Methodological advancements in road safety research reveal an increasing inclination toward integrating spatial approaches in hot spot identification, spatial pattern analysis, and developing spatially lagged models. Previous studies on hot spot identification and spatial pattern analysis have overlooked crash severities and the spatial autocorrelation of crashes by severity, missing valuable insights into crash patterns and underlying factors. This study investigates the spatial autocorrelation of crash severity by taking two capital cities, Addis Ababa and Berlin, as a case study and compares patterns in low and high-income countries. The study used three-year crash data from each city. It employed the average nearest neighbor distance (ANND) method to determine the significance of spatial clustering of crash data by severity, Global Moran\'s I to examine the statistical significance of spatial autocorrelation, and Local Moran\'s I to identify significant cluster locations with High-High (HH) and Low-Low (LL) crash severity values. The ANND analysis reveals a significant clustering of crashes by severity in both cities, except in Berlin\'s fatal crashes. However, different Global Moran\'s I results were obtained for the two cities, with a strong and statistically significant value for Addis Ababa compared to Berlin. The Local Moran\'s I result indicates that the central business district and residential areas have LL values, while the city\'s outskirts exhibit HH values in Addis Ababa. With some persistent HH value locations, Berlin\'s HH and LL grid clusters are intermingled on the city\'s periphery. Socio-economic factors, road user behavior and roadway factors contribute to the difference in the result. Nevertheless, it is interesting to note the similarity of significant HH value locations on the outskirts of both cities. Finally, the results are consistent with previous studies and indicate the need for further investigation in other locations.

The contagious COVID-19 has recently emerged and evolved into a world-threatening pandemic outbreak. After pursuing rigorous prophylactic measures two years ago, most activities globally reopened despite the emergence of lethal genetic strains. In this context, assessing and mapping activity characteristics-based hot spot regions facilitating infectious transmission is essential. Hence, our research question is: How can the potential hotspots of COVID-19 risk be defined intra-cities based on the spatial planning of commercial activity in particular? In our research, Zayed and October cities, Egypt, characterized by various commercial activities, were selected as testbeds. First, we analyzed each activity\'s spatial and morphological characteristics and potential infection risk based on the Centre for Disease Control and Prevention (CDCP) criteria and the Kriging Interpolation method. Then, using Google Mobility, previous reports, and semi-structured interviews, points of interest and population flow were defined and combined with the last step as interrelated horizontal layers for determining hotspots. A validation study compared the generated activity risk map, spatial COVID-19 cases, and land use distribution using logistic regression (LR) and Pearson coefficients (rxy). Through visual analytics, our findings indicate the central areas of both cities, including incompatible and concentrated commercial activities, have high-risk peaks (LR = 0.903, rxy = 0.78) despite the medium urban density of districts, indicating that urban density alone is insufficient for public health risk reduction. Health perspective-based spatial configuration of activities is advised as a risk assessment tool along with urban density for appropriate decision-making in shaping pandemic-resilient cities.

UNASSIGNED: Migraine treatment research has made much great progress over the past decade. However, there have been few bibliometric studies conducted so far. In this study, bibliometric analysis was used to explore the current status and future trends of migraine treatment research.
UNASSIGNED: Migraine treatment-related articles were retrieved from the Web of Science Core Collection on December 7, 2022. Quantitative variables were analyzed by the R-tool bibliometrix and Excel 2020. VOS viewer and CiteSpace software were used to visualize citation, co-authorship, co-occurrence, and co-citation analysis of countries/regions, organizations, authors, references, and keywords.
UNASSIGNED: A total of 3294 articles were included with the global publication output showing a slow upward trend. The United States was the most productive country with 1116 papers and gained the most citations. Albert Einstein College of Medicine was the most active institution with 176 papers. Headache published the most articles in this domain, while Cephalalgia was the most commonly co-cited journal. Lipton, RB published the most articles and had the most citations. Tepper S, 2017, Lancet neurology and Silberstein S, 2004, Cephalalgia were defined as classic articles. The current research mainly focuses on CGRP-related therapeutics, such as fremanezumab, erenumab and ubrogepant.
UNASSIGNED: Based on the analysis of bibliometric data on migraine treatment over the past decade, the trends and the knowledge graph of the country, organization, author, reference, and the keyword were identified, providing accurate and quick positioning of the critical information in the domain.

