Critical Strategy Research Articles

Knowledge and practice of early gastric cancer screening among adults aged ≥ 45 years in China: a cross-sectional study

BackgroundAs the incidence of gastric cancer increases sharply in adults aged over 45 years, a better understanding of gastric cancer screening knowledge and practice is crucial to promote cancer-screening services. This study aimed to evaluate knowledge of early gastric cancer screening, adherence to screening, and perceived barriers hindering screening practices among adults aged ≥ 45 years in China.MethodsA multi-center, face-to-face, cross-sectional study was conducted in community sites in Shijiazhuang, China, through the distribution of structured questionnaires from August to September, 2022.ResultsOf the 1053 respondents, only 13.4% demonstrated a good understanding of early gastric cancer screening. While 64.0% knew that gastroscopy is the gold standard for screening (“how to screen”), only 19.9% were aware of the recommended starting age (“when to screen”). Moreover, less than half could correctly identify high-risk groups (“whom to screen”), with awareness ranging from 20.5% for those infected with H. pylori to 47.8% for those with gastric diseases. Independent factors related to higher screening knowledge included female sex (OR = 1.55, 95% CI = 1.01–2.38), higher education level (OR = 4.03, 95% CI = 2.68–6.06), being with a personal/family experience of gastric diseases (OR = 1.68, 95% CI = 1.12–2.52). In addition, only 23.4% of respondents underwent GC screening. The dominant barrier to early screening was the “absence of symptoms or signs”, followed by “fearing procedural discomfort”.ConclusionThis study highlights significant gaps in early gastric cancer screening knowledge and participation among middle-aged and elderly individuals in China. Addressing these gaps through culturally tailored health education campaigns is a critical strategy for increasing public awareness and participation.

Predictive biomarkers for severity of respiratory syndrome: the role of eGFR and hematological parameters in ICU patients

Introduction: Studying respiratory syndromes like COVID-19 is crucial for understanding viral-induced inflammatory responses and their impact on organ function. Insights from these studies are essential for managing current and future pandemics and improving patient outcomes in respiratory health crises. This study investigates the behavior of the estimated glomerular filtration rate (eGFR) and its correlation with hematological parameters in ICU patients with COVID-19. Objective: To assess eGFR behavior and its correlation with hematological parameters in respiratory syndromes ICU patients. Materials and Methods: 217 patient records were analyzed, with 144 discharged and 73 deaths. Patients were divided into discharge and death groups and laboratory parameters such as glomerular filtration rate and blood count were.evaluated at admission and outcome. Results and Discussion: There was a significant reduction in eGFR among patients in the death group at both admission and outcome. Significant correlations were observed between eGFR and hemoglobin (Hb), total neutrophils (N), and platelets (PLT) at the outcome. These findings suggest that the reduction in eGFR is a predictor of poor prognosis in COVID-19 ICU patients. Additionally, the significant correlations between eGFR and hematological parameters highlight their role in the inflammatory process and clinical deterioration. Conclusion: The reduction of eGFR is a significant predictor of poor prognosis in ICU patients with COVID-19. Significant correlations between eGFR and hematological parameters underscore their importance in the inflammatory process and disease progression. Studying respiratory syndromes like COVID-19 is crucial for understanding viral-induced inflammatory responses and improving critical care strategies for future respiratory health crises.

Dynamic pricing and demand forecasting: Integrating time-series analysis, regression models, machine learning, and competitive analysis

Abstract. Dynamic pricing is a critical strategy for businesses seeking to optimize revenue and stay competitive in fluctuating markets. This paper explores the integration of various demand forecasting techniques, including time-series analysis, regression models, and machine learning algorithms, with competitive analysis methodologies to enhance dynamic pricing strategies. Time-series analysis focuses on decomposing data into trend, seasonality, and random fluctuations, using ARIMA models for accurate demand prediction. Regression models delve into the complexities of variable interactions, extending beyond linear relationships to include advanced techniques like Ridge, Lasso, and Elastic Net regression. Machine learning algorithms, such as decision trees, random forests, gradient boosting, and neural networks, revolutionize demand forecasting by uncovering complex patterns in large datasets. Competitive analysis incorporates market scanning, price elasticity estimation, and competitor behavior modeling to inform dynamic pricing decisions. Optimization algorithms, including linear programming, genetic algorithms, and simulated annealing, are employed to refine pricing strategies, while revenue management techniques like yield management and overbooking ensure maximum revenue from perishable goods and services. This comprehensive approach enables businesses to dynamically adjust prices in real-time, maintaining competitiveness and maximizing profitability in an ever-evolving market landscape.

