We performed a literature review to identify articles regarding the state of urological cancers in low-to-middle income countries (LMIC). The challenges that LMIC face are multifactorial and can include, poor health education, inadequate screening, as well as limited access to treatment options and trained urologists. Many of the gold standard treatments in high income countries (HIC) are scarce in LMIC due to their poor socioeconomic status leading to advanced stage of disease at diagnosis and ultimately a higher mortality rate. These standards of care are vital components of oncological disease management, however, the current and sparse literature available from LMIC indicate that there are many obstacles delaying early diagnosis and management options in LMIC. In the era of evolving medical diagnosis and treatments, sufficient data must be gathered and understood in order to provide appropriate diagnostic and treatment options to curtail rising mortality rates, and therefore help to alleviate the bur-den in LMIC.

The aim of this study was to describe the trend of traffic accident mortality and social inequalities in Ecuador for the period 2011-2020, distributed by year, gender, age group, geographic location, and type of accident, alongside social disparities. An ecological approach was used to evaluate traffic accident mortality data. The average traffic accident mortality rate in the country during this period was 19.1 per 100,000 inhabitants, with rates being significantly higher in males (31.3) than in females (7.2). The highest mortality rate was recorded in 2011, predominantly impacting the 60 and older age group, the Santo Domingo de los Tsáchilas province, and the Amazon region. The study also noted a declining trend in these rates over the years, with an Annual Percentage Variation of -1.2%. A notable finding was the decrease in traffic accident mortality inequality based on per capita income in 2019, compared to 2011, which rose by 247.7%, whereas the inequality based on illiteracy rates decreased by 18.9% from 2014 to 2019. These findings highlight the significant decrease in traffic accident mortality rates across Ecuador, though marked disparities remain evident across different genders, age groups, and provinces.

Foxtail millet (Setaria italica) is an important cereal crop with rich nutritional value. Distinctness, Uniformity, and Stability (DUS) are the prerequisites for the application of new variety rights for foxtail millet. In this study, we investigated 32 DUS test characteristics of 183 foxtail millet resources, studied their artificial selection trends, and identified varieties that conform to breeding trends. The results indicated significant differences in terms of means, ranges, and coefficients of variation for each characteristic. Correlation analysis was performed to determine the correlations between various DUS characteristics. Principal component analysis was conducted on 31 test characteristics to determine their primary characteristics. By plotting PC1 and PC2, all germplasm resources could be clearly distinguished. Trends in foxtail millet breeding were identified through differential analysis of DUS test characteristics between landrace varieties and cultivated varieties. Based on these breeding trends, optimal solution types for multiple evaluation indicators were determined; weight allocation was calculated, and a specific TOPSIS algorithm was designed to establish a comprehensive multi-criteria decision-making model. Using this model, the breeding potential of foxtail millet germplasm resources was ranked. These findings provided important reference for foxtail millet breeding in future.

Wildfires present a significant risk to societies globally due to population growth, con- centrated activities, climate change, and extreme environmental conditions. To establish effective fire suppression and management policies, it is crucial to determine whether the distance between ignition points and urban areas is increasing or decreasing. This study analyses 101,597 fires in Spain and California between 2007 and 2015, using the Mann-Kendall test to determine trends. The study analyses the location and cause of fires to determine their distance from human constructions over time at two spatial scales. The findings suggest that wildfires are not moving away from buildings and, in some areas, are moving significantly closer.

While there has been a tremendous decline in malaria disease burden in the remote parts of the Malaysia, little is known about malaria incidence in its urban localities. This study aimed to analyse trends of malaria cases in urban Kuala Lumpur, Malaysia. All suspected cases presented to a university hospital in Kuala Lumpur from January 2005 to December 2020 were examined by microscopy. Infection status was analysed using descriptive statistics and curve estimation analysis. Of 3105 blood films examined, 92 (3%) were microscopically confirmed malaria cases. Plasmodium vivax infections accounted for the majority (36.9%) of all malaria cases. Nearly half (47.8%) of cases were found among foreign cases (P<0.001). The majority of foreign cases were males (86.4%) and came from Southeast Asian countries (65.9%). Curve estimation analysis showed significant decreases of malaria cases due to P. vivax (R2 = 0.598; P<0.001) and Plasmodium falciparum (R2 = 0.0259, P = 0.029), but increase for Plasmodium knowlesi (R2 = 0.325, P = 0.021) during the 16 years. This study revealed that malaria incidence in urban Kuala Lumpur is low and has remained stable since 2005. However, P. knowlesi played a significant role in the increase of overall malaria in the area, highlighting the importance of continued vigilance and improved surveillance.

The present study analyzed seasonal (i.e., Dec-Jan [DJF] and June – August [JJA]) temperature change for the near (2025-2054) and far future (2070-2099) under SSP245, SSP370, and SSP585 scenarios over Pakistan. The anomalies, Mann-Kendall trend tests, Sequential Mann-Kendall trend test (SQMK), and probability density frequency (PDF) analysis were used to investigate future mean temperature variations. The DJF season projected higher increase in temperature in the northern (3.8 ^oC, 5.1 ^oC and 6.5 ^oC), followed by central regions (3.8 ^oC, 4.9 ^oC and 6.4 ^oC) under SSP245, SSP370 and SSP585 scenarios, respectively. The central region is likely to record significant increase in JJA (3.0 ^oC, 4.4 ^oC and 5.4 ^oC) mean temperature in far future under the given SSP scenarios. Compared to historical (PDF), the far future DJF temperature changes revealed significant higher warming over northern, central and then over southern regions under most of SSP scenarios. The southern regions are projected to possible rise in far future JJA temperatures by 2.7 ^oC, 3.3 ^oC and 4.3 ^oC, under SSP245, SSP370 and SSP585, respectively. The PDFs for JJA further verify the highest positive abrupt shift in temperature across the central region and then southern region. The future diverse seasonal temperature changes supports further examination of the associated mechanisms and factors responsible for temperature changes to address climate change.

The rising popularity of virtual reality has seen a recent push in applications, such as, social media, educational tools, medical simulations, entertainment and training systems. With virtual realitythe ability to engage users for specific purposes, provides opportunities to entertain, develop cognitive abilities and technical skills outside of the standard mediums (e.g., the television or theclassroom) thereby optimizing exposure with realistic (live) opportunities. However, before these applications of virtual reality become more widespread, there are a number of open questions andissues that must be addressed including limitations, challenges, relationships between fidelity, multi-modal cue interaction, immersion, and knowledge transfer and retention. In this article, we begin with a brief overview of virtual reality methods, followed by a discussion of virtual reality and its applications (both historically, currently and in the future). We review virtual reality trends both from the early artistic days through to current day state of the art technological advancements. We explore emerging and futuristic breakthroughs - and their applications in virtual reality - showing how virtual reality will go way beyond anything we could envision. Infact, after reading this article, we hope the reader will agree, that virtual reality, is possibly one of the most powerful mediums of our time. While the earliest mechanistic virtual reality prototypes (e.g., Sensorama) allowed us to view stereoscopic 3D images accompanied by stereosound, smells, as well as wind effect - set the foundation and direction for future pioneers - there have been spearheaded developments which have continually pushed the concept of virtual reality to new domains. As virtual reality evolves, many new and yet-to-be-imagined applications will arise, but we must have understanding and patience as we wait for science, research andtechnology to mature and improve. The article ends with a short overview of future directions based upon recent breakthroughs in research and what this will mean for virtual reality in the coming years.

Background: Intimate partner violence (IPV) is one of the most common forms of domestic violence. Research on Intimate Partner Violence (IPV) prevalence using national samples is scarce. This study aims to estimate trends of IPV prevalence by sex and to identify the correlates of IPV among the Mexican population. Methods: We used data from Mexico's 2011 and 2016 National Survey of Addictions (NSA). The analytic sample consists of 44,963 individuals aged 14 to 65 years old. Poisson regression models regressed IPV on substance use behaviors (alcohol and drug use) and sociodemographic factors. Results: Overall, IPV prevalence did not change in the study period (From 15.6% in 2011 to 14.9% in 2016). The associated factors to IPV were having a partner who uses alcohol (APR) =1.68, 95%CI 1.54-1.84), having a partner who uses drugs (APR=2.80, 95%CI 2.46 -3.18). Living in an urban (APR=1.18, 95% CI 1.05-1.33) or metropolitan area (APR=1.16, 95% CI 1.04-1.28) was associated with a higher prevalence of IPV. Single marital status (APR=0.66, 95% CI 0.56-0.78) was associated with a lower prevalence of IPV. Conclusions: The IPV-associated factors are similar in both sexes, so comprehensive prevention and future interventions must consider the two directions of IPV (victim and perpetrator).

The field of communication and media education is constantly evolving, with recent trends emphasizing the development of abstract thinking skills in students. Abstract thinking, crucial for success in the rapidly changing media landscape, involves critical and conceptual analysis of complex ideas. By prioritizing these skills, educators can better prepare students to interpret information creatively and innovatively. Research shows that incorporating abstract thinking methodologies into communication and media curricula enhances problem-solving abilities and creativity. Encouraging students to look beyond surface-level information and consider deeper underlying principles fosters a more adaptable future workforce. This shift towards abstract thinking reflects a broader recognition of critical thinking's importance in our data-driven society. As communication and media increasingly shape public discourse and perception, the need for skilled professionals who can navigate this complex environment is paramount. By integrating abstract thinking strategies into their curricula, educators can equip students with the tools to become more effective communicators, problem solvers, and leaders in the field. This approach is essential for developing professionals capable of navigating the intricate media landscape with sophistication and discernment.

This research paper presents our investigation of the evolving trends in the field of Learning Sciences (LS) from 2005 to 2021. LS is a multidisciplinary field that integrates various scientific disciplines to study and support learning in real-world contexts (Hoadley, 2018). Our paper aims to understand how LS practices, methods, and disciplines of interest have changed over time, considering factors like the COVID-19 pandemic and the call for more equity-based research. We collected data from the International Society of Learning Sciences (ISLS) repository, which serves as one major platform that showcases LS research. We analysed the proceedings of the ISLS annual meetings and conferences, focusing on variables such as title, abstract, issue date, publisher, and collection using web scraping tools and the RStudio software for data collection and analysis. Our findings indicate several trends in LS research. In terms of methods and practices, design-based research (DBR) and participatory methods were commonly used in LS studies. However, there was a decline in the adoption of DBR in 2021, possibly due to the limitations imposed by the COVID-19 pandemic. Project-based learning, embodied interaction, and game-based learning were the most frequently used techniques among LS researchers. When examining the population of interest and domain focus in LS research. Learners were the primary focus, with a significant increase in the number of studies focusing on students over the years. However, there was also a noticeable increase in studies focusing on teachers in more recent years. In terms of subject disciplines, science and mathematics were the primary areas of focus, but there has been increasing attention to the arts and technology in recent years. Concerning equity-based research in LS, the finding revealed a significant increase in this area from 2005 to 2021, with ICLS studies tending to focus more on equity compared to CSCL studies. These findings contribute to understanding the current status and future development of the learning sciences.

This paper conducts a bibliometric analysis focusing on retail and consumer behavior studies. The research addresses key research questions exploring the landscape and evolution of academic contributions in this domain. Leveraging the Scopus database, the research analyzes a significant corpus of 2,111 documents from 1971 to 2022. The findings reveal a distinct surge in scholarly output, reflecting an escalated interest in understanding consumer behavior, particularly in digital environments, influenced by factors such as sustainability and ethical concerns. The research also identifies leading countries and institutions in document production, highlighting the dominance of the United States, the United Kingdom, India, China, and other leading institutions. The analysis also identifies prolific authors, prevalent research themes, and major publications, emphasizing the evolving demand of retail strategies and consumer behavior research.

Physicians exposed to a risky environment experience burnout, which negatively impacts patient care, stress decreases compassion, harms patient-doctor communication and may jeopardize patient safety. Patients are directly exposed to their physicians' overload and can sense their exhaustion, making them a sensitive seismograph of burnout. This study aimed to compare patients' standpoints towards physicians' burnout before and during the COVID-19 pandemic, characterizing patients' perceptions according to the 4 components of burnout. Two cross-sectional surveys (February-March 2018 and 2021) were conducted. 314 responders, 133 in 2018 and 181 in 2021. Physician burnout, accountability, empathy towards doctors ("mirror reflection") and the expected patient-doctor partnership were measured. Patients' perception of physicians' burnout was significantly associated with urban community, education and religiosity. Perceived accountability was associated with female gender and education. Empathy was associated with employment and gender. Patients described physicians' burnout as significantly higher prior to the COVID-19 pandemic. Physicians' accountability, empathy for doctors, and perceived partnership were more intense in 2021, implying that the emergency emphasized the physician's role and the patient-doctor relationship. Global disasters may have an unexpected positive effect on how patients understand the role and duty of physicians and the health system.

Sufficient evidence is currently available to demonstrate the reality of the warming of our planet's climate system. Global warming has different effects on climate at the regional and local levels. The detection of changes in extreme events using instrumental data provides further evidence of such warming and allows for the characterization of its local manifestations. The present study analyzes changes in temperature and precipitation extremes in the Mexican state of Zacatecas using climate change indices developed by the Expert Team on Climate Change Detection, Monitoring and Indices (ETCCDI). We studied a 40-year period (1976-2015) using annual and seasonal time scales. Maximum and minimum temperature data were used, as well as precipitation statistics from the Mexican climatology database (CLICOM) provided by the Mexican meteorological service. Weather stations with at least 80% of data availability for the selected study period were selected; these databases were subjected to quality control, homogenization, and data filling using Climatol, which runs in the R programming language. These homogenized series were used to obtain daily grides of the three variables at a resolution of 1.3 km. Results reveal important changes in temperature-related indices, such as the increase in maximum temperature and the decrease in minimum temperature. Irregular variability was observed in the case of precipitation, which could be associated with low-frequency oscillations such as the Pacific Decadal Oscillation and the El Niño–Southern Oscillation. The possible impact of these changes in temperature and the increased irregularity of precipitation could have a negative impact on the agricultural sector, especially given that the state of Zacatecas is the largest national bean producer. The most important problems in the short term will be related to the difficulty of adapting to these rapid changes and the new climate scenario, which will pose new challenges in the future.

