Heatmap Research Articles

УПРАВЛЕНИЕ НАДЕЖНОСТЬЮ И РИСКОМ В ХОЗЯЙСТВЕ АВТОМАТИКИ И ТЕЛЕМЕХАНИКИ

the article is devoted to the research of dependability and risk management systems at JSCo “Russian Railways” taking into account the specifics in signaling and interlocking division. On this topic the author reviews the current regulatory framework (domestic and foreign), addressing to the problems of its harmonization and proper adaptation within the company. The state of affairs in automation of dependability and risk management processes is considered, which is implemented in several corporate automated systems: “AS URRAN”, “AS ANSh”, “EKP URRAN-Sh”, “AS ANPSh”, “EK ASUI FA”, “AS FA”. A comparative analysis of these systems made it possible to determine the main directions for their improvement in order to maintain the authenticity, relevance and importance of the information contained in them. Despite many ways to model events that may lead to a violation of the transportation process safety, the article discusses one of them: the creation of the tree structure of probable events that have a sequential connection with each other. The author believes that the application of this method in practice will allow to establish typical trends in the occurrence of such cases. It will have a positive impact on the quality of predictive analytics in the company and will improve the existing risk-based approach. In addition, a rethought way to determine and visually present the level of train delays risk caused by failures in signaling and interlocking division is proposed. The central place here is occupied by heat maps of risk and its components.

The Spatial Variation of Soil Structure Fractal Derived from Particle Size Distributions at the Basin Scale

The accurate characterization of soil structure is fundamental to groundwater science, environmental ecology, and Earth systems science. To address the challenge of quantifying the high spatial variability of large-scale soil structures, this study used a laser particle size analyzer to measure the distribution of soil particle size in 207 samples from ten profiles across the Daqing and Ziya River basins in the North China Plain. The quantified soil structure, expressed as soil fractal dimension D, was derived using monofractal theory. Various spatial analysis techniques, including Moran’s I index, correlation analysis heat maps, the Kolmogorov–Smirnov one-sample test, and geostatistical semivariogram function, were jointly applied to investigate the spatial variability of soil structural fractals across different depths in the piedmont plain–coastal areas of the two river basins. The results indicate the following: (1) Quantitative analysis confirms that under the influence of piedmont alluvial and fluvial dynamics, soil D values homogenize from the piedmont to the coastal areas, with decreasing particle size differences closer to the coast. However, the spatial variability of the soil structural fractals in the Ziya River Basin was greater than that in the Daqing River Basin. (2) The combined effects of climate change, regional differences, and human activity led to greater spatial variability in the soil structural fractals in the Ziya River Basin than in the Daqing River Basin. The correlation between D values and burial depth was strongest in the Xianxian profile (−0.78), whereas the spatial correlation was strongest in the Hengshui and Dacheng profiles (−0.47). (3) The greatest spatial variability in soil D values occurred at depths of 1–2 m, with a coefficient of variation of 23.595%, which was significantly higher than those at depths of 0–1 (14.569%) and 2–3 m (16.284%).

Channeling personal initiative through team coordination: A heat map analysis of soccer players’ aggregate behavioral initiative

Channeling personal initiative through team coordination: A heat map analysis of soccer players’ aggregate behavioral initiative

Rice Disease Image Classification using MobileNetV2 Pretrained Model with AcivationAttention Visualization using Gradient-weighted Class Activation Mapping (Grad-CAM)

Rice diseases is one of the recurring factors of failed harvest and reduced rice production output and it is one of the biggest problems for Indonesia especially because of Indonesia’s population dependent of rice as it’s staple food. Pest control using fast dan early detection is one of the solutions to reduce the potential of failed harvest. Using a pretrained model of MobileNetV2, a model has been successfully built to detect for different types of rice diseases using convolutional neural network architecture and transfer learning method. The modelling process uses 7.077 image sample of rice infected by bacterial blight, blast, brown spot, and tungro. The model then can be accessed through website and has 99% of classification accuracy. Furthermore, using Gradient-Weighted Class Activation Mapping (Grad-CAM) rice disease can then be marked on top of the original image as a heat map, providing the users easier way to localize dan verify the classification result faster.

