Search Results (67)

Understanding the complex interplay between environmental factors and fruit quality development requires sophisticated analytical approaches linking cellular architecture to environmental conditions. This study introduces a novel application of dual-resolution X-ray computed tomography (CT) for the non-destructive characterization of apple internal tissue architecture in relation to fruit growth, thereby advancing beyond traditional methods that are primarily focused on postharvest analysis. By extracting detailed three-dimensional structural parameters, we reveal tissue porosity and heterogeneity influenced by crop load, maturity timing and canopy position, offering insights into internal quality attributes. Employing correlation analysis, Principal Component Analysis, Canonical Correlation Analysis, and Structural Equation Modeling, we identify temperature as the primary environmental driver, particularly during early developmental stages (45 Days After Full Bloom, DAFB), and uncover nonlinear, hierarchical effects of preharvest environmental factors such as vapor pressure deficit, relative humidity, and light on quality traits. Machine learning models (Multiple Linear Regression, Random Forest, XGBoost) achieve high predictive accuracy (R² > 0.99 for Multiple Linear Regression), with temperature as the key predictor. These baseline results represent findings from a single growing season and require validation across multiple seasons and cultivars before operational application. Temporal analysis highlights the importance of early-stage environmental conditions. Integrating structural and environmental data through innovative visualization tools, such as anatomy-based radar charts, facilitates comprehensive interpretation of complex interactions. This multidisciplinary framework enhances predictive precision and provides a baseline methodology to support precision orchard management under typical agricultural variability. Full article

Dewatering of sewage sludge is a key operational element of wastewater treatment plants and has major economic implications, as it entails the costs of thickening, transport, and disposal. The aim of this study was to determine the influence of selected polyelectrolytes and their dosages on dewatering efficiency and to present an innovative, multicriteria method of result evaluation using radar charts. In this research, 10 different polyelectrolytes were assessed in terms of sludge dewaterability, considering conditioning parameters including Specific Resistance to Filtration (SRF), Capillary Suction Time (CST), and centrifugation performance. The results were presented in the form of radar charts, enabling both an overall evaluation of the effectiveness of each product and an assessment of their suitability for specific dewatering technologies, such as belt filter presses and centrifuges. The analysis showed that polyelectrolytes with higher cationic charge provided better dewatering performance. The proposed visualization method allows us to analyze the effects across different conditioners and technologies. The best sludge conditioning effect (maximum radar chart area) was achieved with Praestol 665, a polyelectrolyte with a high cationic charge level. This method is a practical tool for selecting the optimal agent for sewage sludge dewatering. Full article

The aim of the study was to identify doctoral and postdoctoral dissertations that were created between 2003 and 2023 and based on tourism research, and the promotion procedures were conducted within the discipline of ‘Physical Culture Sciences’ (PCS). An attempt was made to identify the connections between such theses and other fields/disciplines of science and the methodological approaches used in them. The conducted research was empirical in nature, and its result is the opinions of the authors of 119 doctoral theses and 42 postdoctoral dissertations addressing tourism issues on the scientific disciplines within which these works were located. An attempt was also made to estimate the contribution that PCS had in their creation. The research results revealed strong connections between ‘tourism’ Ph.D. and postdoctoral theses completed in the PCS discipline, especially with the fields of ‘Social Sciences’ and ‘Humanities’. The results also allowed for determining and performing multi-aspect analyses regarding the methodological profiles of the examined works, visualising such profiles in the form of radar charts, which included information on their 16 most important methodological features. In the research, it was shown that doctoral and postdoctoral dissertations devoted to tourism issues completed within the discipline of PCS are characterised by great diversity concerning the applied methodological approaches. They are largely multi-/inter-disciplinary in nature, and the doctoral theses are dominated by empirical methods focused on cultural research. At the same time, these profiles are strongly diversified depending on the other field of science to which the works formally assigned to the PCS are related. The research results presented in this article suggest that typical bibliometric analyses regarding the disciplinary structure of advance tourism research fail to capture the diversity and methodological specificity of research conducted within various scientific disciplines. This necessitates further research, particularly empirical studies identifying their methodological profiles and demonstrating their differences. These studies can be a valuable source of information not only for methodological refinement and improving the quality of tourism research, but may also provide a basis for discussion on the placement of PCS in the classification of sciences and the role that tourism research should play within this discipline. Full article

