Measurement Model Research Articles

Drivers of supply chain adaptability: insights into mobilizing supply chain processes. A multi-country and multi-sector empirical research

AbstractSupply chain (SC) adaptability (SC-Ad) implies that SC processes should change and adapt to anticipated structural and market changes. However, when these changes are related to shifts from exploitative to explorative focuses, companies face an inflexibility problem because of involved uncertainties, creating a barrier to obtaining SC-Ad. This research proposes to overcome this barrier by integrating new combinations of the product/market strategy and SC processes and securing their fit over time. To get it, this study proposes two SC-Ad drivers (related to the SC process (ASCOS) and new product development competences (PDC)), which secure the aforementioned fit by reducing its uncertainties and thus ensuring a SC-Ad that responds to emerging competitive changes. Measurement and structural models were assessed following PLS-SEM. ASCOS and PDC’ relative importance was analyzed using the importance/performance/analysis procedure. PLS, PLS-predict, and CVPAT were used to analyze model’s in-sample and out-of-sample predictive capacity. ANOVA was used to compare SC-Ad, ASCOS and PDC in different plant groups. Results suggest that ASCOS and PDC are SC-Ad’s drivers, and that the plants with highest SC-Ad values are those with the higher ASCOS and PDC’ values. This expand knowledge about SC-Ad drivers, which represents an important literature gap. In an indirect way, some new light is also added to the topic of ambidextrous management. The adequate generalizability of these results is supported by a) a wide multi-country, multi-informant, and multi-sector sample of 268 plants, b) a good out-of-sample model predictive capacity c) no heterogeneity issues.

Investigating key latent factors influencing alcohol consumption among the tribal male adolescents in Dooars region, West Bengal

Background Alcohol consumption among tribal male adolescents in India is a significant social concern. Tribal adolescents are particularly vulnerable and tend to lean toward alcohol addiction. Therefore, it is crucial to introduce some necessary footsteps to reduce alcohol consumption. The primary objective of this study is to investigate the association of various latent factors with the alcohol-drinking behavior of tribal adolescents. Methods The study collected data from 600 tribal adolescents from the Dooars region, with 241 of them reported consuming alcohol. The study aimed to confirm the theoretical development of hypotheses regarding peer pressure, parental discord, stress, attitude toward alcohol, and food insecurity as exogenous latent factors influencing the alcohol-drinking behavior of tribal adolescents. In this context, the study adopted both measurement and structural models using Partial Least Squares-Structural Equation Modeling (PLS-SEM). Results The findings revealed a significant path relationship between alcohol drinking behavior and various exogenous factors like peer pressure (β = 0.214, p = .000), parental discord (β = 0.121, p = .009), stress (β = 0.170, p = .000), attitude toward alcohol (β = 0.110, p = .004), and food insecurity (β = 0.510, p = .000). This study developed a reflective measurement model, and the evaluation of reflective measurement models was conducted, assessing internal consistency, convergent validity, and discriminant validity, yielding satisfactory results. Conclusion To tackle alcohol issues among tribal adolescents in the Dooars region, effective strategies should be employed. These include educating in schools, highlighting tribal role models, aiding peers with alcohol dependence, providing life skills training, and addressing parental discord and food insecurity through awareness campaigns, workshops, and better infrastructure.

Urban Development in Riyadh: Aligning with Saudi Vision 2030 for Enhanced Quality of Life