Seasonal Study Chemical Profiles and Bioactivities of Hedychium coronarium J. Koenig Essential Oils: Nematicidal, Insecticidal, Phytotoxic, Antifungal Potentials, and Molecular Docking Insights.

Hedychium coronarium plant has attracted considerable attention from researchers due to its diverse phytochemical composition and potential therapeutic applications. The objective of this study was to evaluate the phytochemical profile and biological attributes of H. coronarium essential oils collected during different seasons in the Kumaun region of Uttarakhand. The essential oils were extracted from the rhizomes using hydrodistillation with a Clevenger-type apparatus yielding between 0.07% to 0.38% (w/w). The major compounds identified by GC-MS analysis exhibited seasonal variations and included 1,8-cineole (26.6-38.5%), coronarin E (11.9-18.8%), α-pinene (5.00-14.9%), α-terpineol (4.2-9.3%), (E)-β-caryophyllene (1.2-9.7%), and linalool (0.4-2.9%). To illustrate the variations in oil composition, visualization techniques such as heat map and PCA were employed. The essential oils demonstrated potential biological activity in all tests conducted. The molecular modeling study indicated that the potential mechanism may be associated with acetylcholinesterase. Consequently, this study contributes to the development of novel natural pesticides.

Ultrabroadband chromaticity-programmable random lasing based on waveguide-assisted pumping strategy

Chromaticity-tunable random lasers (RLs) have wide applications in laser display and imaging. However, the achievable chromaticity range poses challenges due to their inherent randomness and the inevitable loss. Here, an ultrabroadband chromaticity-programmable RL has been demonstrated via waveguide-assisted pumping strategy. The unique configuration with destroyed waveguide supplies an excellent platform for achieving full color random lasing through the cascade pumping process. Random lasing with tunable wavelengths spanning the entire visible range is achieved via side-pumping schemes. The eight acceptor RLs can be simultaneously pumped to obtain chromaticity-programmable random lasing, showcasing a Commission Internationale de l'Eclairage (CIE) color map with 155% more perceptible colors than the standard red-green-blue space. This opens the possibilities for programmable RLs with potential applications in biological imaging and smart sensing.

Radiomic detection of abnormal brain regions in tuberous sclerosis complex.

Radiomics refers to the extraction of quantitative information from medical images and is most commonly utilized in oncology to provide ancillary information for solid tumor diagnosis, prognosis, and treatment response. The traditional radiomic pipeline involves segmentation of volumes of interest with comparison to normal brain. In other neurologic disorders, such as epilepsy, lesion delineation may be difficult or impossible due to poor anatomic definition, small size, and multifocal or diffuse distribution. Tuberous sclerosis complex (TSC) is a rare genetic disease in which brain magnetic resonance imaging (MRI) demonstrates multifocal abnormalities with variable imaging and epileptogenic features. The purpose of this study was to develop a radiomic workflow for identification of abnormal brain regions in TSC, using a whole-brain atlas-based approach with generation of heatmaps based on signal deviation from normal controls. This was a retrospective pilot study utilizing high-resolution whole-brain 3D FLAIR MRI datasets from retrospective enrollment of tuberous sclerosis complex (TSC) patients and normal controls. Subjects underwent MRI including high-resolution 3D FLAIR sequences. Preprocessing included skull stripping, coregistration, and intensity normalization. Using the Brainnetome and Harvard-Oxford atlases, brain regions were parcellated into 318 discrete regions. Expert neuroradiologists spatially labeled all tubers in TSC patients using ITK-SNAP. The pyradiomics toolbox was used to extract 88 radiomic features based on IBSI guidelines, comparing tuber-affected and non-tuber-affected parenchyma in TSC patients, as well as normal brain tissue in control patients. For model training and validation, regions with tubers from 20 TSC patients and 30 normal control subjects were randomly divided into two training sets (80%) and two validation sets (20%). Additional model testing was performed on a separate group of 20 healthy controls. LASSO (least absolute shrinkage and selection operator) was used to perform variable selection and regularization to identify regions containing tubers. Relevant radiomic features selected by LASSO were combined to produce a radiomic score ω, defined as the sum of squared differences from average control group values. Region-specific ω scores were converted to heat maps and spatially coregistered with brain MRI to reflect overall radiomic deviation from normal. The proposed radiomic workflow allows for quantification of deviation from normal in 318 regions of the brain with the use of a summative radiomic score ω. This score can be used to generate spatially registered heatmaps to identify brain regions with radiomic abnormalities. The pilot study of TSC showed radiomic scores ω that were statistically different in regions containing tubers from regions without tubers/normal brain (p<0.0001). Our model exhibits an AUC of 0.81 (95% confidence interval: 0.78-0.84) on the testing set, and the best threshold obtained on the training set, when applied to the testing set, allows us to identify regions with tubers with a specificity of 0.91 and a sensitivity of 0.60. We describe a whole-brain atlas-based radiomic approach to identify abnormal brain regions in TSC patients. This approach may be helpful for identifying specific regions of interest based on relatively greater signal deviation, particularly in clinical scenarios with numerous or poorly defined anatomic lesions.