Long-term greenhouse operations face a critical challenge in the form of soil quality degradation, yet the key intervention periods and underlying mechanisms of this process remain unclear. This study aims to quantify the effects of greenhouse lifespan on the evolution of soil quality and to identify critical periods for intervention. We conducted a systematic survey of greenhouse operations in a representative area of Yunnan Province, Southwest China, and adopted a space-for-time substitution design. Using open-field cultivation (OF) as the control, we sampled and analyzed soils from vegetable greenhouses with greenhouse lifespans of 2 years (G2), 5 years (G5), and 10 years (G10). The results showed that early-stage greenhouse operation (G2) significantly increased soil temperature (ST) by 8.38–19.93% and soil porosity (SP) by 16.21–56.26%, promoted nutrient accumulation and enhanced aggregate stability compared to OF. However, as the greenhouse lifespan increased, the soil aggregates gradually disintegrated, particle-size distribution shifted toward finer clay fractions, and pH changed from neutral to slightly alkaline, exacerbating nutrient imbalances. Compared with G2, G10 exhibited reductions in mean weight diameter (MWD) and soil organic matter (SOM) of 2.41–5.93% and 24.78–30.93%, respectively. Among greenhouses with different lifespans, G2 had the highest soil quality index (SQI), which declined significantly with extended operation; at depths of 0–20 cm and 20–40 cm, the SQI of G10 was 32.59% and 38.97% lower than that of G2, respectively (p < 0.05). Structural equation modeling (SEM) and random forest analysis indicated that the improvement in SQI during the early stage of greenhouse use was primarily attributed to the optimization of soil hydrothermal characteristics and pore structure. Notably, the 2–5 years was the critical stage of rapid decline in SQI, during which intensive water and fertilizer inputs reduced the explanatory power of soil nutrients for SQI. Under long-term continuous cropping, the reduction in MWD and SOM was the main reason for the decline in SQI. This study contributes to targeted soil management during the critical period for sustainable production of protected vegetables in southern China. Full article

The recent debate on Environmental, Social, and Governance (ESG) factors has focused primarily on financial decision making and risk management from the perspectives of developed economies. However, in most developing countries, ESG risk models for mortgage lenders are very limited. In most of these countries, ESG-rating providers employ widely varying methodologies and disclosure policies, often resulting in divergent assessments of the same organization. This research develops a pilot statistical-analysis, dual-horizon ESG risk model specific to the Mexican mortgage industry, which provides a better understanding of how ESG risk could evolve over time across financial, operational, regulatory, and reputational dimensions in Mexico. This dual-horizon ESG framework considers a two-year short-term risk assessment and a ten-year long-term risk assessment. This research integrates expert opinions with a scoring system that improves on traditional methods. Dependability and internal consistency are tested using the Intraclass Correlation Coefficient (ICC) and Cronbach’s alpha. Radar chart visualization and cluster analysis are used to visualize the empirical results. The empirical findings show that environmental risk has strong temporal effects, and the perceived severity is 20% higher over the longer time horizon. Furthermore, social risk exhibits high variability, identifying it as a critical risk for financial stability and regulatory compliance. Cluster analysis identifies systematic patterns in expert opinions that determine two groups, making the qualitative findings derived from radar plots more robust. Group 0 (75% of experts) has an institutional view about ESG risks. Group 1 (25% of experts) aligns with an affiliation to large financial institutions. Finally, this research identifies three key sustainability challenges for the mortgage sector in Mexico: exposure to climate-induced stress, fragmented regulatory frameworks, and social inequality. Full article

With the increasing air traffic flow, the workload of air traffic controllers is also growing, and their proficiency directly impacts civil aviation safety and efficiency. To address the lack of clear training objectives and inconsistent evaluation methods in the initial controller training at the Southwest Air Traffic Management Bureau, this study aimed to develop and validate a core competency model for initial air traffic controllers. Referencing ICAO Document 10056, the study first defined core competencies. Subsequently, using job analysis, the behavioral event interview (BEI) method, and expert panels, a core competency model tailored to the training objectives of the Southwest ATMB was constructed. The key findings of this research include: first, the defined structure of the developed model, comprising seven competency dimensions, 21 elements, and 26 observable behaviors (OBs); second, the determination of combined weights for each dimension and indicator using questionnaire surveys, the Analytic Hierarchy Process (AHP), and the Entropy Weight Method; and third, the successful application and validation of the model. Specifically, in its application, the weighted TOPSIS method was employed to evaluate trainees in a specific group. This not only provided a ranking of trainee abilities but also facilitated in-depth analysis through radar charts of competency dimensions and box plots of OB items. These application results demonstrate the model’s effectiveness and practicality. Full article

