Comprehensive Assessment Framework Research Articles

Boosting the transition to biorefineries in compliance with sustainability and circularity criteria

In the transition towards climate neutrality, biorefinery models are considered promising alternatives to processes based on fossil fuels and non-renewable materials. This consideration is attributed to the reduction of dependence on non-renewable systems, in line with greener and lower impact technological strategies. However, they imply a profound transformation of the production system, as the variable and stochastic nature of certain renewable sources makes it necessary to have various tools to provide flexibility and optimize the system. Their market penetration is currently limited by their level of development and the regulatory and financial support to implement the paradigm shift. Traditional linear production is still more economically viable compared to circular production models, based on efficiency parameters achieved by years of manufacturing experience. To reverse this trend, it is necessary to leverage its benefits through the use of appropriate assessment tools, as these are the ones that could drive the move towards biorefineries. This is the focus of this research report, which reviews various evaluation methodologies and indicators to effectively evaluate bio-based process schemes. Life Cycle Assessment (LCA), Product Environmental Footprint (PEF), Green Chemistry Principles (GCP), certification scheme requirements and Circular Economy Action Plans are reviewed and integrated into a precise assessment framework. To determine their effectiveness, three biorefinery models were evaluated: valorization of apple vinasses, grape must and chickpea peel. Through the combined analysis of bio-based scenarios, it has been possible to identify their benefits, bottlenecks and limitations, and to validate a comprehensive assessment framework that allows an accurate, effective and conscious analysis of bio-based models, promoting their development and market penetration.

Advanced data analytics in internal audits: A conceptual framework for comprehensive risk assessment and fraud detection

In the face of increasing complexity and rapid technological advancements, traditional internal audit methods are becoming inadequate for comprehensive risk assessment and effective fraud detection. Advanced data analytics offers a transformative approach that enhances the effectiveness of internal audits. This concept paper presents a framework for integrating advanced data analytics into internal audit processes, aiming to provide more robust risk management and improved fraud detection capabilities. Integrate diverse data sources, including financial, operational, and external data, to provide a holistic view of the organization's risk landscape. Implement rigorous data governance practices to ensure data accuracy, consistency, and reliability. Use machine learning algorithms and predictive analytics to identify patterns, predict future risks, and detect anomalies. Employ real-time data analytics for continuous monitoring, enabling the timely detection and response to emerging threats. Develop adaptive risk assessment models that can evolve with changing business environments and emerging risks. Utilize data-driven insights to prioritize risks based on their potential impact and likelihood. Deploy advanced algorithms to uncover complex fraud schemes that traditional methods might miss. Conduct scenario-based analysis to identify potential fraud patterns and strengthen preventive measures. Use analytics to focus audit efforts on high-risk areas, enhancing the efficiency and effectiveness of audits. Incorporate data-driven procedures into audit execution, reducing manual efforts and increasing precision. Advanced data analytics provides deeper insights, enabling more proactive and comprehensive risk management. Real-time and predictive analytics significantly enhance the ability to detect and prevent fraud, mitigating financial and reputational damage. Data-driven audit processes streamline activities, allowing auditors to focus on high-value tasks and reducing the overall audit cycle time. Analytics provide accurate and timely insights, supporting better decision-making in risk management and fraud prevention. Secure commitment from senior management to support the integration of advanced data analytics into internal audits. Establish a governance framework to oversee the implementation and alignment with organizational objectives. Invest in advanced analytics platforms and tools that support real-time data processing and analysis. Ensure robust data security measures to protect sensitive information. Train audit professionals in data analytics techniques and tools, fostering a culture of continuous learning and innovation. Implement pilot projects to test and refine the data analytics framework, using lessons learned to scale up across the organization. Integrating advanced data analytics into internal audits offers significant benefits, including enhanced risk management, improved fraud detection, and increased audit efficiency. By adopting this conceptual framework, organizations can better protect their assets, ensure compliance, and maintain stakeholder confidence in an increasingly complex and digital business environment. The future of internal auditing is data-driven, and organizations must embrace this transformation to remain competitive and secure. Keywords: Fraud Detection, Risk Assessment, Internal Audits, Advanced Data Analytics, Conceptual Frameworks.