Saudi Urban Development is on the cards, It has been aligned with Vision 2030, since 2016, it refers to the United Nations agenda crafted to meet the needs not only for the present population, rather it guarantees optimum utilisation of resources and preserving them for future generations. The urban development in Saudi Arabia started with a comprehensive idea for tracking its progress on sustainable urban development. The sustainable urban development should be aligned with the Saudi vision 2030, so that the aim and objectives of the vision 2030 can be achieved and better quality of life in the cities can be provided to the residents. To understand this, there is a dire need to investigate the role of built environment on urban quality of life. Previous researches in this area were scarce, Therefore, there is a need to research the above-mentioned constructs in the context of Saudi vision 2030. The research design is cross sectional and collects the data from the population resides in the city of Riyadh (KSA). Responses (n=705) were collected in order to test the model with built environment as independent and urban quality of life as dependent construct. Being an empirical study, the analysis of measurement and structural model was undertaken. On the basis of the findings, implications for improving Quality of life through urban planning for better built environments were also presented. The research makes a contribution by suggesting methods for reducing noise, creating aesthetically beautiful structures and public areas according to the requirements and preferences of the locals, and lowering socio-spatial disparities while supporting housing and mobility for disadvantaged groups.

Reliability and Validity of the Turkish Translation of the PedsQL™ 3.0 Neuromuscular Module for 2-to 4-Year-Olds in Spinal Muscular Atrophy.

The Pediatric Quality of Life Inventory™ (PedsQL™) Neuromuscular Module (PedsQL™ 3.0 NM) evaluates the health-related quality of life in children who are affected by neuromuscular diseases. This study's aim is to assess the adaptation of the PedsQL™ 3.0 NM Turkish version (PedsQL™ 3.0 NM-TR) for 2- to 4-year-olds in spinal muscular atrophy (SMA). The procedure of translating the PedsQL™ 3.0 NM into Turkish was conducted in accordance with the translation methodology outlined by the PedsQL™ measurement model. The PedsQL™ 3.0 NM-TR was administered to 54 parents of children with SMA aged 2 to 4 years. The test-retest reliability and intraclass correlation coefficient (ICC) were measured for reliability analysis. Cronbach's α coefficient and item score correlations were calculated for internal consistency. Concurrent construct validity was evaluated by Pearson correlations between the outcomes of the PedsQL™ 4.0 Generic Core Scale (PedsQL™ 4.0 GCS) and the PedsQL™ 3.0 NM. The PedsQL™ 3.0 NM-TR total score shows excellent reliability. The Cronbach's α values for the PedsQL™ 3.0 NM ranged between 0.871 and 0.906, while those for the PedsQL™ 4.0 GCS ranged between 0.843 and 0.897. Test-retest ICC values for the PedsQL™ 3.0 NM-TR ranged between 0.812 and 0.917, and for the PedsQL™ 4.0 GCS ranged between 0.773 and 0.899. The relationship between the PedsQL™ 3.0 NM-TR and the subscores of the PedsQL™ 4.0 GCS demonstrated a range of correlations from excellent to fair, indicating the interplay between two scales. This study established the PedsQL™ 3.0 NM-TR as reliable, valid, and feasible for use in children aged 2 to 4 years with SMA.

A New Perspective on Strategic Choices for the Survival and Development of Energy Enterprises: An Analysis of Market Power, Innovation Strategy, and Sustainable Development of Major Multinational Oil Companies

As global economic recession and deterioration of the ecological environment become increasingly prominent, every responsible enterprise, especially the energy enterprises with more environmental controversies, will be faced with the most difficult choice regarding sustainable operation in history: market power expansion strategy, or technological innovation strategy? Most of the literature supports the finding that the former can occupy the market advantage and obtain the current market survival, but the future potential is low, while the latter reduces the firm’s negative externality and gains future competitiveness, but current profits are reduced. This paper proposes a new perspective to evaluate the “sustainable development of enterprises” and then constructs a new measurement model, using a linear regression model for empirical analysis, which provides technical support and guidance for energy enterprises facing this decision-making dilemma. For further research, we have proposed more practical business management strategies for the sustainable development of petrochemical companies in developing countries.

