Cost Advantages Research Articles

Role of Health Insurance Policies in Access to Healthcare Services in Bangladesh

Purpose: To aim of the study was to analyze the role of health insurance policies in access to healthcare services in Bangladesh. Methodology: This study adopted a desk methodology. A desk study research design is commonly known as secondary data collection. This is basically collecting data from existing resources preferably because of its low cost advantage as compared to a field research. Our current study looked into already published studies and reports as the data was easily accessed through online journals and libraries. Findings: The role of health insurance policies in Bangladesh is crucial in improving access to healthcare services, particularly for low-income populations. Health insurance has been found to reduce the financial barriers to accessing care, leading to increased utilization of essential health services. However, challenges such as limited coverage, awareness, and administrative inefficiencies persist, hindering the full potential of these policies. Expanding and refining health insurance schemes could significantly enhance healthcare accessibility and reduce out-of-pocket expenditures, ultimately contributing to better health outcomes across the country. Unique Contribution to Theory, Practice and Policy: The health belief model (HBM), the theory of planned behavior (TPB) & Andersen’s behavioral model of health services may be used to anchor future studies on the role of health insurance policies in access to healthcare services in Bangladesh. Health insurance providers should develop targeted outreach and education programs to inform underserved populations about available health insurance options and their benefits. Policymakers should consider implementing universal health insurance coverage mandates to ensure that all individuals, regardless of socioeconomic status, have access to healthcare services.

Impact of Theater Arts Participation on High School Students

Purpose: The aim of the study was to assess the impact of theater arts participation on high school students. Materials and Methods: This study adopted a desk methodology. A desk study research design is commonly known as secondary data collection. This is basically collecting data from existing resources preferably because of its low cost advantage as compared to a field research. Our current study looked into already published studies and reports as the data was easily accessed through online journals and libraries. Findings: Participation in theater arts has been shown to enhance students' academic performance, with improvements in reading comprehension, verbal skills, and overall academic achievement. This is attributed to the discipline, memorization, and comprehension skills that theater activities demand. Additionally, involvement in theater arts fosters critical thinking and creativity, encouraging students to approach problems and situations from multiple perspectives and think outside the box. Beyond academics, theater arts participation positively affects students' social and emotional development. It helps build self-confidence and self-esteem as students perform in front of audiences and receive feedback on their work. Theater also promotes teamwork and collaboration, as students must work closely with their peers to produce successful performances. This collaborative environment helps students develop strong communication skills and a sense of empathy, as they must understand and convey different characters' emotions and motivations. Implications to Theory, Practice and Policy: Constructivist learning theory, social learning theory and arts integration theory may be used to anchor future studies on assessing the impact of theater arts participation on high school students. In practice, schools should actively incorporate theater arts into core academic subjects through interdisciplinary projects that promote creative expression alongside academic learning. From a policy perspective, it is crucial for educational policymakers to prioritize funding for arts education programs, recognizing their significant role in enhancing academic performance.

Effects of Transparent Communication on Employee Trust in Management in Tunisia

Purpose: The aim of the study was to assess the effects of transparent communication on employee trust in management in Tunisia. Materials and Methods: This study adopted a desk methodology. A desk study research design is commonly known as secondary data collection. This is basically collecting data from existing resources preferably because of its low cost advantage as compared to a field research. Our current study looked into already published studies and reports as the data was easily accessed through online journals and libraries. Findings: The study indicated that transparent communication significantly impacts employee trust in management. When management consistently provides clear, accurate, and timely information, it fosters an environment of openness and honesty. Employees are more likely to trust their leaders when they feel informed about organizational changes, goals, and challenges. Transparent communication also helps to reduce uncertainties and rumors, which can lead to anxiety and mistrust. Studies show that when employees trust their management, they exhibit higher levels of engagement, job satisfaction, and commitment to the organization. Moreover, transparency in communication can enhance collaborative efforts and innovation, as employees feel valued and respected, leading to a more positive workplace culture and improved overall organizational performance. Implications to Theory, Practice and Policy: Social exchange theory, organizational justice theory and leader-member exchange theory may be used to anchor future studies on assessing the effects of transparent communication on employee trust in management in Tunisia. From a practical standpoint, organizations should prioritize implementing comprehensive transparent communication strategies to foster employee trust. At the policy level, organizations should institutionalize transparent communication as a core value, embedding it in corporate governance frameworks and organizational policies.

