Hotspot Mapping Research Articles

P-1454. Characterizing the Prevalence of Recurrent Antimicrobial Resistant Infections in an Urban County

Abstract Background Individuals treated for antimicrobial resistant organism (AMRO) infections are exposed to broader antibiotics, which can contribute to an increased likelihood of future resistant infections. Given that community transmission and AMRO recurrence remains underexplored, we conducted spatiotemporal analyses of recurrent AMRO infections in an urban county. Figure 1 Map of spatiotemporal hotspots and recurrent infection counts per 1,000 population (CP1K) for five patterns of antimicrobial resistance at a census block group level. Methods We mapped the spatial prevalence of recurrent AMRO infections for census block groups using electronic health records from two major health systems in Tarrant County (Texas) from 2010-2019. We delineated recurrence across five different patterns: AmpC beta-lactamase producing bacteria (AmpC), Carbapenem-Resistant Enterobacterales (CRE), Extended spectrum beta lactamase (ESBL), Methicillin-Resistant Staphylococcus aureus (MRSA), and Vancomycin-resistant Enterococcus (VRE). We compared recurrent AMRO rates against space-time permutation model identified hotspots. We used spatial lag regressions to explore the relationship between recurrent AMRO rates and Area Deprivation Index (ADI), a measure of neighborhood inequity. Results Of 105,291 patients, 19,161 (18.2%) had at least one AMRO infection. There were 10,611 (10.1%) patients with multiple AMRO infections: AmpC = 202, CRE = 136, ESBL = 741, MRSA = 1255, and VRE = 205. We observed that AmpC, ESBL, and MRSA recurrence had the greatest co-location with spatiotemporal hotspots (Figure 1). In spatial regressions, increasing neighborhood ADI rank (increasing deprivation) was significantly associated with an increase in block group-rates of recurrent AmpC (β = 0.02), ESBL (β = 0.03), MRSA (β = 0.04) and VRE (β = 0.01) (p-value < 0.05). Conclusion We geographically and temporally identified patterns of recurrent AMROs in a large urban county and demonstrated a significant relationship between inequity and AMRO recurrence. This work can enhance our understanding of local infection dynamics and inform antibiotic stewardship. Disclosures John J. Hanna, MD, Pieces Technologies: Advisor/Consultant

Mapping hotspots of zoonotic pathogen emergence: an integrated model-based and participatory-based approach.

An increase in pandemics of zoonotic origin has led to a growing interest in using statistical prediction to identify hotspots of zoonotic emergence. However, the rare nature of pathogen emergence requires modellers to impose simplifying assumptions, which limit the model's validity. We present a novel approach to hotspot mapping that aims to improve validity by combining model-based insights with expert knowledge. We conducted a systematic literature review to identify predictors for zoonotic emergence events in three priority virus families (Filoviridae, Coronaviridae, and Paramyxoviridae). We searched PubMed, Web of Science, Agricola, medRxiv, bioRxiv, Embase, CAB Global Health, and Google Scholar on Oct 14-28, 2021, with no restrictions on language or the date of publication. Articles suggested by subject matter experts and those identified by a review of reference lists were also included. We used regularised regression to fit a model to the data extracted from the literature and produced maps of ranked risk. In a series of workshops in five countries (Kenya, Peru, Senegal, Thailand, and Viet Nam), experts in zoonotic diseases produced qualitative hotspot maps based on their expertise, which were compared with the model-derived maps. 425 articles were analysed, from which 19 predictors and 1068 outcome events were identified. The in-sample misclassification error was 0·365, and 89% of participant-selected zones were ranked as moderate or high risk by the model. Participant-selected zones were too large to be actionable without further refinement. Discordance was probably due to missing predictors for which no valid data exist, and homogeneity imposed by our global model. Concordance between the two sets of maps supports the validity of each. Because model-based and participatory strategies have non-overlapping limitations, the results can be harmonised to minimise bias, and model-based results could be used to refine participant-selected zones. This approach shows potential for refining deployment of countermeasures to prevent future pandemics. US Agency for International Development.

Prevalence of HIV infection and uptake of HIV/AIDs services among fishermen on the shores of Lake Victoria in Kagera region, Northwestern Tanzania.

