Order Preference Research Articles

Evaluation of the Smart Logistics Based on the SLDI Model: Evidence from China

Smart logistics (SL) reflects the digital transformation of the logistics industry, which is key for economic development. Most evaluations are based on the application of technology in SL, and few studies have evaluated SL from a comprehensive perspective. The paper builds the SL development index (SLDI) model from five dimensions based on the driving force, pressure, state, impact, and response (DPSIR) model and identifies the indicator weight by the entropy weight technique. The paper employs the ETDK method, a combined quantitative approach that incorporates entropy weight (E), the technique for order preference by similarity to an ideal solution (TOPSIS) (T), the Dagum Gini coefficient (D), and Kernel density estimation (K), to calculate the closeness degree, analyze spatial-temporal differentiation, and explain the distribution characteristics using data from China spanning 2013 to 2021. The findings show that (1) The SL evaluation is multidimensional and cannot be evaluated only based on technical indicators. A comprehensive evaluation indicator system is necessary. (2) A combined quantitative approach can measure SL development from multiple perspectives and get a clearer picture of the characteristics and regional differences of SL. (3) Influenced by economic development, infrastructure, regional clusters, location, talent, etc., the overall SL development is improving yearly, but SL development in different regions is unbalanced and has different distribution characteristics. The SLDI model developed in this paper will provide a more scientific and reasonable tool for comprehensively evaluating SL. The findings are helpful in proposing suggestions and optimization approaches for subsequent research on SL evaluation and development.

Recommendation for Self-Help Housing Stimulus Assistance (BSPS) Recipient Using Multi-Criteria Decision Making Methods

The Housing and Settlement Area Office aims to improve the accuracy and efficiency of selecting beneficiaries for the Self-Help Housing Assistance (BSPS) program by using the Weighted Product (WP) and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) methods. This study was conducted in Sungkai Tengah Subdistrict, North Lampung, involving six criteria: land ownership, employment, house condition, income, independence, and household size.The WP method is used to determine the weight of each criterion by calculating the product of the criteria values raised to the power of their respective weights, which helps in ranking the alternatives. TOPSIS is applied to identify the best alternative by comparing the geometric distance of each option to the ideal solution, which is either the maximum or minimum value of each criterion The results indicate that the WP and TOPSIS methods can provide more objective and transparent rankings. The application of these methods resulted in the highest ranking for ID V3 with a vector V score of 0.1317. The decision support system developed from this research is expected to assist the Housing and Settlement Area Office in distributing aid more accurately, fairly, and efficiently, thereby supporting the government's goals in improving housing quality and spatial planning

Evaluating Multi-Criteria Decision-Making Methods for Sustainable Management of Forest Ecosystems: A Systematic Review

Multi-criteria decision-making (MCDM) methods provide a framework for addressing sustainable forest management challenges, especially under climate change. This study offers a systematic review of MCDM applications in forest management from January 2010 to March 2024. Descriptive statistics were employed to analyze trends in MCDM use and geographic distribution. Thematic content analysis investigated the appearance of MCDM indicators supplemented by Natural Language Processing (NLP). Factorial Correspondence Analysis (FCA) explored correlations between models and publication outlets. We systematically searched Web of Science (WoS), Scopus, Google Scholar, Semantic Scholar, CrossRef, and OpenAlex using terms such as ‘MCDM’, ‘forest management’, and ‘decision support’. We found that the Analytical Hierarchy Process (AHP) and the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) were the most commonly used methods, followed by the Preference Ranking Organization Method for Enrichment Evaluation (PROMETHEE), the Analytic Network Process (ANP), GIS, and Goal Programming (GP). Adoption varied across regions, with advanced models such as AHP and GIS less frequently used in developing countries due to technological constraints. These findings highlight emerging trends and gaps in MCDM application, particularly for argan forests, emphasizing the need for context-specific frameworks to support sustainable management in the face of climate change.