Impact of Social Media on Cultural Identity in Urban Youth

Purpose: The aim of the study was to assess the impact of social media on cultural identity in urban youth. Materials and Methods: This study adopted a desk methodology. A desk study research design is commonly known as secondary data collection. This is basically collecting data from existing resources preferably because of its low cost advantage as compared to a field research. Our current study looked into already published studies and reports as the data was easily accessed through online journals and libraries. Findings: Social media platforms serve as significant spaces where urban youth engage, express, and negotiate their cultural identities. These platforms provide access to a diverse range of cultural expressions, facilitating the blending and reshaping of traditional and modern cultural elements. Studies have shown that social media influences urban youth by offering a sense of community and belonging, where they can connect with like-minded peers globally, thus fostering a globalized cultural perspective. However, this exposure also poses challenges, such as cultural homogenization and the potential loss of distinct local cultural identities. Additionally, the curated nature of social media content often promotes idealized cultural norms, influencing youth to conform to certain stereotypes and societal expectations. Despite these challenges, social media remains a powerful tool for cultural expression and identity formation, enabling urban youth to navigate and redefine their cultural landscapes in dynamic and innovative ways. Implications to Theory, Practice and Policy: Social identity theory, uses and gratifications theory and cultural convergence theory may be used to anchor future studies on assessing the impact of social media on cultural identity in urban youth. Practically, there is a pressing need for digital literacy programs that educate youth on critical engagement with social media. From a policy perspective, integrating cultural education into school curricula is essential.

The Effectiveness of Disaster Management Policies in Reducing the Impact of Natural Disasters in Canada

Purpose: To aim of the study was to analyze the effectiveness of disaster management policies in reducing the impact of natural disasters. Methodology: This study adopted a desk methodology. A desk study research design is commonly known as secondary data collection. This is basically collecting data from existing resources preferably because of its low cost advantage as compared to a field research. Our current study looked into already published studies and reports as the data was easily accessed through online journals and libraries. Findings: The effectiveness of disaster management policies in reducing the impact of natural disasters largely depends on several key factors: early warning systems, community preparedness, infrastructure resilience, and coordinated response efforts. Studies indicate that well-implemented policies that prioritize risk assessment, public education, and resource allocation significantly mitigate the damage and loss of life during natural disasters. Unique Contribution to Theory, Practice and Policy: Systems theory, social capital theory & vulnerability theory may be used to anchor future studies on the effectiveness of disaster management policies in reducing the impact of natural disasters. Implementing practices that strengthen social capital within communities is essential. Policies should be designed to address the specific vulnerabilities of different population groups. This includes tailoring disaster management strategies to the needs of marginalized communities, women, children, the elderly, and people with disabilities.

Employing Ammonia for the Synthesis of Primary Amines: Recent Achievements over Heterogeneous Catalysts.

Primary amines represent highly privileged chemicals for synthesis of polymers, pharmaceuticals, agrochemicals, coatings, etc. Consequently, the development of efficient and green methodologies for the production of primary amines are of great importance in chemical industry. Owing to the advantages of low cost and ease in availability, ammonia is considered as a feasible nitrogen source for synthesis of N-containing compounds. Thus, the efficient transformation of ammonia into primary amines has received much attention. In this review, the commonly applied synthetic routes to produce primary amines from ammonia were summarized, including the reductive amination of carbonyl compounds, the hydrogen transfer amination of alcohols, the hydroamination of olefins and the arylation with ammonia, in which the catalytic performance of the recent heterogeneous catalysts is discussed. Additionally, various strategies to modulate the surface properties of catalysts are outlined in conjunction with the analysis of reaction mechanism. Particularly, the amination of the biomass-derived substrates is highlighted, which could provide competitive advantages in chemical industry and stimulate the development of sustainable catalysis in the future. Ultimately, perspectives into the challenges and opportunities for synthesis of primary amines with ammonia as N-resource are discussed.