Tumor conspicuity enhancement-based segmentation model for liver tumor segmentation and RECIST diameter measurement in non-contrast CT images

Background and objectiveLiver tumor segmentation (LiTS) accuracy on contrast-enhanced computed tomography (CECT) images is higher than that on non-contrast computed tomography (NCCT) images. However, CECT requires contrast medium and repeated scans to obtain multiphase enhanced CT images, which is time-consuming and cost-increasing. Therefore, despite the lower accuracy of LiTS on NCCT images, which still plays an irreplaceable role in some clinical settings, such as guided brachytherapy, ablation, or evaluation of patients with renal function damage. In this study, we intend to generate enhanced high-contrast pseudo-color CT (PCCT) images to improve the accuracy of LiTS and RECIST diameter measurement on NCCT images. MethodsTo generate high-contrast CT liver tumor region images, an intensity-based tumor conspicuity enhancement (ITCE) model was first developed. In the ITCE model, a pseudo color conversion function from an intensity distribution of the tumor was established, and it was applied in NCCT to generate enhanced PCCT images. Additionally, we design a tumor conspicuity enhancement-based liver tumor segmentation (TCELiTS) model, which was applied to improve the segmentation of liver tumors on NCCT images. The TCELiTS model consists of three components: an image enhancement module based on the ITCE model, a segmentation module based on a deep convolutional neural network, and an attention loss module based on restricted activation. Segmentation performance was analyzed using the Dice similarity coefficient (DSC), sensitivity, specificity, and RECIST diameter error. ResultsTo develop the deep learning model, 100 patients with histopathologically confirmed liver tumors (hepatocellular carcinoma, 64 patients; hepatic hemangioma, 36 patients) were randomly divided into a training set (75 patients) and an independent test set (25 patients). Compared with existing tumor automatic segmentation networks trained on CECT images (U-Net, nnU-Net, DeepLab-V3, Modified U-Net), the DSCs achieved on the enhanced PCCT images are both improved compared with those on NCCT images. We observe improvements of 0.696–0.713, 0.715 to 0.776, 0.748 to 0.788, and 0.733 to 0.799 in U-Net, nnU-Net, DeepLab-V3, and Modified U-Net, respectively, in terms of DSC values. In addition, an observer study including 5 doctors was conducted to compare the segmentation performance of enhanced PCCT images with that of NCCT images and showed that enhanced PCCT images are more advantageous for doctors to segment tumor regions. The results showed an accuracy improvement of approximately 3%–6%, but the time required to segment a single CT image was reduced by approximately 50 %. ConclusionsExperimental results show that the ITCE model can generate high-contrast enhanced PCCT images, especially in liver regions, and the TCELiTS model can improve LiTS accuracy in NCCT images.

What are the roles of positive psychological construct in blended learning contexts? Integrating academic buoyancy into the Community of Inquiry framework.

In the post-epidemic era, blended learning has become a social trend for the future of higher education, and scholars have endeavored to understand the factors that influence student learning in these blended communities. Communities of Inquiry is a conceptual framework that describes the components of blended learning environments, indicating teaching presence, social presence, and cognitive presence. However, the framework fails to adequately explore how individual learning motivational factors influence student learning. Therefore, this study extends the Community of Inquiry framework by drawing on a positive psychological construct-academic buoyancy to reveal the relationship between academic buoyancy and the three presences through empirical research. The theoretical model was validated by SPSS 26.0 and smartPLS4.0. To evaluate the measurement and structural models, structural equation modeling (SEM) was carried out using the partial least squares (PLS) method. (a) Teaching presence positively predicts academic buoyancy, and academic buoyancy positively predicts social presence and cognitive presence; (b) academic buoyancy mediates teaching presence and social presence, as well as teaching presence and cognitive presence; and (c) academic buoyancy acts as a chain mediator between teaching presence and cognitive presence through social presence. The results of this study fill a gap in the multiple roles of individual positive psychological construct-academic buoyancy in blended learning communities, extend the Community of Inquiry theoretical framework, and provide empirical evidence for blended learning quality and practical improvement strategies.

