Foundations of reasoning with uncertainty via real-valued logics.

Interest in logics with some notion of real-valued truths has existed since at least Boole and has been increasing in AI due to the emergence of neuro-symbolic approaches, though often their logical inference capabilities are characterized only qualitatively. We provide foundations for establishing the correctness and power of such systems. We introduce a rich class of multidimensional sentences, with a sound and complete axiomatization that can be parameterized to cover many real-valued logics, including all the common fuzzy logics, and extend these to weighted versions, and to the case where the truth values are probabilities. Our multidimensional sentences form a very rich class. Each of our multidimensional sentences describes a set of possible truth values for a collection of formulas of the real-valued logic, including which combinations of truth values are possible. Our completeness result is strong, in the sense that it allows us to derive exactly what information can be inferred about the combinations of truth values of a collection of formulas given information about the combinations of truth values of a finite number of other collections of formulas. We give a decision procedure based on linear programming for deciding, for certain real-valued logics and under certain natural assumptions, whether a set of our sentences logically implies another of our sentences. The generality of this work, compared to many previous works on special cases, may provide insights for both existing and new real-valued logics whose inference properties have never been characterized. This work may also provide insights into the reasoning capabilities of deep learning models.

scAMAC: self-supervised clustering of scRNA-seq data based on adaptive multi-scale autoencoder.

Cluster assignment is vital to analyzing single-cell RNA sequencing (scRNA-seq) data to understand high-level biological processes. Deep learning-based clustering methods have recently been widely used in scRNA-seq data analysis. However, existing deep models often overlook the interconnections and interactions among network layers, leading to the loss of structural information within the network layers. Herein, we develop a new self-supervised clustering method based on an adaptive multi-scale autoencoder, called scAMAC. The self-supervised clustering network utilizes the Multi-Scale Attention mechanism to fuse the feature information from the encoder, hidden and decoder layers of the multi-scale autoencoder, which enables the exploration of cellular correlations within the same scale and captures deep features across different scales. The self-supervised clustering network calculates the membership matrix using the fused latent features and optimizes the clustering network based on the membership matrix. scAMAC employs an adaptive feedback mechanism to supervise the parameter updates of the multi-scale autoencoder, obtaining a more effective representation of cell features. scAMAC not only enables cell clustering but also performs data reconstruction through the decoding layer. Through extensive experiments, we demonstrate that scAMAC is superior to several advanced clustering and imputation methods in both data clustering and reconstruction. In addition, scAMAC is beneficial for downstream analysis, such as cell trajectory inference. Our scAMAC model codes are freely available at https://github.com/yancy2024/scAMAC.

Fuzzy kernel evidence Random Forest for identifying pseudouridine sites.

Pseudouridine is an RNA modification that is widely distributed in both prokaryotes and eukaryotes, and plays a critical role in numerous biological activities. Despite its importance, the precise identification of pseudouridine sites through experimental approaches poses significant challenges, requiring substantial time and resources.Therefore, there is a growing need for computational techniques that can reliably and quickly identify pseudouridine sites from vast amounts of RNA sequencing data. In this study, we propose fuzzy kernel evidence Random Forest (FKeERF) to identify pseudouridine sites. This method is called PseU-FKeERF, which demonstrates high accuracy in identifying pseudouridine sites from RNA sequencing data. The PseU-FKeERF model selected four RNA feature coding schemes with relatively good performance for feature combination, and then input them into the newly proposed FKeERF method for category prediction. FKeERF not only uses fuzzy logic to expand the original feature space, but also combines kernel methods that are easy to interpret in general for category prediction. Both cross-validation tests and independent tests on benchmark datasets have shown that PseU-FKeERF has better predictive performance than several state-of-the-art methods. This new method not only improves the accuracy of pseudouridine site identification, but also provides a certain reference for disease control and related drug development in the future.

Denoising adaptive deep clustering with self-attention mechanism on single-cell sequencing data.

