Technique For Order Preference By Similarity To An Ideal Solution Research Articles

Vehicle trajectory reconstruction with sparse automatic license plate recognition data for urban road networks

The vehicle trajectory data obtained through automatic license plate recognition (ALPR) technology provide crucial support for Intelligent Transportation Systems. This paper investigates vehicle trajectory reconstruction in urban complex road networks using sparse data. Firstly, the road network topology structure is constructed based on real physical topology relationships. Then, vehicle travel is segmented using the Isolation Forest model. Candidate paths are generated through an improved K-Shortest Paths (KSP) algorithm, which combines the A* algorithm and historical path preferences. Finally, trajectory reconstruction is accomplished using the Extremely Randomised Trees and Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS). The proposed method is applied to the road network in Foshan City, China, and achieves the highest accuracy of 91.19% and 96.06% in dense and sparse road networks, respectively, compared to the baseline model. Furthermore, there is a significant improvement in model accuracy and computational efficiency under severe data loss conditions.

Model test study on the rainfall erosion mechanisms and reclamation potential of open-pit coal mine dump soil improved by fly ash and polyacrylamide

Increasing the soil erosion resistance is one of the core issues in slope erosion control and ecological environmental restoration of open-pit coal mine (OPCM) dumps. In this study, fly ash (FA) and polyacrylamide (PAM) were used to improve the soil quality of an OPCM dump, and an indoor physical model was constructed to investigate the water and soil loss characteristics of the improved soil via simulated rainfall experiments. Scanning electron microscopy and Pore and Crack Analysis System software were employed to systematically investigate the erosion resistance mechanisms of the improved soil qualitatively and quantitatively. Finally, the improved technique for order preference by similarity to an ideal solution (TOPSIS) method was adopted to evaluate the reclamation potential of the improved soil. The results revealed that under the action of PAM (PAM and PAM–FA), the average erosion rate of the slope decreased by more than 90 %. Compared with that in the control group, when FA was applied alone, the slope erosion rate first decreased and then increased with increasing FA content. Upon PAM addition, the erosion pattern changed from the splash erosion stage, cave erosion stage, gully erosion stage, and tensile slip stage to the splash erosion stage and cave erosion stage. However, no obvious change in the runoff pattern. Erosion and runoff patterns are generally affected by the amendment type, addition concentration, porosity, pore shape, pore direction and hydrological environment. The erosion resistance mechanism of the improved soil entailed the formation of more stable soil aggregates via filling, cementation, skeleton support generation, and flocculation of FA and PAM. In addition, the optimal soil improvement was achieved when FA and PAM were added at levels of 16 % and 0.01 %, respectively. The obtained research results could be used for erosion control and ecological environmental protection of coarse-grained soil slopes in mining areas, highways and other fields and could be widely applied.

Comparative assessment of Watershed Hydrological Health (WHH) using multi-criteria decision-making approach based on PSR framework.

The health of a watershed is a crucial aspect that involves evaluating ecological, hydrological, and human factors to determine its overall well-being and functionality. Hydrological components are key parts of a watershed. Therefore, the present study aims to assess Watershed Hydrological Health (WHH) using the Pressure-State-Response (PSR) framework and to compare WHH priorities derived from Multi-Criteria Decision Making (MCDM) approaches in the Gorganroud Watershed, Iran. To do this, with a problem-oriented approach, 16 hydrological criteria were conceptualized in three indices of Pressure (P), State (S), and Response (R) (PSR). In the next step, each of the mentioned criteria was quantified in 16 sub-watersheds of the Gorganroud Watershed. Finally, WHH was calculated based on the PSR framework. According to the criteria Importance Through Intercriteria Correlation (CRITIC) method, the criteria were then weighted, and WHH was calculated using three MCDM methods. To compare the results, the results of the PSR framework were used as the basis and compared with three MCDM methods. The results showed that WHH values in the Gorganroud Watershed ranged from 0.31 to 0.67 in S15 and S9, respectively. Overall, the weighted average WHH in the watershed was 0.52, indicating moderate conditions. Weighting results showed that criteria C4 and C14 had the highest and lowest weights, respectively. WHH priorities based on MCDM methods showed that in the ELimination Et Choice Translating REality (ELECTRE) and Simple Additive Weighting (SAW) methods, sub-watershed S9 had the highest WHH priority, while in the Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) method, sub-watershed S7 achieved the highest priority. Among these, the ELECTRE and SAW methods performed better in determining WHH priorities and are recommended for future research.