Navigating the generative AI travel landscape: the influence of ChatGPT on the evolution from new users to loyal adopters

Purpose This study, rooted in affordance-actualization theory and communication theory, aims to critically examine how ChatGPT influences users’ transition from new adopters to loyal advocates within the context of travel decision-making. It incorporates constructs including communication quality, personalization, anthropomorphism, cognitive and emotional trust (ET), loyalty and intention to adopt into a comprehensive model. Design/methodology/approach This study used quantitative methods to analyze data from 477 respondents, collected online through a self-administered questionnaire by Embrain, a leading market research company in South Korea. Lavaan package within R studio was used for evaluating the measurement model through confirmatory factor analysis and using structural equation modeling to examine the proposed hypotheses. Findings The findings reveal a pivotal need for enhancing ChatGPT’s communication quality, particularly in terms of accuracy, currency and understandability. Personalization emerges as a key driver for cognitive trust, while anthropomorphism significantly impacts ET. Interestingly, the study unveils that in the context of travel recommendations, users’ trust in ChatGPT predominantly operates at the cognitive level, significantly impacting loyalty and subsequent adoption intentions. Practical implications The findings of this research provide valuable insights for improving Generative AI (GenAI) technology and management practices in travel recommendations. Originality/value As one of the few empirical research papers in the burgeoning field of GenAI, this study proposes a highly explanatory model for the process from affordance to actualization in the context of using ChatGPT for travel recommendations.

Scoring PD-L1 Expression in Urothelial Carcinoma: An International Multi-Institutional Study on Comparison of Manual and Artificial Intelligence Measurement Model (AIM-PD-L1) Pathology Assessments.

Assessing programmed death ligand 1 (PD-L1) expression on tumor cells (TCs) using Food and Drug Administration-approved, validated immunoassays can guide the use of immune checkpoint inhibitor (ICI) therapy in cancer treatment. However, substantial interobserver variability has been reported using these immunoassays. Artificial intelligence (AI) has the potential to accurately measure biomarker expression in tissue samples, but its reliability and comparability to standard manual scoring remain to be evaluated. This multinational study sought to compare the %TC scoring of PD-L1 expression in advanced urothelial carcinoma, assessed by either an AI Measurement Model (AIM-PD-L1) or expert pathologists. The concordance among pathologists and between pathologists and AIM-PD-L1 was determined. The positivity rate of ≥ 1%TC PD-L1 was between 20-30% for 8/10 pathologists, and the degree of agreement and scoring distribution for among pathologists and between pathologists and AIM-PD-L1 was similar both scored as a continuous variable or using the pre-defined cutoff. Numerically higher score variation was observed with the 22C3 assay than with the 28-8 assay. A 2-h training module on the 28-8 assay did not significantly impact manual assessment. Cases exhibiting significantly higher variability in the assessment of PD-L1 expression (mean absolute deviation > 10) were found to have patterns of PD-L1 staining that were more challenging to interpret. An improved understanding of sources of manual scoring variability can be applied to PD-L1 expression analysis in the clinical setting. In the future, the application of AI algorithms could serve as a valuable reference guide for pathologists while scoring PD-L1.

Relating violations of measurement invariance to group differences in response times.

Measurement invariance is an assumption underlying the regression of a latent variable on a background variable. It requires the measurement model parameters of the latent variable to be equal across the levels of the background variable. Item-specific violations of this assumption are referred to as differential item functioning and are ideally substantively explainable to warrant theoretically valid and meaningful results. Past research has focused on developing statistical approaches to explain differential item functioning effects in terms of item- or person-specific covariates. In this study, we propose a modeling approach that can be used to test if differences in item response times can be used to statistically explain differential item functioning. To this end, we operationalize a latent response process factor and test if item-specific group differences on this factor can account for the observed differences in item scores. We investigate the properties of the model in a simulation study, and we apply the model to a real data set. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Developing a change management measurement instrument for effective use of examination administration system

