Average Operation Research Articles

A combination prediction model based on Theil coefficient and induced continuous aggregation operator for the prediction of Shanghai composite index

This paper proposes an interval combination prediction model for Shanghai composite index, utilizing the Theil coefficient and the induced continuous generalized ordered weighted logarithmic harmonic averaging (ICGOWLHA) operator. The effectiveness of the proposed model under specific weight conditions and the existence of its analytical solution are demonstrated. Shanghai composite index's case analysis demonstrates that, in terms of interval root mean squared error (IRMSE), interval mean absolute error (IMAE), interval mean absolute percentage error (IMAPE), and interval mean squared percentage error (IMSPE), the proposed model's predictive performance improvements over the best-performing single prediction model are 29.33%, 25.72%, 26.10%, and 28.86%, respectively. At the same time, the theoretical properties of the model are verified in the results of the case analysis, and the model's convergence is reflected in sensitivity analysis. Through extensive model comparisons, it is observed that the model proposed in this paper exhibits strong generalization, without specific limitations on data size or feature count. It demonstrates good aggregation prediction performance for interval data. Moreover, it is applicable to various fields, including finance, environment, and others.

Evaluating the Efficacy of IL-6 as Predictors of Lateral Lumbar Interbody Fusion Success: Insights From Pain Scores and JOABPEQ Assessments.

A prospective study. This study aims to explore the correlation between interleukin (IL)- 6 levels in intervertebral disc (IVD) tissue and clinical outcomes in patients undergoing lumbar surgery for lumbar degenerative disease (LDD). This prospective study analyzed 32 patients (22 men and 10 women, average age 69.6 years) who underwent lateral lumbar interbody fusion (LLIF). IL-6 gene expression in IVD tissues collected during surgery was measured and correlated with pre- and postoperative clinical outcomes, including pain intensity assessed via Numeric Rating Scales (NRS) and quality of life (QOL) evaluated through the Japanese Orthopaedic Association Back Pain Evaluation Questionnaire (JOABPEQ). IL-6 levels showed statistical correlations with postoperative intensity of low back pain (LBP) and several JOABPEQ domains. Patients with higher expression of IL-6 levels experienced more severe postoperative LBP and lower scores in lumbar function, walking ability, social life function, and mental health. The effectiveness rate of JOABPEQ scores was exceptionally high for low back pain (.548), walking ability (.677), and social functioning (.563), demonstrating the effectiveness of LLIF. The average operation time was 105.6 minutes, and the estimated blood loss was 85.6mL. The study underscores IL-6 as a potential biomarker for predicting surgical outcomes in LDD. High IL-6 levels correlate with worse postoperative LBP and lower QOL scores. Integrating molecular markers like IL-6 with patient-reported outcomes could provide a more comprehensive approach to postoperative care in spinal disorders, aiming to improve the overall QOL for LDD patients undergoing LLIF surgery.

Carbonate Ester-Based Electrolyte Enabling Rechargeable Zn Battery to Achieve High Voltage and High Zn Utilization.

Owing to the high H2O activity, the aqueous electrolyte in the Zn battery exhibits a narrow electrochemical window and inevitable hydrogen evolution reaction, limiting the anode utilization ratio and performance at high voltage. Carbonate ester, the well-developed electrolyte solvent in Li-ion batteries, exhibits aprotic properties and high anodic stability. However, its use in Zn metal batteries is limited due to the low solubility of Zn salts in carbonate esters. Herein, we propose a carbonate ester-based electrolyte (EC:DMC:EMC = 1:1:1 wt %), which contains a new Zn salt (Zn(BHFip)2) characterized by low cost, easy synthesis, and excellent aprotic solvent solubility. The BHFip- anion assists in forming Zn2+ conductive SEI on the anode and decomposes at high voltage to generate a protective CEI layer on the cathode. The Zn//Zn symmetric cell using such electrolyte achieves a remarkable Zn utilization ratio of 91% for 125 h, which has rarely been reported before. Furthermore, the Zn//LiMn2O4 full cell with an average operation voltage of 1.7 V demonstrates reliable cycling for 135 cycles with an N/P ratio of 1:1. In addition, the Zn//LiNi0.5Mn1.5O4 full cell exhibits a high discharge median voltage exceeding 2.2 V for 280 cycles, with the high voltage plateau (above 2 V) constituting 82% of the total capacity.