Developing a Framework for Assessing Transformative Competencies of Junior High School Students Through Construct Modelling Approach

This research is designed to develop a comprehensive assessment framework to study junior high school students’ transformative competencies in three dimensions: creating new value, reconciling tensions and dilemmas, and taking responsibility. Researchers developed and verified the quality of the assessment framework among the 120 junior high school students purposively selected from four schools in the northeastern and central regions of Thailand as the test-takers. The project is segmented into four phases: creation of the construct map, item design, outcome space, and Wright map. The framework’s efficacy is analyzed using the Rasch model and the Maximum Likelihood Estimation method. Validity is rigorously evaluated through empirical evidence derived from the Wright map and cognitive interviews. Reliability is assessed via the standard error of measurement, test information function, expected-a-posteriori reliability, and Infit Mean Square value. Key findings included the development of a five-level construct map and the formulation of scaled open-ended questions based on the test takers’ responses. A cut-off point is determined by dividing the threshold level by the number of tests at corresponding levels, adhering to Wright map criteria for each dimension. Subsequent item analysis and modeling confirmed the internal structure’s validity and reliability. This framework equips them with skills to navigate and effectively address complex real-world challenges, enhancing targeted educational strategies, promoting superior learning outcomes, and preparing students for advanced academic and professional environments.

A comprehensive risk assessment framework for occupational health and safety in pharmaceutical warehouses using Pythagorean fuzzy Bayesian networks

Risk assessment plays a crucial role in managing occupational health and safety in various industries, including pharmaceutical warehouses. Bayesian Networks (BN) have been widely employed for risk assessment due to their ability to handle uncertainty and quantify risks. However, the traditional BN approach has limitations in dealing with ambiguity and continuous variables. To address this, the fuzzy BN technique, combining fuzzy logic with BN, has emerged as an effective method for risk assessment. In this study, a fuzzy BN model using Pythagorean fuzzy sets is proposed for risk assessment in a pharmaceutical warehouse. The model incorporates 24 identified risk factors, and survey data is used to determine the conditional probabilities of these factors. The novelty of the study lies in the application of Pythagorean fuzzy sets and the development of risk assessment criteria specifically for pharmaceutical warehouses. The results of a comprehensive literature review and the proposed methodology are presented. A real case analysis is conducted, followed by validation and sensitivity analysis. The results provide valuable insights into enhancing occupational health and safety practices in pharmaceutical warehouses. The study contributes to enhancing occupational health and safety practices in pharmaceutical warehouses and provides a framework for future research.

An integrated group decision-making framework for assessing S3PRLPs based on MULTIMOORA-WASPAS with q-rung orthopair fuzzy information

Sustainable third-party reverse logistics has gradually risen to prominence as a component of contemporary commercial development as a result of the acceleration of global economic integration and the prominent growth of information technology in the logistics industry. In the procedure of sustainable third-party reverse logistics providers (S3PRLPs) selection, indeterminacy and conflict information bring great challenges to decision experts. In view of the significant superiority of q-rung orthopair fuzzy (q-ROF) set in expressing uncertain and vague assessment information, this essay designs a comprehensive assessment framework through merging the best and worst method (BWM), Multiplicative Multi-objective Optimization by Ratio Analysis with Full Multiplicative Form (MULTIMOORA) and weighted aggregated sum product assessment (WASPAS) method to address the S3PRLPs selection issue with entirely unknown weight information under q-ROF setting. Firstly, we present a novel score function for comparing q-ROF numbers after analyzing the inadequacies of previous works. Secondly, the q-ROF Frank interactive weighted average (q-ROFFIWA) and q-ROF Frank interactive weighted geometric (q-ROFFIWG) operators are advanced based on the constructed operations to take into consideration the interactive impact of information fusion procedure. Thirdly, the q-ROF-MULTIMOORA-WASPAS decision framework is built based on novel score function and the developed operators, in which the synthetic weights of the criterion are determined by the modified BWM and entropy weight method to reflect both the subjectivity of the decision expert and the objectivity of the decision information. Ultimately, an empirical example was used to evaluate S3PRLPs to demonstrate the applicability and feasibility of the developed methodology, and a comparative analysis was conducted with other existing methods to highlight its advantages in dealing with complex decision problems. The discussion from the research indicates that the developed methodology can be used to evaluate S3PRLPs and further improve the quality of logistics services for organizations.

