Measure Method Research Articles

On the semiclassical limit of the Schrödinger-Lohe model and concentration estimates

We study the semiclassical limit of quantum synchronization model and concentration estimates for the resulting limit model. From the Schrödinger-Lohe model, we rigorously derive the Vlasov-Lohe model using Wigner transform and Wigner measure method. In semiclassical limit, generalized Wigner distributions to the Schrödinger-Lohe model converge to a set of Wigner measures which corresponds to a weak solution to the Vlasov-Lohe model, and then we show the asymptotic collective behaviors of the Vlasov-Lohe model. When one-body potentials are identical, we show that complete synchronization emerges for the Vlasov-Lohe model. In contrast, for non-identical potentials the lack of boundedness results in practical synchronization for the integrals of solutions. Moreover, we construct a global existence of classical solutions to the Vlasov-Lohe model using the standard method of characteristics. Analysis in this work can deal with possibly non-identical potentials in which their differences are constant.

Global exponential stability for quaternion-valued neural networks with time-varying delays by matrix measure method

Global exponential stability for quaternion-valued neural networks with time-varying delays by matrix measure method

Unexpected good news: the impacts of land decentralization reform on energy development in China

PurposeThe purpose of this paper is to investigate the impact of land approval decentralization on energy efficiency and then analyze the mechanisms that land decentralization improves energy efficiency.Design/methodology/approachThis paper has been based on the dynamic slack based measure (DSBM) method to measure energy efficiency using input-output data at the city level in China. In the empirical analysis, this paper employed difference in differences (DID), instrumental variable (IV), propensity score matching–difference in differences (PSM–DID) and triple differences (DDD) to examine the research hypotheses.FindingsFirstly, this paper finds unexpected good news and shows that decentralizing land approvals significantly improves regional energy efficiency. Secondly, this paper explores two mechanisms for this effect, one is the mitigation of land misallocation and the other one is the cleaner allocation of land resources across industries. Thirdly, this paper provides micro-evidence from Chinese listed firms and finds that the decentralization of land approvals similarly improve firms’ energy efficiency.Originality/valueExisting studies have mainly explored the environmental impacts of land marketization reforms, with limited attention to decentralization of land approval. As a result, the promotion effect of decentralization of land approval on energy efficiency is still unclear, and the mechanism need to be further refined. Based on empirical analysis, this paper not only helps to clarify the relationship between land decentralization reform and energy efficiency, but also explores the mechanisms of transmission.

Study on the Assessment of STEM Instructional Design Plan Based on Information Flows

In recent years, the growing incidence of blended and online learning has highlighted instructional design concerns, especially STEM instructional design. Existing studies have often adopted observations, questionnaires, or interviews to evaluate STEM instructional design plans. However, there is still a lack of quantitative, measurable, and objective assessment methods. To close this research gap, this study proposes an innovative method for assessing STEM instructional design plans based on information flows. In this study, 20 STEM instructional design plans were designed, analysed, and assessed using the proposed information flow-based assessment method. The results indicated that the proposed method is feasible and effective for assessing STEM instructional design plans. STEM instructional design plans could be significantly improved based on the proposed method. The results and implications for instructors and practitioners are discussed in depth.

Limfatičke malformacije

Lymphatic malformations are abnormal cystic dilatations of lymphatic vessels. They are present at birth, but often can only be detected later in life. Simple lymphatic malformations are limited to certain anatomical regions, while complex diffuse abnormalities can affect the entire organism. Simple lymphatic malformations are divided into macrocystic (cysts >1cm) and microcystic (cysts <1cm). According to the ISSVA classification from 2015, the term lymphangioma is abandoned and the term lymphatic malformation is introduced, which is intended to clearly indicate that it is not a tumor formation. The clinical course is characterized by long asymptomatic periods with short-term inflammatory or infectious episodes and episodes of intracystic bleeding. In the case of voluminous changes, the diagnosis can be made at the transition from the second to the third trimester of pregnancy by ultrasound examination or fetal magnetic resonance. Ultrasound and magnetic resonance imaging are diagnostic methods of choice in the postpartum period as well. The purpose of the treatment is not to completely remove the formation, but to limit the symptoms to the extent that would preserve functionality and provide an appropriate appearance. Treatment methods are the expectant method for asymptomatic disease, sclerotherapy, surgical resection, drug treatment, laser treatment and physical therapy measures. When there is no life-threatening airway obstruction, sclerosing is the method of choice. Bleomycin as a sclerosant provides good results with mild side effects. Therapy with rapamycin (sirolimus) allows for a large percentage of reduction in formation, but it is important to monitor possible side effects