Multiple-attribute group decision-making approach using power aggregation operators with CRITIC-WASPAS method under 2-dimensional linguistic intuitionistic fuzzy framework

The 2-dimensional linguistic intuitionistic fuzzy variables (2DLIFVs) provide an efficient tool to model the cognitive information of experts in practical circumstances while considering the reliability of the evaluation results. It is a hybrid model representing qualitative information, which derives from integrating the 2-dimensional linguistic variables (2DLVs) with linguistic intuitionistic fuzzy numbers (LIFNs). In this work, we first define a novel score function to establish a proper ranking order among the 2DLIFVs. Next, we define a new distance measure under a 2-dimensional linguistic intuitionistic fuzzy framework to determine the difference between 2DLIFVs. Some mathematical properties and special cases of the defined distance measure are also discussed. The power average (PA) operators provide aggregation tools, allowing aggregated arguments to support each other in the aggregation process. Motivated by the notion of power average (PA) and power geometric (PG), we define four new aggregation operators (AOs) to aggregate 2DLIFVs by considering information correlation in terms of support degree during the aggregation process. Several mathematical properties of the proposed AOs are also investigated. In addition, a new integrated 2DLIF-CRITIC-WASPAS methodology is developed in the 2-dimensional linguistic intuitionistic fuzzy environment to deal with complex decision issues. In this method, a “Criteria Importance Through Intercriteria Correlation (CRITIC)” approach is used to obtain the associate weights of the attributes, and the “Weighted Aggregated Sum Product Assessment (WASPAS)” method is executed to establish a valid ranking order among the alternatives.Moreover, a real problem of venture capital investment related to renewable energy projects is considered to demonstrate the applicability of the proposed methodology. The evaluation process considers four aspects: the management team, service or product, finance, and the market, which are related to 14 attributes. A sensitivity analysis is carried out to explain the reliability of the obtained results. Comparing the proposed method with some extant methods establishes the strength and robustness of the formulated method in real-world situations.

Iterative learning based convergence analysis for nonlinear impulsive differential inclusion systems with randomly varying trial lengths

SummaryThis paper studies the finite‐time tracking problem for nonlinear impulsive differential inclusion systems with randomly varying trial lengths. First, we convert the set‐valued mapping in the differential inclusion systems to single‐valued mapping by a Steiner‐type selector. For the tracking problem of random discontinuous output trajectories, this paper defines a piecewise continuous variable by zero‐order holder to correct the tracking error of segmented continuity. Then, we introduce the average operator with forgetting factor to design three novel learning schemes, and establish convergence results by using the mathematical analysis tools such as impulsive Gronwall inequality and ‐norm. Finally, a numerical example verifies the validity of the theoretical results, and we compare the tracking performance of the output trajectories for different forgetting factors.

A novel neutrosophic cubic MADM method based on Aczel-Alsina operator and MEREC and its application for supplier selection

Neutrosophic cubic set (NCS) can process complex information by choosing both interval value and single value membership and indeterminacy and falsehood components. The aggregation operators based on Aczel-Alsina t-norm and t-corm are quite effective for evaluating the interrelationship among attributes. The purpose of this paper is to diagnose the interrelationship among attributes with neutrosophic cubic information, and propose a multi-attribute decision-making(MADM) method for supplier selection problem with unknown weight under neutrosophic cubic environment. We defined neutrosophic cubic Aczel-Alsina (NC-AA) operator and neutrosophic cubic Aczel–Alsina weighted arithmetic average (NCAAWAA) operator, then we discussed various important results and some properties of the proposed operators. Additionally, we proposed a MADM method under the presence of the NC-AAWAA operator. When the weights of attributes are unknown, we use the MEREC method to determine the weights. Later, the NC-AAWAA operator and MEREC method are applied to address the supplier selection problem. Finally, a sensitivity analysis and a comparative analysis are conducted to illustrate the stability and superiority of the proposed method. The results show the NC-AAWAA operator can handle the interrelationship among complex information more effectively, and MEREC method can weight the attributes based on the removal effect of a neutrosophic cubic attribute.

