Search Results (139)

This research aims to identify and prioritize key planning elements for the redevelopment of such housing complexes by incorporating perspectives from both experts (supply-side) and residents (demand-side). To achieve this, a hybrid multi-criteria decision-making framework was developed by integrating the Analytic Hierarchy Process (AHP) and the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS). A total of 25 planning elements were identified through Focus Group Interviews and organized into five domains: legal and institutional reforms, project feasibility, residential conditions, social integration, and complex design. The AHP was used to assess the relative importance of each element based on responses from 30 experts and 130 residents. The analysis revealed a clear divergence in priorities: experts emphasized feasibility and regulatory considerations, while residents prioritized livability and spatial quality. Subsequently, the TOPSIS method was applied to evaluate four real-world redevelopment cases. From the supply-side perspective, Seoul A District received the highest score (0.58), whereas from the demand-side perspective, Gyeonggi D District ranked highest (0.69), illustrating the differing priorities of stakeholders. Overall, Gyeonggi D District emerged as the most favorable option in the combined evaluation. This research contributes a structured and inclusive decision-making framework for the regeneration of public housing. By explicitly comparing and quantifying the contrasting preferences of key stakeholders, it underscores the critical need to balance technical feasibility with resident-centered values in future redevelopment initiatives. Full article

The shikimate pathway is the fundamental metabolic route for aromatic amino acid biosynthesis in bacteria, plants, and fungi, but is absent in mammals. This review explores how multi-enzyme synergy and allosteric regulation coordinate metabolic flux through this pathway by focusing on three key enzymes: 3-deoxy-d-arabino-heptulosonate-7-phosphate synthase, chorismate mutase, and tryptophan synthase. We examine the structural diversity and distribution of these enzymes across evolutionary domains, highlighting conserved catalytic mechanisms alongside species-specific regulatory adaptations. The review covers directed evolution strategies that have transformed naturally regulated enzymes into standalone biocatalysts with enhanced activity and expanded substrate scope, enabling synthesis of non-canonical amino acids and complex organic molecules. Industrial applications demonstrate the pathway’s potential for sustainable production of pharmaceuticals, polymer precursors, and specialty chemicals through engineered microbial platforms. Additionally, we discuss the therapeutic potential of inhibitors targeting pathogenic organisms, particularly their mechanisms of action and antimicrobial efficacy. This comprehensive review establishes the shikimate pathway as a paradigmatic system where understanding allosteric networks enables the rational design of biocatalytic platforms, providing blueprints for biotechnological innovation and demonstrating how evolutionary constraints can be overcome through protein engineering to create superior industrial biocatalysts. Full article

The increasing release of nanoparticles into aquatic environments, particularly via wastewater, raises concerns about their biological effects on microbial communities. This study investigated the molecular response of Pseudomonas putida to aluminum oxide nanoparticles (Al₂O₃NPs) under controlled conditions and in synthetic wastewater, both before and after biological treatment. Acute toxicity was evaluated using growth inhibition assays, while the expression of katE, ahpC, and ctaD—genes associated with oxidative stress and energy metabolism—was quantified via RT-qPCR. Exposure to pristine Al₂O₃NPs induced a strong, time-dependent upregulation of all tested genes (e.g., katE and ahpC up to 4.5-fold). In untreated wastewater, this effect persisted but at a lower intensity; bulk Al₂O₃ caused only moderate changes. Treated wastewater samples showed markedly reduced gene expression, indicating partial detoxification. Nanoparticles elicited stronger biological responses than their bulk counterparts, confirming the material form-specific effects. Comparative analysis with Daphnia magna revealed similar patterns of oxidative stress gene activation. These findings highlight the influence of nanoparticle form and environmental matrix on microbial responses and support the use of gene expression analysis as a sensitive biomarker for nanoparticle-induced stress in environmental risk assessment. Full article

