Trends In Patterns Research Articles

Explore Global Fashion Trends and Cultural Influences

This paper discusses the relationship between cultural identity and diversity, cultural export and import, social values and behavioral patterns in global fashion trends, as well as the challenges and future prospects. It emphasizes that culture, as the core of fashion design, is spread globally through commercial operation, but it is necessary to balance cultural protection and commercialization to avoid cultural distortion. Meanwhile, changes in social values and behavioral patterns drive fashion innovation, while the application of technology such as AI accelerates the intelligence of the industry. In the future, the fashion industry will pay more attention to diversification, intelligence and sustainability, balancing the needs of culture, commerce and environmental protection, and jointly promoting the fashion industry to a better and sustainable future.

Concurrent trend turnings of drought severity across Afro-Eurasian continent since 1950

Rapidly intensifying global land drought poses severe threats to human societies, economies, and ecosystems. While previous studies have primarily investigated long-term drought trends, the frequency and concurrence of trend turnings have been largely neglected. In this study, we address this gap by employing the Running Slope Difference (RSD)-t-test to quantify trend turning frequency in Afro-Eurasian drought severity. Based on Palmer Drought Severity Index (PDSI), our analysis indicates that the PDSI trend in most parts of the Afro-Eurasian continent has experienced two turnings since 1950, although, the types of trend turnings vary regionally. The concurrence of these PDSI trending turnings is further investigated. Around 1985, a dipole pattern emerged - Eastern Europe experiences a drying trend turning, accompanied by decreased P-E and intensified drought, while Sahel exhibits a wetting trend turning, with increased P-E and mitigated drought. Around 2000, a tripole pattern is observed in Eastern Eurasia: The Russian Far East and South Asia experienced a drying trend turning, with reduced P-E and intensified drought, while Northeast Asia exhibited a further wetting trend, characterized by increased P-E and mitigated drought. We further investigate the influence of large-scale circulation changes. The enhanced Northern Hemisphere warming trend before and after 1985 contributes to increased land surface high pressure and an amplified meridional temperature gradient, favoring cross-equatorial water vapor transport. This mechanism potentially drives the dipole pattern of trend turning observed around 1985. Additionally, the North Pacific Ocean Sea Surface Temperature (SST) exhibited an enhanced North Pacific Gyre Oscillation (NPGO) pattern around 2000, which induced a tripole atmospheric circulation pattern over East Asia, corresponding to the observed tripole pattern of PDSI trend turnings. The identified dipole and tripole patterns of drought trend turnings, and their potential links to large-scale atmospheric circulation changes, provide insights into the complex dynamics of land drought variability across Afro-Eurasian.

Seasonal And Trend Analysis of Drug Sales using Time Series Analysis

The SARIMA model is to analyse and forecast seasonal drug sales data, enhancing accuracy and understanding the complex of seasonal and trend patterns in fields like pharmaceutical sales, unlike the ARIMA model that only handles non-seasonal data. The model aims to improve predictions and provide deeper insights into the intricate seasonal and trend patterns in drug sales data. This study analyses drug sales behaviour across different seasons using drug store data from 1992 to 2010, grouping data by seasonal periods. Time series analysis is a method used to analyse data points over time to identify trends, patterns, and seasonal variations, providing valuable insights into drug sales. The study emphasizes the significance of drug sales data in real-time monitoring of best values of p,d,q activity

Deep learning-based time series forecasting

With the advancement of deep learning algorithms and the growing availability of computational power, deep learning-based forecasting methods have gained significant importance in the domain of time series forecasting. In the past decade, there has been a rapid rise in time series forecasting approaches. This paper comprehensively reviews the advancements in deep learning-based forecasting models spanning 2014 to 2024. We provide a comprehensive examination of the capabilities of these models in capturing correlations among time steps and time series variables. Additionally, we explore methods to enhance the efficiency of long-term time series forecasting and summarize the diverse loss functions employed in these models. Moreover, this study systematically evaluates the effectiveness of these approaches in both univariate and multivariate time series forecasting tasks across diverse domains. We comprehensively discuss the strengths and limitations of various algorithms from multiple perspectives, analyze their capacity to capture different types of time series information, including trend and season patterns, and compare methods for enhancing the computational efficiency of these models. Finally, we summarize the experimental results and discuss the future directions in time series forecasting. Codes and datasets are available at https://github.com/TCCofWANG/Deep-Learning-based-Time-Series-Forecasting.