Novel method for total organic carbon content prediction based on non-equigap multivariable grey model

Predicting total organic carbon content plays a crucial role in shale gas reservoir evaluation. To address multi-variable, imperfect logging data and non-equigap characteristics, this study developed a multi-variable grey derivative model for predicting the total organic carbon content trend. To begin with, the non-equigap factor is introduced and the parameter identification of the new model is established on the basis of least square method. Then, to reduce fluctuations in the original data, the weighted geometric average weakening operator is introduced. In addition, considering the system stability, the derivation method is used to calculate the time response formula of the model. Furthermore, two validation cases are provided to verify the validity of this novel model. Finally, the model is applied to the Fuling shale gas field in China for practical forecasting. Compared to other models, such as the grey model, statistical model, and artificial intelligence model, the proposed model achieved higher prediction accuracy in the three cases. Specifically, this model obtained 95.62%, 95.88% and 94.55% prediction accuracy for total organic carbon content. Results that the new model effectively addresses the limitations of previous studies that focus on single-parameter or equal spacing issues.

Analysis of effectiveness of one-hole split endoscopy technique in treatment of lumbar spinal stenosis

To investigate the effectiveness of one-hole split endoscope (OSE) technique in the treatment of single segment lumbar spinal stenosis (LSS). The clinical data of 32 single segment LSS patients treated with OSE technique for simple spinal canal decompression between January 2022 and December 2022, who met the selection criteria, were retrospectively analysed. There were 18 males and 14 females, the age ranged from 45 to 82 years, with an average of 65.1 years. The disease duration was 9-72 months, with an average of 34.9 months. The surgical segments included L 3, 4 in 3 cases, L 4, 5 in 19 cases, and L 5, S 1 in 10 cases. The incision length, operation time, intraoperative blood loss, intraoperative radiation exposure frequency, postoperative mobilization time, and the area of the patient's lesion segment dural sac before operation and at 1 month after operation were recorded. Low back pain and leg pain were assessed by visual analogue scale (VAS) score before operation and at 3 days, 3 months, and 12 months after operation; functional recovery was assessed by Oswestry disability index (ODI) before operation and at 3 months and 12 months after operation; the effectiveness was assessed by modified MacNab criteria at last follow-up. All 32 patients successfully completed the operation, with an average incision length of 2.05 cm, an average operation time of 88.59 minutes, an average intraoperative blood loss of 46.72 mL, an average intraoperative radiation exposure frequency of 3.84 times, and an average postoperative mobilization time of 11.66 hours. All patients were followed up 12-16 months, with an average of 13.5 months. One patient experienced lower limb numbness, pain, and decreased muscle strength after operation, while the remaining patients did not experience complications such as dural tear or important nerve damage. The VAS scores of low back pain and leg pain and ODI in patients at various time points after operation were significantly better than preoperative ones, and each indicator further improved with time. The differences between time points were significant ( P<0.05). At 1 month after operation, the area of the patient's lesion segment dural sac was (123.13±19.66) mm 2, which significantly increased compared to preoperative (51.25±9.50) mm 2 ( t=-18.616, P<0.001). At last follow-up, the improved MacNab criteria were used to evaluate the effectiveness, with 18 cases achieving excellent results, 11 cases being good, and 3 cases being fair, with an excellent and good rate of 90.6%. The effectiveness of using OSE technique for simple spinal canal decompression treatment of single segment LSS is satisfactory, with the advantages of minimal surgical trauma and fast recovery.

A MAGDM approach for evaluating the impact of artificial intelligence on education using 2-tuple linguistic q-rung orthopair fuzzy sets and Schweizer-Sklar weighted power average operator.

The impact of artificial intelligence (AI) in education can be viewed as a multi-attribute group decision-making (MAGDM) problem, in which several stakeholders evaluate the advantages and disadvantages of AI applications in educational settings according to distinct preferences and criteria. A MAGDM framework can assist in providing transparent and logical recommendations for implementing AI in education by methodically analyzing the trade-offs and conflicts among many components, including ethical, social, pedagogical, and technical concerns. A novel development in fuzzy set theory is the 2-tuple linguistic q-rung orthopair fuzzy set (2TLq-ROFS), which is not only a generalized form but also can integrate decision-makers quantitative evaluation ideas and qualitative evaluation information. The 2TLq-ROF Schweizer-Sklar weighted power average operator (2TLq-ROFSSWPA) and the 2TLq-ROF Schweizer-Sklar weighted power geometric (2TLq-ROFSSWPG) operator are two of the aggregation operators we create in this article. We also investigate some of the unique instances and features of the proposed operators. Next, a new Entropy model is built based on 2TLq-ROFS, which may exploit the preferences of the decision-makers to obtain the ideal objective weights for attributes. Next, we extend the VIseKriterijumska Optimizacija I Kompromisno Resenje (VIKOR) technique to the 2TLq-ROF version, which provides decision-makers with a greater space to represent their decisions, while also accounting for the uncertainty inherent in human cognition. Finally, a case study of how artificial intelligence has impacted education is given to show the applicability and value of the established methodology. A comparative study is carried out to examine the benefits and improvements of the developed approach.