AIOps and DevOps : Catalysts of Digital Transformation in the Age of Automated Operations

This article examines the pivotal roles of Artificial Intelligence for IT Operations (AIOps) and Development Operations (DevOps) as key drivers of digital transformation in contemporary organizations. As businesses increasingly rely on complex, distributed IT systems, the integration of AIOps and DevOps emerges as a critical strategy for maintaining operational efficiency, scalability, and innovation. Through a comprehensive analysis of current literature and industry case studies, we explore how these technologies are reshaping the landscape of IT operations, accelerating software delivery cycles, and enabling more resilient and adaptive infrastructures. The article delves into the evolving nature of release engineering, where automation and machine learning are becoming central to ensuring agile and robust IT systems. Furthermore, we critically assess the ethical implications of automated decision-making in operations, including issues of transparency, bias, and job displacement. Our findings suggest that while AIOps and DevOps offer significant benefits in terms of operational optimization and cost reduction, their successful implementation requires careful consideration of organizational culture, skill development, and ethical guidelines. This article contributes to the growing body of knowledge on digital transformation strategies and provides insights for practitioners navigating the complex intersection of artificial intelligence, automation, and IT operations.

Critical construction waste minimization strategies for a circular economy in developing countries: A contractor’s perspective in China

AbstractDeveloping countries are often burdened by substantial construction waste (CW) generated through urbanization and urban renewal activities, highlighting the urgent need for effective CW minimization strategies to facilitate their transition towards a circular economy. Although previous studies have examined similar topics at various stages of construction projects from different perspectives, a comprehensive study integrating all critical stages from a contractor’s perspective is still lacking. To fill this gap, this study aims to identify critical CW minimization strategies in developing countries, with a holistic concentration on the planning, design, and construction stages, using China as a case study. æThe research began by compiling a comprehensive list of CW minimization strategies tailored to developing countries, based on an extensive desktop survey and a focus group interview, resulting in 32 strategies. A subsequent questionnaire survey with leading CW management experts and rigorous statistical analyses have identified 9 strategies as critical for minimizing CW in developing countries. Finally, through exploratory factor analysis, seven fundamental principles for CW minimization have been established: “Planning for CW Minimization” for the planning stage; “Optimized Design of Building Structures,” “Optimization of Design Process,” and “Stakeholders’ Efforts in the Design Stage” for the design stage; and “Optimization of Construction Techniques,” “Stakeholders’ Efforts in the Construction Stage,” and “Efforts on CW Disposal” for the construction stage. This study offers valuable insights for stakeholders in developing countries, empowering them to effectively minimize CW through targeted strategies, facilitating the transition to a circular economy and supporting the realization of the "zero-waste city" goal.

Different mechanisms of two oil-soluble additives to reduce heavy crude oil viscosity

The depletion of light crude oil resources necessitates the efficient development of heavy crude oil, characterized by high density and viscosity. In this work, we used molecular dynamics (MD) simulations to investigate the mechanisms by which two oil-soluble viscosity reducers (VRs) reduce the viscosity of heavy crude oil. Our results reveal that direct interaction between VRs and asphaltene (ASP) aggregates is not the sole pathway for viscosity reduction. Instead, targeting resin (RES) emerges as a critical strategy. The first kind of VR (VR1), with its polar acrylamide head, binds effectively to ASP, reducing the exposure of ASP to other heavy oil components and thereby decreasing the friction between ASP and these components. In contrast, the second kind of VR (VR2), with a maleic anhydride head, tends to disperse away from ASP aggregates, reducing viscosity by weakening the intermolecular interactions between RES and lighter components. These findings elucidate distinct mechanisms by which VRs mitigate the viscosity of heavy crude oil and provide insights for the design of more effective oil-soluble VRs for future heavy oil exploitation.

Empowering Global Health Systems to Protect, Promote and Support Optimal Breastfeeding.