Assessing risks for the use of drones for wind turbine inspections

This paper presents a comprehensive risk assessment framework for integrating drones into wind turbine inspections, addressing technical, operational, human factors, environmental, and regulatory risks. Utilizing a multidimensional approach based on Failure Modes Effects and Criticality Analysis (FMECA), the study evaluates potential risks in drone-assisted wind turbine inspections, considering various factors like communication reliability, sensor functionality, battery performance, and environmental impacts. The research emphasizes the importance of systematic risk identification, evaluation, and prioritization in enhancing the safety and reliability of drone operations in the renewable energy sector. The framework’s practical application is demonstrated through a case study involving real-world offshore wind farms, engaging industry experts and employing structured data collection methods. The study identifies, assesses, and offers mitigation strategies for critical risks, underlining the significant potential of drones in optimizing maintenance processes and advancing sustainable energy production. Future research directions include refining risk assessment methodologies, advancing drone technology, and fostering international collaboration for broader adoption of drone-based inspections in renewable energy.

Appraising Linguistic and Reading Impairments in Kannada-Speaking Persons With Central Alexia.

Alexia is an acquired condition resulting in impaired abilities to comprehend and/or read aloud written scripts secondary to lesions in the brain involved in reading processes. Just as how linguistic aspects are multi-faceted in persons with aphasia (PWAs), the reading impairments also vary extensively across each PWA depending on the type and nature of the language deficits. Each language has its unique linguistic properties. Whether the impairment is in the spoken form or written form, the dissociations in both aspects across the linguistic distinctions are paramount. Given the diverse orthographic features of Kannada, alongside inquiries into the impairment in oral language skills among individuals with acquired reading difficulties, the study emphasizes distinguishing the concurrent language processes associated with reading impairment. The study aimed to explore the relationship between spoken language and reading abilities in Kannada-speaking post-stroke survivors with acquired alexia. The study recruited 15 Kannada-speaking adults with complaints of reading impairment post-ictus through convenient sampling. The enrolled participants included both males and females with a mean age of 42.2 years (SD=15.58; range=20-68) with 16 years of formal education. The study performed three primary investigations; a) linguistic, b) oral reading, and c) reading comprehension. Linguistic tasks entailed semantics, syntax, and phonological tasks. Oral reading entailed real word and non-word reading tasks. Reading comprehension tasks entailed single-word level, sentence-level, and paragraph-level tasks. Results of overall domain performance across linguistics, oral reading, and reading comprehension revealed superior performance in linguistics (M=71.77, SD=19.18) followed by reading comprehension (M=70.55, SD=24.10) and oral reading (M=41.55, SD=24.66), which was significant (p<0.05). Performance in phonology was weakest (M=58.06, SD=12.44) compared to syntax (M=71.13) and semantics (M=85.33) on comparing PWAs' abilities within the linguistic domain. Reading abilities were compared across oral reading and reading comprehension, which significantly varied (p<0.05), and reading comprehension abilities were superior (M=72.65, SD=24.10). Task-specific variabilities were significant (p<0.05) in both oral reading and reading comprehension, wherein, real word reading (M=58.22) and comprehension at a single level (M=80.22) were better performed. Results of correlation analysis revealed semantics (r=0.494, p<0.05) and phonology (r=0.428, p>0.05) were highly positively correlated to oral reading abilities. Syntax (r=0.412, p>0.05), and semantics (r=0.377, p>0.05) were strongly positively correlated to reading comprehension abilities. The study has convincingly shown that performance on reading-related tasks reflects the functionality of central semantic, phonological, and syntactic processing elements. The literate adults routinely interact with both spoken and written language and a comprehensive assessment framework of language processing must encompass both modalities (linguistic and reading) for individuals with alexia, specific to Indian languages. Owing to the transparent writing system of the Kannada language, a multimodal phonological and lexical-based reading treatment may prove to be beneficial in remediating reading impairments among adult post-stroke survivors.

