Term Trends Research Articles

Unlocking the Green Growth Puzzle: Exploring the Nexus of Renewable Energy, CO2 Emissions, and Economic Prosperity in G7 Countries

The primary aim of this research endeavor is to elucidate the intricate dynamics among renewable energy utilization, CO2 emissions, and the trajectory of economic growth within the framework of G7 Countries over the extensive timeframe from 1997 to 2021. Through the meticulous application of cointegration analysis and the Panel Vector Error Correction Model, our empirical investigation unveils a substantiated positive impact of the utilization of renewable energy sources and CO2 emissions on economic growth trends in the long term. Nevertheless, our comprehensive analysis suggests a notable absence of any observable relationship between renewable energy adoption, CO2 emissions, and short-term economic growth dynamics. In light of our findings, it is recommended that policymakers focus on fostering sustainable long-term strategies for renewable energy adoption and CO2 emissions reduction, while also acknowledging the limited short-term impact on economic growth dynamics.

Mapping the knowledge of omics in myocardial infarction: A scientometric analysis in R Studio, VOSviewer, Citespace, and SciMAT.

Many researchers nowadays choose multi-omics techniques for myocardial infarction studies. However, there's yet to be a review article integrating myocardial infarction multi-omics. Hence, this study adopts the popular bibliometrics. Based on its principles, we use software like R Studio, Vosviewer, Citespace, and SciMAT to analyze literature data of myocardial infarction omics research (1991-2022) from Web of Science. By extracting key information and calculating weights, we conduct analyses from 4 aspects: Collaboration Network Analysis, Co-word Analysis, Citing and Cited Journal Analysis, and Co-citation and Clustering Analysis, aiming to understand the field's cooperation, research topic evolution, and knowledge flow. The results show that myocardial infarction omics research is still in its early stage with limited international cooperation. In terms of knowledge flow, there's no significant difference within the discipline, but non-biomedical disciplines have joined, indicating an interdisciplinary integration trend. In the overall research field, genomics remains the main topic with many breakthroughs identifying susceptibility sites. Meanwhile, other omics fields like lipidomics and proteomics are also progressing, clarifying the pathogenesis. The cooperation details in this article enable researchers to connect with others, facilitating their research. The evolution trend of subject terms helps them set goals and directions, quickly grasp the development context, and read relevant literature. Journal analysis offers submission suggestions, and the analysis of research base and frontier provides references for the research's future development.

Comparing the Influences on NO2 Changes in Terms of Inter-Annual and Seasonal Variations in Different Regions of China: Meteorological and Anthropogenic Contributions

NO2 primarily originates from natural and anthropogenic emissions. Given China’s vast territory and significant differences in topography and meteorological conditions, a detailed understanding of the impacts of weather and human emissions in different regions is essential. This study employs Kolmogorov–Zurbenko (KZ) filtering and stepwise multiple linear regression to isolate the effects of meteorological conditions on tropospheric NO2 vertical column densities. Long term trends indicate an overall decline, with anthropogenic contribution rates exceeding 90% in Shanghai, Changchun, Urumqi, Shijiazhuang, and Wuhan, where interannual variations are primarily driven by human emissions. In Guangzhou, the anthropogenic contribution rate exceeds 100%, highlighting the significant impact of human factors in this region, although meteorological conditions somewhat mitigate their effect on NO2. In Chengdu, meteorological factors also play a role. Seasonal variations display a U-shaped trend, and there are significant differences in the impact of meteorological factors on seasonal variations among different regions. Meteorological contribution rates in Changchun and Chengdu are below 36.90% and anthropogenic contributions exceed 63.10%. This indicates that changes in NO2 are less influenced by meteorological factors than by human activities, with human emissions dominating. In other regions, meteorological contributions are greater than those from human activities.