Identifying time patterns of highland and lowland air temperature trends in Italy and the UK across monthly and annual scales

Abstract. This paper presents a statistical analysis of air temperature data from 32 stations in Italy and the UK up to 2000 m above sea level from 2002 to 2021. The data came from both highland and lowland areas in order to evaluate the differences due to both location and elevation. The analysis focused on detecting trends at annual and monthly timescales, employing ordinary least-squares (OLS), robust S-estimator regression, and Mann–Kendall (MK) and Sen's slope methods. Hierarchical clustering (HCA) using dynamic time warping (DTW) was then applied to the monthly data to analyze the intra-annual pattern similarity of trends within and across the groups. Two different regions of Europe were chosen because of the different climate and temperature trends – namely, the northern UK (smaller trends) and the northwest Italian Alps (larger trends). The main novelty of the work is to show that stations with similar locations and altitudes have similar monthly slopes by quantifying them using DTW and clustering. These results reveal the nonrandomness of different trends throughout the year and between different parts of Europe, with a modest influence of altitude in wintertime. The findings revealed that group average trends were close to the National Oceanic and Atmospheric Administration (NOAA) values for the areas in Italy and the UK, confirming the validity of analyzing a small number of stations. More interestingly, intra-annual patterns were detected commonly at the stations of each of the groups and are clearly different between them. Confirming the different climates, most highland and lowland stations in Italy exhibit statistically significant positive trends, while in the UK, both highland and lowland stations show statistically nonsignificant negative trends. Hierarchical clustering in combination with DTW showed consistent similarity between monthly patterns of means and trends within the group of stations and inconsistent similarity between patterns across groups. The use of the 12 distance correlation matrices (dcor) (one for each month) also contributes to what is the main result of the paper, which is to clearly show the different temporal patterns in relation to location and (in some months) altitude. The anomalous behaviors detected at 3 of the 32 stations, namely Valpelline, Fossano, and Aonoch Mòr, can be attributed, respectively, to the facts that Valpelline is the lowest-elevation station in its group; Fossano is the southernmost of the Italian stations, with some sublittoral influence; and Aonoch Mòr has a large number of missing values. In conclusion, these results improve our understanding of temperature spatio-temporal dynamics in two very different regions of Europe and emphasize the importance of consistent analysis of data to assess the ongoing effects of climate change. The intra-annual time patterns of temperature trends could also be compared with climate model results.

Processes Driving the Intermodel Spread of the Southern Hemisphere Hadley Circulation Expansion in CMIP6 Models

AbstractThe Hadley circulation (HC) has been expanding poleward in recent decades. The Coupled Model Intercomparison Project Phase 6 (CMIP6) models predict that the expansion will accelerate in the future, more so in the Southern Hemisphere (SH). However, the extent of the expansion varies widely among the models. We investigate the mechanisms driving the intermodel spread in SH HC expansion predictions. The intermodel spread is obtained by an empirical orthogonal function analysis on the SH HC trend patterns of 16 CMIP6 model simulations using the historical and shared socioeconomic pathway 5–8.5 scenarios. The leading mode, showing a mean meridional stream function anomaly at the poleward SH HC extent, explains 49.73% of the variance and significantly correlates (r = 0.94) with the SH HC expansion. By analyzing the extended Kuo‐Eliassen equation, we find that the intermodel difference in the representation of diabatic heating is responsible for about 14% of the intermodel spread. The meridional eddy momentum and heat fluxes contribute to about 21% and 18% of the intermodel spread, respectively. The models simulating a relatively large SH HC expansion tend to show increased precipitation in the Southern Pacific Convergence Zone, reduced baroclinic instability in the subtropics, and an enhanced poleward shift of jet stream in the midlatitudes. This suggests that the uncertainty in the HC projection may be constrained by reducing the bias in the trend of the mean fields.

Bibliometric Analysis of Spline Regression Model for Trend Mapping and Strategy Development Research Using Vosviewer

Bibliometric analysis is a quantitative method used to measure and analyze scientific literature. This technique involves the collection and analysis of publication data, such as journal articles, books, and conferences, to evaluate and understand patterns of scientific communication, research productivity, author collaboration, journal impact, and trends within a scientific field. Bibliometric analysis has a limitation in that it is challenging to visualize effectively, necessitating the use of software for proper visualization. Therefore, this article discusses bibliometric analysis on spline regression for trend mapping and strategy development using VOSviewer software. The research results show that the visualization of spline regression keywords with VOSviewer can help in understanding patterns of relationships between variables, research trends, and network structures in scientific literature. Based on the analysis results, bibliometric analysis on spline regression can be visualized in trend mapping, which can aid in planning further research strategies, including identifying collaboration opportunities and underexplored research areas.