Quality Evaluation of Public Spaces in Traditional Villages: A Study Using Deep Learning and Panoramic Images

In the context of rapid urbanization, public spaces in traditional villages face challenges such as material ageing, loss of characteristics, and functional decline. The scientific and objective assessment of the quality of these public spaces is crucial for the sustainable development of traditional villages. Panoramic images, as an important source of spatial data, combined with deep learning technology, can objectively quantify the characteristics of public spaces in traditional villages. However, existing research has paid insufficient attention to the evaluation of the quality of public spaces in traditional villages at the micro-scale, often relying on questionnaires and interviews, which makes it difficult to meet the needs of planning and construction. This study constructs an evaluation system for the quality of public spaces in traditional villages, taking national-level traditional villages in the Fangshan District of Beijing as an example, based on traditional field research, using deep learning and panoramic images to automatically extract the features of public spaces in traditional villages, using a combination of the Analytic Hierarchy Process (AHP) and Criteria Importance Through Intercriteria Correlation (CRITIC) methods to determine the weights of the indicators and applying the Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) method to evaluate the quality of public spaces in traditional villages. The study found that the quality of public spaces in Nanjiao Village is Grade I; Shuiyu Village and Liulinshui Village, Grade III; and Heilongguan Village, Grade IV and that there is still much room for improvement in general. The evaluation results match well with the public’s subjective perceptions, with an R2 value of 0.832, proving that the constructed evaluation system has a high degree of accuracy. This study provides a scientific basis and an effective tool for the planning, design, and management of public spaces in traditional villages, which helps decision-makers better protect and utilize them.

Driving risk prediction of urban arterial and collector roads using multi-dimensional real-time data

With the objective of predicting driving risks on urban arterial and collector roads, this paper primarily collects and processes vehicle trajectory data and driver-vehicle-road-environment data. Using Entropy-Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) and K-means, risk scores and levels for the target roads are determined. An original dataset comprising 10,320 samples is established, with risk levels as labels and multi-dimensional data as features. SHapley Additive exPlanations (SHAP) analysis is utilized to extract important features. The performance of four ensemble models are compared, including Random Forest (RF), eXtreme Gradient Boosting (XGBoost), Light Gradient Boosting Machine (LightGBM) and Stacking. Three data resampling techniques are integrated to address data imbalance. The combination of LightGBM and Deep Convolutional Generative Adversarial Network (DCGAN) outperforms other approaches, showing potential for real-time risk prediction. Besides, DCGAN is employed to update the multi-dimensional features. The models' predictive performance is evaluated under various update ratios using F1-score and training time, with LightGBM demonstrating superior accuracy and efficiency compared to its counterparts (RF, XGBoost, and Stacking). This study develops a driving risk prediction system based on multi-dimensional features, offering a theoretical foundation and technical approach to enhance urban road traffic safety management.

A multi-criteria decision-making approach for prioritizing factors influencing healthcare workers' safety performance: A case of a women's hospital

This study was designed to examine the influence of organizational and individual-level factors on the safety performance of healthcare workers at a women's hospital. Healthcare workers in different occupational groups enrolled in the current study. A questionnaire was used for data collection, and the data were analyzed using an integrated multi-criteria decision-making (MCDM) approach. The entropy method was applied to prioritize influential factors, including safety climate, perceived organizational support for safety, perceived supervisor support for safety, safety voice, organizational resilience, and individual resilience, and the technique for order preference by similarity to an ideal solution (TOPSIS) was employed to rank the alternatives (healthcare workers in different occupational groups). The finding of the entropy method illustrated that perceived organizational support for safety and organizational resilience had the highest influence on healthcare workers' safety performance. The other most influential factor was individual resilience. Regarding safety performance components, safety compliance was more important than safety participation. TOPSIS results suggested that radiologists, nurses, and midwives experienced higher safety performance levels than the other occupational groups in the study women's hospital. The findings of this study demonstrated that organizational and individual-level factors such as safety climate, perceived organizational and supervisor support for safety, and resilience-related factors significantly influence healthcare workers' safety performance, and compliance with safety rules and procedures is essential to achieve better safety performance.

