Relative Degree Research Articles

Human Behavior Analysis Using Machine Learning

The study and analysis of human behavior have been longstanding challenges in various fields such as psychology, sociology, and anthropology. With the advent of advanced technologies and the proliferation of data, machine learning has emerged as a powerful tool for deciphering and understanding complex human behaviors. This research aims to explore the application of machine learning techniques in the analysis of human behavior, leveraging diverse datasets and cutting-edge algorithms to gain insights into individual and collective actions. The research methodology involves the collection of multidimensional data sources, including but not limited to social media interactions, physiological measurements, and environmental factors. These datasets are preprocessed and integrated to create a comprehensive representation of human behavior. Machine learning models, ranging from traditional statistical methods to deep learning architectures, are then applied to uncover patterns, correlations, and predictive insights from the data. Machine learning algorithms can analyze patterns in behavior to identify potential signs of mental health issues, enabling early intervention and treatment. Tracking behavioral data can help in managing chronic conditions by providing insights into lifestyle choices, adherence to treatment plans, and overall well-being. Machine learning models can identify unusual patterns of behavior, which is crucial for security purposes. This can include recognizing suspicious activities in public spaces or flagging unusual behavior in online environments. The purpose of this study is to explore the challenges of multiple attribute decision-making when dealing with intuitionist fuzzy information. In this scenario, the attribute weights are not entirely known, and the attribute values are represented by intuitionist fuzzy numbers. To determine the attribute weights, an optimization model is constructed based on the traditional grey relational analysis (GRA) fundamental principles. The proposed method involves calculating the grey relation degree between each alternative and the positive-ideal solution and negative-ideal solution. This degree is then used to define a relative relational degree, which enables the ranking of all alternatives simultaneously with respect to both the positive-ideal solution (PIS) and negative-ideal solution (NIS). From the result 22 is ranked at first position and 45 is ranked at fifth position

Rational Thinking in Complex Qualitative Business and Management Research Using Systems Methodologies.

This paper describes the application of Systems Methodologies when dealing with complex qualitative business and management research studies, when seen in their true light, as “wicked projects”. It will demonstrate the power, flexibility and effectiveness of these approaches. This paper focuses on studies undertaken by mostly part-time postgraduate students on Business and Management related degrees who are managers in their full-time roles. Such research studies in the “messy” real world often deal with complex, difficult, unstructured and ill-defined problematic situations, with many stakeholders, multi-perspectives, uncertainties, soft factors. Hence “wicked projects”. Systems methodologies and Soft OR (Operational Research) are very suitable approaches for structuring the research project, gathering data, carrying out rigorous analysis, and enabling clear, unbiased and rational thinking. They provide the researcher with a fully informed holistic approach. They have been employed successfully by the author, when supervising, or in collaboration with students undertaking research studies or dissertations. Soft systems methodologies are not mainstream, and thus are often ignored in university research methods courses, and rarely mentioned in business research methods books. Yet they help the student design the project, help the supervisor guide the project, enable clear communications between the participants in the research, and aid collaboration. They are highly efficient and effective, and will deliver “joined-up thinking!” The paper will describe the studies carried out, and methods and techniques chosen. They have proved of real value to the students, not only in their degrees but also in the learning and development needed for their ongoing managerial roles, to the organisations in question, and to advance knowledge in the particular field.

In-silico mechanistic analysis of adsorption of Iodinated Contrast Media agents on graphene surface

The study aims at assessing the potential of graphene-based adsorbents to reduce environmental impacts of Iodinated Contrast Media Agents (ICMs). We analyze an extensive collection of ICMs. A modeling approach resting on molecular docking and Density Functional Theory simulations is employed to examine the adsorption process at the molecular level. The study also relies on a Quantitative Structure-Activity Relationship (QSAR) modeling framework to correlate molecular properties with the adsorption energy (Ead) of ICMs, thus enabling identification of the key mechanisms underpinning adsorption and of the key factors contributing to it. A collection of distinct QSAR-based models is developed upon relying on Multiple Linear Regression and a standard genetic algorithm method. Having at our disposal multiple models enables us to take into account the uncertainty associated with model formulation. Maximum Likelihood and formal model identification/discrimination criteria (such as Bayesian and/or information theoretic criteria) are then employed to complement the traditional QSAR modeling phase. This has the advantage of (a) providing a rigorous ranking of the alternative models included in the selected set and (b) quantifying the relative degree of likelihood of each of these models through a weight or posterior probability. The resulting workflow of analysis enables one to seamlessly embed DFT and QSAR studies within a theoretical framework of analysis that explicitly takes into account model and parameter uncertainty. Our results suggest that graphene-based surfaces constitute a promising adsorbent for ICMs removal, π-π stacking being the primary mechanism behind ICM adsorption. Furthermore, our findings offer valuable insights into the potential of graphene-based adsorbent materials for effectively removing ICMs from water systems. They contribute to ascertain the significance of various factors (such as, e.g., the distribution of atomic van der Waals volumes, overall molecular complexity, the presence and arrangement of Iodine atoms, and the presence of polar functional groups) on the adsorption process.

