Search Results (213)

Industrial heritage is increasingly becoming an important resource for sustainable urban renewal. With the acceleration of deindustrialization and urban transformation, Adaptive Reuse (AR) is regarded as the core path connecting heritage protection and functional renewal. Balancing the diverse value dimensions of AR has also become a key research focus. However, existing research mostly focuses on financial returns and investment efficiency, ignoring the long-term impact of community space and cultural dimensions on economic feasibility; at the same time, culture is often simplified into a tool for asset appreciation and urban branding, lacking a systematic model that reveals the structural role of culture in economic feasibility. Therefore, this study constructs a multi-attribute decision-making framework that integrates economic performance, community space, and cultural value. Using Guangzhou Guanggang New City as a representative case, the Fuzzy Delphi Method (FDM), Analytic Network Process (ANP), and Grey Relational Analysis (GRA) were employed to screen and rank the highest-priority reuse schemes. The results show that the economic dimension holds the highest overall weight, followed by the community and cultural dimensions. This suggests that economic feasibility remains a key prerequisite for industrial heritage renewal, while cultural and community factors play an important supporting role in achieving long-term sustainability. This study provides a quantifiable assessment path for the adaptive reuse of industrial heritage and offers a basis for decision making in other cities seeking a balance between economic rationality and cultural sustainability. Full article

To address the limitation that single-index evaluation fails to fully reflect the development performance of reservoirs of different types and at various development stages, a multi-index comprehensive evaluation system featuring the workflow of “index screening–weight determination–model evaluation–strategy guidance” was established. Firstly, the grey correlation analysis method (with a correlation degree threshold set at 0.65) was employed to screen 12 key evaluation indicators, including reservoir physical properties (porosity, permeability) and development dynamics (recovery factor, water cut, well activation rate). Subsequently, the fuzzy analytic hierarchy process (FAHP, for subjective weighting, with the consistency ratio (CR) of expert judgments < 0.1) was coupled with the attribute measurement method (for objective weighting, with information entropy redundancy < 5%) to determine the indicator weights, thereby balancing the influences of subjective experience and objective data. Finally, two evaluation models, namely the fuzzy comprehensive decision-making method and the unascertained measurement method, were constructed to conduct evaluations on 308 reservoirs in the Liaohe Oilfield (covering five major categories: integral medium–high-permeability reservoirs, complex fault-block reservoirs, low-permeability reservoirs, special lithology reservoirs, and thermal recovery heavy oil reservoirs). The results indicate that there are 147 high-efficiency reservoirs categorized as Class I and Class II in total. Although these reservoirs account for 47.7% of the total number, they control 71% of the geological reserves (154,548 × 10⁴ t) and 78% of the annual oil production (738.2 × 10⁴ t) in the oilfield, with an average well activation rate of 65.4% and an average recovery factor of 28.9. Significant quantitative differences are observed in the development characteristics of different reservoir types: Integral medium–high-permeability reservoirs achieve an average recovery factor of 37.6% and an average well activation rate of 74.1% by virtue of their excellent physical properties (permeability mostly > 100 mD), with Block Jin 16 (recovery factor: 56.9%, well activation rate: 86.1%) serving as a typical example. Complex fault-block reservoirs exhibit optimal performance at the stage of “recovery degree > 70%, water cut ≥ 90%”, where 65.6% of the blocks are classified as Class I, and the recovery factor of blocks with a “good” rating (42.3%) is 1.8 times that of blocks with a “poor” rating (23.5%). For low-permeability reservoirs, blocks with a rating below medium grade account for 68% of the geological reserves (8403.2 × 10⁴ t), with an average well activation rate of 64.9%. Specifically, Block Le 208 (permeability < 10 mD) has an annual oil production of only 0.83 × 10⁴ t. Special lithology reservoirs show polarized development performance, as Block Shugu 1 (recovery factor: 32.0%) and Biantai Buried Hill (recovery factor: 20.4%) exhibit significantly different development effects due to variations in fracture–vug development. Among thermal recovery heavy oil reservoirs, ultra-heavy oil reservoirs (e.g., Block Du 84 Guantao, with a recovery factor of 63.1% and a well activation rate of 92%) are developed efficiently via steam flooding, while extra-heavy oil reservoirs (e.g., Block Leng 42, with a recovery factor of 19.6% and a well activation rate of 30%) are constrained by reservoir heterogeneity. This system refines the quantitative classification boundaries for four development levels of water-flooded reservoirs (e.g., for Class I reservoirs in the high water cut stage, the recovery factor is ≥35% and the water cut is ≥90%), as well as the evaluation criteria for different stages (steam huff and puff, steam flooding) of thermal recovery heavy oil reservoirs. It realizes the transition from traditional single-index qualitative evaluation to multi-index quantitative evaluation, and the consistency between the evaluation results and the on-site development adjustment plans reaches 88%, which provides a scientific basis for formulating development strategies for the Liaohe Oilfield and other similar oilfields. Full article