Understanding nitrogen allocation dynamics in Indian mustard: Insights from enzyme activity and ideotype analysis

The study explores the effect of varying levels of nitrogen (N) application on N allocation in Indian mustard (Brassica juncea) genotypes from vegetative to the generative phases. The use of a heat map and PCA biplot analysis identifies two distinct sets of ideotypes. The first set is represented by genotype IC212031 having “Stay-green” phenotype with effective N regulating enzymes, high N uptake (NUpt) and better root development. The second set of ideotype is represented by HB9902 having delayed flowering phenology phases. The study highlights the relevance of Nitrate reductase (NR) in the final oil buildup in seeds (r = 0.5, p = 0.09). The considerable rise in NR activity from pre-anthesis to post-anthesis stages translates to higher seed storage compounds such as oil (r=−0.65, p = 0.03). And the developed prediction models confirmed the significance of these stages (pre-anthesis and post-anthesis) in the accumulation of seed storage components with emphasis on oil content. Similarly, this prediction model helps us to study the cause and effect relationship between various factors and oil content. The notable rise in Nitrite reductase (NiR) activity during the post-anthesis phase suggests its role in assimilating N for the storage of seed components, most likely proteins. Further, the inverse relationship between seed N (−0.53,p= 0.09) and oil content (−0.65, p= 0.03) confirmed that at the post anthesis stage the allocation of N towards precursor carbon compounds happens, which in turn forms either proteins or lipids. This becomes a critical factor in determining the final composition of the seed. Irrespective of N treatment and genotype, the N allocation follows the pattern: seed > siliquae husk > stover > root. However, higher N application rates primarily result in increased NUpt, but not necessarily improve utilisation. Overall, the study highlights the roles of root characteristics, N metabolising enzymes, and flowering phenology in enhancing N allocation and seed production in Indian mustard offering valuable insights for breeding strategies to enhance seed yield, oil content and nutrient efficiency in oilseed crops.

Characterizing m6A modification factors and their interactions in colorectal cancer: implications for tumor subtypes and clinical outcomes