A large number of works have presented the single-cell RNA sequencing (scRNA-seq) to study the diversity and biological functions of cells at the single-cell level. Clustering identifies unknown cell types, which is essential for downstream analysis of scRNA-seq samples. However, the high dimensionality, high noise and pervasive dropout rate of scRNA-seq samples have a significant challenge to the cluster analysis of scRNA-seq samples. Herein, we propose a new adaptive fuzzy clustering model based on the denoising autoencoder and self-attention mechanism called the scDASFK. It implements the comparative learning to integrate cell similar information into the clustering method and uses a deep denoising network module to denoise the data. scDASFK consists of a self-attention mechanism for further denoising where an adaptive clustering optimization function for iterative clustering is implemented. In order to make the denoised latent features better reflect the cell structure, we introduce a new adaptive feedback mechanism to supervise the denoising process through the clustering results. Experiments on 16 real scRNA-seq datasets show that scDASFK performs well in terms of clustering accuracy, scalability and stability. Overall, scDASFK is an effective clustering model with great potential for scRNA-seq samples analysis. Our scDASFK model codes are freely available at https://github.com/LRX2022/scDASFK.

Structures of calmodulin-melittin complexes show multiple binding modes lacking classical anchoring interactions.

Calmodulin (CaM) is a Ca2+ sensor protein found in all eukaryotic cells that regulates a large number of target proteins in a Ca2+ concentration-dependent manner. As a transient-type hub protein, it recognizes linear motifs of its targets, though for the Ca2+-dependent binding, no consensus sequence was identified. Its complex with melittin, a major component of bee venom, is often used as a model system of protein-protein complexes. Yet, the structural aspects of the binding are not well understood, as only diverse, low-resolution data are available concerning the association. We present the crystal structure of melittin in complex with Ca2+-saturated CaMs from two, evolutionarily distant species, Homo sapiens and Plasmodium falciparum, representing three binding modes of the peptide. Results-augmented by molecular dynamics simulations-indicate that multiple binding modes can exist for CaM-melittin complexes, as an intrinsic characteristic of the binding. While the helical structure of melittin remains, swapping of its salt bridges and partial unfolding of its C-terminal segment can occur. In contrast to the classical way of target recognition by CaM, we found that different sets of residues can anchor at the hydrophobic pockets of CaM, which were considered as main recognition sites. Finally, the nanomolar binding affinity of the CaM-melittin complex is created by an ensemble of arrangements of similar stability-tight binding is achieved not by optimized specific interactions but by simultaneously satisfying less optimal interaction patterns in co-existing different conformers.

Living cells and biological mechanisms as prototypes for developing chemical artificial intelligence.

Artificial Intelligence (AI) is having a revolutionary impact on our societies. It is helping humans in facing the global challenges of this century. Traditionally, AI is developed in software or through neuromorphic engineering in hardware. More recently, a brand-new strategy has been proposed. It is the so-called Chemical AI (CAI), which exploits molecular, supramolecular, and systems chemistry in wetware to mimic human intelligence. In this work, two promising approaches for boosting CAI are described. One regards designing and implementing neural surrogates that can communicate through optical or chemical signals and give rise to networks for computational purposes and to develop micro/nanorobotics. The other approach concerns "bottom-up synthetic cells" that can be exploited for applications in various scenarios, including future nano-medicine. Both topics are presented at a basic level, mainly to inform the broader audience of non-specialists, and so favour the rise of interest in these frontier subjects.

scHFC: a hybrid fuzzy clustering method for single-cell RNA-seq data optimized by natural computation.

Rapid development of single-cell RNA sequencing (scRNA-seq) technology has allowed researchers to explore biological phenomena at the cellular scale. Clustering is a crucial and helpful step for researchers to study the heterogeneity of cell. Although many clustering methods have been proposed, massive dropout events and the curse of dimensionality in scRNA-seq data make it still difficult to analysis because they reduce the accuracy of clustering methods, leading to misidentification of cell types. In this work, we propose the scHFC, which is a hybrid fuzzy clustering method optimized by natural computation based on Fuzzy C Mean (FCM) and Gath-Geva (GG) algorithms. Specifically, principal component analysis algorithm is utilized to reduce the dimensions of scRNA-seq data after it is preprocessed. Then, FCM algorithm optimized by simulated annealing algorithm and genetic algorithm is applied to cluster the data to output a membership matrix, which represents the initial clustering result and is taken as the input for GG algorithm to get the final clustering results. We also develop a cluster number estimation method called multi-index comprehensive estimation, which can estimate the cluster numbers well by combining four clustering effectiveness indexes. The performance of the scHFC method is evaluated on 17 scRNA-seq datasets, and compared with six state-of-the-art methods. Experimental results validate the better performance of our scHFC method in terms of clustering accuracy and stability of algorithm. In short, scHFC is an effective method to cluster cells for scRNA-seq data, and it presents great potential for downstream analysis of scRNA-seq data. The source code is available at https://github.com/WJ319/scHFC.

circRNA-binding protein site prediction based on multi-view deep learning, subspace learning and multi-view classifier.