CURRENT ISSUES OF TERM FORMATION IN THE MODERN KAZAKH LANGUAGE

The article is devoted to the current issues of Kazakh Terminology formation after the country gained independence. The use of Kazakh words as terms has a positive effect on the formation of Kazakh scientific speech. Nevertheless, new problems in the practice of terminology formation in the Kazakh Language and the need to solve them are revealed. The article describes the main reasons for changes in Kazakh Terminology, considers the problems of the term and new trends in modern terminology. The main goal of the article is to present to the scientific community the ways that can have a positive impact on the systematization and unification of the term, clarifying the current problems of terminology in the Kazakh Language. The scientific novelty of the article is expressed in the identification of new trends in the practice of terminology. From a new point of view, such issues as competence, variability of terms, translation, difficulties in terminology and determining their place in the systematization of Kazakh Terminology are considered. The article also considers the ability to determine the main provisions in the process of term formation in the modern Language, find rational approaches taking into account the nature of the development of the current Language situation and identify the optimality of views.

Dissolved oxygen criteria attainment in Chesapeake Bay: Where has it improved since 1985?

Dissolved oxygen criteria attainment in Chesapeake Bay: Where has it improved since 1985?

Does anyone live here? Mine closures and depopulation in Spanish coal mining areas

One of the most pressing socio-economic issues across EU countries has been the depopulation of a significant part of its territory. Less urbanized areas are perceived as non-attractive places to live and have been losing population steadily in the latest decades. For the case of Spain, this European-wise phenomenon has been exacerbated for several territories characterized by a large presence of primary and extractive industries in the past. We quantify empirically the contribution that the closure of the heavily subsidized coal mining had on the depopulation trends experienced in mining-intensive areas in Spain. This poses an interesting research question, since both non-mining and mining territories in Spain suffered a remarkable negative down trend in demographic terms since early nineties, which was the period on which the coal mining industry started to cease steadily its activity. Our empirical strategy relies on matching estimators that compare the demographic trend across mining-intensive and non-mining intensive municipalities in four provinces, controlling for observable characteristics and isolating the net effect of the “shock” originated by the termination of this mining activities. Our analysis finds a statistically significant and sizable negative effect on the fall of population for mining-intensive municipalities between 1991 and 2011.

Time Series Forecasting using RNN

Time series forecasting is essential across various fields such as finance, economics, meteorology, and healthcare, where accurate predictions are crucial for effective decision- making. Traditional statistical methods often fall short in capturing the intricate patterns and long- term dependencies inherent in time series data, limiting their practical applicability. This research investigates the application of Long Short-Term Memory (LSTM) networks, a type of Recurrent Neural Network (RNN), to enhance forecasting accuracy. LSTMs are particularly well-suited for this task due to their ability to process and retain information over extended sequences, allowing them to capture complex temporal relationships that conventional methods might overlook. This work employs a comprehensive approach that includes advanced data preprocessing, feature engineering, and model architecture design, combined with meticulous hyperparameter tuning to optimize performance. The effectiveness of the LSTM- based approach is evaluated using the M4 competition dataset, which is widely recognized for its complexity and diversity in time series data. The results demonstrate that the optimized LSTM model consistently outperforms traditional statistical methods. Specifically, it shows superior accuracy in capturing both short-term fluctuations and long- term trends. Performance metrics, including Mean Absolute Scaled Error (MASE) and Symmetric Mean Absolute Percentage Error (sMAPE), reveal significantly lower error rates with the LSTM model compared to conventional approaches. The LSTM model’s proficiency in predicting long-term trends further highlights its effectiveness in practical time series forecasting scenarios. Overall, LSTMs prove to be a robust tool for time series forecasting, offering substantial improvements over traditional methods by effectively leveraging long-term dependencies in data. These models are especially valuable in fields like finance, economics, and healthcare. Future studies could aim to further optimize LSTM models and explore their application to other complex datasets, pushing the boundaries of this field even further.