Building Information Modeling (BIM) is an emerging information technology tool and management concept in the construction industry, enabling the transition from traditional 2D drawings to 3D models. It helps improve efficiency and promote industrial upgrading in the construction sector. However, in actual project practices, the effectiveness of BIM application has not been as expected, and the return on investment (ROI) may even be negative. Through a literature review, we found that risk identification, correlation analysis, and risk assessment related to BIM implementation require further research. To better promote the application of BIM in the construction industry, this study employs relevant methods to analyze the risk factors of BIM implementation. Through the literature review, 31 BIM implementation risk factors were identified, and 24 major risk factors were extracted using the AHP (Analytic Hierarchy Process) method. The ISM (Interpretative Structural Modeling) method was then used to determine the interrelationships among these major risk factors, establishing a hierarchical model with seven levels. Through MICMAC (Matrices Impacts Corises-Multiplication Appliance Classment) analysis, the BIM implementation risk factors were categorized into three groups, and three-tiered response strategies were proposed at the industry, organizational, and project levels. By analyzing the main risk factors of BIM application in China’s construction industry and formulating corresponding response strategies to promote its successful application, this study contributes to the knowledge system. The findings also provide a reference for other countries and regions to clarify major risk factors and their interrelationships, thereby improving the effectiveness of BIM implementation. Full article

Polyembryonic maize, capable of producing multiple seedlings from a single kernel, holds great potential value in agricultural and industrial applications, but the seedling quality needs to be improved. In this study, seedlings of two waxy maize (Zea mays L. sinensis Kulesh) inbred lines, D35 (a polyembryonic line with twin shoots) and N6110 (single-shoot), exhibited similar relative growth rates during 1 to 5 days post-germination. UPLC-MS/MS profiling of 3- to 5-day-old seedling roots and shoots revealed that H2JA, MeSAG, and IAA-Val-Me were the common differentially accumulated metabolites (DAMs) of the 3-day-old vs. 5-day-old seedlings of D35 and N6110 in the same tissues, and MeSAG, tZ9G, cZROG, and DHZROG were identified in D35 vs. N6110 across the same tissues and the same periods. RNA-seq analyses showed various processes involved in seedling development, including DNA replication initiation, rhythmic processes, the cell cycle, secondary metabolic processes, and hormone biosynthetic regulation. The differentially expressed genes (DEGs) between D35 and N6110 were significantly enriched in organic hydroxy compound biosynthetic, alcohol biosynthetic, organic hydroxy compound metabolic, abscisic acid biosynthetic, and apocarotenoid biosynthetic processes. The KEGG-enriched pathways of DAMs and DEGs identified that AUX1, AHP, A-ARR, JAR1, SIMKK, ERF1, and GID2 might be conserved genes regulating seedling growth. The integrated analyses revealed that 98 TFs were potentially associated with multiple hormones, and 24 of them were identified to be core genes, including 11 AP2/ERFs, 4 Dofs, 2 bZIPs, 2 MADS-box genes, 2 MYBs, 1 GATA, 1 LOB, and 1 RWP-RK member. This study promotes a valuable understanding of the complex hormone interactions governing twin-shoot seedling growth and offers potential targets for improving crop establishment via seedling quality. Full article

Gas accumulation triggered by coal and gas outbursts is the core cause of secondary disasters in coal mines. This study focuses on the risk assessment of gas accumulation during disaster scenarios, proposing a multidimensional evaluation method integrating the analytic hierarchy process (AHP), information entropy theory, kernel density estimation, and dynamic risk propagation modeling. A unified intelligent prevention system encompassing “monitoring–prediction–decision making” is established. Leveraging the TFIM3D simulation platform and case studies from the Qunli Coal Mine accident, this research reveals spatiotemporal evolution patterns of gas concentration and explosion risk thresholds. A ventilation optimization strategy based on risk classification is proposed. The results demonstrate that the dynamic risk index (DRI), derived from the coupling of the roadway air volume stability coefficient and gas concentration information entropy, can accurately identify high-risk zones. The findings provide theoretical foundations and practical pathways for dynamic risk management in ventilation systems during coal and gas outburst disasters. Full article

