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This paper aims to present findings from a study of non-profit organizations (NPOs), including a model of knowledge needs that can be applied by practitioners and scholars to further develop the NPO sector.

A survey was conducted with NPOs operating in Canada and Australia. An analysis of survey responses identified the different types of knowledge essential for each organization. Respondents identified the importance of three pre-determined themes (quantitative data) related to knowledge needs, as well as a fourth option, which was a free text box (qualitative data). The quantitative and qualitative data were analyzed using descriptive statistical analyses and a grounded theory approach, respectively.

Analysis of the quantitative data indicates that NPOs ' needs are comparable in both countries. Analysis of qualitative data identified five major categories and multiple sub-categories representing the types of knowledge needs of NPOs. Major categories are knowledge about management and organizational practices, knowledge about resources, community knowledge, sectoral knowledge and situated knowledge. The paper discusses the results using semantic proximity and presents an emergent, evidence-based knowledge management (KM)-NPO model.

The findings contribute to the growing body of literature in the KM domain, and in the understudied research domain related to the knowledge needs and experiences of NPOs. NPOs will find the identified categories and sub-categories useful to undertake KM initiatives within their individual organizations. The study is also unique, as it includes data from two countries, Canada and Australia.

This paper aims first to identify key interorganisational partnership types among non-profit organisations (NPOs) and second to determine how knowledge sharing takes place within each type of partnership. Results explore the value of social media specifically in facilitating external relationships between NPOs, firms and the communities they serve.

Empirical qualitative analysis of exploratory interviews with 16 Canadian NPOs generates a non-exhaustive classification of partnership types emerging from these organisations, and their defining characteristics in the context of interorganisational knowledge sharing.

Overall eight categories of partnerships from the sampled NPOs emerged from the analysis of the data. These include business partnerships, sector partnerships, community partnerships, government partnerships, expert partnerships, endorsement partnerships, charter partnerships and hybrid partnerships. Using examples from interviews, the sharing of knowledge within each of these partnerships is defined uniquely in terms of directionality (i.e. uni-directional, bi-directional, multi-directional knowledge sharing) and formality (i.e. informal, semi-formal or formal knowledge sharing).Specific practices within these relationships also arise from examples, in particular, the use of social media to support informal and community-driven collaborations. Twitter, as a popular social networking tool, emerges as a preferred medium that supports interorganisational partnerships relevant to NPOs.

This research is valuable in identifying the knowledge management practices unique to NPOs. By examining and discussing specific examples of partnerships encountered among NPOs, this paper contributes original findings about the implications of interorganisational knowledge sharing, as well as the impact of emerging social technologies on same.

This study aims to investigate the potential use of potato peel extracts as antibacterial finishes for cotton fabrics against Staphylococcus aureus and Escherichia coli. Potato peels are abundant as waste and provide a natural, cheaper and sustainable alternative means of preventing the spread of bacterial infections on cotton fabric.

This research included the characterization of potato peel extracts, application of the extract onto cotton fabric and efficacy testing of the treated cotton fabric against bacteria. Phytochemical screening, agar well diffusion antibacterial test, minimum inhibitory concentration and Fourier transform infrared (FTIR) tests were used to characterize the extract. Antibacterial efficacy of the treated fabric was determined qualitatively using the disc diffusion assay and quantitatively using the bacteria reduction test.

Phytochemical screening confirmed the presence of several secondary metabolites including phenols and flavonoids. Antibacterial tests revealed a positive response in Escherichia coli and Staphylococcus aureus with a zone of inhibition of 6.50 mm and 5.60 mm, respectively. Additional peaks on the FTIR spectroscopy confirmed the presence of potato peel extract on the treated cotton fabric. The treated cotton fabrics showed efficacy against Staphylococcus aureus and Escherichia coli up to 20 washes.

This study introduced the application of potato peel extracts onto cotton fabrics and assessment of the antibacterial properties before and after washing. Results of this study suggest that potato peel extracts can be used as an organic eco-friendly antibacterial finish for cotton fabrics.

Students with developmental/intellectual disabilities (ID/DD) often have serious health issues that require additional medical care and supervision. Serious health issues also mean increased absence and additional lags in academic achievement and development of adaptive and social skills. The incorporation of artificial intelligence in the education of a child with ID/DD could ameliorate the educational, adaptive and social skill gaps that occur as a direct result of persistent health problems.

The literature regarding the use of artificial intelligence in education for students with ID/DD was collected systematically from international online databases based on specific inclusion and exclusion criteria. The collected articles were analyzed deductively, looking for the different gaps in the domain. Based on the literature, an artificial intelligence–based architecture is proposed and sketched.

The findings show that there are many gaps in supporting students with ID/DD through the utilization of artificial intelligence. Given that the majority of students with ID/DD often have serious and chronic and comorbid health conditions, the potential use of health information in artificial intelligence is even more critical. Therefore, there is a clear need to develop a system that facilitates communication and access to health information for students with ID/DD, one that provides information to caregivers and education providers, limits errors, and, therefore, improves these individuals' education and quality of life.

This review highlights the gap in the current literature regarding using artificial intelligence in supporting the education of students with ID/DD. There is an urgent need for an intelligent system in collaboration with the updated health information to improve the quality of services submitted for people with intellectual disabilities and as a result improving their quality of life.

This study contributes to the literature by highlighting the gaps in incorporating artificial intelligence and its service to individuals with ID/DD. The research additionally proposes a solution based on the confounding variables of students’ health and individual characteristics. This solution will provide an automated information flow as a functional diagnostic and intervention tool for teachers, caregivers and parents. It could potentially improve the educational and practical outcomes for individuals with ID/DD and, ultimately, their quality of life.