The quantitative change management measurement for effective use, which consists of operationalized change drivers, dimensions and sub-dimensions for effective use of Examination Administration System (EAS) in the South African context, has been scarcely discussed in the literature. This paper intends to develop and validate a change management measurement instrument (CHAMI) to measure the effective use of EAS in South Africa’s (SA) Technical and Vocational Education and Training (TVET) colleges. The CHAMI was developed drawing on the survey measurement instruments literature for change management and effective use and assessed quantitatively using data collected from 215 EAS users across all 318 TVET colleges from nine provinces of SA. The empirical results found the existence of construct validity of the CHAMI with 11 dimensions (i.e. user involvement and change recognition, user satisfaction, performance measurement, technology use, EAS adaptive use, EAS verification, user learning, transparent interaction, representational fidelity, informed action, and effective use) and 63 questions. Another contribution this study stems from the way the research constructs in a measurement model are operationalized as they incorporate measurement class (i.e. reflective or formative). This paper presents a CHAMI for effective use of EAS. In the future, management, researchers and practitioners can use the CHAMI to measure effective use, espouse or adapt instrument as an additional use measurement dimension on United Nations digital transformation programmes index or test it in different settings to advance its generalizability.

Optimizing organic fertilization towards sustainable vegetable production evaluated by long-term field measurement and multi-level fuzzy comprehensive model

Precision and efficient management of organic substitution fertilization is crucial in balancing soil health, environmental risks, and economic benefits for intensive crop cultivation. However, effectively determining the optimal organic substitution ratio in vegetable production remains uncertain and still relatively unexplored. We conducted a five-year vegetable field experiment and established four nitrogen-equivalent fertilization treatments: mineral fertilizer alone, and organic fertilizer substituting a quarter, half, and entirety of the mineral nitrogen. Employing a specialized multi-level fuzzy comprehensive evaluation model, our study assessed impacts of different organic substitution ratios on soil qualities, microbial diversity, vegetable yield and quality, environmental risks, and economic benefits in sustainable vegetable production of eastern China. Our results showed that the 50% organic substitution was optimal, significantly improving soil qualities and microbial characteristics, particularly in the topsoil layer. Compared to the control, this approach significantly reduced methane by 6.6–48.3%, nitrous oxide by 49.5–79.1%, and nitric oxide by 36.5–50.0%, while enhancing vegetable yield by 16.0–28.3% and its five-year average stability as well as quality as evidenced by increased protein, soluble sugar, and vitamin C contents. Economic analysis revealed that while it maximized environmental benefits, complete organic substitution did not consistently yield positive economic returns. In contrast, a 50% substitution ratio was identified as the most economically viable, representing a sustainable compromise that balances environmental health with economic feasibility. Further modeling analysis indicated an optimal comprehensive range between 42.9% and 64.5% for achieving the best balance of multiple objectives in vegetable fields. The study underscores the intricate organic substitution effects on intensive vegetable production, highlighting the necessity for tailored organic substitution strategies that integrate environmental sustainability with agricultural productivity and economic practicality.

Orbit Determination for Impulsively Maneuvering Spacecraft Using Modified State Transition Tensor

This paper proposes a method to accurately resolve orbit determination for a spacecraft with unknown impulsive maneuvers. The proposed method handles the unknown impulsive maneuver by incorporating the magnitude, direction, and time of the impulsive maneuver into the estimation parameter vector. First, a modified state transition tensor (STT) is proposed via orbit division and segment connection, allowing the orbit to be directly propagated under the effects of impulsive maneuver uncertainties. Then, based on the modified STT, a second-order measurement model is established with the estimation parameter vector as the input. Combining the second-order measurement model with observations, a second-order optimal solution is derived to correct the estimation parameters. The spacecraft orbit, together with the magnitude, direction, and time of the impulsive maneuver, are simultaneously estimated in an iterative framework. The performance of the proposed method is validated in a low-Earth-orbit case and a high-Earth-orbit case. Simulations show that the proposed method outperforms its linear version in terms of convergence, accuracy, and uncertainty quantification capacity. Its maneuver reconstruction and orbit estimation errors are one order of magnitude less than those of competitive methods. Moreover, the proposed method can handle severe conditions and is robust to initial guesses.