Mortality related to different circulatory diseases: a multiple causes of death analysis, 2008-2022

Abstract Background Mortality from circulatory diseases significantly increased in 2020 compared to pre-pandemic years in many countries. However, data were mostly limited to the underlying cause of death and to the first phases of the pandemic. Analyses of any mention of a disease in death certificates (multiple causes of death approach, MCOD) are more robust to changes in coding rules/practices and to the role of COVID-19 as a competing cause of death. Methods Mortality records were extracted from 2008 to 2022 among residents of the Veneto Region (Northeastern Italy, 4.9 million population). Based on MCOD, age-standardized mortality rates (2013 European standard) were computed for ischemic heart diseases (IHD), cerebrovascular diseases (CVD), atrial fibrillation (AF), and hypertensive diseases (HD). The annual percent change (APC) was estimated through the pre-pandemic period (2008-2019), and change in rates during pandemic years were computed compared to 2019. Results Before the pandemic, rates were steeply declining for IHD (APC -5.1%; 95%CI -5.5, -4.6) and CVD (-4.0%; -4.4, -3.5); mortality related to HD reduced at a slower pace (-1.9%; -2.3, -1.4), whereas AF-related mortality was increasing (+1.0%; 0.1, 1.8). During the first year of the pandemic, the growth in mortality compared to 2019 was +26% for HD, +18% for AF, +13% for CVD and +12% for IHD. In 2021-2022, rates for CVD and IHD returned to pre-pandemic levels; rates for HD reduced with respect to 2020 but remained above the baseline; AF-related mortality was still increasing. Conclusions The pandemic differentially impacted mortality associated to different circulatory diseases, depending on pre-existing long-term trends (increasing for AF) and on susceptibility to severe COVID-19 disease (higher for HD). Continuous surveillance based on MCOD is warranted to properly assess changes in mortality associated to specific circulatory diseases after the end of the pandemic. Key messages • Multiple causes of death analyses are warranted to assess how the pandemic affected pre-existing long term trends in cause-specific mortality. • Mortality related to different circulatory diseases increased in 2020, but pre-pandemic trends, the extent of the increase in 2020, and further changes observed in 2021-2022 largely diverged.

Long-term trends in abundance and potential drivers for eight species of coastal birds in the U.S. South Atlantic

Long-term trends in abundance and potential drivers for eight species of coastal birds in the U.S. South Atlantic

Variability Effects of Meteorological Factors on Reported Malaria Cases in Kano and Lagos States of Nigeria

Abstract: Malaria is one of life’s threatening disease that contributed significantly to morbidity and mortality in Nigeria. The variability effects of meteorological factors between the Northern and Southern region of Nigeria on malaria transmission remain unclear and not well researched. This research was aimed at comparing the non-linear effects of seasonal, trend and meteorological variables on monthly malaria cases in Kano and Lagos State of Nigeria. The monthly malaria cases for age group above 5-year for Kano and Lagos State were obtained from DHIS for the period of January, 2016 to December, 2022. Also, the predictors/meteorological variables (Rainfall, Relative Humidity, Minimum Temperature and Maximum Temperature) were obtained from Nigeria Meteorological Agent. Generalized Additive Model was fitted using Negative Binomial to apply smooth function to estimate and compare the effects of the selected predictors on the malaria transmission in Kano and Lagos States. Rainfall and Maximum temperature were found not significant in explaining the effect of malaria transmission. Malaria transmission was found to be seasonal and varying in Kano State with highest transmission between September and October while the seasonal effect was reduced to linear in Lagos State. The long term trend had a significant increasing effect on malaria transmission in Kano Sate compared to non-significant effect in Lagos State. The effect of Relative Humidity was reduced to linear and increasing in the both State. Paying attention to the variability in the influence of season, trend and meteorological variables on malaria transmission will enhance respective State Government to develop an early warning and awareness that will help in controlling the malaria outbreak in their State.

Electroacupuncture at Traditional Acupoints or Myofascial Trigger Points for Chronic Nonspecific Low Back Pain: High or Alternated Frequency? A Double-Blinded Randomized Controlled Trial.