The Tanzania HIV Impact Survey (THIS) 2022-2023 showed that HIV prevalence among the general population stabilises but varies geographically across the country. Despite this, disproportionate burdens of HIV continue among specific subpopulations, such as fishermen. Fishermen are particularly vulnerable to HIV infection and have a low uptake of HIV prevention and treatment services. This study aimed to understand the prevalence of HIV infection, uptake of HIV/AIDs services, and associated risk factors of HIV Infection among fishermen residing along the shores of Lake Victoria in the Kagera region, Tanzania, in 2024. A cross-sectional study among fishermen was employed from February to April 2024, using convenient sampling of 10 beach management units (BMUs) to obtain a robust sample of fishermen aged ≥15 years in Kagera. Participants were randomly selected across BMUs. Information was collected using an interviewer-administered questionnaire, and HIV testing was offered on-site according to national testing guidelines. Multivariable logistic regression was used to determine factors associated with HIV infection, adjusted for potential confounders. A total of 774 fishermen with a median age of 31 years (interquartile range: 25-38 years) were recruited. The study found an HIV prevalence of 11.3% (95% CI: 9.2-13.8). HIV prevalence varied across selected districts: 12.7% in Muleba District, 10.1% in Bukoba Rural District, and 8.6% in Bukoba Urban District. The HIV prevalence was highest among fishermen aged 20-24 years (14.7% (95% CI: 9.7-21.5). The majority, 60.9% (95% CI: 57.4-64.2), had tested for HIV in the past 12 months. Of those living with HIV, 77.6% (95% CI: 67.3-85.9) self-reported using anti-retroviral therapy. In adjusted analysis, using alcohol before sex (aOR = 2.32, 95 CI: 1.42-3.80), not testing for HIV in the last 12 months (aOR = 4.69, 95% CI: 2.79-7.88), and not using condoms (aOR = 1.94, 95% CI: 1.13-3.27) were significantly associated with HIV infection among fishermen. HIV prevalence among fishermen was nearly twice as high as in the general population in Kagera. HIV programming should be strengthened to reduce new HIV infections. Hotspot mapping to expand HIV prevention and treatment services is pivotal for controlling the HIV epidemic.

Smart Urban Forest Initiative: Nature-Based Solution and People-Centered Approach for Tree Management in Chiang Mai, Thailand

This research created urban forest management using GIS as the primary instrument to act as a combined technique that allows the locals to participate in the survey. To maintain a sustainable urban green, urban tree management is necessary to reduce complexity and conflict. The initiative used a nature-based solution for tree care depending on species combined with a people-centered smart city approach to better assess tree health in historic urban areas. A total of 4607 records were obtained from the field survey event utilizing a mobile application as a tool. The tree’s basic name, spatial character, position, and potential risk were all gathered during the field survey. As GIS converted the tree’s general or local name into its scientific name, it was able to view and evaluate the data. The findings indicate that trees are most in danger from animals and insects, accounting for 56.39% (2748) of the total risk. Most of them are in areas with poor soil suitability. Through optimized hot-spot analysis mapping, the study recommended that tree care be prioritized. Maps of tree blooming and fruiting indicate the possibility of enhancing the advantages of urban trees in the research region in accordance with their phenological patterns.

Genome-wide mapping of spontaneous DNA replication error-hotspots using mismatch repair proteins in rapidly proliferating Escherichia coli.

Fidelity of DNA replication is crucial for the accurate transmission of genetic information across generations, yet errors still occur despite multiple control mechanisms. This study investigated the factors influencing spontaneous replication errors across the Escherichia coli genome. We detected errors using the MutS and MutL mismatch repair proteins in rapidly proliferating mutH-deficient cells, where errors can be detected but not corrected. Our findings reveal that replication error hotspots are non-randomly distributed along the chromosome and are enriched in sequences with distinct features: lower thermal stability facilitating DNA strand separation, mononucleotide repeats prone to DNA polymerase slippage and sequences prone to forming secondary structures like cruciforms and G4 structures, which increase likelihood of DNA polymerase stalling. These hotspots showed enrichment for binding sites of nucleoid-associated proteins, RpoB and GyrA, as well as highly expressed genes, and depletion of GATC sequence. Finally, the enrichment of single-stranded DNA stretches in the hotspot regions establishes a nexus between the formation of secondary structures, transcriptional activity and replication stress. In conclusion, this study provides a comprehensive genome-wide map of replication error hotspots, offering a holistic perspective on the intricate interplay between various mechanisms that can compromise the faithful transmission of genetic information.

