Search Results (37)

Drought stress has a significant impact on cotton growth, development, and productivity. This study conducted drought stress treatment and normal water treatment (control group) on 502 cotton accessions and analyzed data on eight phenotypic traits closely related to drought stress tolerance. The results showed that all indicators changed significantly under drought stress conditions compared to the control group, with varying degrees of response among different indicators. To comprehensively evaluate the drought resistance of cotton during the germination period, the values of drought resistance comprehensive evaluation (D-value), weight drought resistance coefficient (WDC-value), and comprehensive drought resistance coefficient (CDC-value) were calculated based on membership function analysis and principal component analysis. Cluster analysis based on the D-value divided the germplasm into five drought-resistant grades, followed by the selection of one extreme material, each from the strongly drought-resistant and strongly drought-sensitive groups. An evaluation model was established using stepwise regression analysis, including the following effective indicators: Relative Fresh Weight (RFW), Relative Hypocotyl Length (RHL), Relative Seeds Water Absorption Rate (RAR), Relative Germination Rate (RGR), Relative Germination Potential (RGP), and Relative Drought Tolerance Index (RDT). The validation of the D-value prediction model based on the Best Linear Unbiased Prediction (BLUP) showed that the results obtained from two independent biological replicates were highly consistent. The comprehensive evaluation system and screening indicators established in this study provide a reliable method for identifying drought tolerance during the germination period. Full article

Drought is one of the main environmental disturbances limiting the growth and production of turfgrass in China and around the world. To study the performance under drought conditions of different mixing ratios (Lolium perenne L., Festuca elata Keng., Poa pratensis L.), a water-controlled pot experiment was conducted. The mixing ratios used were 2:3:5, 2:6:2, and 2:2:6 for Lolium perenne, Festuca elata, and Poa pratensis, respectively. The relative water content (RWC), proline (Pro) content, and other physiological and ecological variables of three turfgrass monocultures and their three ratio mixtures (a total of six different treatments) were measured under drought as well as dust stress at various time points. The results revealed that, under drought stress, the dust retention performance of the mixing ratio treatments was better than the monocultures, with the best performance in the 2:6:2 mix and the worst in the Poa pratensis monoculture. Additionally, during the 21 days of drought stress, as time increased, the appearance quality (TQ) of the turfgrass gradually declined over time; its RWC gradually decreased; its chlorophyll (Chl) content, peroxidase (POD) activity, and superoxide dismutase (SOD) activity all showed a trend of initially increasing then decreasing; and its soluble sugar (Sol), malondialdehyde (MDA), and Pro content increased continuously. A comprehensive evaluation of physiological and ecological variables, using the membership function method, showed that the six types of turfgrass treatments ranked as follows (from strongest to weakest) in drought resistance: 2:6:2 mix > Festuca elata monoculture > 2:3:5 mix > 2:2:6 mix > Lolium perenne monoculture > Poa pratensis monoculture. The dust retention capability was assessed through quantitative measurements, and the ranking of dust retention amounts in descending order was as follows: Festuca elata > 2:6:2 mix > 2:2:6 mix > Poa pratensis > Lolium perenne > 2:3:5 mix. We conclude that, in practical applications, the degree of drought can be appropriately controlled within a certain range to achieve maximum dust retention benefits from turfgrass. Full article

In view of the diversity and varying complexity of ship equipment, and the difficulty of existing state assessment methods in effectively handling the differences in the influence of different characteristic parameters on the equipment’s health state, leading to poor evaluation results, this paper proposes a ship equipment health state assessment method based on BP-RF and combined weighting. This method utilizes the BP-RF model to mine the implicit relationship between ship equipment feature parameters and state patterns, converting monitoring data into state pattern probability information. A trapezoidal membership function is used to determine the membership degree of each state pattern probability to different health state levels. The combined weighting method, which reflects a symmetric concept, balances expert experience and data information by integrating the subjective and objective weights of each state pattern probability, thus determining the equipment’s health state level. Through a case study of a specific type of ship’s gas turbine, the BP-RF model achieves a diagnostic accuracy of 98.3%, with $F_1$ scores improved by 5.1%, 5.0%, 8.6%, 9.9%, 6.7%, 3.4%, and 5.3% compared to the BP neural network, RF, Support Vector Machine (SVM), Convolutional Neural Network (CNN), BP-SVM, SVM-RF, and CNN-SVM models, respectively. Additionally, the evaluation results of this method exhibit clear boundaries for each state membership degree, effectively addressing the problem of unbalanced contributions from characteristic parameters and comprehensively reflecting the relative importance and correlation of each parameter. Overall, this method provides a more accurate and comprehensive assessment of ship equipment health compared to other methods, offering reliable support for ship equipment maintenance and assurance. Full article