BACKGROUND: Polycystic ovary syndrome (PCOS) is a common gynecological endocrine disease that has a great impact on women\'s physical and mental health. It is a burden to social and patients\' economy. In recent years, researchers\' understanding of PCOS has reached a new level. However, many PCOS reports have different directions, and overlapping phenomena exist. Therefore, clarifying the research status of PCOS is important. This study aims to summarise the research status of PCOS and predict the hot spots of PCOS in the future by Bibliometricx.
RESULTS: The keywords of PCOS research focused on PCOS, insulin resistance (IR), obesity and metformin. Keywords plus co-occurrence network showed that PCOS, IR and prevalence were hot spots in the recent 10 years. Moreover, we found that gut microbiota may be a carrier that can be used to study hormone levels, IR-related mechanisms, prevention and treatment in the future.
CONCLUSIONS: This study is helpful for researchers to quickly grasp the current situation of PCOS research and enlighten researchers to explore new problems in PCOS.

The aim of this study was to evaluate the global scientific output of research on infertility and psychology; explore the current status and trends in this field through the cooperation of authors, countries, and institutions; shed light on the direction of clinical infertility research in the future, and provide inspiration for targeted diagnosis and treatment of infertility.
Research publications on infertility and psychology from the past two decades were retrieved from the Web of Science Core Collection (WoSCC). Bibliometric analyses were performed using VOSviewer software and the bibliometrix R package. Network maps were generated to evaluate the collaborations between different authors, countries, institutions, and keywords.
A total of 151 articles related to the study of infertility and psychology were identified. We observed a gradual increase in the number of publications from 2001 to 2021, and the trend has been relatively stable in the past eight years. Human Reproduction (England), as the leading journal publishing the most papers (29 articles), was cited in the most journals (1208 times). Boivin J was the most prolific author (16 articles), with the largest number of citations (890 times) and the highest h-index (14) during the past decades. Boivin J was also the leader with the highest publication frequency and more active cooperation with other top authors. The United Kingdom (34 papers) and Cardiff University (25 articles) contributed the most publications and were the leading contributors in this field. Active cooperation between countries and between institutions was observed, and analyses of articles and references were also shown. The main hot topics included matters related to women (39 times), in-vitro salt (31 times), infertility (30 times), couples (25 times), and impact (24 times).
Our study results provide a comprehensive overview of the development of scientific literature, allowing relevant authors and research teams to recognize the current research status in this field. At the same time, infertility and psychology may soon become hotspots and should be closely monitored.

Light enhancement occurs strongly within the plasmonic clusters by interaction with surface plasmons. Surface-enhanced Raman spectroscopic (SERS) characteristics of a series of silver@silica trimer core-shell (CS) nanosphere (NS) clusters are investigated in this paper. It is significant to understand the electric field (EF) enhancement mechanism behind the SERS technique. The effect of symmetry breaking is studied for the series starting from the highly symmetric trimer cluster and transformed to linear dimer geometry which progresses through the gradual reduction in the vertex NS. The optical activity such as the evolution of LSPR peak is discussed, the formation of hot spots is demonstrated and the strength of the local EF enhancement is calculated and correlated with the plasmon dipolar modes by using plasmon hybridization theory to understand the underlying physical concepts.

OBJECTIVE: To describe epidemiological trends and spatial distributions of HIV/AIDS among older adults (aged ≥50) in Sichuan Province, China during 2008-19, and provide scientific reference for HIV/AIDS prevention, intervention and treatment.
METHODS: Data on HIV/AIDS cases reported in 2008-19 was extracted from the Case Report System. The Cochran-Armitage trend test was used to determine epidemic trends. Spatial autocorrelation and space-time analysis were conducted with ArcGIS10.6 and ArcGIS Pro2.4, respectively.
RESULTS: A total of 77854 HIV/AIDS cases among older adults were included in the study. Newly reported cases increased from 320 in 2008 to 22189 in 2019, and the reported incidence rate (number of new reported cases/older adult population) rose from 0.001% to 0.077%. Infections through heterosexual transmission increased from 65.3% to 98.2% of total cases in older adults in this period. Spatial analysis at the county-level showed significant clustering throughout Sichuan, with the main hot spots concentrated in the southeast. Spatiotemporal analysis indicated that most of the southeastern counties/districts were Consecutive Hot Spots.
CONCLUSIONS: Older adults have become a key population in the HIV/AIDS epidemic in Sichuan; comprehensive prevention and intervention measures targeted to older adults are urgently needed to control the spread of HIV/AIDS.