Cloud maps highlighting dynamic characteristics of surface signal to improve time-varying wear evaluation accuracy

This paper proposes a high accuracy time-varying wear evolution method with cloud maps highlighting dynamic characteristics of surface signal. Firstly, the cloud map method is established by arrangement of measured data, thus time-varying wear evolution process is visualized on a series of plane figures for preliminary qualitative analysis. Then, high accuracy recognition of time-varying wear states is realized by cloud map shape parameters, including kurtosis and 1-D kernel density function highlighting distribution information of surface signal. The relative recognition degree by cloud map shape parameters are higher than those of friction coefficient, Root-Mean-Square (RMS) deviation and fractal dimension. Finally, high accuracy time-varying wear life prediction is realized by cloud map size growing speed highlighting waviness information of surface signal. The relative prediction error is reduced from more than 20% to less than 10% compared with friction coefficient and roughness.

Исследование кинетики формообразования деталей сферического подшипника скольжения из коррозионно-стойких сталей, полученных объемной штамповкой пористых заготовок

Introduction. Spherical powder sliding bearings are widely used in various branches of mechanical engineering. Therefore, the development of a promising method of production of spherical sliding bearing parts from powders of corrosion-resistant steels with specified properties is an urgent task. Purpose of work: is to study the kinetics of forming during cold die forging of spherical sliding bearing parts from stainless steel powder blanks, and to assess the effect of the chemical composition of lubricants and the design of the pressing tool on the structure and properties of the bearing outer ring. Materials from sprayed powders of stainless chromium-nickel steels obtained by cold die forging of sintered blanks coated with lubricants are studied in the work. The following research methods were used: mechanical tensile testing, metallographic studies and cold die forging process simulation. Results and its discussion. It is revealed that the resistance and work of deformation, as well as the kinetics of forming of the outer ring of the spherical sliding bearing are influenced by chemical composition of powders and lubricants, microstructure and mechanical properties of the blank material, configurations of the end surfaces of punches. The top and bottom edges of the outer bearing are most intensively sealed when the punch faces are made with a chamfer angle of 30–40 degrees. With an increase in the relative strain degree by height up to 0.30–0.35 its residual porosity amounted to 0.5–2.0 %. The features of definition of strain state and calculation of strain energy in the implementation of the offered method and the choice of technological parameters of the cold die forging process of sliding bearings parts are shown. A simple method for calculating and experimentally determining the coefficient of contact friction in the process of cold die forging of porous stainless steel blanks is developed, which allows to establish the effect of lubricant composition on the strain resistance at different values of the degree of radial deformation and to develop optimal methods of cold die forging of porous blanks in the production of parts of different complexity.

Evaluation of mine ecological environment based on fuzzy hierarchical analysis and grey relational degree

In order to improve the level of mine ecological environment management and restoration, and to improve and enhance the overall environmental quality of mines. This study takes coal mine as the perspective, and constructs evaluation indexes in two steps, i.e., social network analysis method and relevant policy documents are combined to construct evaluation indexes. The indicator system contains 5 first-level indicators and 23 s-level indicators, and the triangular fuzzy number hierarchical analysis method is introduced to determine the comprehensive weight of each evaluation indicator, which overcomes the inadequacy of the objective empowerment method or the subjective empowerment method of single empowerment. The grey correlation analysis theory is used to establish a grey correlation evaluation model of mine ecological environment, which is applied to the evaluation of ecological environmental protection level of four coal mines in a province of China, making full use of the information of each index for quantitative evaluation, and finally obtaining the evaluated value of ecological environmental protection level of each coal mine. It is demonstrated that the evaluation model can make scientific and effective evaluation of the level of ecological environmental protection in mines. The study concludes that coal mines should pay attention to improving the comprehensive utilization rate of coal gangue in the next mine ecological environment management and restoration, and at the same time, reduce the ton of coal power consumption, ton of coal water consumption, and Increase in vegetation cover. This study provides a useful evaluation method for ecological environment management in mining areas, which is helpful to improve the quality of ecological management in mining areas.