Sustainable rural development: differentiated paths to achieve rural revitalization with case of Western China

Rural Revitalization (RR) is a key national strategy in China aimed at sustainable rural development and has gained significant attention. Given the unique characteristics of different villages, understanding differentiated paths to achieve RR is essential. This study introduces a new “5I Framework” (INDUS-INHAB-INDOC-INFRA-INCOM) to assess RR’s overall development status (ODS) and differentiated paths. Then, using western China as a case, this study employs entropy weight, the technique for order preference by similarity to an ideal solution (TOPSIS), fuzzy set qualitative comparative analysis (fsQCA), and hierarchical clustering to categorize differentiated RR paths into “4L Category” (Lots-Learning-Labor-Living). Context-specific recommendations are provided for each category. “5I” and “4L” are derived from their initial letters for easier recall and global dissemination. Our findings reveal ODS’s temporal fluctuations between Acceptable and Good Grades (0.3 and 0.6), with Chongqing’s villages showing a downward trend (from 0.439 to 0.407); the findings also reveal spatial differences in ODS, as northern villages generally score higher due to distinct characteristics. Seven differentiated paths to RR are identified, highlighting varying dimensions essential for RR success: INFRA is crucial across all high ODS paths, while INHAB and INDOC are not always central. Besides, these paths are grouped into four clusters via hierarchical clustering, indicating that RR can be achieved by integrating “lots” of aspects, enhancing “learning” and cultural status among residents, requiring villagers’ “labor” to support agriculture, or improving the “living” environment. The clustered categories enhance the applicability of our proposed countermeasures in similar contexts. The “5I Framework” facilitates theoretical understanding and evaluation of RR, while the “4L Category” underpinned by the “5I Framework” promotes effective implementation practices—both representing innovations of this study.

Determination of The Antifungal Capacity of Kefir Components Against Spoilage Fungi

The purpose of this study is to investigate the potential alternative use of kefir and its derived components in the biocontrol of fungi that cause economic losses during the harvesting and storage of fruits. The antifungal properties of kefir and its derived components (the non-protein liquid fraction of kefir, kefiran, and yeast cells isolated from kefir) were examined using the disk diffusion method, and the minimum inhibitory concentration (MIC) against naturally occurring fungi during the storage of hazelnuts, oranges, and apples were determined. Fungal isolates obtained from different fruits were identified as Aspergillus niger, Penicillium digitatum, and Penicillium expansum through 18S rRNA Polymerase Chain Reaction. The inhibition zones of A. niger, P. digitatum, and P. expansum were evaluated using the disk diffusion method. The minimum inhibitory concentrations (MICs) for kefir against A. niger, P. digitatum, and P. italicum were found to be 0.4, 0.2, and 0.25 µg mL-1, respectively. The effectiveness of these applications was analyzed using a technique known as TOPSIS (Technique for Order Preference by Similarity to an Ideal Solution). With the development of the results of this study, alternative biocontrol applications using kefir and its components could be developed to prevent fungi-related microbial spoilage, providing an alternative to chemical treatments.

