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The study aims to identify the areas of flood susceptibility and to categorize the Gangarampur sub-division into various flood susceptibility zones. It also aspires to evaluate the efficacy of integrating Geographic Information Systems (GIS) with Artificial Neural Networks (ANN) for flood susceptibility analysis.

The factors contributing to floods such as rainfall, geomorphology, geo-hazard, elevation, stream density, land use and land cover, slope, distance from roads, Normalized Difference Water Index (NDWI) and distance from rivers were analyzed for flood susceptibility analysis. The use of the ANN model helps to construct the flood susceptibility map of the study area. For validating the outcome, the Receiver Operating Characteristic (ROC) is employed.

The results indicated that proximity to rivers, rainfall deviation, land use and land cover are the most significant factors influencing flood occurrence in the study area. The ANN model demonstrated a prediction accuracy of 85%, validating its effectiveness for flood susceptibility analysis.

The research offers a novel approach by integrating Geographic Information Systems (GIS) with Artificial Neural Networks (ANN) for flood susceptibility analysis in the Gangarampur sub-division. By identifying key factors such as proximity to rivers, rainfall deviation and land use, the study achieves 85% prediction accuracy, showing the effectiveness of ANN in flood risk mapping. These findings provide critical insights for planners to devise targeted flood mitigation strategies.

To meet future healthcare needs, primary care is undergoing a transformation in which innovations and new ways of working play an important role. However, successful innovations depend on joint learning and rewarding collaborations between healthcare and other stakeholders. This study aims to explore how learning develops when entrepreneurs, healthcare professionals and older people collaborate in a primary care living lab.

The study had an action research design and was conducted at a clinically embedded living lab at a primary care centre on the west coast of Sweden. Data consisted of e-mail conversations, recordings from design meetings and three group interviews with each party (entrepreneurs, healthcare professionals and older people). Data were analysed with inductive qualitative content analysis.

An overarching theme, “To share each other’s worlds in an arranged space for learning”, was found, followed by three categories, “Prerequisites for learning”, “Strategies to achieve learning” and “To learn from and with each other”. These three categories comprise eight subcategories.

This research contributes to knowledge regarding the need for arranged spaces for learning and innovation in primary care and how collaborative learning can contribute to the development of practice.

This paper presents an experimental investigation in establishing the relationship between FDM process parameters and tensile strength of polycarbonate (PC) samples using the I-Optimal design.

I-optimal design methodology is used to plan the experiments by means of Minitab-17.1 software. Samples are manufactured using Stratsys FDM 400mc and tested as per ISO standards. Additionally, an artificial neural network model was developed and compared to the regression model in order to select an appropriate model for optimisation. Finally, the genetic algorithm (GA) solver is executed for improvement of tensile strength of FDM built PC components.

This study demonstrates that the selected process parameters (raster angle, raster to raster air gap, build orientation about Y axis and the number of contours) had significant effect on tensile strength with raster angle being the most influential factor. Increasing the build orientation about Y axis produced specimens with compact structures that resulted in improved fracture resistance.

The fitted regression model has a p-value less than 0.05 which suggests that the model terms significantly represent the tensile strength of PC samples. Further, from the normal probability plot it was found that the residuals follow a straight line, thus the developed model provides adequate predictions. Furthermore, from the validation runs, a close agreement between the predicted and actual values was seen along the reference line which further supports satisfactory model predictions.

This study successfully investigated the effects of the selected process parameters - raster angle, raster to raster air gap, build orientation about Y axis and the number of contours - on tensile strength of PC samples utilising the I-optimal design and ANOVA. In addition, for prediction of the part strength, regression and ANN models were developed. The selected ANN model was optimised using the GA-solver for determination of optimal parameter settings.

The proposed ANN-GA approach is more appropriate to establish the non-linear relationship between the selected process parameters and tensile strength. Further, the proposed ANN-GA methodology can assist in manufacture of various industrial products with Nylon, polyethylene terephthalate glycol (PETG) and PET as new 3DP materials.

This paper focuses on knowledge sharing in health care. The aim of the paper is to further understand how digital applications can facilitate knowledge sharing between different care providers and health-care professionals.

The paper is based on a qualitative action case study, performed as a formative intervention study as a Change Laboratory, where a digital application concerning wound support was used. The Change Laboratory was used for knowledge sharing in the assessment and treatment process of wounds. The collected data was then thematically analyzed.

The findings show how digital applications can facilitate knowledge sharing, but also the need for complementary collaborative sessions. The main contribution is the rich description of how digital applications together with these sessions can facilitate knowledge sharing.

This paper shows that activities as collaborative sessions performed on the organizational level prove to support knowledge sharing and learning when a new digital application has been implemented in the work process. It also shows that these sessions contributed to identifying new knowledge that has potential for being included in the application and hence are important to keeping the application updated and relevant over time.

This study aims to explore the management learning during a long-term crisis like a pandemic. The paper addresses both what health-care managers have learnt during the COVID-19 pandemic and how the management learning is characterized.

The paper is based on a qualitative case study carried out during the COVID-19 pandemic at two different public hospitals in Sweden. The study, conducted with semi-structured interviews, applies a combination of within-case analysis and cross-case comparison. The data were analyzed using thematic deductive analysis with the themes, i.e. sensemaking, decision-making and meaning-making.

The COVID-19 pandemic was characterized by uncertainty and a need for continuous learning among the managers at the case hospitals. The learning process that arose was circular in nature, wherein trust played a crucial role in facilitating the flow of information and enabling the managers to get a good sense of the situation. This, in turn, allowed the managers to make decisions meaningful for the organization, which improved the trust for the managers. This circular process was iterated with higher frequency than usual and was a prerequisite for the managers’ learning. The practical implications are that a combined management with hierarchical and distributed management that uses the normal decision routes seems to be the most successful management method in a prolonged crisis as a pandemic.

The gained knowledge can benefit hospital organizations, be used in crisis education and to develop regional contingency plans for pandemics.

This study has explored learning during the COVID-19 pandemic and found a circular process, “the management learning wheel,” which supports management learning in prolonged crises.