The impact on ecosystem services from rural revitalization activity in China Shandong Province, from a human well-being perspective of local residential

Rural decline is a global concern, and Ecosystem Services (ES) play a crucial role in both rural revitalization and the Human Well-being (HW) of residents. This study investigated 9 revitalization cases in Shandong province, China, employing both quantitative and qualitative methods. It confirmed the effect on HW based on ES, using a comprehensive assessment framework covering local ecosystem services contribution and economic/social effect derived from the Millennium Ecosystem Service Assessment (MA) and Japan Biodiversity Outlook (JBO) and consolidated variations into four ES dimensions from a HW perspective. As a result, 4 common types of rural revitalization were identified: (A) Large-scale Agriculture, (B) Local Specialty/Brand Making, (C) Industry Integration, and (D) Urbanization Village, based on actors, core industries, and key revitalization activities. According to interviews with local residents, the residents express high levels of satisfaction with revitalization activities. Using TOPSIS analysis, we found that urbanization-led revitalization (D) excels in “economic/social” dimensions but falls short in “Interaction with nature and health" and “Life and culture with nature." In contrast, agriculture-based revitalization activities (A and B) offer a more balanced impact across various ES dimensions. The well-being of residents varies depending on how they utilize and connect with nature. These findings underscore the potential of large-scale agricultural development to address rural population decline and suggest that fostering a positive exchange of ES, HW and capital between rural and urban areas can enhance the resilience of the rural-urban system. These insights are especially relevant for addressing the urban-rural imbalances in developing countries experiencing urban expansion and rural decline.

Allergenicity assessment of new or modified protein-containing food sources and ingredients

The growing world population, changing dietary habits, and increasing pressure on agricultural resources are drivers for the development of novel foods (including new protein sources as well as existing protein sources that are produced or used in an alternative way or in a different concentration). These changes, coupled with consumer inclination to adopt new dietary trends, may heighten the intake of unfamiliar proteins, or escalate consumption of specific ones, potentially amplifying the prevalence of known and undiscovered food allergies. Assessing the allergenicity of novel or modified protein-based foods encounters several challenges, including uncertainty surrounding acceptable risks and assessment criteria for determining safety. Moreover, the available methodological tools for gathering supportive data exhibit significant gaps. This paper synthesises these challenges, addressing the varied interpretations of "safe" across jurisdictions and societal attitudes towards allergenic risk. It proposes a comprehensive two-part framework for allergenicity assessment: the first part emphasises systematic consideration of knowledge and data requirements, while the second part proposes the application of a generic assessment approach, integrating a Threshold of Allergological Concern. This combined framework highlights areas that require attention to bridge knowledge and data gaps, and it delineates research priorities for its development and implementation.

From drought hazard to risk: A spring wheat vulnerability assessment in the Canadian Prairies

The Canadian drought monitor (CDM) provides monthly assessments of drought severity but does not provide guidance on the potential agricultural impacts. This study extends the CDM to a comprehensive risk assessment framework by assessing the vulnerability of spring wheat to drought timing and severity in the Canadian Prairies. To achieve this, a bayesian neural network (BNN) model was developed that predicts high-resolution wheat yields and uncertainties using the CDM and normalized difference vegetation index (NDVI) data from 2010 to 2021. Model performance is comparable to previous studies, with errors ranging from 13% to 18% of mean yields and R2 values from 0.43 to 0.70 that peak in early August. The model was then applied under various drought conditions to evaluate how different drought timings and severities affect yield vulnerability. The results demonstrate that drought exposure from June to August reduces wheat yields in proportion to drought severity while abnormally dry conditions may have a positive impact of yield, particularly in August. Yield uncertainty decreases substantially from June to July while yield prediction accuracy increases, suggesting that mitigation efforts are most effective prior to July. The final component of the research involved creating operational risk maps that integrate the yield vulnerability assessment with the CDM. These maps were applied in a case study to demonstrate their utility in providing actionable, real-time information for agricultural stakeholders. Overall, this study contributes to the body of knowledge regarding crop sensitivities to varying drought conditions and represents a methodological advancement by enabling high-resolution, operational agricultural risk assessments at scale. By integrating detailed vulnerability data with drought hazard monitoring from the CDM, the information provided by these risk maps can be leveraged by agricultural stakeholders in near real-time to make well-informed decisions that mitigate the impacts of drought on wheat production, enhancing the resilience of agricultural systems to climatic variability.