Analysis of the Brand Internationalization Status and Development Environment of Chinese Enterprises

Driven by the wave of economic globalization in the 21st century, brand internationalization has become an important strategic choice for the enterprise and even the national level. With the internationalization of the market, the transnational of enterprises, the multinational capital and the intensification of competition, the brand has become a key indicator to measure the economic strength of a country or region. Although Chinese enterprises have cost and product advantages in the international market, their brand advantages are relatively weak, which leads to the limited image of Chinese products in overseas markets, and faces intellectual property challenges and dumping charges. To meet this challenge, the Chinese government has included independent innovation and brand building as the focus of the 11th Five-Year Plan since 2005, with the aim of enhancing international competitiveness. However, Chinese enterprises still face many problems in the process of brand internationalization, including the lack of brand strategy, the lack of understanding of the importance of brand internationalization, and the lack of grasp of development models and methods. At present, the theoretical research of brand internationalization has not yet formed a systematic framework, and there is a relative lack of systematic research on enterprise brand internationalization. Moreover, the practical history of Chinese enterprises in this field is relatively short, which provides a broad space for theoretical research and practical exploration. This paper aims to provide theoretical reference and strategic guidance for Chinese enterprises to enhance their brand competitiveness in the international market through in-depth study of the theory and practice of brand internationalization. Through the elaboration of the relevant theory of brand internationalization, the analysis of the development environment of Chinese brand internationalization, and the construction of the development strategy and mode of enterprise brand internationalization, this paper discusses the support system of brand internationalization development, in order to provide systematic theoretical support for the road of brand internationalization of Chinese enterprises.

Role of Machine Learning Algorithms in Enhancing Customer Relationship Management Systems in Rwanda

Purpose: The aim of the study was to assess the role of machine learning algorithms in enhancing customer relationship management systems in Rwanda. Materials and Methods: This study adopted a desk methodology. A desk study research design is commonly known as secondary data collection. This is basically collecting data from existing resources preferably because of its low cost advantage as compared to a field research. Our current study looked into already published studies and reports as the data was easily accessed through online journals and libraries. Findings: Algorithms analyze vast amounts of data, allowing businesses to personalize interactions and anticipate customer needs more effectively. For instance, predictive analytics can forecast customer churn, prompting proactive retention strategies. Moreover, sentiment analysis tools gauge customer emotions from feedback, enabling tailored responses and improved service delivery. Machine learning also automates routine tasks like email marketing segmentation, optimizing resource allocation and enhancing overall efficiency. Consequently, CRM systems empowered by machine learning algorithms foster stronger customer relationships, driving business growth through enhanced customer satisfaction and loyalty. Implications to Theory, Practice and Policy: Technology acceptance model (TAM), resource-based view and diffusion of innovations theory may be used to anchor future studies on assessing the role of machine learning algorithms in enhancing customer relationship management systems in Rwanda. Businesses should adopt advanced ML techniques, such as Long Short-Term Memory (LSTM) models and ensemble learning, to enhance the accuracy of customer segmentation and churn prediction, leading to more personalized marketing strategies and improved customer retention. Policymakers should develop and enforce stringent data privacy and security regulations to protect customer data used in ML-enhanced CRM systems.