This paper presents a unique multi-Q valued bipolar picture fuzzy set (MQVBPFS) methodology to tackle issues in cybersecurity risk assessment under conditions of ambiguity and contradicting data. The MQVBPFS framework enhances classical fuzzy theory through three key innovations: (1) multi-granular Q-valued membership, (2) integrated bipolarity for representing conflicting evidence, and (3) refined algebraic operations, encompassing union, intersection, and complement. Contemporary fuzzy set methodologies, such as intuitionistic and image fuzzy sets, inadequately encapsulate positive, negative, and neutral membership degrees while maintaining bipolar information. Conversely, our MQVBPFS architecture effectively resolves this restriction. Utilizing this framework for threat assessment and risk ranking, we create a tailored cybersecurity algorithm that exhibits 91.7% accuracy (in contrast to 78.2–83.5% for baseline methods) and attains 94.6% contradiction tolerance in empirical evaluations, alongside an 18% decrease in false negatives relative to conventional approaches. This study offers theoretical progress in fuzzy set algebra and practical enhancements in security analytics, improving the handling of ambiguous and conflicting threat data while facilitating new research avenues in uncertainty-aware cybersecurity systems. Full article

Harmonic distortion caused by phase jumps in the phase-locked loop (PLL) during asymmetric faults poses a significant threat to the secure operation of renewable energy grid-connected systems. A harmonic suppression strategy based on Vague set theory is proposed for offshore wind power AC transmission systems. By employing the three-dimensional membership framework of Vague sets—comprising true, false, and hesitation degrees—phase-locked errors are characterized, and dynamic, real-time PLL proportional-integral (PI) parameters are derived. This approach addresses the inadequacy of harmonic suppression in conventional PLL, where fixed PI parameters limit performance under asymmetric faults. The significance of this research is reflected in the improved power quality of offshore wind power grid integration, the provision of technical solutions supporting efficient clean energy utilization in alignment with “Dual Carbon” objectives, and the introduction of innovative approaches to harmonic suppression in complex grid environments. Firstly, an equivalent circuit model of the offshore wind power AC transmission system is established, and the impact of PLL phase jumps on grid harmonics during asymmetric faults is analyzed in conjunction with PLL locking mechanisms. Secondly, Vague sets are employed to model the phase-locked error interval across three dimensions, enabling adaptive PI parameter tuning to suppress harmonic content during such faults. Finally, time-domain simulations conducted in PSCAD indicate that the proposed Vague set-based control strategy reduces total harmonic distortion (THD) to 1.08%, 1.12%, and 0.97% for single-phase-to-ground, two-phase-to-ground, and two-phase short-circuit faults, respectively. These values correspond to relative reductions of 13.6%, 33.7%, and 80.87% compared to conventional control strategies, thereby confirming the efficacy of the proposed method in minimizing grid-connected harmonic distortions. Full article

Soil reclamation is essential for restoring the ecological environment in coal mining subsidence areas, with reclaimed soil quality serving as a key indicator of success. Traditional evaluation methods often rely on subjective judgment, leading to potential biases. This study proposes an approach combining cluster analysis (CA), correlation degree analysis (CDA), principal component analysis (PCA), and membership function (MF) to evaluate soil reclamation quality in the Ezhuang subsidence area, Shandong Province, China. A minimum dataset (MDS) was established, including seven indicators: exchangeable magnesium, total nitrogen, available copper, available manganese, zinc, free iron, and available silicon. Soil quality indices (SQIs) were calculated using membership functions, revealing moderate soil quality across the reclamation area, with significant spatial variations. The northeastern section exhibited relatively good soil quality, while the northwestern and southeastern sections were poorer. Key factors influencing soil quality included variations in organic matter, exchangeable magnesium, and available copper. The accuracy of the CA-CDA-PCA-MF method was validated, with a coefficient of determination (R²) of 0.877 and a coefficient of deviation (CV) of 0.053, demonstrating its reliability. This method provides a robust tool for evaluating and improving soil restoration in mining areas, with potential applications in similar reclamation projects. Full article