On the effectiveness of virtual reality technology for relaxation of locomotive crew workers

Reducing the risks of occupational stress requires improving the methods of psychological support for employees of locomotive crews. Virtual reality technology offers restorative measures comparable to traditional techniques. The study aims to analyze the effectiveness of the impact of virtual reality technology on the basis of the analysis of the psycho-emotional state of employees of locomotive crews before and after rehabilitation measures; to evaluate the ratio of stress biomarkers before and after exposure to classical and technical techniques; to compare the effectiveness of psychological influence carried out by various methods. The scientists conducted a survey and diagnosis of the functional state of locomotive crew workers, followed by rehabilitation measures using various methods. The specialists performed a saliva analysis based on the concentration levels of cortisol and dehydroepiandrosterone. 120 people took part in the study, of which: 60 people according to the classical program and 60 people according to the virtual reality program, of which 15 people are at risk according to psychophysiological indicators. The session lasted 21 minutes. The average age of employees of locomotive crews participating in the study was 35.4 years. 88% of the subjects underwent functional state diagnostics and a full course of relaxation sessions in virtual reality. Of these, 73% have improved psychophysiological indicators: response accuracy, increased neuropsychiatric stability, and decreased anxiety levels. The authors noted calmness, improved mood and relief of muscle tension. 60% of the employees have completed a full course of rehabilitation measures using classical techniques. Of these, 70% have an improvement in their functional state according to certain parameters. There is a decrease in cortisol levels (unit of measurement — nanograms per milliliter) from 6.75 ng/ml to 6.28 ng/ml with a simultaneous increase in dehydroepiadrosterone levels (unit of measurement — micrograms per milliliter) from 0.53 mcg/100 ml to 1.77 mcg/100 ml, which corresponds to a decrease in the overall stress level. The analysis of the obtained results showed sufficient effectiveness of virtual methods comparable to classical approaches to relaxation. Immersive technologies more effectively activate natural compensatory mechanisms by expanding the individual regulatory capabilities of the nervous system. Scientists have identified areas for improving the latest methods of rehabilitation measures in order to correct the functional condition of locomotive crews. Limitations. The representativeness of the sample was ensured by the participation of 120 employees of locomotive crews in the study. The limitations are related to the gender homogeneity of the sample: only men are included, due to their professional status. Ethics. The study was performed in accordance with the rules of good clinical practice and the Helsinki Declaration of the World Medical Association, the conclusion of the ethics committee was not required.

Online interventions for the mental health and well-being of parents of children with additional needs: Systematic review and meta-analysis.

Parents of children with additional needs experience compromised physical and mental health and higher stress, and their ability to engage in meaningful occupations is impacted by their additional caregiving tasks. Online interventions targeting mental health, stress, and well-being for parents of children with additional needs have potential to increase supports alongside occupational therapy direct services. The aims of this review are to systematically identify and synthesise evidence of effectiveness of online interventions aimed at improving mental health, stress, and well-being of parents of children with additional needs. Eight databases were searched up to July 2024 to identify online interventions for parents of children with additional needs. Studies were appraised for methodological quality. Standardised effect sizes were calculated, and meta-analyses of randomised control trials (RCTs) were conducted on outcomes of mental health, well-being, and stress. There was no consumer or community involvement. Systematic screening identified 30 papers that met inclusion criteria. Most were RCTs or controlled clinical trials (CCTs) with 'moderate' quality ratings: Three were designed by occupational therapists. There was substantial variation in intervention types, methods of delivery, outcomes, and outcome measures used. The pooled standardised effect size estimates (ES) and the lower and upper confidence intervals (CI) of online interventions from RCTs post-interventions were significant for mental health ES = 0.47, 95% CI (0.18, 0.77), p = 0.002; stress ES = 1.27, 95% CI (0.56, 1.98), p = 0.000; and well-being ES = 0.65, 95% CI (1.2, 0.06), p = 0.03, respectively. The online interventions that aimed to improve mental health, stress, and well-being included in this meta-analysis were effective. Occupational therapists supporting families play an important role in guiding parents of children with additional needs to evidence-based interventions to support them with addressing their own mental health and well-being needs.

