Proactive Management Research Articles

Digital monitoring of heavy equipment: advancing cost optimization and operational efficiency

The monitoring of heavy equipment using digital technologies has become a crucial solution for cost optimization and improving operational efficiency in essential sectors such as construction, mining, and infrastructure. The integration of technologies such as advanced sensors, the Internet of Things (IoT), Big Data, and Artificial Intelligence (AI) enables proactive management of machines, allowing for early failure detection, real-time monitoring, and the adoption of predictive maintenance. These advancements result in significant reductions in downtime, extend the lifespan of equipment, and minimize emergency repair costs. The research by Khan et al. (2022), Ma et al. (2020), and Rohith et al. (2023) demonstrates how technological innovations such as AI, ML, BIM, and GIS are transforming maintenance strategies. These tools not only optimize machine performance but also provide intelligent, data-driven decision support, creating a more efficient and sustainable environment. The implementation of these technologies allows for the integration of new automated systems, which can revolutionize equipment management in various sectors. However, the adoption of these technologies also faces challenges, as indicated by Dagsa et al. (2022) and Shekari and Ray (2024), who highlight the limitations of monitoring systems and potential contractual disputes. While the benefits are evident, the excess of data and contractual complications require managers to adopt balanced solutions to ensure that risks are minimized while benefits are maximized. In summary, digital technologies in heavy equipment monitoring represent a significant advancement, offering a clear path toward long-term sustainability and operational efficiency.

Associations Between Diabetic Retinopathy and Frailty: Insights From the National Health and Nutrition Examination Survey and Mendelian Randomization.

To elucidate the relationship between diabetic retinopathy (DR) and frailty and investigate genetic correlations and causality. We analyzed data from the US National Health and Nutrition Examination Survey, which included 1003 individuals with diabetes. DR was evaluated via nonmydriatic retinal imaging, and frailty was measured using a 49-item frailty index. The association between DR and frailty was assessed using survey-weighted logistics regression adjusted for multiple covariates, including age, sex, race, education level, family income-to-poverty ratio, marital status, and Healthy Eating Index. Genetic correlations and causal relationships were investigated through linkage disequilibrium score regression and bidirectional Mendelian randomization. DR was significantly associated with higher odds of frailty after full adjustment (odd ratio [OR] = 4.25; 95% confidence interval [CI], 1.08-16.67; P = 0.040). The association was robust and did not significantly differ across age (P interaction = 0.080), sex (P interaction = 0.216), or race (P interaction = 0.749) groups. DR exhibited a moderate but significant genetic correlation with frailty (rg = 0.27, standard error = 0.04; P = 2.43 × 10-10). Genetically inferred DR was significantly associated with a greater frailty index (β = 0.03; 95% CI, 0.01-0.05; P < 0.001), whereas frailty was not associated with DR risk (OR = 1.20; 95% CI, 0.80-1.81; P = 0.376). Our findings suggest that DR is associated with an increased risk of frailty, indicating that DR not only impairs vision but also accelerates physical decline. This study highlights the critical need for integrated care approaches that incorporate frailty screening and proactive management in individuals with DR to prevent further health deterioration and improve both quality of life and long-term outcomes.

Modeling smart rejuvenation on a series system with different failure modes

Abstract Software rejuvenation is a proactive fault management technique that is used to counteract aging phenomena in continuously running software systems. To mitigate such phenomena, rejuvenation includes preventive periodic stoppage of the running software, cleaning its internal state by garbage collection, flushing operating system kernel tables, defragmentation and reinitialization of internal data structures, and then restarting it. In this paper, a two-unit series software system is considered which can experience different failure modes. Each software component can experience both soft and hard failures. A hard failure is counteracted by a hardware reboot, though a soft failure is recovered by software rejuvenation. Additionally, rejuvenation is proactively initiated when a software component transitions into a degraded, failure-prone state. This paper introduces the innovative concept of smart rejuvenation, which strategically leverages system downtime caused by a hard failure in one component to simultaneously rejuvenate another component. To model the entire system’s evolution in time, a semi-Markov process is used. The aim of this work is twofold: firstly, to distinguish the rejuvenation policy for each software component that optimizes the entire system availability and operational cost, and secondly to examine if smart rejuvenation can improve these measures for the software system.