Hotspot mapping of pest introductions in the EU: A regional analysis of environmental, anthropogenic and spatial effects

Plant pests may pose a significant threat to global agriculture, natural ecosystems and biodiversity, causing severe ecological and economic damage. Identifying regions more susceptible to pest introductions is crucial for developing effective prevention, early detection and outbreak response strategies. While historical data on pest introductions in the European Union (EU) exist, they are typically reported at the regional level. This broad aggregation has posed a challenge for accurate analysis in plant health research. This study addresses this gap by leveraging existing regional data to identify hotspots of pest introductions within the EU and the UK, through a Bayesian hierarchical spatial model. Specifically, we employed the Besag, York, and Mollié (BYM) model to identify higher risk regions (NUTS2) by incorporating covariates and spatial effects to consider information from neighbouring areas. The results showed a positive effect of annual average temperature, annual average precipitation, and human population density on the risk of pest introduction, highlighting the relevance of the spatial component. Our analysis pinpoints high-risk regions in southern Europe, particularly northern Italy. Additionally, the high human population density and documented pest introductions in the Netherlands contributed to its elevated risk. While limitations exist due to the regional nature of the data, this study represents a methodological advancement, demonstrating the effectiveness of spatial models and offering a robust framework for future studies using regional data. It also provides insights that can inform targeted prevention, early detection and preparedness strategies, ultimately contributing to safeguarding agriculture, natural ecosystems and biodiversity in the EU and UK.

The sacred and the wild: Unpacking human-wildlife interaction in Indian part of Kailash Sacred Landscape

The negative interaction between humans and wildlife in the Indian Himalayas has increased in recent decades. Studies on human-wildlife negative interactions have primarily focused on protected areas. However, research on such interactions is limited in regions like the Indian part of Kailash Sacred Landscape (KSL), where wildlife and humans share a common space. This study examined the patterns of crop damage, livestock depredation, human fatalities, seasonal and temporal variations, location-specific factors, and critical variables contributing to negative humanwildlife interactions in KSL India. We examined 574 incidences of livestock depredation, 123 incidences of human injury/death between 1984-2016, and 560 crop damage incidences between 2002-2017. Most incidences of human injury/death and livestock depredation were by common leopards, followed by Asiatic black bears. Maize was the highest raided crop (n=202), followed by wheat (n=190) and paddy (n=168). Maximum incidences of crop loss were reported in 2017 (n=76), and the lowest was in 2002 (n=5). The hotspot maps of crop damage and livestock depredation revealed that the negative interaction between humans and wildlife was highest in the southern part of the landscape. The result predicted 195 villages as hotspots in the Indian part of KSL. Villages were identified at the forest range level. We found that 11 villages in Askot, 12 in Munshiyari, 17 in Berinag, 15 in Dharchula, 36 in Didihat, 61 in Gangolihat, and 43 in Pithoragarh forest ranges are severely affected by negative interaction with wildlife.

A GIS‐Based Decision Support System to Study the Impact of Land Use Policies on Water‐Related Ecosystem Services—A Case Study of Kozhikode, Kerala

ABSTRACTThis study tries to review the land use policies from the perspective of their impact on the ecosystem and identify significant negative LULC changes and their spatial distribution. The study proposes a decision support system to evaluate land‐use policies based on the coupling coordination degree (CCD) of LULC and water‐related ecosystem service (WES) changes. By studying the nature of the interaction, changes in the intensity and significance of land use and land cover (LULC) changes with negative WES changes, the implications of the government LULC policies and schemes are assessed. Unlike general LULC‐WES studies that focus on the influence of the LULC on WES in a cross‐section of time, this study focuses on the changes in the WES due to changes in LULC over the longitudinal section of time at pixel‐level resolution. For illustration, one of the fastest urbanizing districts in Kerala, Kozhikode, was selected. From 2003 to 2013, LULC‐driven WES changes were highest in municipal areas and the western coast, shifting to midlands and urban centers in 2013–2022. Critical land use changes included the conversion of plantations and farms to built‐up areas (2003‐2013), and plantations to farms and barren lands (2013–2022). The 2018 Kerala Forest Policy effectively reduced forest conversion, while several agricultural and conservation policies proved ineffective. Key ecological concerns include farm‐to‐urban conversion and plantation abandonment in urbanizing areas. The study emphasizes the need for targeted, ecologically informed agricultural policies to address these issues and improve WES management. The spatial mapping of LULC changes hotspots that are detrimental to WES and assists urban decision‐makers in the formulation of decentralized, participatory, and spatially targeted land use controls.