Temporal trends of aortic valve replacement modalities by age group in the United-States 2015 - 2023

Abstract Background/Introduction Transcatheter aortic valve implantation (TAVI) has led to a paradigm shift in treating aortic stenosis (AS) across patients of all surgical-risk categories. To guide the clinical decision, the ACC/AHA guidelines recommended an age group classification; with SAVR being recommended in patients < 65 years, and TAVI being favored over SAVR in patients > 80 years. In patients between 65-80 years, both modalities are to be considered after shared decision making. The impact of those guidelines-derived age group classification on the contemporary AVR practice remains of interest and is dynamically changing. Purpose This study aims to examine the temporal trends in the modality of AVR (TAVI vs SAVR) in the U.S. among different age groups using a contemporary nationwide database. Methods We conducted a retrospective study using de-identified data from the Vizient Clinical Database, which gathers patient and hospital data from more than 1000 medical Centre. We analysed TAVI and SAVR procedures from October 1, 2015, to November 30, 2023, excluding patients under 18. We examined the national trends of TAVI vs SAVR in 3 age groups (<65, 65-80, >80 years) with AS. Trend analysis was performed using linear regression. This study was deemed exempt from the requirements of 45 CFR 46.104(d) by The Miriam Hospital Institutional Review Board given that it involved de-identified data. Results During the study period, 467,832 underwent AVR (TAVI n= 252,834, SAVR n= 214,998). The patient and hospital characteristics by age group are summarized in the Table. We observed a significant rise in TAVI adoption from 33.5% in 2015 to 59.96% in 2023. However, the pattern of temporal trends varied among different age groups. In patients > 80 years, TAVI showed a steep rise from 69.95% in 2015 to 92.96% in 2023 (p<0.002). The 65-80 years age cohort demonstrated the most pronounced growth in TAVI, rising from 26.23% in 2015 to about 60% in 2020 (p.001), after which a plateau in percentage was observed. TAVI in patients <65 years has also been increasingly adopted, rising from 7.18% in 2015 to 19.12% in 2020 (p<0.002), however, followed with a slight downtrend to 16.36% in 2023 (Figure). Conclusion In this most contemporary analysis from the U.S., we demonstrate an increase in the adoption of TAVI across all age groups over the study period, with the steepest rise observed in patients between 65-80 years of age. Since the 2020 ACC/AHA guidelines, there has been a gradual downtrend in the utilisation of TAVI in patients <65 years of age indicating a possible change in practice based on scientific societal recommendations. The stabilization in TAVI percentage in the age groups 65-80 years and > 80 years in the last few years may hint towards approaching equilibrium between TAVI and SAVR for these age groups.Proportions of AVR across age groups

Establishing Climate Change on Rainfall Trend, Variation and Change Point Pattern in Umuahia, Nigeria

This study investigates the prevailing conditions of changing climatic trends and change point dates in Umuahia, Nigeria, using daily annual maximum series (AMS) rainfall data spanning 31 years. The Indian Meteorological Department (IMD) was employed to downscale the time series data into durations ranging from 5 minutes to 24 hours. Mann-Kendall (MK) trend and Sen's Slope Estimator (SSE) tests revealed a statistically significant increasing trend across all durations. The Sen's Slope magnitude ranged from 0.1373 mm/year for 5-minute duration to 0.9045 mm/year for 24-hour duration, corresponding to a variation rate of 9.05 mm/decade for the 24-hour duration. Trend change-point analysis, utilizing Distribution-free CUSUM and Sequential Mann-Kendall tests, identified 2002 as the initial rainfall change-point date. These results provide robust evidence of positive changing climatic conditions for rainfall in Umuahia, indicating that non-stationary Intensity-Duration-Frequency (IDF) modelling may be more appropriate for flood risk management and urban planning in this area.