Breastfeeding is a critical public health strategy for optimal child development and maternal health across the life course. The 2021 Global Burden of Diseases, Injuries and Risk Factors Study reveals that, despite reductions in mortality and disability-adjusted life years (DALYs) attributed to suboptimal breastfeeding practices-namely, non-exclusive breastfeeding during the first 6 months or early discontinuation within the first 2 years-low- and middle-income countries (LMICs) continue to bear a staggering 50-fold higher burden compared to high-income nations. This inequity signals a pressing global health priority. Hence, we propose to address these challenges by first, expanding the Baby-Friendly Hospital Initiative (BFHI) reach through community-driven efforts such as the Baby-Friendly Community Initiative (BFCI) to enhance breastfeeding support in both clinical and community settings; second, embedding breastfeeding counselling within national health and social protection programmes to fill in gaps in culturally competent care, drawing on large scale breastfeeding peer counselling programme experiences like the one being implemented by the US Supplemental Nutrition Program for Women, Infants and Children (WIC); third, improving maternity leave policies and workplace accommodations for breastfeeding women and fourth, stringent regulation of exploitative commercial milk formula (CMF) marketing to combat misinformation and reduce health inequities. Governments must implement strong, evidence-driven policies-such as strict monitoring and regulation of product labelling and digital media marketing-to establish safeguards against the powerful influence of the CMF industry. Collectively, these strategies will enhance breastfeeding outcomes, reduce health disparities and drive progress across countries towards meeting the UN Sustainable Development Goals.

Integrated bagging-RF learning model for diabetes diagnosis in middle-aged and elderly population

As the population ages, the increase in the number of middle-aged and older adults with diabetes poses new challenges to the allocation of resources in the healthcare system. Developing accurate diabetes prediction models is a critical public health strategy to improve the efficient use of healthcare resources and ensure timely and effective treatment. In order to improve the identification of diabetes in middle-aged and older patients, a Bagging-RF model is proposed. In the study, two diabetes datasets on Kaggle were first preprocessed, including unique heat coding, outlier removal, and age screening, after which the data were categorized into three age groups, 50–60, 60–70, and 70–80, and balanced using the SMOTE technique. Then, the machine learning classifiers were trained using the Bagging-RF integrated model with eight other machine learning classifiers. Finally, the model’s performance was evaluated by accuracy, F1 score, and other metrics. The results showed that the Bagging-RF model outperformed the other eight machine learning classifiers, exhibiting 97.35%, 95.55%, 95.14% accuracy and 97.35%, 97.35%, 95.14% F1 Score at the Diabetes Prediction Dataset for diabetes prediction for the three age groups of 50–60, 60–70, and 70–80; and 97.03%, 94.90%, 93.70% accuracy and 97.03%, 94.90%, 93.70% F1 Score at the Diabetes Prediction Dataset. 95.55%, 95.13% F1 Score; and 97.03%, 94.90%, 93.70% accuracy; and 97.03%, 94.89%, 93.70% F1 Score at Diabetes Prediction Dataset. In addition, while other integrated learning models, such as ET, RF, Adaboost, and XGB, fail to outperform Bagging-RF, they also show excellent performance.

A Theoretical Review on Non-Formal Education and Women Empowerment in Bhutan

The exponential growth of the formal education system, albeit literacy, has remained a far-fetched ideology for many Bhutanese, especially among adult women and men in the less-sourced parts of the country. Therefore, non-formal education in Bhutan has been a critical and essential strategy for inclusion and empowerment of a sizeable population of citizens who missed the opportunity to acquire literacy due to several factors; including social, economic, and occupational circumstances at the time of their regular schooling. The non-formal education program, conducted through a somewhat structured network of learning centers across the country, has grown in relevance and popularity over the years. It is viewed as a significant enabler of learning, surpassing its original goal of promoting the national language, Dzongkha. The non-formal education programme has emerged as a veritable tool for self-realization and participation in local governance, social economic activities for the women in an increased space created by the advent of democracy. Thus, non-formal education has proven to be beneficial not only in enhancing literacy and numeracy but also as a crucial means of conveying important messages regarding issues that impact people's lives and broader societal concerns. We were able to delve into extensive research carried out by other researchers as our mode of collecting data for analysis. Our findings culminated to the fact that, till date the Non-formal Education (NFE) and Continuous Education (CE)programme have benefitted about 170,000 and 11,000 learners respectively. Numerous success stories illustrate how the program has transformed the lives of individuals, particularly in rural Bhutan. The programme helped to read and write, take better care of family, do simple calculations and helped toestablish small businesses.However, the drop-out numbers from the programme are significant and the reasons cited range from closure of centers following the departure of the instructor, competing claims of housework, distance to learning centers, shortage of helping hands, to perceptions of gender roles.Recommendations have been put forward to ensure that the programme is beneficial to the society, is viable and experiences longevity.