Circular RNAs (circRNAs) generally bind to RNA-binding proteins (RBPs) to play an important role in the regulation of autoimmune diseases. Thus, it is crucial to study the binding sites of RBPs on circRNAs. Although many methods, including traditional machine learning and deep learning, have been developed to predict the interactions between RNAs and RBPs, and most of them are focused on linear RNAs. At present, few studies have been done on the binding relationships between circRNAs and RBPs. Thus, in-depth research is urgently needed. In the existing circRNA-RBP binding site prediction methods, circRNA sequences are the main research subjects, but the relevant characteristics of circRNAs have not been fully exploited, such as the structure and composition information of circRNA sequences. Some methods have extracted different views to construct recognition models, but how to efficiently use the multi-view data to construct recognition models is still not well studied. Considering the above problems, this paper proposes a multi-view classification method called DMSK based on multi-view deep learning, subspace learning and multi-view classifier for the identification of circRNA-RBP interaction sites. In the DMSK method, first, we converted circRNA sequences into pseudo-amino acid sequences and pseudo-dipeptide components for extracting high-dimensional sequence features and component features of circRNAs, respectively. Then, the structure prediction method RNAfold was used to predict the secondary structure of the RNA sequences, and the sequence embedding model was used to extract the context-dependent features. Next, we fed the above four views' raw features to a hybrid network, which is composed of a convolutional neural network and a long short-term memory network, to obtain the deep features of circRNAs. Furthermore, we used view-weighted generalized canonical correlation analysis to extract four views' common features by subspace learning. Finally, the learned subspace common features and multi-view deep features were fed to train the downstream multi-view TSK fuzzy system to construct a fuzzy rule and fuzzy inference-based multi-view classifier. The trained classifier was used to predict the specific positions of the RBP binding sites on the circRNAs. The experiments show that the prediction performance of the proposed method DMSK has been improved compared with the existing methods. The code and dataset of this study are available at https://github.com/Rebecca3150/DMSK.

Structured Sparse Regularized TSK Fuzzy System for predicting therapeutic peptides.

Therapeutic peptides act on the skeletal system, digestive system and blood system, have antibacterial properties and help relieve inflammation. In order to reduce the resource consumption of wet experiments for the identification of therapeutic peptides, many computational-based methods have been developed to solve the identification of therapeutic peptides. Due to the insufficiency of traditional machine learning methods in dealing with feature noise. We propose a novel therapeutic peptide identification method called Structured Sparse Regularized Takagi-Sugeno-Kang Fuzzy System on Within-Class Scatter (SSR-TSK-FS-WCS). Our method achieves good performance on multiple therapeutic peptides and UCI datasets.

Analyzing fuzzy boundary value problems: a study on the influence of mitochondria and ER fluxes on calcium ions in neuron cells.

Cytosolic-free calcium ions play an important role in various physical and physiological processes. A vital component of neural signaling is the free calcium ion concentration often known as the second messenger. There are many parameters that effect the cytosolic free calcium concentration like buffer, voltage-gated ion channels, Endoplasmic reticulum, Mitochondria, etc. Mitochondria are small organelles located within the nervous system that are involved in processes within cells such as calcium homeostasis management, energy generation, response to stress, and cell demise pathways. In this work, a mathematical model with fuzzy boundary values has been developed to study the effect of Mitochondria and ER fluxes on free Calcium ions. The intended findings are displayed utilizing the physiological understanding that amyloid beta plaques and tangles of neurofibrillary fibers have been identified as the two main causes of AD. The key conclusion of the work is the investigation of [Formula: see text] for healthy cells and cells affected by Alzheimer's disease, which may aid in the study of such processes for computational scientists and medical practitioners. Also, it has been shown that when a unique solution is found for a specific precise problem, it also successfully deals with any underlying ambiguity within the problem by utilizing a technique based on the principles of linear transformation. Furthermore, the comparison between the analytical approach and the generalized hukuhara derivative approach is shown here, which illustrates the benefits of the analytical approach. The simulation is carried out in MATLAB.

Risky-Choice Framing Effects Result Partly From Mismatched Option Descriptions in Gains and Losses.