Temporal trends in weight-based disparities in physical fitness of children: 30-years of continuous surveillance through Slovenian national system

BackgroundObesity in childhood has deleterious consequences for health while improving physical fitness can significantly reduce health risks related to high body mass index. We aimed to examine the evolution of disparities in physical fitness based on weight status among 7–15-year-olds in Slovenia between 1989 and 2019 and compare these trends across sex, age and socioeconomic status.MethodsWe used annual data collected within the SLOfit monitoring system in the period between 1989 and 2019, totalling 4,256,930 participants (about 137 000 per year). We examined cardiorespiratory fitness (600-m run test), muscular fitness (60-s sit-ups, bent arm hang, and standing broad jump test) and skill-related fitness (backwards obstacle course, 60-m dash, arm plate tapping). We grouped children according to the IOTF cut-offs for BMI to those living with normal weight or excess weight and estimated changes in physical fitness over time by fitting quantile regression models separately by sex and age group, and then using segmented regression to identify the patterns of trends over time.ResultsWeight-based disparities in physical fitness were large in 1989 and have further increased by 2019. The increase in disparities was generally around 5 percentiles larger in boys, and 10–15-year-olds compared to younger children. It was particularly pronounced for body core strength and speed in boys (up to 15 and 19 percentiles, respectively) and upper body strength and speed among girls (up to 13 percentiles). Most of the increase in disparities in health-related fitness accumulated during the 2010s, when the fitness of children generally improved, but much less so in children living with excess weight.ConclusionsDespite recent improvements in population fitness levels, children with excess weight seem to be left behind, which adds to existing health inequalities. Our results should encourage policymakers to redesign policies aimed at promoting physical activity and enhancing physical fitness to make them more equitable and ultimately lead to reducing inequalities in fitness.

Differential responses to weather and land‐cover conditions explain spatial variation in winter abundance trends in a migratory bird of conservation concern

Abstract Effective conservation strategies for animal populations require knowledge of relationships between population dynamics and their environmental drivers. However, these processes often vary within animal populations, requiring site‐specific conservation planning. Given limited financial resources, identifying groups of sites with similar population dynamics can help practitioners efficiently implement conservation programs to larger areas. We evaluated spatial patterns and environmental drivers of wintering site trends in a migratory bird of conservation concern, the Greenland white‐fronted goose (Anser albifrons flavirostris). We used latent class analysis to identify trend patterns in 35 years of abundance data among 59 geographically discrete Greenland white‐fronted goose wintering sites. We developed a state‐space abundance model in a Bayesian framework to quantify the effects of weather and land‐cover conditions experienced throughout spring migration, summer breeding, autumn migration and wintering periods on variation in wintering site abundance. We identified two main patterns in Greenland white‐fronted goose abundance trends: northeastern wintering sites declined on average by 3% per year, while southwestern wintering sites declined on average by 14% per year. Differential responses to weather and habitat conditions likely explained variation among groups, as geese at southwestern wintering sites were more negatively affected by harsh weather conditions (e.g. low temperatures and high precipitation on breeding areas) and poor habitat conditions (i.e. low‐quality grasslands and croplands) on wintering areas. Future conservation efforts to improve the suitability and nutritional quality of agricultural areas, especially cereal croplands in autumn and early winter and grasslands in late winter and early spring, could potentially improve local habitat conditions, especially in the southwestern wintering sites where abundance declines were steepest. Synthesis and applications. We demonstrate the potential to delineate animal populations based on spatial patterns in population dynamics using long‐term abundance monitoring data, which are commonly the only available data for conservation practitioners. By grouping sites based on spatial patterns in local abundance trends, we can further test hypotheses about how these groups are differentially affected by changing environmental conditions. This information is important for informing efficient conservation planning over large areas when financial resources are limited.

Incidence of Self-Reported Bothersome Tinnitus Versus Tinnitus Diagnosis Among U.S. Army Soldiers.