Optimizing additive manufacturing parameters for graphene-reinforced PETG impeller production: A fuzzy AHP-TOPSIS approach

Additive manufacturing, particularly 3D printing, has transformed production by enabling precise, layer-by-layer construction with minimal material waste. This study aims to optimize the mechanical properties and production efficiency of impellers manufactured using Graphene-Reinforced Polyethylene Terephthalate Glycol (G-PETG) filament. By employing the Fuzzy Analytic Hierarchy Process (FAHP) and the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS), we identified optimal 3D printing parameters. The results showed that a 65 % infill density, 0.20 mm layer height, 50 mm/s printing speed, 90 °C platform temperature, 240 °C extruder temperature, and 90 mm/s traverse speed led to a 15 % improvement in tensile strength and a 12 % reduction in production time compared to baseline settings. Additionally, the impellers produced demonstrated superior surface finish and structural integrity, making them suitable for high-performance applications. These findings underscore the importance of parameter optimization in enhancing the performance of 3D-printed components, particularly for applications requiring high mechanical strength and precision.

Combined application of silica nanoparticles and brassinolide promoted the growth of sugar beets under deficit irrigation

Silica nanoparticles (SiNPs) and brassinolide (BR) have been used as nano-fertilizer and growth regulator, respectively to enhance crop tolerance to abiotic stress. However, it is unclear whether a combination of the two (BR + SiNPs) is more beneficial than single application of BR or SiNPs to improve the growth of deficit-irrigated sugar beets. In this study, a two-year (2022–2023) field experiment was conducted to investigate the effects of foliar spraying of water (CK), SiNPs, BR, and BR + SiNPs on the antioxidant defense, photosynthetic capacity, dry matter accumulation, nutrient uptake, and yield of sugar beets under full irrigation (100% of crop evapotranspiration (ETc), W1) and deficit irrigation (60% ETc, W2). The results showed that compared with the application of BR or SiNPs, the application of BR + SiNPs could enhance the antioxidant defense, osmoregulation, and photosynthesis of the full-irrigated and deficit-irrigated sugar beet leaves, and ultimately improved the water status, growth, and yield of sugar beet plants. There was no significant difference in the net revenue (NR) between BR + SiNPs treatment and CK under W1 conditions. However, the NR of the BR + SiNPs treatment increased by 27.0% (p < 0.05) compared with that of CK under W2 conditions, and there was no significant difference in NR between BR + SiNPs and SiNPs treatments. A comprehensive evaluation using entropy weight combined with technique for order preference by similarity to ideal solution method found that under deficit irrigation condition, spraying SiNPs could improve the growth of sugar beet, increase the TY, NR, and water use efficiency, and reduce costs compared with spraying BR + SiNPs. Therefore, foliar spraying of SiNP on deficit-irrigated sugar beets can be used to improve sugar beet growth and reduce the potential economic losses caused by deficit irrigation.

The combination of Multi-Criteria Decision-Making (MCDM) and morphometric parameters for prioritizing the erodibility of sub-watersheds in the Ouljet Es Soltane basin (North of Morocco)