Chronic nonspecific low back pain (cNLBP) can be effectively treated by electroacupuncture (EA) at traditional acupoints (TAPs) and myofascial trigger points (MTrPs). However, the optimal type and frequency of stimulation (alternated frequency [AF] and high frequency [HF]) remain unclear. This study aimed to explore this. A double-blinded randomized controlled trial was conducted with four treatment groups: EA using AF at MTrPs (MTP-AF group), HF at MTrPs (MTP-HF group), AF at TAPs (TAP-AF group), and HF at TAPs (TAP-HF), each with 40 middle-aged cNLBP patients. The AF was 2/100 Hz and HF was 100 Hz. Pain-visual analog scale (pain-VAS), paracetamol requirement, Oswestry disability index (ODI) score, global improvement, and adverse effects (AEs) were monitored. After 4 weeks, groups of EA targeting MTrPs and TAPs with the same frequency showed no significant differences. Groups using AF demonstrated significantly superior pain-VAS and ODI percentage score reductions compared with HF groups, extending at least 4 weeks post-EA. All groups showed consistent results in paracetamol use, global improvement, and safety. Subgroup analysis indicated that EA with AF at MTrPs provided better results in patients aged ≥60-65 years. EA at MTrPs and TAPs demonstrated similar effects on cNLBP. However, an AF proves more effective than an HF, potentially maintaining this trend in the short term. Older patients may respond better to EA at MTrPs with AF. Future studies may explore combined MTrPs and TAPs for cNBLP treatment with a broader age range and more diverse demographic groups.

The employment, retention and exit of public school teachers in New Brunswick, Canada: an analysis using linked administrative data

ObjectiveAs in many jurisdictions, New Brunswick (Canada) is facing increasing shortages of K-12 school teachers as retirements loom at the same time that the school age population of NB continues to exceed long term trends. The purpose of this retrospective study is to analyze the recruitment, retention and exit decisions of teachers in the NB public education system in order to support ongoing planning around teacher staffing. ApproachThe analysis uses a unique linked administrative data combining province-wide individual-level teacher employment data, immigration records, university graduation data and public health insurance registration on NB teachers and individuals who obtained a B.Ed. degree in NB. Data are accessed through the secure facilities of the NB Institute for Research, Data and Training. The analysis includes both descriptive statistics and econometric methods appropriate to the particular outcomes of interest. ResultsThe analysis will present results on three types of labour market transitions: 1) from NB university education to employment as a NB teacher. 2) exits from employment as a teacher, including both retirement and pre-retirement exits, and 3) decisions of ex-teachers about remaining in NB. The potential effects of a range of demographic, geographic and system-level factors on these outcomes are considered. Conclusions and ImplicationResults from the analysis of entry to and exit from K-12 teaching in NB will be vital to human resource planning for a province dealing with looming retirements, growing labour shortages and an increasing population. Future work will consider subject-specific teacher shortages in critical fields like STEM.

(245) ASSESSMENT OF PUBLIC INTEREST IN VASECTOMY AFTER DOBBS VS. JACKSON WOMEN’S HEALTH ORGANIZATION

(245) ASSESSMENT OF PUBLIC INTEREST IN VASECTOMY AFTER DOBBS VS. JACKSON WOMEN’S HEALTH ORGANIZATION

Toward Systematic Theology: Problems and Possibilities for Christian Theology in the Academy

This article comprises a set of reflections on the future of systematic theology in academic institutions. It identifies three long term trends that have been shaping the current institutional context of the field, and the ways theology has (partially) responded. The article next identifies developments in some of Christian theology’s more important academic conversation partners that are promising potential sources for its continued vitality. In the third part, theology’s future direction is generally indicated by asserting that successful theologians are going to be those who find ways to bridge the current cultural fault-line in the academic field. In the final parts, this general suggestion is made more specific by naming four developments in a future systematic theology (concerning the God–world relation, the interconnection of core doctrines, the anthropology of salvation, and the hermeneutic of scripture) that can meet the challenges and exploit the possibilities named in the first three parts.