Responsible Email Marketing in Higher Education

In higher education, responsible email marketing emerges as a critical strategy for engaging students, alums, and potential students meaningfully and ethically. By comprehensively examining best practices and regulatory compliance, we aim to provide educational institutions with actionable insights for cultivating trust and enhancing communication effectiveness in their email marketing campaigns. This paper explores the nuanced landscape of responsible email marketing within higher education, focusing on balancing effective communication and ethical considerations. The paper aims to identify the starting points for creating a responsible email marketing campaign in higher education. Sources such as Marketing Compare, OmniUpdate, IBM Marketing Cloud and CoSchedule were used for secondary research. The results were formulated based on data published in surveys by RNL, Emma, Oberlo, LeadSquared, Full Fabric, Barnes and Noble, and WordStream. The following scientific research methods were used to create individual parts of the paper: extraction, information collection and processing, comparison, analysis, synthesis and interpretation. By examining current practices, regulatory frameworks, and case studies, we identify strategies institutions can employ to respectfully and effectively engage their audiences. Our analysis highlights the importance of transparency, consent, and personalisation in fostering trust and building long-term relationships with students, alums, and prospective learners.

Harnessing the Unconventional Cubic Phase in 2D LaNiO3 Perovskite for Highly Efficient Urea Oxidation

AbstractPhase engineering is a critical strategy in electrocatalysis, as it allows for the modulation of electronic, geometric, and chemical properties to directly influence the catalytic performance. Despite its potential, phase engineering remains particularly challenging in thermodynamically stable perovskites, especially in a 2D structure constraint. Herein, we report phase engineering in 2D LaNiO3 perovskite using the strongly non‐equilibrium microwave shock method. This approach enables the synthesis of conventional hexagonal and unconventional trigonal and cubic phases in LaNiO3 by inducing selective phase transitions at designed temperatures, followed by rapid quenching to allow precise phase control while preserving the 2D porous structure. These phase transitions induce structural distortions in the [LaO]+ layers and the hybridization between Ni 3d and O 2p states, modifying local charge distribution and enhancing electron transport during the six‐electron urea oxidation process (UOR). The cubic LaNiO3 offers optimal electron transport and active site accessibility due to its high structural symmetry and open interlayer spacing, resulting in a low onset potential of 1.27 V and a Tafel slope of 33.1 mV dec−1 for UOR, outperforming most current catalysts. Our strategy features high designability in phase engineering, enabling various electrocatalysts to harness the power of unconventional phases.

Harnessing the Unconventional Cubic Phase in 2D LaNiO3 Perovskite for Highly Efficient Urea Oxidation

AbstractPhase engineering is a critical strategy in electrocatalysis, as it allows for the modulation of electronic, geometric, and chemical properties to directly influence the catalytic performance. Despite its potential, phase engineering remains particularly challenging in thermodynamically stable perovskites, especially in a 2D structure constraint. Herein, we report phase engineering in 2D LaNiO3 perovskite using the strongly non‐equilibrium microwave shock method. This approach enables the synthesis of conventional hexagonal and unconventional trigonal and cubic phases in LaNiO3 by inducing selective phase transitions at designed temperatures, followed by rapid quenching to allow precise phase control while preserving the 2D porous structure. These phase transitions induce structural distortions in the [LaO]+ layers and the hybridization between Ni 3d and O 2p states, modifying local charge distribution and enhancing electron transport during the six‐electron urea oxidation process (UOR). The cubic LaNiO3 offers optimal electron transport and active site accessibility due to its high structural symmetry and open interlayer spacing, resulting in a low onset potential of 1.27 V and a Tafel slope of 33.1 mV dec−1 for UOR, outperforming most current catalysts. Our strategy features high designability in phase engineering, enabling various electrocatalysts to harness the power of unconventional phases.