According to ISO/TR 23383, smart textiles reversibly interact with their environment and respond or adapt to changes in the environment. The present review and bibliometric analysis was performed on 5,810 documents (1989–2022) from the Scopus database, using VOSviewer and Bibliometrix/Biblioshiny for science mapping. The results show that the field of smart textiles is highly interdisciplinary and dynamic, with an average growth rate of 22% and exponential growth in the last 10 years. Beeby, S.P., and Torah, R.N. have published the highest number of papers, while Wang, Z.L. has the highest number of citations. The leading journals are Sensors, ACS Applied Materials and Interfaces, and Textile Research Journal, while Advanced Materials has the highest number of citations. China is the country with the most publications and the most extensive cooperative relationships with other countries. Research on smart textiles is largely concerned with new materials and technologies, particularly in relation to electronic textiles. Recent research focuses on energy generation (triboelectric nanogenerators, thermoelectrics, Joule heating), conductive materials (MXenes, liquid metal, silver nanoparticles), sensors (strain sensors, self-powered sensors, gait analysis), specialty products (artificial muscles, soft robotics, EMI shielding), and advanced properties of smart textiles (self-powered, self-cleaning, washable, sustainable smart textiles).

We have developed additive logistic models for the occurrence of lightning, large (≥ 2 cm), and very large (≥ 5 cm) hail to investigate the evolution of these hazards in the past, in the future, and for forecasting applications. The models, trained with lightning observations, hail reports, and predictors from atmospheric reanalysis, assign an hourly probability to any location and time on a 0.25° × 0.25° × 1-hourly grid as a function of reanalysis-derived predictor parameters, selected following an ingredients- based approach. The resulting hail models outperform the Significant Hail Parameter and the simulated climatological spatial distributions and annual cycles of lightning and hail are consistent with observations from storm report databases, radar, and lightning detection data. As a corollary result, CAPE released above the -10°C isotherm was found to be a more universally skilful predictor for large hail than CAPE. In the period 1950–2021, the models applied to the ERA5 reanalysis indicate significant increases of lightning and hail across most of Europe, primarily due to rising low-level moisture. The strongest modelled hail increases occur in northern Italy with increasing rapidity after 2010. Here, very large hail has become 3 times more likely than it was in the 1950s. Across North America trends are comparatively small, apart from isolated significant increases in the direct lee of the Rocky Mountains and across the Canadian Plains. In the southern Plains, a period of enhanced storm activity occurred in the 1980s and 1990s.

This study aims to identify the knowledge structure and research trends of soccer psychology research during the last 33 years (1990–2022). Data were obtained from 1,863 papers from the Web of Science database. The data were collected through keyword text mining and data preprocessing to determine the keywords needed for analysis. Based on the keywords, latent Dirichlet allocation-based topic modeling analysis was performed to analyze the topic distribution of papers and explore research trends by topic area. The topic modeling process included four topic area and fifty topics. The “Coaching Essentials in Football” topic area had the highest frequency, but it was not statistically identified as a trend. However, coaching, including training, is expected to continue to be an important research topic, as it is a key requirement for success in the highly competitive elite football world. Interest in the research field of “Psychological Skills for Performance Development” has waned in recent years. This may be due to the predominance of other subject areas rather than a lack of interest. Various high-tech interventions and problem-solving attempts are being made in this field, providing opportunities for qualitative and quantitative expansion. “Motivation, cognition, and emotion” is a largely underrated subject area in soccer psychology. This could be because survey-based psychological evaluation attempts have decreased as the importance of rapid field application has been emphasized in recent soccer-related studies. However, measuring psychological factors contributes to the study of football psychology through a new methodology and theoretical background. Recognizing the important role of psychological factors in player performance and mental management, as well as presenting new research directions and approaches that can be directly applied to the field, will advance soccer psychology research.

This scholarly article provides an overview of the present and future issues and trends in social work on a global scale. Drawing upon recent bibliographic references and a review of the literature, this article explores the challenges faced by social workers in various regions and highlights emerging trends that are reshaping the field. The analysis incorporates insights from renowned authors and researchers to provide a comprehensive understanding of the current state and anticipated future of social work practice, education, and research worldwide.

Background: United Nations Sustainable Development Goals state that by 2030, the Global maternal mortality rate (MMR) should be lower than 70 per 100,000 live births. MMR is still one of Africa's leading causes of death among women. This research aims to study regional trends in maternal mortality in Africa. Methods: We extracted data for Maternal mortality rates per 100,000 births from the UNICE data bank from 2000 to 2017, being 2017 the last date available. Joinpoint regression was used to study the trends and estimate the annual percent change (APC). Results: Maternal mortality has decreased in Africa over the study period by an average APC of -3.0% (95% CI -2.9;-3,2%). All regions showed significant downward trends, with the sharpest decreases in the South. Only the North African region is close to the United Nations' sustainable development goals for Maternal mortality. The remaining sub-Saharan African regions are still far from achieving the goals. Conclusions: maternal mortality has decreased in Africa, especially in the South Africa region. The only region closed to the United Nations target is North Africa. The remaining sub-Saharan African regions are still far from achieving the goals. These results could be used for the development of Regional Policies.

Background: United Nations Sustainable Development Goals state that by 2030, the Global maternal mortality rate (MMR) should be lower than 70 per 100,000 live births. MMR is still one of Africa's leading causes of death among women. This research aims to study regional trends in maternal mortality in Africa. Methods: We extracted data for Maternal mortality rates per 100,000 births from the World Bank database from 1990-2015. Joinpoint regression was used to study the trends and estimate the annual percent change (APC). Results: Maternal mortality has decreased in Africa over the study period by an average APC of -2.6%. All regions showed significant downward trends, with the sharpest decreases in East Africa. Only the North African region is close to the United Nations' sustainable development goals for Maternal mortality. The remaining sub-Saharan African regions are still far from achieving the goals. Conclusions: maternal mortality has decreased in Africa, especially in East Africa. The only region closed to the United Nations target is North Africa. The remaining sub-Saharan African regions are still far from achieving the goals. These results could be used for the development of Regional Policies.

Digital medicine based on the integration of all medical data of a particular patient, has become a reality today, thanks to information technology. Traditional medical examinations can be supple-mented by assessment results of the oxidative-anti-oxidative (OAO) status of the body [1]. Elec-trochemical sensors are able to not only determine the integral indicators of the OAO system of the body, but also to depict details of the processes occurring in the system. The main obstacle to the widespread use of electrochemical sensors in medical diagnostics is the extremely small amount of the received information in comparison with tens of thousands of known human dis-eases. The problem can be eliminated only by rethinking the purpose of electrochemical measure-ment within the framework of thermodynamics of information processes and information theory. In the information paradigm of electrochemical analysis of biological fluids, a sample is considered as an electrochemical message created by a sensor. The purpose of electrochemical measurement is to obtain information in a volume sufficient to identify the sample composition within the range of possible concentrations of its components. The fundamentals of the thermodynamics of infor-mation processes are considered and conclusions that are of practical importance for the devel-opment of electrochemical sensors and analyzers are derived. It is shown that potentiostatic con-trol of the sensor is physically impacted by the electromechanical instability of the electrical double layer, which is the main source of sensor signal noise. Estimates are of a minimum amount of an-alytical signal information required for identification of a sample of a known composition, such as a biological fluid, are provided. Examples of highly informative analytical signals for flowing and stationary samples are presented. Problems related to the visualization of such signals are noted.

Background/Objectives: Accurate prediction of stock prices is an extremely challenging task because of factors such as political conditions, global economy, unexpected events, market anomalies, and relevant companies’ features. In this work, the random forest has been used to forecast the prices of the four major Greek systemic banks Methods/Analysis: We make use of a set of financial variables based on intraday data: (i) Open stock price, (ii) High stock price, (iii) Low stock price, and (iv) Close stock price of a particular Greek systemic bank. Results/Findings: The variables used here are crucial in predicting systemic banks' stock closing prices. These provide a better prediction of the next day's closing price of the bank series. Novelty /Improvement: To our knowledge, this is the first study that employs machine learning techniques in Greek systemic banks.

In this study, we have first studied the trend in meteorological data from the Harmaleh, Vanai and Farsesh stations in the 50-year period in the Dez catchment area. The meteorological data will be then forecasted using SWAT and Mann-Kendall. Forecasting the results in the Mann-Kendall and SWAT model has been done using the code written in MATLAB software and RCP (4.5, 8.5) scenarios, respectively. Studying the results of the trend in the data of meteorological stations in this catchment area indicated that these two parametric and non-parametric methods have been used to determine trends in meteorological data. The results of the parametric method are positive in all meteorological parameters. Non-parametric method over a period of 50 years shows the presence of trends in the data. The comparison on the forecasting results at maximum temperature suggested that during summer, we will see an increase in temperature compared to the ground state in all three forecasts. The results of the minimum temperature forecast show a decrease in the minimum increase during the winter and the precipitation forecast indicates that at the end of autumn (Nov) precipitation decreased by 20 mm in the Mann-Kendall and 4.5 RCP while RCP8.5 suggests the increase in precipitation compared to the ground state. Studying the runoff forecast results using SWOT show that at the end of winter (Feb) and almost all spring (Mar, Apr) a decrease of about 40%, 15% and 14% will be seen, respectively

The aim of this study is to explore the extent of English language teaching in Higher Institutions of Malaysia and investigate the current changes, trends and challenges in this niche. A sample of 100 English learners from public and private institutions participated in this study. Analysis of the responses indicated that English language is remaining to be difficult to master, due to speaking environment, proficiency of the teachers and other factors. However, the Government is on the right direction to improve this situation by following Common European Framework of Reference of Languages. As any other system, it takes time to put into realization and start up the mechanism. This observation carries a pedagogical perspective and includes the overview of the general picture based on Private, Public and Stand Alone English educational institutions. This study has highlighted the practical importance of British Framework of English learning and suggests to focus on emphasis of the learning process instead of a result.

The Tibetan Plateau(TP) is known as ‘the water tower of Asian’, its precipitation variation play an important role in the eco-hydrological processes and water resources regimes. based on the monthly mean precipitation data of 65 meteorological stations over the Tibetan Plateau and the surrounding areas from 1961-2015,variations, trends and temporal-spatial distribution were analyzed, furthermore, the possible reasons were also discussed preliminarily. The main results are summarized as follows: the annual mean precipitation in the TP is 465.54mm during 1961-2015, among four seasons, the precipitation in summer accounts for 60.1% of the annual precipitation, the precipitation in summer half year (May.- Oct.) accounts for 91.0% while that in winter half year (Nov.- Apr.) only accounts for 9.0%; During 1961-2015, the annual precipitation variability is 0.45mm/a and the seasonal precipitation variability is 0.31mm/a, 0.13mm/a, -0.04mm/a and 0.04mm/a in spring, summer, autumn and winter respectively on the TP; The spatial distribution of precipitation can be summarized as decreasing from southeast to northwest in the TP, the trend of precipitation is decreasing with the increase of altitude, but the correlation is not significant. The rising of air temperature and land cover changes may cause the precipitation by changing the hydrologic cycle and energy budget, furthermore, different pattern of atmospheric circulation can also influence on precipitation variability in different regions.

This paper presents a comprehensive bibliometric analysis of research on form and typology in architectural publications, spanning from 1999 to 2024. Utilizing the Web of Science database and the bibliometrix package in R, the study examines trends, influential works, and thematic evolution in the field. The findings reveal a progression from foundational themes such as "architecture," "culture," and "planning" to more specialized and diverse topics, including "sustainability," "parametric design," and "digital fabrication." Key influential works by authors like Jane Jacobs, Kevin Lynch, and Bill Hillier are highlighted. The study also underscores the collaborative nature of contemporary architectural research, with significant international co-authorship. While providing valuable insights into the research landscape, the study acknowledges limitations, such as database reliance and the need for qualitative analysis. The paper concludes with recommendations for future research, emphasizing the exploration of emerging technologies, interdisciplinary collaborations, and regional research dynamics.

Recently, interest in STEM education has grown among researchers and educators, also considering the skills required to address the challenges of the 21st century. Supporting learning in STEM subjects is an open challenge, especially during secondary education, which is reflected in the implementation of innovative pedagogical practices, including approaches focused on Game-Based Learning (GBL). This study analyzed the main issues addressed by research on game-based educational approaches applied to STEM teaching and learning in secondary school and their evolving trends. A text analysis of abstracts published in the past decade was conducted using T-lab software. The “Word Analysis” function was used for preliminary visual mapping; then the “Specificity Analysis” and “Thematic Analysis of Elementary Contexts” functions were applied to explore changes in research directions and to map the themes covered by the most recent studies. The results showed that the implementation of games in STEM disciplines is a promising area of inquiry. A shift in research directions also emerged, currently more focused on studying how GBL can foster engagement and the emotional component of learning. Adapting to rapidly changing technology and integrating essential skills such as coding and computational thinking are additional challenges that the research community is facing.

Suicide is an important public health problem, fundamentally affecting the younger population and responding to multiple biological, psychological, and social causes. The objective of this study was to characterize changes suicide mortality, suicide methods and years of potential life lost during 2005 and 2019 in Paraguay. This observational, descriptive study used data from the Vital Statistics Information Subsystem of the Ministry of Public Health and Social Welfare. The average mortality rate from suicide was 4.9 per 100,000 inhabitants, with an increase from 4.2 between 200-2009 to 5.8 during 2014 and 2019. Suicide was more common in men (75%) than in women. In men, the highest mortality rate was observed among the 20-24 years-old whereas in women the age most affected were the 15–19-year-old age group. The most used method for suicide was strangulation. The most frequent places of suicide occurrence were at home (73%). The seasonality of suicide occurrence showed a slight increase in the spring-summer months compared with autumn winter (53% vs 47%). The rate of potential years of life lost statistically significantly increased from2005 to 2019. Public health measures need to be implemented to investigate the underlying reasons and to implement interventions in the population to decrease suicide mortality in Paraguay.