Spatially resolved plasma composition evolution in a solar flare – The effect of reconnection outflow

Context. Solar flares exhibit complex variations in elemental abundances compared to photospheric values. These abundance variations, characterized by the first ionization potential (FIP) bias, remain challenging to interpret. Aims. We aim to (1) examine the spatial and temporal evolution of coronal abundances in the X8.2 flare on 2017 September 10, and (2) provide a new scenario to interpret the often observed high FIP bias loop top, and provide further insight into differences between spatially resolved and Sun-as-a-star flare composition measurements. Methods. We analyzed 12 Hinode/Extreme-ultraviolet Imaging Spectrometer (EIS) raster scans spanning 3.5 hours, employing both Ca XIV 193.87 Å/Ar XIV 194.40 Å and Fe XVI 262.98 Å/S XIII 256.69 Å composition diagnostics to derive FIP bias values. We used the Markov Chain Monte Carlo (MCMC) differential emission measure (DEM) method to obtain the distribution of plasma temperatures, which forms the basis for the FIP bias calculations. Results. Both the Ca/Ar and Fe/S composition diagnostics consistently show that flare loop tops maintain high FIP bias values of > 2–6, with peak phase values exceeding 4, over the extended duration, while footpoints exhibit photospheric FIP bias of ∼1. The consistency between these two diagnostics forms the basis for our interpretation of the abundance variations. Conclusions. We propose that this variation arises from a combination of two distinct processes: high FIP bias plasma downflows from the plasma sheet confined to loop tops, and chromospheric evaporation filling the loop footpoints with low FIP bias plasma. Mixing between these two sources produces the observed gradient. Our observations show that the localized high FIP bias signature at loop tops is likely diluted by the bright footpoint emission in spatially averaged measurements. The spatially resolved spectroscopic observations enabled by EIS prove critical for revealing this complex abundance variation in loops. Furthermore, our observations show clear evidence that the origin of hot flare plasma in flaring loops consists of a combination of both directly heated plasma in the corona and from ablated chromospheric material; and our results provide valuable insights into the formation and composition of loop top brightenings, also known as EUV knots, which are a common feature at the tops of flare loops.

Research on the Synergistic Development of China’s Pension Industry and Pension Career

To adapt to the new demographic trends and foster high-quality and sustainable population growth, this study analyzes the synergistic development of the pension industry and pension career based on the data of 31 provinces in China from 2016 to 2022, using a distance coordination model, the latest Vague set similarity measure method, new grey relational analysis, kernel density estimation, and Markov chain. The findings indicate that (1) the development levels of China’s pension industry and pension career have shown a steady upward trend, with the gap between them gradually narrowing, though the overall development of the pension career remains higher than that of the pension industry. (2) The synergistic degree of China’s pension industry and pension career is high, with the degree of synergy fluctuating but generally increasing. (3) The synergistic development degree of the pension industry and pension career has continued to rise. The increase in the synergistic development degree shows a spatial distribution pattern of “Central > East > West”. (4) The spatial distribution of synergistic development of China’s pension industry and pension career is uneven, and in the long term, regions with higher synergistic development levels tend to positively influence less developed regions.

Social Cost Benefit Analysis – a powerful tool for decision making

Abstract Health Impact Assessment provides policy makers with important information about health effects of programs, policies, plans or projects (PPPP). However, PPPP may have many more effects, both positive and negative, direct and indirect, intended and unintended, immediate and in the far future. Ex-ante insights in all these effects may help policy makers to optimize decision-making, but also to communicate realistically to all stakeholders about the PPPP. Weighing effects that may be very different in scale and reach different stakeholders in different ways is inevitable. One powerful tool to help decision makers in this task is Social Cost Benefit Analysis (SCBA). SCBA requires to specify and quantify all effects of PPPP over a specified time horizon. It is also necessary to specify a base-case scenario, i.e. how the problem under study would develop without further investments in PPPP. All effects of PPPP need to be quantified in monetary terms, these can be financial, e.g. healthcare costs or costs of productivity losses, or non-financial, for instance by placing a value of €50,000 on a QALY gained or a DALY averted or a value of €3,5 million on a lost life. PPPP with positive net monetary value are considered welfare enhancing, while PPPP with a negative monetary value do not add to welfare in society. In the Netherlands, government asks any bigger PPPP to be quantified with SCBA methodology. Guidance on unit values for non-monetary effects is given. This contribution will elaborate on results of the SCBA method for different alcohol policy measures, such as increase in excise taxes, minimum unit pricing, closure of vending spots and a total marketing ban. SCBA is a powerful tool to inform policy makers and assist in their decision making processes. It is also a labour and data intensive process that should only be used for larger PPPP investments and not for local decision making.