A specific challenge in robotic control applications is the identification and regulation of actuators that provide mechanical traction and motion to the robot links. The design of actuator control laws, grounded in parametric identification and experimental motor characterization, enables numerical simulations to explore diverse operating scenarios. This article presents the initial phases in the development of a robotic rehabilitation system, focused on the kinematic modeling of a parallelogram-configuration robot for upper-limb therapy, the fuzzy identification of its actuators, and their closed-loop evaluation using a fuzzy Parallel Distributed Compensation (PDC) controller with state feedback (Ackermann), whose poles are optimized via the Grey Wolf Optimizer (GWO) metaheuristic. This controller was selected for its congruence with the nonlinear universe of discourse defined by the identified model, a key feature for operation within specific functional ranges in medical applications. The simulation and hardware platform results provide evidence that fuzzy dynamic models constitute a valuable tool for application in rehabilitation systems. This work serves as a foundation for future physical implementations with the fully coupled robotic system, in order to ensure operational safety prior to the start of clinical trials. Full article

The formation of strategies and tactics of destructive impact (DI) at the stages of complex computer attacks (CCAs) largely depends on the content of intelligence data obtained by the intruder about the attacked elements of distributed information systems (DISs). This study analyzes scientific papers, methodologies and standards in the field of assessing the indicators of awareness of the intruder about the objects of DI and symmetrical indicators of intelligence security of the elements of the DIS. It was revealed that the aspects of changing the quantitative and qualitative characteristics of intelligence data (ID) at the stages of CCA, as well as their impact on the possibilities of using certain types of simple computer attacks (SKAs), are poorly studied and insufficiently systematized. This paper uses technologies for modeling the process of an intruder obtaining ID based on the application of the methodology of black, grey and white boxes and the theory of fuzzy sets. This allowed us to identify the relationship between certain arrays of ID and the possibilities of applying certain types of SCA end-structure arrays of ID according to the levels of identifying objects of DI, and to create a scale of intruder awareness symmetrical to the scale of intelligence protection of the elements of the DIS. Experiments were conducted to verify the practical applicability of the developed models and techniques, showing positive results that make it possible to identify vulnerable objects, tactics and techniques of the intruder in advance. The result of this study is the development of an intruder awareness scale, which includes five levels of his knowledge about the attacked system, estimated by numerical intervals and characterized by linguistic terms. Each awareness level corresponds to one CCA stage: primary ID collection, penetration and legalization, privilege escalation, distribution and DI. Awareness levels have corresponding typical ID lists that can be potentially available after conducting the corresponding type of SCA. Typical ID lists are classified according to the following DI levels: network, hardware, system, application and user level. For each awareness level, the method of obtaining the ID by the intruder is specified. These research results represent a scientific contribution. The practical contribution is the application of the developed scale for information security (IS) incident management. It allows for a proactive assessment of DIS security against CCAs—modeling the real DIS structure and various CCA scenarios. During an incident, upon detection of a certain CCA stage, it allows for identifying data on DIS elements potentially known by the intruder and eliminating further development of the incident. The results of this study can also be used for training IS specialists in network security, risk assessment and IS incident management. Full article

Electric discharge machining processes are prominent in the fastest-growing industries because of their accuracy, achievable complex workpiece shapes, and cost-effectiveness. Furthermore, the machining of high-quality difficult-to-machine alloys is becoming critical in the aerospace, manufacturing, and defence industries. While the optimisation of EDM parameters is essential for improving machining outcomes, it is also important to consider the trade-offs between different performances metrics, such as material removal rate and part accuracy. Part accuracy in terms of dimensional and geometric deviations from nominal values was rarely considered in the literature, if not by the authors. Balancing these factors remains a challenge in the field of EDM. Therefore, this work aims to carry out a multi-objective optimisation of both MRR and part accuracy. A Ni-based alloy (Inconel-625) was used that is widely used in creep-resistant turbine blades and vanes and turbine disks in gas turbine engines for aerospace and defence industries. Four performance indices were optimised simultaneously: two related to the performance of the EDM process and two connected with the form deviations of the manufactured surfaces. Multi-hole copper electrodes having different diameters and three process parameters were varied during the experimental tests. Grey relational analysis and the Adaptive Neuro-Fuzzy Inference System method were used for optimisation. Grey relational analysis found that the following values of the process parameter—0.16 mm of multi-hole electrode diameter, 12 Amperes of Peak current, 200 µs of pulse on time and 0.2 kg/m² as dielectric pressure—produce the optimal performance, i.e., a material removal rate of 0.099 mm³/min, an electrode wear rate of 0.0002 g/min, a circularity deviation of 0.0043 mm and a cylindricity deviation of 0.027 mm. From the experimental examination using multi-hole electrodes, it is concluded that the material removal rate increases and the electrode wear rate decreases because of the availability of higher spark discharge areas between the electrode and work material interface. The Adaptive Neuro-Fuzzy Inference System models showed minimum mean percentage error and, therefore, better performance in comparison with regression models. Full article