BackgroundThe study aims to comprehensively combine colorectal cancer data cohorts in order to analyze the effects of various DNA methylation-coding genes on colorectal cancer patients. The annual incidence and mortality of colorectal cancer are very high, and there are no effective treatments for advanced colorectal cancer. DNA methylation is a method widely used to regulate epigenetics in the molecular mechanism study of tumors.MethodThree single-cell cohorts GSE166555, GSE146771, and EMTAB8107, and five transcriptome cohorts GSE17536, GSE39582, GSE72970, and TCGA-CRC (TCGA-COAD and TCGA-READ) were applied in this study. 2 erasers (ALKBH5 and FTO), There are 7 writers (METTL3, METTL14, WTAP, VIRMA, RBM15, RBM15B, and ZC3H13) and 11 readers (YTHDC1, IGF2BP1, IGF2BP2, IGF2BP3, YTHDF1, YTHDF3, YTHDC2, and HNRNPA2B1, YTHDF2, HNRNPC and RBMX), a total of 20 M6A regulators, were used as the basis of the dataset in this study and were applied to the construction of molecular typing and prognostic models. Drugs that are differentially sensitive in methylation-regulated gene-related prognostic models were identified using the ConsensusClusterPlus package, which was also used to identify distinct methylation regulatory expression patterns in colorectal cancer and to model the relationship between tissue gene expression profiles and drug IC50 values. Finally, TISCH2 assessed which immune cells were significantly expressed with M6A scores. The immunosuppression of M6A methylation is spatially explained.ResultsThis study used data from 583 CRC patients in the TCGA-CRC cohort. Firstly, the mutation frequency and CNV variation frequency of 20 m6A modification-related factors were analyzed, and the corresponding histogram and heat map were drawn. The study next analyzed the expression variations between mutant and wild forms of the VIRMA gene and explored differences in the expression of these variables in tumor and normal tissues. In addition, the samples were divided into different subgroups by molecular clustering method based on m6A modification, and each subgroup’s expression and clinicopathological characteristics were analyzed. Finally, we compared prognostic differences, tumor microenvironment (TME) characteristics, immune cell infiltration, and gene function enrichment among different subpopulations. We also developed a colorectal cancer m6A-associated gene signature and validated its prognostic effects across multiple cohorts. Finally, using single-cell RNA sequencing data, we confirmed that tumor cells show elevated expression of m6A-related gene signatures.DiscussionThis study explored the mutation frequency, expression differences, interactions, molecular clustering, prognostic effect, and association with tumor characteristics of m6A modification-related factors in CRC and validated them at the single-cell level. These results clarify the association between m6A alteration and colorectal cancer (CRC) and offer important insights into the molecular recognition and management of cancer.

Characterization of volatile profiles and markers prediction of eleven popular edible boletes using SDE-GC–MS and FT-NIR combined with chemometric analysis

Wild edible boletes mushrooms are regarded as a delicacy in many countries and regions due to their rich nutritional contents and strong aromatic compounds. This study aimed to identify 445 samples of 11 boletes species collected from Yunnan and Sichuan provinces through molecular analysis. Using simultaneous distillation–extraction (SDE) combined with gas chromatography-mass spectrometry (GC–MS), 97 volatile compounds were identified. Chemometric methods were then applied to analyze the heterogeneity of these volatile compounds among the different species. The results showed that, 22 and 21 volatile compounds were selected using variable importance in projection (VIP > 1) and relative odor activity values (ROAV > 0.1), respectively. Partial least squares discrimnatint analysis (PLS-DA) was then employed to develop pattern recognition models for 11 species, which demonstrated strong identification performance. Furthermore, correlation heat maps, volcano plots, and Fisher linear discriminant analysis identified five volatile organic compounds, including methyl (9E)-9-octadecenoate, 2, 6-dimethylpyrazine, 1-decen-3-one, furfural, and methional as markers for distinguishing 11 boletes species. Ultimately, the rapid content prediction models of partial least squares regression (PLSR) were established by combining Fourier Transform Near-Infrared Spectroscopy (FT-NIR) with the concentrations of these five marker compounds. These findings provide a methodological strategy for the effective species identification of wild edible mushrooms and the rapid prediction of their characteristic aroma compounds.

Fertility, economic development, and remittances in post-communist times

ABSTRACT This study investigates fertility responses to remittances across developed and developing countries in post-communist times. We first collected fertility, remittances, and income statistics over the 1995–2020 period and created a new vulnerability index that identifies less economically developed states with high fertility rates and dependent on remittances. We then examined the global fertility effects of remittances between 1995 and 2015 via Ordinary Least Squares, Fixed Effects, and Instrumental Variable estimation methods. The baseline regression results suggest that the relationship between remittances and fertility rates of remittances-receiving countries is generally inverse. We also found that the fertility-reducing power of remittances is heterogeneous worldwide. To illustrate the findings, we constructed two heat maps for 196 countries. The first one depicts the distribution of the vulnerability index, and the second one is dedicated to variations in the fertility effects of remittances across countries based on the vulnerability index.

3-generation family histories of mental, neurologic, cardiometabolic, birth defect, asthma, allergy, and autoimmune conditions associated with autism: An open-source catalog of findings.