In recent decades, there have been significant problems worldwide related to global warming and the intensification of extreme temperature and rainfall events. This research evaluated daily temperature and rainfall indices trends to identify whether warming signals have occurred in South Australia over the past fifty years. Extreme cold, hot, and rainfall events were calculated using climatic data from 37 weather stations. A Mann-Kendall test was used for trend analysis with Sen's estimator. As a result, we determined that for the 1970-2021 period, the maximum and minimum temperatures increased by 1.1 and 0.7 °C, respectively, while precipitation had a negative trend (-52.2 mm/period). Since statistical significance was found in analysing extreme cold and hot events, we find that warming signals have several impacts on South Australia. In the case of rainfall events, we identified a heterogeneous pattern characterised by a reduction in the annual amount, and an increase in extreme rainfall events. The findings enable us to conclude that the area shows signs of global warming that could affect the intensity and magnitude of droughts. This information is essential for continuing with current management strategies to adapt and mitigate the effects of global warming in South Australia.

This study aims to assess trends and significant changes in precipitation and river flow in the Senegal River basin and its upper basin over the period 1982-2021. Eight hydroclimate indicators, namely maximum river discharge (QMAX), standardized flow index, mean daily rainfall intensity index (SDII), maximum 5-day consecutive precipitation (RX5DAY), annual precipitation exceeding the 95th percentile (R95p), annual precipitation exceeding the 99th percentile (R99P), annual flows exceeding the 95th percentile (Q95p), and annual flows exceeding the 99th percentile (Q95p) were considered.. The modified Mann-Kendall test (MMK) was used to analyze trends in extremes while standard normal homogeneity and Pettit’s tests were employed to detect potential breakpoints in these trends. The results indicated an irregular precipitation pattern, with high values of extreme precipitation indices (R95p, R99p, SDII, and RX5DAY) in the southern part, while the northern part exhibits lower values. Inter-annual analysis revealed a significant increase in extreme precipitation events between 1982 and 2021, with a notable breakpoint around the years 2006 and 2007. This breakpoint marks a transition to a much wetter period starting from 2008. Regarding extreme flows, a significant increase was observed between 1982 and 2021. This study provides insights into past hydrological extremes in the study area and can serve as a basis for future research in this field.

(1) Background: With the United Nations Sustainable Development Goals (SDG) (2015-2030) fo-cusing on reducing maternal mortality, monitoring and forecasting Maternal Mortality Rates (MMR) in regions like Africa become crucial for health strategy planning by policymakers, in-ternational organizations, and NGOs. (2) Methods: We collected maternal mortality rates per 100,000 births from the World Bank database between 1990 and 2015. Join Point regression was applied to assess trends, and the autoregressive integrated moving average (ARIMA) model was used on 1990-2015 data to forecast the MMR for the next 15 years. (3) Results: The study found a decline in MMR in Africa with an average annual percentage change (APC) of -2.6% (95% CI -2.7; -2.5). North Africa reported the lowest MMR, while East Africa experienced the sharpest decline. The region-specific ARIMA models predict that the maternal mortality rate (MMR) in 2030 will vary across regions, ranging from 65 deaths per 100,000 births in North Africa to 249 deaths per 100,000 births in Central Africa., averaging 197 per 100,000 births for the continent. (4) Conclusions: Despite the observed decreasing trend in maternal mortality rate (MMR), the MMR in Africa remains relatively high. The results indicate that MMR in Africa will continue to decrease by 2030. However, only North and South Africa will likely reach the SDG target.

We explored the trends and the diversity of the mortality and risk factors of age-standardized cardiovascular diseases (CVD) and cerebrovascular disease (CBD) from 2000 to 2020 in five advanced Asian countries, including China, Japan, Singapore, South Korea, and Taiwan. Data were obtained from the Global Burden of Disease (GBD) report. Overall, the CVD mortality rates decreased gradually in all five Asian countries. Singapore had the highest rates before 2011, but Taiwan replaced Singapore as the nation of the highest mortality rates after 2012. Additionally, South Korea had the highest CBD mortality rates before 2016, while Taiwan experienced a peak in 2011 and remained high thereafter. The absolute difference (AD) and relative difference (RD) for CVD mortality rates showed clear declining trends in all five countries, with South Korea and China displaying the highest reductions, respectively. It is noteworthy that overweight and obesity exhibited an increasing trend in all these five Asian countries, with China demonstrating the highest AD and RD. Except for Taiwan, where a negative association of r=-0.732 was observed, CVD mortality was positively associated with CBD, hypertension, and DM mortality in all other four countries, therefore, further investigation into these relationships are needed.

A better understanding of background tropospheric ozone delivers multiple benefits. Robust estimates of regional background ozone assist policy makers to understand the limits to reducing ozone by controlling anthropogenic emissions alone and assist in characterising changes in the atmospheric environment and its composition. Long time series of background ozone measurements can also help quantify the influence of human activity on the global and regional atmosphere. Background tropospheric ozone measurements representative of continental air masses are however scarce in Australia, limiting our knowledge of background atmospheric composition. Here we use k-means clustering to identify a cluster of measurements from the long-term monitoring station at Oakdale, NSW which are likely to be representative of background air. The cluster is associated with NOx-limited air masses of continental origin. From this analysis we estimate background ozone representative of Eastern Australia. We find recent (2017-2022) mean ozone mixing ratios of 28.5 ppb and identify a statistically significant (α=0.05) trend in the mean of +1.8 (1.0 – 2.8) ppb/decade. Our methods demonstrate that some long-term monitoring stations within or near urban areas can provide suitable conditions and datasets for regional Global Atmosphere Watch monitoring.

Methane (CH4) is the second most significant contributor to climate change after carbon dioxide (CO2), accounting for approximately 20% of the contributions from all the well-mixed greenhouse gases. Understanding the spatiotemporal distributions, and the relevant long-term trends are crucial to identifying the sources, sinks, and impacts on climate. Hyperspectral thermal infrared (TIR) sounders, including the Atmospheric Infrared Sounder (AIRS), the Cross-track Infrared Sounder (CrIS), and the Infrared Atmospheric Sounding Interferometer (IASI), have been used to measure global CH4 concentrations since 2002. This study analyzed nearly twenty years of data from AIRS and CrIS and confirmed a significant increase in CH4 concentrations in the mid-upper troposphere (around 400 hPa) from 2003 to 2020, with a total increase of approximately 85 ppb, representing a +4.8% increase in 18 years. The rate of increase was derived using global satellite TIR measurements is consistent with in-situ measurements, indicating a steady increase starting in 2007 and became stronger in 2014. The study also compared CH4 concentrations derived from the AIRS and CrIS against ground-based measurements from NOAA Global Monitoring Laboratory (GML) and found phase shifts in the seasonal cycles in the middle to high latitudes in the northern hemisphere, which is attributed to the influence of stratospheric CH4 that varies at different latitudes. These findings provide insights into the global budget of atmospheric composition and the understanding of satellite measurement sensitivity of CH4.

Trends in excess winter mortality (EWM) were investigated from the winter of 1900/01 to 2019/20. During the 1918-1919 Spanish flu epidemic a maximum EWM of 100% was observed in both Denmark and the USA. During the Spanish flu epidemic in the USA 70% of excess winter deaths were coded to influenza. EWM steadily declined from the Spanish flu peak to a minimum around the 1970’s to 1980’s. There is evidence that this decline was accompanied by a shift in deaths away from the winter, and that the EWM calculation shifted from a maximum around April to June in the early 1900’s to around March since 1967. EWM has a good correlation with the number of estimated influenza deaths, but in this context influenza pandemics after the Spanish flu only had an EWM equivalent to that for seasonal influenza. Using data from 1980 onward the effect of influenza vaccination on EWM was examined using a large international data set. No effect of increasing influenza vaccination could be discerned; however, there are multiple competing forces influencing EWM which will obscure any underlying trend, e.g., increasing age at death, multimorbidity, dementia, polypharmacy, diabetes, and obesity – all of which either interfere with vaccine effectiveness or are risk factors for influenza death. After adjusting the trend in EWM in the USA influenza vaccination can be seen to be masking higher winter deaths among a high morbidity US population. Winter deaths are clearly the outcome of a complex system of competing long-term trends.

The public interest towards renewable and clean energy is an important part of shaping consumer behavior and policy towards these topics. A “big data” approach towards assessing the public interest for various topics consists in using freely available search frequency data for the Google search engine, through the Google Trends service. The search data frequency can be used to assess public opinion for a variety of topics, such as medicine, climate change and environmental concerns, finance, and economics etc. A study of the public interest towards wind energy topics is reported here. Six Google search keywords (“Wind power”, “Wind energy”, “Offshore wind”, “Wind farm”, “Wind turbine” and “Wind generator”) were investigated in the 2004–2020-time range. All keywords except “Offshore wind” show a steady decrease from a 2008 – 2010 maximum up to 2015, followed by a period limited change in the 2015 – 2020 range. The interest towards offshore wind topics follows a similar trend but increases in frequency starting from 2015 and reaches a maximum in 2018. Overall, the Google Trends data show a decrease of public interests towards most wind energy topics, with the exception of “Offshore wind”, for English speaking users, in the 2004–2020-time range.

The article is devoted to a bibliometric analysis of scientific publications reflecting trends in scientific publications related to the energy transition of leading universities in Saudi Arabia. The universities were selected based on the presence of joint publications with the Saudi Arabian Oil Company, they are: King Fahd University of Petroleum and Minerals, King Abdullah University of Science and Technology, and King Saud University. The importance of studying the experience of Saudi Arabia for Russia in the context of the energy transition was highlighted. The analysis was based on Scopus platform data related to Subject Areas: Physical Sciences. Scientists from Saudi Arabian universities are increasingly publishing with co-authors from China, India, Egypt, Pakistan, and Malaysia, while maintaining high interaction with co-authors from the United States, South Korea, and the United Kingdom. Authors from the Russian Federation publish poorly with co-authors from Saudi Arabia. The trends in the topics of scientific publications were investigated, and their connection with the renewable energy topic was shown. The VOSviewer software was used to determine the main research areas using the cluster analysis method based on the co-occurrence of key terms. The analysis was done for both Author's keywords and Index keywords of the Scopus system. Graphical representation of the final choice of terms to describe research trends was proposed. TRANSLATE with x English ArabicHebrewPolish BulgarianHindiPortuguese CatalanHmong DawRomanian Chinese SimplifiedHungarianRussian Chinese TraditionalIndonesianSlovak CzechItalianSlovenian DanishJapaneseSpanish DutchKlingonSwedish EnglishKoreanThai EstonianLatvianTurkish FinnishLithuanianUkrainian FrenchMalayUrdu GermanMalteseVietnamese GreekNorwegianWelsh Haitian CreolePersian TRANSLATE with COPY THE URL BELOW Back EMBED THE SNIPPET BELOW IN YOUR SITE Enable collaborative features and customize widget: Bing Webmaster Portal Back

As the coronavirus disease 2019 (COVID-19) continues to rage worldwide, the United States has become the most affected country with more than 34.1 million total confirmed cases up to June 1, 2021. In this work, we investigate correlations between online social media and Internet search for the COVID-19 pandemic among 50 U.S. states. By collecting the state-level daily trends through both Twitter and Google Trends, we observe a high but state-different lag correlation with the number of daily confirmed cases. We further find that the predictive accuracy measured by the correlation coefficient is positively correlated to a state’s demographic, air traffic volume and GDP development. Most importantly, we show that a state’s early infection rate is negatively correlated with the lag to the previous peak in Internet search and tweeting about COVID-19, indicating that earlier collective awareness on Twitter/Google correlates with lower infection rate. Lastly, we demonstrate that correlations between online social media and search trends are sensitive to time, mainly due to the attention shifting of the public.

Precipitation plays vital role in the economy of agricultural country like Pakistan. Baluchistan being the largest province of Pakistan in term of land is facing reoccurring droughts as well as flashflood due unprecedent torrential precipitation pattern.

Rationale: Examining trends in patient characteristics, processes of care and outcomes, across an epidemic, provides important opportunities for learning. Objectives: To report and explore changes in admission rates, patient characteristics, processes of care and outcomes for all patients with COVID-19 admitted to intensive care units (ICUs) in England, Wales and Northern Ireland. Methods: Population cohort of 10,287 patients with COVID-19 in the Case Mix Programme national clinical audit from 1 February to 2 July, 2020. Analyses were stratified by time period (pre-peak, peak, post-peak) and geographical region. Multivariable logistic regression was used to estimate differences in 28-day mortality, adjusting for patient characteristics over time. Main results: Admissions to ICU peaked simultaneously across regions on 1 April, with ongoing admissions peaking ten days later. Compared with pre- and post-peak periods, patients admitted during the peak were slightly younger but had greater respiratory and renal dysfunction. Use of invasive ventilation and renal replacement reduced over time. Twenty-eight-day mortality reduced from 43.5% (95% CI 41.6% to 45.5%) pre-peak to 34.3% (95% CI 32.3% to 36.2%) post-peak; a difference of −8.8% (95% CI: −5.2%, −12.3%) after adjusting for patient characteristics. London experienced the highest admission rate and had higher mortality during the peak period but a greater reduction in post-peak mortality. Conclusion: This study highlights changes in patient characteristics, processes of care and outcomes, during the UK COVID-19 epidemic. After adjusting for the changes in patient characteristics and first 24-hour physiology, there was substantial improvement in 28-day mortality over the course of the epidemic.

This study investigated the trends of registered Death and Birth in Nigeria using Generalized Linear Models. Annual data on Death and Birth was collected from National Population Commission for the period of 2004 to 2017. The Natural increase calculated revealed a positive trend in the natural increase in Nigeria from 2004 to 2017. Evidence from summary statistics revealed some level of over dispersion (variance > mean). This study explored Poisson Regression Models and Negative Binomial Regression Models using two links (identity and log). The results revealed a positive increase in registration of birth and death rates in Nigeria and among the competing the models, Negative Binomial regression model with identity link emerged as the best model for modeling birth and death rates registration in Nigeria. Data on numbers of deaths and causes of death are essential if countries are to determine priorities, formulate and monitor policies for public health care as well as other government policies that may be based on such data

Air pollution is an important public health issue. High levels of carbon monoxide in the atmosphere are hazardous to human health. Studies regarding the concentration of this and other gases in the atmosphere allow political actions to manage and reduce the emission of pollutants. In this context, this paper studied the annual, seasonal and daily variations of carbon monoxide (CO) concentration for the Metropolitan Region of São Paulo (MRSP). We studied three sites in the MRSP, chosen due to the longer time series and the localities. Two of them are located in areas under the influence of heavy vehicle traffic (Osasco and Congonhas) and the third one in a city park (Ibirapuera Park). The results showed high influence of gasoline vehicles on CO emission. In the annual scale, instead of following the increasing number of vehicles, CO emission and concentration decreased due to improvements in emission technology. CO emission showed a seasonal, weekly and diurnal cycle. The highest values of mean concentration were observed in June/July for Osasco (2.20 ppm), Congonhas (2.04 ppm) and Ibirapuera (1.04 ppm), during the morning, due to weak dispersion of the polluting gases.