Clinical and genetic analysis of a patient with Ataxia and vitamin E deficiency due to homozygous variant of TTPA gene

To explore the clinical phenotype and genetic characteristics of a patient with Ataxia and vitamin E deficiency syndrome (AVED) due to a variant of TTPA gene. A patient diagnosed with AVED (proband), intended for assisted reproductive technology for pregnancy in Zhongnan Hospital of Wuhan University in July 2023, was selected as research subject. Clinical data of the proband were collected, and 2 mL of peripheral venous blood samples were collected from the proband and her father and siblings for serum vitamin E level testing. Whole exome sequencing (WES) was carried out. Pathogenic variants were selected based on American public archive of interpretations of clinically relevant variants (ClinVar). Sanger sequencing was performed to validate the candidate variants detected by WES. Pathogenicity of variants was classified based on the guidelines from the American College of Medical Genetics and Genomics (ACMG), and the impact of variants was analyzed using multiple bioinformatics tools including SIFT, Mutation Taster, CADD, and SpliceAI. Information on the protein domains was obtained from ClinVar and dbSNP databases, and a hotspot map for the variants of protein-coding region was constructed. This study was approved by the Medical Ethics Committee of Zhongnan Hospital of Wuhan University (No. 2023068K). The proband has a significantly low serum level of vitamin E (5.186 μg/mL), while her father and siblings were normal. WES revealed that she has harbored a homozygous missense c.2T>A (p.0?) variant of the TTPA gene, for which her father and younger sister were heterozygous carriers. Based on the guidelines from the ACMG, the missense c.2T>A (p.0?) variant of the TTPA gene was classified as pathogenic (PVS1+PM2+PM3). Multiple bioinformatics tools had predicted this variant to be located in the initiation codon region and may lead to abnormal synthesis of the TTPA protein, indicating it was deleterious. The hotspot map based on ClinVar and dbSNP databases showed an even distribution of variants across 5 structural domains of the TTPA protein, with high conservation of the first amino acid residue across various species. The homozygous c.2T>A (p.0?) variant of the TTPA gene probably underlie the AVED in the proband. Above discovery has enriched the mutational spectrum of AVED and provided a basis for the diagnosis, genetic counseling, and assisted reproductive strategies for this family.

Application of GIS for Monitoring Firefly Population Abundance (Pteroptyx tener) and the Influence of Abiotic Factors

This study focuses on the <i>Pteroptyx tener</i> species in the Sepetang River, Malaysia, aiming to evaluate the firefly’s abundance and explore its correlation with various biotic and abiotic parameters. The study was conducted over six months, from November 2021 to April 2022, utilizing Geographic Information System (GIS) software to apply hotspot mapping and Inverse Distance Weighting (IDW) analysis to elucidate the spatial distribution of firefly populations. A total of 111,615 individuals were recorded, with a particular focus on this firefly species’ presence on their display trees. Hotspot analysis showed that Station 6, located at the mouth of a river with dense mangroves, hosted 55,723 fireflies (50.01%). In contrast, Stations 9 and 10, near ponds and shrimp settlements, recorded 517–723 fireflies (0.65% and 0.46%). Pearson’s correlation coefficient (r) unveiled a statistically significant positive correlation (r = 0.88, p < 0.05) between wind speed and the abundance of firefly populations within the Sepetang River. However, no statistically significant correlation (p > 0.05) was found between firefly abundance and various other abiotic parameters, including relative humidity (RH), air temperature, tide level, pH, electrical conductivity (EC), salinity, total dissolved solids (TDS), and water clarity. Thus, the results revealed the preference for fireflies due to the availability of vegetation, wind speed and minimal disturbance in this area. In conclusion, this study’s information significantly adds to our understanding of these interesting insects and their complicated relationships in nature. It underscores the importance of preserving their habitats and ecosystems.