Digital Twin-Enabled Response Function Analysis: A Synthetic Approach to Ship's Propulsion System Assessment

Analyzing the behavior of vessels in actual sea conditions is crucial for conceptual system design, safety and energy efficiency considerations. However, the essential seakeeping problem is reduced to the analysis of wave-hull interactions often neglecting consideration of the propulsion. But in the meantime, the synthetic consideration of wave–propulsion interactions is a key for safety and energy efficiency. Furthermore, safety evaluation of a ship's design with reduced propulsion power in adverse seas is vital for risk management. Response functions are commonly used to estimate propulsion system responses in incoming seaways. This paper proposes a synthetic approach using digital twin technology for rapid response function estimation. It introduces a companion linearized state-space model linked with the digital twin, enabling immediate retrieval of coefficients for response function analysis at the desired operating point. This integrated methodology provides a comprehensive representation of ship propulsion behavior in wave environments, offering a comprehensive framework for system performance assessment.

Responsible Integration of Large Language Models (LLMs) in Navy Operational Plan Generation

This paper outlines an approach for assessing and quantifying the risks associated with integrating Large Language Models (LLMs) in generating naval operational plans. It aims to explore the potential benefits and challenges of LLMs in this context and to suggest a methodology for a comprehensive risk assessment framework.

Integrated Framework for Light Pollution Assessment and Mitigation: A Multifaceted Approach with TOPSIS and Machine Learning

In this study, we propose the development of an evaluation system to quantify the degree of light pollution and offer three strategies for mitigating its negative effects. Our research identifies 4 distinct types of cities and employs a 16 key indicators to assess the level of light pollution risk, forming the basis for the construction of the Light Pollution Assessment (LPA) Model. Utilizing the TOPSIS and EWM methods, we ranked each city and determined a composite score interval, categorizing pollution levels from I to IV. Our evaluation system designates level I with an overall score of 0.7466, establishing it as a benchmark for assessing the risk level of light pollution. Recognizing that certain factors influencing LPA levels may not directly contribute to light pollution, we introduce the ML-based Strategy Evaluation (MLE) method, this approach classifies 11 out of 16 indices based on their relevance to light pollution. Our analysis reveals that the number of streetlights, the range of ULOR (Upward Light Output Ratio), and the presence of natural protected areas are the top three factors affecting light pollution. With an accuracy of 79.1%, the MLE method demonstrates a strong alignment with the data. Based on our findings, we propose three targeted strategies focusing on luminance regulation, greenery implementation, and legal enhancements to address and alleviate light pollution challenges in urban environments. This research contributes a comprehensive assessment framework and actionable strategies for policymakers and urban planners to effectively manage and reduce light pollution.

GreenR: An open-source framework for quantifying urban greenness

How do we quantify the levels of greenness within urban street networks? Numerous attempts to quantify this factor have been made through survey methodologies, remote sensing data, and street view imagery. The results are promising, but are often limited by scalability constraints, including limited data availability, high requirements of computational power, and validation challenges. This study introduces a comprehensive framework for urban greenness assessment, leveraging open-source data to overcome these limitations. Central to this framework is the development of the greenR R package and an accompanying Shiny app, designed to compute, visualize, and analyze a novel proximity-based green index for individual street segments. Beyond this, the framework innovatively extends its analytical capability by integrating accessibility analysis, Green View Index quantification as well as introducing the Green Space Similarity Index, a novel metric that evaluates and compares the characteristics of urban green spaces across different regions. This extension enriches the proposed framework, providing not just a measure of proximity to green spaces, but also insights into their spatial connectivity and distribution. The efficacy of greenR is demonstrated by studying urban greenness patterns across several cities, highlighting the potential impact of such an open-source framework for citizens, urban planners, and policy-makers. This study not only advances our methodological approach to quantifying urban greenness but also provides practical tools and metrics that can inform sustainable urban planning and policy decisions.