This scholarly investigation conducts a rigorous exploration of Bangladesh's textile sector spanning the years 2011 to 2022, centering its attention primarily on the development of an intricate econometric framework. The study unveils profound insights into the sector's growth trajectories, the intricate dynamics of global demand, the undulating fluctuations of interest rates, and other pivotal economic gauges. The core component of this research, the econometric model, adeptly prognosticates textile exports through the incorporation of multifarious variables, encompassing the count of garment establishments, the magnitude of the workforce, market penetration, worldwide demand patterns, currency exchange rates, and interest rate fluctuations. Notably, the model attains a lofty degree of explicative potency, with an R-squared coefficient approximating 0.756, thereby attesting to its remarkable capacity to elucidate variances in textile export values. These discoveries carry substantial consequences for policymakers and stakeholders within the industry, as they bestow upon them a potent instrument for judicious decision-making and strategic blueprinting within Bangladesh's textile domain. The model accentuates the paramount significance of global demand and market share, concurrently accentuating the latent repercussions posed by fluctuations in interest rates.This research provides valuable insights into promoting the industry's sustainable growth, diversification, and resilience in the face of economic challenges.

Coffee is not only a delicious beverage but also an important dietary source of natural antioxidants. We live in an oxidative world where it is impossible to avoid pollution, stress, food additives, radiation, and other sources of oxidants that eventually lead to severe health disorders. Fortunately, there are chemicals in our diet that counteract the hazards posed by the reactive species that trigger oxidative stress. They are usually referred to as antioxidants; some of them can be versatile compounds that exert such a role in various ways. This review summarizes, from a chemical point of view, the antioxidant effects of relevant molecules found in coffee. Their ways of action and trends in activity are analyzed, considering the data gathered so far from both theory and experiments. The influence of the media and pH in aqueous solution, and structure-activity relationships are discussed. The protective role of the explored compounds is examined. A particular section is devoted to derivatives of some coffee components, and another one to their bioactivity. The data used in the analysis come from theoretical anc computational protocols, which have been proven to be very usefull in this context. Hopefully, the information provided here will promote further investigations into the amazing chemistry contained in our morning cup.

Coffee is not only a delicious beverage but also an important dietary source of natural antioxidants. We live in an oxidative world where it is impossible to avoid pollution, stress, food additives, radiation, and other sources of oxidants that eventually lead to severe health disorders. Fortunately, there are chemicals in our diet that counteract the hazards posed by the reactive species that trigger oxidative stress. They are usually referred to as antioxidants; some of them can be versatile compounds that exert such a role in various ways. This review summarizes, from a chemical point of view, the antioxidant effects of relevant molecules found in coffee. Their ways of action and trends in activity are analyzed, considering the data gathered so far from both theory and experiments. The influence of the media and pH in aqueous solution, and structure-activity relationships are discussed. The protective role of the explored compounds is examined. A particular section is devoted to derivatives of some coffee components, and another one to their bioactivity. Hopefully, the information provided here will promote further investigations into the amazing chemistry contained in our morning cup.

Background: Understanding the factors that influence the market entry, exit, and stability of community pharmacies (i.e., market dynamics) is important for stakeholders ranging from patients to health policymakers and small business owners to large corporate institutions. The purpose of this article is to provide an update to a 15-year ecological comparison for the market dynamics of Minnesota community pharmacies from 2002 to 2017 [1]. Objectives: The first objective was to update the 15-year study [1] by describing the market dynamics of community pharmacies for each county in the state of Minnesota every five years over a 20-year period (i.e., 2002-2022). The second objective was to update the 15-year study by describing the number and proportion of community pharmacies for each county in the state of Minnesota every five years over a 20-year period (i.e., 2002-2022) by (1) ‘Business Organization Structure’ and (2) ‘Pharmacy Type.’ The third and fourth objectives were new for the 2022 study. In response to evolutionary trends in the pharmacy market, objective three described the number of Minnesota licensed pharmacies located in-state and out-of-state between 2002 and 2022. Objective four described the primary type of service provided by licensed pharmacies categorized as out-of-state in 2002 and in 2022. Methods: Data were obtained from the Minnesota Board of Pharmacy, US Census Bureau, and Minnesota State Demographic Center for 2002, 2007, 2012, 2017, and 2022. Descriptive statistics were used to tabulate and summarize the findings and test associations between study variables. Results: Over the 20-year study period, the total number of community pharmacies in Minnesota remained steady. Findings for 2022 were similar to those for 2017 regarding the effects of population density and metropolitan designation on the market dynamics of community pharmacies and regarding the organizational structures and types of pharmacies. The findings revealed significant growth in the number of proportion of pharmacies licensed in Minnesota that were located out-of-state. In 2002, there were 283 of these pharmacies (19% of the total). By 2022, this grew to 960 (46% of the total). Over that 20-year period, the number of mail order and medical supply service pharmacies rose from 196 to 413. The number of specialty pharmacy distributors rose from 37 to 230 and the number of compounding pharmacies rose from 33 to 202. Discussion: The findings confirmed the shift over the past 20 years from traditional retail convenience models to healthcare access models based on population health metrics. National scalability was another detected shift in order to have profitable margins for some services and was feasible due to advances in technologies. Conclusion: The findings showed that community pharmacy distribution in Minnesota’s 87 counties shifted between 2002 and 2022 from traditional retail convenience models to healthcare access models based on population health metrics and to nationally scalable models based on economic pressures. This signals the need for not only new approaches for tracking community pharmacy market dynamics but also adjustments by community pharmacies to remain relevant in a new environment of patient care services.

As the global energy crisis continues, efficient hydrogen production is one of the hottest topics these days. In this sense, establishing catalytic trends for hydrogen production is essential for choosing proper H2 generation technology and catalytic material. Volcano plots for hydrogen evolution in acidic media are well-known, while volcano plot in alkaline media was constructed ten years ago using theoretically calculated hydrogen binding energies. Here we show for the first time that the volcano-type relationships are largely maintained in a wide range of pH values, from acidic to neutral and alkaline solutions, using theoretically calculated hydrogen binding energies on clean metallic surfaces and experimentally measured hydrogen evolution overpotentials. If metallic surfaces are exposed to high anodic potentials, hydrogen evolution can be boosted or significantly impeded, depending on the metal and the electrolyte in which the reaction occurs. Such effects are discussed here and can be used to properly tailor catalytic materials for hydrogen production via different water electrolysis technologies.

The objective of this article: analysis of research trends on the topic "Materials Science for Energy Engineering" for the period 2012-2021. Materials and methods: bibliometric analysis of data from the Scopus abstract database.Results. It is shown that this topic is developing faster than the general energy topics.The key feature of the research trends is a significant increase in work on the development of materials for renewable energy and a decrease in the number of publications related to nuclear power.The primary goal of many studies is to increase the efficiency of renewable energy production through the use of new materials.The importance of the topic is evidenced by the emergence of new journals that quickly entered the first quartile of the abstract databases Scopus and Web of Science, for example: ACS Applied Energy Materials.There has been a significant increase of interest in perovskite solar cells, layered semiconductors, triboelectric nanogenerators, cobalt compounds, sulfur compounds, and oxygen and hydrogen evolution reaction during Electrolysis.Publications on electric batteries are well represented in all time intervals. The focus is on electrode materials and battery efficiency.

While the ongoing climate change is well documented, the impacts exhibit a substantial variability, both in direction and magnitude, visible even at regional and local scales. However, the knowledge of regional impacts is crucial for the design of mitigation and adaptation measures, particularly when changes in the hydrological cycle are concerned. In this paper we present hydro-meteorological trends based on observations from a hydrological research basin in Eastern Austria between 1979-2019. The analysed state variables include the air temperature, the precipitation, and the catchment runoff. Additionally, trends for the catchment evapotranspiration were derived. The analysis shows that while the mean annual temperature was decreasing and annual temperature minima remained constant, the annual maxima were rising. The long-term trends indicate a shift of precipitation to the summer with minor variations observed for the remaining seasons and at an annual scale. Observed precipitation intensities mainly increased in spring and summer between 1979-2019. The catchment evapotranspiration, computed based on catchment precipitation and outflow, showed an increasing trend for the observed time period.

Dendroclimatology has gained relevance during the XXI century. We analyze the state of-the-art of dendroclimatology in Latin America during the past 28 years (1990 to 2019), identifying the current state and recent advances in the application of dendroclimatology in this region. We carried out a systematic review in ScienceDirect, Web of Science, and Scopus databases using Boolean operators to logically connect the keywords “dendrochronology,” “dendroclimatology,” “trend,” “climatic variability,” and “climatic variability trend”, for each country. Dendroclimatological research conducted in the region focused on climatic reconstruction and the evaluation of new tree species with dendrochronological potential, which increase in 2010, then there was a gap between 1995 and 1996, later increase to present. Dendroclimatological studies in Latin America have been mainly developed in temperate climate zones (82.4 %) followed by tropical or subtropical areas (17.6 %). Dendroclimatological research in Latin America has provided advances in the study of climate variability by defining response functions of tree rings to climate. The generated information allows for a better understanding of the spatial and temporal dynamics of climatic variability and about its effects on ecosystems and society. We also call for increased dendroclimatological research based on subtropical and tropical forests limited studied until today.

COVID-19 pandemic and its restrictive measures have present serious unprecedented challenges to human eating behavior. Given that Google Search has become a valuable information resource to examine, predict, and estimate human online interests and behavior, that somehow linked to real people concerns. This study aimed to investigate the features and evaluate the impacts of the COVID-19 pandemic and its lockdown on consumer worldwide interest in eating behavior and its related factors. Google Trends-Relative Search Volumes (RSV) of distinct keywords related to eating behavior, were obtained from a timeframe before and during the COVID-19 pandemic, from January 1, 2018, to December 13, 2020. During the global lockdown from March 11, 2020, to June 30, 2020, RSV curves exhibited a short-term fluctuation of interest in multiple keywords related to eating behavior and its related factors such as food purchasing, food security, food poisoning, panic buying, stocking up, health awareness and mental illness. Spearman’s correlation analysis showed a strong correlation between daily confirmed cases and examined keywords. Univariate repeated measures ANOVA following with Bonferroni Post-hoc test revealed that during the year with the presence of COVID-19 pandemic, people worldwide pay more concerns in each keyword (1) environmental and economic factors (unemployment: +269%, food shortage: +180%, food bank: +50%); (2) health- and food-safety concerns (immunity: +138%, vitamin C: +90%, vitamin D: +55%, zinc: +47%, food storage containers: +40%, food packaging: +31%); (3) food choices and interest (local meat: +84%, frozen food: +67%, CSA: +65%, flour: +66%, bread: +53%, soybean oil: +45%, local fruit: +43%, canned tomato: +42%, refrigerated food: +41%, canned meat: +39%, pancake: +37%, cookie: +29%, butter: +29%, canned fish: +29%, liquior: +20%); (4) social and individual factors (take-out: +128%, deliver: +53%), (6) lifestyle factors (stationary bicycle: +110%, dumbbell: +89%, yoga mat: +84%, treadmill: +65%, grocery store: +51%); (6) psychological factors (isolation: +113%). COVID-19 pandemic and its lockdown have had far-reaching effects on global concerns in many factors related to human eating behavior. Swift action is necessarily performed to strengthen the resilience of the food supply chain system, support and adapt to the new normal behavior, and mitigation the profound negative changes, especially targeting those in high-risks and vulnerable groups and food-insecure regions.

Objective: In recent years, the field of Essential Thrombocythemia (ET) research has seen a significant accumulation of scientific findings, but comprehensive bibliometric analyses remain lacking. This study aims to fill that gap by performing a thorough bibliometric analysis of ET research, identifying key contributors and collaboration networks, and mapping the development trends to provide insights for future research directions. Methods: A bibliometric analysis of ET-related publications from 2001 to 2024 was conducted using CiteSpace, VOSviewer, and R packages. Data were retrieved from the Web of Science Core Collection, with a focus on publication volume, citation analysis, co-authorship networks, co-citation relationships, and citation bursts. Results: A total of 4,297 studies published in 778 journals were included in the analysis. ET research has seen rapid growth, with researcher clusters in the United States and Europe driving progress through extensive regional and international collaborations. Leading researchers, such as Ayalew Tefferi from the Mayo Clinic and Alessandro M. Vannucchi from the University of Florence, have made significant advances in ET classification, molecular mechanisms, targeted therapies, and disease management. The discovery of driver mutations, such as JAK2, has revolutionized the diagnostic and therapeutic approaches to ET. Research focus has shifted from clinical morphological diagnosis to molecular diagnostics, with the field now entering the era of targeted therapies. However, the inherent heterogeneity of ET continues to present challenges for the widespread implementation of personalized precision treatment. Conclusion: Bibliometric analysis demonstrates significant advances in ET research, particularly in molecular pathology and targeted therapies. However, the heterogeneity of ET remains a major obstacle to personalized treatment. Future research should focus on further elucidating the pathogenesis of ET and improving stratified management approaches to achieve individualized precision therapy.

The broad adoption of information and communication technology and the shift to a fully digital society are together referred to as the "digital revolution." Similar to how the first industrial revolution affected western nations in the 19th century, the digital revolution has the power to drastically develop and transform Africa. The majority of African nations, if not all of them, are still searching for answers to the political, social, and educational issues that their citizens deal with. The principal objectives of the research were to ascertain the obstacles and possible patterns in African nations concerning the execution of STEM education and 4IR across the region, in addition to offering a plan and approach for doing so. In order to explore the sought papers and find answers to the research questions, the study employed the archival research study process. The results show that certain patterns that were adopted in Africa must be cooperatively adopted throughout the continent.