Thousands of deaths associated with air pollution each year could be prevented by forecasting the behavior of factors that pose risks to people\'s health and their geographical distribution. Proximity to pollution sources, degree of urbanization, and population density are some of the factors whose spatial distribution enables the identification of possible influence on the presence of respiratory diseases (RD). Currently, Bogotá is among the cities with the poorest air quality in Latin America. Specifically, the locality of Kennedy is one of the zones in the city with the highest recorded concentration levels of local pollutants over the last 10 years. From 2009 to 2016, there were 8619 deaths associated with respiratory and cardiovascular diseases in the locality. Given these characteristics, this study set out to identify and analyze the areas in which the primary socioeconomic and environmental conditions contribute to the presence of symptoms associated with RD. To this end, information collected in field by performing georeferenced surveys was analyzed through geostatistical and machine learning tools which carried out cluster and pattern analyses. Random forests and AdaBoost were applied to establish hot spots where RD could occur, given the conjugation of predictor variables in the micro-territory. It was found that random forests outperformed AdaBoost with 0.63 AUC. In particular, this study\'s approach applies to densely populated municipalities with high levels of air pollution. In using these tools, municipalities can anticipate environmental health situations and reduce the cost of respiratory disease treatments.

Understanding which drivers cause diversity patterns is a key issue in conservation. Here we applied a spatially explicit model to predict marine benthic diversity patterns according to environmental factors in the NW Mediterranean Sea. While most conservation-oriented diversity studies consider species richness only and neglect equitability, we measured separately species richness, equitability, and \'overall\' diversity (i.e., the Shannon-Wiener H\' function) on a dataset of 890 benthic species × 209 samples. Diversity values were predicted by means of Random Forest regression, on the basis of 10 factors: depth, distance from the coast, distance from the shelf break, latitude, sea-floor slope, sediment grain size, sediment sorting, distance from harbours and marinas, distance from rivers, and sampling gear. Predictions by Random Forests were accurate, the main predictors being latitude, sediment grain size, depth and distance from the coast. Based on predicted values, diversity hotspots were identified as those localities where indices were in the 15% top segment of ranked values. Only a minority of the diversity hotspots was included within the boundaries of the protection institutes established in the region. Marine protected areas are often created in sites harbouring important coastal habitats, which risks neglecting the diversity hidden in the sedimentary seafloor. We suggest that marine protected areas should accommodate portions of sedimentary habitat within their boundaries to improve diversity conservation.

To verify the feasibility of portable X-ray fluorescence (PXRF) for rapidly analyzing, assessing and improving soil heavy metals mapping, 351 samples were collected from Fuyang District, Hangzhou City, in eastern China. Ordinary kriging (OK) and co-ordinary kriging (COK) combined with PXRF measurements were used to explore spatial patterns of heavy metals content in the soil. The Getis-Ord index was calculated to discern hot spots of heavy metals. Finally, multi-variable indicator kriging was conducted to obtain a map of multi-heavy metals pollution. The results indicated Cd is the primary pollution element in Fuyang, followed by As and Pb. Application of PXRF measurements as covariates in COK improved model accuracy, especially for Pb and Cd. Heavy metals pollution hot spots were mainly detected in northern Fuyang and plains along the Fuchun River in southern Fuyang because of mining, industrial and traffic activities, and irrigation with polluted water. Area with high risk of multi-heavy metals pollution mainly distributed in plain along the Fuchun River and the eastern Fuyang. These findings certified the feasibility of using PXRF as an efficient and reliable method for soil heavy metals pollution assessment and mapping, which could contribute to reduce the cost of surveys and pollution remediation.