The Effect of Public Safety Policies on Crime Reduction in France

Purpose: To aim of the study was to analyze the effect of public safety policies on crime reduction in France. Methodology: This study adopted a desk methodology. A desk study research design is commonly known as secondary data collection. This is basically collecting data from existing resources preferably because of its low cost advantage as compared to a field research. Our current study looked into already published studies and reports as the data was easily accessed through online journals and libraries. Findings: Leadership in Indian organizations plays a pivotal role in enhancing organizational learning and knowledge management by promoting knowledge sharing, continuous learning, and innovation. Effective leaders facilitate the retention of critical knowledge and improve decision-making processes, thereby driving competitive advantage and organizational success. This leadership approach fosters a culture of continuous improvement and adaptability, essential for thriving in dynamic business environments. Unique Contribution to Theory, Practice and Policy: Routine activity theory, social disorganization theory & broken windows theory may be used to anchor future studies on the effect of public safety policies on crime reduction in France. Enhance training programs for police officers to include comprehensive modules on community engagement, cultural competency, and communication skills. Encourage the adoption of diverse public safety policies tailored to the unique needs of different communities. Policymakers should prioritize flexibility and adaptability in policy design, allowing for the integration of multiple strategies based on local crime dynamics and community feedback.

Impact of Data Governance on Data Quality in Healthcare Institutions

Purpose: The aim of the study was to assess the impact of data governance on data quality in healthcare institutions. Materials and Methods: This study adopted a desk methodology. A desk study research design is commonly known as secondary data collection. This is basically collecting data from existing resources preferably because of its low cost advantage as compared to a field research. Our current study looked into already published studies and reports as the data was easily accessed through online journals and libraries. Findings: The study found that institutions with robust data governance protocols experience fewer instances of data inconsistency and errors. This, in turn, improves the reliability of clinical decision-making processes and patient outcomes. Moreover, compliant data governance practices contribute to better data integration across various healthcare systems and departments, facilitating comprehensive patient care and longitudinal health monitoring. By promoting transparency and accountability in data management, healthcare institutions can mitigate risks associated with data breaches and ensure compliance with regulatory standards, thereby fostering trust among patients and stakeholders alike. Implications to Theory, Practice and Policy: Control theory, information theory and agency theory may be used to anchor future studies on assessing the impact of data governance on data quality in healthcare institutions. Healthcare institutions should prioritize the adoption of comprehensive data governance frameworks that integrate policy development, stakeholder engagement, and technological infrastructure. Policymakers should align regulatory frameworks with evolving data governance standards to promote interoperability and data exchange across healthcare networks.

Effect of Data Integration Techniques on Operational Efficiency in Manufacturing Industries in Iran

Purpose: The aim of the study was to assess the effect of data integration techniques on operational efficiency in manufacturing industries in Iran. Materials and Methods: This study adopted a desk methodology. A desk study research design is commonly known as secondary data collection. This is basically collecting data from existing resources preferably because of its low cost advantage as compared to a field research. Our current study looked into already published studies and reports as the data was easily accessed through online journals and libraries. Findings: The study found that by seamlessly amalgamating data from disparate sources across production lines, supply chains, and customer feedback systems, manufacturers have been able to streamline processes, optimize resource allocation, and achieve significant cost savings. For instance, real-time data integration facilitates timely decision-making, allowing for adaptive production planning and inventory management. Furthermore, the integration of advanced analytics and machine learning algorithms has enabled predictive maintenance strategies, reducing downtime and enhancing overall equipment effectiveness (OEE). These technological advancements not only bolster operational efficiency but also foster innovation, as companies leverage integrated data insights to drive continuous improvement initiatives and meet evolving consumer demands. Implications to Theory, Practice and Policy: Resource-based view (RBV), technology acceptance model and dynamic capabilities theory may be used to anchor future studies on assessing the effect of data integration techniques on operational efficiency in manufacturing industries in Iran. Manufacturing firms should prioritize strategic implementation plans for data integration technologies to ensure that investments in IoT, AI, ETL, data warehousing, data virtualization, and APIs are aligned with their overall business objectives. Policymakers should provide incentives and support for the adoption of advanced data integration technologies in the manufacturing sector.