Enhancing the Pitch-Rate Control Performance of an F-16 Aircraft Using Fractional-Order Direct-MRAC Adaptive Control

This study presents a comparative analysis of classical model reference adaptive control (IO-DMRAC) and its fractional-order counterpart (FO-DMRAC), which are applied to the pitch-rate control of an F-16 aircraft longitudinal model. The research demonstrates a significant enhancement in control performance with fractional-order adaptive control. Notably, the FO-DMRAC achieves lower performance indices such as the Integral Square-Error criterion (ISE) and Integral Square-Input criterion (ISU) and eliminates system output oscillations during transient periods. This study marks the pioneering application of FO-DMRAC in aircraft pitch-rate control within the literature. Through simulations on an F-16 short-period model with a relative degree of 1, the FO-DMRAC design is assessed under specific flight conditions and compared with its IO-DMRAC counterpart. Furthermore, the study ensures the boundedness of all signals, including internal ones such as ω(t).

Analysis of the coupling coordination between traditional Chinese medicine medical services and economy and its influencing factors in China.

The coordinative relationship between medical treatment and the economy is an important part of promoting the sustainable and healthy development of a social economy, and it is conducive to promoting the coordinated development of the two systems. Based on constructing a comprehensive evaluation index system for the relationship between traditional Chinese medicine medical services and economic development, the entropy method, coupling coordination model, and Tobit model are used to calculate the comprehensive development level, coupling coordination degree, and influencing factors of the two systems in 31 provinces of China from 2015 to 2021. Suggestions are proposed for the coordinated development of traditional Chinese medicine medical services, and economy. The results showed that the development level of traditional Chinese medicine was higher than that of social and economic development, the relative development gap between the two systems was gradually narrowed, and the coupling coordination degree increased steadily; however, the regional differences were large. The relative development level and coupling coordination degree of each region show a development pattern of eastern > central > western > northeast; the coupling coordination degree of the two systems in the spatial region was positively correlated and was enhanced year by year. From the perspective of influencing factors, the level of economic development is the decisive factor in the coupling and coordination between traditional Chinese medicine medical treatment and the social economy. Factors such as human capital and health level in traditional Chinese medicine have an inhibitory effect on the coordinated development of the two systems. So, China should focus on optimizing the regional allocation of traditional Chinese medicine medical personnel, improving the quality of medical services, and enhancing the health level of medical personnel.

Generative Artificial Intelligence for Journalistic Content in Ibero-America : Perceptions, Challenges and Regional Projections

Introduction: The increasing availability of artificial intelligence (AI) tools has brought with it various, increasingly advanced, uses for content production. Digital media editors have not been immune to the phenomenon, which is why there is growing interest, both professional and academic, regarding its possible uses.<br /> Objectives: In this paper we seek to detect the experiences, challenges and projections regarding the use of artificial intelligence in the generation of news content. In turn, the specific objectives aim both to offer a general overview of journalistic experiences with AI and to study the results of a survey regarding the use of this technology in the production of content in Ibero-America.<br /> Methodology: A survey was conducted among 154 participants through a cycle of webinars held at four universities in Ibero-America during the second semester of 2023, corresponding to 14 countries in the region, including academics, researchers and students of Journalism (and related degrees in Social Communication), along with managers and journalists from prominent media outlets.<br /> Results: although journalistic experiences with AI are still incipient in the region, the responses obtained show that most participants use generative AI tools every week, there is greater knowledge (and preference) for ChatGPT, and there is a consensus regarding how artificial intelligence can bring great benefits if it has the proper regulations.