Using coal gangue in highway base construction provides a sustainable and high-value solid waste recycling approach. This research focused on the mechanical and durability properties of coal gangue from tunneling operations. Six experimental tests, such as unconfined compressive strength (UCS), flexural–tensile strength (FTS), etc., were carried out. The impact of aggregate gradation on coal gangue mixtures’ performance was systematically evaluated. XRD and SEM were used to explore the microstructural mechanisms in cement-stabilized coal gangue–gravel mixtures (CGM). An improved evaluation model, the Delphi–entropy weight–TOPSIS (DET) method, integrating Delphi and entropy weighting, was proposed. Together with an advanced radar chart, it evaluates eight performance criteria, including mechanical, durability, economic, and environmental aspects. The results show that increasing the coal gangue content in mixtures decreases UCS, dynamic compressive rebound modulus (DCRM), FTS, fatigue life, and drying shrinkage performance. Coarse aggregates relieve drying shrinkage, while fine ones improve long-term mechanical properties. Gradation T1~3 promotes the formation of C–S–H gel and AFt crystals, enhancing compactness. Based on the DET model’s quantitative evaluation, T1~3 was determined as the optimal mix for expressway bases, achieving a balance between mechanical performance, durability, and sustainability. Full article

In light of the challenges confronting urban areas due to increasing populations and spatial constraints, urban green infrastructure is vital for fostering environmental balance, enhancing community well being, and promoting sustainable urban development. This situation underscores the necessity for strategies that reconcile the escalating demand for constructed environments with the enhancement of urban green infrastructure in urban areas. This study seeks to empirically investigate an integrated spatial analysis approach that synthesises the quality of urban green infrastructure and land characteristics by incorporating diverse perspectives, utilising the Altstetten-Albisrieden district of Zurich as a case study. It systematically evaluates factors including development density, green surface coverage, leaf area, green ratio and connectivity, and the accessibility of public green spaces within the studied district. A 10-m rectangular grid was employed to visualise and integrate the evaluation results from different perspectives. Furthermore, clustering algorithms were utilised to generate spatial patterns indicative of unique land characteristics. By comparing the results from various clustering algorithms, this study adopted the fifteen clusters derived from the K-Means method, employing radar charts to describe the characteristics of each cluster, and partitioned the district into five zones to provide recommendations regarding the provision and optimisation of urban green infrastructure within the district. Ultimately, it highlighted the necessity of increasing community gardens and green spaces in densely built areas and leveraging existing structures to augment vegetation and plant life for the enhancement of ecological benefits. Full article

This study establishes a comprehensive framework for evaluating sustainability by integrating radar chart analysis with fuzzy linguistic methods, helping enterprises select suppliers that align with sustainable development principles. Testing within the textile industry confirmed the framework’s reliability, effectively identifying strengths and areas for improvement across key sustainability dimensions: environmental, social, economic, and governance. The analysis highlighted strong supplier performance in governance and social responsibility, particularly in human rights protection, while emphasizing the need for improvements in environmental aspects such as water resource management and product recycling. Economic factors, including product quality and delivery capability, demonstrated significant potential, though cost efficiency remained a challenge. Recommendations focus on enhancing board diversity, employee satisfaction, and circular economy initiatives. The framework demonstrates strong applicability within the textile industry and has the potential for adaptation in other sectors. Future research should conduct industry-specific validations to refine weight allocation for dynamic markets. Full article

The interior of a pigsty is a nonlinear system formed by multiple interacting environmental factors, making it challenging to reasonably and accurately assess the environmental comfort levels. To address this, we propose an environmental comfort evaluation index based on livestock farming standards. By combining the analytic hierarchy process (AHP) and entropy weight method (EWM), we determine the weights of each evaluation index. Finally, the evaluation results are visualized using radar charts, and the model is validated. We apply this model to monitor and analyze environmental factors in a fattening pigsty at a farm in Central Jilin Province. The results demonstrate that the AHP-EWM multi-factor comprehensive evaluation method effectively reflects overall environmental comfort variations in the pigsty and captures interactions among environmental factors across different time periods. This study establishes a methodological foundation for comprehensive pigsty environmental assessment, precision control, and enhanced environmental comfort. Full article

This paper introduces a novel topology (upper approximated G-topology) on vertex sets of graphs using rough upper approximation neighborhoods, extending prior work on graph-induced topologies. Key results include characterizing discrete/indiscrete topologies for complete graphs, cycle graphs, and bipartite graphs (Theorems 1–3). The discrete topology for cycle graphs $C_n$, $n>5$, is particularly insightful. Exploring further, we delve into the continuity and isomorphism of graph mappings. Subsequently, we apply these findings to enhance radar chart graphical methods through the analysis of corresponding graph structures. These applications demonstrate practical relevance, linking graph structures to data visualization. Full article