Heat transfer measurements on a flat plate and cylinders at high velocity conditions: Comparison of calculation models with infrared thermography

In this study, various heat transfer measurement models using infrared (IR) thermography were applied to a flat plate and cylinders to examine the accuracy and suitability of the measurements in a transonic blow-down test facility recently developed at Korea Aerospace University. Experiments were performed under three inlet velocity conditions for the flat plate, and for the cylinders, two diameters, two turbulence intensity levels, and five inlet velocity conditions. The surface temperature distribution during the experiment was acquired using an IR camera, and the surface heat transfer coefficient (HTC) was calculated using four HTC calculation models based on the one-dimensional semi-infinite (1D SI) solid assumption: step change (STEP), Duhamel's superposition (DUH), 1D finite volumetric model (1D FVM), and linear regression model (LRM). The LRM showed minimum deviation with reference correlation for the flat plate heat transfer measurements, while STEP, DUH, and 1D FVM showed increased deviations at higher mainstream velocity conditions. The measurements by LRM at the cylinder stagnation location showed the most comparable value with the reference correlation, whereas STEP and FVM showed about 10% deviation from the reference. It is concluded that the LRM is the most suitable heat transfer calculation model for high velocity tests because the model excludes the ramping data during the transient tests, and it can also obtain the local adiabatic wall temperature experimentally.

Combining Nonparametric and Parametric Item Response Theory to Explore Data Quality: Illustrations and a Simulation Study

ABSTRACT Researchers who use measurement models for evaluation purposes often select models with stringent requirements, such as Rasch models, which are parametric. Mokken Scale Analysis (MSA) offers a theory-driven nonparametric modeling approach that may be more appropriate for some measurement applications. Researchers have discussed using MSA as a preliminary procedure with which to evaluate data quality before applying a parametric model. However, the literature includes only a few examples in which researchers have integrated MSA techniques with parametric models throughout the analytic procedure. We consider a systematic approach for integrating results from nonparametric MSA techniques with parametric measurement models to evaluate measurement quality and construct scales with useful measurement properties. We use real-data illustrations and a simulation study to demonstrate and systematically explore our approach. We discuss implications for research and practice.

Measurement and Policy Optimization of Regional Preschool Education Development Level Based on Generalized Orthogonal Fuzzy Sets and Prospect Theory

Preschool education belongs to non-compulsory enlightenment education, and it is difficult to measure and analyze the development of preschool education in different regions because of its multiple attributes and diversity of influencing factors. In addition, decision makers will be limited by their own cognition when facing multi-attribute factors and uncertain factors, and there is a big gap between the decision results given and the actual situation. Therefore, this chapter introduces generalized orthogonal fuzzy sets and prospect theory into the measurement model of preschool education development level based on technique for order of preference by similarity to ideal solution (TOPSIS) method to improve the decision accuracy of decision makers. The experimental results show that the model can effectively deal with uncertain information and improve the analysis accuracy, and the results are more in line with the actual situation than other models. At the same time, it can intuitively compare and analyze the development of preschool education in different regions, and provide reliable data support for decision makers.

Adverse childhood event subtypes and routine diabetes care utilization among U.S. adults with Type 2 diabetes.

