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This paper proposes a methodology based on an uncertain mining technology that identifies the linguistic relationships of ESG and its components with a financial performance metric to help the sustainability diagnosis of a region, specifically Latin America.

First, based on a relevant dataset of companies in a region, a procedure is formulated whereby an uncertain mining technology extracts the mathematically significant linguistic relationships of ESG and its components with a financial performance metric. Second, a knowledge management process is designed based on the linguistic summaries obtained from the mining process. As a final step and drawing upon the two preceding processes, a diagrammatic system of signals is proposed for diagnosing the sustainability of the region as contributed by its companies.

After this methodology is instantiated on a group of Multilatinas, it is observed that their sustainability contributions to the region are limited and that none of the identified linguistic relationships between ESG and the financial performance metric are favorable for the region.

This is the first proposal of its kind and it can be applied to any region of the world to assess the financial performance of its companies regarding their ESG commitments. In addition, it enables the region to comprehensively monitor compliance with the 2030 SDG agenda.

This paper aims to critically evaluate the role of advanced artificial intelligence (AI)-enhanced image fusion techniques in lung cancer diagnostics within the context of AI-driven precision medicine.

We conducted a systematic review of various studies to assess the impact of AI-based methodologies on the accuracy and efficiency of lung cancer diagnosis. The focus was on the integration of AI in image fusion techniques and their application in personalized treatment strategies.

The review reveals significant improvements in diagnostic precision, a crucial aspect of the evolution of AI in healthcare. These AI-driven techniques substantially enhance the accuracy of lung cancer diagnosis, thereby influencing personalized treatment approaches. The study also explores the broader implications of these methodologies on healthcare resource allocation, policy formation, and epidemiological trends.

This study is notable for both emphasizing the clinical importance of AI-integrated image fusion in lung cancer treatment and illuminating the profound influence these technologies have in the future AI-driven healthcare systems.

Cataract, or lens opacification, is a major public health burden accounting for more than half of all blindness worldwide. Plant-based dietary indices provide a unique approach to investigating a modifiable risk for age-related cataracts (ARC). The purpose of this study was to investigate the association between plant-based diet indices and risk of ARC.

This case-control study was conducted on 97 patients with newly diagnosed ARC and 198 healthy people (as a control group) in Iran. Convenience sampling and a food frequency questionnaire were used. Three plant-based dietary indices were used based on the health promoting qualities of food items, the overall plant-based diet index (PDI), healthful plant-based diet index (H-PDI) and unhealthful plant-based diet index (U-PDI) which comprised refined carbohydrates and highly processed foods. The plant-based dietary indices were used to investigate relationships with risk of ARC.

After adjusting for potential covariates, no significant association between a higher adherence to O-PDI and risk of ARC. As well, a higher adherence to H-PDI was inversely associated with the risk of ARC (OR = 0.35,95%CI:0.16–0.78). In contrast, there was a significant positive association between a higher adherence to U-PDI and the risk of ARC (OR = 3.67,95%CI:1.66 – 8.15).

The findings of this study have underscored the potential impact of the quality of plant-based food selections on the likelihood of developing ARC. Therefore, adopting a plant-based diet that is rich in nutrient-dense plant-based foods while being low in unhealthy options may have the potential to reduce the risk of ARC.

Coronary artery disease is one of the most common cardiovascular disorders in the world, and it can be deadly. Traditional diagnostic approaches are based on angiography, which is an interventional procedure having side effects such as contrast nephropathy or radio exposure as well as significant expenses. The purpose of this paper is to propose a novel artificial intelligence (AI) approach for the diagnosis of coronary artery disease as an effective alternative to traditional diagnostic methods.

In this study, a novel ensemble AI approach based on optimization and classification is proposed. The proposed ensemble structure consists of three stages: feature selection, classification and combining. In the first stage, important features for each classification method are identified using the binary particle swarm optimization algorithm (BPSO). In the second stage, individual classification methods are used. In the final stage, the prediction results obtained from the individual methods are combined in an optimized way using the particle swarm optimization (PSO) algorithm to achieve better predictions.

The proposed method has been tested using an up-to-date real dataset collected at Basaksehir Çam and Sakura City Hospital. The data of disease prediction are unbalanced. Hence, the proposed ensemble approach improves majorly the F-measure and ROC area which are more prominent measures in case of unbalanced classification. The comparison shows that the proposed approach improves the F-measure and ROC area results of the individual classification methods around 14.5% in average and diagnoses with an accuracy rate of 96%.