Coalbed methane (CBM) resources hold broad development prospects in China, with deep CBM reservoirs increasingly becoming a focal point for exploration. However, compared to shallow CBM, the factors influencing the productivity of deep CBM are more complex and less studied. This study integrates statistical methods—grey correlation analysis and principal component analysis—with the machine learning approach of random forests, and further employs a fuzzy mathematics-based comprehensive evaluation method to propose a systematic evaluation framework for identifying key controlling factors of productivity. Using field data from the No. 8+9 coal seam in the eastern margin of the Ordos Basin, the results indicate that the primary geological factors affecting cumulative gas production are gas content and coal seam thickness, while the key engineering factors are proppant intensity and proppant volume. These findings align with practical field experience and provide a rational basis for the design of fracturing strategies in deep CBM reservoirs. Full article

The current green building assessment system suffers from issues such as insufficient coverage of smart indicators, significant biases in subjective weighting, and weak dynamic adaptability, which restrict the scientific promotion of green buildings. This study focuses on the gaps in the quantitative assessment of smart technologies in China’s green building evaluation standards (such as the current Green Building Evaluation Standard). While domestic standards are relatively well-established in traditional dimensions like energy conservation and environmental protection, there are fragmentation issues in the assessment of smart technologies such as the Internet of Things (IoT) and BIM. Moreover, the coverage of smart indicators in non-civilian building fields is significantly lower than that of international systems such as LEED and BREEAM. This study determined the basic framework of the evaluation indicator system through the Delphi method. Drawing on international experience and contextualized within China’s (GB/T 50378-2019) standards, it systematically integrated secondary indicators including “smart security,” “smart energy,” “smart design,” and “smart services,” and constructed dual-drive evaluation dimensions of “greenization + smartization.” This elevated the proportion of the smartization dimension to 35%, filling the gap in domestic standards regarding the quantitative assessment of smart technologies. In terms of research methods, combined weighting using the Analytic Hierarchy Process (AHP) and entropy weight method was adopted to balance subjective and objective weights and reduce biases (the resource conservation dimension accounted for 39.14% of the combined weights, the highest proportion). By integrating the grey clustering model with the whitening weight function to handle fuzzy information, evaluations were categorized into four grey levels (D/C/B/A), enhancing the dynamic adaptability of the system. Case verification showed that Project A achieved a comprehensive evaluation score of 5.223, with a grade of B. Among its indicators, smart-related ones such as “smart energy” (37.17%) and “smart design” (37.93%) scored significantly higher than traditional indicators, verifying that the system successfully captured the project’s high performance in smart indicators. The research results indicate that the efficient utilization of resources is the core goal of green buildings. Especially under pressures of energy shortages and carbon emissions, energy conservation and resource recycling have become key priorities. The evaluation system constructed in this study can provide theoretical guidance and technical support for the promotion, industrial upgrading, and sustainable development of green buildings (including non-civilian buildings) under the dual-carbon goals. Its characteristic of “dynamic monitoring + smart integration” forms differentiated complementarity with international standards, making it more aligned with the needs of China’s intelligent transformation of buildings. Full article

In response to the challenges of reduced efficiency and compromised seeding accuracy in conventional soybean planters operating at high speeds, this research introduces a novel precision seeding system powered by an electric drive, aiming to enhance both operational reliability and sowing precision. The entire system is powered by the tractor’s 12 V battery and incorporates an OGWO-Fuzzy PID control strategy to regulate the seeding motor speed. To achieve faster and more accurate regulation of the seeding motor speed, this study employs a ternary phase-diagram-based strategy to optimize the weight allocation among the α, β, and δ wolves within the Grey Wolf Optimization (GWO) algorithm. Based on engineering requirements, the optimal weight ratio was determined to be 16:2:1. Simulation results indicate that the optimized OGWO-Fuzzy PID control strategy achieves a settling time of only 0.17 s with no overshoot. In bench tests, the OGWO-Fuzzy PID control strategy significantly outperformed both GWO-Fuzzy PID and Fuzzy PID in terms of seed-metering speed regulation time and accuracy. The average qualified seeding index reached 95.68%, demonstrating excellent seeding performance at medium-to-high operating speeds. This study provides a practical and technically robust approach to ensure seeding quality during medium–high-speed soybean planting Full article