Overextraction of groundwater, as well as rapidly changing land use patterns, climatic change, and anthropogenic activities, in the densely populated Melur of Tamil Nadu state in India, has led to aquifer degradation. This study maps the groundwater potential (GWPZ) by evaluating 678 km² of this region in the Analytical Hierarchy Processes (AHP) and by using remote sensing and GIS tools as part of SDG 6 for the sustainable management of drinking, irrigation, and industrial uses for future generations. Data information layers, such as aquifer (a), topography (t), lineaments (l), land-use/land-cover (LuLc), soil (s), rainfall (r), and drainage (d) characteristics, separated the study area between poor and excellent groundwater potential zones with 361 km² or 53% of the study area remaining as low GWP and the prospective excellent groundwater potential zone covering only 9 km² (1.3% of total area). The integrated approach of the GWPZ and Water Quality Index (WQI) can effectively identify different zones based on their suitability for extraction and consumption for better understanding. This study also evaluates the performance of three machine learning models, such as Random Forest (RF), Gradient Boosting, and Support Vector Machine (SVM), based on a classification method using the same layers that govern the groundwater potential. The results indicate that both the RF model and Gradient Boosting achieved 100% accuracy, while SVM had a lower accuracy of 50%. Performance metrics such as precision, recall, and F1-score were analyzed to assess classification effectiveness. The findings highlight the importance of model selection, dataset size, and feature importance in achieving optimal classification performance. Results of this study highlight that the aquifer system of Melur has a low groundwater reserve, and it requires adequate water resource management strategies such as artificial recharge, pumping restriction, and implementation of groundwater tariffs for sustainability. Full article

A central focus of this study was the methodology used to evaluate both humans and AI platforms, particularly in terms of their competitiveness and the implications of six key challenges to society resulting from the development and increasing use of artificial intelligence (AI) technologies. The list of challenges was compiled by consulting various online sources and cross-referencing with academics from 15 countries across Europe and the USA. Professors, scientific researchers, and PhD students were invited to independently and remotely evaluate the challenges. Rather than contributing another discussion based solely on social arguments, this paper seeks to provide a logical evaluation framework, moving beyond qualitative discourse by incorporating numerical values. The pairwise comparison of AI challenges was conducted by two groups of participants using the multicriteria decision-making model known as the analytic hierarchy process (AHP). Thirty-eight humans performed pairwise comparisons of the six challenges after they were listed in a distributed questionnaire. The same procedure was carried out by four AI platforms—ChatGPT, Gemini (BardAI), Perplexity, and DedaAI—who responded to the same requests as the human participants. The results from both groups were grouped and compared, revealing interesting differences in the prioritization of AI challenges’ impact on society. Both groups agreed on the highest importance of data privacy and security, as well as the lowest importance of social and cultural resistance, specifically the clash of AI with existing cultural norms and societal values. Full article

With rapid urbanization, urban underground space (UUS) development has become crucial for sustainable urban growth. This paper systematically reviews geological suitability evaluation (GSE) methods for UUS, integrating theoretical frameworks, indicator systems, and assessment techniques. We establish a comprehensive evaluation framework based on environmental strategic assessment (ESA) principles, analyzing key geological factors, including rock/soil properties, hydrogeological conditions, geological hazards, and existing underground structures. The study compares weighting methods (AHP, EWM, CRITIC) and comprehensive evaluation models (FCE, TOPSIS, BNM), highlighting their advantages and application scenarios. A case study of Xiong’an New Area demonstrates how multi-layer UUS planning integrates geological constraints with sustainable development goals. The results show that combining 3D geological modeling with hybrid evaluation methods significantly improves decision-making accuracy. The review provides practical guidance for optimizing UUS utilization while addressing current challenges in indicator selection, weight rationalization, and heterogeneity management. Full article

