Iskanje po katalogu digitalne knjižnice

Opis: Beauty is subjective, and as such it, of course, cannot be defined in absoluteterms. But we all know or feel when something is beautiful to us personally.And in such instances, methods of statistical physics and network sciencecan be used to quantify and to better understand what it is that evokesthat pleasant feeling, be it when reading a book or looking at a painting.Indeed, recent large-scale explorations of digital data have lifted the veilon many aspects of our artistic expressions that would remain foreverhidden in smaller samples. From the determination of complexity andentropy of art paintings to the creation of the flavour network and the prin-ciples of food pairing, fascinating research at the interface of art, physics andnetwork science abounds. We here review the existing literature, focusing inparticular on culinary, visual, musical and literary arts. We also touch uponcultural history and culturomics, as well as on the connections betweenphysics and the social sciences in general. The review shows that the syner-gies between these fields yield highly entertaining results that can oftenbe enjoyed by layman and experts alike. In addition to its wider appeal,the reviewed research also has many applications, ranging from improvedrecommendation to the detection of plagiarism.
Ključne besede: complexity, entropy, network science, data science, self-organization
Objavljeno v DKUM: 17.09.2024; Ogledov: 0; Prenosov: 414
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Opis: Microbiota analysis can provide valuable insights in various fields, including diet and nutrition, understanding health and disease, and in environmental contexts, such as understanding the role of microorganisms in different ecosystems. Based on the results, we can provide targeted therapies, personalized medicine, or detect environmental contaminants. In our research, we examined the gut microbiota of 16 animal taxa, including humans, as well as the microbiota of cattle and pig manure, where we focused on 16S rRNA V3-V4 hypervariable regions. Analyzing these regions is common in microbiome studies but can be challenging since the results are high-dimensional. Thus, we utilized machine learning techniques and demonstrated their applicability in processing microbial sequence data. Moreover, we showed that techniques commonly employed in natural language processing can be adapted for analyzing microbial text vectors. We obtained the latter through frequency analyses and utilized the proposed hierarchical clustering method over them. All steps in this study were gathered in a proposed microbial sequence data processing pipeline. The results demonstrate that we not only found similarities between samples but also sorted groups’ samples into semantically related clusters. We also tested our method against other known algorithms like the Kmeans and Spectral Clustering algorithms using clustering evaluation metrics. The results demonstrate the superiority of the proposed method over them. Moreover, the proposed microbial sequence data pipeline can be utilized for different types of microbiota, such as oral, gut, and skin, demonstrating its reusability and robustness.
Ključne besede: machine learning, NLP, hierarchical clustering, microbial data, microbiome, n-grame
Objavljeno v DKUM: 04.09.2024; Ogledov: 38; Prenosov: 12
Celotno besedilo (4,48 MB)

Opis: Maritime transportation for Tunisia plays an important role in trade exchange with other countries. Therefore, the objective of this paper is to measure the efficiency scores of 7 seaports in Tunisia by applying the Stochastic Frontier Analysis (SFA) with Cobb-Douglas production function and Data envelopment analysis (DEA) with CCR and BCC models. The annual data collected cover the 2007-2017 period for each port. Thus, the sample size for the analysis comprises a total of 77 observations. The empirical result shows that the total average scores of operating efficiency scores were DEA-BCC (0.746)>SFACD (0.536)>DEA-CCR (0.334) from 2007 to 2017. Given these results, the port of Gabes can be considered as the best efficient port in the 3 models (DEA-BCC, DEACCR and SFA-CD).
Ključne besede: efficiency, data envelopment analysis (DEA), stochastic frontier analysis (SFA), Tunisian seaports
Objavljeno v DKUM: 22.08.2024; Ogledov: 44; Prenosov: 4
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Opis: The importance and value of real-world data in healthcare cannot be overstated because it offers a valuable source of insights into patient experiences. Traditional patient-reported experience and outcomes measures (PREMs/PROMs) often fall short in addressing the complexities of these experiences due to subjectivity and their inability to precisely target the questions asked. In contrast, diary recordings offer a promising solution. They can provide a comprehensive picture of psychological well-being, encompassing both psychological and physiological symptoms. This study explores how using advanced digital technologies, i.e., automatic speech recognition and natural language processing, can efficiently capture patient insights in oncology settings. We introduce the MRAST framework, a simplified way to collect, structure, and understand patient data using questionnaires and diary recordings. The framework was validated in a prospective study with 81 colorectal and 85 breast cancer survivors, of whom 37 were male and 129 were female. Overall, the patients evaluated the solution as well made; they found it easy to use and integrate into their daily routine. The majority (75.3%) of the cancer survivors participating in the study were willing to engage in health monitoring activities using digital wearable devices daily for an extended period. Throughout the study, there was a noticeable increase in the number of participants who perceived the system as having excellent usability. Despite some negative feedback, 44.44% of patients still rated the app’s usability as above satisfactory (i.e., 7.9 on 1–10 scale) and the experience with diary recording as above satisfactory (i.e., 7.0 on 1–10 scale). Overall, these findings also underscore the significance of user testing and continuous improvement in enhancing the usability and user acceptance of solutions like the MRAST framework. Overall, the automated extraction of information from diaries represents a pivotal step toward a more patient-centered approach, where healthcare decisions are based on real-world experiences and tailored to individual needs. The potential usefulness of such data is enormous, as it enables better measurement of everyday experiences and opens new avenues for patient-centered care.
Ključne besede: multilingual framework, risk assessment, symptom tracking, chronic diseases, patient-centered care, real-world data
Objavljeno v DKUM: 12.08.2024; Ogledov: 74; Prenosov: 34
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Opis: The publication "Analysis of quantitative research data in nursing research: A guide to SPSS" provides nursing students and nurses with the knowledge and skills to interpret the different statistical methods in their field, which can improve users' skills in collecting, analysing and interpreting results from clinical practice, thus contributing to improving the quality of health care. It provides detailed instructions on how to use IBM SPSS and perform statistical analyses that nurses need to be familiar with as they use and generate data in their daily work with patients. The main aim of patient care is to provide high quality, evidence-based care, so nurses have a duty to keep up to date with the latest research and evidence and apply it to their work. The knowledge gained in this book can also help nurses to better understand and interpret previously published results, and thus critically assess the validity and reliability of the results they will use in clinical practice.
Ključne besede: quantitative analysis, statistics, IBM SPSS, reliability, validity, data analysis
Objavljeno v DKUM: 18.07.2024; Ogledov: 113; Prenosov: 27
Celotno besedilo (18,18 MB)
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