Inequalities in emergency service accessibility: spatial analysis of urban infrastructure

PurposeThis study aims to investigate the relationship between emergency response times and crash severity in New York City, focusing on spatial disparities and their implications. It examines how these disparities impact disadvantaged neighborhoods, particularly regarding traffic safety and emergency service accessibility.Design/methodology/approachThe research uses comprehensive spatial analysis techniques, including hotspot mapping, network analysis for travel time modeling, local bivariate correlation analysis and service area calculations. It maps crash data alongside emergency facility locations, considering peak-hour traffic. The Area Deprivation Index (ADI) is integrated to evaluate socioeconomic factors influencing accessibility. This approach provides a nuanced understanding of how emergency response times correlate with crash severity at the census block level, accounting for socioeconomic disparities.FindingsThis study reveals significant disparities in emergency response times across New York City. In high-poverty, predominantly minority areas, response times are 2–3 min longer than average, correlating with a 15% increase in severe injury rates. Over 20% of neighborhoods show correlations between response times and crash severity, with positive linear (5.51%), negative linear (10.72%), concave (2.44%) and convex (2.80%) relationships. Blocks with positive linear relationships have a mean ADI rank of 3.918. During peak hours, 69.7% of Manhattan blocks show negative relationships, the highest among boroughs.Originality/valueThis research highlights spatial justice issues in urban emergency response systems, emphasizing the need for localized, data-driven planning and infrastructure adjustments. By integrating the ADI, the multifaceted approach reveals the complex dynamics of socioeconomic factors and emergency service accessibility that have not yet been investigated in diverse urban communities.

Identification and Ranking of Binding Sites from Structural Ensembles: Application to SARS-CoV-2.

Target identification and evaluation is a critical step in the drug discovery process. Although time-intensive and complex, the challenge becomes even more acute in the realm of infectious disease, where the rapid emergence of new viruses, the swift mutation of existing targets, and partial effectiveness of approved antivirals can lead to outbreaks of significant public health concern. The COVID-19 pandemic, caused by the SARS-CoV-2 virus, serves as a prime example of this, where despite the allocation of substantial resources, Paxlovid is currently the only effective treatment. In that case, significant effort pre-pandemic had been expended to evaluate the biological target for the closely related SARS-CoV. In this work, we utilize the computational hot spot mapping method, FTMove, to rapidly identify and rank binding sites for a set of nine SARS-CoV-2 drug/potential drug targets. FTMove takes into account protein flexibility by mapping binding site hot spots across an ensemble of structures for a given target. To assess the applicability of the FTMove approach to a wide range of drug targets for viral pathogens, we also carry out a comprehensive review of the known SARS-CoV-2 ligandable sites. The approach is able to identify the vast majority of all known sites and a few additional sites, which may in fact be yet to be discovered as ligandable. Furthermore, a UMAP analysis of the FTMove features for each identified binding site is largely able to separate predicted sites with experimentally known binders from those without known binders. These results demonstrate the utility of FTMove to rapidly identify actionable sites across a range of targets for a given indication. As such, the approach is expected to be particularly useful for assessing target binding sites for any emerging pathogen, as well as for indications in other disease areas, and providing actionable starting points for structure-based drug design efforts.

Air pollution’s numerical, spatial, and temporal heterogeneous impacts on childhood hand, foot and mouth disease: a multi-model county-level study from China