TOPSIS approach based adaptability analysis for power substation area communication schemes

Abstract With the development of new power systems, various new services emerge in substation areas, increasing the number of end devices as well as the frequency of data collection. As a result, end terminal communication in substation areas faces significant challenges such as capacity, coverage, reliability and so on. A unified analytical model is required to evaluate various networking schemes. Therefore, in this paper we propose a technique for order preference by similarity to an ideal solution (TOPSIS) based compatibility analysis model for power communication demand indicators, which provides a theoretical evaluation criteria for substation area communication networks. Based on the model, we also introduce multiple networking schemes for comparison. The comparative results show that for three typical services, emergency response, electric frequency regulation, and peak shaving, there are three distinct networking schemes with the highest compatibility, respectively. Wherein joint HPLC, HRF, and LTE networking provide well-suited performance for substation area communication.

Assessing raw material efficiency and waste management for Sustainable Development: A VIKOR and TOPSIS Multi-Criteria Decision Analysis

Abstract This study explores the integration of sustainability in decision-making processes within a steel manufacturing company in Poland. As global clients increasingly demand sustainable practices, companies must adapt their operations to meet these expectations. We applied Multi-Criteria Decision Analysis (MCDA) methods, specifically TOPSIS (Technique for Order Preference by Similarity to an Ideal Solution) and VIKOR (in Serbian: VlseKriterijuska Optimizacija I Komoromisno Resenje), along with three weighting techniques, namely Entropy, Standard Deviation, and CRITIC (Criteria Importance Through Inter-criteria Correlation), to evaluate the sustainability of four products. Nine criteria were considered, including material type, corrosion protection, surface treatment, and various manufacturing processes. Our findings reveal that the MCDA framework effectively ranks products from most to least sustainable, highlighting the importance of raw material efficiency and waste management. This research demonstrates the practical application of MCDA methods in assessing sustainability within the steel industry, providing a basis for future studies to extend this framework to other manufacturing sectors and regions. Overall, this approach supports informed decision-making, aligning with broader sustainability goals while satisfying the demands of business partners and clients.

Multi-objective optimization design of steel cross section of integrated supports and hangers based on NSGA-Ⅱ and MDOS

The stability and safety of building Mechanical, Electrical, and Plumbing (MEP) systems are significantly influenced by the steel section design of integrated supports and hangers. Traditional design methods, reliant on empirical steel section selection, are often inefficient and can result in unnecessary material usage. This paper introduces a novel multi-objective optimization approach that enhances the section design process by minimizing material costs and maximizing bending performance of supports and hangers. Firstly, the Non-dominated Sorting Genetic Algorithm II (NSGA-II) is employed to identify an array of optimal cross-sectional designs that reconcile economic considerations with structural mechanical performance. Subsequently, in order to pinpoint the most suitable design from the resulting Pareto-optimal scheme, the Method of Determining the Optimal Scheme (MDOS) is proposed. MDOS harnesses expert scoring, the Continuous ordered weighted averaging (COWA) operator, game theory, and the Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) to objectively evaluate and prioritize design schemes according to stakeholder preferences and project-specific criteria. Finally, the optimization results are analyzed and the influence of different spacing of supports and hangers and different subjective weight factors on the selection of cross-section is studied. The findings reveal that the method can precisely determine all cross-section designs that adhere to the established constraints, achieving a 4.09-fold enhancement in design efficiency. It was observed that the two outlined objectives are predominantly affected by the height of the web. The distribution trend of the optimal solution value of the independent variable is almost the same under the condition of different values of the supports and hangers spacing. With the change of subjective weight, the cross section selection focuses on three optimal schemes. Therefore, the proposed method can be used as reference for similar steel design optimization.

Delineation of Groundwater Potential Using the Bivariate Statistical Models and Hybridized Multi-Criteria Decision-Making Models

Identifying groundwater potential zones in a basin and developing a sustainable management plan is becoming more important, especially where surface water is scarce. The main aim of the study is to prepare the groundwater potential maps (GWPMs) considering the bivariate statistical models of frequency ratio (FR), weight of evidence (WoE), and the multi-criteria decision-making (MCDM) model of Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) hybridized with FR and WoE. Two distance measures, Euclidean and Manhattan, were used in TOPSIS to evaluate their effect on GWPMs. The research focused on the Burdur Lake catchment located in the southwest of Türkiye. In total, 74 wells with high yields were chosen randomly for the analysis, 52 (70%) for training, and 22 (30%) for testing processes. Sixteen groundwater conditioning factors were selected. The area under the receiver operating characteristic (AUROC) and true skill statistics (TSS) were utilized to examine the goodness-of-fit and prediction accuracy of approaches. The TOPSIS-WoE-Manhattan model and the FR and WoE models gave the best AUROC values of 0.915 and 0.944 for the training and testing processes, respectively. The best TSS values of 0.827 and 0.864 were obtained by the TOPSIS-FR-Euclidean and WoE models for the training and testing processes, respectively.