The relatively few conditions and family member types (e.g., sibling, parent) considered in investigations of family health history in autism spectrum disorder (ASD, or autism) limits understanding of the role of family history in autism etiology. For more comprehensive understanding and hypothesis-generation, we produced an open-source catalog of autism associations with family histories of mental, neurologic, cardiometabolic, birth defect, asthma, allergy, and autoimmune conditions. All live births in Denmark, 1980-2012, of Denmark-born parents (1,697,231 births), and their 3-generation family members were followed through April 10, 2017 for each of 90 diagnoses (including autism), emigration or death. Adjusted hazard ratios (aHR) were estimated via Cox regression for each diagnosis-family member type combination, adjusting for birth year, sex, birth weight, gestational age, parental ages at birth, and number of family member types of index person; aHRs also calculated for sex-specific co-occurrence of each disorder. We obtained 6462 individual family history aHRS across autism overall (26,840 autistic persons; 1.6% of births), by sex, and considering intellectual disability (ID); and 350 individual co-occurrence aHRS. Results are cataloged in interactive heat maps and down-loadable data files: https://ncrr-au.shinyapps.io/asd-riskatlas/ and interactive graphic summaries: https://public.tableau.com/app/profile/diana.schendel/viz/ASDPlots_16918786403110/e-Figure5. While primarily for reference material or use in other studies (e.g., meta-analyses), results revealed considerable breadth and variation in magnitude of familial health history associations with autism by type of condition, family member type, sex of the family member, side of the family, sex of the index person, and ID status, indicative of diverse genetic, familial, and nongenetic autism etiologic pathways. Careful attention to sources of autism likelihood in family health history, aided by our open data resource, may accelerate understanding of factors underlying neurodiversity.

Synesthetic Color Mapping of Chinese Characters and Kanji: Comparative Analysis among Grapheme-Color Synesthetes in Taiwan and Japan

Synesthetic Color Mapping of Chinese Characters and Kanji: Comparative Analysis among Grapheme-Color Synesthetes in Taiwan and Japan

Application of a Real-Time Field-Programmable Gate Array-Based Image-Processing System for Crop Monitoring in Precision Agriculture

Precision agriculture (PA) technologies combined with remote sensors, GPS, and GIS are transforming the agricultural industry while promoting sustainable farming practices with the ability to optimize resource utilization and minimize environmental impact. However, their implementation faces challenges such as high computational costs, complexity, low image resolution, and limited GPS accuracy. These issues hinder timely delivery of prescription maps and impede farmers’ ability to make effective, on-the-spot decisions regarding farm management, especially in stress-sensitive crops. Therefore, this study proposes field programmable gate array (FPGA)-based hardware solutions and real-time kinematic GPS (RTK-GPS) to develop a real-time crop-monitoring system that can address the limitations of current PA technologies. Our proposed system uses high-accuracy RTK and real-time FPGA-based image-processing (RFIP) devices for data collection, geotagging real-time field data via Python and a camera. The acquired images are processed to extract metadata then visualized as a heat map on Google Maps, indicating green area intensity based on romaine lettuce leafage. The RFIP system showed a strong correlation (R2 = 0.9566) with a reference system and performed well in field tests, providing a Lin’s concordance correlation coefficient (CCC) of 0.8292. This study demonstrates the potential of the developed system to address current PA limitations by providing real-time, accurate data for immediate decision making. In the future, this proposed system will be integrated with autonomous farm equipment to further enhance sustainable farming practices, including real-time crop health monitoring, yield assessment, and crop disease detection.

Tracking Varroa Parasitism Using Handheld Infrared Cameras: Is Eusocial Fever the Key?