Multi-attribute decision-making methods based on Aczel-Alsina power aggregation operators for English blended teaching quality evaluation under single-valued neutrosophic sets

As a new form of teaching, blended teaching can integrate the advantages of classroom teaching and online teaching and improve the overall quality and effectiveness of English teaching under the premise of changing the orientation of teachers and students. The English blended teaching quality evaluation is a multiple attribute decision making (MADM) issue. Aczel-Alsina t-norm and t-conorm operations have the true advantage of changeability by adjusting a decision parameter. power averaging (PA) operator allowed decision attributes to strengthen each other through weight information during fusion, thereby eliminating the influence of subjective weights on the decision results. Motivate through classical Aczel-Alsina operators, PA operator and the single-valued neutrosophic sets (SVNSs), in this paper, the single-valued neutrosophic number Aczel-Alsina power averaging (SVNNAAPA) operator and the single-valued neutrosophic number Aczel-Alsina power weighted averaging (SVNNAAPWA) operator are constructed under SVNSs. The SVNNAAPWA operator is built for MADM. Eventually, an example about English blended teaching quality evaluation and some selected comparative decision analysis is used to depict the SVNNAAPWA method.

Process design and economic analysis for the production of microalgae from anaerobic digestates in open raceway ponds

A model based framework was established for large scale assessment of microalgae production using anaerobically digested effluent considering varied climatic parameters such as solar irradiance and air temperature. The aim of this research was to identify the optimum monthly average culture depth operation to minimize the cost of producing microalgae grown on anaerobic digestion effluents rich in ammoniacal nitrogen with concentration of 248 mg L−1. First, a productivity model combined with a thermal model was developed to simulate microalgae productivity in open raceway ponds as a function of climatic variables. Second, by combining the comprehensive open pond model with other harvesting equipment, the final techno economic model was developed to produce a microalgae product with 20 wt% biomass content and treated water with <1 mg L−1 ammoniacal nitrogen. The optimization approach on culture depth for outdoor open raceway ponds managed to reduce the cost of microalgae production grown in anaerobic digested wastewater up to 16 %, being a suitable solution for the production of low cost microalgae (1.7 AUD kg−1 dry weight) at possible scale of 1300 t dry weight microalgae yr−1.

An intuitive fuzzy multi-attribute decision making method based on a herding psychology improved score function for trading decisions

The amount of used new energy vehicle transactions is increasing quickly as the social economy matures, yet prices are typically low, making it increasingly difficult to select a fair trading system. Enhancing the score function is crucial in order to account for how different people’s attitudes affect the outcome of decisions and to choose an acceptable trading strategy that is applicable to other scenarios and has a favorable impact on transaction flow. The choice of a trading scheme for new energy-using vehicles is usually regarded as a multi-attribute decision problem. In this paper, the Intuitionistic Fuzzy Hybrid Averaging (IFHA) operator integration operator with an improved score function is proposed based on the influence of herd mentality on decision-makers. In order to examine the correlation between the score function and the decision outcome using the Spearman rank correlation coefficient, an application to a real situation and some comparative analyses are provided. The outcomes demonstrate that the decision-making process for used car trading schemes can make use of the proposed improved score function.

