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The purpose of the study is to classify the radiographic images into three categories such as fillings, cavity and implant to identify dental diseases because dental disease is a very common dental health problem for all people. The detection of dental issues and the selection of the most suitable method of treatment are both determined by the results of a radiological examination. Dental x-rays provide important information about the insides of teeth and their surrounding cells, which helps dentists detect dental issues that are not immediately visible. The analysis of dental x-rays, which is typically done by dentists, is a time-consuming process that can become an error-prone technique due to the wide variations in the structure of teeth and the dentist's lack of expertise. The workload of a dental professional and the chance of misinterpretation can be decreased by the availability of such a system, which can interpret the result of an x-ray automatically.

This study uses deep learning (DL) models to identify dental diseases in order to tackle this issue. Four different DL models, such as ResNet-101, Xception, DenseNet-201 and EfficientNet-B0, were evaluated in order to determine which one would be the most useful for the detection of dental diseases (such as fillings, cavity and implant).

Loss and accuracy curves have been used to analyze the model. However, the EfficientNet-B0 model performed better compared to Xception, DenseNet-201 and ResNet-101. The accuracy, recall, F1-score and AUC values for this model were 98.91, 98.91, 98.74 and 99.98%, respectively. The accuracy rates for the Xception, ResNet-101 and DenseNet-201 are 96.74, 93.48 and 95.65%, respectively.

The present study can benefit dentists from using the DL model to more accurately diagnose dental problems.

This study is conducted to evaluate dental diseases using Convolutional neural network (CNN) techniques to assist dentists in selecting the most effective technique for a particular clinical condition.

COVID-19 has occurred in more than 150 countries and causes a huge impact on the health of many people. The main purpose of this work is, COVID-19 has occurred in more than 150 countries and causes a huge impact on the health of many people. The COVID-19 diagnosis is required to detect at the beginning stage and special attention should be given to them. The fastest way to detect the COVID-19 infected patients is detecting through radiology and radiography images. The few early studies describe the particular abnormalities of the infected patients in the chest radiograms. Even though some of the challenges occur in concluding the viral infection traces in X-ray images, the convolutional neural network (CNN) can determine the patterns of data between the normal and infected X-rays that increase the detection rate. Therefore, the researchers are focusing on developing a deep learning-based detection model.

The main intention of this proposal is to develop the enhanced lung segmentation and classification of diagnosing the COVID-19. The main processes of the proposed model are image pre-processing, lung segmentation and deep classification. Initially, the image enhancement is performed by contrast enhancement and filtering approaches. Once the image is pre-processed, the optimal lung segmentation is done by the adaptive fuzzy-based region growing (AFRG) technique, in which the constant function for fusion is optimized by the modified deer hunting optimization algorithm (M-DHOA). Further, a well-performing deep learning algorithm termed adaptive CNN (A-CNN) is adopted for performing the classification, in which the hidden neurons are tuned by the proposed DHOA to enhance the detection accuracy. The simulation results illustrate that the proposed model has more possibilities to increase the COVID-19 testing methods on the publicly available data sets.

From the experimental analysis, the accuracy of the proposed M-DHOA–CNN was 5.84%, 5.23%, 6.25% and 8.33% superior to recurrent neural network, neural networks, support vector machine and K-nearest neighbor, respectively. Thus, the segmentation and classification performance of the developed COVID-19 diagnosis by AFRG and A-CNN has outperformed the existing techniques.

This paper adopts the latest optimization algorithm called M-DHOA to improve the performance of lung segmentation and classification in COVID-19 diagnosis using adaptive K-means with region growing fusion and A-CNN. To the best of the authors’ knowledge, this is the first work that uses M-DHOA for improved segmentation and classification steps for increasing the convergence rate of diagnosis.

The purpose of this paper is to design and manufacture an intelligent 3D printed sensor to monitor the re-occurrence of diaphragmatic hernia (DH; after surgery) in bovines as an Internet of Things (IOT)-based solution.

