Organisational learning in forensic fingerprint investigation : Solving critical challenges with organisational rule construction.

The present study analyses data collected from a series of developmental seminars in a fingerprint laboratory during which fingerprint examiners jointly discussed and developed their work processes, analytical methods, decision-making criteria and rules of documentation. The analysed organizational development took place in the context of moving from paper to digital documentation and from individually to collectively mastered work process. The fingerprint examiners who participated in the seminar series jointly reflected on their existing professional rules and operational practices, improvement of which was called for to facilitate organizational learning. The analysed data set consists of 10 audio-recorded developmental seminars with written documents as well as notes and decisions that were made during the seminar. The results of the study will reveal the complex ways in which the fingerprint examiners share their practical professional knowledge and collectively create decision-making criteria and rules of investigative practices so as to adapt their work practices to the changing quality requirements, evolving international standards and digitalization of research documentation.Peer reviewe

Recommender system for modelling subject combination in Ugandan senior secondary schools

Subject combinations at A-level in Ugandan Senior Secondary Schools have made or marred the future career of many prospective students, many students have ended up doing courses they had not planned to do because they made wrong choices at their A-level. This recommender system offers the decision-making process for students based on their subject performance coupled with interest, passion, skills and talents to enable them make right choices. It is person-centred and there are three (3) main actors: the student (who are interested in making appropriate career choice), the documents (which contains available information about interest and passion, skills and talents and subject performances) and access metrics (which aids the student of A-level in extracting knowledge from available resources). A hybrid matrix factorization model using ANFIS and R were used to represents students and subjects as linear combinations derived from their characteristics and interactions, this is combined with rule-based model to offer a unified approach of presenting any student with a list of subjects that will lead to prospective career choices. This offer higher predictive accuracy in career choice matchmaking and overcoming challenges of parental influence, peer influence and others while expanding opportunities of career guidance in Uganda.

Understanding the bi-directional relationship between analytical processes and interactive visualization systems

Interactive visualizations leverage the human visual and reasoning systems to increase the scale of information with which we can effectively work, therefore improving our ability to explore and analyze large amounts of data. Interactive visualizations are often designed with target domains in mind, such as analyzing unstructured textual information, which is a main thrust in this dissertation. Since each domain has its own existing procedures of analyzing data, a good start to a well-designed interactive visualization system is to understand the domain experts' workflow and analysis processes. This dissertation recasts the importance of understanding domain users' analysis processes and incorporating such understanding into the design of interactive visualization systems. To meet this aim, I first introduce considerations guiding the gathering of general and domain-specific analysis processes in text analytics. Two interactive visualization systems are designed by following the considerations. The first system is Parallel-Topics, a visual analytics system supporting analysis of large collections of documents by extracting semantically meaningful topics. Based on lessons learned from Parallel-Topics, this dissertation further presents a general visual text analysis framework, I-Si, to present meaningful topical summaries and temporal patterns, with the capability to handle large-scale textual information. Both systems have been evaluated by expert users and deemed successful in addressing domain analysis needs. The second contribution lies in preserving domain users' analysis process while using interactive visualizations. Our research suggests the preservation could serve multiple purposes. On the one hand, it could further improve the current system. On the other hand, users often need help in recalling and revisiting their complex and sometimes iterative analysis process with an interactive visualization system. This dissertation introduces multiple types of evidences available for capturing a user's analysis process within an interactive visualization and analyzes cost/benefit ratios of the capturing methods. It concludes that tracking interaction sequences is the most un-intrusive and feasible way to capture part of a user's analysis process. To validate this claim, a user study is presented to theoretically analyze the relationship between interactions and problem-solving processes. The results indicate that constraining the way a user interacts with a mathematical puzzle does have an effect on the problemsolving process. As later evidenced in an evaluative study, a fair amount of high-level analysis can be recovered through merely analyzing interaction logs

Data science for engineering design: State of the art and future directions

Abstract Engineering design (ED) is the process of solving technical problems within requirements and constraints to create new artifacts. Data science (DS) is the inter-disciplinary field that uses computational systems to extract knowledge from structured and unstructured data. The synergies between these two fields have a long story and throughout the past decades, ED has increasingly benefited from an integration with DS. We present a literature review at the intersection between ED and DS, identifying the tools, algorithms and data sources that show the most potential in contributing to ED, and identifying a set of challenges that future data scientists and designers should tackle, to maximize the potential of DS in supporting effective and efficient designs. A rigorous scoping review approach has been supported by Natural Language Processing techniques, in order to offer a review of research across two fuzzy-confining disciplines. The paper identifies challenges related to the two fields of research and to their interfaces. The main gaps in the literature revolve around the adaptation of computational techniques to be applied in the peculiar context of design, the identification of data sources to boost design research and a proper featurization of this data. The challenges have been classified considering their impacts on ED phases and applicability of DS methods, giving a map for future research across the fields. The scoping review shows that to fully take advantage of DS tools there must be an increase in the collaboration between design practitioners and researchers in order to open new data driven opportunities

Innovation for the base of the pyramid: Critical perspectives from development studies on heterogeneity and participation

This article criticises current BoP approaches for under-appreciating two issues that play vital roles in projects targeting the poor at the BoP: heterogeneity among the poor, and the intricacies of participatory partnerships between TNCs, the non-profit sector (NGOs) and local poor communities in the global south. Our main contention is that the extant BoP literature has a naive view of what working with the poor really involves, which grossly underestimates adverse power relationships and disregards the hierarchies between the poor and outsiders who administer development interventions. To unpack the hidden complexities associated with heterogeneity and partnership dynamics, we draw on extensive knowledge from the field of development studies, which has accumulated key insights about working in and with poorer communities over several decades.innovation and development, participation, poverty alleviation, TNCs

ChatGPT for Shaping the Future of Dentistry: The Potential of Multi-Modal Large Language Model

The ChatGPT, a lite and conversational variant of Generative Pretrained Transformer 4 (GPT-4) developed by OpenAI, is one of the milestone Large Language Models (LLMs) with billions of parameters. LLMs have stirred up much interest among researchers and practitioners in their impressive skills in natural language processing tasks, which profoundly impact various fields. This paper mainly discusses the future applications of LLMs in dentistry. We introduce two primary LLM deployment methods in dentistry, including automated dental diagnosis and cross-modal dental diagnosis, and examine their potential applications. Especially, equipped with a cross-modal encoder, a single LLM can manage multi-source data and conduct advanced natural language reasoning to perform complex clinical operations. We also present cases to demonstrate the potential of a fully automatic Multi-Modal LLM AI system for dentistry clinical application. While LLMs offer significant potential benefits, the challenges, such as data privacy, data quality, and model bias, need further study. Overall, LLMs have the potential to revolutionize dental diagnosis and treatment, which indicates a promising avenue for clinical application and research in dentistry

A terminal assessment of stages theory : introducing a dynamic states approach to entrepreneurship

Stages of Growth models were the most frequent theoretical approach to understanding entrepreneurial business growth from 1962 to 2006; they built on the growth imperative and developmental models of that time. An analysis of the universe of such models (N=104) published in the management literature shows no consensus on basic constructs of the approach, nor is there any empirical confirmations of stages theory. However, by changing two propositions of the stages models, a new dynamic states approach is derived. The dynamic states approach has far greater explanatory power than its precursor, and is compatible with leading edge research in entrepreneurship