Preserving water and soil resources ranks among the top priorities outlined in the national water strategy. Indeed, the integrated management of water resources in vulnerable territories, particularly in Morocco, requires a deep knowledge of the hydrological functioning and use of water resources in these regions. The diverse hydroclimatic and morphological features within the Ouljet Es Soltane watershed, which is a sub-basin of the extensive Oued Sebou watershed, present significant challenges in managing its water and soil resources. Identifying areas susceptible to soil erosion is crucial for implementing preventive measures in the Ouljet Es Soltane basin and ensuring its sustainable development. Morphometric analysis plays an important role in the effective management and sustainable utilization of the basin's resources. This study used four MCDM models, including the CF (Compound Factor), VIKOR (VIseKriterijumska Optimizacija I Kompromisno Resenje), TOPSIS (Technique for Order Preference by Similarity to Ideal Solution), and SAW (Simple Additive Weighing), to prioritize 20 sub-watersheds of the Ouljet Es Soltane watershed.Based on the sub-watershed prioritization results obtained from the VIKOR, TOPSIS, and SAW models, sub-watershed 16 achieved scores of 0, 0.59, and 0.8, respectively, positioning it as the first rank. These findings highlight that sub-watershed 16 exhibits a high susceptibility to erosion and is classified as one of the most vulnerable areas in terms of erosion risk.Based on the results obtained from the VIKOR, TOPSIS, and SAW models, the susceptibility of the sub-watersheds to erosion can be classified into four categories: low, moderate, high, and very high. On the other hand, the CF model only has two categories: low and moderate susceptibility. Overall, the findings suggest that morphometric parameters are highly effective in identifying areas at risk of erosion. Furthermore, the VIKOR, TOPSIS, and SAW methods exhibit greater predictive accuracy compared to the CF model. The comparison of these models involved the use of Spearman correlation coefficient test (SCCT).The findings of this study can provide valuable insights for making informed decisions in developing an effective framework for soil erosion control strategies.

Assessing Southeast Asia countries’ potential in the semiconductor supply chain: an objectively weighting multi-criteria decision-making approach

The semiconductor industry serves as a key enabler of technological progress and economic growth on a global scale. With Southeast Asia emerging as a region of increasing importance in the global supply chain landscape, understanding the potential for its countries to participate in the semiconductor supply chain becomes crucial. Despite extensive research on global supply chain dynamics, a focused analysis on the role of Southeast Asia within this crucial industry has been notably absent, particularly considering recent geopolitical and economic shifts. This gap has been filled by employing a rigorously designed Multi-Criteria Decision Making (MCDM) approach that integrates the Criteria Importance Through Intercriteria Correlation (CRITIC) method and the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS). Data on fifteen indicators, including economic stability, technological innovation capacity, and infrastructural capabilities, were collected and identified through an extensive literature review and consultations with stakeholders. These indicators were weighted using the CRITIC method to reflect their relative importance, and each country’s position relative to an ideal solution was assessed using the TOPSIS method. It was revealed that countries such as Singapore and Malaysia possess significant potential due to their advanced technological infrastructure and stable economic environments. Other countries, like Vietnam and Indonesia, were also found to exhibit promising capabilities, albeit with certain limitations. The study highlights the varying strengths and strategic areas that should be considered by policymakers and investors to enhance integration into the global semiconductor supply chain. Significant contributions to the strategic decision-making process have been made by providing a nuanced analysis of the region’s readiness and identifying specific areas for investment and policy intervention. By offering a methodological framework that can be adapted to other regions and sectors, not only has an existing research void been filled, but the practical understanding of effectively leveraging Southeast Asia’s potential in the semiconductor industry has also been enhanced.

Entropy-TOPSIS Method Approach: A Comprehensive Quarterly Assessment with Application in Turkey's Cement Sector

The objective of this research is to determine the financial performance of Turkish cement firms using quarterly data from the period between 1997 and 2022 through the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS). The research takes into account company mergers that occurred in 2020 and analyzes two distinct periods: from 1997 to 2020, and post-2020. Fourteen companies were examined in the first period, while ten companies were assessed in the latter period. To measure financial performance, ten common financial ratios selected based on literature were utilized. Determining the importance of these financial ratios is a crucial step in multi-criteria decision-making techniques; hence, the entropy method was employed to address this issue. According to empirical results, before the company mergers, UNYE and MARDIN cement firms were observed to consistently perform well financially, often ranking at the top based on the financial parameters considered. However, after the merger, it has been noted that BUCIM consistently held the top position in every quarter.

