A musical score. I. Crisp Canape. II. Creole Gumbo. III. Salade. IV. Filet Mignon. V. Crepes Suzette. VI. Demitasse.

Heterogeneity in the school education quality plays an important role for those who want to pursue a bachelor's degree in Peru since access to higher education is highly correlated with socioeconomic status. In that sense, an intervention for disadvantaged students took place for the first time in 2022 and was constrained to the assessment of a scholarship called “Beca 18”, the biggest scholarship that the public institution called PRONABEC addresses every year since 2012. The intervention included additional tools for a group of applicants: (i) full-time online classes for 2 to 4 months; (ii) an electronic device with an internet connection; and (iii) the admission exam payment fully covered up to 2 times. The objective of this thesis is to evaluate the effectiveness of this intervention in increasing the likelihood of the treated to access higher education through the 2022 “Beca 18” scholarship process. As the treatment was not randomly assigned, a control group was estimated using the Propensity Score Matching methodology based on individual characteristics. Results showed that there is no statistically significant effect of the intervention in the treated applicants and invite to re-evaluate its design and implementation.

Analyzing a person's gait is important in determining their physical and neurological health. However, typical motion analysis laboratories are only in urban specialty care facilities and can be expensive due to the specialized personnel and technology needed for these examinations. Many patients, especially those who reside in underdeveloped or isolated locations, find it impractical to go to such facilities. With the help of recent developments in high-performance computing and artificial intelligence models, it is now feasible to evaluate human movement using digital video. Over the past 20 years, various visual gait analysis tools and scales have been developed. A study of the literature and discussions with physicians who are domain experts revealed that the Edinburgh Visual Gait Score (EVGS) is one of the most effective scales currently available. Clinical implementations of EVGS currently rely on human scoring of videos. In this thesis, an algorithmic implementation of EVGS scoring based on hand-held smart phone video was implemented. Walking gait was recorded using a handheld smartphone at 60Hz as participants walked along a hallway. Body keypoints representing joints and limb segments were then identified using the OpenPose - Body 25 pose estimation model. A new algorithm was developed to identify foot events and strides from the keypoints and determine EVGS parameters at relevant strides. The stride identification results were compared with ground truth foot events that were manually labeled through direct observation, and the EVGS results were compared with evaluations by human scorers. Stride detection was accurate within 2 to 5 frames. The level of agreement between the scorers and the algorithmic EVGS score was strong for 14 of 17 parameters. The algorithm EVGS results were highly correlated to scorers' scores (r>0.80) for eight of the 17 factors. Smartphone-based remote motion analysis with automated implementation of the EVGS may be employed in a patient's neighborhood, eliminating the need to travel. These results demonstrated the viability of automated EVGS for remote human motion analysis.

Mule deer are an important rangeland grazer, large prey species, and game animal for the state of Utah, so herd size is monitored and managed actively. A significant cause of population decline is poor overwintering survival, including from the lack of available forage during winter months. Mule deer energy storage is correlated with greater overwintering success, so physical estimates of energy storage including body condition score (BCS), rump fat (RF), and loin thickness (LT), can be used to track and predict a herd's health. Current methods of collecting deer information are costly, time consuming, and cause physical stress to deer, so here we sought to test if a microbiome analysis could be used to predict deer overwintering success. We analyzed nearly 1000 fecal samples collected from deer in Utah over a five-year period. We found that the microbiome composition of these samples shared characteristics with published reports of other reported ruminant species. Also, the location and time when the samples were collected significantly influenced mule deer microbiota composition and abundance. We found that there is a relationship between microbes and health measures (BCS, RF, LT), including some microbial abundances that could predict the health measures of mule deer several months ahead of time. There were also microbial groups whose abundances were significantly correlated with the latitude and elevation of the deer. Finally, a longitudinal analysis on a subset of sampled deer produced slightly different results than the broad analysis of all samples, including suggesting that some of the differences in microbiota composition with time may have been related more to sampling distinct deer at different time points, rather than that the deer microbiota composition changed with time. . These results suggest possible candidate microbial taxa for use in developing assays to replace current methods of measuring and tracking deer health.

Bipartite experiments have recently emerged as a focal point in causal inference. In these experiments, treatment is administered to one set of units, while outcomes of interest are gauged on a distinct set of units. Such experiments are especially valuable in scenarios where pronounced interference effects transpire between units on a bipartite network. For instance, in market experiments, designating treatment at the seller level and assessing outcomes at the buyer level (or vice-versa) can lead to causal models that more accurately reflect the inherent interference between buyers and sellers. Although bipartite experiments can enhance the precision of causal effect estimations in specific contexts, it's imperative to conduct the analysis judiciously to avoid introducing undue bias through the network. Drawing from the generalized propensity score literature, we demonstrate that it's feasible to achieve unbiased estimates of causal effects for bipartite experiments, given a conventional set of assumptions. Furthermore, we delve into the formulation of confidence sets with accurate coverage probabilities. By employing a bipartite graph from a publicly accessible dataset previously explored in bipartite experiment studies, we illustrate, via simulations, a notable reduction in bias and augmented coverage. / Statistics

