Fokus i metoden var att hitta vilken algoritm som presterade bäst, det vill säga den algoritm som predikterade närmst de faktiska värdena för Initial Margin. Resultatet visade sig vara en modell som kombinerade klassificering och regression, där icke-parametriska algoritmer såsom Random Forest och KNN var de som presterade bäst i båda fallen. Vår slutsats är att den utvecklade modellen har en god förmåga att beräkna storleken på Initial Margin och därmed uppfyller den sitt syfte.

Bakgrund: I Sverige drabbas årligen tusentals människor av trauman. Beroende på skademekanism och verkliga skador som patienten ådragit sig aktiveras ett traumalarm utifrån de nationella traumalarmskriterierna. Vid nivå 1 larm sammankommer ett fullskalig traumateam där intensivvårdssjuksköterskan blir delaktig. Intensivvårdssjuksköterskan besitter kunskap och erfarenhet av att vårda kritiskt sjuka och skadade patienter. Syfte: Studiens syfte var att undersöka intensivvårdssjuksköterskors erfarenheter av att vårda trauma 1 patienter på akutmottagningen. Metod: En kvalitativ metod användes med ett strategiskt urval för att finna informanter. Tolv stycken semistrukturerade intervjuer utfördes. En kvalitativ innehållsanalys på latent nivå användes för att analysera data. Resultat: Består av tre teman, Upplevelser innan, under och efter traumaomhändertagandet, följsamhet i traumaomhändetagandet och att vara förberedd. Att som intensivvårdssjuksköterska gå på traumalarm innebär olika känslor innan, under och efter traumaomhändertagandet. Skillnaden i arbetet mellan IVA och akutrummet. Hur det är att vara delaktig i teamet, upplevelser att vara med och följa patienten. Yrkeserfarenhet, simulering och utbildning påverkar intensivvårdssjuksköterskors upplevelser av omhändertagandet. Konklusion: Intensivvårdssjuksköterskor har olika erfarenheter av att vårda traumanivå 1 patienter på akutmottagningen. Det är associerat med både positiva och negativa känslor och tankar. Det är fördelaktigt med utbildning och övning vid vårdandet av patienter med trauma. / Background: Thousands of people suffer every year from trauma in Sweden. Due to damage mechanism and actual damage the patient suffers from is a trauma call activated through the national trauma criteria. In case of a level one trauma call a trauma team assembles that intensive care nurse becomes participants. The intensive care nurse has knowledge and experience in caring for critical and injured patients. Purpose: The purpose of the study was to investigate intensive care nurses' experiences of caring for trauma 1 patients in the emergency department. Method: A qualitative method was used with a strategic selection to find informants and 12 semi-structured interviews were conducted. A manifest level of qualitative content analysis was used to analyse the data. Results: Consists of three themes; experiences before, during and after trauma call, compliance in trauma care and being prepared. As an intensive care nurse, going to the trauma call involves different emotions before, during and after trauma care. The difference in work between the ICU and the emergency room. What it is like to be part of the team, experiences of being with and following the patient. Professional experience, simulation and training influence intensive care nurses' experiences of care. Conclusion: Intensive care nurses have different experiences of caring for trauma level 1 patients in the emergency department. It is associated with both positive and negative feelings and thoughts. Education and practice in the care of patients with trauma is beneficial.

OpenStack is currently the most popular open source platform for Infrastructure as a Service (IaaS) clouds. OpenStack lets users deploy virtual machines and other instances, which handle different tasks for managing a cloud environment on the fly. A lot of cloud platform offerings, including the Ericsson Cloud System, are based on OpenStack. Despite the popularity of OpenStack, there is currently a limited understanding of how much resource is consumed/needed by components of OpenStack under different operating conditions such as number of compute nodes, number of running VMs, the number of users and the rate of requests to the various services. The master thesis attempts to model the resource demand of the various components of OpenStack in function of different operating condition, identify correlations and evaluate how accurate the predictions are. For this purpose, a physical OpenStack is setup with one strong controller node and eight compute nodes. All the experiments and measurements were on virtual OpenStack components on top of the main physical one. In conclusion, a simple model is generated for idle behavior of OpenStack, starting and stopping a Virtual Machine (VM) API calls which predicts the total CPU utilization based on the number of Compute Nodes and VMs.