Integrating FinOps in healthcare for optimized financial efficiency and enhanced care

The integration of Financial Operations (FinOps) in healthcare is increasingly recognized as a critical strategy for optimizing financial efficiency and enhancing patient care. This paper explores the principles and practices of FinOps, emphasizing its role in improving cost management, fostering financial transparency, and ensuring better resource allocation in healthcare organizations. By addressing traditional inefficiencies in healthcare financial management, FinOps promotes collaboration between finance and operations, enabling real-time financial decision-making that aligns with both operational goals and patient outcomes. Additionally, the paper outlines the challenges healthcare organizations face in managing rising costs and complex billing systems, and provides practical strategies for the successful implementation of FinOps, including the use of advanced technologies such as cloud-based platforms and data analytics tools. Ultimately, the adoption of FinOps not only enhances short-term financial efficiency but also contributes to the long-term sustainability and scalability of healthcare services.

Effective Machine Learning Solution for State Classification and Productivity Identification: Case of Pneumatic Pressing Machine

The fourth industrial revolution (Industry 4.0) brought significant changes in manufacturing, driven by technologies like artificial intelligence (AI), Internet of Things (IoT), 5G, robotics, and big data analytics. For industries to remain competitive, the primary goals must be the improvement of the efficiency and safety of machinery, the reduction of production costs, and the enhancement of product quality. Predictive maintenance (PdM) utilizes historical data and AI models to diagnose equipment’s health and predict the remaining useful life (RUL), providing critical insights for machinery effectiveness and product manufacturing. This prediction is a critical strategy to maximize the useful life of equipment, especially in large-scale and important infostructures. This study focuses on developing an unsupervised machine state-classification solution utilizing real-world industrial measurements collected from a pneumatic pressing machine. Unsupervised machine learning (ML) models were tested to diagnose and output the working state of the pressing machine at each given point (offline, idle, pressing, defective). Our research contributes to extracting valuable insights regarding real-world industrial settings for PdM and production efficiency using unsupervised ML, promoting operation safety, cost reduction, and productivity enhancement in modern industries.

Illustrating the Anticipate, Recruit, Retain, Adapt, Sustain (ARRAS) Framework for Surge Capacity. How Bangladesh, Sri Lanka, and Nepal Maintained Their Health Workforce During COVID-19.

Surge capacity-the ability to acquire additional workers and resources during unexpected increases in service demand-is often perceived as a luxury. However, the COVID-19 pandemic necessitated an urgent expansion of surge capacity within health systems globally. Health systems in Bangladesh, Nepal, and Sri Lanka managed to scale up their capacities despite severely limited budgets. This study employs a mixed-methods approach, integrating qualitative interviews with quantitative data analysis, to propose a comprehensive framework for understanding Human Resources for Health (HRH) surge capacity from 2018 to 2021, termed ARRAS: Anticipate, Recruit, Retain, Adapt, Sustain. We present national-level data to demonstrate how each country was able to maintain their per capita health care workforce during the crisis. Interviews with key informants from each country reinforce the ARRAS framework. Quantitative data revealed ongoing increases in doctors and nurses pre- and post-pandemic, but no country could rapidly expand its health workforce during the crisis. Qualitative findings highlighted critical strategies such as pre-crisis planning, financial incentives, telemedicine, and re-skilling the workforce. Despite adaptive measures, challenges included inadequate funding, poor data systems, and coordination issues. This study underscores the necessity for robust, long-term strategies to enhance surge capacity and better prepare health systems for future crises.

Association between achieved cholesterol levels and the risk of major adverse cardiovascular events after acute coronary syndromes