Response of sand added with various biopolymer contents under repetitive loading and freeze–thaw cycles

Biopolymers have recently been used as ecofriendly materials for soil improvement in terms of stabilization, compressibility, and engineering parameters. The objective of this study is to assess the effect of biopolymer content in sand mixtures during freeze–thaw repetitive loading cycles. The biopolymers were mixed at weight ratios of 0.0, 0.5, 1.0, 2.0, 5.0, and 10 % (BPC 0.0—BPC 10), and the relative density and degree of saturation were fixed at 60 % and 20 %, respectively. The measurement system was located in an ice chamber for freeze–thaw, and 100 cycles of repetitive loads were applied. The test results showed that the deformation decreased from BPC 0.0, to BPC 1.0 owing to the cementation effect produced by the biopolymer chain and coating. However, the deformation increased from BPC 1.0 to BPC 5.0 because high-viscosity solutions might separate the sand particles, causing a density reduction and generating more deformation by compaction. The relative permittivity varied with respect to BPC and freeze–thaw repetitive loading stages that were affected by unfrozen water content, volume contraction, and water consumption during dehydration. The shear wave velocity gradually increased from BPC 2.0 to BPC 10 because the effect of fines in coarse–fine mixtures, rather than the cementation effect. Therefore, the module containing the sensors used in this study can be used to understand the role of biopolymers as reinforcing materials in railway subgrades.

Multi-attribute quantum group interference decision model considering minimum constraint and information entropy

About multi-attribute group decision making process, the opinions of different decisions will interfere with each other, or be affected by the decision-making environment, resulting in an unobserved interference effect, which is regarded as quantum entanglement in quantum theory. In this paper, an interference effect model based on minimum constraint and information entropy is proposed to measure the interference value in quantum decision models. Firstly, considering the interference effects of decision makers’ opinions, the evaluation opinions of decision makers are aggregated using Quantum-like Bayesian Network (QLBN), which are regarded as wave functions superimposed in the quantum model. Secondly, due to the uncertainty of decision process, information entropy and minimum constraint are combined to solve the interference effect. The minimum constraint is used to limit the range of interference values and the information entropy represents the uncertainty in the decision process. The grey relational degree represents the conditional probability of the decision maker about the alternatives. Thirdly, the weights of attributes and the probabilities of the alternatives are aggregated about decision makers by the proposed interference effect model, and then the alternatives are ranked by the aggregated probabilities. Subsequently, a numerical example is used to verify the validity and rationality of the model. Comparative analyses and sensitivity analyses are also implemented to demonstrate the stability and reliability of our method.

Feedback linearization control to avoid saturation of the high frequency transformer of a dual active bridge DC–DC converter for a DC microgrid

This paper introduces an innovative control strategy for a dual active bridges DC–DC converter employed to regulate voltage levels between two feeders in a DC microgrid. The controller is specifically designed to regulate the output voltage at a predetermined reference value and to ensure a zero mean value for both primary and secondary currents of the high-frequency transformer, regardless of any imbalance in one of the active bridges. This is particularly important in the face of potential imbalances in one of the active bridges resulting from construction disparities in converters or variations in conduction resistances in power transistors. Given the nonlinear nature of the converter, the controller is designed using feedback linearization. The output is selected with a relative degree equal to the system order, resulting in a stable controller exhibiting good dynamic performance under scenarios such as reference changes, linear load fluctuations, and the integration of constant power loads. The performance of the proposed control strategy is validated through both simulation and experimental results.

Variation in cancer risk between organs can not be explained by the degree of somatic clonal expansion

Somatic clonal expansion refers to the proliferation and expansion of a cell clone within a multicellular organism. Since cancer also results from the uncontrolled proliferation of few cell clones, it is generally believed that aging-associated somatic clonal expansion observed in normal tissues represents a precancerous condition. For instance, hematological malignancy is often preceded by clonal hematopoiesis. However, the precise connection between cancer and somatic clonal expansion remains elusive in solid organs. In this study, we utilized a straightforward method to assess the relative quantitative degrees of clonal expansion in nine human organs. Our findings reveal that the degree of clonal expansion varies across different organs while remaining consistent among different individuals. Contrary to the general belief, we did not identify any significant correlation between lifetime cancer risk and the degree of lifetime somatic clonal expansion. For example, the lifetime risk of colorectal cancer is approximately 20 times higher than that of esophageal cancer, yet the former exhibited the lower degree of clonal expansion than the latter. Our results suggest that somatic clonal expansion represents an evolutionary process distinct from carcinogenesis in normal tissues, providing novel perspectives on precancerous conditions.