Design of a sweeping robot based on fuzzy QFD and ARIZ algorithms

The design of a multifunctional intelligent robotic vacuum cleaner is essential for meeting user needs and replacing human cleaning tasks, thereby improving the effectiveness and convenience of human-robot interaction. Existing robotic vacuum cleaners can address the issues of small dust and hair accumulation; however, a comprehensive and integrated solution for sweeping, mopping, sanitizing, and general waste cleaning remains necessary. This paper employs the Fuzzy Quality Function Deployment (FQFD) tool and the Algorithm for Inventive Problem Solving (ARIZ) algorithm to address these challenges. The research framework integrates FQFD with the Fuzzy Analytic Hierarchy Process (FAHP) methodology to evaluate the weights of user requirements for a robotic vacuum cleaner. Subsequently, these requirements were translated into technical specifications, thus establishing a link between user needs and technical requirements. The ARIZ algorithm is subsequently employed to resolve conflicts and inconsistencies within the FQFD model. Finally, an innovative design for a robotic vacuum cleaner was proposed by utilizing the ARIZ algorithm's problem-solving principles and qualitative and quantitative insights derived from the FQFD. The design is evaluated using the Fuzzy Order Preference Technique (FTOPSIS) method, which resembles the ideal solution. Furthermore, the operational dynamics of the critical components of the design solution that navigate obstacles are analyzed to ensure their effectiveness. The final product can effectively replace humans in complex cleaning tasks while navigating obstacles during sweeping, mopping, and sanitizing for comprehensive space cleaning. The results of this study provide valuable insights for designers to enhance user experience, improve customer emotional satisfaction, and guide future product designs in related product development initiatives.

Generation of nematic order in itraconazole by cryo-milling

Mechanical milling is a well-known method of solid-state amorphization of crystalline materials. Here, it is revealed that cryo-milling of itraconazole, a model thermotropic liquid-crystalline active pharmaceutical ingredient, leads to a preferred nematic order of molecules in solid particles instead of isotropic disorder − usually observed for most molecules that do not form liquid-crystalline mesophases. While standard quenching of itraconazole isotropic liquid down to room temperature results in the formation of glass with some degree of smectic order, cryo-milling easily destroys not only crystalline order but also smectic organization of molecules into layers, omitting the melting. The cryo-milled itraconazole samples exhibit faster α-relaxation and slightly slower δ-mode above the glass transition temperature. They are also more physically stable, with the stability dependent on the grinding time, below this temperature, with respect to the ordinary glass. The explanation for this behavior is believed to be increased short-range intermolecular disorder. Moreover, the cryo-milled particles with nematic order are better soluble than ordinary glass. Therefore, this work can guide the formulation of stable pharmaceuticals with nematic order/disorder and enhanced solubility.

Optimization of vacuum membrane distillation and advanced design of compact solar water heaters with heat recovery

With the escalating global population, freshwater scarcity has become a pressing challenge. To address this issue, this paper presents a novel compact solar water heater (CSWHT) system incorporating heat recovery and submerged-membrane filtration. The research aims to develop an efficient desalination method that reduces water production costs and maximizes output. The system comprises a feed storage tank, a vacuum membrane distillation (VMD) system positioned atop the submerged CSWHT, a liquid ring vacuum pump (LRVP) utilizing waste heat from the outlet to preheat the feed, and hollow fiber membranes.Three critical parameters, namely the mass of the feed in the storage tank, the number of hollow fiber membrane rows, and the feed volumetric flow rate, were optimized using a multi-objective non-dominated sorting genetic algorithm II (NSGA II) to minimize water production cost (WPC) while maximizing water production. Subsequently, the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) was employed to identify the optimal scenario from among the selected best solutions.For Mediterranean seawater and a 6 m2 solar collector area, the multi-objective NSGA II algorithm identified 20 membrane rows per collector, 334.4 kg feed mass in the storage tank, and 0.027 kg.s−1 feed mass flow rate as the optimal system parameters. Under these optimized conditions, the system exhibited a daily production capacity of 92.5 kg water, 4.6 kg chloride, and 0.03 g bromide, while achieving a production cost of 12.2 USD.m−3 and a gain output ratio (GOR) of 1.6. These results highlight the system's remarkable potential for desalination with high efficiency and low cost. Future research should focus on scaling up the system and exploring its applicability in various geographical locations.