Research has provided evidence of the protective characteristics of extended family for U.S. emerging adults, but no research has specifically explored cousin relationships. The current study fills this gap by analyzing qualitative data collected from 192 U.S. 18–29-year-old adults (M age = 25.6 years). As this topic is relatively unexplored, examining qualitative data provides scope and vocabulary for further exploration. Participants completed an online survey asking them to describe interactions with extended family, identifying 561 cousins (M age = 28.2 years). A thematic analysis based on the process defined by Braun and Clark generated four primary themes: (1) emerging adults feel varying degrees of closeness and distance with their cousins, (2) relational maintenance with cousins is both planned and incidental, (3) family membership provides resources, and (4) cousins share the same generational position. These results describe important characteristics of the cousin relationship, including moments of unexpected closeness and shared experience of family. The findings also highlight the relevance of sharing a similar life stage within the same family system. Practitioners can utilize findings to help clients identify extended family members that can be tapped for bonding and support. Full article

Exogenous biostimulants (EB) are crucial for reducing abiotic stress in plants. It is currently unclear how EB such as melatonin (MT), betaine (BA), and salicylic acid (SA) regulate the stress in Camellia oleifera seedlings under alkali stress (XP). This study demonstrates the moderating effect of SA (0.5, 1, and 2 mmol/L), BA (0.2, 0.4, and 0.8 g/L), and MT (200, 400, and 800 μmol/L) on the relative chlorophyll content, photosynthetic parameters, chlorophyll fluorescence parameters, osmoregulatory substances, and antioxidant enzymes in C. oleifera seedlings under XP. The results showed that spraying different types and different concentrations of EB under alkali stress had a certain alleviating effect on the phenotype of C. oleifera seedlings. Whether 7 or 15 days after the application of EB, the relative chlorophyll content (SPAD) and the degree of yellowish-green in the control group were different from those in the other 10 treatment groups, but the difference in brightness was not significant. As far as the malondialdehyde (MDA) content is concerned, the SA2, BA3, MT2, and MT3 treatment groups can significantly reduce the MDA content on the 7th day of EB application. The electrolytic leakage (EL) is also significantly reduced by MT2 and MT3. It was found that treatment groups SA3 and MT2 could improve the photosynthetic parameters of C. oleifera seedlings to different degrees on the 7th day of EB application. On the 15th day of EB application, treatment groups SA1, SA3, BA1, and BA2 all increased the photosynthetic rate of C. oleifera compared to the XP treatment group, but other treatments did not increase. At the same time, the results showed that the fluorescence parameters of the seedlings showed different degrees of improvement under different EB spraying conditions. Under alkali stress, soluble proteins (SP) and soluble sugars (SS) increased in the XP group, but it was found that the SA3, BA3, and MT2 treatment groups could reduce the content of osmoregulatory substances both on the 7th and 15th days of EB application. In terms of proline (Pro) content, BA1, BA2, and MT2 treatment groups could reduce Pro content on the 7th and 15th days of EB spraying, respectively. As for the antioxidant enzymes, the SA2, BA3, MT2, and MT3 treatment groups could basically increase the activity of antioxidant enzymes and further reduce oxidative damage on the 7th day of application of EB. According to the comprehensive results of the membership function, whether on the 7th or 15th day of EB spraying, the MT2 treatment group has the best overall mitigation effect of the three EB applications, ranking in the top three. This study will help to improve the scientific understanding of C. oleifera’s alkali resistance and interaction with EB while filling the knowledge gap on the physiological response to oleofylline stress. Full article

The study aimed to evaluate the cold tolerance of various peach cultivars under diverse low-temperature conditions (−5, −10, −15, −20, −25, −30, and −35 °C). A comprehensive assessment of their responses to cold was performed by integrating LT50 values with membership functions and evaluating local adaptability among the selected peach cultivars. The findings revealed that as temperatures dropped, electrical conductivity (REC), malondialdehyde (MDA), and hydrogen peroxide (H₂O₂) levels initially rose, then fell, and subsequently increased once more. Soluble sugar (SS) and soluble protein (SP) concentrations peaked at −25 °C and showed a significant negative correlation with semi-lethal temperature (LT50). The expression of free proline varied among different samples. Combining physiological analyses with field adaptation correlation assessments, it was found that ‘Ziyan Ruiyang’ exhibited a relatively low LT50 value of −29.67 °C and a membership function degree of 0.76, suggesting robust field adaptation abilities. At the same time, ‘Ganlu Shumi’ demonstrated stable trends in H₂O₂ and MDA levels, maintaining them at relatively low concentrations; it also had the lowest LT50 value, the highest membership function score, and the highest survival rate. Consequently, this cultivar could be a valuable resource for enhancing cold resistance under low-temperature stress. In summary, by correlating LT50 values with membership functions and observing local adaptability in these peach cultivars, we have established reliable data that can serve as a basis for identifying potential cross-breeding parents to develop new cold-resistant varieties. Full article