U.S. Army Soldiers undergo annual audiometric surveillance to facilitate prevention of auditory injury. Soldiers are screened for bothersome tinnitus and subsequently referred for a clinical audiological evaluation as warranted. Presumably, most Soldiers seen in the clinic for bothersome tinnitus receive a tinnitus diagnosis. The incidence of self-reported bothersome tinnitus at the time of annual audiometric surveillance and subsequent diagnosis of tinnitus in the medical record has not been examined to date. This study estimated the incidence of tinnitus diagnosis in a population of Soldiers who reported new onset bothersome tinnitus and explored trends associated with tinnitus diagnosis to refine hearing health education. A subset of hearing conservation records retrieved for a previous study were selected based on change in self-reporting of bothersome tinnitus. Corresponding medical records were retrieved for this sample of Active Duty (AD) Soldiers. The data were retrospectively examined, and analyses were conducted to identify statistically significant differences between Soldiers with and Soldiers without a tinnitus diagnosis. In addition, trend patterns of bothersome tinnitus and significant threshold shift (STS) were examined. Of the sample of 730,350 AD Soldiers, 16.7% self-reported new onset bothersome tinnitus and 7.1% of those recorded as having bothersome tinnitus had a diagnosis of tinnitus in their medical record. Soldiers with a tinnitus diagnosis were older and had higher pure-tone averages than Soldiers without a tinnitus diagnosis. Counterintuitively, overall trend percentages of bothersome tinnitus increased per calendar year, while the percentages of STS decreased throughout the study period. The rate of tinnitus diagnosis in the medical record is vastly lower than the prevalence of bothersome tinnitus recorded in the annual surveillance system. Steps should be taken to screen, refer, and diagnose tinnitus more consistently. Based on the outcome of the study analysis, we recommend that screening for bothersome tinnitus more closely follows procedure recommendations from the Department of Veterans Affairs and Department of Defense (VA/DOD) Tinnitus Working Group, which may reduce the rate at which Soldiers report bothersome tinnitus.

Mapping the Evolution of Digital Health Research: Bibliometric Overview of Research Hotspots, Trends, and Collaboration of Publications in JMIR (1999-2024).

While bibliometric studies of individual journals have been conducted, to the best of our knowledge, bibliometric mapping has not yet been utilized to analyze the literature published by the Journal of Medical Internet Research (JMIR). In celebration of the journal's 25th anniversary, this study aimed to review the entire collection of JMIR publications from 1999 to 2024 and provide a comprehensive overview of the main publication characteristics. This study included papers published in JMIR during the 25-year period from 1999 to 2024. The data were analyzed using CiteSpace, VOSviewer, and the "Bibliometrix" package in R. Through descriptive bibliometrics, we examined the dynamics and trend patterns of JMIR literature production and identified the most prolific authors, papers, institutions, and countries. Bibliometric maps were used to visualize the content of published articles and to identify the most prominent research terms and topics, along with their evolution. A bibliometric network map was constructed to determine the hot research topics over the past 25 years. This study revealed positive trends in literature production, with both the total number of publications and the average number of citations increasing over the years. And the global COVID-19 pandemic induced an explosive rise in the number of publications in JMIR. The most productive institutions were predominantly from the United States, which ranked highest in successful publications within the journal. The editor-in-chief of JMIR was identified as a pioneer in this field. The thematic analysis indicated that the most prolific topics aligned with the primary aims and scope of the journal. Currently and in the foreseeable future, the main themes of JMIR include "artificial intelligence," "patient empowerment," and "victimization." This bibliometric study highlighted significant contributions to digital health by identifying key research trends, themes, influential authors, and collaborations. The findings underscore the necessity to enhance publications from developing countries, improve gender diversity among authors, and expand the range of research topics explored in the journal.

The generalized STAR modelling with three-dimensional of spatial weight matrix in predicting the Indonesia peatland’s water level

The release rate of CO2 gas can be influenced by peatlands’ physical properties, such as water level and soil moisture, and rainfall. To anticipate the unstable condition which is when the peatland emit more carbon, we developed the Generalized Space Time Autoregressive (GSTAR) model in predicting these physical properties for the following weeks. As the innovation in modelling, the spatial weight matrix was based on three-dimensional coordinates with a modification on the height factor. The data we used are real-time data of water level on the peatlands in Pulang Pisau Regency, Central Kalimantan Province from 20 February 2021 to 18 March 2023. We then used Ordinary Kriging interpolation on the prediction results to create contour maps on different dates. There were empty data on several dates, especially from 24 March until 3 August 2022. To fill the empty data, we used linear interpolation and then we added white noise to the interpolation results. From the data, the water level has a downward trend pattern from around November to September and an upward trend pattern from October to November. Furthermore, we found that the best model for water level was GSTAR (2;0.1) with a modified matrix a=0.1\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$a=0.1$$\\end{document} and b=1.1\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$b=1.1$$\\end{document}. Based on the predicted water level, there is a risk of changes in the properties of the peatlands in several areas in Pulang Pisau Regency.