Multi-objective optimal sizing and techno-economic analysis of on- and off-grid hybrid renewable energy systems for EV charging stations

Integrating electric vehicle charging stations (EVCSs) with renewable energy systems requires the consideration of several factors during the planning stage, including environmental impact, economic viability, grid reliability, and self-sufficiency. Therefore, this study conducts a multi-objective optimal sizing of on- and off-grid hybrid renewable energy systems for EVCSs. The sizing problem is solved using the Non-dominated Sorting Genetic Algorithm (NSGA-II). Subsequently, the best suitable solutions from the obtained non-dominated solutions are selected using the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) method, prioritizing the objective functions based on diverse interests of different stakeholders (large and small private investors and governmental entities). Finally, a techno-economic analysis is made considering payback period, profitability index (PI), and internal rate of return (IRR). The results show that on-grid systems show high economic viability with payback periods between 1.98 and 7.72 years, an average PI of 5.07 and an average IRR of 23.97%. Although off-grid systems present lower economic viability with payback periods between 8.77 and 22.42 years, an average PI of 1.68 and an average IRR of 4.91%, in certain cases they reach investable levels with payback periods below 10 years, PI above 2, and IRR above the interest rate.

Three-dimensional numerical investigation on flow and heat transfer characteristics and multi-objective optimization of interconnected microchannels

The interconnected microchannel demonstrates excellent performance in enhancing flow heat transfer. However, the optimal width of the interconnecting slot has not been thoroughly investigated. To address this gap, this paper numerically simulates the three-dimensional flow and heat transfer of interconnected microchannels featuring varied slot widths and channel depths. The investigated slot widths range from 50 to 1000 μm and depths of 100, 200, and 300 μm. Evaluation of overall performance encompasses heat transfer and flow characteristics such as average wall temperature, heat transfer coefficient, thermal resistance as well as pressure drop. It is the interconnected slots that interrupt the velocity and thermal boundary layers, contributing to enhanced heat transfer. Furthermore, the back propagation neural network and non-dominated sorting genetic algorithm-II are utilized for multi-objective optimization of thermal resistance and pressure drop. The technique for order preference by similarity to an ideal solution (TOPSIS) method combined with the entropy weight method is employed to select the compromise optimal solution from the Pareto optimal solutions. The optimized slot widths are 369, 424, and 167 μm for channel depths of 100, 200, and 300 μm, respectively. This research provides data support for the optimization design and engineering manufacturing of interconnected microchannels.

Impact of regulated deficit irrigation on the dynamics of quality changes in processing tomato fruits during ripening

Quality is a key factor restricting the development and economic benefits of the tomato processing industry, and improving the quality of tomatoes has become a hotspot in the development of the tomato processing industry in Xinjiang. Regulated deficit irrigation (RDI) is an abiotic means of crop yield and quality control widely used for crop yield and quality improvement. This study aimed to investigate the impact of RDI on the dynamics of quality changes in processing tomato fruits during ripening via a 2-year (2022–2023) filed experiment with five water irrigation treatments in Xinjiang, China. The results showed that compared with conventional irrigation, regulated deficit irrigation significantly saved 315–1260 m3 ha−1 irrigation water. Mild RDI increased the single fruit weight and fruit hardness by 0.15 % and 3.29 kg cm2, respectively, thus improved the yield and storage and transportation quality of processed tomatoes. Moderate RDI increased the contents of soluble solid, soluble sugar and lycopene in fruit to 0.6 %, 0.56 % and 3.53 μg/g, respectively, therefore significantly improved the nutritional quality and flavor quality of processed tomato. Ultimately, a comprehensive evaluation using a coupled Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) model, taking into account the appearance, nutrition, flavor, and storage and transportation indexes of processed fruits, concluded tha W1 treatment is a sustainable water management approach that balances yield and quality. Therefore, the optimal deficit irrigation model for processing tomato fruits in Xinjiang was recommended to be the W1 treatment, whereas the W2 treatment was considered as an alternate model. The study supported the large-scale development of the tomato processing industry in Xinjiang and the implementation of effective water-saving farming.