Relationship between Data Privacy Regulations and Consumer Trust in E-Commerce Platforms in Vietnam

Purpose: The aim of the study was to assess the relationship between data privacy regulations and consumer trust in e-commerce platforms in Vietnam. Materials and Methods: This study adopted a desk methodology. A desk study research design is commonly known as secondary data collection. This is basically collecting data from existing resources preferably because of its low cost advantage as compared to a field research. Our current study looked into already published studies and reports as the data was easily accessed through online journals and libraries. Findings: The study found that stringent data privacy regulations positively influence consumer trust in e-commerce platforms. When consumers perceive that their data is adequately protected, they are more likely to trust the platform, resulting in higher confidence levels and increased willingness to engage in online transactions. Moreover, compliance with these regulations can differentiate e-commerce platforms in competitive markets, as consumers tend to prefer platforms that prioritize data privacy and security. This relationship underscores the pivotal role of regulatory frameworks in shaping consumer perceptions and behaviors in the digital marketplace, emphasizing the importance of maintaining robust data protection standards to foster trust and sustainable business relationships. Implications to Theory, Practice and Policy: Trust theory, regulation theory and technology acceptance model may be used to anchor future studies on assessing the relationship between data privacy regulations and consumer trust in e-commerce platforms in Vietnam. E-commerce platforms should prioritize enhancing data transparency and accountability to build consumer trust. Policymakers should work towards harmonizing international data privacy standards to create a cohesive regulatory environment.

Effect of Transparency Initiatives on Public Trust in Government in Kenya

Purpose: To aim of the study was to analyze the effect of transparency initiatives on public trust in government in Kenya. Methodology: This study adopted a desk methodology. A desk study research design is commonly known as secondary data collection. This is basically collecting data from existing resources preferably because of its low cost advantage as compared to a field research. Our current study looked into already published studies and reports as the data was easily accessed through online journals and libraries. Findings: Transparency initiatives in Kenya have shown a positive impact on public trust in government. Efforts such as open access to government information, transparent budgeting processes, and public participation in decision-making have increased accountability and reduced corruption. These initiatives have led to greater citizen engagement and trust, as the public perceives the government to be more accountable and responsive to their needs. However, challenges remain in fully implementing these initiatives, and inconsistencies in transparency efforts can still undermine public trust. Unique Contribution to Theory, Practice and Policy: Social exchange theory, institutional theory & information processing theory may be used to anchor future studies on the effect of transparency initiatives on public trust in government in Kenya. Continuously improve online transparency portals by enhancing user interfaces, ensuring data accuracy, and expanding the scope of information available. Develop sector-specific transparency policies tailored to the unique needs of different governmental sectors (e.g., healthcare, education). This approach ensures that transparency initiatives address sector-specific challenges and enhance sector-specific public trust. Invest in training programs for public officials on transparency practices and the effective use of transparency tools.

Influence of Big Data Analytics on Decision-Making Processes in Financial Firms in South Africa

Purpose: The aim of the study was to assess the influence of big data analytics on decision-making processes in financial firms in South Africa. Materials and Methods: This study adopted a desk methodology. A desk study research design is commonly known as secondary data collection. This is basically collecting data from existing resources preferably because of its low cost advantage as compared to a field research. Our current study looked into already published studies and reports as the data was easily accessed through online journals and libraries. Findings: By harnessing vast volumes of structured and unstructured data, these firms can derive valuable insights into market trends, customer behavior, and risk assessment with unprecedented accuracy and speed. This capability enables more informed and data-driven decision-making across various facets of financial operations, including investment strategies, risk management, and customer relationship management. Moreover, big data analytics facilitates the identification of previously hidden patterns and correlations within financial data, enhancing predictive modeling and forecasting capabilities. This proactive approach allows firms to anticipate market shifts, optimize portfolio performance, and mitigate potential risks effectively. Additionally, the adoption of advanced analytics techniques, such as machine learning and artificial intelligence, further enhances decision-making by automating processes and refining predictive accuracy. Implications to Theory, Practice and Policy: Diffusion of innovation theory, technology acceptance model and resource-based view may be used to anchor future studies on assessing influence of big data analytics on decision-making processes in financial firms in South Africa. In practice, financial firms should establish robust data governance frameworks to ensure data accuracy, privacy, and security. Policymakers should develop and enforce regulatory frameworks that support the ethical and effective use of BDA in financial firms.