Multi-Objective Decision-Making Evaluation Method of Environmental Impact Associated with the Life Cycle of Agro-Friendly Biochar Materials

The urgency of addressing farmland contamination is undeniable. However, the environmental impacts associated with soil remediation, especially during the production of remediation materials, are often overlooked. This study seeks to fill this gap by conducting a comprehensive environmental impact assessment of remediation material production processes. We apply a Life Cycle Assessment (LCA) framework, enhanced by a multi-objective optimization model combining the Analytic Hierarchy Process (AHP) and Techniques for Order Preference by Similarity to an Ideal Solution (TOPSIS). This method enables the integration of multiple environmental indicators into a high-dimensional reference system, reducing subjectivity in decision-making. The study focuses on the environmental impacts of 11 types of biochar materials used in soil remediation. Among these, alkali-modified biochar loaded with nano TiO2 exhibited the highest environmental impact index. Sensitivity analysis further confirmed the robustness of the method, with impact variations ranging from 0.44 to 0.52, suggesting the model’s reliability in comparing different remediation materials. Our findings highlight the significant environmental variability between remediation materials and underscore the necessity of incorporating comprehensive environmental assessments in material selection processes. This study provides a valuable framework for optimizing the environmental sustainability of soil remediation efforts.

Power consumption state evaluation of important power customers based on AHP‐TOPSIS algorithm

AbstractIn the construction of a new power system, the identification and evaluation of power consumption status of power customers will become an important basis for them to participate in the emerging businesses such as demand response and virtual power plants. In order to ensure the power safety of important power customers, a new evaluation of power consumption status of important power customers based on the AHP (analytic hierarchy process)‐TOPSIS (technique for order preference by similarity to an ideal solution) algorithm is proposed by fully mining and applying the power big data. Firstly, a power consumption big data analysis platform based on the Hadoop architecture is built to provide a high‐performance platform support for big data analysis. Secondly, nine evaluation indexes are constructed from the three dimensions of voltage, load and synthesis, which objectively and scientifically describes the power consumption status of important power customers. Finally, the AHP‐TOPSIS algorithm is used to evaluate and analyse the voltage, load and comprehensive indicators respectively, thus, obtaining the evaluation values of three kinds of indicators. The power consumption status scores of important power customers are determined by the variable weight weighted summation. The rationality and feasibility of the method and algorithm are proved by example analysis and field verification. This method helps to promote the transformation from post fault emergency repair to warning beforehand. It has the multiple effects of ensuring safe power consumption, supporting accurate patrolling and active emergency repair serving.

Health Insurance Provider Selection Through Novel Correlation Measure of Neutrosophic Sets Using TOPSIS