ADDITIVE HOLT-WINTERS METHOD FOR FORECASTING GROSS REGIONAL DOMESTIC PRODUCT AT CONSTANT PRICES OF EXPENDITURE OF WEST SUMATRA

Regional disparities often pose significant challenges, with some areas experiencing rapid economic growth while others lag behind. An essential macro benchmark to gauge the success of development initiatives is economic growth, as indicated by changes in a region's Gross Regional Domestic Product (GRDP). Using GRDP at constant prices (GRDP CP) helps eliminate the impact of price fluctuations, focusing on real increases in production activities. Expenditure GRDP reflects the value of goods and services produced within a region and consumed by the community. In the case of West Sumatra, the GRDP CP expenditure data reveals simultaneous seasonal and trend elements. The seasonal pattern, occurring quarterly each year, exhibits an additive seasonal effect. The Additive Holt-Winters method has been proven effective for data containing seasonal patterns with constant seasonal variation (additive) and linear trends, where the level, trend, and seasonal pattern can change. The data used is secondary data of GRDP CP of expenditure quarterly of West Sumatra in 2010 - 2022 obtained from the official website of the Indonesian Central Bureau of Statistics. According to research findings, the GRDP CP expenditure with a Mean Absolute Percentage Error (MAPE) value of 1.03% for quarter I to quarter IV in 2023 are Rp46,284,010.59, Rp46,472,223.99, Rp47,512,197.79, and Rp48,445,184.94, respectively. This suggests that the model equation performs exceptionally well predicting future economic trends.

Analyzing Public Interest in Geohazards Using Google Trends Data

This study investigates public interest in geological disasters by analyzing Google Trends data from 2023. This research focuses on earthquakes, hurricanes, floods, tornadoes, and tsunamis to understand how search behaviors reflect public awareness and concern. This study identifies temporal and geographical patterns in search trends. Key findings reveal that public interest spikes during significant disaster events, such as the February 2023 earthquake in Turkey and Syria and the August 2023 hurricanes in the United States. This study highlights the importance of timely and accurate information dissemination for disaster preparedness and response. Google Trends proves to be a valuable tool for monitoring public interest, offering real-time insights that can enhance disaster management strategies and improve community resilience. This study’s insights are essential for policymakers, disaster management agencies, and educational efforts aimed at mitigating the impacts of natural disasters.

Navigating global horizons: a review of export promotion and foreign market entry strategies for small and medium enterprises (SMEs)

PurposeThis study aims to conduct a systematic review of the existing literature on export promotion and foreign market entry strategies for small and medium enterprises (SMEs).Design/methodology/approachThis study incorporates systematic literature review by using bibliometric technique and content analysis of 195 scholarly papers published between 2000 and 2024 obtained from Scopus database, using the PRISMA principles and a methodological framework.FindingsThe use of quantitative analysis has shown dynamic patterns in publication trends, characterized by notable increases in recent years. These trends align with the changing dynamics of the global economy and shifting priorities in international commerce. The study reveals thematic frameworks using author collaboration analysis, word cloud visualizations and a thematic map, providing insights into a significant motor, niche and developing issues that shape the discussion on the international growth of SMEs.Research limitations/implicationsIt offers significant insights that can benefit academics, policymakers and practitioners who are interested in understanding and navigating the intricate terrain of export promotion and market access strategies specifically tailored for small firms.Originality/valueTo the best of the authors’ knowledge, the study is the first of a kind that presents a systematic examination of the existing body of knowledge on the internationalization of SMEs.

Disaster Risk Assessment and Analysis of Physical Infrastructure: A Comprehensive Review of Scientific Methods and Techniques