The Varroa destructor is the most significant bee parasite and the greatest threat to bee health all around the world. Due to its hidden lifestyle, detection within the brood cell is only possible through invasive techniques. Enhancing detection methods is essential for advancing research on population dynamics, spread, selection efforts, and control methodologies against the mite. In our study, we employed infrared imaging to measure the thermal differences in parasite and intact Apis mellifera worker broods. Experiments were conducted over two years at the MATE Kaposvár Campus in Hungary involving five beehives in 2022 and five beehives in 2023. A FLIR E5-XT WIFI handheld infrared camera was used to create a heat map of capped brood frames. Our results indicate that the resolution of these cameras is sufficient to provide detailed IR images of a bee colony, making them suitable to detect temperature differences in intact and Varroa parasitized capped brood cells. Mite parasitism causes a time-dependent and sustained temperature increase in developing bee pupae, observable regardless of mite number. Our work demonstrates two different heating patterns: hotspot heating and heating cells that are responsible for the elevated temperature of the Varroa-infested cells as a social fever response by the worker bees. Based on our results, future research combined with AI-based image evaluation software could offer beekeepers and researchers practical and valuable tools for high-throughput, non-invasive Varroa detection in the field.

Relationships between the qNOX, qCON, burst suppression ratio, and muscle activity index of the CONOX monitor during total intravenous anesthesia: a pilot study.

Processed electroencephalographic (EEG) indices can help to navigate general anesthesia. The CONOX (Fresenius Kabi) calculates two indices, the qCON (hypnotic level) and the qNOX (nociception). The CONOX also calculates indices for electromyographic (EMG) activity and EEG burst suppression (BSR). Because all EEG parameters seem to influence each other, our goal was a detailed description of parameter relationships. We used qCON, qNOX, EMG, and BSR information from 14 patients receiving propofol anesthesia. We described index relationships with linear models, heat maps, and box plot representations. We also evaluated associations between qCON/qNOX and propofol/remifentanil effect site concentrations (ceP/ceR). qNOX and qCON (qCON = 0.79*qNOX + 5.8; p < 0.001; R2 = 0.84) had a strong linear association. We further confirmed the strong relationship between qCON/qNOX and BSR for qCON/qNOX < 25: qCON=-0.19*BSR + 25.6 (p < 0.001; R2 = 0.72); qNOX=-0.20*BSR + 26.2 (p < 0.001; R2 = 0.72). The relationship between qCON and EMG was strong at higher indices: qCON = 0.55*EMG + 33.0 (p < 0.001; R2 = 0.68). There was no qCON > 80 without EMG > 0. The relationship between ceP and qCON was qCON=-3.8*ceP + 70.6 (p < 0.001; R2 = 0.11). The heat maps also suggest that the qCON and qNOX can at least partially separate the hypnotic and analgetic components of anesthesia. We could describe relationships between qCON, qNOX, EMG, BSR, ceP, and ceR, which may help the anaesthesiologist better interpret the information provided. One major finding is the dependence of qCON > 80 on EMG activity. This may limit the possibility of detecting wakefulness in the absence of EMG. Further, qNOX seems generally higher than qCON, but high opioid doses may lead to higher qCON than qNOX indices.

Characterization of mango seed kernel starch: Extraction and Analysis

During mango (Mangifera indica) processing, significant by-products like peel and kernel are generated. Mango seed kernels are cost-effective and readily available materials for starch extraction. This study explores low-cost methods to extract mango kernel starch (MKS) from Surjapuri (X) and Amrapali (Y) varieties using distilled water and alkaline solution. Proximate analysis revealed starch yield variations from 44.49 % to 62.11 %, with moisture content between 4.32 % and 5.89 %, protein content from 0.067 % to 0.069 %, fat content from 0.22 % to 0.24 %, and ash content from 0.12 % to 0.14 %. X-variety starch extracted using distilled water had a higher yield (62.11 %), foaming capacity (5.94 %), and emulsion capacity (6.36 %), while alkaline extraction showed higher water absorption (0.16 %) and oil absorption (0.15 %). Y-variety starch showed higher amylose content (23.50 %) with distilled water extraction and higher pH (7.94) with alkaline extraction. X-variety starch had higher swelling power (11.35 g g-1) with distilled water extraction and higher solubility (0.27 %) with alkaline extraction at 90 °C. Principal component analysis (PCA), Heat map, and Pearson correlation analyses highlighted significant relationships between proximate and physicochemical properties. These results suggest that distilled water extraction is an effective, low-cost method for isolating high-quality MKS, with potential applications in various industries.