Fast and accurate modelling of twin-screw compressors: A generalised low-order approach

Twin-screw compressors (TSC) are commonly used in heat pump processes due to their robustness and flexibility. They exhibit two core properties, i.e. the swept volume and the built-in volume ratio (BVR), which heavily influence their capacity limits and off-design efficiency. This work presents a new low-order model (i.e. a polynomial model), which can accurately predict a TSC’s behaviour. The model uses the external pressure ratio and volumetric compressor inlet flow rate to calculate isentropic efficiency and compressor speed. The input parameters are normalised with a reference flow rate (calculated from the swept volume) and the BVR, respectively. This results in a generalised model of low numerical cost, which can be used for explorative studies independent of the specific machine size and BVR. A gain in computational speed by a factor of 375 is achieved compared to a semi-empiric reference model. The model displays very good predictive accuracy when used to predict the performance of machines with similar BVRs, but different sizes. A mean deviation from the manufacturer data of 4.29%, 0.88°C and 1.38% for the shaft power, the outlet temperature and the compressor speed can be observed, respectively. When there is a difference in size and BVR, the prediction accuracy is still reasonable but significantly declines for small and very large pressure ratios. Nevertheless, the proposed new approach extends the state-of-the-art by introducing a low-order model, which combines the advantages of low computational cost, high accuracy, physically correct predictions over a wide operational range and scalability to different machine capacities and BVRs. The validation for different fluids indicates a good general prediction accuracy relatively independent of the used fluid.

The pharmacokinetics of ganciclovir during prolonged intermittent kidney replacement therapy in a cardiac transplant recipient.

Ganciclovir, a guanine analogue, is used intravenously (IV) first-line for the prophylaxis and treatment of cytomegalovirus (CMV) infection in solid organ transplant recipients. The pharmacokinetics (PK) of ganciclovir are highly variable, with myelosuppression occurring at high concentrations. Ganciclovir is primarily renally excreted as the parent compound, and clearance is significantly reduced in renal impairment. Acute kidney injury (AKI) is a common post-operative complication of cardiac transplantation, reducing the clearance of ganciclovir. In the intensive care unit (ICU), AKI is often managed by kidney replacement therapy (KRT). One form of KRT, prolonged intermittent kidney replacement therapy (PIKRT) is increasingly used for cost and flexibility advantages. Ganciclovir dosing recommendations are available for varying degrees of renal impairment and KRT, except for PIKRT. In this case of cardiac transplantation, complicated by anuric AKI, a ganciclovir dose of 2.0-2.5 mg/kg of adjusted body weight given after each PIKRT session was demonstrated to achieve PK targets.

Flowerlike Co-MOFs catalyzed chemiluminescence for sensitive detection of tetrachlorophenquinone in environmental waters

Tetrachlorobenzoquinone (TCBQ) as one of the most toxic quinones has potential risk of causing canceration, and the monitoring of TCBQ in the environment is of significant importance. In this study, a flowerlike Co-PTA-400(Ar) metal organic frameworks (MOF) catalyzed TCBQ-H2O2 chemiluminescence (CL) system was established for sensitive determination of trace TCBQ in environmental waters. The reaction between TCBQ and H2O2 can result in weak CL emission, and the CL signal was enhanced about 10-fold using flowerlike Co-PTA-400(Ar) as catalyst. The luminescence mechanism was investigated through kinetic studies and free radical scavenging experiments. The strong CL emission of TCBQ mainly relied on the generation of abundant •OH by the catalysis of Co-PTA-400(Ar). This strategy allows to develop a highly sensitive method for the determination of TCBQ in the linear range of 1 ∼ 50 μM, with a detection limit of 0.56 μM (3σ). The proposed method was applied for analysis of three spiked water samples with recoveries ranging from 95.7% to 100.5%. This method offering the advantages of rapid analysis, low costs, and high sensitivity, is appealing for detection of TCBQ in environmental waters.