Exploring Corporate Capital Structure and Overleveraging in the Pharmaceutical Industry

This paper applies an empirical model of corporate capital structure, optimal debt, and overleveraging to estimate overleveraging measured as the difference between actual and optimal debt. Estimated using a sample of the twenty largest pharmaceutical firms, covering the time span from 2000 to 2018, the model sheds light on an industry-specific default risk. The analysis presented in this paper reveals a concerning trend in the pharmaceutical industry, with corporate excess debt steadily increasing over the past two decades, particularly peaking during the 2008 crisis and after 2013. These findings underscore the critical role of excess debt in exacerbating financial instability and highlight the pharmaceutical sector’s unique challenges, including high R&amp;D intensity and regulatory pressures. By quantifying overleveraging and linking it to financial risk, the paper offers valuable policy implications, emphasizing the need for proactive management of optimal debt levels to mitigate default risks and enhance macroeconomic resilience.

How eHealth use and cancer information-seeking influence older adults' acceptance of genetic testing: Mediating roles of PIGI and cancer worry.

This study aims to investigate how eHealth use and information-seeking behavior affect older adults' acceptance of genetic testing, focusing on their participation in genetic tests and their willingness to adopt lifestyle changes based on test results. The research highlights the mediating roles of the perceived importance of genetic information (PIGI) and cancer worry. This cross-sectional study used secondary data from the Health Information National Trends Survey (HINTS 5, Cycle 4), conducted in 2020. The analysis included 1852 adults aged 60 and above. Two mediation models were tested using SPSS 25. Model 1 examined the relationship between eHealth use, perceived importance of genetic information (PIGI), and genetic test behavior. Model 2 analyzed how cancer information-seeking influences willingness to change lifestyle behavior (WCLB) based on genetic test results, with cancer worry as a mediator. Both models showed statistically significant mediation effects (p < 0.05). Model 1 found that eHealth use positively influences genetic test behavior through PIGI. Model 2 revealed that cancer information-seeking indirectly impacts willingness to change lifestyle behavior (WCLB) based on the genetic test results via cancer worry, confirming a full mediation effect. Additionally, among demographic variables, educational level was the strongest predictor of genetic test behavior, while gender significantly predicted WCLB, with older female adults showing higher intentions to change their lifestyle based on genetic test results than males. The study highlights the pivotal roles of PIGI and cancer worry in shaping older adults' acceptance of genetic testing, encompassing both performing genetic test behaviors and adopting lifestyle changes based on test results. These findings offer actionable insights for designing targeted health communication strategies and interventions to enhance genetic testing uptake and foster proactive health management among older populations.

Integrating Process Safety and Process Security Risk Management: Practitioner Insights for a Resilience-Oriented Framework

Integrating process safety and process security risk management is increasingly essential for enhancing resilience in the chemical process industry. This study addresses how practitioners perceive the integration of these two domains, identifying key benefits, barriers, and strategies for effective implementation. A mixed-methods approach was applied, combining quantitative survey data from 47 industry professionals with qualitative insights from open-ended responses. The findings highlight significant advantages of integration, such as optimized resource use, reduced operational redundancies, and improved risk management. However, barriers such as knowledge gaps, resource constraints, and communication silos were identified. Respondents emphasized the importance of adopting a resilience-oriented approach involving proactive risk management, continuous improvement, and adaptability in both safety and security practices. Critical enablers for integration include strong leadership, alignment of societal values, cross-disciplinary training, and integrated risk assessment methodologies. Emerging technologies and regulatory alignment were also identified as critical factors in facilitating integration. The study contributes to the theoretical understanding of integrated risk management by supporting resilience engineering and systems theory. It offers actionable strategies for overcoming barriers and leveraging enablers, laying the groundwork for developing a resilience-oriented framework for process safety and process security risk management.