A multiscale examination of heat health risk inequality and its drivers in mega-urban agglomeration: A case study in the Yangtze River Delta, China

Increased frequency of extreme urban heat and its exposure to urban populations is one of the challenges presented by climate change, especially in urban clusters. Due to the rapid but unequal development, heat exposure disproportionately increased in the underdeveloped regions compared to the developed regions in urban agglomeration. To address this issue, it is crucial to clarify the spatial pattern of heat health risk (HHR) inequality for urban heat resilience. However, analyses for the disparity of HHR inequality often used a single scale, neglecting important spatial context effects at other scales. Moreover, the rationale of HHR inequality remains unclear. Here, we took the well-developed and highly urbanized Yangtze River Delta (YRD) region as a case study and employed multiscale approaches to examine how and why the HHR inequality varied at and within the regional scale. We first assessed HHR using a comprehensive assessment framework at a 1 km grid level. Then, we quantified the inequality between regions using local Moran's I and KS distance. Therefore, we utilized the Gini coefficient and Bayes quantile regression to quantify inequality and identify its drivers within the regional scale. Finally, we proposed a conceptual framework to inform policymaking in regions with different patterns of multiscale equality. Our results found that the HHR in YRD exhibited significant spatial inequality at the regional scale (Moran's I = 0.562, P < 0.001) and within the regional scale (Gini coefficient: 0.27–0.54). Higher population concentrations and building densities often led to higher HHR. In high HHR areas, intra-regional inequality was often lower due to high and coordinated socioeconomic levels (Gini coefficient: 0.27–0.34). Additionally, in areas with low and medium levels of risk, healthcare resource availability and local temperatures had a greater impact on intra-regional inequities, which varied at different levels of inequality. This study contributes to a better understanding of multiscale HHR inequality, which helps optimize heat risk management strategies and regional sustainable development.

Enhancing Interdisciplinary English Learning: A Systematic Review of the HyFlex Model's Strategies, Challenges, and Implications

This systematic review offers a thorough analysis of the existing research on the utilization of the HyFlex (Hybrid-Flexible) learning model in interdisciplinary English courses. Drawing insights from 24 empirical studies published from 2008 to 2023, the review aims to showcase successful tactics for enhancing the HyFlex methodology while pinpointing notable deficiencies in its integration within the context of interdisciplinary English education. The review reveals promising strategies, such as leveraging peer interactions, utilizing authentic assessments, and implementing discipline-specific guidelines. However, it also uncovers critical gaps, including insufficient research on the model's effectiveness, a lack of frameworks for assessing higher-order skills, and challenges in facilitating meaningful discourse across modes. The review further highlights the need for more robust faculty training, consistent institutional support, and greater accessibility and inclusiveness for diverse student populations. To address these gaps, the review recommends the development of comprehensive assessment frameworks, investment in faculty training, integration of work-integrated learning, and implementation of blended synchronous approaches. By addressing these areas, the HyFlex model can be optimized to create more inclusive, flexible, and effective learning environments for students in interdisciplinary English courses. This review provides a solid foundation for understanding the current research landscape and identifies key areas for future development, offering valuable insights for educators, policymakers, and researchers seeking to enhance student engagement and learning outcomes.

Implementing the model-based systems engineering (MBSE) approach to develop an assessment framework for healthcare facility design

AbstractThe global elderly population rises, increasing dementia cases. Built environment impact on dementia health outcomes is known, forming the basis for evidence-based design studies. There's a need for a comprehensive assessment framework due to the complexity of interactions among Architectural Variables (AVs) and Health and Care Outcomes (HCOs). This paper proposes using Model-Based Systems Engineering (MBSE) to create such a framework. It collects data from 105 studies on 40 AVs, 36 HCOs, and 396 interactions. MBSE offers a holistic understanding, aiding healthcare facility design decisions.

