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This study investigates how a hospital can increase the flow of patients through its emergency department by using benchmarking and process improvement techniques borrowed from the manufacturing sector.

An in-depth case study of an Australasian public hospital utilises rigorous, multi-method data collection procedures with systems thinking to benchmark an emergency department (ED) value stream and identify the performance inhibitors.

High levels of value stream uncertainty result from inefficient processes and weak controls. Reduced patient flow arises from senior management’s commitment to simplistic government targets, clinical staff that lack basic operations management skills, and fragmented information systems. High junior/senior staff ratios aggravate the lack of inter-functional integration and poor use of time and material resources, increasing the risk of a critical patient incident.

This research is limited to a single case; hence, further research should assess value stream maturity and associated performance enablers and inhibitors in other emergency departments experiencing patient flow delays.

This study illustrates how hospital managers can use systems thinking and a context-free performance benchmarking measure to identify needed interventions and transferable best practices for achieving seamless patient flow.

This study is the first to operationalise the theoretical concept of the seamless healthcare system to acute care as defined by Parnaby and Towill (2008). It is also the first to use the uncertainty circle model in an Australasian public healthcare setting to objectively benchmark an emergency department's value stream maturity.

The research aims to explore the wisdom, knowledge and practices in vernacular housing settlements with their sustainability underpinnings as tools for modelling rural affordable housing in tropical regions. The study is based on a weaving settlement in Bargarh district of Odisha, which is globally acclaimed for its Ikkat style of weaving.

A hierarchical framework of sustainability resting on the three pillars of ecological, economical and environmental dimensions is derived from existing theoretical research. This framework of 22 indicators is subsequently assigned to assess the sustainability of the vernacular weavers' settlement through quantitative evaluation. A qualitative assessment through observation and deduction also verifies the result.

Since the vernacular weavers settlement performs very well on the sustainability scorecard, the paper suggests that its best practices can be incorporated while designing affordable housing so that social, cultural and heritage values are retained and a climate conscious, energy-efficient sustainable approach is ensured.

The recommendations from the assessment has many lessons while framing policies for rural affordable housing as it cannot have one size that fits all settlement typology irrespective of the occupational, climatic and social needs.

The sustainable design and planning principles embedded in this vernacular settlement offers a valuable blueprint to re-imagine the affordable housing in rural areas which can be myopic if it does not take into account the occupational needs and life style of craftsmen dwellers.

The use of generative artificial intelligence (genAi) language models such as ChatGPT to write assignment text is well established. This paper aims to assess to what extent genAi can be used to obtain guidance on how to avoid detection when commissioning and submitting contract-written assignments and how workable the offered solutions are.

Although ChatGPT is programmed not to provide answers that are unethical or that may cause harm to people, ChatGPT’s can be prompted to answer with inverted moral valence, thereby supplying unethical answers. The authors tasked ChatGPT to generate 30 essays that discussed the benefits of submitting contract-written undergraduate assignments and outline the best ways of avoiding detection. The authors scored the likelihood that ChatGPT’s suggestions would be successful in avoiding detection by markers when submitting contract-written work.

While the majority of suggested strategies had a low chance of escaping detection, recommendations related to obscuring plagiarism and content blending as well as techniques related to distraction have a higher probability of remaining undetected. The authors conclude that ChatGPT can be used with success as a brainstorming tool to provide cheating advice, but that its success depends on the vigilance of the assignment markers and the cheating student’s ability to distinguish between genuinely viable options and those that appear to be workable but are not.

This paper is a novel application of making ChatGPT answer with inverted moral valence, simulating queries by students who may be intent on escaping detection when committing academic misconduct.

The author reflects on her journey to becoming a Black female full professor at a Historically Black College and University (HBCU). Additionally, she summarizes the research on the successes and challenges for women professors, Black professors more broadly, and Black female professors, more specifically, to contribute to a deeper understanding of the positionality and stance of Black women professors. Although HBCUs are higher education institutions where Black female professors achieve tenure in the greatest percentages, the author highlights the intersection of race and gender and the unfortunate gendered power dynamics in these spaces that frequently place Black female faculty at a disadvantage. The chapter concludes with strategies for Black women professors to survive and thrive in academia, in general, and at HBCUs, more specifically.

Small- and medium-sized enterprises (SMEs) carry huge economic importance worldwide. At the same time, SMEs face specific challenges, some of which may be alleviated by employing accountants. However, research on the role and impact of accountants in SMEs has long remained fragmented and scarce. This paper aims to encourage more research on accountants in SMEs by providing the first comprehensive and systematic review of relevant research.

Based on systematic review methods, the authors critically examine 68 research articles dealing with accountants in SMEs.

The review identifies three dominant roles for accountants in SMEs: providers of reporting services, sources of SME owners’ self-validation and translators between capital providers and SMEs and advisors. Implicitly, many studies assume a value-enhancing effect of employing accountants in SMEs regardless of these specific roles. At the same time, available studies seldom make use of existing theoretical frameworks to more closely analyze the value-enhancing potential of human resources such as accountants. The authors, thus, propose the resource-based view as a robust theoretical framework to improve theory building in research on accountants in SMEs.

To the best of the authors’ knowledge, this paper is the first systematic review of accountants in SMEs. In addition, the authors develop a resource-based model on accountants in SMEs to guide future research on this topic.