The increasingly serious imbalance between the supply and demand of water resources necessitates the establishment of a scientific and reasonable comprehensive evaluation method for industrial water use efficiency (WUE). In this study, a general method for industrial WUE evaluation on an enterprise scale was proposed by combining the analytic hierarchy process (AHP), entropy weight method (EWM), and self-organizing map (SOM), and it was tested in several areas of Zhejiang Province, China. The results show that the composite indexes generated using the AHP and EWM were different and were employed as the input of the SOM to divide enterprises into four categories. Most enterprises were classified as Class A, with a relatively high WUE, accounting for 82.5% of the total, while those in Class D, with a relatively low WUE, only accounted for 0.5% of the total. Furthermore, the differences in WUE for industry classification and spatial distribution were also analyzed. The classification results of several industries were more diverse, especially for those industries in which water plays an important role in production. Moreover, the spatial distribution of WUE classifications also implied that the clustering of enterprises has a positive effect on the improvement in WUE. In other words, it is feasible to improve WUE through industry clustering and sub-industry management. In summary, a comprehensive, detailed evaluation of industrial WUE was conducted on an enterprise scale, which can also be applied to other areas and used as a reference for local water resource managers for formulating targeted policies. Full article

Metabolic engineering of the shikimate pathway offers a promising strategy for enhancing the production of aromatic compounds in microbial hosts. However, feedback inhibition of key enzymes, such as the 3-deoxy-D-arabino-heptulosonate 7-phosphate synthase (DAHP synthase), often limits the yield of target products. In this study, we focused on the DAHP synthase (AroF-I) from Pseudomonas putida. Through computational modeling and experimental validation, we identified specific amino-acid residues responsible for tyrosine-mediated feedback inhibition. By targeted mutagenesis, we engineered DAHP synthase variants that exhibit reduced sensitivity to feedback inhibition. The introduction of these engineered enzymes into a metabolically engineered Pseudomonas putida strain resulted in significantly increased production of p-coumaric acid. Our findings provide valuable insights into the regulation of the shikimate pathway and demonstrate the potential of protein engineering to improve microbial production of aromatic compounds. Full article

The current examination used a multi-criteria decision-making (MCDM) approach to optimize the roughness parameters of S-shaped ribs (SSRs) in a solar thermal collector (STC) duct using air as the working fluid. Different SSRs were tested to identify the combination of parameters resulting in the best performance. Geometrical parameters such as relative roughness pitch ($P_R/e_{RH}$) varied from 4 to 12, relative roughness height ($e_{RH}/D_{hd}$) from 0.022 to 0.054, arc angle (${\alpha }_{Arc}$) from 30° to 75°, and relative roughness width ($W_{Duct}/w_{RS}$) from 1 to 4. The Nusselt number (${Nu}_{RP}$) and friction factor ($f_{RP}$), findings which impact the STC performance, rely on SSRs. The performance measurements show that no combination of SSR parameters lead to the best enhancement heat transfer rate at low enhancement in the friction. So, a hybrid multi-criteria decision-making strategy using the Analytical Hierarchy Process (AHP) for criterion significance and Multi Attributive Border Approximation Area Comparison (MABAC) for alternative ranking was used to determine which combination of geometrical parameters will result in the optimum performance of a roughened STC. This work employs a hybrid MCDM technique to optimise the effectiveness of an STC roughened with SSRs. To optimize the SSR design parameters, this study used the hybrid AHP-MABAC technique for analytical assessment of a roughened STC. The optimization results showed that the STC roughened with SSRs achieved the optimum performance at $P_R/e_{RH}$ = 8, $e_{RH}/D_{hd}$ = 0.043, ${\alpha }_{Arc}$ = 60° and $W_{Duct}/w_{RS}$ = 3. Full article