Abstract Background European guidelines recommend reducing lipid levels to defined targets to lower cardiovascular risk after acute coronary syndromes (ACS). Purpose This study aims to evaluate the association of achieved and reduced lipid levels one year post-ACS with the risk of subsequent major adverse cardiovascular events (MACE) in a real-world setting. Methods Data from SPUM-ACS, a Swiss multicentric cohort of patients hospitalized with ACS between 2007 and 2021, were used to assess the association between lipid levels (LDL-C, non-HDL-C, HDL-C, triglycerides), relative difference from baseline lipid levels, and lipid targets achievement (LDL-C <1.8 and ≥50% reduction from baseline, non-HDL-C <2.2 and triglycerides <1.7mmol/l) one year after ACS with the risk of MACE (myocardial infarction, stroke, coronary revascularization, and cardiovascular death). The observation period for MACE started after a follow-up visit one year post-ACS, during which lipid levels were measured, extending over a median duration of 5.5 years. Cox proportional hazards models, adjusted age, sex, BMI, smoking status, statin therapy intensity, diabetes, systemic inflammatory disease, and creatinine were used. Predicted adjusted hazard ratios (HR) were represented graphically for continuous exposures. Analyses were repeated with interaction terms for predefined subgroups. Results 2579 ACS survivors (mean age 62 years; 80% male), primarily undergoing percutaneous revascularization, attended a follow-up visit one year after ACS and were followed up for a median of 5.5 years. 98% were prescribed statin therapy (59% high-intensity) at discharge. During the first year post-ACS, median LDL-C decreased from 3.7 to 2.4 mmol/l (-34%, p<0.001), non-HDL-C from 3.9 to 2.6 mmol/l (-32%, p<0.001), and triglycerides from 1.21 to 1.16 mmol/l (-6%, p<0.001); HDL-C increased from 1.1 to 1.2 mmol/l (+6%, p<0.001). 92% of patients were on statin therapy (50% high-intensity) one year after ACS. In log-linear models, one year after ACS, a 1 mmol/l reduction of LDL-C, non-HDL-C, and triglycerides levels to the target was associated with a lower risk of subsequent MACE by 23%, 24%, and 14%, respectively (p values <0.001). A 10% relative reduction from baseline levels of LDL-C, non-HDL-C and triglycerides was associated with a lower risk of MACE by 8%, 9%, and 4%, respectively (p values <0.001). Achieving LDL-C <1.8 mmol/l (21%, HR=0.71 [0.54, 0.93]), LDL-C reduction ≥50% from baseline (26%, HR=0.63 [0.48, 0.84]), non-HDL-C <2.2 mmol/l (30%, HR=0.65 [0.51, 0.83]), and triglycerides <1.7 mmol/l (76%, HR=0.76 [0.62, 0.94]) were all associated with a reduction in the risk of MACE. Neither age, sex, BMI, diabetes, or smoking status affected the results (p for interaction ≥0.05). Conclusion These findings affirm current recommendations and underscore lipid reduction and targets achievement as critical strategies for mitigating residual cardiovascular risk in patients with ACS.

Enhancing Equity in Clinical Research: A Multifaceted Proposal for Spondyloarthritis.

Clinical research advances medical knowledge and improves healthcare outcomes. However, disparities in research participation hinder progress. The Unmet Research Needs in Spondyloarthritis Conference IV highlighted critical insights and strategies to enhance equity in clinical research. Talks focused on engaging underrepresented communities and addressing disparities in rheumatic diseases, particularly spondyloarthritis (SpA), to ensure research results are generalizable and inclusive. Disparities in SpA management, such as greater back pain severity among Black and Hispanic Americans and sex-based differences in pain management, emphasize the need for equitable research. Dr. Elizabeth Ferucci discussed the racial disparities in rheumatologic care, highlighting the importance of early access to rheumatologists and culturally informed primary care to improve outcomes. Dr. Hani El-Gabalawy's talk on engaging Indigenous communities stressed the importance of community consent and reciprocal benefits. Dr. Sarfaraz Hasni's presentation on mitigating disparities in research participation underscored the need for inclusive practices and strategies to promote diverse representation. Finally, Dr. Edith Williams emphasized institutional approaches to fostering equity, including diverse recruitment practices and institutional review board alignment with diversity priorities. Strategies to enhance equity in clinical research include community engagement, addressing logistical barriers to participation, and ensuring diverse research teams. These approaches can dismantle barriers for underrepresented communities, making research more accessible and reflective of the broader population. The SpA research community must commit to creating structures that foster inclusivity, ensuring medical advancements benefit all populations, especially historically underrepresented groups. The principles and strategies proposed serve as a roadmap for achieving equity in SpA research.