On the granular representation of fuzzy quantifier-based fuzzy rough sets

Rough set theory is a well-known mathematical framework that can deal with inconsistent data by providing lower and upper approximations of concepts. A prominent property of these approximations is their granular representation: that is, they can be written as unions of simple sets, called granules. The latter can be identified with “if…, then…” rules, which form the backbone of rough set rule induction. Alternatively, this property of granular representability can be defined as being free from inconsistencies, i.e. there are no elements within the set that are indiscernible to elements outside the set. It has been shown previously that this property can be maintained for various fuzzy rough set models, including those based on ordered weighted average (OWA) operators. In this paper, we will focus on some instances of the general class of fuzzy quantifier-based fuzzy rough sets (FQFRS). In these models, the lower and upper approximations are evaluated using binary and unary fuzzy quantifiers, respectively. One of the main targets of this study is to examine the granular representation of different models of FQFRS. The main findings reveal that Choquet-based fuzzy rough sets can be represented granularly under the same conditions as OWA-based fuzzy rough sets, whereas Sugeno-based FRS can always be represented granularly. Additionally, we provide counterexamples for the granular representability of other FQFRS models, and show that despite this, these approaches still demonstrate effectiveness in mitigating inconsistencies in real-life datasets. This observation highlights the potential of these models for resolving data inconsistencies and managing noise.

Long-Term Efficacy of Single-Incision Laparoscopic Intragastric Resection in Treating Gastrointestinal Stromal Tumors.

Gastrointestinal stromal tumors (GISTs), the most common mesenchymal tumors in the gastrointestinal tract, are increasingly treated with minimally invasive surgeries. Developed techniques include laparoscopic, endoscopic, and hybrid methods for gastric GIST resection. Our study, focusing on single-incision laparoscopic intragastric resection for gastric GISTs, aims to evaluate its safety, efficacy, and long-term outcomes. In a retrospective study of GIST surgery involving 14 patients who underwent single-incision laparoscopic intragastric resections, we analyzed and compared their preoperative demographics, American Society of Anesthesiologists (ASA) scores, tumor size, neoadjuvant treatment, operation duration, hospital stay, mitotic and Ki-67 indexes, and histological features with those of patients who underwent open and laparoscopic wedge resections, to assess the impact on both survival and disease-free survival. Average operation time was 93.07 minutes (range 81-120 minutes). Average blood loss: 67 ± 20 mL (range 40-110 mL). Postoperative hospital stay averaged 6.79 days (range 4-16 days). Strong correlations were observed between preoperative and pathological tumor sizes (P = .001, P < .001). Survival analysis indicated a significant association with ASA scores (P = .031), but not with mitotic index, Ki-67, or tumor size. Average survival was 80.57 months, with no recurrence or metastasis during follow-up. Based on our experience, the single-incision laparoscopic intragastric resection method emerges as a highly efficient, timesaving, and gentle oncological procedure, providing a safe and minimally invasive alternative resulting in shorter hospital stays and excellent long-term outcomes with minimal recurrence. For more definitive conclusions, larger, multicenter, and prospective studies are recommended.

Comparative analysis of surgical outcomes in children with type 3 and type 4 lateral physeal condylar humerus fractures in China: Closed Reduction-PerCutaneous Pinning (CRPP) vs. Open Reduction-Internal Fixation (ORIF)

BackgroundLateral physeal condylar humerus fractures in pediatric patients aged 1–13 rank as the second most common elbow injury, occurring with a frequency ranging from 5% to 16.8%. There exists an ongoing debate regarding the surgical management of these fractures. This study aims to evaluate the efficacy of Open Reduction and Internal Fixation (ORIF) and Closed Reduction and Percutaneous Pinning (CRPP) as suitable surgical treatments for displaced unstable fractures of the lateral condyle physeal humerus in children. The comparison encompasses the results of ORIF and CRPP, alongside clinical and radiographic outcomes and complication rates. MethodA retrospective review was conducted at the Department of Orthopedic Surgery in the research hospital. A cohort of 27 patients treated between 2016 and 2023 were analyzed, 19 patients meeting inclusion criteria. The fracture pattern and degree of displacement were assessed, with specialized radiologists, doctors, and surgeons in agreement. Among the patients, 11 underwent CRPP 7 type 3and 4 type 2, while 16 received ORIF 12 type 4 and 4 type 5. Data collection included fracture type, surgical method, operation time, pre and post-operative displacement, casting period, bone union condition, follow-up records, range of motion, complications, delayed union, lateral spurring, and pin removal records. ResultsFor CRPP, the mean time for pin removal was 5.42 weeks, with excellent bone union and an average operation time of 34.57 min. Criteria of Hardacre showed 28.57% of cases as good and 71.42% as excellent. Similarly, ORIF demonstrated a mean operation time of 42.5 min, with the fracture healing within 5.33 weeks and the pin being removed after 15 days on average. Criteria of Hardacre indicated 25% of cases as good and 75% as excellent. Both groups showed satisfactory outcomes, with no complications such as osteomyelitis, nonunion, malunion, delayed union, myositis ossificans, physeal growth arrest, tardy ulnar nerve palsy, cubitus valgus, or varus, and no cases requiring re-surgery. ConclusionBoth CRPP and ORIF are effective surgical methods for treating lateral physeal condylar humeral fractures (types 3 and 4 according to the Song classification) in children, demonstrating satisfactory outcomes. Notably, regardless of displacement (2 mm and >2 mm), both methods yield similar results, albeit with CRPP offering the advantage of avoiding incisions. Overall, both procedures are safe, with favorable bone healing outcomes.