Urban sprawl is recognized as one of the most critical drivers disrupting the functional integrity of rural and natural landscapes. A systematic multiscale study has yet to be conducted to measure the Megalopolis of Mexico City's regional extent, comprising ca. 23 million inhabitants. This paper investigates land cover, land use, vegetation and soil changes at macro-regional, regional, local and site scales. Land cover/use data from the 1970s and 2018 were used as the primary input to analyze changes using remote sensing and geographical information systems. At the local scale, fieldwork data from the 1970s, 1990s, and 2020 served as the primary input and were subjected to multivariate analyses. At site scale, soils profiles and hydrological balance analyses at micro-basin level were conducted. Furthermore, social perception of changes was investigated among local residents/stakeholders using semi-structured interviews with four communities. Outcomes were validated to reach 85 percent accuracy in cartographic and 95 % statistical outputs. At the regional scale, land-use changes, especially human settlements, increased from about four to almost 40 % between 1970 and 2018 , with significant incursion into rural landscapes. At the local scale, analyses revealed significant decline in predominantly cold-humid vegetation types and conversion of agricultural fields into peri-urban landscapes. Site scale revealed the predominant importance of soils as the core landscape component for water infiltration and carbon sequestration. Nested multi-scale analyses revealed different disrupting processes which are hidden at one scale and evident at another. Poorly implemented, disconnected urban and environmental policies prevail as the main driving force, with implications for the weak local governance of rural communities. Regional integrated analyses are discussed in light of the relevance of urban resilience theory.

The integration of Artificial Intelligence (AI) with the Internet of Things (IoT) has propelled technological innovation across various industries. This systematic literature review explores the current state and future trajectories of AI in IoT, with a particular focus on emerging trends in intelligent data analysis and privacy protection. The proliferation of IoT devices, marked by voluminous data generation, has reshaped data processing methods, providing actionable insights for informed decision-making. While previous reviews have offered valuable insights, they often fall short of comprehensively addressing the multifaceted dimensions within the AI-driven IoT landscape. This review aims to bridge this gap by systematically examining existing literature and acknowledging the limitations of past studies. To achieve this aim, the study uses a meticulous approach guided by established methodologies. The chosen methodology ensures the rigor and validity of the review, aligning with PRISMA guidelines for systematic reviews. This systematic literature review serves as a comprehensive guide for researchers, practitioners, and policymakers, offering insights into the current landscape and paving the way for future research directions. The identified trends and challenges provide a valuable resource for navigating the evolving domain of AI in IoT, fostering a balanced, secure, and sustainable advancement in this dynamic field.

The paper presents a thorough examination of the burgeoning field of utilizing manufacturing waste for sustainable energy generation, aligning with the global imperative for resource efficiency and clean energy solutions. With the manufacturing sector being a significant contributor to waste generation, this study explores innovative approaches to transform waste materials into valuable resources, thereby mitigating environmental impacts and contributing to a circular economy. This work critically reviews current trends, emphasizing technological advancements, policy interventions, and market dynamics shaping the manufacturing waste utilization landscape. Noteworthy developments, such as advanced sorting technologies and the integration of digital solutions, are discussed in detail, showcasing their role in enhancing the efficiency of waste management processes. The study outlines future trends in the field, anticipating a shift towards closed-loop systems guided by circular economy principles. This comprehensive review aims to contribute to the discourse on sustainable waste management and energy generation by providing a holistic perspective on the field's current state and offering insights into the future trajectories that will propel manufacturing waste toward a more sustainable and circular future. The synthesis of technological innovation, collaborative efforts, and evolving regulatory frameworks presents a compelling case for the continued exploration of manufacturing waste as a valuable resource in the pursuit of a more sustainable and energy-efficient world.

In the educational process, quality has long been considered a critical component. It may get impacted due to different factors especially when talking about the COVID19 pandemic. The purpose of this investigation is to explore the impact and relationship between COVID-19 and the quality of education using bibliometric analysis. As face-to-face learning shifts to online learning, this research investigates how the COVID-19 epidemic has affected education quality, concentrating on Programme Educational Outcomes (PEOs), Programme Learning Outcomes (PLOs), and Course Learning Outcomes (CLOs). The research covers pandemic online education, literature, and trends. There are two important steps in any bibliometric analysis selecting keywords and databases. The initial set of keywords was selected by the authors' experience and then an online survey study through Google Forms was conducted among experts in the research area. This was followed by a search of the selected keywords in the databases consisting of PubMed, Web of Science (WoS), and SCOPUS. Therefore, the final data was collected from the SCOPUS database on August 24, 2021. Hence, 182 documents were considered for the current study. The document's data were analysed by using the “visualization of similarities” (VOS) Viewer software and the R-Tool of the Bibliometrix-package that was specifically designed for quantitative Bibliometrics research. Based on the obtained data, the results showed the quality of education that may get impacted by the changing of the education delivery from face-to-face to online-based learning. In conclusion, this study showed the importance of studying the correlation and impact of COVID-19 on the quality of education.

Veganism and the interest towards a plant-based diet are experiencing exponential growth worldwide, and in Italy more and more individuals and families are adopting this lifestyle and diet, too.Such a change in eating habits also imperatively implies the support and expertise of medical and health care professionals, who to date are still often unprepared and skeptical about this dietary pattern, despite scientific evidence.

In Brazil, the production of corn and soybeans has been growing significantly in recent years. Considering that the strategies for investing in the production of these crops are conditioned by production costs and risk, the objective of this article is to propose a simulation model that indicates the trend of production costs for these commodities, considering the dispersion and correlation of selected key variable prices and corn and soybean production costs practiced between the years of 2018 and 2022. Fifty specialized companies in the state of Sao Paulo, Brazil, provided data for this study, and Monte Carlo simulations showed that the total cost of corn production/ha is between USD 600.00 and USD 1150.00, with a level of certainty of 84.7%, and soybeans are in the range of USD 260.00 to USD 420.00, with a level of certainty of 86.4%. The model evidenced a trend of decreasing production costs for the crops for the 2023/24 and 2024/25 harvests, as the input with the greatest impact (fertilizer) is showing a downward trend. On the other hand, costs related to labor, soybean seed, and fungicides are showing an upward trend, while dolomitic limestone corrective remains stable.

This paper aims to explore the development of the field of Intangible Cultural Heritage Design. It seeks to understand the global trends in this field and provide insights for its growth and development. The objectives are achieved through a mixed-methods approach that combines bibliometric methods and content analysis. This approach allows for a quantitative and qualitative analysis of the scientific literature related to Intangible Cultural Heritage Design. The findings of the work include an analysis of the most productive countries/regions, institutions, journals, and authors in the field of Intangible Cultural Heritage Design. It also includes citation and co-citation analysis, which helps identify influential scholars and popular journals in the field, as well as patterns of col-laboration within the field. The outcomes and implications for practice include a better understanding of the global trends in Intangible Cultural Heritage Design and insights for its growth and development. The research find-ings have important theoretical and practical implications for the field, and future research directions are pro-posed. The value of the paper lies in its contribution to a better understanding of the global trends in Intangible Cultural Heritage Design. It provides insights and assistance for its growth and development, mak-ing it valuable to researchers and practitioners in the field.

Background: The progression of periodontal diseases at national Portuguese level and its public awareness are of great interest, mainly due to the high burden of periodontitis. Objectives: To evaluate the prevalence progression of periodontal diseases in Portugal and correspondent public awareness, between 2004 and 2017, by using data from the Global Burden of Disease (GBD), Directorate-General of Health (DGH) and Google® Trends (GT). Methods: For the period 2004-2017, Portuguese national data of periodontal diseases prevalence were searched in the Institute for Health Metrics and Evaluation of GBD and DGH and for public awareness, GT comparison tool between Portuguese words for “Periodontitis”, “Gingivitis”, “Gums” and “Periodontal disease” trends was used. Results: For the period 2004-2017, the overall prevalence of periodontitis slightly increased from 11.3% to 11.7%. During that period the GT search term “Gums” (“Gengivas”) was the most relevant. It increased steadily over time while the search term “Periodontal disease” (“Doença periodontal”) decreased, being these search trends significantly correlated (

It is required to manage sustainable Short-Rotation Coppices (SRCs) as an important role on carbon sink and bioenergy output, because most of SRCs were established in reclaimed land in South Korea. However, during the last three years, growth pattern of the SRCs was remarkably changed with soil condition. This study aimed to identify the sustainability of SRCs on carbon storage, biomass and fuel pellet production, monitoring the neighboring vegetation of SRCs by land-use exchange, physiological change of poplar on seasonal trend, and to evaluate whether poplar is suitable for making wood pellets. The calculated biomass yield per area of poplar grown was 103.07 Mg per total area (55.6 ha), and volumes of carbon dioxide absorption was estimated to be 330 Mg CO2. Wood pellet quality based on the criteria scored third grade, indicating that poplar is suitable for manufacturing fuel pellets. Moreover, monitoring of the flora distribution in SRCs revealed changes in species composition. As halophyte was increased during drought, soil organic matter, net growth and total chlorophyll of poplar were significantly decreased. These findings indicated that photosynthesis and growth pattern of SRCs may be negatively affected by microclimate and will provide valuable information for effective management of SRCs.

Georgia is ranked fifth highest among states for rates of HIV diagnosis. About 4% of persons living with HIV infection in the United States reside in Georgia, and almost 19% of these people do not know their HIV status. The present study examined the trends and associated factors of HIV testing among adults in Georgia between 2010 and 2014 by analyzing data of the Behavioral Risk Factor Surveillance System (BRFSS). A total of 30,791 persons aged ≥18 years were identified who responded to the question “Have you ever been tested for HIV?” Overall, there were 11,543 respondents who had been tested for HIV, with a decrease in percentage from 49.4% in 2010 to 43.7% in 2014 (p<0.001). Factors associated with HIV testing were being black (p<0.001), being younger than 55 years (p<0.001), single (p=0.02), attaining education level above high school (P<0.001), engaging in HIV high-risk behaviors (p<0.001), and not having healthcare coverage (p=0.03). Overall in Georgia, there has been a decline in the temporal trend of HIV testing, and more than half of adults have never been tested for HIV. For reducing HIV transmission in Georgia, enhancing access and utilization of HIV testing should be a public health priority.

Detection and quantification of disease-related biomarkers in wastewater samples, denominated Wastewater Based Surveillance (WBS), has proven a valuable strategy for studying the prevalence of infectious diseases within populations in a time and resource-efficient manner, as wastewater samples are representative of all cases within the catchment area, whether they are clinically reported or not. However, analysis and interpretation of WBS datasets for decision-making during public health emergencies, such as the COVID-19 pandemic, remains an area of opportunity. In this article, a database obtained from wastewater sampling at wastewater treatment plants (WWTPs) and university campuses in Monterrey and Mexico City between 2021 and 2022 was used to train simple clustering and regression-based risk assessment models to allow for informed prevention and control measures in high-affluence facilities, even if working with low-dimensionality datasets and a limited number of observations. When dividing weekly data points based on whether the seven-day average daily new COVID-19 cases were above a certain threshold, the resulting clustering model could differentiate between weeks with surges in clinical reports and periods between them with an 83.3% accuracy rate. Moreover, the clustering model provided satisfactory forecasts one week (79.2% accuracy) and two weeks (72.9%) into the future. However, the prediction of the weekly average of new daily cases was limited (R2 = 0.452, MAPE = 180.2%), likely because of insufficient dimensionality in the database. Overall, while simple, WBS-supported models can provide relevant insights for decision-makers during epidemiological outbreaks, regression algorithms for prediction using low-dimensionality datasets can still be improved.

A significant part of the global economy is focused on financial technologies, such as digital wallets, a rapidly growing sector. In 2020, 2.4 billion people used digital banking services globally, and this figure is projected to rise to 3.6 billion by 2026. This study aims to evaluate the current state and future trends in research on digital wallets using scientific databases. The methodology employed was a scoping review that analyzed 778 documents after the eligibility process. The terms used were “digital wallet,” “mobile wallet,” and “e-wallet.” Results indicate that India, the United States, and China lead in digital wallet research. Factors such as data security, ease of use, and integration with traditional payment systems significantly influence the adoption of digital wallets. Despite numerous studies, this article provides the first in-depth bibliometric analysis using Scopus and Web of Science. There has been an exponential research increase over the last decade, with a shift towards technological adoption and innovation. Less developed but key topics such as “digital wallets,” “blockchain,” and “block-chain” are fundamental yet have potential for further development. Driving themes like “electronic money,” “e-wallet,” and “electronic commerce” are central and highly developed, indicating their relevance and high research activity.

The article is aimed at a brief comparison and analysis of the results of queries to IEEE Xplore and the leading abstract databases Scopus and Web of Science to identify research trends. Some errors in the Author Keywords in Web of Science have been revealed. Therefore, a more detailed analysis was conducted by comparing different types of key terms for IEEE Xplore and Scopus platforms only. I used IEEE Access journal metadata as indexed on both platforms. The sample match for IEEE Xplore and Scopus was achieved by comparing DOI. The IEEE Xplore metadata contains more types of key terms, which provides an advantage in analyzing research trends. Using NSPEC Controlled Terms from expert-compiled vocabulary provides a more stable data, which gives an advantage when considering the change of terms over time. Apriori, an algorithm for finding association rules, has been used to compare co-occurrence of terms for a more detailed description of sample subjects on both platforms. VOSviewer was used to analyze trends in scientific research based on IEEE Xplore data. The 2011-2021 ten-year period was divided into two sub-intervals for comparing the occurrence of Author Keywords, IEEE Terms, and NSPEC Controlled Terms. Using the IEEE conference proceedings bibliometric data, I illustrated the importance of context in estimating the rate of growth of publishing activity on a topic of interest.