Advanced Deep Learning Approaches for Forecasting High-Resolution Fire Weather Index (FWI) over CONUS: Integration of GNN-LSTM, GNN-TCNN, and GNN-DeepAR

Wildfires in the United States have increased in frequency and severity over recent decades, driven by climate change, altered weather patterns, and accumulated flammable materials. Accurately forecasting the Fire Weather Index (FWI) is crucial for mitigating wildfire risks and protecting ecosystems, human health, and infrastructure. This study analyzed FWI trends across the Continental United States (CONUS) from 2014 to 2023, using meteorological data from the gridMET dataset. Key variables, including temperature, relative humidity, wind speed, and precipitation, were utilized to calculate the FWI at a fine spatial resolution of 4 km, ensuring the precise identification of wildfire-prone areas. Based on this, our study developed a hybrid modeling framework to forecast FWI over a 14-day horizon, integrating Graph Neural Networks (GNNs) with Temporal Convolutional Neural Networks (TCNNs), Long Short-Term Memory (LSTM), and Deep Autoregressive Networks (DeepAR). The models were evaluated using the Index of Agreement (IOA) and root mean squared error (RMSE). The results revealed that the Southwest and West regions of CONUS consistently exhibited the highest mean FWI values, with the summer months demonstrating the greatest variability across all climatic zones. In terms of model performance on forecasting, Day 1 results highlighted the superior performance of the GNN-TCNN model, achieving an IOA of 0.95 and an RMSE of 1.21, compared to the GNN-LSTM (IOA: 0.93, RMSE: 1.25) and GNN-DeepAR (IOA: 0.92, RMSE: 1.30). On average, across all 14 days, the GNN-TCNN outperformed others with a mean IOA of 0.885 and an RMSE of 1.325, followed by the GNN-LSTM (IOA: 0.852, RMSE: 1.590) and GNN-DeepAR (IOA: 0.8225, RMSE: 1.755). The GNN-TCNN demonstrated robust accuracy across short-term (days 1–7) and long-term (days 8–14) forecasts. This study advances wildfire risk assessment by combining descriptive analysis with hybrid modeling, offering a scalable and robust framework for FWI forecasting and proactive wildfire management amidst a changing climate.

Investigating challenges in Agile software development: a cross-country comparative analysis

Agile software development has been a significant methodology in software engineering for over two decades, offering enhanced adaptability to software requirement changes, improved delivery, and better quality. However, prevalent misunderstandings in Agile implementation have limited its benefits. Hence, this study investigates the challenges faced during Agile implementation. Initial analysis was carried out from cases in Indonesia, a leading country in the software industry within the Asia-Pacific region. Using Kitchenham’s systematic literature review (SLR) methodology, fourteen distinct obstacles were identified from a database of research reports on Agile software development in Indonesia. Subsequent interviews with Agile experts in the country were conducted to validate the SLR findings. The results emphasize the critical need for a top-down strategic approach, the active participation of senior management, and the essential roles of competent scrum masters or Agile coaches. A comparative analysis with reports from other developing countries Saudi Arabia, India, Malaysia, Brazil and developed countries the UK, Belgium, Singapore, and the USA reveals common challenges. They highlight the imperative for proactive upper management leadership to steer successful Agile adoptions, particularly in organizations with entrenched top-down practices and complex hierarchical systems.

Trajectory Analysis in FBG and the Incidence of Chronic Kidney Disease: A Nationwide Population-Based Study

Objectives: This study aimed to classify fasting blood glucose (FBG) trajectories by sex and examine their associations with the risk of chronic kidney disease (CKD). Methods: Using data from the National Health Insurance Service-National Sample Cohort in Korea, participants aged 40 years and above, without CKD or diabetes mellitus (DM), were followed from 2002 to 2009. Based on their FBG trajectories, participants were categorized into two classes and stratified by sex. CKD incidence rates were analyzed according to these FBG trajectories, and the impact of additional risk factors on CKD incidence was assessed. Results: A total of 91,131 participants were analyzed. Among individuals classified in Class 1, FBG levels gradually increased from 90.7 (men) and 88.7 (women) in 2002 to 96.6 (men) and 93.2 (women) in 2009. In contrast, participants classified as Class 2 exhibited a rapid increase in FBG levels, rising from 106 (men) and 106 (women) in 2002 to 144 (men) and 132 (women) in 2009. The incidence of CKD increased over time in both men and women classified as Class 2 compared to Class 1, with respective hazard ratios (HR) of 1.35 for men and 1.53 for women. Additionally, increased age, hypertension, and body mass index (BMI) were independently associated with an elevated risk of CKD. Conclusions: The Class 2 group demonstrated a significantly higher incidence of CKD compared to the Class 1 group. This finding indicates the need for the proactive management of individuals with relatively high FBG levels featuring rapid FBG increases in order to mitigate the risk of CKD development.