Adverse childhood events (ACEs) have significant impacts on adulthood health, including greater risk of Type 2 diabetes (T2D). While there is a known connection to increased use of outpatient and emergency healthcare services, the potential role of ACEs in routine diabetes care utilization remains unclear. This study uses ACE subtypes to explain pathways to routine diabetes care utilization among adults with T2D. Cross-sectional data were obtained from the 2019 Behavioral Risk Factor Surveillance System, a survey of a representative sample of U.S. adults. Eligible participants resided in a state that completed both the Adverse Childhood Experiences (ACEs) and Diabetes modules and were diagnosed with non-gestational diabetes (N = 9,904), of whom the majority were presumed to have T2D. Confirmatory factor analysis was used to test the measurement model for three ACE factors: household dysfunction, physical and emotional abuse, and sexual abuse. Structural equation modeling was used to relate factors to four routine diabetes care utilization outcomes. Factor loadings were strong, and fit indices indicated good measurement model and full structural model fits. In the full structural equation model, household dysfunction was associated with decreased likelihood of meeting frequency recommendations for A1C testing (β = -0.22, p < .05) and foot exams (β = -0.29, p < .01). Physical and emotional abuse were associated with greater likelihood of meeting A1C testing frequency recommendations (β = 0.21, p < .05). Specific types of ACEs may differentially relate to routine diabetes care utilization in adulthood. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

The Mediating Role of Career Futures between Prospective Teachers’ Career Calling and Career Adaptability

This study aims to evaluate the mediating role of career futures in the relationship between prospective teachers' career adaptability and career calling. This aim was carried out according to the quantitative research patterns relational model. The sample of this study consists of 225 prospective teachers between 18 and 39 years of age ( = 21.49, SD = 1.81). Data were collected using the Career Adapt-Abilities Scale, Career Futures Inventory, and Career Calling Scale. The two-stage Structural Equation Modeling technique tested the hypothetically determined model. Accordingly, the measurement model was first examined. Afterwards, the structural model was tested. In this model test, the Maximum Likelihood estimation technique was used. Bootstrapping analysis showed that this indirect effect was significant in 1,000 bootstrapping samples. As a result of the model test, it was determined that the career futures partially mediate the relationship between career adaptability and career calling. In line with the findings of this study, recommendations for future researchers and practitioners are presented

Study on preparation and properties of silver alloy for Mongolian medicine acupuncture

The Mongolian medical silver needles often encounter issues of bending, fracturing, and blunting in clinical applications. Similarly, Mongolian warm needles can cause burns on patients due to inaccurate temperature control. In this study, we developed an Ag85Cu15 alloy specifically for acupuncture needles based on material preparation. By incorporating appropriate amounts of Mn and Ti elements, we were able to enhance the mechanical properties and biocompatibility of the acupuncture needles. Compared to commercially available silver needles, this alloy exhibited a significant increase in microhardness up to 210.2 Hv0.2 and an improved tensile strength of 880.2 MPa. Furthermore, we designed a thermoelectric effect-based temperature measurement model for precise control of the warm needle's temperature, enhancing the therapeutic effectiveness of the treatment.

Research on test method of multi-channel ultrasonic wave detection for gas well injection volume

In the early stage of natural gas extraction, the fracturing and blowout process is often used to obtain stable production. However, during the fracturing process, natural gas combustion is frequently directly released, which causes significant pollution to the environment and a waste of resources. In order to measure the flow rate of the gas-liquid mixture in the process of gas well blowout, a multi-channel velocity measurement model is established. The multi-channel sensor structure is designed based on an analysis of the gas well blowout process and fluid configuration simulations. The influence of pipe diameter on the flow characteristics of gas-liquid two-phase flow was analyzed by FLUENT software. The simulation results show that the distribution of flow velocity in a gas-liquid two-phase flow tube is not uniform, the flow velocity of different flow layers is different, and the stratification phenomenon is more obvious with the increase of pipe diameter, highlighting the necessity of multi-channel measurements. Therefore, we developed a gas-liquid two-phase flow velocity measuring device. The experimental results demonstrate that accurately describing velocity in the tube can be achieved by measuring velocities in different channels using the gas-liquid two-phase flow velocity measurement device.