Techno-financial analysis of 100 % renewable electricity for the south west region of the UK by 2050

In the UK, the transition to 100 % Renewable Energy Systems is a critical strategy in addressing climate change, aligning with the UK government's 2050 net-zero objective. This study explores the potential for such a transformation within the South West region of the UK by 2050. EnergyPLAN16.2 software was utilised for scenario modelling and the fmincon algorithm in MATLAB for optimisation analysis. The focus was on two scenarios involving hybrid renewable energy systems incorporating onshore wind, solar, offshore wind, and two energy storage systems which are lithium-ion battery energy storage and hydrogen fuel cell energy storage. The optimisation process entailed two stages: determining renewable energy configurations for cost-efficient coverage of annual electricity demand and computing the minimum energy storage capacity to ensure a constant power supply. The results suggest an optimal configuration of 4,460 MW onshore wind, 9,880 MW solar, 6,982 MW offshore wind, and 2,273,662 MWh of lithium-ion battery energy storage for 100 % hybrid renewable energy systems in the South West region of the UK in 2050. This configuration achieves the lowest annual cost of 3208 M£/year, with a balanced wholesale electricity price ranging from 4.3p/kWh to 6.6p/kWh and a levelized cost of energy of 6.44p/kWh. Furthermore, hydrogen fuel cell storage systems can be another alternative, however, the present study showed it is not economical enough compared to lithium-ion battery storage systems. This research broadens the understanding of hybrid renewable energy systems implementation in large regions, offering valuable insights for sustainable energy planning in the South West region of the UK.

Dual Metal Sites Coengineered Graphitic Carbon Nitride as Green Electrocatalyst for Highly Selective Overall Water Splitting—A Sustainable Approach

The development of efficient, low‐cost, non‐noble metal‐oxide‐based nanohybrid materials for overall water splitting is a critical strategy for enhancing clean energy use and addressing environmental issues. In this study, an interfacial engineering strategy for the development of bimetallic Co–Ni nanoparticles on graphitic carbon nitride (g‐C3N4) using ultrasonication followed by coprecipitation is conveyed. These nanoparticles demonstrate high efficacy as bifunctional electrocatalysts for the hydrogen evolution reaction (HER) and oxygen evolution reaction (OER) in alkaline conditions. Co–Ni nanoparticles on graphitic carbon nitride demonstrate an increased surface area via ultrasonication and subsequent coprecipitation. The g‐C3N4 combined with Co–Ni nanoparticles leads to the development of bifunctional catalysts that exhibit significant efficiency in both HER and OER, and their interfacial properties are investigated for the first time. The chemical composition and morphology of g‐C3N4 integrated with Co–Ni nanoparticles significantly influence the modulation of redox‐active sites and the facilitation of electron transfer, resulting in improved splitting efficiency. The interactions between the Co–Ni bimetal and g‐C3N4 demonstrate exceptional electrochemical performance for water splitting. Consequently, the 20% 20–Co–Ni–graphitic carbon nitride electrode demonstrated superior HER performance, comparable to the other electrodes. In the results, it is indicated that an increased molar ratio of Co and Ni incorporated in graphitic carbon nitride significantly improves HER performance.

The relationship between cyberchondria and health literacy among first-year nursing students: the mediating effect of health anxiety

BackgroundFirst-year nursing students are at a critical juncture in their education. They are transitioning from laypersons to healthcare professionals, and students are frequently exposed to medical information in academic settings and through personal research. This exposure can sometimes lead to cyberchondria; improving health literacy and managing health anxiety are critical strategies for reducing the incidence of cyberchondria.AimInvestigate the mediating effect of health anxiety on cyberchondria and health literacy among first-year nursing students.MethodsA cross-sectional correlational research design was used in this study. The study was conducted with 475 students in October 2023 during the first semester of the academic year 2023–2024.ResultsCyberchondria and health literacy levels were both moderate. In addition, the degree of health anxiety among first-year Nursing Students was mild, too. The results showed that the association between Cyberchondria and Health Literacy was partially mediated by health anxiety.Conclusion and implicationsThis study highlights the sophisticated relationship between cyberchondria, health literacy, and anxiety among first-year nursing students. It demonstrates that lower health literacy can lead to increased health anxiety, which in turn exacerbates cyberchondria. To mitigate these issues, it is essential to enhance health literacy and provide support for managing health anxiety within nursing education programs. By doing so, we can help nursing students navigate online health information effectively and reduce unnecessary health-related anxieties, promoting better educational outcomes and overall well-being.