The Role of Customer Satisfaction in Moderating the Effect of Brand Image and Service Quality on Loyalty

This study examines the factors that influence customer loyalty at Islamic banks in West Java in the face of increasingly fierce competition in the Islamic banking industry. The research focuses on brand image, service quality, and customer satisfaction, highlighting the moderating role of customer satisfaction. The uniqueness of this study lies in the in-depth exploration of how customer satisfaction serves as a moderator that strengthens the relationship between brand image and loyalty in the religious preferences of the people of West Java. Data was collected from Islamic bank customers in West Java using purposive sampling method, with a total of 259 respondents. Data analysis was conducted using Structural Equation Modeling (SEM) with AMOS software version 24. The results showed that brand image, service quality, and customer satisfaction have a positive and significant effect on loyalty. In addition, customer satisfaction moderates the relationship between brand image and loyalty, strengthening customer trust and long-term commitment. This research introduces a new perspective by integrating the religiosity dimension as an intrinsic factor that influences the effectiveness of Islamic banks' strategies in increasing customer loyalty. The practical implication is that Islamic banks should enhance customer experience through specific strategies, such as providing intensive training to improve employees' service skills, using technology to ensure service efficiency and convenience, and developing marketing campaigns that are consistent with Islamic values. In addition, proactive and transparent complaint management can strengthen customer relationships. These measures are important to strengthen brand image, improve service quality, and foster loyalty in the long run.

Coexistence between people and polar bears supports Indigenous knowledge mobilization in wildlife management and research

Polar bears are coming into northern communities more frequently, and human-polar bear conflict is increasing. However, in the community of Churchill, Manitoba, Canada, people live alongside polar bears with high tolerance and reciprocal respect. Through this case study, we explored human-polar bear coexistence in the community through Indigenous voices, documented social-ecological change, and mobilized recommendations as future visions to inform inclusive management and research strategies: elevate Indigenous knowledge, support proactive management and less invasive research, cultivate a culture of coexistence, improve education and safety awareness, and protect polar bears to support tourism. We used community-based participatory research, coproduction of knowledge, hands back, hands forward, and storytelling, mixing methods from the social sciences and Indigenous ways of knowing. Our study revealed coexistence can be a tool to bridge social and ecological knowledge, examine and facilitate wildlife conservation, and promote well-being through applied research on global issues at the local level.

Antecedents of university students' self-leadership: The roles of teacher developmental feedback and student proactive vitality management.

Self-leadership is of significant importance to the growth and development of college students. However, few studies have systematically examined how teachers and college students can work together to cultivate the self-leadership of college students. Based on the social cognitive theory, this paper aims to propose and teste a comprehensive model examining the comprehensive influence effects of teacher developmental feedback, student proactive vitality management, and psychological resilience on student self-leadership. Data from 807 valid respondents were collected via online questionnaire surveys administered at several universities in Southeast China. The validation and robustness testing of the variables were conducted using SPSS version 22.0. The discriminant validity of our measures was assessed using AMOS version 24.0. Finally, the proposed model was tested using Hayes' PROCESS macro in SPSS version 22.0. Both teacher developmental feedback and student proactive vitality management were positively related to student self-leadership. Moreover, psychological resilience mediated the relationships between teacher developmental feedback and student self-leadership, as well as between student proactive vitality management and student self-leadership. Additionally, teacher developmental feedback was more strongly related to student self-leadership when student proactive vitality management was higher. Our research demonstrates the potential of using social cognitive theory to understand the multifaceted drivers of students' self-leadership. Specifically, both teacher developmental feedback and student proactive vitality management contribute to enhancing student self-leadership through psychological resilience.

Clinical predictors for restrictive allograft syndrome: a nested case-control study.