This study presents a low-cost and low-risk AI-based approach for diagnosing heart disease compared to traditional diagnostic methods. Most of the existing research studies focus on base classification methods. In this study, we mainly investigate an effective ensemble method that uses optimization approaches for feature selection and combining stages for the medical diagnostic domain. Furthermore, the approaches in the literature are commonly tested on open-access dataset in heart disease diagnoses, whereas we apply our approach on a real and up-to-date dataset.

Adolescence and emerging adulthood are key developmental stages with high risk for trauma exposure and the development of mental and substance-use disorders (SUDs). This study aims to compare the clinical profiles of adolescents (aged 12–17 years) and emerging adults (aged 18–25 years) presenting for treatment of posttraumatic stress disorder (PTSD) and SUD.

Data was collected from the baseline assessment of individuals (n = 55) taking part in a randomized controlled trial examining the efficacy of an integrated psychological therapy for co-occurring PTSD and SUDs (PTSD+SUD) in young people.

Both age groups demonstrated complex and severe clinical profiles, including high-frequency trauma exposure, and very poor mental health reflected on measures of PTSD, SUD, suicidality and domains of social, emotional, behavioral and family functioning. There were few differences in clinical characteristics between the two groups.

Similarity between the two groups suggests that the complex problems seen in emerging adults with PTSD + SUD are likely to have had their onset in adolescence or earlier and to have been present for several years by the time individuals present for treatment.

To the best of the authors’ knowledge, this is the first study to compare the demographic and clinical profiles of adolescents and emerging adults with PTSD + SUD. These findings yield important implications for practice and policy for this vulnerable group. Evidence-based prevention and early intervention approaches and access to care are critical. Alongside trauma-focused treatment, there is a critical need for integrated, trauma-informed approaches specifically tailored to young people with PTSD + SUD.

Workflow optimisation is crucial for establishing a viable acute stroke (AS) intervention programme in a large tertiary care centre. This study aims to utilise Lean Six Sigma (LSS) principles to enhance the hospital's workflow.

The Action Research methodology was used to implement the project and develop the case study. The study took place in a large tertiary care academic hospital in India. The Define-Measure-Analyse-Improve-Control approach optimised the workflow within 6 months. Lean tools such as value stream mapping (VSM), waste audits and Gemba were utilised to identify issues involving various stakeholders in the workflow. Sigma-level calculations were used to compare baseline, improvement and sustainment status. Additionally, statistical techniques were effectively employed to draw meaningful inferences.

LSS tools and techniques can be effectively utilised in large tertiary care hospitals to optimise workflow through a structured approach. Sigma ratings of the processes showed substantial improvement, resulting in a five-fold increase in clinical outcomes. Specifically, there was a 43% improvement in outcome for patients who underwent acute stroke revascularisation. However, certain sigma ratings deteriorated during the control and extended control (sustainment) phases. This indicates that ensuring the sustainability of quality control interventions in healthcare is challenging and requires continuous auditing.

The article presents a single case study deployed in a hospital in India. Thus, the generalisation of outcomes has a significant limitation. Also, the study encounters the challenge of not having a parallel control group, which is a common limitation in quality improvement studies in healthcare. Many studies in healthcare quality improvement, including this one, are limited by minimal data on long-term follow-up and the sustainability of achieved results.

This study pioneers the integration of LSS methodologies in a large Indian tertiary care hospital, specifically targeting AS intervention. It represents the first LSS case study applied in the stroke department of any hospital in India. Whilst most case studies discuss only the positive aspects, this article fills a critical gap by unearthing the challenges of applying LSS in a complex healthcare setting, offering insights into sustainable quality improvement and operational efficiency. This case study contributes to the theoretical understanding of LSS in healthcare. It showcases its real-world impact on patient outcomes and process optimisation.

The aim of this study was to explore empirical literature on the experiences of pre-registration student nurses during mental health clinical placements and identify factors that enhance practice learning.

An integrative mixed-methods approach and constant comparative synthesis were chosen. Eligible studies were from 2009 onwards sampling student experiences of mental health placements within undergraduate and postgraduate degree entry to practice nursing programmes, excluding academic-only experiences. The search was last conducted on 14th August 2021 and included MEDLINE, CINAHL and APA PsycINFO databases.