The main objective of the study is to analyse time trends in excessive body weight (obesity, overweight) of Czech adolescents between 2002 and 2018 with regard to the socioeconomic status (SES) of adolescents’ families and to find SES-mediated correlates of adolescents’ obesity. A nationally representative sample of 29,879 adolescents (49.6% of them boys) aged 10.5-16.5 years was drawn from the Health Behaviour in School-aged Children cross-sectional, self-reported questionnaire surveys conducted in 2002, 2006, 2010, 2014, and 2018 in the Czech Republic. Across the quadrennial surveys from 2002 to 2018, we observed a clear increase in the prevalence of excessive body weight (overweight/obesity) in all SES categories of adolescents, which was most striking (p < 0.05) in adolescents with low SES (boys: +5.2/+7.5 percent points (p.p.); girls +6.3/+2.4 p.p.). When all the survey cycles were compared, the highest prevalence of overweight/obesity was evident in the low-SES adolescents in 2018, both in girls (14.9%/5.1%) and boys (20.4%/12.0%). The lower odds of obesity were significantly (p < 0.05) associated with regular vigorous physical activity, participation in organized sport. An unreasonable increase in the prevalence of obesity in adolescents with low SES suggests that national health-related and sports programmes have the least impact on children from low-SES families.

BACKGROUND Epidemiological sciences have been evolving at an exponential rate paralleled only by the comparable growth within the discipline of data science. Digital epidemiological studies are playing a vital role in medical science analytics for the past few decades. To date, there are no published attempts at deploying the use of real-time analytics in connection with the disciplines of Dentistry or Medicine. AIMS AND OBJECTIVES We deployed a real-time statistical analysis in connection with topics in Dental Anatomy and Dental Pathology represented by the maxillary sinus, posterior maxillary teeth, related oral pathology. The purpose is to infer the digital epidemiology based on a continuous stream of raw data retrieved from Google Trends database. MATERIALS AND METHODS Statistical analysis was carried out via Microsoft Excel 2016 and SPSS version 24. Google Trends database was used to retrieve data for digital epidemiology. Real-time analytics and the statistical inference were based on encoding a programming script using Python high-level programming language. A systematic review of the literature was carried out via PubMed-NCBI, the Cochrane Library, and Elsevier databases. RESULTS The comprehensive review of databases of the literature, based on specific keywords search, yielded 491813 published studies. These were distributed as 488884 (PubMed-NCBI), 1611 (the Cochrane Library), and 1318 (Elsevier). However, there was no single study attempting real-time analytics. Nevertheless, we succeeded in achieving an automated real-time stream of data accompanied by a statistical inference based on data extrapolated from Google Trends. CONCLUSION Real-time analytics are of considerable impact when implemented in biological and life sciences as they will tremendously reduce the required resources for research. Predictive analytics, based on artificial neural networks and machine learning algorithms, can be the next step to be deployed in continuation of the real-time systems to prognosticate changes in the temporal trends and the digital epidemiology of phenomena of interest.

This article examines the profound impact of immigration on Brazil's human capital formation and economic growth from the late 19th century to the early 20th century. It highlights how the influx of European and Asian immigrants filled the labor void created by the abolition of slavery in 1888, fostering agricultural expansion, industrialization, and urbanization. Immigrants brought diverse skills, technologies, and cultural practices that enriched Brazil's socio-economic fabric. The article also compares these historical dynamics with contemporary demographic trends, notably the challenges posed by a declining fertility rate and an aging population. It underscores the necessity of re-evaluating inclusion policies and investing in human capital to sustain economic growth. By drawing lessons from the past, the article suggests that Brazil can navigate current demographic challenges through strategic policy interventions aimed at enhancing human capital as a tool for economic revitalization.

Keywords: Academic Trends; Energy Consumption; Higher Education Institutions; Systematic Literature Review; Scholarly output; University buildings

Because of ambiguous and widely debated observations concerning incidence trend and management of TC, we performed this analysis. We drew attention to some events, such as “cancer screening activity,” introduction of noninvasive follicular neoplasm with papillary-like nuclear features (NIFTP) to TC types, possibility of papillary thyroid microcarcinoma (PTMC) active surveillance (AS), occurrence of personalized medicine in TC management and finally COVID-19 pandemic time. Because of the opinion that all changes have been made mostly by PTC, we compared it to the rest of types of TC in terms of incidence, clinical and pathological characteristics and treatment. The prevalence of TC significantly increased from 5.15% in 2008 to 13.84% in 2015 and then significantly decreased to 1.33% in 2022 when the pandemic lasted (p<0.0001). A similar trend was observed for PTC, when the incidence significantly increased to 13.99% in 2015 and then decreased to 1.38% in 2022 (p<0.0001). At that time, the NIFTP category was introduced, and observation of PTMC began. The prevalence of FTC and MTC also increased until 2015 and then decreased. Significant differences in age, types of surgery, necessity of reoperation, pTNM, between PTCs and other types of TCs were observed. The average age was significantly lower in PTC patients than in patients with the remaining types of TC (p<0.0001). Four milestones, including NIFTP introduction, the possibility of PTMC AS, personalized cancer medicine and the COVID-19 pandemic, may have influenced the general statistics of TC.

Background: This study aimed to estimate the prevalence of sexualized drug use (SDU) in gay, bisexual, and other men who have sex with men living with HIV (HIV+GBMSM) in Madrid during 2019/2020 and compare it with data from 2016/2017. Methods: We analysed the frequency of SDU in a sample of HIV+GBMSM attending HIV clinics, who participated in an anonymous online survey regarding sexual behaviour and recreational drug use. The association between SDU, sexual risk behaviours, and STIs was evaluated. The methodology employed in the present study(U-SEX-2) in 2019/2020 was consistent with that of a previous study(U-SEX-1) in 2016/2017. Results: The study included 424 HIV+GBMSM, with a mean age of 40(10.43) years. Overall, 94% (396) reported being sexually active. Additionally, 33% (140) had been diagnosed with an STI within the previous year. Moreover, 54% (229) had used drugs in the last year, 25% (107) engaged in SDU, and 15% (17) reported engagement in slamsex. After adjusting for confounding factors, SDU was associated with STIs, fisting, unprotected anal intercourse, and having &gt; 24 sexual partners in the last year. According to the DUDIT test scores, 76% (81) probably had problematic drug use (≥6 points), and 5% (8) probable drug dependence (≥25 points). When comparing the U-SEX-1(2016/2017) data with the U-SEX-2(2019/2020) data, no significant differences were found in the proportion of participants practicing SDU or slamsex. Conclusion: The prevalence of SDU among HIV+GBMSM has remained high in recent years and without significant changes. We observed a clear association between SDU, high-risk sexual behaviors, STIs, and the risk of drug related problems.

The lack of reliable rainfall projection records remains a major challenge to Uganda. In the advent of extreme wetness or drought events, reliable rainfall estimates for local planning and adaptation are essential. The present study used two main datasets to conduct a historical analysis from 1981 to 2019, coupled with future projections under representative concentration pathway (RCP 8.5) for the period 2020-2050. Historical analysis revealed bimodal annual rainfall pattern for March-May (MAM) and September-November (SON) gradients representing heavier to lighter rainfall events respectively over the study area. Investigation of recent trends in rainfall patterns revealed an upward trend from 2010 onwards in annual and seasonal rainfall. Moreover, results for future projections show wet conditions are projected to occur over the study area between the months of April/May and October. Contrarily, March is likely to experience a reduction in wet conditions. Mann-Kendall test employed to make future projections of rainfall depicted decreasing patterns during MAM season whilst increasing tendencies with strong shift was highlighted for SON season over the study region. Meanwhile, annual projections indicate huge variations with linear trends showing a marginal increase as compared to historical trends. Findings would serve as baseline print to propel further studies that could delve into impact analysis of drought extreme events which pose significant threats to the agricultural sector which is heavily reliant on rainfall.

There is much discussion among clinicians, epidemiologists, and public health experts about why case fatality rate from COVID-19 in Italy (at 13.3% as of April 20, 2020, versus a global case fatality rate of 6.9%) is considerably higher than estimates from other countries (especially China, South Korea, and Germany). In this article, we propose several potential explanations for these differences. We suggest that Italy’s overall and relative case fatality rate, as reported by public health authorities, is likely to be inflated by such factors as heterogeneous reporting of coronavirus-related fatalities across countries and the iceberg effect of under-testing, yielding a distorted view of the global severity of the COVID-19 pandemic. We also acknowledge that deaths from COVID-19 in Italy are still likely to be higher than in other equally affected nations due to its unique demographic and socio-economic profile. Lastly, we discuss the important role of the stress imparted by the epidemic on the Italian healthcare system, which weakened its capacity to adequately respond to the sudden influx of COVID-19 patients in the most affected areas of the country, especially in the Lombardy region.

This qualitative study explores how businesses are adapting their Supplier Relationship Management (SRM) strategies to address the challenges and capitalize on the opportunities presented by evolving e-commerce trends. Through in-depth interviews with key industry stakeholders, the research investigates the integration of advanced technologies such as artificial intelligence (AI), blockchain, and the Internet of Things (IoT) into SRM practices. Findings reveal that these technologies enhance efficiency, transparency, and responsiveness within supply chains, enabling businesses to navigate complexities such as supply chain dynamics, fluctuating consumer demand, and regulatory compliance. The study identifies effective strategies employed by businesses, including enhancing communication channels with suppliers, diversifying supplier bases, and investing in technological advancements. These strategies enable businesses to mitigate risks, improve collaboration, and drive innovation, thereby maintaining competitive advantage in the digital marketplace. Collaborative practices such as joint product development, shared risk management, and co-investment in technology emerge as critical drivers of successful SRM. Ultimately, this research contributes to a deeper understanding of SRM in the context of e-commerce, offering insights into how businesses can optimize their supply chain operations amidst technological disruptions and market fluctuations. The findings underscore the importance of proactive adaptation and strategic alignment with suppliers to foster resilient and agile supply chains. As e-commerce continues to evolve, businesses must leverage these insights to refine their SRM strategies and sustain growth in an increasingly competitive environment.

Sri Lanka's vulnerability to climate change-induced natural disasters, particularly floods, under-scores the urgent need for an integrated approach to disaster risk management (DRM) that ad-dresses the interconnected challenges within the water-food-land-energy nexus. The Deduru Oya basin, located in the north-western region, exemplifies this vulnerability, with its predominantly intermediate climatic zone susceptible to flooding. While the focus has traditionally been on flood risk reduction, it is imperative to recognize the broader implications of floods on water, food, land, and energy systems. This study aims to strengthen data acquisition and management efforts to better understand rainfall trends and their impact on the water-food-land-energy nexus in the Deduru Oya basin. By compiling 60 years of rainfall data and conducting time series analysis, we observed a decreasing trend in monthly rainfall, particularly during the South-West Monsoon, Second Inter Monsoon, and North-East Monsoon seasons. These findings have significant implica-tions for water availability, agricultural productivity, land use patterns, and energy generation in the basin. Moreover, the malfunction of stakeholders' responsibilities for local-level flood risk management exacerbates the challenges faced by communities living in flood-prone areas. By in-tegrating insights from this study into flood risk management strategies, stakeholders can develop holistic approaches that not only mitigate flood risk but also enhance resilience across the water-food-land-energy nexus. This interdisciplinary approach will inform policymakers, flood manag-ers, practitioners, and the community in formulating effective strategies to address the multifacet-ed challenges posed by floods in the Deduru Oya basin and beyond.

The human-centric approach is a leading trend for future production processes, and collaborative roboticists are key to its realization. This article addresses the challenge of designing a new cus-tom-made non-conventional machine or robot involving toolpath control (interpolated axes) with collaborative functionalities but by using “general-purpose standard" safety and motion control technologies. This is done on a non-conventional Cable-Driven Parallel Robot (CDPR). Safety is assured by safe commands to individual axes, known as Safe Motion Monitoring Functionalities, which limits the axes speed in the event of human intrusion. At the same time, the robot's Motion Controller applies an override to the toolpath speed to accommodate the robot's path speed to the axis’s limitations. The implementation of a new Pre-Warning Zone, prevent unnecessary stops due to the approach of the human operator. The article also details a real experiment that validates the effectiveness of the proposed strategy.

We analysed changes in magnitude and timing of the largest annual observed daily flow (Amax), in each water year, for 596 stations in high-value water resources catchments and flood risk locations across Australia. These stations are either included in the Bureau of Meteorology's Hydrologic Reference Stations, or used in its operational flood forecasting services. Monotonic trend (which are either consistently increasing or decreasing) analyses of the magnitude and timing of flood peaks (estimated using Amax) were performed using Theil-Sen and Mann-Kendell approach and circular statistics to identify strength of seasonality and timing. Regional significance at the drainage division scale was analysed using the Walker test. Monotonic decreasing trends in Amax flood magnitude were detected in the Murray-Darling River basin and in other drainage divisions in Victoria, south-west and mid-west of Western Australia and South Australia. No significant obvious pattern in Amax magnitude was detected in northern Queensland, coastal NSW, central Australia and Tasmania. Only the Tanami-Timor Sea Coast drainage division in northern Australia showed monotonic increasing trends. Monotonic trends in Amax magnitude were regionally significant at the drainage division scale. We found two distinct patterns in flood seasonality and timing. In the northern and southern parts of Australia, flood peaks generally occur during February to March and August to October, respectively. The strength of this seasonality varies across the country. Weaker seasonality was detected for locations in the Murray-Daring River basin, and stronger seasonality was evident in northern Australia, south-west of Western Australia, South Australia, Victoria and Tasmania. The trends of seasonality and timing reveal that in general, flood peaks are occurring later in the water year in recent years. In northern Australia, flood peaks are generally occurring earlier – at a rate of 12 days/decade. In Victoria, New South Wales and Tasmania, trends in timing are generally mixed. However, in the south-west of Western Australia, the largest change in timing was evident – with Amax peaks commencing later at a rate of 15 days/decade. Decadal variability in flood timing was found at the drainage division scale as well. Most stations show a decreasing trend in Amax magnitude, but how that trend is associated with the change in timing is not clear.

