9,498 research outputs found

    A Multivocal Literature Review on Non-Technical Debt in Software Development: An Insight into Process, Social, People, Organizational, and Culture Debt

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    Software development encompasses various factors beyond technical considerations. Neglecting non-technical elements like individuals, processes, culture, and social and organizational aspects can lead to debt-like characteristics that demand attention. Therefore, we introduce the non-technical debt (NTD) concept to encompass and explore these aspects. This indicates the applicability of the debt analogy to non-technical facets of software development. Technical debt (TD) and NTD share similarities and often arise from risky decision-making processes, impacting both software development professionals and software quality. Overlooking either type of debt can lead to significant implications for software development success. The current study conducts a comprehensive multivocal literature review (MLR) to explore the most recent research on NTD, its causes, and potential mitigation strategies. For analysis, we carefully selected 40 primary studies among 110 records published until October 1, 2022. The study investigates the factors contributing to the accumulation of NTD in software development and proposes strategies to alleviate the adverse effects associated with it. This MLR offers a contemporary overview and identifies prospects for further investigation, making a valuable contribution to the field. The findings of this research highlight that NTD's impacts extend beyond monetary aspects, setting it apart from TD. Furthermore, the findings reveal that rectifying NTD is more challenging than addressing TD, and its consequences contribute to the accumulation of TD. To avert software project failures, a comprehensive approach that addresses NTD and TD concurrently is crucial. Effective communication and coordination play a vital role in mitigating NTD, and the study proposes utilizing the 3C model as a recommended framework to tackle NTD concerns

    Graduate Catalog of Studies, 2023-2024

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    Proceedings of the 10th International congress on architectural technology (ICAT 2024): architectural technology transformation.

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    The profession of architectural technology is influential in the transformation of the built environment regionally, nationally, and internationally. The congress provides a platform for industry, educators, researchers, and the next generation of built environment students and professionals to showcase where their influence is transforming the built environment through novel ideas, businesses, leadership, innovation, digital transformation, research and development, and sustainable forward-thinking technological and construction assembly design

    Machine Learning Model for Repurposing Drugs to Target Viral Diseases

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    With recent events, such as the Covid-19 pandemic, it is increasingly important to develop strategies to combat viral diseases. Due to technological advancements, computer-aided drug design and machine learning (ML)-based hit identification strategies have gained popularity. Applying these techniques to identify novel scaffolds and/or repurpose existing therapeutics for viral diseases is a promising approach. As an avenue to improve existing classification models for antiviral applications, this thesis aimed to make improvements to non-binding data selection within these models. We created a classification model using molecular fingerprints to assess the performance of machine learning predictions when the model is trained using randomly selected and rationally selected non-binding datasets. Our analyses revealed that machine learning predictions can be improved using a rational selection approach. We further used this approach and trained three machine learning models based on XGBoost, Random Forest, and Support Vector Machine to predict potential inhibitors for the SARS-CoV2 main protease (Mpro) enzyme. Probability-ranked hits from the combined model were further analyzed using classical structure-based methods. The binding modes and affinities of the hits were identified using AutoDock Vina, and molecular dynamics simulations-enabled MM-GBSA calculations. The top hits identified from this multi-step screening approach revealed potential candidates that show improved affinity and stability than existing non-covalent Mpro inhibitors. Thus, our approach and the model could be useful for screening large ligand libraries