The complex interdependencies among numerous safety risk factors influencing oil tanker loading/unloading operations constitute a focal point in academic research. To enhance safety management in oil port operations, this study conducts a risk analysis of oil tanker berthing and cargo transfer safety. Initially, safety risk factors are identified based on the Wu-li Shi-li Ren-li (WSR) systems methodology. Subsequently, a hybrid weighting approach integrating the Fuzzy Analytic Hierarchy Process (FAHP), G2 method, and modified CRITIC technique is employed to calculate indicator weights. These weights are then synthesised into a combined weight (GVW) using cooperative game theory and variable weight theory. Further, by integrating grey theory with the cloud model (GCM), a risk assessment is performed using Tianjin Port as a case study. Results indicate that the higher-risk indicators for Tianjin Port include vessel traffic density, safety of berthing/unberthing operations, safety of cargo transfer operations, safety of pipeline transfer operations, psychological resilience, proficiency of pilots and captains, and emergency management capability. The overall comprehensive risk evaluation value for Tianjin Port is 0.403, corresponding to a “Moderate Risk” level. Comparative experiments demonstrate that the results generated by this model align with those obtained through Fuzzy Comprehensive Evaluation Methods. However, the proposed GVW-GCM framework provides a more objective and accurate reflection of safety risks during tanker operations. Based on the computational outcomes, targeted recommendations for risk mitigation are presented. The integrated weighting model—incorporating game theory and variable weight concepts—coupled with the grey cloud methodology, establishes an interpretable and reusable analytical framework for the safety assessment of oil port operations under diverse port conditions. This approach provides critical decision support for constructing comprehensive management systems governing oil tanker loading/unloading operations. Full article

While blockchain technology (BT) has gained attention in the construction industry, limited research has focused on its application in sustainable construction procurement management (SCPM). Addressing this gap, the present study investigates the key drivers influencing BT adoption in SCPM using a hybrid methodological approach. This study includes a systematic review of academic and grey literature, expert consultations, and quantitative analysis using advanced fuzzy-based algorithms, k-means clustering, and social network analysis (SNA). Data were collected through an online survey distributed to professionals experienced in SCPM and blockchain implementation. The Fuzzy DEMATEL results identify “high quality”, “decentralization and data security”, and “cost of the overall project” as the most critical drivers. Meanwhile, SNA highlights “stability of the system”, “overall performance of the project”, and “customer satisfaction” as the most influential nodes within the network. These insights provide actionable guidance for industry stakeholders aiming to advance SCPM through blockchain integration and contribute to theoretical advancements by proposing novel analytical frameworks. Full article

“The Internet of Things” is a relatively new idea that refers to objects that can connect to the Internet and exchange data. The Internet of Things (IoT) enables novel interactions between objects and people by interconnecting billions of devices. While there are many IoT-related products, challenges pertaining to their effective implementation, particularly the lack of knowledge and confidence about security, must be addressed. To provide IoT-based enterprises with a platform for efficiency and sustainability, this study aims to identify the critical elements that influence the growth of a successful company integrated with an IoT system. This study proposes a decision support tool that evaluates the influential features of IoT using the Pythagorean Fuzzy and Interval Grey approaches within the Analytical Hierarchy Process (AHP). This study demonstrates that security, value, and connectivity are more critical than telepresence and intelligence indicators. When both strategies are used, market demand and information privacy become significant indicators. Applying the Pythagorean Fuzzy approach enables the identification of sensor networks, authorization, market demand, and data management in terms of importance. The application of the Interval Grey approach underscores the importance of data management, particularly in sensor networks. The indicators that were finally ranked are compared to obtain a good coefficient of agreement. These findings offer practical insights for promoting sustainability in enterprise operations by optimizing IoT infrastructure and decision-making processes. Full article