China’s grand strategy within 21st-century megatrends

The main aim of this study is to identify possible components of China’s grand strategy and check how they can influence global megatrends. Based on a literature review, four grand strategy components are identified: 1) building up military power, 2) consolidating power at the peripheries and pushing for more control over disputed territories, 3) expanding economic influence through the Belt and Road Initiative, and 4) establishing new standards in digital technologies and promoting cyber sovereignty. These strategic tools can directly affect the following megatrends that are commonly mentioned in the literature: shift of economic power, emerging markets, new economic order; digitalisation; globalisation; deglobalization, trade reduction; diversity of governance, trust in the political system; and scarce resources. Using data taken from Google Trends it is shown that in the 2004-2023 period the trends of Chinese grand strategy terms correlate most with terms that represent the following three megatrends: digitalisation, globalisation, and scarce resources.

Long term relationships of electron density with solar activity

Greenhouse gases such as carbon dioxide and methane that are causing climate change may cause long term trends in the thermosphere and ionosphere. The paper aims to contribute to explore long term effects in the ionosphere focusing on the impact of solar activity changes. Peak electron density data derived from vertical sounding measurements covering 65 years at the ionosonde stations Juliusruh (JR055), Boulder (BC840) and Kokubunji (TO536), have been utilized to estimate the long-term behavior of daytime ionospheric F2 layer ionization in relation to the solar 10.7 cm radio flux index F10.7. In parallel, Global Navigation Satellite System (GNSS) based vertical total electron content (TEC) data over the ionosonde stations in combination with the peak electron density data have been used to derive the equivalent slab thickness τ for estimating long-term behavior in the time period 1996-2022. A new approach has been developed for deriving production and loss term proxies for studying long-term ionization effects from F2 layer peak electron density and TEC data. The derived coefficients allow estimating the long-term variation of atomic oxygen and molecular nitrogen concentrations including their ratio during winter months. The noon-time slab thickness values over Juliusruh correlate well with the decrease of F10.7 and the F2 layer peak height and enable estimating the neutral gas temperature. The equivalent slab thickness decreases by about 20 km per decade in the period 1996-2022, indicating a thermospheric cooling by about 100 K per decade for Juliusruh. Whereas the oxygen concentration decreases, the loss term, considered as a proxy for molecular components of the neutral gas, in particular N2, increases with the long-term solar activity variation. Considering 11 years averages of the production and loss terms under wintertime conditions, the long-term study reveals for the O/N2 ratio a percentage decrease of 5% per decade and for F10.7 about 3.1% per decade in a linear approach referred to the year 1970. Linear models of 11 years averaged NmF2 and foF2 from corresponding F10.7 show a very close correlation with the temporal variation of F10.7 until about 1990. The root mean square errors are in the order of 1.0 -1.3 ‧1010m-3 for NmF2 and 0.03-0.05 MHz for foF2. After 1990 the linear models clearly deviate from F10.7 at all selected ionosonde stations indicating a non-local effect.

Long Term Trends in Security Threats and an Approach for Integrating Them into System Architecture and Design

AbstractSecurity is an important consideration in today's systems, and it promises to be both relevant and challenging in the future. Our design and development processes are evolving to incorporate modern methods such as agile development and model‐based engineering with the intent to provide flexible and resilient capabilities to our customers quickly and with high quality. As we continue to enhance our practices to achieve these aspirations, security capabilities both at initial deployment and throughout the system lifespan are an important consideration. A literature review of security concerns in general and challenges across a range of domains including civil aviation, smart cities, eLearning, finance and defense reveals a set of recurring long‐term trends. These trends suggest challenges to security capabilities in the future. In this paper, we summarize and analyze these trends and propose an approach for addressing them in architecture and design activities.

Do Competition, Size, and Macro Variables Impact Bank Risk?