Risk monitoring aims to recognize and control potential threats to our assets or health activities. Insurance is vital, as it compensates for any unexpected loss of property or life. Clients selecting the best health insurance provider must consider various factors and their relative significance to their circumstances. Multi-criteria decision-making (MCDM) helps people analyze and compare policy alternatives to find the best option. This research aims to assist potential health policy purchasers by addressing the selection of health insurance providers as an MCDM problem. The correlation coefficient is useful for identifying the importance of several conflicting criteria. The idea of correlation coefficients is extended in a neutrosophic context to capture the indeterminacy and incomplete information in the relationship among the criteria. The technique for order preference by similarity to an ideal solution (TOPSIS) approach is a useful and straightforward approach to solving MCDM problems. However, it often became ambiguous to researchers due to its involvement in the distance measure technique. The proposed neutrosophic correlation measure may also replace the ambiguity of using a suitable distance measure in the TOPSIS approach. This study extends the TOPSIS method by using the proposed neutrosophic correlation coefficient on single-valued neutrosophic sets (SVNSs). The criteria preferences are computed using a method based on the removal effects of the criteria (MEREC) approach. Some valuable concepts, like the weighted closeness measure of type I and type II and the weighted index parameter, are introduced with their properties to establish the proposed neutrosophic TOPSIS approach. An MCDM approach for health insurance providers has been constructed to illustrate the proposed approach numerically. The proposed method suggest that the health insurance provider ϒ2 is the most beneficial alternative, whereas ϒ1 is the least suitable. The client considers the terms and conditions for non-coverage and the facilities provided for pediatric and maternity care while buying health insurance. The comparative analysis of the suggested technique demonstrates the merit of the research in terms of consistency. The sensitivity analysis demonstrates the flexibility and robustness of the obtained results.

A Crowd-Intelligence-Driven, Multi-Attribute Decision-Making Approach for Product Form Design in the Cloud Environment

In the traditional decision-making process for product form design, designers and experts often prioritize schemes based on their own knowledge and experience. This approach can lead to an oversight of user preferences, ultimately affecting decision outcomes. In contrast, crowd-intelligence-driven, multi-attribute decision-making for product form design in the cloud environment builds upon traditional approaches by leveraging the vast and diverse expertise of individuals on cloud platforms, engaging participants from various fields and roles in the decision-making process to enhance comprehensiveness and accuracy. To address the issue of a single decision-maker and limited user participation in the decision-making process for product form design schemes in the cloud environment, a multi-attribute decision-making method integrating expert knowledge and user preferences is proposed. This method aims to select a product form design scheme that optimally balances expert and user satisfaction. Initially, the Pythagorean Hesitant Fuzzy Set (PHFS) is used to quantify qualitative product attributes and to establish a comprehensive multi-attribute evaluation system. In the aspect of expert decision-making, a gray correlation coefficient decision matrix based on expert knowledge is established and the overall score of the base alternative is calculated by the ViseKriterijumska Optimizacija I Kompromisno Resenje (VIKOR) method and the Improved Osculating Value method. In terms of user decision-making, weights are determined by calculating the similarity between user evaluation matrices, and the Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) is used to calculate scores for product form designs based on user preferences. Ultimately, optimal selection is achieved by aggregating the aforementioned expert evaluation values and user preference values. The method’s effectiveness and feasibility are confirmed through a case study of coffee machine product form design schemes.

Evaluation of thermal destratification performance and optimization of jetting parameters in a spray-bar thermodynamic vent system

Future operations in space exploration require the spray-bar thermodynamic vent system (TVS) to control self-pressurization in the cryogenic propellant tank. The submerged buoyant jetting, which is followed by thermal destratification due to mixing, leads to a decrease in tank pressure. In this study, a three-dimensional (3D) numerical model is developed to study the mixing and cooling characteristics induced by submerged buoyant jets in a fully filled, thermally stratified cryogenic propellant tank. The finite volume method is employed to perform transient simulations, solving the governing equations and turbulence models. The SIMPLEC algorithm is applied for pressure–velocity coupling, and the pressure terms are discretized spatially using the PRESTO! technique. Subsequently, dynamic response studies for various parameters are carried out. The results reveal that as the jetting cryogenic energy increases from 5.5 W to 88 W, both the average and maximum temperatures decrease by 0.8 K and 0.4 K, respectively. Adjusting the jetting angle to an upward 45° is beneficial in reducing the maximum temperature by approximately 0.2 K. Moreover, the Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) is integrated with the Entropy Weight Method (EWM) to derive a comprehensive evaluation index that includes the average temperature, maximum temperature, temperature standard deviation, and jetting cryogenic energy. Additionally, by correlating dimensionless jetting parameters with the evaluation index, it is identified that an inlet Reynolds number of 158 and a Grashof number of 19,161 correspond to the optimal thermal destratification performance. This study offers significant insights for the design and improvement of the TVS.