Deep learning has been extensively used in many areas because of its proven benefit. However, developing deep learning is challenging. The master thesis aims to investigate suitable information visualization for the output of an automated deep learning model platform. The thesis has been carried with Bitynamics AB. The methodologies used are: 1) user research; 2) prototyping; 3) user evaluation. The design requirements are gathered from user research and study literature. The prototype offered the visualization, including a list of models, model comparison, model training, testing, and prediction result. Ten people have evaluated the prototype by using usability testing, subjective expert interview, and questionnaires. From the user evaluation, it indicates the prototype has addressed the user problems in deep learning. The result shows the prototype has good usability based on the SUS and has a completion rate of 100%. The participants’ feedback has been categorized into five labels: 1) defining and designing the necessary functionalities; 2) the importance of customization; 3) designing the information visualization; 4) user interaction with data; and 5) trustworthiness of the recommended actions for parameter tuning. These labels should be considered when designing the visual analytics of an automated deep learning output platform. / På grund av att dess bevisade förmåner har deep learning har använts utförligt inom många områden. Dock är utvecklingen av deep learning utmanande. Målet med denna masterexamensarbetesrapport är att undersöka lämpliga visualiseringar för produkten från en automatiserad deep learning -modellsplattform. Arbetet har utförts tillsammans med Bitynamics AB. Metodologierna som har använts är: 1) användarintervjuer; 2) utveckla prototyp; 3) användarutvärdering. Designkraven är hämtade från användarforskning och studieliteratur. Prototypen erbjöd visualiseringen, inklusive en lista av modeller, modelljämförelse, modellträning, testning och resultatförutspåelse. Tio personer har utvärderat prototypen genom användarvänlighetstest, subjektiv expertintervju och frågeformulär. Från användarutvärderingen, är det indikerat att prototypen har hanterat användarnas problem med deep learning. Resultaten visar att prototypen är användarvänlig, baserat på SUS och en slutförandeandel på 100%. Deltagarnas återkoppling har kategoriserats in i fem kategorier: 1) definera och designa de nödvändiga funktionaliteterna; 2) betydelsen av anpassning; 3) designa informationsvisualiseringen; 4) användarinteraktion med data; 5) tillförlitlighet till de rekommenderade handlingarna för att ställa in parametrar. Dessa kategorier bör tas I hänsyn när man designar de visuella analyserna från en automatiserad deep learning -plattform.

Bakgrund: Sepsis som i vardagligt tal benämns blodförgiftning är ett allvarligt tillstånd och uppstår när kroppens immunsystem överreagerar på en infektion. Är de metabola och cirkulatoriska förändringarna påtagligt förhöjda i kombination med organdysfunktion benämns tillståndet som septisk chock. Tidig identifikation har en betydande roll för patientens överlevnad och livskvalité. Sjuksköterskan arbetar patientnära och med rätt förutsättningar möjliggörs tidig identifikation. Syfte: Syftet är att belysa sjuksköterskans omvårdnadsåtgärder för tidig upptäckt av sepsis. Metod: Studien utfördes som en litteraturöversikt där både kvalitativa och kvantitativa artiklar användes. Fribergs granskningsmall användes för att granska kvalitén. Resultat: Två huvudteman identifierades; Sjuksköterskans kunskap och Ett gemensamt förhållningssätt som sedan följs av sex subteman; Kunskapsutveckling och sepsisprotokoll, Vikten av praktisk kunskap, Teamsamverkan, Stödjande bedömningsverktyg - NEWS, Vikten av att tala samma språk och Kliniska blicken. Konklusion: Denna litteraturöversikt belyser faktorer som kan inverka på sjuksköterskans möjlighet att identifiera sepsis i tid. Eftersom sepsis är ett vanligt och allvarligt tillstånd är det av betydelse att sjuksköterskor ges möjlighet att öka kompetensen inom området. Eftersom det brister inom hälso- och sjukvården avseende huruvida sepsis upptäcks i tid, bör vidare forskning inom området studeras för att minska patientens lidande och för att minska dödlig utgång. / Background: Sepsis also known as blood poisoning, is a serious condition and occurs when the body's immune system overreacts to an infection. If the metabolic and circulatory changes are significantly increased in combination with organ dysfunction, the condition is called septic shock. Early identification has a significant role for the patient's quality of life and survival. The nurse works close to the patient and with the right conditions early identification is possible. Aim: The aim is to shed light on the nurse´s care measures for early detection of sepsis. Method: The study was conducted as a literature review where both qualitative and quantitative articles were used. Friberg's review template was used to review the quality. Findings: Two main themes were identified; The nurse's knowledge and A joint approach which is then followed by six sub-themes; Knowledge development and sepsis protocols, The importance of practical knowledge, Team collaboration, Supporting assessment tools - NEWS, The importance of speaking the same language and The clinical gaze. Conclusion: This literature review highlights factors that can influence the nurse's ability to identify sepsis in time. As sepsis is a common and serious condition, it is important that nurses are given the opportunity to increase their competence in the area. Since there are deficiencies in the healthcare system regarding whether sepsis is detected in time, further research in the field should be studied to reduce patient suffering and to reduce mortality.

Opinion Mining is becoming the fastest growing area to extract useful and insightful information to support decision making. In the age of social media, user’s opinions and discussions have become a highly valuable source to look for users preferences, likes, and dislikes. The industry of wildlife parks (or zoos) is a competitive domain that requires careful analysis of visitor’s opinions to understand and cater for their preferences when it comes to wildlife. In this thesis, an opinion mining approach was proposed and applied on textual posts on the social media platform, Twitter, to extract the popularity, polarity (sentiment), and emotions toward birds and bird types such as owls, sparrows, etc. Then, the thesis provides recommendations based on popularity of birds and bird types and a ranked list of the most desired birds based on consumer emotions toward them. The findings of this thesis can help wildlife parks in the decision-making process on the types of birds to acquire.

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