Correction to: Funnel control of linear systems with arbitrary relative degree under output measurement losses

Correction to: Funnel control of linear systems with arbitrary relative degree under output measurement losses

Coevolution of superconductivity and Hall coefficient with anisotropic lattice shrinkage in compressed KCa2Fe4As4F2

Abstract The stability of superconductivity in superconductors is widely recognized to be determined by various factors, including charge, spin, orbit, lattice, and other related degrees of freedom. Here, we report our findings on the pressure-induced coevolution of superconductivity and Hall coefficient in KCa2Fe4As4F2, an iron-based superconductor possessing a hybrid crystal structure combining KFe2As2 and CaFeAsF. Our investigation, involving high-pressure resistance, Hall effect and x-ray diffraction (XRD) measurements, allows us to observe the connection of the superconductivity and Hall coefficient with the anisotropic lattice shrinkage. We find that its ambient-pressure tetragonal (T) phase presents a collapse starting at around 18 GPa, where the sign of the Hall coefficient (R H) changes from positive to negative. Upon further compression, both superconducting transition temperature (T c) and R H exhibit a monotonous decrease. At around 41 GPa, the superconductivity is completely suppressed (T c = 0), where the parameter a begins to decline again and the Hall coefficient remains nearly unchanged. Our experiment results clearly demonstrate that the pressure-induced anisotropic lattice collapse plays a crucial role in tuning the interplay among multiple degrees of freedom in the superconducting system and, correspondingly, the stability of the superconductivity.

Impact of cladribine, cytarabine, and G-CSF (CLAG) as a bridging therapy prior to allogeneic hematopoietic stem cell transplantation in relapsed or refractory acute myeloid leukemia.

The combination of cladribine, cytarabine, and G-CSF (CLAG) has exhibited robust synergistic anti-leukemia activity as an induction therapy (IT) in acute myeloid leukemia (AML). However, the impact of CLAG as a bridging therapy (BT) administered between IT and allogeneic hematopoietic stem cell transplantation (allo-HSCT) for patients with relapsed or refractory (R/R) AML remains uncertain. In this retrospective study, we examined the efficacy of CLAG as a transitional strategy prior to allo-HSCT in R/R AML. We included 234 patients with R/R AML who received the modified busulfan plus cyclophosphamide conditioning regimen for allo-HSCT in our center during the past 6years, performed a propensity-score matching analysis, partitioned them into four distinct cohorts, and further integrated them into the CLAG group and non-CLAG group based on response to IT and utilization of CLAG. Our cohorts encompassed 12 patients in Cohort A (modified composite complete remission (mCRc) after IT, CLAG), 31 in Cohort B (mCRc after IT, non-CLAG), 35 in Cohort C (non-complete remission (non-CR) after IT, CLAG), and 80 in Cohort D (non-CR after IT, non-CLAG). Intriguingly, among patients with non-CR status, the administration of CLAG correlated with a notably statistically diminished risk of relapse and improved survival at 2-year follow-up (Cohort C vs. Cohort D). Employing CLAG as a BT prior to allo-HSCT demonstrates substantial effectiveness, a relative degree of safety, and manageable toxicity in selected R/R AML cases.

A related degree-based frequent pattern mining algorithm for railway fault data

It is of great significance to improve the efficiency of railway production and operation by realizing the fault knowledge association through the efficient data mining algorithm. However, high utility quantitative frequent pattern mining algorithms in the field of data mining still suffer from the problems of low time-memory performance and are not easy to scale up. In the context of such needs, we propose a related degree-based frequent pattern mining algorithm, named Related High Utility Quantitative Item set Mining (RHUQI-Miner), to enable the effective mining of railway fault data. The algorithm constructs the item-related degree structure of fault data and gives a pruning optimization strategy to find frequent patterns with higher related degrees, reducing redundancy and invalid frequent patterns. Subsequently, it uses the fixed pattern length strategy to modify the utility information of the item in the mining process so that the algorithm can control the length of the output frequent pattern according to the actual data situation and further improve the performance and practicability of the algorithm. The experimental results on the real fault dataset show that RHUQI-Miner can effectively reduce the time and memory consumption in the mining process, thus providing data support for differentiated and precise maintenance strategies.