Physical infrastructure plays a crucial role in fostering economic development and societal well-being. However, they are susceptible to potential impacts when exposed to various hazards, which makes risk assessment and analysis essential for ensuring resilience. This paper presents a comprehensive review of the extant literature to provide an in-depth understanding of the research landscape developed to assess and analyze disaster risk to physical infrastructure. The objectives of this study are 1) to comprehend the disaster risk assessment methods and techniques from the global literature, 2) to analyze the trend and growth pattern of research on disaster risk assessment and 3) to identify the nuances in the methods for assessing disaster risk. This study employs bibliometric analysis along with a comprehensive literature review of selected papers extracted from the Scopus database. The study realized that research focusing on disaster risk assessment is scarce in developing countries at risk, especially India, Bangladesh, China, and Pakistan. Existing studies have attempted to develop conceptual as well as theoretical frameworks for risk assessment, and also employed assessment methods such as digital twins, risk indices, risk curves, and the like. Furthermore, the study realized that a higher emphasis on assessing the risk of transportation infrastructure disregards the other major physical infrastructure. The bibliometric analysis further indicates the need for a comprehensive and robust risk analysis tool for physical infrastructure with a standardized quantitative analysis model that can analyze the impacts of hazards. Deep learning coupled with GIS and remote sensing techniques can be considered a promising approach for risk assessment in future research. In conclusion, this comprehensive bibliometric analysis provides a detailed overview of the research landscape of physical infrastructure risk assessment and analysis. The insights derived from this analysis can help to identify the research gap in the disaster risk assessment globally highlighting the potential methods and techniques for the same.

Mapping the scientific landscape and evolution of the international journal of surgery: A scientometric analysis (2004-2024).

The purpose of this study was to analyze the publication characteristics and development of the International Journal of Surgery over its 20-year history. This study included articles published in IJS during the 20 years from 2004 to 2024. The data were analyzed using the Citespace, VOS viewer, and the 'Bibliometrix' package in R software. We studied the dynamics and trend patterns of IJS literature production through descriptive bibliometrics and identified the most prolific authors, publications, institutions, and countries. Bibliometric maps were utilized to visualize published articles' content and identify the most prolific research terms and topics in IJS, as well as their evolution over time. A total of 5964 publications in the IJS from 2004 to 2023 and 356 publications in 2024 were included in the analysis. The study revealed a positive trend in literature production, although the number of articles published in IJS has slightly decreased recently. The most productive country was China (n=1,211), the most productive institution was Sichuan University (n=151), and the most prolific author was Wang Yang (n=87). The top 15 most cited articles focus primarily on research reporting standard guidelines for surgical studies. Research published in IJS mainly targeted middle-aged and older adults, emphasizing postoperative complications and treatment outcomes. The future research focus in IJS might center on integrating artificial intelligence and deep learning technologies to revolutionize surgical research and practice. The International Journal of Surgery significantly contributes to advances in surgical research. IJS prioritized enhancing patient outcomes and advancing surgical techniques, focusing on middle-aged and older adults, postoperative complications, and treatment outcomes. The journal emphasized robust evidence through retrospective, controlled, and cohort surgical studies. Integrating artificial intelligence and deep learning represented a significant frontier poised to revolutionize surgical care, shaping the future landscape of research and practice.

Metropolization process in the rural settlement system of the Central Chernozem region of Russia

The Central Chernozem region of Russia has undergone significant changes in socio-demographic processes over the past half-century. The aim of this study was to establish the impact of metropolization on the demographic transformation of sparsely populated rural settlements for the Central Chernozem region and to develop a demographic forecast for 2030. The study’s goals were to (1) determine the scale of regional metropolises; (2) identify the impact of metropolization on the quantitative indicators of sparsely populated rural settlements; (3) establish trends in their transformation; and (4) compile a medium-term demographic forecast for the Central Chernozem region. The set objectives were achieved by integrating methods for spatial-temporal and comparative geographical analysis, statistical methods, and a combination of official statistics with sociological methods. Analysis of the level of urbanization of the regions in modern conditions (20th and 21st centuries) showed that, although this process was and remained below the national average, it did not prevent the formation of regional metropolises (Belgorod, Stary Oskol, Kursk, Voronezh, Lipetsk, Tambov) as an obvious outcome of urbanization processes. A regional pattern of degradation of the network of rural settlements, mainly the smallest and small in terms of population (1-25, 26-50 people), which make up over one third of all settlements in the Central Chernozem region of Russia, was determined. A stable long-term trend of growth in the number of abandoned villages and hamlets was established. Between the last two All-Russian population censuses (11 years), their number increased by 45%. The forecasting results obtained confirm the established patterns in the medium-term trend of “compression” of rural settlement around regional metropolis (with some exceptions). The medium-term demographic forecast showed that population concentration in metropolitan areas and further socio-demographic desertification in peripheral areas will continue with growing territorial differentiation. The obtained results of the study of the transformation of settlement systems of the Central Chernozem region as a result of the metropolization of regional centers can help government institutions in strategic planning of territorial development.