This paper presents a comprehensive analysis of the scholarly footprint of ChatGPT, an AI language model, using bibliometric and scientometric methods. The study aims to understand the evolution of research output, citation patterns, collaborative networks, application domains, and future research directions related to ChatGPT. By analyzing data from the Scopus database, 533 relevant articles were identified for analysis. The findings reveal the prominent publication venues, influential authors, and countries contributing to ChatGPT research. Collaborative networks among researchers and institutions are visualized, highlighting patterns of co-authorship. The application domains of ChatGPT, such as customer support and content generation, are examined. Moreover, the study identifies emerging keywords and potential research areas for future exploration. The methodology employed includes data extraction, bibliometric analysis using various indicators, and visualization techniques such as Sankey diagrams. The analysis provides valuable insights into ChatGPT's influence in academia and offers researchers guidance for further advancements. This study stimulates discussions, collaborations and innovations to enhance ChatGPT's capabilities and impact across domains.

Strong numerical and functional response of owls to voles in cyclic environments is well known, but there is insufficient knowledge from boreonemoral region, in particular, with depleted populations of the small mammals. In this study we describe the dynamics of the small mammal population in Latvia from 1991 to 2016 and link them to owl population characteristics. We used food niche breadth, number of fledglings and population trends to describe the numerical response of six owl species to dampened small mammal population cycles. We found temporarily in-creasing food niche breadth in tawny and Ural owls. There were no other responses in tawny owl, whereas the breeding performance of three forest specialist species – pygmy, Tengmalm`s and Ural owls – were similar to vole crash years in Fennoscandia. Moreover, the populations of forest specialist owls are decreasing and the change in Ural owl can be attributed to the depletion of small mammal populations. We found evidence of carry-over effect in eagle owl arising from strong correlation of declining breeding performance with the small mammal abundance indices in previous autumn. We conclude that dampening of the small mammal population cycles is an important covariate to overwhelming impacts of habitat destruction with stronger response in more specialized (to prey or habitat) species.

During virus outbreaks in the recent past web behavior mining, modeling, and analysis have served as means to examine, explore, interpret, assess, and forecast the worldwide perception, readiness, reactions, and response linked to these virus outbreaks. The recent outbreak of the Marburg Virus disease (MVD), the high fatality rate of MVD, and the conspiracy theory linking the FEMA alert signal in the United States on October 4, 2023, with MVD and a zombie outbreak, resulted in a diverse range of reactions in the general public which has transpired in a surge in web behavior in this context. This resulted in “Marburg Virus” featuring in the list of the top trending topics on Twitter on October 3, 2023, and “Emergency Alert System” and “Zombie” featuring in the list of top trending topics on Twitter on October 4, 2023. No prior work in this field has mined and analyzed the emerging trends in web behavior in this context. The work presented in this paper aims to address this research gap and makes multiple scientific contributions to this field. First, it presents the results of performing time series forecasting of the search interests related to MVD emerging from 216 different regions on a global scale using ARIMA, LSTM, and Autocorrelation. The results of this analysis present the optimal model for forecasting web behavior related to MVD in each of these regions. Second, the correlation between search interests related to MVD and search interests related to zombies (in the context of this conspiracy theory) was investigated. The findings show that there were several regions where there was a statistically significant correlation between MVD-related searches and zombie-related searches (in the context of this conspiracy theory) on Google on October 4, 2023. Finally, the correlation between zombie-related searches (in the context of this conspiracy theory) in the United States and other regions was investigated. This analysis helped to identify those regions where this correlation was statistically significant.

40 years of cloud observations are available globally from satellites allowing derivation of climate data records (CDRs) for climate change studies. The aim of this study is to investigate how stable these cloud CDRs are and whether they qualify stability requirements recommended by the WMO’s Global Climate Observing System (GCOS). We also investigate robust trends in global total cloud amount (CA) and cloud top temperature (CTT) that are significant and common across all CDRs. The latest versions of four global cloud CDRs, namely CLARA-A3, ESA Cloud CCI, PATMOS-x and ISCCP-HGM, are analysed. This assessment finds that all three AVHRR based cloud CDRs satisfy even the strictest GCOS stability requirements for CA and CTT when averaged globally. While CLARA-A3 is most stable in global averages, PATMOS-x offers the most stable CDR spatially. While we find these results highly encouraging, there however remain large spatial differences in the stability of and across the CDRs. All four CDRs continue to agree on the statistically significant decrease in global cloud amount over the last four decades, although this decrease is now weaker compared to the previous assessments. This decreasing trend is stabilizing or even reversing in the last two decades; the latter is seen also in MODIS-Aqua and CALIPSO GEWEX datasets. Statistically significant trends in CTT are observed in global averages in the AVHRR-based CDRs, but the spatial agreement in the sign and the magnitude of the trends is weaker compared to those in CA. We also present maps of Common Stability Coverage and Common Trend Coverage that could provide a valuable metric to carry out an ensemble-based analysis of the CDRs.

Background: Several GLP-1 receptor agonists (GLP-1 RAs) are used to treat type 2 diabetes (T2DM). Their cardio- and renal protective effects and substantial weight loss have been evident and progressively expanded their role in the American Diabetes Association (ADA) guidelines, which are endorsed by the European Association for the Study of Diabetes (EASD). The increased demand leads to a global shortage. Methods: We utilized a repeated cross-sectional design, drawing data from national prescribing databases, to analyze six GLP-1 RAs: Dulaglutide, Enatide, Liraglutide, Lisenatide, Semaglutide, and Tirzepatide. AutoRegressive Integrated Moving Average (ARIMA) models with exogenous variables were applied to assess trends over time and regions. Results: The prescription rates significantly differ between regions. Wales shows the highest prescribing for most GLP-1 RAs. The ARIMA models indicated a significant increase in their prescribing after ADA guidelines 2022 (e.g., Dulaglutide: Post-ADA effect of 15.22, 95% CI: [12.97, 17.47]). Following GLP-1 RAs shortages in July 2023, the prescribing rates, particularly for Semaglutide, increased (Shortage effect: 74.36, 95% CI: [71.92, 76.80]). Model diagnostics, including the Akaike Information Criterion (AIC) and Durbin-Watson statistics, confirmed the robustness of these trends. Conclusion: Informed decisions should be made by considering prescribing trends before and after important events such as the issuing of new guidelines or safety alerts.

The Automatic Dependent Surveillance-Broadcast (ADS-B) protocol stands as a cornerstone in modern aviation surveillance and Aircraft Traffic Control systems, integral to the NextGen project initiated by the US Department of Federal Aviation Administration (FAA) in 2005. ADS-B utilizes data links to autonomously broadcast Aircraft navigational and identification information to augment air transportation system capacity and safety. However, concerns regarding the protocol's security persist, particularly as its adoption expands. The unencrypted nature of transmitted aircraft data and the absence of robust authentication mechanisms render the protocol susceptible to exploitation by malicious actors. In this study, we comprehensively reviewed existing research in the field, identifying a crucial gap necessitating a holistic survey. Prior surveys have predominantly focused on specific aspects, such as vulnerabilities, attacks, or critiques of existing solutions. Our survey addresses this gap by thoroughly exploring the aviation system, providing readers with a nuanced understanding of ADS-B security. Utilizing a detailed SWOT analysis diagram, our paper delves into the vulnerabilities inherent in the ADS-B protocol outlines potential threats and scrutinizes various attack scenarios. We systematically categorize and analyze existing security solutions, considering cost-effectiveness, scalability, implementation complexity, and coverage against diverse attack vectors. Furthermore, we critically evaluate these solutions, elucidate ADS-B security requirements, discuss current challenges, and propose future research directions. This survey serves as a comprehensive resource for researchers and practitioners alike, shedding light on the multifaceted landscape of ADS-B security and paving the way for enhanced aviation system resilience in the face of evolving cybersecurity threats.

Background: In America, vaccine-related deaths constitute a significant contributor to child mortality. An essential means of reducing this is through broad vaccine coverage. The COVID-19 pandemic has posed a potential disruption to vaccine coverage due to its effects on the healthcare system. Objectives: This study aims to evaluate the impact of the COVID-19 pandemic on DTP3 vaccination coverage in the Americas, investigating trends from 2012 to 2022 to identify significant changes, regional disparities, and the overall effect of the pandemic on progress towards global immunization targets. Methods: This study used the coverage data for the third dose of the Diphtheria, Tetanus, and Pertussis Vaccine (DTP3) pulled from UNICEF databases spanning 2012 to 2022. We conducted a JoinPoint regression to identify points of significant trend changes. The annual percentage change (APC) and 95% confidence intervals (95% CI) were calculated for America and its regions. We also used segmented regression analysis. Using the Chi-square test, we compared DTP3 vaccination coverage for each country between 2019 and 2022. Results: Overall, America saw a decrease in vaccine coverage during this period, with an APC of -1.4 (95% CI -1.8.; -1.0). This trend varied across regions. In North America, the decrease was negligible (-0.1% APC). South America showed the steepest decrease, with an APC of -2.5%. Central America also signif-icantly declined, with an APC of -1.3%. Our findings suggest a concerning trend of declining DTP vaccination rates in the Americas, exacerbated in certain regions, in the wake of the COVID-19 pandemic. The absolute decrease in vaccine coverage in the Americas was -4 % between 2019 and 2022, with the most significant drop observed in Central America (-7 %). However, six countries reported increased vaccination rates post-COVID-19, led by Brazil, with a 7% increase. Conversely, twenty-two countries registered a decline in DTP3 vaccine coverage, with the average decrease being -7.37%. This decline poses a significant challenge to achieving the WHO's target of 90% coverage for the third dose of DTP by 2030, as evidenced by the reduction in the number of countries meeting this target from 2019 to 2022. Conclusions: The COVID-19 pandemic has impacted vaccine coverage in America, leading to a decrease, especially across Central America.

Recent years have experienced an increase in construction and demolition waste (C&amp;DW), which is the most significant solid waste stream. C&amp;DW is contributing to exceeding all planetary boundaries and presents a range of other issues. In order to better understand the existing research on C&amp;DW, a global bibliographic analysis was undertaken through seven groups of keyword searches and the results visualised using VOCviewer. The results show that C&amp;DW receives only a modest research attention compared to other areas of waste, and this is despite an exponential increase in C&amp;DW research since 2016. The analyses also show that concrete is the most researched material in terms of C&amp;DW, and that reuse, recycling, and circular economy are so far attracting only proportionally modest research attention. This signals a need for further acceleration of the C&amp;DW research, and specifically for more research on reuse, recycling, and circular economy for materials other than concrete.

This study assessed health inequality trends and the degree of maternal healthcare services utilization in the DRC, using two consecutive Demographic and Health Surveys, 2007 and 2013-2014. First, we assessed the changes in the magnitude of inequality in the utilization of MHCS using logistics and regressions. Second, we analyzed the distribution of inequality in each MHCS utilization variable using the Gini coefficient and the Lorenz curve. Third, we used the Wagstaff two groups concentration indices comparison method to assess health inequality trends. Finally, we fitted the concentration curves to estimate the inequality in the utilization of MHCS to the economic condition of women. Women were less likely to have their first ANC visit within the first trimester, less likely to receive checkups during ANC visits, and less likely to attend more ANC visits when living in eastern DRC compared to western DRC. Women in rural areas were less likely to have their last birth by C-section, and less likely to receive PNC than women in urban areas. Women with middle, richer, and richest wealth indexes were more likely to complete more ANC visits, more likely to deliver by C-section, most likely to receive PNC, and more likely to receive ANC than those with lower wealth indexes. Over time, inequality in the utilization of MHCS decreased for ANC and PNC but increased for the delivery by C-sections. These findings suggest that innovative strategies are still needed to improve the utilization of MHC services among poorer, rural, and underserved women in post-conflict DRC.

The World Health Organization (WHO) added Disease X to their shortlist of blueprint priority diseases to represent a hypothetical, unknown pathogen that could cause a future epidemic. During different virus outbreaks of the past, such as COVID-19, Influenza, Lyme Disease, and Zika virus, researchers from various disciplines utilized Google Trends to mine multimodal components of web behavior to study, investigate, and analyze the global awareness, preparedness, and response associated with these respective virus outbreaks. As the world prepares for Disease X, a dataset on web behavior related to Disease X would be crucial to contribute towards the timely advancement of research in this field. Furthermore, none of the prior works in this field have focused on the development of a dataset to compile relevant web behavior data, which would help to prepare for Disease X. To address these research challenges, this work presents a dataset of web behavior related to Disease X, which emerged from different geographic regions of the world, between February 2018 to August 2023. Specifically, this dataset presents the search interests related to Disease X from 94 geographic regions. These regions were chosen for data mining as these regions recorded significant search interests related to Disease X during this timeframe. The dataset was developed by collecting data using Google Trends. The relevant search interests for all these regions for each month in this time range are available in this dataset. This paper also discusses the compliance of this dataset with the FAIR principles of scientific data management. Finally, a brief analysis of specific features of this dataset is presented to uphold the applicability, relevance, and usefulness of this dataset for the investigation of different research questions in the interrelated fields of Big Data, Data Mining, Healthcare, Epidemiology, and Data Analysis.

Abstract: This paper presents a comprehensive analysis of links between satellite-measured vegetation vigor and climate variables in Armenian mountain grassland ecosystems in years 1984–2018. NDVI is derived from MODIS and Landsat data, temperature and precipitation data are from meteorological stations. Two study sites were selected, representing arid and semi-arid grassland vegetation types, respectively. Various trend estimators including Mann-Kendall (MK) and derivatives were combined for vegetation change analysis at different time scales. Results suggest that temperature and precipitation had negative and positive impacts on vegetation growth, respectively, in both areas. NDVI-to-precipitation correlation was significant but with an apparent time-lag effect that was further investigated. No significant general changes were observed in vegetation along the observed period. Further comparisons between results from corrected and uncorrected data led us to conclude that MODIS and Landsat data with BRDF, topographic and atmospheric corrections applied are best suited for analyzing relationships between NDVI and climatic factors for the 2000-2018 period in grassland at a very local scale, but in the absence of correction tools and information, uncorrected data can still provide meaningful results. Future refinements will include removal of anthropogenic impact, and deeper investigation of time-lag effects of climatic factors on vegetation dynamics.

pH is considered one of the paramount factors in bodily functions, because most of the cellular tasks exclusively rely on precise pH values. The regulation of pH is a necessary feature of the intracellular atmosphere and can be established as a strong indicator to judge a physiological abnormality in most of the cases. In this context, the current techniques for pH sensing provide us with the futuristic insight to further design therapeutic and diagnostic tools. Thus, pH-sensing (electrochemically and optically) is rapidly evolving toward exciting new applications and expanding researchers’ interests in many chemical contexts, especially in biomedical applications. The adaptation of cutting-edge technology is subsequently producing the modest form of these biosensors as wearable devices, which are providing us the opportunity to target the real-time collection of vital parameters, including pH for improved healthcare systems. The motif of this review is to provide an insight of trending tech-based systems employed in real time or in-vivo pH responsive monitoring. Herein, we briefly go through the pH regulation in the human body to help the beginners and scientific community with quick background knowledge, recent advances in the field, and pH detection in cancerous environments. In the end, we summarize our review by providing an outlook; challenges that need to be addressed and prospective integration of various pH in vivo platforms with modern electronics that can open new avenues of cutting-edge techniques for disease diagnostics and prevention.