The purpose of this study is to investigate and demonstrate the advancements achieved in the field of chest X-ray image captioning through the utilization of dynamic convolutional encoder–decoder networks (DyCNN). Typical convolutional neural networks (CNNs) are unable to capture both local and global contextual information effectively and apply a uniform operation to all pixels in an image. To address this, we propose an innovative approach that integrates a dynamic convolution operation at the encoder stage, improving image encoding quality and disease detection. In addition, a decoder based on the gated recurrent unit (GRU) is used for language modeling, and an attention network is incorporated to enhance consistency. This novel combination allows for improved feature extraction, mimicking the expertise of radiologists by selectively focusing on important areas and producing coherent captions with valuable clinical information.

In this study, we have presented a new report generation approach that utilizes dynamic convolution applied Resnet-101 (DyCNN) as an encoder (Verelst and Tuytelaars, 2019) and GRU as a decoder (Dey and Salemt, 2017; Pan et al., 2020), along with an attention network (see Figure 1). This integration innovatively extends the capabilities of image encoding and sequential caption generation, representing a shift from conventional CNN architectures. With its ability to dynamically adapt receptive fields, the DyCNN excels at capturing features of varying scales within the CXR images. This dynamic adaptability significantly enhances the granularity of feature extraction, enabling precise representation of localized abnormalities and structural intricacies. By incorporating this flexibility into the encoding process, our model can distil meaningful and contextually rich features from the radiographic data. While the attention mechanism enables the model to selectively focus on different regions of the image during caption generation. The attention mechanism enhances the report generation process by allowing the model to assign different importance weights to different regions of the image, mimicking human perception. In parallel, the GRU-based decoder adds a critical dimension to the process by ensuring a smooth, sequential generation of captions.

The findings of this study highlight the significant advancements achieved in chest X-ray image captioning through the utilization of dynamic convolutional encoder–decoder networks (DyCNN). Experiments conducted using the IU-Chest X-ray datasets showed that the proposed model outperformed other state-of-the-art approaches. The model achieved notable scores, including a BLEU_1 score of 0.591, a BLEU_2 score of 0.347, a BLEU_3 score of 0.277 and a BLEU_4 score of 0.155. These results highlight the efficiency and efficacy of the model in producing precise radiology reports, enhancing image interpretation and clinical decision-making.

This work is the first of its kind, which employs DyCNN as an encoder to extract features from CXR images. In addition, GRU as the decoder for language modeling was utilized and the attention mechanisms into the model architecture were incorporated.

The purpose of this study is to plan and develop a cost-effective health-care robot for assisting and observing the patients in an accurate and effective way during pandemic situation like COVID-19. The purposed research work can help in better management of pandemic situations in rural areas as well as developing countries where medical facility is not easily available.

It becomes very difficult for the medical staff to have a continuous check on patient’s condition in terms of symptoms and critical parameters during pandemic situations. For dealing with these situations, a service mobile robot with multiple sensors for measuring patients bodily indicators has been proposed and the prototype for the same has been developed that can monitor and aid the patient using the robotic arm. The fuzzy controller has also been incorporated with the mobile robot through which decisions on patient monitoring can be taken automatically. Mamdani implication method has been utilized for formulating mathematical expression of M number of “if and then condition based rules” with defined input X_j (j = 1, 2, ………. s), and output y_i. The inputs and output variables are formed by the membership functions µ_Aij(x_j) and µ_Ci(y_i) to execute the Fuzzy Inference System controller. Here, Aij and Ci are the developed fuzzy sets.

The fuzzy-based prediction model has been tested with the output of medicines for the initial 27 runs and was validated by the correlation of predicted and actual values. The correlation coefficient has been found to be 0.989 with a mean square error value of 0.000174, signifying a strong relationship between the predicted values and the actual values. The proposed research work can handle multiple tasks like online consulting, continuous patient condition monitoring in general wards and ICUs, telemedicine services, hospital waste disposal and providing service to patients at regular time intervals.

The novelty of the proposed research work lies in the integration of artificial intelligence techniques like fuzzy logic with the multi-sensor-based service robot for easy decision-making and continuous patient monitoring in hospitals in rural areas and to reduce the work stress on medical staff during pandemic situation.

This paper aims to present relevant literature review to build up the case pertaining to the impact of traditional leadership in the demarcation of municipal boundaries in South Africa. Municipal boundary demarcation remains a major contentious issue during the process of establishing municipalities. Little or no attention has been paid to the significance of traditional leadership in resolving issues around boundary demarcation between municipalities, hence this paper.

This paper adopted case study design. Data were collected from secondary sources and the contents were analyzed for this research purpose.

This paper put boundary in municipal context as a result of spatial reconfiguration process. It also discussed the impactful roles of traditional leadership as an institution involved in municipal (re)demarcation processes.

This paper focusses on the contemporary roles that traditional leadership is at vantage position to play in the process of municipal boundary demarcation. Therefore, the paper concludes that traditional leadership could resolve issue of ethnicity as a causal factor mitigating the redemarcation of municipal boundaries.

It contributes to existing knowledge by providing information on the roles of traditional leadership that could complement the resolution of the ethnic complexities arising from municipal (re)demarcation processes.