Assessing the geological suitability of urban underground space development is crucial for mitigating geological risks. Traditional 2D evaluation methods fail to capture complex vertical variations in underground space, hindering precise planning. This paper presents an innovative 3D-CWC framework, combining a weighted cloud model with three-dimensional geological modeling, to address vertical complexity and uncertainty in geological assessments. The study area, located in the northern part of Kunming’s Second Ring Road, is divided into 22 million 25 m × 25 m × 1 m 3D units for evaluation. The framework uses the improved AHP and CRITIC methods to assign weights to key geological indicators, addressing both subjective and objective uncertainty, and employs a cloud model to determine geological suitability levels. The results are visualized using 3D geological modeling. The key findings include the following: (1) approximately 71% of the area within a −50 m depth range is suitable or more suitable for underground space development; (2) active fractures and groundwater are the main unfavorable factors; and (3) the geological suitability varies significantly with depth, with shallow areas being less suitable due to soft soil and complex hydrogeological conditions. The framework is further applied to assess the geological suitability of Kunming Metro Line 10, providing valuable decision support for infrastructure development. Compared to existing methods, this framework integrates cloud modeling and 3D geological modeling, offering a more comprehensive approach to handling underground space complexity. It is adaptable and holds potential for global applications, supporting urban underground space development in diverse geological conditions. Full article

In coal mine roadways excavated along the goaf with water accumulation, the roadway is subjected to the combined effects of water infiltration and multiple stresses from excavation activities, leading to significant deformation and challenges in determining the appropriate coal pillar width. This study, based on the Jianxin Coal Mine 4301 tailgate, utilizes the advanced three-dimensional numerical calculation software FLAC^3D 6.0 to develop a comprehensive seepage flow model. By analyzing the distribution of key roadway surrounding rock properties, such as deviatoric stress, plastic zone, and dissipated energy, the influence of coal pillar width on roadway deformation and failure characteristics is systematically investigated. The findings provide novel insights into the roadway stability control under complex geological conditions. Specifically, the results reveal that: (1) When the coal pillar width is less than 9 m, stress concentration zones are observed, fully connected by plastic zones and dissipated energy. For widths exceeding 9 m, the influence of the goaf diminishes, leading to a stress reduction zone within the coal pillar and a shift in dissipated energy density distribution from a penetrating shape to an independent double-core shape. The plastic zones on both the goaf and roadway sides become independent, indicating a transition from an unstable to a stable coal pillar state. (2) Using the Analytic Hierarchy Process (AHP), a zoning control system for the roadway surrounding rock is established, dividing the roadway into three regions: normal support, reinforced support, and special support. Industrial experiments corroborate the simulation results, and on-site monitoring demonstrates that the control measures significantly improve roadway stability. This study presents an innovative approach to the design and control of coal pillars in water-affected mine roadways, offering valuable contributions to both the scientific understanding and practical application of mining engineering in similar geological settings. Full article

This study examines the impact of noise inside the cabin of an agricultural tractor on the operator, focusing on operational parameters: agrotechnical surfaces, speed of movement, and tire pressure. Noise measurements were conducted on a LANDINI POWERFARM 100 tractor on agricultural fields and access roads. The tests followed HRN ISO 5008:2015 standards for smooth and rough tracks, and the noise was measured with a Metrel Multinorm device using an “A” filter, positioned according to HRN ISO 6396:2018 and ISO 5131:2018 standards. A two-factor analysis of variance was performed, considering tractor speed (“B”) and tire pressure (“C”). The Analytical Hierarchy Process (AHP) was applied to group the agronomic surfaces (“A”) into smooth and rough categories. By conducting a one-way ANOVA on the mean values of the dependent variables grouped by AHP, significant differences in noise were found. On the right side of the operator, significant noise differences were found between speeds B2, B3, and B4 (from 73.60 dB(A) to 73.99 dB(A)), with a decrease in noise as speed increased at C2. The coefficient of determination decreased from 78.99% to 44.93% with increasing speed. On the left side, significant differences in noise were found at tire pressure C1 at lower speeds, with increasing R² values up to 50.89%, and similarly for C3 at higher speeds, reaching 72.61%. Full article