A reasonable water price for interbasin water transfer projects (IWTPs) is vital for solving the problem of unequal water use among different water users caused by different water source supply prices, promoting external water transfer consumption, and ensuring the stable and equitable project operation. However, the formulation of the water price is influenced by many factors, and it is necessary to identify the key factors and their interactions in the water prices formulation for IWTPs. In this study, we identified 15 factors that affect it. This paper used the fuzzy decision-making trial and evaluation laboratory (DEMATEL) to analyze the causal relationships and importance levels among the influencing factors. A four-level hierarchical structural model was established using an interpretive structural model (ISM), which intuitively displayed the hierarchical structure and pathways of each factor. The role of each influencing factor was determined by using MICMAC. Finally, the grey relational analysis method was used to identify the top five key factors: the socioeconomic development level, diversification of water resources, water demand of water users, cost of the project’s water supply, and national policies and regulations. Strategies to improve the formulation of water prices have also been proposed. The results show that the top five factors influencing the water price for IWTPs are the socio-economic development level, diversification of water resources, water demand of water users, cost of the project’s water supply, and national policies and regulations. The water price should be formulated based on the water resource cost, supply–demand relationships of water resources, and policy objectives to ensure scientific and reasonable cost allocation and differentiated pricing. For water-transfer projects with strong public welfare, the government may lower water prices through financial subsidies to alleviate the burden on water users. Full article

Plasma jet cutting is a non-conventional process commonly used in modern industry for processing metal sheets and preparing them for subsequent technological steps. In this context it is very important to achieve the best possible final-quality workpiece to minimize additional post-processing costs, and time. This is especially challenging by the plasma jet processing of aluminum and its alloys. In this paper, a comprehensive analysis regarding the machinability and optimal circular quality of aluminum alloy 5083 was performed. Process parameters whose effects were analyzed are the cutting speed, arc current and cutting height. The circular quality was considered through responses: the circular kerf width, circular bevel angle, and circularity error on the top and bottom sheet of the metal side. To design functional relations between the process inputs and quality performances, an artificial intelligence fuzzy logic technique supported by ANOVA was applied. In order to define the process conditions that result in optimal cut quality responses, the multi-objective optimization of hybrid grey relational analysis (GRA) and the fuzzy logic approach was presented. Corresponding surface plots were created to determine the Pareto front of optimal solutions that simultaneously optimize all circular quality objective functions. The optimization procedure was confirmed through a test in which the mean absolute percentage error represented as the validation metric. Full article

Blue-and-white porcelain patterns embody rich symbolic meanings and play a pivotal role in the transmission of Chinese intangible cultural heritage. However, their contemporary application often faces challenges due to complex visual forms and contextual interpretations. This study adopts a semiotic perspective to reinterpret blue-and-white porcelain motifs as cultural heritage symbols, aiming to assess their potential for sustainable preservation and modern revitalization. A hybrid evaluation framework is proposed, combining Grey System Theory and the Fuzzy Evaluation Method to quantitatively analyze 40 representative patterns across five key dimensions: cultural symbolism, esthetic value, communicative potential, modern applicability, and sustainability. Data were collected from expert panels, public surveys, and market performance, with the Analytic Hierarchy Process (AHP) employed to determine the relative importance of each dimension. The results reveal that plant and geometric patterns exhibit high adaptability and symbolic clarity, making them ideal for reinterpretation in modern design. Conversely, complex narrative and animal-based motifs demonstrate weaker performance in communicative efficiency and sustainability, indicating the need for visual simplification and semantic transformation. This study provides a theoretical and methodological foundation for the revitalization of traditional porcelain heritage in contemporary design practice, contributing to the global dissemination and sustainable development of cultural heritage symbols. Full article

Talent significantly influences urban technological innovation and sustainable economic development. Enhancing urban talent competitiveness (UTC) necessitates a systemic perspective on upgrading and optimizing the combination of both tangible and intangible resources, such as economic vitality, livability, and social harmony, which is a typical configurational issue. This paper utilizes empirical data from 96 Chinese cities and applies an innovative grey quantitative comparative analysis (GQCA) method to investigate the impact and mechanisms of different urban talent ecology (UTE) on talent competitiveness. The findings reveal that there are no bottleneck factors constraining UTC within the sample, interactions, and couplings among six urban talent elements that generated 30 distinct UTEs. By calculating the possibility of each UTE achieving talent competitiveness, it was found that 16 UTEs, characterized by vibrant business-led development, comprehensive development and integrated leadership, regional development leadership, and economy-led international innovation, lead to high UTC. Conversely, 14 UTEs result in low UTC. These findings were further validated through fuzzy set qualitative comparative analysis (fsQCA) for robustness testing. Finally, the study offers policy recommendations for urban talent strategies from both short-term and long-term perspectives. Full article