The approach used in this study is based on a bibliographic analysis for the re-occurrence of DH in the bovine after surgery. Using SolidWorks and ANSYS, the computer-aided design model of the implant was 3D printed based on literature and discussions on surgical techniques with a veterinarian. To ensure the error-proof design, load test and strain–stress rate analyses with boundary distortion have been carried out for the implant sub-assembly.

An innovative IOT-based additive manufacturing solution has been presented for the construction of a mesh-type sensor (for the health monitoring of bovine after surgery).

An innovative mesh-type sensor has been fabricated by integration of metal and polymer 3D printing (comprising 17–4 precipitate hardened stainless steel and polyvinylidene fluoride-hydroxyapatite-chitosan) without sacrificing strength and specific absorption ratio value.

This study aims to analyze the precursors of high and low intrapreneurial intentions among fitness center employees, considering various variables (gender, age, organization size and job satisfaction).

The study involved 166 fitness center employees of the Portuguese fitness center. The study used a two-part questionnaire to gather sociodemographic data and assess variables related to intrapreneurial intentions and job satisfaction among fitness employees. The first part collected basic demographic information, while the second used validated scales to measure intrapreneurial intentions (innovation and risk-taking) and job satisfaction (intrinsic and extrinsic).

This study underscores intrapreneurship as key for the evolving global fitness sector, highlighting job satisfaction as critical for fostering intrapreneurial intentions. Age, organizational size and gender diversity are also significant, suggesting that fostering a diverse and satisfied workforce under transformational leadership can enhance fitness organizations’ adaptability and growth.

This research supports the growth of the fitness sector by demonstrating how intrapreneurship, propelled by job satisfaction, can resolve challenges, benefiting fitness centers regardless of size, age or gender diversity.

The study highlights the vital role of intrapreneurs in the fitness industry, advocating a nongender-biased approach to intrapreneurship and identifying job satisfaction as key to fostering intrapreneurial intentions, beneficial for all fitness centers.

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 purpose of the study is an in-depth exploration of the processes through which a leader develops their leader identity in strength, meaning and integration, with resulting enrichment outcomes.

Using multi-domain leader identity theory, this study provides an in-depth exploration of the processes through which a leader develops their leader identity. Set in a healthcare context, 26 participants took part in an 18-month multi-domain leadership development program.

Findings indicate a typology of leader identities, capturing the dynamic nature of leader identity based on combinations of strength and meaning. Our research also suggests that as the leader develops, their leader identity can change from a differentiated identity as a leader to a more integrated leader identity, with resulting enrichment outcomes.

The results suggested value in inherently multi-domain focus using event-based reflection and, as such, are useful in leader identity development programs. We recommend future research generalize to other settings and a larger population.

By taking a multi-domain approach to leader identity development, the leader has the opportunity to learn and develop in a more holistic way. They are encouraged to reflect on and learn from leadership experiences throughout their entire lives, adding breadth and depth that are often overlooked in development programs.

Developing leaders who understand who they are and are capable of critical self-reflection and learning is a fundamental requirement for the positive advancement of society.

The value of the study lies in the first longitudinal, work-based empirical study taking an explicitly multi-domain approach to leader identity development.

The digitalization of archival management has rapidly developed with the maturation of digital technology. With data's exponential growth, archival resources have transitioned from single modalities, such as text, images, audio and video, to integrated multimodal forms. This paper identifies key trends, gaps and areas of focus in the field. Furthermore, it proposes a theoretical organizational framework based on deep learning to address the challenges of managing archives in the era of big data.

Via a comprehensive systematic literature review, the authors investigate the field of multimodal archive resource organization and the application of deep learning techniques in archive organization. A systematic search and filtering process is conducted to identify relevant articles, which are then summarized, discussed and analyzed to provide a comprehensive understanding of existing literature.

The authors' findings reveal that most research on multimodal archive resources predominantly focuses on aspects related to storage, management and retrieval. Furthermore, the utilization of deep learning techniques in image archive retrieval is increasing, highlighting their potential for enhancing image archive organization practices; however, practical research and implementation remain scarce. The review also underscores gaps in the literature, emphasizing the need for more practical case studies and the application of theoretical concepts in real-world scenarios. In response to these insights, the authors' study proposes an innovative deep learning-based organizational framework. This proposed framework is designed to navigate the complexities inherent in managing multimodal archive resources, representing a significant stride toward more efficient and effective archival practices.