Electrochemical properties of tungsten doped LiNi0.9Co0.1O2 lithium-ion battery cathode materials

High-nickel layered cathode materials for lithium-ion batteries have received widespread attention in new energy vehicles for their excellent specific energy and cost advantages, and are considered to be the most promising cathode materials for high-energy density lithium-ion batteries. Despite its many advantages, poor thermal stability and rapid capacity degradation have greatly limited its large-scale application. Ion doping is considered to be an effective way to improve its drawbacks, and in this paper, a series of W-doped LiNi0.9Co0.1O2 cathode materials were prepared using WO3 as a tungsten source. Among them, 1 mol% W doping was the most effective, and its reversible capacity of 204.44 mA g −1 still had 93.25 % capacity retention after 100 this cycles at 1C. Combined with DFT calculations, it is found that the introduction of W does not change the original layered structure and the material transitions from semi-metallic to metallic at lower doping concentrations, resulting in more electrons occupying the Fermi energy levels to enhance its electrical conductivity and leading to an elevated spin state and a lower oxidation state of Ni.

Nanozyme-catalyzed and zwitterion-modified swabs based for the detection of Listeria monocytogenes in complex matrices

In recent years, nanozymes have been widely used in the field of biosensing and food safety testing due to their advantages of low cost, high stability, easy modification and adjustable catalytic activity. However, how to reduce the signal interference generated by reducing substances, macromolecules and colored substances in the food matrix in nanozymes-based colorimetric sensing is still a major challenge. In this paper, using Listeria monocytogenes as a model analyte, sodium sulfonyl methacrylate (SBMA) polymers were modified onto cotton swabs by photothermal polymerization and combined with Listeria monocytogenes-specific aptamer (Apt1) to prepare swabs that can specifically capture and isolate Listeria monocytogenes from complex matrices (SBMA/Apt1 cotton swab). In addition, in combination with the inhibitory effect of the aptamer (Apt2) on the oxidase activity of Mn3O4 NPs, a colorimetric biosensor based on nanozymes that can quantitatively, sensitively, and specifically identify Listeria monocytogenes in food products was constructed. The results showed that the colorimetric signal of the method was linear with the concentration of Listeria monocytogenes in the range of 2.83–2.83 × 105 CFU/mL, and the limit of detection was 2.64 CFU/mL, which can be used for the detection of Listeria monocytogenes in complex environments and food samples.

Analysis of green energy-oriented sustainable development goals for emerging economies

Countries should focus on the improvements to achieve sustainable development goals. However, taking these actions create some operational costs. Thus, it is appropriate for rising nations to prioritize sustainable development objectives. Due to this condition, more critical sustainable development goals should be identified. Accordingly, this study evaluates green energy-oriented sustainable development goals for emerging economies. Within this context, a model is generated by considering the golden cut and bipolar q-rung orthopair fuzzy sets (q-ROFSs). The novelty of this study is to identify key green energy-oriented sustainable development goals for the improvement of emerging economies by an original model based on the golden cut and bipolar q-ROF decision making approach. All results are similar, indicating the proposed model's validity and coherency. It is concluded that increasing renewable energy is the most significant target with a weight of 0.235 regarding green energy-oriented sustainable development. Cooperation and efficiency are other significant issues for this condition, with weights of 0.198 and 0.196. It is also identified that Turkey and Mexico are the most successful countries in reaching green energy-oriented sustainable development goals. Increasing the share of renewable energy in total energy production and consumption is important for emerging countries. For this situation, governments should provide subsidies for renewable energy investors to provide a cost advantage. In this context, issues such as tax cuts and loans with low-interest rates can increase the motivation of clean energy investors.