Textbook psychology holds that people usually prefer a certain option over a risky one when options are framed as gains but prefer the opposite when options are framed as losses. However, this pattern can be amplified, eliminated, or reversed depending on whether option descriptions include only positive information (e.g., "200 people will be saved"), only negative information (e.g., "400 people will not be saved"), or both. Previous studies suggest that framing effects arise only when option descriptions are mismatched across frames. Using online and student samples (Ns = 906 and 521), we investigated 81 framing-effect variants created from matched and mismatched pairs of 18 option descriptions (nine in each frame). Description valence or gist explained substantial variation in risk preferences (prospect theory does not predict such variation), but a considerable framing effect remained in our balanced design. Risky-choice framing effects appear to be partly-but not completely-the result of mismatched comparisons.

Enhancing Control Over Nitric Oxide Photorelease via a Molecular Keypad Lock.

Based on Boolean logic, molecular keypad locks secure molecular information, typically with an optical output. Here we investigate a rare example of a molecular keypad lock with a chemical output. To this end, the light-activated release of biologically important nitric oxide from a ruthenium complex is studied, using proton concentration and photon flux as inputs. In a pH-dependent equilibrium, a nitritoruthenium(II) complex is turned into a nitrosylruthenium(II) complex, which releases nitric oxide under irradiation with visible light. The precise prediction of the output nitric oxide concentration as function of the pH and photon flux is achieved with an artificial intelligence approach, namely the adaptive neuro-fuzzy inference system. In this manner an exceptionally high level of control over the output concentration is obtained. Moreover, the provided concept to lock a chemical output as well as the output prediction may be applied to other (photo)release schemes.

Binary classification with fuzzy logistic regression under class imbalance and complete separation in clinical studies.

BACKGROUND: In binary classification for clinical studies, an imbalanced distribution of cases to classes and an extreme association level between the binary dependent variable and a subset of independent variables can create significant classification problems. These crucial issues, namely class imbalance and complete separation, lead to classification inaccuracy and biased results in clinical studies. METHOD: To deal with class imbalance and complete separation problems, we propose using a fuzzy logistic regression framework for binary classification. Fuzzy logistic regression incorporates combinations of triangular fuzzy numbers for the coefficients, inputs, and outputs and produces crisp classification results. The fuzzy logistic regression framework shows strong classification performance due to fuzzy logic's better handling of imbalance and separation issues. Hence, classification accuracy is improved, mitigating the risk of misclassified conditions and biased insights for clinical study patients. RESULTS: The performance of the fuzzy logistic regression model is assessed on twelve binary classification problems with clinical datasets. The model has consistently high sensitivity, specificity, F1, precision, and Mathew's correlation coefficient scores across all clinical datasets. There is no evidence of impact from the imbalance or separation that exists in the datasets. Furthermore, we compare the fuzzy logistic regression classification performance against two versions of classical logistic regression and six different benchmark sources in the literature. These six sources provide a total of ten different proposed methodologies, and the comparison occurs by calculating the same set of classification performance scores for each method. Either imbalance or separation impacts seven out of ten methodologies. The remaining three produce better classification performance in their respective clinical studies. However, these are all outperformed by the fuzzy logistic regression framework. CONCLUSION: Fuzzy logistic regression showcases strong performance against imbalance and separation, providing accurate predictions and, hence, informative insights for classifying patients in clinical studies.

To risk or not: The impact of socioeconomic status on preschoolers' risky decision-making for gains and losses.

Disparities in socioeconomic status (SES) may affect individuals' risk preferences, which have important developmental consequences across the lifespan. Yet, previous research has shown inconsistent associations between SES and risky decision-making, and little is known about how this link develops from a young age. The current research is among the first to examine how SES influences preschoolers' risky decisions in both gain and loss frames. Across two studies, children aged 5 to 6 years (total N = 309, 154 boys) were asked to choose between certain and risky options. The risky option was more advantageous, equal to, or less advantageous than the certain option. Study 1 revealed that in the loss frame, high-SES children (n = 84, 44 boys) chose more risky options and were more sensitive to the expected value compared to low-SES children (n = 78, 42 boys), especially when the risk was more advantageous. However, this SES difference was not significant in the gain frame. Supporting the potential causal link between SES and risky decision-making, Study 2 further found that experimentally increasing low-SES children's (n = 68, 30 boys) status by providing additional resources increased their risk-seeking behavior in the loss frame. Overall, our findings suggest an interaction between environmental cues (gain vs. loss) and early life circumstances (SES) in shaping children's risk preferences. RESEARCH HIGHLIGHTS: This research is among the first to examine how school backgrounds and experimentally manipulated SES influence preschoolers' risk preferences in gain and loss frames. Children were more risk-seeking for losses than for gains; this framing effect was stronger in higher-SES than lower-SES children. Lower-SES children exhibited fewer risk-seeking behaviors and decreased sensitivity to the expected value of options for losses, but not for gains. A temporary boost in SES increased children's risk-seeking behavior, but not sensitivity to expected values.