Association of type 2 diabetes with family history of diabetes, diabetes biomarkers, mental and physical disorders in a Kenyan setting

This study aimed to determine the degree of family relations and associated socio-demographics characteristics, clinical/physical and mental disorders in type 2 diabetes mellitus in a Kenyan diabetes clinic. This study was part of a large multicentre study whose protocol and results had been published. It took place at the outpatient diabetes clinic at a County Teaching and Referral Hospital in South East Kenya involving 182 participants. We used a socio-demographic questionnaire, the Hamilton Depression (HAM-D) and PHQ-9 rating scales for depression, the MINI International Neuropsychiatric Interview (MINI; V5 or V6) for DSM-5 diagnoses, the WHO-5 Well-being scale and Problem Areas in Diabetes Scale (PAID). We extracted from the notes all physical conditions. We enquired about similar conditions in 1st and 2nd degree relatives. Descriptive, Chi-square test, Fisher’s exact test, one way ANOVA, and Multinomial logistic regression analysis were conducted to test achievements of our specific aims. Of the 182 patients who participated in the study, 45.1% (82/182) reported a family history of diabetes. Conditions significantly (p < 0.05) associated with a degree of family history of diabetes were retinopathy, duration of diabetes (years), hypertension, and depressive disorder. On average 11.5% (21/182) scored severe depression (≥ 10) on PHQ-9 and 85.2% (115/182) scored good well-being (≥ 13 points). All DSM-5 psychiatric conditions were found in the 182 patients in varying prevalence regardless of relations. In addition, amongst the 182 patients, the highest prevalence was poor well-being on the WHO quality of life tool. This was followed by post-traumatic disorders (current), suicidality, and psychotic lifetime on DSM-5. The least prevalent on DSM-5 was eating disorders. Some type 2 diabetes mellitus physical disorders and depression have increased incidence in closely related patients. Overall, for all the patients, the prevalence of all DSM-5 diagnoses varied from 0.5 to 9.9%.

Hyperspectral estimation model of soil organic matter content based on principal gradient grey information

PurposeIn order to explore a new estimation approach of hyperspectral estimation, this paper aims to establish a hyperspectral estimation model of soil organic matter content with the principal gradient grey information based on the grey information theory.Design/methodology/approachFirstly, the estimation factors are selected by transforming the spectral data. The eigenvalue matrix of the modelling samples is converted into grey information matrix by using the method of increasing information and taking large, and the principal gradient grey information of modelling samples is calculated by using the method of pro-information interpolation and straight-line interpolation, respectively, and the hyperspectral estimation model of soil organic matter content is established. Then, the positive and inverse grey relational degree are used to identify the principal gradient information quantity of the test samples corresponding to the known patterns, and the cubic polynomial method is used to optimize the principal gradient information quantity for improving estimation accuracy. Finally, the established model is used to estimate the soil organic matter content of Zhangqiu and Jiyang District of Jinan City, Shandong Province.FindingsThe results show that the model has the higher estimation accuracy, among the average relative error of 23 test samples is 5.7524%, and the determination coefficient is 0.9002. Compared with the commonly used methods such as multiple linear regression, support vector machine and BP neural network, the hyperspectral estimation accuracy of soil organic matter content is significantly improved. The application example shows that the estimation model proposed in this paper is feasible and effective.Practical implicationsThe estimation model in this paper not only fully excavates and utilizes the internal grey information of known samples with “insufficient and incomplete information”, but also effectively overcomes the randomness and grey uncertainty in the spectral estimation. The research results not only enrich the grey system theory and methods, but also provide a new approach for hyperspectral estimation of soil properties such as soil organic matter content, water content and so on.Originality/valueThe paper succeeds in realizing both a new hyperspectral estimation model of soil organic matter content based on the principal gradient grey information and effectively dealing with the randomness and grey uncertainty in spectral estimation.

Development and Perspective of Gold and Silver Jewelry Imports to Slovakia Considering the Impact of the COVID-19 Pandemic

This study focuses on gold and silver jewelry import to Slovakia between the years 2010–2022 for hallmarking according to the country of origin. The aim of the research was to analyze and predict the import of gold and silver jewelry to Slovakia using statistical methods, namely the correlation coefficient and time series analysis. Using the correlation coefficient, we determined the relationship or degree of relation between selected countries of origin based on the amount of gold and silver jewelry (in kg) imported to Slovakia for hallmarking. We obtained an estimate of the future development trend of gold and silver imports into the domestic market from abroad using the time series method, particularly regression analysis and Holt-Winterson methods. It was found that the import of gold jewelry to Slovakia from the studied countries has an upward trend and, conversely, the import of silver jewelry has a downward trend.