Global Exponential Synchronization of Delayed Quaternion-Valued Neural Networks via Decomposition and Non-Decomposition Methods and Its Application to Image Encryption

With the rapid advancement of information technology, digital images such as medical images, grayscale images, and color images are widely used, stored, and transmitted. Therefore, protecting this type of information is a critical challenge. Meanwhile, quaternions enable image encryption algorithm (IEA) to be more secure by providing a higher-dimensional mathematical system. Therefore, considering the importance of IEA and quaternions, this paper explores the global exponential synchronization (GES) problem for a class of quaternion-valued neural networks (QVNNs) with discrete time-varying delays. By using Hamilton’s multiplication rules, we first decompose the original QVNNs into equivalent four real-valued neural networks (RVNNs), which avoids non-commutativity difficulties of quaternions. This decomposition method allows the original QVNNs to be studied using their equivalent RVNNs. Then, by utilizing Lyapunov functions and the matrix measure method (MMM), some new sufficient conditions for GES of QVNNs under designed control are derived. In addition, the original QVNNs are examined using the non-decomposition method, and corresponding GES criteria are derived. Furthermore, this paper presents novel results and new insights into GES of QVNNs. Finally, two numerical verifications with simulation results are given to verify the feasibility of the obtained criteria. Based on the considered master–slave QVNNs, a new IEA for color images Mandrill (256 × 256), Lion (512 × 512), Peppers (1024 × 1024) is proposed. In addition, the effectiveness of the proposed IEA is verified by various experimental analysis. The experiment results show that the algorithm has good correlation coefficients (CCs), information entropy (IE) with an average of 7.9988, number of pixels change rate (NPCR) with average of 99.6080%, and unified averaged changed intensity (UACI) with average of 33.4589%; this indicates the efficacy of the proposed IEAs.

Correlation models for next-generation amplitude and cumulative intensity measures using artificial neural networks

Various next-generation intensity measures (IMs) have emerged in recent years. For instance, filtered incremental velocity, FIV3, was shown to be efficient in predicting the collapse of structures, or average spectral acceleration, Saavg, shown to be well-correlated with a wide range of response, from initiation of structural damage yielding to structural collapse. Different ground motion models (GMMs) are available to predict the probabilistic distribution of these IMs, given a set of seismological parameters. Nonetheless, a few key correlation models are still missing in the literature, which are elemental for a more holistic approach to ground motion record selection. These models are vital when performing seismic hazard analysis and ground motion record selection using state-of-the-art approaches like the conditional spectrum or generalized conditional intensity measure (GCIM) methods. This study uses a single machine-learning-based generalized ground motion model (GGMM) to estimate all the IMs. The model is based on the Next-Generation Attenuation (NGA)-West2 database and was utilized to quantify the correlations between the residuals. Correlations were calculated for intra- and inter-event residuals, but only those for total residuals are presented here, given their utility. Since the calculation of residuals comes from the same GMM and the same database of ground motions, this produces more consistent correlation coefficients between all IMs. To facilitate the usage of these correlation coefficients, predictive models of the empirical data were developed using machine-learning-based techniques, namely artificial neural networks (ANNs). It was found that FIV3 is strongly correlated with Sa(~1 s) and itself across all periods and has a weak negative correlation with duration at short periods and near-zero correlation for longer periods. Also, a stronger negative correlation between Sa and significant duration was found, compared with other prominent existing models. Direct correlation models between Sa and Saavg are also proposed.

Research on evaluation of knowledge interaction indicators in multi-team systems based on management entropy

Efficient knowledge interaction among teams can enhance the collaborative performance of Multi-team Systems (MTSs). Identifying and addressing issues within MTS knowledge interaction is a current topic in knowledge-driven MTS collaboration. However, there is a lack of scientific and measurable evaluation indicators and methods for effectively evaluating MTS knowledge interactions to aid decision-making. This study, integrating a network perspective with complex systems thinking and the theoretical principles of management entropy, constructs a systematic evaluation system for MTS knowledge and a corresponding methodological framework. Validated and analyzed through two case studies in diverse contexts, this system and method enable managers to pinpoint crucial indicators affecting the effectiveness of MTS knowledge at various stages, especially critical junctures that may arise during the evolving development of the MTS. This provides a basis for decision-making to enable timely managerial interventions at crucial moments.