Preference learning based on adaptive graph neural network for multi-criteria decision support

Intelligent decision-making assists decision-makers (DMs) in making choices through data analysis, model prediction, and automated processes. Central to this field are two key concepts: multi-criteria decision making (MCDM) and preference learning (PL). While MCDM and PL both aim to develop decision models that rank alternatives based on observed or revealed preferences, they diverge in focus. MCDM concentrates on the DMs' perspectives, whereas PL emphasizes model-driven approaches. This divergence presents significant challenges in integrating these methodologies, particularly in ensuring the integrated method remains scalable and interpretable amidst the complexity of decision scenarios. To bridge this gap, our study introduces the use of graph structures to frame decision problems and proposes a novel PL method employing graph neural network (GNN) for multi-criteria decision support. This method is anchored in the Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) technique and combines an adaptive GNN model with a weight determination model. The GNN model updates embeddings from the alternative's criteria and category features, utilizing an attention mechanism to adaptively assess their importance. Concurrently, the weight determination model contains a weight neural network module to set objective criterion weights and a game theory-based module for calculating combined criterion weights. The method not only inherits the interpretability and intuitive appeal of decision models but also leverages the computational efficiency and high accuracy of machine learning. In experiments conducted on benchmark datasets, our method exhibits significant performance improvements, especially in ranking-related evaluation metrics, outperforming the best baseline by 5.78 %.

A service failure assessment model for smart product consumption experience based on customer perception

Customer perception is an important consideration factor in evaluating the quality of human-computer interaction services. Sustainable user experiences and marketing strategies can be created by analyzing customer perception. By understanding consumer satisfaction with product services in the customer perception area, appropriate product service failure prevention strategies can be formulated. A service failure evaluation model is proposed in this study, which considers the customer tolerance area to accurately evaluate consumers’ behavioral experiences from purchasing to using products. The concept of tolerance area is introduced, and a combination of the fuzzy Failure Mode and Effect Analysis (FMEA) method and the Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) method is used to construct a human-computer interaction service failure evaluation model. Potential service failure factors of smart speakers are accurately evaluated by this model, and these service failure factors are ranked within the tolerance area. The research identifies voice misinterpretation and signal connectivity issues as the primary risk factors impacting the quality of human-computer interaction for smart speakers. The application of this method not only enhances the evaluation of smart speaker human-computer interaction services quality but also aids in the precise identification and prioritization of critical failure modes. The proposed service failure prevention strategies can reduce consumer dissatisfaction and provide innovative references for smart product design and marketing. The findings bolster empirical evidence for service failure prevention strategies in smart products and pave the way for novel perspectives on enhancing the quality of human-computer interaction services.

Selection of refrigerant based on multi-objective decision analysis for different waste heat recovery schemes

Waste heat generation, upgrading, and refrigeration are the fundamental ways to recover and utilize waste heat. The rationalizing the use of refrigerants also contribute in creating energy savings and minimizing carbon emissions. This study evaluated the thermodynamics, economics, and environmental of different types of refrigerants in three kinds of waste heat recovery schemes: generate electricity, heat pump, and refrigeration. Based on this, the entropy weight and Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) were combined to assess the overall performance of the refrigerants. A thorough analysis revealed that R1234ze(E) can replace R245fa and R123 in the organic rankine cycle (ORC). The best refrigerant for vapor compression refrigeration (VCR) and high temperature heat pump (HTHP) systems is R1243zf. In addition, the multi-objective decision analysis shows that the performance difference among the nine selected refrigerants is the total cost, followed by the environmental impact. The approach is successful in recognizing the variations between different refrigerants in the same waste heat recovery scheme and gives a thorough evaluation. It sets instructions for the future use of eco-friendly refrigerants and their application waste heat recovery schemes.

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.

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.

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.