This study comprehensively reviews the existing literature on multimodal archive resources organization. Additionally, a theoretical organizational framework based on deep learning is proposed, offering a novel perspective and solution for further advancements in the field. These insights contribute theoretically and practically, providing valuable knowledge for researchers, practitioners and archivists involved in organizing multimodal archive resources.

The purpose of this study is to examine the state of research into adoption of machine learning systems within the health sector, to identify themes that have been studied and observe the important gaps in the literature that can inform a research agenda going forward.

A systematic literature strategy was utilized to identify and analyze scientific papers between 2012 and 2022. A total of 28 articles were identified and reviewed.

The outcomes reveal that while advances in machine learning have the potential to improve service access and delivery, there have been sporadic growth of literature in this area which is perhaps surprising given the immense potential of machine learning within the health sector. The findings further reveal that themes such as recordkeeping, drugs development and streamlining of treatment have primarily been focused on by the majority of authors in this area.

The search was limited to journal articles published in English, resulting in the exclusion of studies disseminated through alternative channels, such as conferences, and those published in languages other than English. Considering that scholars in developing nations may encounter less difficulty in disseminating their work through alternative channels and that numerous emerging nations employ languages other than English, it is plausible that certain research has been overlooked in the present investigation.

This review provides insights into future research avenues for theory, content and context on adoption of machine learning within the health sector.

The purpose of this paper is to report on the implementation of the World Health Organization (WHO) trauma care checklist (TCC) (WHO, 2016) in an emergency department in a tertiary hospital in Nepal. This research was undertaken as part of a Hybrid International Emergency Medicine Fellowship programme (Subedi et al., 2020) across UK and Nepal, incorporating a two-year rotation through the UK National Health Service, via the Medical Training Initiative (MTI) (AoMRC, 2017). The WHO TCC can improve outcomes for trauma patients (Lashoher et al., 2016); however, significant barriers affect its implementation worldwide (Nolan et al., 2014; Wild et al., 2020). This article reports on the implementation, barriers and recommendations of WHO TCC implementation in the context of Nepal and argues for Transformational Leadership (TL) to support its implementation.

Explanatory mixed methods research (Creswell, 2014), comprising quasi-experimental research and a qualitative online survey, were selected methods for this research. A training module was designed and implemented for 10 doctors and 15 nurses from a total of 76 (33%) of clinicians to aid in the introduction of the WHO TCC in an emergency department in a hospital in Nepal. The quasi-experimental research involved a pre- and post-training survey aimed to assess participant’s knowledge of the WHO TCC before and after training and before the implementation of the WHO TCC in the emergency department. Post-training, 219 patients were reviewed after four weeks to identify if process measures had improved the quality of care to trauma patients. Subsequently six months later, a qualitative online survey was sent to all clinical staff in the department to identify barriers to implementation, with a response rate of 26 (n = 26) (34%) (20 doctors and 6 nurses). Descriptive statistics were used to evaluate quantitative data and the qualitative data were analysed using the five stepped approach of thematic analysis (Braun and Clarke, 2006).

The evaluation of the implementation of the WHO TCC showed an improvement in care for trauma patients in an emergency setting in a tertiary hospital in Nepal. There were improvements in the documentation in trauma management, showing the training had a direct impact on the quality of care of trauma patients. Notably, there was an improvement in cervical spine examination from 56.1% before training to 78.1%; chest examination 125 (57.07%) before training and 170 (77.62%) post-training; abdominal examination 121 (55.25%) before training and 169 (77.16%) post-training; gross motor examination 13 (5.93%) before training and 131 (59.82%) post-training; sensory examination 4 (1.82%) before training and 115 (52.51%) post-training; distal pulse examination 6 (2.73%) before training and 122 (55.7%) post-training. However, while the quality of documentation for trauma patients improved from the baseline of 56%, it only reached 78% when the percentage improvement target agreed for this research project was 90%. The 10 (n = 10) doctors and 15 (n = 15) nurses in the Emergency Department (ED) all improved their baseline knowledge from 72.2% to 87% (p = 0.00006), by 14.8% and 67% to 85%) (p = 0.006), respectively. Nurses started with lower scores (mean 67) in the baseline when compared to doctors, but they made significant gains in their learning post-training. The qualitative data reported barriers, such as the busyness of the department, with residents and medical officers, suggesting a shortened version of the checklist to support greater protocol compliance. Embedding this research within TL provided a steer for successful innovation and change, identifying action for sustaining change over time.