The Curve Fitting Problem, Data Validation, and Inductive Generalization in Machine Learning

AbstractAris Spanos and Deborah Mayo’s error-statistical approach to statistical modeling and inference adopts the reliability of inductive inference as a primary criterion for statistical model and estimator selection (e.g., curve fitting). In this paper, we expand the error-statistical approach’s adoption of reliable inductive inference by scrutinizing the epistemic legitimacy of contemporary techniques leveraged in data science. We argue that data validation and testing potentially provides a direct, measurable method of evaluating evidence for reliable inductive inferences in cases where the error-statistical approach is not easily applied, and conclude with an exploration of core methodological foils to the reliability of inductive inference revealed by this argument.

Different manifestations of a loop-like transient brightening in solar atmospheres

Small-scale transient brightenings that are the consequence of magnetic reconnection play pivotal roles in the heating process of solar atmospheres. These phenomena contain key information about the dynamic evolution of the solar magnetic field. The fine-scale structures triggered by instabilities in these brightenings are intimately connected with the release of magnetic energy. To better understand the conversion and release of magnetic energy in small-scale heating events, we investigated the thermal-dynamical behaviors of a loop-like transient brightening (LTB) with plasma blobs. We used the spectroscopic and slit-jaw imaging observations taken from the Interface Region Imaging Spectrograph and the extreme-ultraviolet images taken from the Atmospheric Imaging Assembly on board the Solar Dynamics Observatory to analyze the plasma properties of an LTB that occurred on February 28, 2014. The space-time maps were created to present the spatial evolution of the LTB, and the light curves were calculated to illustrate the heating process. Additionally, we employed the differential emission measure (DEM) method to compute the temperature and emission measure of the LTB. In order to investigate the plasma motion along the line-of-sight direction, a double-Gaussian function was used to fit the Si IV spectral profiles. The spectrum and DEM analysis indicate that the LTB was constituted by multithermal plasma with temperatures reaching up to $5.4 $ K. The space-time maps of the emission and the Gaussian-fitting results of the Si IV line demonstrate that the LTB not only exhibited bidirectional flows, but was also twisted. Several plasma blobs were identified in the spine of the LTB, suggesting the potential presence of a tearing-mode instability. The low-temperature bands peaked approximately one minute prior to the high-temperature bands, suggesting the occurrence of a heating process driven by magnetic reconnection. The appearance of plasma blobs closely coincided with the sudden increase in the velocity and the quick rise of light curves, providing evidence that plasma blobs facilitate the release of magnetic energy during solar activity. Based on these findings, we speculate that the LTB was a complex structure that occurred in the upper chromosphere-transition region. These results clearly demonstrate that plasma blobs are important for the conversion and release processes of magnetic energy.

Managing the risk of suicide in a psychiatry clinic: An ethnographic study on the work atmosphere of nurses during the Covid-19 pandemic

The aim of this study was to describe the approaches of nurses working in an inpatient psychiatry clinic during the pandemic period to managing suicide risk in the context of their work atmosphere. This ethnographic research design used a sample of 13 psychiatric nurses in a psychiatric clinic in Ankara, Turkey. Data were collected with in-depth interviews and 612h of observation. Qualitative thematic analysis was used for data analysis. Two themes emerged from the data analysis, including ‘De-prioritizing the uniqueness of the patient in managing suicide risk’ and ‘A condition that remains important for uniqueness: personality disorders’. The current risk assessment methods for suicide, pandemic measures, and past inadequacies in the approach to patients with personality disorders have revealed the risk of alienating from the individual risk factors as well as the patients' history in risk management. Risk management practices that consider the psychosocial assets of patients at risk of suicide, addressing the risk level with a safe measurement scale and performing triage according to this grading system can provide an atmosphere that allows nurses to make risk assessments greater self-confidence.

Wideband microwave frequency response measurement based on optical phase-locked loop.