Decision algorithm for picture fuzzy sets and Aczel Alsina aggregation operators based on unknown degree of wights

Aggregation operators (AOs) are well-known and efficient mathematical tools that are utilized to overcome the impact of imprecise and vague information during the aggregation process. The theoretical concepts of Aczel Alsina aggregation expressions are an extension of triangular norms and become a hot research topic in the environment of the fuzzy framework. The power operators provide a smooth approximation and are used to mitigate the influence of redundant or insufficient information on the attributes or criteria. Some robust aggregation approaches are developed by combining two different theories, like power operators and Aczel Alsina aggregation tools. This article aims to explore the theory of picture fuzzy sets (PFSs), an extended version of fuzzy sets, and intuitionistic fuzzy sets. Some robust operations of Aczel Alsina aggregation tools are also present in light of the picture fuzzy environment. We established a class of new methodologies in the light of picture fuzzy information, including picture fuzzy Aczel Alsina power weighted average (PFAAPWA) and picture fuzzy Aczel Alsina power ordered weighted average (PFAAPOWA) operators. We also developed an appropriate approach like picture fuzzy Aczel Alsina power weighted geometric (PFAAPWG) and picture fuzzy Aczel Alsina power ordered weighted geometric (PFAAPOWG) operators. Notable properties and characteristics of proposed methodologies are also demonstrated. Our invented approaches not only aggregate complicated information but can clearly define interrelationships among several arguments. Moreover, we establish an algorithm for the multi-attribute group decision-making (MAGDM) problem to handle the impact of redundant and vague information on human opinions. Finally, we study an experimental case study to evaluate an appropriate optimal option from available options. To reveal consistency and effectiveness of developed approaches, influence study by changing various parametric values and comparative study by comparing results of existing approaches.

Decision analysis with IDOCRIW-QUALIFLEX approach in the 2TLq-ROF environment: An application of accident prediction models in Pakistan

In Pakistan, the assessment of road safety measures within road safety management systems is commonly seen as the most deficient part. Accident prediction models are essential for road authorities, road designers, and road safety specialists. These models facilitate the examination of safety concerns, the identification of safety improvements, and the projection of the potential impact of these modifications in terms of collision reduction. In the context described above, the goal of this paper is to utilize the 2-tuple linguistic q-rung orthopair fuzzy set (2TLq-ROFS), a new and useful decision tool with a strong ability to address uncertain or imprecise information in practical decision-making processes. In addition, for dealing with the multi-attribute group decision-making problems in road safety management, this paper proposes a new 2TLq-ROF integrated determination of objective criteria weights (IDOCRIW)-the qualitative flexible multiple criteria (QUALIFLEX) decision analysis method with a weighted power average (WPA) operator based on the 2TLq-ROF numbers. The IDOCRIW method is used to calculate the weight of attributes and the QUALIFLEX method is used to rank the options. To show the viability and superiority of the proposed approach, we also perform a case study on the evaluation of accident prediction models in road safety management. Finally, the results of the experiments and comparisons with existing methods are used to explain the benefits and superiority of the suggested approach. The findings of this study show that the proposed approach is more practical and compatible with other existing approaches.