    Workflow to detect ship encounters at sea with GIS support

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    Dissertation presented as the partial requirement for obtaining a Master's degree in Geographic Information Systems and ScienceAccording to the United Nations, more than 80% of the global trade is currently transported by sea. The Portuguese EEZ has a very extensive area with high maritime traffic, among which illicit activities may occur. This work aims to contribute to the official control of illegal transshipment actions by studying and proposing a new way of detecting encounters between ships. Ships with specific characteristics use an Automatic Identification System (AIS) on board which transmits a signal via radio frequencies, allowing shore stations to receive static and dynamic data from the ship. Thus, there is an increase in maritime situational awareness and, consequently, in the safety of navigation. The methodology of this dissertation employs monthly and daily AIS data in the study area, which is located in southern mainland Portugal. A bibliometric and content analysis was performed in order to assess the state of the art concerning geospatial analysis models of maritime traffic, based on AIS data, and focus on anomalous behaviour detection. Maritime traffic density maps were created with the support of a GIS (QGIS software), which allowed to characterize the maritime traffic in the study area and, subsequently, to pattern the locations where ship encounters occur. The algorithm to detect ship-to-ship meetings at sea was developed using a rule-based methodology. After analysis and discussion of results, it was found that the areas where the possibility of ship encounters at sea is greatest are away from the main shipping lanes, but close to areas with fishing vessels. The study findings and workflow are useful for decision making by the competent authorities for patrolling the maritime areas, focusing on the detection of illegal transhipment actions.Segundo as Nações Unidas, mais de 80% do comércio global é, atualmente, transportado por via marítima. A ZEE portuguesa tem uma área muito extensa, com tráfego marítimo elevado, entre o qual podem ocorrer atividades ilícitas. Este trabalho pretende contribuir para o controlo oficial de ações de transbordo ilegal, estudando e propondo uma nova forma de deteção de encontros entre navios. Os navios com determinadas características, utilizam a bordo um Automatic Identification System (AIS) que transmite sinal através de frequências rádio, permitindo que estações em terra recebam dados estáticos e dinâmicos do navio. Deste modo, verifica-se um aumento do conhecimento situacional marítimo e, consequentemente, da segurança da navegação. Foi realizada uma análise bibliométrica e de conteúdo a fim de avaliar o estado da arte referente a modelos de análise geoespacial do tráfego marítimo, com base em dados AIS, e foco na deteção de comportamentos anómalos. Na metodologia desta dissertação, são utilizados dados AIS mensais e diários na área de estudo, situada a sul de Portugal Continental. Foram criados mapas de densidade de tráfego marítimo com o apoio de um SIG (software QGIS), o que permitiu caracterizar o tráfego marítimo na área de estudo e, posteriormente, padronizar os locais onde ocorrem encontros entre navios. O algoritmo para detetar encontros entre navios no mar foi desenvolvido através de uma metodologia baseada em regras. Após análise e discussão de resultados, constatou-se que as áreas onde a possibilidade de ocorrer encontros de navios no mar é maior, encontram-se afastadas dos corredores principais de navegação, mas próximas de zonas com embarcações de pesca. Os resultados do estudo e o workflow desenvolvidos são úteis à tomada de decisão pelas autoridades competentes por patrulhar as áreas marítimas, com incidência na deteção de ações de transbordo ilegal

    Relatedly: Scaffolding Literature Reviews with Existing Related Work Sections

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    Scholars who want to research a scientific topic must take time to read, extract meaning, and identify connections across many papers. As scientific literature grows, this becomes increasingly challenging. Meanwhile, authors summarize prior research in papers' related work sections, though this is scoped to support a single paper. A formative study found that while reading multiple related work paragraphs helps overview a topic, it is hard to navigate overlapping and diverging references and research foci. In this work, we design a system, Relatedly, that scaffolds exploring and reading multiple related work paragraphs on a topic, with features including dynamic re-ranking and highlighting to spotlight unexplored dissimilar information, auto-generated descriptive paragraph headings, and low-lighting of redundant information. From a within-subjects user study (n=15), we found that scholars generate more coherent, insightful, and comprehensive topic outlines using Relatedly compared to a baseline paper list

    Genomic architecture of selection for adaptation to challenging environments in aquaculture

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    Aquaculture, including freshwater and marine farming, has been important for global fish production during the past few decades. However, climate change presents a major risk threatening both quality and quantity of aquaculture production. The environmental stressors in aquaculture resulting from climate change, are temperature rise, salinity changes, sea level rise, acidification and changes of other chemical properties and changes of oxygen levels. Although a reasonable genetic gain can be achieved by selective breeding, this genetic response may not be enough to adapt fish species to the effects of climate change. Marker assisted selection focusing on specific genes or alleles that allow fish to cope with these changes would allow more rapid adaptation of fish to these new environments. In this thesis, I focused on three essential environmental stressors - dissolved oxygen, salinity and temperature as primarily determined in aquaculture production. The main objective is to provide insight in the genomic architecture underlying the mechanism of adaptation to challenging environments of aquaculture species under farming conditions. First, I determined candidate QTL associated with phenotypic variation during adaptation to hypoxia or normoxia. I identified overrepresented pathways that could explain the genetic regulation of hypoxia on growth. To identify fish with better hypoxia tolerance and growth under a hypoxic environment, I quantified the genetic correlations between an indicator trait for hypoxia tolerance (critical swimming performance) and growth. Moreover, the genomic architecture associated with swimming performance was demonstrated, while the effect of significant QTLs on growth was estimated. Beyond applying genome-wide association studies, I used selection signatures to identify QTLs and genes contributing to salinity tolerance. In addition, I also compared the genome of the saline-tolerant and highly productive tilapia “Sukamandi”, that was developed by the aquaculture research institute in Indonesia, to that of blue tilapia and Nile tilapia, to identify the QTLs contributing to salinity tolerance. Finally, I investigated QTLs associated with growth-related traits and organ weights at two distinct commercial Mediterranean product sites differing in temperature (farms in Spain and Greece). Overall, this thesis considerably adds to insight into how fish adapt to challenging environments, which will aid marker-assisted selection for improved resilience of aquaculture species under climate change