The paper investigates the correlation between bank competition, performance, and risk-taking behavior in MENA economies. Furthermore, it explores the risk level associated with dual banking as well as the bidirectional connectedness of competition and jeopardy in terms of size, GDP, and inflation rate. This work employs Generalised Method Moments (GMM) for regression findings and Two-Stage Least Squares (2SLS) for robustness tests. It uses an unbalanced panel dataset from 256 banks and 14 MENA countries from the year 2011 to 2022. According to the study’s findings, financial stability improves, and bank risk-taking decreases with less bank competition. The findings of this investigation corroborate the competition fragility theory. The competition square concept suggests that in a market with competition, risk will eventually rise. In terms of economic progression, risk-taking behavior is exactly the reverse of bank competition. Again, the finding shows that good performance reduces the bank’s risk, finally turning to greater financial stability. Furthermore, there is a substantial association between inflation rates and the extent of banks’ risk-taking activity. In a competitive market scenario, MENA countries inflation rate risk-taking behavior initially increases and then declines over time, while bank size exhibits a similar trend in the near term. When the inflation rate rises, banks’ risk-taking behavior initially decreases and then rises over time. This research will provide policymakers in MENA nations with a better grasp of how to establish strategies that benefit the banking environment.

Advancing Regional–Scale Spatio–Temporal Dynamics of FFCO2 Emissions in Great Bay Area

Estimating city–scale emissions using gridded inventories lacks direct, precise measurements, resulting in significant uncertainty. A Kalman filter integrates diverse, uncertain information sources to deliver a reliable, accurate estimate of the true system state. By leveraging multiple gridded inventories and a Kalman filter fusion method, we developed an optimal city–scale (3 km) FFCO2 emission product that incorporates quantified uncertainties and connects global–regional–city scales. Our findings reveal the following: (1) Kalman fusion post–reconstruction reduces estimate uncertainties for 2000–2014 and 2015–2021 to ±9.77% and ±11.39%, respectively, outperforming other inventories and improving accuracy to 73% compared to ODIAC and EDGAR (57%, 65%). (2) Long–term trends in the Greater Bay Area (GBA) show an upward trajectory, with a 2.8% rise during the global financial crisis and a −0.19% decline during the COVID-19 pandemic. Spatial analysis uncovers a “core–subcore–periphery” emission pattern. (3) The core city GZ consistently contributes the largest emissions, followed by DG as the second–largest emitter, and HK as the seventh–highest emitter. Factors influencing the center–shift of the pattern include the urban form of cities, population migration, GDP contribution, but not electricity consumption. The reconstructed method and product offer a reliable solution for the lack of directly observed emissions, enhancing decision–making accuracy for policymakers.

Predicting Future Birth Rates with the Use of an Adaptive Machine Learning Algorithm: A Forecasting Experiment for Scotland.

The total fertility rate is influenced over an extended period of time by shifts in population socioeconomic characteristics and attitudes and values. However, it may be impacted by macroeconomic trends in the short term, although these effects are likely to be minimal when fertility is low. With the objective of forecasting monthly deliveries, this study concentrates on the analysis of registered births in Scotland. Through this approach, we examine the significance of precisely forecasting fertility trends, which can subsequently aid in the anticipation of demand in diverse sectors by allowing policymakers to anticipate changes in population dynamics and customize policies to tackle emerging demographic challenges. Consequently, this has implications for fiscal stability, national economic accounts and the environment. In conducting our analysis, we incorporated non-linear machine learning methods alongside traditional statistical approaches to forecast monthly births in an out-of-sample exercise that occurs one step in advance. The outcomes underscore the efficacy of machine learning in generating precise predictions within this particular domain. In sum, this research will comprehensively demonstrate a cutting-edge model of machine learning that utilizes several attributes to assist in clinical decision-making, predict potential complications during pregnancy and choose the appropriate delivery method, as well as help in medical diagnosis and treatment.