Frequency response measurement, or the forward transmission coefficient (S21) measurement for a two-port network, is the key function of a vector network analyzer (VNA). In this paper, a broadband and high dynamic range (DR) microwave S21 parameter measurement scheme based on an optical phase-locked loop (OPLL) is proposed. By heterodyning two phase-locked hybrid integrated ultra-narrow linewidth lasers, a microwave signal with low phase noise and spurious level is generated as the incident signal and reference signal, and the signal frequency can be easily manipulated over a wide range by tuning the master laser wavelength. In the receiver, the radio frequency (RF) signals are down-converted to intermediate frequency (IF) signals with the phase-locked lasers. By sampling and processing the IF signals the S21 parameter of the DUT can be acquired. A proof-of-concept experiment is performed, and with available photodetectors, phase modulators and phase-locked loops, a measurable range of 2 to 18 GHz is achieved. The demonstrated minimum frequency resolution of the OPLL-based RF signal synthesizer is 10 Hz. The system DR exceeds 68 dB at an equivalent resolution bandwidth of 1 kHz. The S21 parameters of a power divider and a bandpass filter are measured, and the results are well consistent with those of a commercial VNA. The DR and measurable range limit factors and possible extension methods are discussed. The proposed approach offers a high potential way to develop a wideband, high DR, and fully integrated VNA.

The pedagogical effectiveness of the programmed learning of English tenses: A pedagogical experiment

The current study aims to investigate the effectiveness of a programmed approach to teaching English verb tenses. This approach is essential for enhancing grammar instruction in the classroom. A total of 23 first-year students from Toraighyrov University participated in both studies. Before the experiments, the participants were divided into two groups. During the first stage of the experiment (2021–2022), both groups started with a similar level of English proficiency assessed through admission exams. There was a noticeable difference in the initial proficiency between the two groups during the second stage (2022–2023). After completing the learning process, the participants underwent a post-assessment to measure their progress in using tense forms. In a second experiment, the order of events was reversed. In this study, the criterion measures method, statistical method, statistical analysis of the test, and data collection and analysis were employed. Based on the preliminary findings, students experienced enhanced learning and engagement when studying English grammar using programmed learning methods compared to traditional approaches. It was concluded that programmed learning helps students to memorize and comprehend grammar concepts more efficiently through structured progression and immediate feedback, indicating that it is a viable approach to teaching grammar.

Strain Gauge Location Optimization for Operational Load Monitoring of an Aircraft Wing Using an Improved Correlation Measure

Operational loads of an aircraft are the prerequisite for assessing its safety or fatigue life. Traditionally, numerous strain gauge sensors are installed to monitor the operational loads, which inevitably increase the weight and system complexity of the aircraft. Therefore, in order to decrease the maintenance costs and data redundancy, the number and location of strain sensors should be optimized for accurate and reliable operational load monitoring. In this paper, a novel two-stage strain gauge location optimization method is proposed to reduce the number of strain gauges while maintaining the operational load monitoring accuracy, which is validated by a numerical case study of an aircraft wing. In the first stage, the traditional Pearson correlation measure is harnessed to initially eliminate numerous correlated strain gauge monitoring points, reducing 996 original strain gauge measurement points to 13 for the aircraft wing box. In the second stage, an improved correlation measure method is proposed to further reduce the 13 strain gauge points to 2, which can evaluate the correlation degree of several variables and simultaneously determine the optimal strain monitoring locations for the two load actuators in this study. The relative errors between the predicted loads and the actual loads for both load actuators are less than 4% when only two optimized monitoring points are adopted. In addition, a comparison study with LASSO regression and principal component regression methods is conducted. The results demonstrate that the proposed method has the characteristics of less monitoring points and higher load prediction precision.

Comparison of indicators to evaluate the performance of climate models

AbstractThe evaluation of climate models is a crucial step in climate studies. It consists of quantifying the resemblance of model outputs to reference data to identify models with superior capacity to replicate specific climate variables. Clearly, the choice of the evaluation indicator significantly impacts the results, underscoring the importance of selecting an indicator that properly captures the characteristics of a “good model”. This study examines the behaviour of six indicators, considering spatial correlation, distribution mean, variance and shape. Monthly data for precipitation, temperature and teleconnection patterns in Central America were utilized in the analysis. A new multicomponent measure was selected based on these criteria to assess the performance of 32 CMIP6 models in reproducing the annual seasonal cycle of these variables. The top six models were determined using multicriteria methods. It was found that even the best model reproduces one derived climatic variable poorly in this region. The proposed measure and selection method can contribute to enhancing the accuracy of climatological research based on climate models.