    Enhancing the forensic comparison process of common trace materials through the development of practical and systematic methods

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    An ongoing advancement in forensic trace evidence has driven the development of new and objective methods for comparing various materials. While many standard guides have been published for use in trace laboratories, different areas require a more comprehensive understanding of error rates and an urgent need for harmonizing methods of examination and interpretation. Two critical areas are the forensic examination of physical fits and the comparison of spectral data, which depend highly on the examiner’s judgment. The long-term goal of this study is to advance and modernize the comparative process of physical fit examinations and spectral interpretation. This goal is fulfilled through several avenues: 1) improvement of quantitative-based methods for various trace materials, 2) scrutiny of the methods through interlaboratory exercises, and 3) addressing fundamental aspects of the discipline using large experimental datasets, computational algorithms, and statistical analysis. A substantial new body of knowledge has been established by analyzing population sets of nearly 4,000 items representative of casework evidence. First, this research identifies material-specific relevant features for duct tapes and automotive polymers. Then, this study develops reporting templates to facilitate thorough and systematic documentation of an analyst’s decision-making process and minimize risks of bias. It also establishes criteria for utilizing a quantitative edge similarity score (ESS) for tapes and automotive polymers that yield relatively high accuracy (85% to 100%) and, notably, no false positives. Finally, the practicality and performance of the ESS method for duct tape physical fits are evaluated by forensic practitioners through two interlaboratory exercises. Across these studies, accuracy using the ESS method ranges between 95-99%, and again no false positives are reported. The practitioners’ feedback demonstrates the method’s potential to assist in training and improve peer verifications. This research also develops and trains computational algorithms to support analysts making decisions on sample comparisons. The automated algorithms in this research show the potential to provide objective and probabilistic support for determining a physical fit and demonstrate comparative accuracy to the analyst. Furthermore, additional models are developed to extract feature edge information from the systematic comparison templates of tapes and textiles to provide insight into the relative importance of each comparison feature. A decision tree model is developed to assist physical fit examinations of duct tapes and textiles and demonstrates comparative performance to the trained analysts. The computational tools also evaluate the suitability of partial sample comparisons that simulate situations where portions of the item are lost or damaged. Finally, an objective approach to interpreting complex spectral data is presented. A comparison metric consisting of spectral angle contrast ratios (SCAR) is used as a model to assess more than 94 different-source and 20 same-source electrical tape backings. The SCAR metric results in a discrimination power of 96% and demonstrates the capacity to capture information on the variability between different-source samples and the variability within same-source samples. Application of the random-forest model allows for the automatic detection of primary differences between samples. The developed threshold could assist analysts with making decisions on the spectral comparison of chemically similar samples. This research provides the forensic science community with novel approaches to comparing materials commonly seen in forensic laboratories. The outcomes of this study are anticipated to offer forensic practitioners new and accessible tools for incorporation into current workflows to facilitate systematic and objective analysis and interpretation of forensic materials and support analysts’ opinions

    Synergi: A Mixed-Initiative System for Scholarly Synthesis and Sensemaking

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    Efficiently reviewing scholarly literature and synthesizing prior art are crucial for scientific progress. Yet, the growing scale of publications and the burden of knowledge make synthesis of research threads more challenging than ever. While significant research has been devoted to helping scholars interact with individual papers, building research threads scattered across multiple papers remains a challenge. Most top-down synthesis (and LLMs) make it difficult to personalize and iterate on the output, while bottom-up synthesis is costly in time and effort. Here, we explore a new design space of mixed-initiative workflows. In doing so we develop a novel computational pipeline, Synergi, that ties together user input of relevant seed threads with citation graphs and LLMs, to expand and structure them, respectively. Synergi allows scholars to start with an entire threads-and-subthreads structure generated from papers relevant to their interests, and to iterate and customize on it as they wish. In our evaluation, we find that Synergi helps scholars efficiently make sense of relevant threads, broaden their perspectives, and increases their curiosity. We discuss future design implications for thread-based, mixed-initiative scholarly synthesis support tools.Comment: ACM UIST'2
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