This study employs an adaptive neuro-fuzzy inference system (ANFIS) to identify critical success factors (CSFs) crucial for the success of pavement construction projects. Challenges such as construction cost delays, budget overruns, disputes, claims, and productivity losses underscore the need for effective project management in pavement projects. In contemporary construction management, additional performance criteria play a vital role in influencing the performance and success of pavement projects during construction operations. This research contributes to the existing body of knowledge by comprehensively identifying a multidimensional set of critical success performance factors that impact pavement and utility project management. A rigorous literature review and consultations with pavement experts identified sixty CSFs, categorized into seven groups. The relative importance of each element and group is determined through the input of 287 pavement construction specialists who participated in an online questionnaire. Subsequently, the collected data undergo thorough checks for normality, dependability, and independence before undergoing analysis using the relative importance index (RII). An ANFIS is developed to quantitatively model critical success factors and assess the implementation performance of construction operations management (COM) in the construction industry, considering aspects such as clustering input/output datasets, fuzziness degree, and optimizing five Gaussian membership functions. The study confirms the significance of three primary CSFs (financial, bureaucratic, and governmental) and communication-related variables through a qualitative structural and behavioral validation process, specifically k-fold cross-validation. The outcomes of this research hold practical implications for the management and assessment of overall performance indices in pavement construction projects. The ANFIS model, validated through robust testing methodologies, provides a valuable tool for industry professionals seeking to enhance the success and efficiency of pavement construction endeavors. Full article

In order to avoid potential personnel and financial losses, the evaluation of pre-drilling safety is of great importance in oil and gas exploration and development. This paper presents a method of evaluating pre-drilling safety through combining the Analytic Hierarchy Process (AHP) with the Alternating Condition Expectation (ACE) method. An indicator system with a 9-3-1 structure was established, incorporating various unrestricted variables to describe the technical factor. Additionally, nine membership functions and weights were determined in order to build the AHP model by connecting the independent variables in the basic layer to dependent variables in the middle layer. Four transformed functions were also formulated to construct the ACE model by linking the middle variables to the pre-drilling safety value in the final layer. A total of 28 sets of on-site drilling data from three oilfields were collected for the establishment and verification of the AHP-ACE model. Average absolute error (AAE) and average absolute relative error (AARE) of the model to predict the training data are 0.03 and 4.29%, respectively, whereas the AAE and AARE for verification samples are 0.03 and 4.51%, respectively. The sensitivity ranking of the three potential variables is as follows: human factor exhibits the highest degree of sensitivity, followed by natural factor and technical factor, in descending order. The AHP-ACE model for pre-drilling safety assessment faces limitations in universal applicability and scope, particularly in real-time drilling activities. However, its potential for improvement lies in integrating insights from past operations and expanding the dataset to enhance accuracy and broaden safety assessment coverage. This method is not limited by blocks, which is of great significance to ensure drilling safety. Full article

Efficient deep-water offshore wind power installation platforms with a pressurized self-elevating mat are a new type of equipment used for installing offshore wind turbines. However, the unstable internal pressure of the pressurized self-elevating mat can cause serious harm to the platform. This paper studies the pneumatic control system of the self-elevating mat to improve the precision of its pressure control. According to the pneumatic control system structure of the self-elevating mat, the pneumatic model of the self-elevating mat is established, and a conventional PID controller and fuzzy PID controller are designed and established. It can be seen via Simulink simulation that the fuzzy PID controller has a smaller adjustment time and overshoot, but its anti-interference ability is relatively weak. The membership degree and fuzzy rules of the fuzzy PID controller are optimized using a neural network algorithm, and a fuzzy neural network PID controller based on BP neural network optimization is proposed. The simulation results show that the overshoot of the optimized controller is reduced by 9.71% and the stability time is reduced by 68.9% compared with the fuzzy PID. Finally, the experiment verifies that the fuzzy neural network PID controller has a faster response speed and smaller overshoot, which improves the pressure control accuracy and robustness of the self-elevating mat and provides a scientific basis for the engineering applications of the self-elevating mat. Full article