Bi-level optimization and integrated multiple-criteria decision making under uncertainty for structural design of insulated bearings

To address the premature failure of insulated bearings, this article proposes a design method for the structure of bearings to prolong their service life. First, a bi-level optimization model is established to determine the structural parameters of bearings considering their insulating and mechanical properties. Upper-level problems include bearing voltage ratio, porosity of insulating coatings, minimum thickness of oil film and Hertz contact area. Lower-level problems include fatigue life of bearings and adhesion of insulating coatings. Second, a non-dominated sorting genetic algorithm-III based on space decomposition and penalty-based boundary intersection (NSGA-III-SD-PBI) is designed to solve the optimization model. Third, the integrated multiple-criteria decision-making (MCDM) using the fuzzy best–worst method (BWM) and fuzzy technique for order preference by similarity to an ideal solution (TOPSIS) is developed to determine the best structural parameters of bearings. A case study demonstrates the effectiveness and superiority of the proposed method, with fatigue life increased by 20%.

Multi-Criteria Decision-Making Framework (AHP-TOPSIS): Pavement Preventive Maintenance Case Study for Ordinary National Trunk Highways

Pavement maintenance and rehabilitation decision-making needs to weigh multiple strategic goals to achieve sustainable development through the pavement maintenance management system. Making decisions regarding pavement preventive maintenance is both intricate and costly. This study introduces a multi-criteria decision-making framework aimed at enhancing the scientific basis of such decisions. The framework first establishes an evaluation system for preventive maintenance strategies by considering three primary evaluation criteria—service functionality, pavement performance, and economic benefits, and then identifies nine specific evaluation indicators to influence these criteria, with a comparison matrix constructed to determine the weight of each indicator in relation to the maintenance decision hierarchy. Following this, the technique for order preference by similarity to ideal solution (TOPSIS) is employed to prioritize four commonly utilized preventive maintenance strategies. The results reveal that pavement condition and maintenance costs are the most influential factors in determining the appropriate preventive maintenance strategies for national highways. The priority rankings for the four strategies—slurry seal, micro-surfacing, chip seal, and ultra-thin overlays—are found to be 56.12%, 63.86%, 12.12%, and 83.52%, respectively, with ultra-thin overlays identified as the optimal choice for second-class highways. The decision-making model utilized in this study enables a multi-dimensional analysis, reducing the subjectivity inherent in expert evaluations and facilitating the prompt identification of the most suitable maintenance strategy.

Why do flowers wilt?

Resources salvaged when flowers wilt on a perennial plant could promote reproduction by, in preference order, the same flowers (Hypothesis 1), adjacent flowers on the same plant (Hypothesis 2), or during the next flowering season by the same plant (Hypothesis 3). We tested the above hypotheses for Blandfordia grandiflora, a perennial species, where some plants included flowers that were allowed to wilt, while equivalent flowers on other plants were prevented from wilting. The abilities of these plants to produce seed were determined by liberally pollinating all flowers. To test Hypotheses 1 and 2, seed set per flower and per plant were compared between plants with and without wilting flowers. To specifically test Hypothesis 3, reproduction was prevented in all flowers. For each experiment, flowering was monitored in the same plants during the next flowering season, thus also enabling Hypothesis 3 to be tested. The results were consistent with Hypothesis 3, but not with Hypotheses 1 and 2. Hence, we verified, for the first time, that plants may benefit from salvaging resources from wilting flowers and re-using these resources for subsequent reproduction. However, contrary to expectations, plants re-used these resources to promote reproduction during subsequent flowering, and not during current flowering by either the same flowers or other flowers on the same plant. The plants must have transferred resources from wilting flowers to underground corms and roots, which provided resources necessary for subsequent flowering. This is likely part of a general plant strategy to salvage resources invested in reproduction during one flowering season and reuse these resources during subsequent flowering.