Risk factors for restrictive allograft syndrome (RAS), a severe phenotype of chronic lung allograft dysfunction after lung transplantation, are currently not well known. In this retrospective nested case-control-study, we analyzed 69 RAS patients and 69 matched non-CLAD controls to identify clinical risk factors for RAS. RAS patients demonstrated overall higher blood eosinophils (p=0.02), increased bronchoalveolar eosinophils (p<0.001) and lymphocytes (p=0.03), and a higher incidence of infections, particularly Pseudomonas (p=0.003), invasive fungal disease (p<0.001, mainly Aspergillus species), SARS-CoV-2 (p<0.001) and CMV (p=0.04), compared to non-CLAD controls. Anti-HLA antibodies, especially persistent donor-specific antibodies (p<0.001), especially targeting HLA-DQ and HLA-DR loci, and antibody-mediated rejection (p<0.001), were strongly associated with later RAS. Histopathological lung injury patterns on transbronchial biopsy (p<0.001), and persistent chest CT opacities in absence of pulmonary dysfunction (p<0.001) were identified as early indicators of later RAS. Proactive detection and management of these risk factors could help mitigate future decline in allograft function, and reduce progression to clinical RAS. Future studies should explore early treatment strategies targeting these modifiable factors to preserve allograft function and improve long-term outcomes for lung transplant recipients.

Integrating machine learning models for optimizing ecosystem health assessments through prediction of nitrate-N concentrations in the lower stretch of Ganga River, India.

Nitrate, a highly reactive form of inorganic nitrogen, is commonly found in aquatic environments. Understanding the dynamics of nitrate-N concentration in rivers and its interactions with other water-quality parameters is crucial for effective freshwater ecosystem management. This study uses advanced machine learning models to analyse water quality parameters and predict nitrate-N concentrations in the lower stretch of the Ganga River from the observations of six annual periods (2017 to 2022). The parameters include water temperature, pH, specific conductivity (Sp_Con), dissolved oxygen (DO), nitrate-N, total phosphate (TP), turbidity, biochemical oxygen demand (BOD), silicate, total dissolved solids (TDS), and rainfall. The present study evaluated the predictive performance of five models-Multiple Polynomial Regression (MPR), Generalized Additive Models (GAMs), Decision Tree Regression, Random Forest (RF), and XGBoost (Extreme Gradient Boosting)-using RMSE, MAE, MAPE, NSE and R2 metrics. XGBoost emerged as the top performer, with an RMSE of 0.024, MAE of 0.018, MAPE of 51.805, NSE of 0.855 and R2 of 0.85, explaining 85% of the variance in nitrate-N concentrations. Random Forest also demonstrated strong predictive capability, with an RMSE of 0.028, MAE of 0.021, MAPE of 57.272, NSE of 0.804 and R2 of 0.80. MPR effectively modelled non-linear relationships, explaining 75% of the variance, while Decision Tree Regression and GAMs were less effective, with R2 values of 0.60 and 0.48, respectively. Variables (BOD, pH, Rainfall, water temperature, and total phosphate) were the best predictors of nitrate-N dynamics. Comparative analysis with previous studies confirmed the robustness of XGBoost and Random Forest in environmental data modelling. The findings highlight the importance of advanced machine learning models in accurately predicting water quality parameters and facilitating proactive management strategies.

Impact of positive psychological capital and shared leadership on nurses’ organizational well-being: a descriptive survey study

BackgroundOrganizational well-being is a critical issue that should be addressed within nursing organizations as it boosts the morale and work motivation of its members by enhancing their satisfaction and happiness.AimThis study aimed to evaluate the levels of positive psychological capital, shared leadership, and organizational well-being among nurses, and examine the impact of positive psychological capital and shared leadership on organizational well-being.MethodsA descriptive survey research design was used, involving 177 nurses with at least one year of work experience at highly specialized hospitals in Korea treating patients with severe conditions. Data were collected via an online survey from July 1 to August 31, 2023, using a questionnaire that included items on general characteristics, job-related characteristics, positive psychological capital, shared leadership, and organizational well-being. The collected data were analyzed using the SPSS 29.0 software, employing statistical measures such as frequency, percentage, mean, standard deviation, t-test, ANOVA, Pearson correlation coefficient, and hierarchical regression analysis.ResultsThe participants’ scores were 3.32 ± 0.51 out of 5 for organizational well-being, 4.12 ± 0.58 out of 6 for positive psychological capital, and 3.64 ± 0.59 out of 5 for shared leadership. Factors identified that influenced organizational well-being included positive psychological capital (β = 0.462, p < .001) and shared leadership (β = 0.442, p < .001), which explained 58% of the total variance in the model (F = 48.74, p < .001).ConclusionsTo improve the organizational well-being of nurses, it is essential to develop capacity-building and intervention programs that boost positive psychological capital and shared leadership. Achieving this goal necessitates not only the individual efforts of organizational members but also robust organizational support, engagement, and proactive management.