BackgroundWhile stationary links between childhood hand, foot and mouth disease (HFMD) and air pollution are known, a comprehensive study on their heterogeneous relationships (nonstationarity), jointly considering numerical, temporal and spatial dimensions, has not been reported.MethodsMonthly HFMD incidence and air pollution data were collected at the county level from Sichuan-Chongqing, China (2009–2011), alongside meteorological and social environmental covariates. Key influential factors were identified using random forest (RF) under the stationary assumption. Factors’ numerically, temporally, and spatially heterogeneous relationships with HFMD were assessed using generalized additive model (GAM) and geographically and temporally weighted regression (GTWR).ResultsOur findings highlighted the relatively higher stationary contributions of fine particulate matter (PM2.5) and ozone (O3) to HFMD incidence across Sichuan-Chongqing counties. We further uncovered heterogeneous impacts of PM2.5 and O3 from three nonstationary perspectives. Numerically, PM2.5 showed an inverse ‘V’-shaped relationship with HFMD incidence, while O3 exhibited a complex pattern, with increased HFMD incidence at low PM2.5 and moderate O3 concentrations. Temporally, PM2.5’s impact peaked in autumn and was weakest in spring, whereas O3’s effect was strongest in summer. Spatially, hotspot mapping revealed high-risk clusters for PM2.5 impact across all seasons, with notable geographical variations, and for O3 in spring, summer, and autumn, concentrated in specific regions of Sichuan-Chongqing.ConclusionsThis study underscores the nuanced and three-perspective heterogeneous influences of air pollution on HFMD in small areas, emphasizing the need for differentiated, localized, and time-sensitive prevention and control strategies to enhance the precision of dynamic early warnings and predictive models for HFMD and other infectious diseases, particularly in the fields of environmental and spatial epidemiology.

Wasted shores: Using drones to monitor the spatio-temporal evolution of debris accumulation hotspots on South Africa's Umgeni River

Rivers are major contributors of plastic waste to the oceans. Running through the northern part of the 1.3 million-inhabitants City of Durban, South Africa, the Umgeni River is estimated to flush in the order of tens to hundreds of tonnes of plastic waste into the Indian Ocean every year. The riverbanks are lined with plastic and other macro-waste accumulation zones formed due to direct littering and occasional deposition of river debris loads. This study presents the use of Unmanned Aerial Vehicles (UAVs) and hydro-meteorological sensors to (1) identify, quantify and monitor such anthropogenic macro-waste hotspots; and (2) investigate the influence of rainfall, river water level and a major flood event on the spatio-temporal evolution of the hotspots, evidencing the debris' availability to leak into the Indian Ocean. The one-year aerial monitoring (2021−2022) of waste hotspots shows that extreme hydrometeorological events have an immediate but short-term effect on the erosion of debris stocks in riverine systems. We observe that reduction in mean index changes of hotspot surface area after flooding were 2–5 times higher than in non-flood conditions. Despite visual evidence of seasonality in debris erosion between the wet and dry season, only the 'natural' type hotspots showed a significant change. Our findings support reported inconsistencies of macro-debris erosion with hydrological factors. Although the data contributes a baseline for macro-debris erosion in the Umgeni River, future ground truth sampling and finer monitoring scales are important to fully understand debris transfer in river systems. The mapping of waste hotspots and understanding their spatio-temporal transfer dynamics supports policymakers in planning and timing to mitigate environmental pollution.

Enhancing public opinion monitoring for social hot events with a time series neural network-based logic map

A method for constructing a commercial map of social hotspots based on time-series neural networks is proposed. It explores map construction through three stages: atomic event graph building, fusion, and public opinion analysis. The neural network captures event dynamics and public opinion evolution. An unsupervised learning approach trains the model to explore deep event connections. A time map reflects event trends and opinion changes. A sentence-level event extraction model provides rich information. An empirical mode decomposition method analyzes IMF data to understand intrinsic patterns. Time-series neural networks classify and reconstruct event graphs, constructing a knowledge graph. Experiments show this method reduces storage costs, simplifies prediction, and exhibits robustness. It offers new insights for commercial mapping and contributes to public opinion monitoring.

Seasonal anomaly of particulate matter concentration in an equatorial climate: Evaluating the transboundary impact from neighboring provinces on Padang City, Indonesia.

This study investigated the anomalous seasonal variations in particulate matter (PM) concentrations-specifically PM2.5 and PM10-in Padang City, Indonesia, situated within the Equatorial climate zone. A one-year dataset of half-hourly PM measurements from January to December 2023, collected by the Air Quality Monitoring System (AQMS) managed by the Environmental Agency of West Sumatra (DLH), was utilized. Maps of hotspots and air mass backward trajectories were used to identify possible transboundary emissions affecting Padang City. Despite the region experiencing nearly continuous rainfall, significant elevations in PM levels were observed during the typically drier months of August to October. Specifically, PM2.5 levels peaked at 36.57µg/m3 and PM10 at 39.58µg/m3 in October, significantly higher than in other months and indicating a substantial deviation from the typical expectations for equatorial climates. These results suggest that the high PM concentrations are not solely due to local urban emissions or normal seasonal variations but are also significantly influenced by transboundary smoke from peatland fires and agricultural burning in neighboring provinces such as Bengkulu, Riau, Jambi, and South Sumatra. Backward trajectory analysis further confirmed the substantial impact of regional activities on degradation of air quality in Padang City. The study underscores the need for integrated air quality management that includes both local and transboundary pollution sources. Enhanced monitoring, public engagement, and inter-regional collaboration are emphasized as crucial strategies for mitigating the adverse effects of PM pollution in equatorial regions like Padang City.