Aczel–Alsina T-norm based group decision-making technique for the evaluation of electric cars using generalized orthopair fuzzy aggregation information with unknown weights

Data management and finding precise outcomes from large amounts of information are among the biggest challenges for scientists. The technique of multi-attribute group decision-making (MAGDM) is a valuable tool for investigating fuzzy data precisely. The key objective of the paper is to redefine the q-rung orthopair (q-RO) fuzzy set (FS) (q-ROFS) in the term of interval-valued and proposed new aggregation operators (AOs) based on the Aczel-Alsina (AA) t-norm (TN) and t-conorm (TCN) operations. The AA operational laws are a generalized form of existing TNs and TCNs and give more reliable results because they can fluctuate in their parametric values. The concept of interval-valued enlarges the space of membership degree (MD) and non-membership degree (NMD) for decision-makers. By taking qth power, the interval-valued q-ROFS (IV-q-ROFS) structure. The IV-q-ROFS can handle the uncertainty and vagueness in data, then interval-valued intuitionistic FS (IV-IFS) and interval-valued Pythagorean FS (PyFS) (IV-PyFS) and provide accurate results. The thought of power AOs (PAOs) makes the relationship between weight vectors and reduces the chances of uncertainty in aggregated results. By taking advantage of PAOs, this article is devoted to introducing the interval-valued q-ROF Aczel-Alsina power-weighted averaging (IV-q-ROFAAPWA) and interval-valued q-ROF Aczel-Alsina power-weighted geometric (IV-q-ROFAAPWG) operators. The fundamental axioms of AOs, idempotency, boundedness, and monotonicity, are also discussed. To illustrate the importance of suggested AOs, the real-life problem of electric car selection was solved by applying the MAGDM method using the proposed IV-q-ROFAAPWA and IV-q-ROFAAPWG operators. The comparison of proposed AOs with currently present AOs is also part of the article. We finally constructed solid conclusions.

Ultrasonic flexible array for non-destructive testing of complex curved surfaces

Ultrasound detection technology in Non-Destructive Testing (NDT) is widely used. Ultrasonic transducer arrays effectively solved the problem of limitation in the detection range of single beam scanning transducers. However, the common used ultrasonic transducer arrays are rigid that are difficult to achieve complete contact with complex curved surfaces. Flexible arrays can be bent to a certain extent and can adaptively fit the surface of different curvature structures for detection. This work reported ultrasonic transducer flexible arrays that combine 2D arrays and linear arrays. The arrays have eight lines and each can be individually controlled. Each line has eight elements. The arrays expand the detection range and greatly reduce the control circuit difficulty and signal processing difficulty. The average operating frequency of the measured arrays is 2.5 MHz, the bandwidth is close to 30% and the insertion loss is close to −33 dB. Also, the arrays can detect defects through curved surface testing. Based on these advantages, this study paved the way for further investigation into the flexible ultrasonic arrays.

Irreversible Mixing Induced by Geostrophic Turbulence over the Global Ocean

Abstract Two recently proposed mixing diagnostics are employed to estimate the global surface irreversible mixing based on particle and tracer simulation driven by satellite-derived geostrophic velocities. These two novel diagnostics, similar to the traditional dispersion diffusivity and Nakamura’s effective diffusivity but defined in a localized and instantaneous sense, have the following advantages: 1) they reconcile the theoretical discrepancies between Eulerian-, particle-, and contour-based diffusivities and 2) they do not rely on the stationary and homogeneous assumptions of the turbulent ocean and are free from traditional average operators (e.g., Eulerian time–space or along-contour mean). Our results show that evident discrepancies among these three types of diffusivities do emerge when employing traditional estimates. However, these discrepancies could be significantly mitigated with the adoption of new diagnostic methods, implying that the three types of diffusivities can be effectively reconciled within a global framework. Moreover, finescale mixing structures and transient elevated mixing events due to geostrophic stirring can be clearly identified by the two new diagnostics, in contrast to previous estimates that are spatially and/or temporally smoothed. In particular, it is interesting to note that large values of the new diagnostics usually occur along narrow filaments/fronts associated with mesoscale eddies, and elevated mixing is observed to be located at the periphery of eddies. Our study presents a novel revisit of the global surface mixing induced by geostrophic eddies with an emphasis on irreversibility and provides new insights into previous questions regarding different mixing diagnostics in the community. Significance Statement Previous estimates of eddy mixing over the global ocean, using particle-based, tracer-based, and Eulerian-based diffusivities, have shown evident discrepancies. By using recently proposed novel mixing diagnostics, this study demonstrates that the three types of diffusivity estimates agree well with each other, indicating a practical unification of the three types of diffusivities. Also, since the new mixing diagnostics do not involve any traditional average operator, the local and instantaneous mixing maps over the global ocean are presented here, in contrast to previous spatial- or temporal-averaged ones. These new insights can address several unresolved issues in the mixing community.