Predictors of Severe Coughing and Its Impact on Bronchoalveolar Lavage and Transbronchial Lung Biopsy in Patients with Diffuse Lung Disease: Evaluation of Bronchoscopy Safety

Background/Objectives: Bronchoscopy is an invasive procedure, and patient coughing during the examination has been reported to cause significant distress. This study aimed to identify predictors of coughing severity and assess its impact on the procedure during bronchoalveolar lavage (BAL) and transbronchial lung biopsy (TBLB). Methods: We conducted a prospective study involving 119 consecutive patients with diffuse lung disease who underwent BAL and TBLB at Kyorin University Hospital from April 2019 to December 2023. Cough severity was scored on a scale of 0 to 3, with scores of 0–1 considered mild and 2–3 considered severe. Multivariate logistic regression analysis was performed to identify factors associated with severe coughing during the procedure. Results: Severe coughing was significantly associated with Grade 2 or higher bleeding (OR 6.230, 95% CI 2.220–17.400, p &lt; 0.001), fewer TBLB specimens collected (OR 0.708, 95% CI 0.530–0.945, p = 0.019), and pre-procedural dyspnea (OR 2.560, 95% CI 1.110–5.870, p = 0.027). Conclusions: Severe coughing during bronchoscopy is associated with increased bleeding and reduced specimen collection. For patients with pre-procedural dyspnea, proactive cough management may improve procedural safety and outcomes.

Prevalence and gender distribution of mitral annular calcification and associated mitral valve dysfunction across age groups: a retrospective analysis

Abstract Background Mitral Annular Calcification (MAC) is a significant predictor of cardiovascular events and mitral valve dysfunctions (MVD), including mitral regurgitation and mitral stenosis. This study investigates the prevalence of MAC and its severity across different age groups and genders, and examines its association with MVD in a single tertiary care center. Methods A retrospective analysis was conducted on an echocardiography database of 48,912 consecutive patients. The prevalence of MAC, including both any MAC and moderate to severe MAC was assessed. Patients were categorized into different age groups: &amp;lt;55 years, 55-65 years, 66-75 years, and &amp;gt;75 years. The study also analyzed the prevalence of MVD in these patients and examined gender differences. Chi-square tests were used to evaluate statistical significance in gender and age group differences in association with MVD. Results The overall prevalence of any MAC was 12.5% (6,104), while moderate or severe MAC was observed in 2.2% (1,104) of the patients. When stratified by age, the prevalence of any MAC was 3% in patients &amp;lt;55 years, 15.0% in patients 55-65 years, 28.6% in patients 66-75 years, and 40.9% in patients &amp;gt;75 years. The prevalence of moderate or severe MAC followed a similar trend, with 0.2%, 1.9%, 5.5%, and 10.7% in the respective age groups. Gender distribution analysis revealed a significant female predominance among patients with moderate or severe MAC (59.5% vs. 40.5%), p &amp;lt; 0.0001. Patients with moderate or severe mitral annular calcification (MAC) had a more than four-fold increased risk of mitral valve disease (MVD) compared to those with mild MAC (57.9% vs. 13.1%, p &amp;lt; 0.0001).Age group was not a significant factor in MVD prevalence. Conclusion The age-related prevalence of mitral annular calcification (MAC), especially in older women, represents a significant clinical concern given its association with increased risk of mitral valve disease (MVD). This highlights the importance of vigilant MAC screening and proactive MVD management in this population. Future research should focus on identifying modifiable risk factors and developing effective screening strategies to mitigate the progression of MAC and its associated valvular complications.

Global prevalence and moderating factors of malnutrition in colorectal cancer survivors: A meta-analysis.