A comprehensive study of optical resonances in metals, dielectrics, and excitonic materials in double interface structures

From an optical perspective, depending on the relationship between the real (n) and imaginary (k) parts of its refractive index, three broad categories of materials can be distinguished: metals (k ≫ n), dielectrics (n ≫ k), and materials in which n ≈ k (termed here excitonic materials). The modes and optical resonances that appear in a thin film bounded by two dielectrics with similar refractive index, what we call here a double interface structure, have been widely studied in the case of metals, but not for dielectrics, or materials with n ≈ k. In this work, we propose a new approach, based on employing the phase matching condition to correlate the resonances that appear in the wavelength versus incident angle color maps of the reflected power with the modal analysis of the cross section of the structure. This analysis is performed, using an attenuated total reflection (ATR) setup, for thin film materials that belong to each of the mentioned categories: a metal (gold, Au), a dielectric (titanium dioxide, TiO2), and a material with n ≈ k (chromium, Cr). The theoretical analysis is supported with experimental results. It is demonstrated that this method enables to identify any resonance at any wavelength or incident angle, being valid for all three types of materials. Therefore, it is considered the suggested approach will help the research in these materials and in the double interface structure in the optics and photonics field.

Imaging of Volatile Organic Compounds Using a Single Nanowire-Based Electronic Nose for Future Biomedical Applications.

This study introduces an array of semiconductor oxide single nanowires fabricated using advanced semiconductor processing techniques, including electron beam lithography and thin-film deposition, which is well-suited for large-scale nanowire integration. A four-channel nanowire array consisting of tin oxide (SnO2), indium oxide (In2O3), ferric oxide (Fe3O4), and titanium oxide (TiO2) was developed. As a proof of concept, we converted the response curves of the sensor array to heat maps, enabling comprehensive feature representation. The fabricated electronic nose (E-nose) was utilized to detect three types of volatile organic compounds (VOCs), with the results visualized in a heat map format. Additionally, the performance of each individual sensor was quantitatively studied, highlighting the array's potential for enhanced gas detection and analysis. To further illustrate the interaction between gas molecules and the nanowires, we visualized the gas response results by mapping the sensor's signal changes. These visualizations provide a clear representation of how different gas molecules interact with specific nanowires. For example, the heat maps reveal distinct response patterns for each type of VOC, allowing for the identification and differentiation of gases based on their unique signatures. This visualization technique not only enhances the understanding of gas-nanowire interactions but also demonstrates the effectiveness of the E-nose in distinguishing between various VOCs. The SnO2 nanowire gas sensor showed enhanced gas response compared to other materials. The SnO2 and TiO2 gas sensors showed enhanced response (62 and 56 s) and recovery times (100 and 37 s).

Using heat map data to measure population development trends and urban disparities of large-scale affordable communities in China

Over the past decade, large-scale affordable communities have proliferated in the suburban areas of major Chinese cities. Most of the housing in these communities consists of affordable housing provided by the government and constructed by developers, targeted at middle to low-income individuals through restricted-price sales and rentals. With more than a decade of occupancy, it is crucial to examine the development status of these communities. This study employs regular collection of heat map data to observe the population density trends in communities from 2020 to 2024. By analyzing population density and its derived variables, the study examines changes in daytime and nighttime populations during weekdays, as well as the differences between the Spring Festival period and regular population data. Combining urban context, we use both quantitative and qualitative methods to analyze population quantity and structure trends and further explore the development disparities among cities. The study reveals a widespread increase in population across 35 communities in five cities from 2020 to 2021, peaking in 2021 and 2022, with differentiation among cities after 2023. Before 2022, there was a pattern in the development of community population quantity and structure: communities with faster population growth exhibited a simultaneous increase in commuter proportions. The impetus for community population changes is largely attributed to the varying attractiveness of cities to the floating population, with the five cities demonstrating distinct patterns. The method used in this study to collect population data, as opposed to traditional questionnaire surveys, presents advantages in terms of real-time, efficiency, convenience, and high sensitivity. This approach can offer feasible insights for establishing a real-time monitoring system for the development of large-scale affordable communities in China in the future.