Super-Resolution Surface-Enhanced Raman Scattering: Perspectives on the Past, Present, and Future.

Super-resolution surface-enhanced Raman scattering (SERS) allows researchers to overcome the resolution limit of far field optical microscopy and peer into electromagnetic hot spots with nanoscale resolution. By localizing the signal from single (or few) molecules on the surface of plasmonic nanoparticle aggregates, relationships between the spatial origin of the SERS signal, local electromagnetic field enhancements, and SERS intensity can be determined. This Perspective describes the successes and challenges of super-resolution SERS, from the earliest mapping of single-molecule SERS hot spots to the current state-of-the-art, while highlighting open questions and future opportunities to advance the field. Comparisons with fluorescence-based super-resolution imaging are discussed to help frame the unique challenges associated with performing SERS in the super-resolution regime.

The Effects of Layout Order on Interface Complexity: An Eye-Tracking Study for Dashboard Design.

This study investigated the effect of layout order on the complexity of the dashboard interface based on screen-based eye trackers. By simplifying and abstracting dashboard interfaces and incorporating subjective ratings (symmetry and unity calculations), we successfully manipulated the levels of complexity and layout order of the interface materials. Using four types of eye movement data (total fixation count, total gaze duration, scanning paths, and hotspot maps) and behavioral data, we compared participants' visual search behavior on interfaces with different layout orders and complexity levels. Experiment 1 revealed a significant interaction between layout order and interface complexity, with participants performing significantly better in the high-level layout order condition. Experiment 2 confirmed that the position of the core chart plays a crucial role in users' visual search behavior and that the optimal layout order for the dashboard is to place the core chart on the left side of the interface's horizontal axis, with partial symmetry in the no-core chart areas. This study highlights the effectiveness of eye-tracking techniques in user interface design research and provides valuable insights into optimizing dashboard interface design. Designers should adopt the design principle of "order is more" in addition to "less is more" and consider designing the core chart in the left-center position.

Weight Impact on Comparative Evaluation of Topographic Data

The paper addresses the problem of weighting in an analysis that supports the selection of a categorical data set according to user needs. Using the Relative Change (RC) of the Compound Correspondence Index (CCI), it is shown that weights have a significant impact on user choice – reaching extreme values in both urbanized and forested areas. Decreasing the weights from 0.25 to 0.17 in forested and built-up areas resulted in the maximum variations that were seen in the hot spot maps, with cold areas generally corresponding to builtup regions and hot areas to forested areas. The analysis covers seven counties that are located in different regions of Poland: Pomerania, Podlasie, Mazovia, Greater Poland and the Beskidy Mountains.

Surface and average volume temperature measurements in semitransparent media based on multispectral thermotransmittance

Temperature plays an essential role in maintaining the performance of microsystems, which calls for three-dimensional mapping of heat sources, hot spots and thermal properties. In this context, infrared (IR) thermography provides information for opaque materials but faces limitations when applied to semitransparent samples such as microfluidic chips or semiconductor materials in the IR range. In this article, we present temperature maps obtained for the surface and average volume of two semitransparent samples with a detection limit of 1 K. To achieve such measurements, we demonstrated that thermotransmittance signals can be decomposed into two parts: the first originating from interfaces and the second originating from the volume. We quantified the contributions of both components by combining contactless measurements with a heat transfer model. We then validated the method using two Borofloat glass samples with different thicknesses. We were able to discriminate the contributions of surface and volume temperatures by performing spectroscopic thermotransmittance measurements at wavelengths ranging from 3100 to 3400 nm due to the different spectral behaviors of these two components. This new technique is an important improvement for temperature measurements using IR cameras in semitransparent media.