This meta-analysis aims to estimate the global prevalence of severe, moderate, overall malnutrition and moderating factors of malnutrition in colorectal cancer (CRC) survivors. A comprehensive search was conducted in Embase, CINAHL, Medline-OVID, PubMed, Scopus, and Web of Science from inception to February 8, 2024, without language, region, or publication date restrictions. A generalized linear mixed model and random-effects model were used to examine the pooled prevalence, and moderator analyses were implemented to investigate variations in the pooled prevalence. In 35 studies involving 9,278 colorectal cancer survivors, the global prevalence was 12.10% for severe malnutrition (95% confidence interval (CI): 7.28-16.92; n = 507), 33.13% for moderate malnutrition (95% CI: 28.93-37.34; n: 2,192), and 47.78% for overall malnutrition (95% CI: 41.60-53.96; n: 3,812). Asia showed higher rates of severe malnutrition 16.67% (95% CI: 4.66-28.68, n: 232) and overall malnutrition 53.17% (95% CI: 39.66-66.69, n: 1,913), whereas low-middle income countries demonstrated higher rates of overall malnutrition 67.46% (95% CI: 30.25-100.00, n: 82). Male sex, colon cancer, advanced stage, metastasis, chemotherapy, surgery, adjuvant treatment, smoking, alcohol consumption, hypertension, and diabetes significantly moderated overall malnutrition prevalence. This meta-analysis reports detailed data on the global prevalence of CRC survivors experience malnutrition, highlighting that health-care professionals should consider the identified moderating factors. Addressing malnutrition in CRC survivors is critical, as early and proactive nutritional management can enhance recovery, improve quality of life, and potentially reduce cancer-related complications associated with malnutrition.

Exploring Gendered Perspectives on Personality Traits and Entrepreneurial Performance in Lebanon during the COVID-19 Crisis

We investigate the impact of gendered personality traits on the entrepreneurial performance of male and female entrepreneurs in Lebanon during the COVID-19 crisis. Using the Big Five personality model and survey data from 500 entrepreneurs, the research examines how traits such as agreeableness, conscientiousness, neuroticism, extraversion, and openness to experience influence business outcomes in high-pressure environments. The findings reveal that agreeableness and neuroticism benefit female entrepreneurs more than males during crises, while conscientiousness significantly deteriorates the financial performance of female entrepreneurs. Extraversion and openness exhibit no differential effects on financial performance by gender. By examining the interplay between gendered personality traits and entrepreneurial performance within the unique context of Lebanon during the COVID-19 pandemic, this study contributes novel insights to the literature. It challenges traditional views on gendered advantages in entrepreneurship, particularly by highlighting the underexplored positive implications of neuroticism and the nuanced role of agreeableness. These findings provide actionable insights for policymakers and support organisations. Specifically, fostering relational skills such as agreeableness and leveraging neuroticism’s sensitivity for proactive crisis management can enhance entrepreneurial resilience. Additionally, training programmes aimed at addressing the rigidity associated with conscientiousness can help entrepreneurs adapt more effectively to volatile environments. By tailoring interventions to gender-specific personality dynamics, these insights can strengthen entrepreneurial ecosystems and improve resilience in times of uncertainty.

High metabolic dysfunction-associated steatotic liver disease prevalence in type 2 diabetes: Urgent need for integrated screening and lifestyle intervention.

This letter discusses the recent study by Mukherjee et al, which identifies a significant prevalence of metabolic dysfunction-associated steatotic liver disease (MASLD) among newly diagnosed type 2 diabetes mellitus (T2DM) patients in Bihar, India, and underscores the pressing need for integrated MASLD management within T2DM care. With 72.3% of the study cohort affected by MASLD, implementing routine liver function tests and ultrasound screenings is recommended as a standard practice in diabetes care, especially in regions with high prevalence rates. The study also advocates for dietary and lifestyle modifications, particularly the reduction of saturated fats, to slow MASLD progression. Patient education on monitoring body mass index and waist circumference, coupled with the integration of these metrics into digital health records, could enhance patient involvement and support proactive health management. Moreover, the letter emphasizes the advantages of developing a region-specific MASLD risk model that incorporates local dietary patterns and socioeconomic factors. Continued research into genetic and environmental determinants of MASLD remains essential for advancing our understanding of its etiology and informing targeted public health strategies.