Highwall mining is a method that involves using a continuous highwall miner system (CHM) to extract coal from the remaining coal seams, which has proven to be an effective and safe method for extracting coal after open-pit mining. However, application cases globally have shown that the feasibility of highwall mining in open-pit coal mines is subject to geological conditions, mining techniques, and other factors. If application conditions are not suitable, equipment may be trapped under collapsed coal–rock masses and unable to be retrieved, resulting in severe safety issues for slope stability. To meet the real-world demand for extracting the remaining coal in open-pit coal mines in China, it is urgent to conduct a feasibility evaluation of highwall mining in these areas. This paper establishes a mathematical evaluation framework for assessing the feasibility of highwall mining by summarizing a large number of engineering application cases globally and analyzing various technical characteristics such as geological deposit conditions, mining techniques, and technical equipment. The analytic hierarchy process (AHP), fuzzy comprehensive evaluation (FCE) and variable weight theory (VWT) are utilized in conjunction to form this framework, which includes four secondary indicators: geological deposit factors, mining technique factors, safety impact factors, and economic evaluation factors, and 20 tertiary sub-indicators, along with their corresponding characteristic values. The feasibility sub-set is divided into four categories: infeasible, basically feasible, relatively feasible, and highly feasible, and the values of the sub-indicators strictly follow and represent these four levels of feasibility. Weight vectors for the sub-indicators are obtained through a judgment matrix established within the mathematical evaluation framework. The fuzzy relationship matrix of the sub-indicators is constructed using fuzzy mathematical membership functions, and the final feasibility evaluation is determined through two-level comprehensive evaluation. The accuracy of the model is verified using the characteristic parameters of open-pit coal mines under two different conditions (JZT coal mine in Inner Mongolia, China, and GC coal mine in Australia). The results demonstrate that the maximum evaluation membership degree for the JZT mine is 0.7113, belonging to the “highly feasible” level, while the GC mine is 0.3304, belonging to the “basically feasible” level, which aligns well with real-world usage, proving that the evaluation model can effectively reveal the performance and membership degree of each indicator in different application cases. By quantitatively characterizing the feasibility level of highwall mining technology under different application conditions, this evaluation model can provide scientific guidance for coal mining enterprises to introduce CHM for highwall mining operation in open-pit coal mines. Full article

The fire risk of cables constantly changes over time and is affected by the materials and working conditions of cables. To address its internal timing property, it is essential to use a dynamic analysis method to assess cable fire risk. Meanwhile, data uncertainty resulting in the deviation of risk values must also be considered in the risk assessment. In this regard, this study proposes a hybrid cloud model (CM)-enabled Dynamic Bayesian network (DBN) method to estimate the cable fire risk under uncertainty. In particular, the CM is initially applied to determine the membership degrees of the assessment data relative to different states of the root nodes; then, these degrees are considered the prior probabilities of DBN, where the dynamic risk profiles are reasoned. Subsequently, the Birnbaum and Fussell–Vesely importance measures are constructed to identify the key nodes for risk prevention and control, respectively. Moreover, a case study of the Chongqing Tobacco Logistics Distribution Center is conducted, the computational results of which indicate the proposed method’s decision-making effectiveness. Finally, a comparison of the reasoning results between the proposed and traditional methods is performed, presenting strong evidence that demonstrates the reliability of the proposed method. Full article

Semi-supervised clustering typically relies on both labeled and unlabeled data to guide the learning process towards the optimal data partition and to prevent falling into local minima. However, researchers’ efforts made to improve existing semi-supervised clustering approaches are relatively scarce compared to the contributions made to enhance the state-of-the-art fully unsupervised clustering approaches. In this paper, we propose a novel semi-supervised deep clustering approach, named Soft Constrained Deep Clustering (SC-DEC), that aims to address the limitations exhibited by existing semi-supervised clustering approaches. Specifically, the proposed approach leverages a deep neural network architecture and generates fuzzy membership degrees that better reflect the true partition of the data. In particular, the proposed approach uses side-information and formulates it as a set of soft pairwise constraints to supervise the machine learning process. This supervision information is expressed using rather relaxed constraints named “should-link” constraints. Such constraints determine whether the pairs of data instances should be assigned to the same or different cluster(s). In fact, the clustering task was formulated as an optimization problem via the minimization of a novel objective function. Moreover, the proposed approach’s performance was assessed via extensive experiments using benchmark datasets. Furthermore, the proposed approach was compared to relevant state-of-the-art clustering algorithms, and the obtained results demonstrate the impact of using minimal previous knowledge about the data in improving the overall clustering performance. Full article