The study is a single-centre study that involved trauma patients in an emergency department in one hospital in Nepal. There is a lack of internationally recognised trauma training in Nepal and very few specialist trauma centres; hence, it was challenging to teach trauma to clinicians in a single 1-h session. High levels of transformation of health services are required in Nepal, but the sample for this research was small to test out and pilot the protocol to gain wider stakeholder buy in. The rapid turnover of doctors and nurses in the emergency department, creates an additional challenge but encouraging a multi-disciplinary approach through TL creates a greater chance of sustainability of the WHO TCC.

International protocols are required in Nepal to support the transformation of health care. This explanatory mixed methods research, which is part of an International Fellowship programme, provides evidence of direct improvements in the quality of patient care and demonstrates how TL can drive improvement in a low- to medium-income country.

The Nepal/UK Hybrid International Emergency Medicine Fellowships have an opportunity to implement changes to the health system in Nepal through research, by bringing international level standards and protocols to the hospital to improve the quality of care provided to patients.

To the best of the authors’ knowledge, this research paper is one of the first studies of its kind to demonstrate direct patient level improvements as an outcome of the two-year MTI scheme.

Mucormycosis has evolved as a post-COVID-19 complication globally, especially in India. The research on fungus has been very primitive, and many scientific publications have been discovered. The current COVID-19 pandemic needs further investigation into this unusual fungal infection. This review study aims to provide a pen-picture to researchers, science policymakers and scientists about different bibliometric indicators related to the research literature on mucormycosis.

The quantitative research was conducted using the established procedure of bibliometric investigation on data collected from Scopus from 2011 to 2020 using a validated search query. The search query consisted of keywords “Mucormycosis” or “Mucormycoses” or “Mucormycose” or “Mucorales Infection” or “Mucorales Infections” or “Black Fungus Infection” or “Black Fungus Infections” or “Zygomycosis” in the “Title-Keyword-Abstract” search option for data extraction. The analysis of data is performed using MS-Excel. Mapping was done with state-of-the-art visualization tools Biblioshiny and VOSviewer, using bibliometric indicators as units of analysis.

The analysis reveals that the first publication on this topic was reported from 1923 onwards. In total, 9,423 authors contributed 1,896 papers with 11,437 collaborated authors, documents per author are 0.201, authors per document are 4.97 and co-authors per document are 6.03. Total records were published in 779 journals in the English language from 75 countries globally. Mucormycosis literature is mostly open access, with 1,210 publications available via different open access routes. The highest number of articles (204) published in the journal “Mycoses” with 1,333 authors received 4,875 cited references, and the h-index has 24. The growth of publications is exponential, as depicted by the Price Law. The USA has recorded a maximum number of publications at both country and institutional levels compared to the other nations. There has been extensive research on mucormycosis before the outbreak as a post-COVID complication, as indicated by the highest number of publications in 2019.

The research hot spots have altered from “Mucormycosis,” “fungi,” “Zygomycosis” and “Drug efficacy”, “Drug Safety” to “Microbiology,” “Pathology,” “nucleotide sequence,” “surgical debridement” which indicates that potential area of research in the near future will be concerned with more extensive research in mucormycosis to develop standard treatment procedures to fight this infection. The quantity of scientific publications has also increased over time. The research and health community are called upon to join forces to activate existing knowledge, generate new insights and develop decision-supporting tools for health authorities in different nations to leverage vaccination in its transformational role toward successfully attaining nil cases of COVID-19.

The analysis of collaboration, findings, the research networks and visualization makes this study novel and separates from traditional metrics analysis. To the best of the authors’ knowledge, this work is original, and no similar studies have been found with the objectives included here.