This paper examines the potential of Artificial Intelligence (AI) to address societal megatrends, with a specific focus on OpenAI’s Generative Pre-Trained Transformer 3 (GPT-3). To do this, we con-ducted an analysis using GPT-3 in order to explore the benefits of AI for digitalization, urbanization, globalization, climate change, automation and mobility, global health issues and aging population. We also looked at emerging markets as well as sustainability in this study. Interaction with GPT-3 was done solely through prompt questions and generated responses were analyzed. Our results indicate that AI can significantly improve our understanding of these trends by providing insights into how they develop over time and what solutions could be implemented. Further research is needed to determine how effective AI will be in addressing them successfully but initial findings are encouraging. Our discussion focuses on the implications of our findings for society going forward and suggests that further investigation should be done into how best to utilize new technologies like GPT-3 when tackling these challenges. Lastly, we conclude that while there is still much work left to do before any tangible effects can be seen from utilizing AI tools such as GPT-3 on societal mega-trends; early indications suggest it may have a positive impact if used correctly.

Malawi has developed an excellent, nation-wide system for monitoring people infected with HIV and keeping track of key epidemic markers. Their success lies in two things: the focus on simplicity and the use of data collection not only to track the epidemic and identify problems but also to give regular feedback and support to every clinic in the country. This achievement is the more remarkable given that Malawi is one of the poorest countries in the world, ranking 190 out of 194 countries by GDP, but has one of the most severe epidemics of HIV in the world, ranking 9th out of 168 countries by HIV prevalence. We first discuss the current state and likely future epidemic trends in Malawi: unless we know where we are and where we are going we cannot decide what to do or how to do it to in order to achieve a better outcome. We then discuss the history and development of Malawi’s patient monitoring system, as reported in their Integrated HIV Program Reports,ix which have been published quarterly since the beginning of 2004. We consider the current state of patient monitoring and support as reflected in the most recent report for the third quarter (Q3) of 2016 and comment on some of the questions that this raises. Finally, we consider ways in which the current system could be improved by strengthening Malawi’s analytical capacity and making better use of this unique data set. The focus here is on HIV in adultsv because if ART is initiated early in all adults living with HIV this should include testing all pregnant women for HIV and starting them on treatment immediately. However, PMTCT is especially important and care must be given to reducing MTCT and identifying the long-term child survivors of mother-to-child transmission and this demands a complementary assessment. There is an ongoing debate about the relative merits of treatment and prevention in reducing transmission and it should be made clear that the primary reason for starting people on treatment early is that it is in the best interest of the individual patient to start treatment as soon as possible after becoming infected. Allowing a person’s immune system to deteriorate to any degree is not consistent with the clinician’s commitment to ‘first do no harm’ and even those with the highest CD4⁺ cell count are at a substantially increased risk of death. What matters, therefore, is to get as many people as possible onto ART, ensure that they remain virally suppressed, and consider prevention in this context.

The growing adoption of electric propulsion systems in Maritime Autonomous Surface Ships (MASS) necessitates advancements in shipboard electronics for safe, efficient, and reliable operation. These advancements are crucial for tasks like real-time sensor data processing, control algorithms for autonomous navigation, and robust decision-making capabilities. This study investigates research trends in MASS using bibliographic analysis to inform policy and future research directions in this evolving field. We analyze 3,363 MASS-related articles from the Web of Science database, employing co-occurrence word analysis and latent Dirichlet allocation (LDA) topic modeling. Findings reveal a rapidly growing field dominated by image recognition research. Keywords like "datum," "image," and "detection" suggest a focus on collecting and analyzing marine data, particularly with deep learning for synthetic aperture radar imagery. LDA confirms this, with "Image analysis and classification research" as the leading topic. The study also identifies national and organizational leaders in MASS research. However, research on Arctic routes lags behind other areas. This work provides valuable insights for policymakers and researchers, promoting a deeper understanding of MASS and informing future policy and research agendas for the integration of electric propulsion systems in the maritime industry.

In this paper, the Shannon entropy measure was used to assess changes in precipitation and temperature conditions. Due to the short, low-volume sequences of precipitation and temperature data analysed, a bootstrap method was used in the procedure for calculating Shannon entropy. The analysis used minimum and maximum values of monthly precipitation totals and monthly mean temperatures for 377 catchments distributed across the globe. A 110-year data series from 1901 to 2010 was analysed. Entropy values for the estimated parameters of the generalised extreme value distribution (GEV) were calculated for the adopted data. Entropy value calculations were performed for the left-hand constraint, based on minimum values, and for the right-hand constraint, based on maximum values. The applicability of Shannon's entropy measure in the analysis of climate change was demonstrated by allowing the degree of disorder and complexity of the distributions describing climate variables in the form of precipitation and temperature to be measured. This made it possible to obtain information on the directions of changes occurring with regard to minimum and maximum values in the field of monthly precipitation and mean temperatures in the analysed catchments. The study demonstrated the existence of Shannon entropy trends. The evaluation of entropy trends for precipitation and temperature sequences was performed using non-parametric tests. Mann -Kendall tests at the 5% significance level were used for trend analyses. The Pettitt test was performed to determine the point of change in trend for the rainfall and temperature data. The performed analysis was supported by graphical presentations.

This article presents an analysis of monthly precipitation totals based on the Global Precipitation Climatology Centre database and monthly mean temperatures based on National Oceanic and Atmospheric Administration data for 377 catchments located around the world. Data sequences covering 110 years from 1901 to 2010 were analysed. The long-term sequences of precipitation and temperature were used to assess the variability of climate extremes, referred to here as polarisation. Measures of polarisation used in the natural sciences are discussed. A simple measure of polarisation was presented and applied to long-term sequences of monthly precipitation totals and monthly mean temperatures. Due to the nature of the proposed polarisation measure, other characteristics of precipitation and temperature sequences are also presented as a background for the discussion of the polarisation index. The study showed the existence of trends related to the polarisation of temperature and precipitation phenomena. As a result of the analysis, trends of polarisation factors in the area of precipitation and temperature were identified in 11 catchments out of the 377 analysed catchments. The trend analysis used Mann-Kendall tests at a level of significance of 5%. The Pettitt test was used to determine the point of trend change for precipitation and temperature data. The whole investigation is supported by rich graphical analyses, and the results are presented in tabular form.

Knowledge of wave climate is essential for efficient management of the world's coastal areas. Senegal is a relevant case given its high coastal vulnerability to energetic wave conditions. This study investigates wave climates along the coastal zone of Senegal based on a new high-resolution hindcast covering the period 1980-2021. This study evaluates the average, seasonal and extreme values for the wave significant heights (Hs), periods (Tm02/Tp) and mean directions (DIR). In boreal Winter, the wave climate is dominated by swells coming from the North-Atlantic lows, while in boreal Summer, the Southern Coast (from Dakar to Casamance) is exposed to swells generated in the South-Atlantic Ocean. Throughout their refraction around Dakar Peninsula, NW swells rotate by ~100° from NW to SW, while their Hs is roughly halved when reaching the Southern Coast of Senegal. Over the studied period, trends in Hs are weak (~0.6 cm.decade-1) on the Northern Coast, double on the Southern coast (~1.2 cm.decade-1), mostly due to an increase during boreal Summer (2 cm.decade-1). The wave periods show weak trends (~0.05 s.decade-1) and DIRs show weak counterclockwise rotation (-1°.decade-1). These trends are explained by the main climate modes of the Atlantic Ocean (NAO/EA during Winter, SAM during Summer) and are important for future research and long-term monitoring of the Senegalese Coast.

The article presents an analysis of monthly precipitation totals based on the GPCC database, and monthly mean temperatures (NOAA data) for 377 catchments distributed across the globe. The analysis included 110-year data sequences from 1901 to 2010 calculated from grid data with a spatial resolution of 0.5°x 0.5° longitude and latitude. Long-term sequences of precipitation and temperature were used to assess the polarization of climatic phenomena. The noticeable impact of polarization in the area of extreme changes in temperature and precipitation is related to anthropogenic factors such as greenhouse gas emissions, deforestation and pollution, which affect ecosystem functioning, biodiversity, water resources and economies. The paper demonstrates the existence of trends related to the polarization of temperature and precipitation phenomena. The measures of polarization used in science are discussed. A simple measure of polarization was proposed and applied to both long-term sequences of monthly precipitation totals and monthly mean temperature. Due to the nature of the proposed polarization measure, other characteristics of the precipitation and temperature sequences are also presented as background for the discussion of the polarization index. The paper presents, for a selection of several hundred catchments from around the world, analyses of the assessment of precipitation and temperature trends using non-parametric tests. The trend analyses use Mann -Kendall tests at the 5% significance level. A Pettitt test was used to determine the trend change point for precipitation and temperature data. The whole is supported by rich graphical analyses and the results are presented tabularly.

Google Trends data can be informative for infectious disease incidences, including Lyme disease. However, the use of Google Trends for predictive purposes is underutilized. In this study, we tested the ability of Google Trends search data to predict monthly state-level Lyme disease case counts in the United States. We requested Lyme disease data for the years 2010-2021. We downloaded Google Trends search data on terms for Lyme disease, symptoms of Lyme disease, and diseases with similar symptoms as Lyme disease. We built mixed negative binomial models based on a training dataset (2010-2016) and tested the models on a test dataset (2017-2021). A model was built for each search term and monthly lags of search terms were included as predictors. The highest performing models had high predictive ability, indicated by low Root Mean Squared Errors (RMSEs) and close association between observed and predicted case counts. The highest performing model was for the search term “Summer Flu”, which indicates low specificity of some of the terms. We outline challenges of using Google Trends data, including data availability and a mismatch between geographic units. We discuss opportunities for Google Trends data, including prediction of additional zoonotic diseases and incorporating environmental and companion animal data.

Solid-state batteries (SSBs) hold the potential to revolutionize energy storage systems by offering enhanced safety, higher energy density, and longer life cycles compared with conventional lithium-ion batteries. However, the widespread adoption of SSBs faces significant challenges, including low charge mobility, high internal resistance, mechanical degradation, and the use of unsustainable materials. These technical and manufacturing hurdles have hindered the large-scale commercialization of SSBs, which are crucial for applications such as electric vehicles, portable electronics, and renewable energy storage. This paper provides a systematic review and cross-sectional bibliometric and thematic analysis of the global patent landscape for SSB technologies. The study classifies innovations into key problem and solution areas by meticulously examining 244 patents across multiple dimensions, including geographic distribution, inventor engagement, award latency, and technological focus. Key findings reveal progress in addressing performance, safety, material sustainability, and manufacturing scalability, with significant innovations in electrolyte, electrode, architectural, and process engineering. This research contributes a comprehensive analysis of the technological landscape, offering valuable insights into ongoing advancements and providing a roadmap for future research and development. This work will benefit researchers, industry professionals, and policymakers by highlighting the most promising areas for innovation, thereby accelerating the commercialization of SSBs, and supporting the transition toward more sustainable and efficient energy storage solutions.

The origin and climate impact of Arctic aerosols, like the Arctic Haze, are not fully understood. Therefore, long-term aerosol observations in the Arctic are performed. In this study we present a homogenised data set from sun and star photometer operated in the European Arctic, in Ny-Ålesund, Svalbard, from 2004 – 2023. Due to polar day and polar night it is crucial to use observations of both instruments. Their data is evaluated in the same way and follows the cloud-screening procedure of AERONET. Additionally, an improved method for the calibration of the star photometer is presented. We found out, that winter months are generally more polluted and have larger particles than summer. While the monthly median Aerosol Optical Depth (AOD) decreases in spring, the AOD increases significantly in autumn months. The Arctic Haze is not characterised by large particles and can not be distinguished from large aerosols in winter months. With autocorrelation analysis we found out, that AOD events usually occur with duration of several hours and are therefore caused by large-scale processes and may have a periodicity of several days. Local processes can be neglected in Ny-Ålesund but long-range transport plays a major role for the Arctic aerosol budget. We also compared AOD events with large-scale processes, like large-scale oscillation patterns, sea ice, weather conditions or wildfires on the Northern Hemisphere but did not find one single cause, which clearly determines the Arctic AOD.

Aim: This study aims to analyze the structure and impact of research collaborations in colonoscopy from 2000 to 2023 by examining co-authorship networks. The goal is to understand the evolution of these networks, identify key contributors, and assess how collaborative relationships influence the field. Method: I conducted a comprehensive network analysis of co-authorship patterns using data from the Web of Science (WoS) Core Collection database. The analysis included 41,213 articles related to colonoscopy published between 2000 and 2023. Using Python (Version 3.10.5) and PyCharm (Software Version 2022.1.3), we evaluated the networks through macro-level indicators (network density, clustering coefficient, number of components, average distance) and micro-level indicators (degree centrality, closeness centrality, betweenness centrality).Result: The analysis revealed a network with increasing but still fragmented collaboration over the years. The network density showed low proportions of realized connections relative to potential ones, with a high clustering coefficient indicating tight-knit groups when collaborations occurred. The number of components remained high, reflecting a fragmented structure. Prominent researchers such as Hassan, Cesare (Italy) and Dekker, Evelien (Netherlands) consistently emerged as central figures across different periods, demonstrating significant roles in facilitating connections between various research clusters.Conclusion: The study highlights a growing but fragmented collaborative landscape in colonoscopy research. Key researchers have played central roles in connecting different parts of the network, yet there remains substantial room for further integration. Strengthening collaborations across isolated groups could enhance the cohesiveness and impact of research in this field.