In the current work, authors reviewed the latest research results in landslide susceptibility mapping (LSM) using artificial intelligence (AI) methods. Based on an overall review of collected publications, the review was classified into four sections based on their complexity: single-model approaches, enhanced models with optimization, ensemble models, and hybrid models. Each category offers distinct advantages and is suited to specific geographic and data conditions, enabling the selection of an optimal model type based on the complexity and requirements of the mapping task. Among models, random forest (RF), support vector machine (SVM), convolutional neural network (CNN), and multilayer perception (MLP) are used as the baseline to compare any new model introduced to develop LSM. Moreover, compared to previous review works, the number of LSM conditioning factors used in AI models are significantly increased, up to 122 factors. Their relation to the AI models is illustrated using Sankey diagram, while a radar chart is used to further visualize the dataset size per reviewed work for comparative purposes. In the main part of the current review work, the main findings are summarized into a table form, where the reader can find the overall relations between landslide conditioning factors, landslide dataset size, applied AI models, and their accuracy on predicting LSM for selected geographical locations. In terms of the regions, Asia is leading in the application of AI models to generate LSM, and in such regions with dense populations falling into higher landslide risk categories, there are more ongoing research activities, using modern AI methods. This trend underscores the increased use of AI in disaster management, with implications for improving practical applications, such as early warning systems and informing policy decisions aimed at risk reduction in vulnerable areas. Full article

Accurate estimation of construction costs is of foremost importance in construction management processes. Considering the changes and unexpected situations, cost estimations should be revised during the construction process. This study investigates the predictability of earned value management (EVM)-based approaches using machine learning (ML) methods. A total of 2318 data points via 19 EVM-based cost estimation methods were created and six ML methods were used for the analyses. The planned and actual project data of the rough construction activities of a housing project completed in Türkiye were used. The ML methods considered consisted of adaptive neuro-fuzzy inference systems (ANFISs), artificial neural networks (ANNs), Gaussian process regression (GPR), long-short-term memory (LSTM), M5 model trees (M5TREEs), and support vector machines (SVMs). The created models were compared using performance criteria such as mean absolute percentage error (MAPE), relative root means square error (RRMSE), coefficient of determination (R²), Nash–Sutcliffe efficiency coefficient (NSE), and overall index of model performance (OI). Moreover, radar charts, trend graphs, Taylor diagrams, violin plots, and error boxplots were used to evaluate the performance of the estimation models. The results revealed that the classical ANN model outperforms EVM-based cost methods that utilize current ML methods. Full article

In agriculture, one of the most crucial elements for sustained plant production is light. Artificial lighting can meet the specific light requirements of various plants. However, it is a challenge to find optimal lighting schemes that can facilitate a balance of plant growth and nutritional qualities. In this study, we experimented with the light intensity required for plant growth and nutrient elements. We designed three light intensity treatments, 180 μmol m⁻² s⁻¹ (L1), 210 μmol m⁻² s⁻¹ (L2), and 240 μmol m⁻² s⁻¹ (L3), to investigate the effect of light intensity on lettuce growth and quality. It can be clearly seen from the radar charts that L2 significantly affected the plant height, fresh weight, dry weight, and leaf area. L3 mainly affected the canopy diameter and root shoot ratio. The effect of L1 on lettuce phenotype was not significant compared with that of the others. The total soluble sugar, vitamin C, nitrate, and free amino acid in lettuce showed more significant increases under the L2 treatment than under the other treatments. In addition, the transpiration rate and stomatal conductance were opposite to each other. The comprehensive evaluation of the membership function value method and heatmap analysis showed that lettuce had the highest membership function value in L2 light intensity conditions, indicating that the lettuce grown under this light intensity could obtain higher yield and better quality. This study provides a new insight into finding the best environmental factors to balance plant nutrition and growth. Full article

The proposal of sintered sludge cement (SSC) paste aligns with the low-carbon development goals of building materials. However, there is a lack of scientific guidance for the preparation of sintered sludge ash (SSA). Herein, this study systematically investigates the influence mechanism of mechanical–thermal activation parameters of SSA on the mechanical properties and life cycle assessment (LCA) of SSC paste, and conducts a comprehensive evaluation using a radar chart and the TOPSIS method. The results show that with the increase in calcination temperature and duration, the compressive and flexural strengths of the SSC paste are improved, especially at 600 °C and above, increasing by 57.92% and 62.52%, respectively. The longer calcination time at 1000 °C results in a decrease in its mechanical properties. The addition of SSA significantly reduces the LCA indicators of cement paste. Specifically, 30% SSA only contributes 8.1% to the global warming potential. Compared to calcination, the LCA indicators have less sensitivity to ball milling, and prolonging the time hardly increases them. Based on performance and environmental impact, the optimal SSA is obtained by calcining at 800 °C for 2 h and ball milling for 10 min. This study can provide theoretical guidance for efficient building material utilization of dredged sludge. Full article