Evaluation of ecological environment governance quality in the top ten cities of Zhejiang province using entropy weight method and Rank-Sum Ratio method: A case study of air quality indicators

All In this study, we focused on evaluating the quality of ecological environment m governance in eleven cities within Zhejiang Province. We employed a comprehensive set of key indicators, including the proportion of days with air quality meeting or exceeding Grade II standards, annual average concentrations of sulfur dioxide (SO_2), nitrogen dioxide (NO_2), particulate matter with a diameter of 10 micrometers or less (PM10), the 95th percentile daily concentration of carbon monoxide (CO), and the 90th percentile 8-hour maximum concentration of ozone (O_3). We utilized methods such as entropy values, information utility values, and weight allocation techniques to conduct a holistic assessment of urban ecological environment governance.Our findings revealed significant advantages in terms of Ritchies fitting values (RSR fitting values) for Zhoushan and Lishui cities, ranking them in the third tier. This underscores the commendable efforts of these two cities in improving air quality. Simultaneously, Huzhou city, while displaying relatively lower fitting values, demonstrates substantial potential in ecological environment governance. Additionally, key indicators such as the 90th percentile 8-hour maximum concentration of ozone (O_3) and annual average concentration of particulate matter (PM10) play pivotal roles in the overall evaluation.The comprehensive assessment framework presented in this study offers a thorough and accurate perspective on urban ecological environment governance. This approach can guide policy formulation and environmental management, albeit requiring further refinement and practical implementation. In summary, our research provides valuable insights for advancing ecological civilization construction through an in-depth analysis of urban ecological environment governance in Zhejiang Province.

An integrated Fine-Kinney risk assessment model utilizing Fermatean fuzzy AHP-WASPAS for occupational hazards in the aquaculture sector

The multidisciplinary field of occupational health and safety (OHS) aims to identify, assess, and mitigating hazards that arise in the workplace or due to the execution of the work. The Fine-Kinney method is a quantitative risk assessment widely applied in many industries to identify, evaluate and prevent potential hazards. Despite the widespread usage of the classic Fine-Kinney approach in several research, its effectiveness in addressing the issue of objective risk appraisal among decision-makers is limited. Therefore, this study proposes a comprehensive occupational risk assessment framework that integrates the Fine-Kinney method with the analytical hierarchy process (AHP) and weighted aggregated sum product assessment (WASPAS) under the Fermatean fuzzy environment. The Fermatean fuzzy set (FFS) offers a more flexible solution in determining the uncertainty and vagueness of information, as it covers membership and non-membership degrees in a wider area compared to intuitionistic and Pythagorean fuzzy sets. The AHP approach is used to calculate the weightage of each Fine-Kinney risk parameter (probability (P), exposure (E), and consequence (C)), and WASPAS method is used to find the ranking of the hazards. In the proposed model, Fermatean fuzzy weighted geometric (FFWG) operator is used for aggregation of expert opinions. Finally, sensitivity and comparative analyses are also performed to further highlight the adaptability and efficiency of the proposed occupational health and safety risk assessment (OHSRA) model, and a case study analyzing occupational hazards for aquaculture operations is presented to demonstrate the model's rationality and feasibility.

The Global Impact of CEFR in Higher Education: A Case Study in Kazakhstan and Insights from a Pedagogical Experiment

The article explores the transformative impact of the Common European Framework of Reference for Languages (CEFR) on global language pedagogy, with a particular focus on its implications for higher education and its application in Kazakhstan. Introduced by the Council of Europe in the early 2000s, the CEFR offers a comprehensive framework for language teaching, learning, and assessment, spanning six competency levels from A1 to C2. Its widespread adoption has reshaped language education policies and practices worldwide, emphasizing communication skills across reading, writing, speaking, and listening. The study investigates the evolution of the CEFR and its integration into higher education systems, examining its influence in countries such as the UK, Germany, France, and Canada. Notably, Kazakhstan has embraced the CEFR as a guiding framework for language instruction, aiming to enhance graduates’ employability in the international job market. Additionally, the article presents findings from a pedagogical experiment conducted at a Kazakhstani university, assessing the effectiveness of CEFR-aligned speaking assessment criteria in improving students’ proficiency. The study employs a mixed-methods approach, combining quantitative data analysis with qualitative student feedback, highlighting the positive impact of aligning teaching strategies with the CEFR. Overall, the research contributes valuable insights into the practical application of the CEFR in Kazakhstani higher education and its broader implications for language instruction worldwide. Keywords: CEFR, higher education system, teaching methods, assessment criteria, student feedback, survey.