The search strategy identified 579 studies, of which 10 met the eligibility criteria. Seven of the articles reported qualitative research; two were based on quantitative studies, and one had a mixed-methods design. There was international representation across six countries. All studies examined the experiences of pre-registration student nurses during mental health clinical placements. The total number of participants was 447, comprised of students, nongovernmental organisations and community members.

The review identified four influential themes that enhance practice learning: immersion in the nursing role; relationships that empower autonomous learning; opportunity for defined and subtle skill development; and student experiences of people with mental health needs. Further research is required on culture, subtle skill development and the socialisation process of students with the mental health nurse professional identity.

This study aims to address the critical issue of machine breakdowns in industrial settings, which jeopardize operation economy, worker safety, productivity and environmental compliance. It explores the efficacy of a predictive maintenance program in mitigating these risks by proactively identifying and minimizing failures, thereby optimizing maintenance activities for higher efficiency.

The article implements Logical Analysis of Data (LAD) as a predictive maintenance approach on an industrial machine maintenance dataset. The aim is to (1) detect failure presence and (2) determine specific failure modes. Data resampling is applied to address asymmetrical class distribution.

LAD demonstrates its interpretability by extracting patterns facilitating the failure diagnosis. Results indicate that, in the first case study, LAD exhibits a high recall value for failure records within a balanced dataset. In the second case study involving smaller-scale datasets, enhancement across all evaluation metrics is observed when data is balanced and remains robust in the presence of imbalance, albeit with nuanced differences in between.

This research highlights the importance of transparency in predictive maintenance programs. The research shows the effectiveness of LAD in detecting failures and identifying specific failure modes from diagnostic sensor data. This maintenance strategy exhibits its distinction by offering explainable failure patterns for maintenance teams. The patterns facilitate the failure cause-effect analysis and serve as the core for failure prediction. Hence, this program has the potential to enhance machine reliability, availability and maintainability in industrial environments.

Over the last two decades, most organizations have considered technologies to drive digital transformation, and the recent pandemic has brought significant changes in the healthcare sector. Therefore, this study explores the technological nexus in supporting digital transformation as a process to govern the healthcare sector more effectively.

This study uses quantitative and qualitative methods to analyse the impact of ArogyaSetu (a health and wellness app) on India’s digital transformation process. The study involves 212 responses to understand how the app enables digital transformation and its impact on governance, healthcare systems and stakeholders. Additionally, 31 semi-structured interviews were conducted to validate the quantitative study’s findings.

Referring quantitative part of research design, ArogyaSetu has had a positive impact on the digital transformation of India’s healthcare industry, which has in turn affected stakeholders and improved governance. Moreover, qualitative findings suggest that a governance system like ArogyaSetu can aid in the development of dynamic capabilities within the healthcare system and governance.

This study adds to our understanding of the digital transformation of healthcare by examining it through the lens of dynamic capability. In this framework, “sense” refers to the stakeholders, “seize” the healthcare system and “transform” governance. The study also provides practical implications for managers, academics and government administrators responsible for digital healthcare transformation.

As school districts evolve in their ability to actively support schools and educators, they must simultaneously contend with external policies that create additional demands on time and resources. This includes accountability policies aimed at increasing district and school capacity. This study uses Malen and Rice’s (2004) dual dimensions of capacity building to look at how district and charter leaders responded to the demands of Michigan’s Partnership Model, a district-based approach to school turnaround, focusing on how they tried to build capacity in response to the policy and whether and why these capacity building approaches were perceived as productive.

Semi-structured interviews were conducted with 22 out of 29 Partnership leaders between October 2019 to March 2020 in the second year of policy implementation. Data were analyzed using a combination of index-coding and thematic analysis.

Most leaders perceived the resources associated with the reform as useful, but the productivity of capacity building efforts was limited because some resources were not adequately matched to what they perceived as a core problem: the recruitment and retention of teachers. Engagement with the reform resulted in building informational and social capital because it fostered collaboration and continuous improvement processes, but leaders perceived technical partnerships as more productive than community partnerships.

Turnaround reforms like the Partnership Model that increase resources for districts and schools likely offer a better chance at success than those that simply focus on accountability threats without accompanying support because they give leaders new opportunities to coordinate and align resources, processes and ideas.