Re-ordered fuzzy conformance checking for uncertain clinical records.

Modern hospitals implement clinical pathways to standardize patients' treatments. Conformance checking techniques provide an automated tool to assess whether the actual executions of clinical processes comply with the corresponding clinical pathways. However, clinical processes are typically characterized by a high degree of uncertainty, both in their execution and recording. This paper focuses on uncertainty related to logging clinical processes. The logging of the activities executed during a clinical process in the hospital information system is often performed manually by the involved actors (e.g., the nurses). However, such logging can occur at a different time than the actual execution time, which hampers the reliability of the diagnostics provided by conformance checking techniques. To address this issue, we propose a novel conformance checking algorithm that leverages principles of fuzzy set theory to incorporate experts' knowledge when generating conformance diagnostics. We exploit this knowledge to define a fuzzy tolerance in a time window, which is then used to assess the magnitude of timestamp violations of the recorded activities when evaluating the overall process execution compliance. Experiments conducted on a real-life case study in a Dutch hospital show that the proposed method obtains more accurate diagnostics than the state-of-the-art approaches. We also consider how our diagnostics can be used to stimulate discussion with domain experts on possible strategies to mitigate logging uncertainty in the clinical practice.

Implementation and evaluation of a system for assessment of the quality of long-term management of patients at a geriatric hospital.

BACKGROUND: The increasing aging population presents a significant challenge, accompanied by a shortage of professional caregivers, adding to the therapeutic burden. Clinical decision support systems, utilizing computerized clinical guidelines, can improve healthcare quality, reduce expenses, save time, and boost caregiver efficiency. OBJECTIVES: 1) Develop and evaluate an automated quality assessment (QA) system for retrospective longitudinal care quality analysis, focusing on clinical staff adherence to evidence-based guidelines (GLs). 2) Assess the system's technical feasibility and functional capability for senior nurse use in geriatric pressure-ulcer management. METHODS: A computational QA system using our Quality Assessment Temporal Patterns (QATP) methodology was designed and implemented. Our methodology transforms the GL's procedural-knowledge into declarative-knowledge temporal-abstraction patterns representing the expected execution trace in the patient's data for correct therapy application. Fuzzy temporal logic allows for partial compliance, reflecting individual and grouped action performance considering their values and temporal aspects. The system was tested using a pressure ulcer treatment GL and data from 100 geriatric patients' Electronic Medical Records (EMR). After technical evaluation for accuracy and feasibility, an extensive functional evaluation was conducted by an experienced nurse, comparing QA scores with and without system support, and versus automated system scores. Time efficiency was also measured. RESULTS: QA scores from the geriatric nurse, with and without system's support, did not significantly differ from those provided by the automated system (p < 0.05), demonstrating the effectiveness and reliability of both manual and automated methods. The system-supported manual QA process reduced scoring time by approximately two-thirds, from an average of 17.3 min per patient manually to about 5.9 min with the system's assistance, highlighting the system's efficiency potential in clinical practice. CONCLUSION: The QA system based on QATP, produces scores consistent with an experienced nurse's assessment for complex care over extended periods. It enables quick and accurate quality care evaluation for multiple patients after brief training. Such automated QA systems may empower nursing staff, enabling them to manage more patients, accurately and consistently, while reducing costs due to saved time and effort, and enhanced compliance with evidence-based guidelines.

The effects of parental retirement on adult children's health: Evidence from China.

Using representative data from China, we examine the causal effects of parental retirement on the health of adult children. To do so, we adopt a fuzzy regression discontinuity design and exploit the mandatory retirement ages in China as cut-off points. We find no evidence that parental retirement has significant effects on the mental health, healthcare utilization, or risky health behaviors of adult children. However, paternal retirement and maternal retirement have different effects on adult children's Self-reported health (SRH). Paternal retirement has a significantly negative effect only on the SRH of sons, while maternal retirement does not induce such effects. Potential mechanisms of intergenerational transfer through which parental retirement might affect adult children's health are also explored.

Flamingo Jelly Fish search optimization-based routing with deep-learning enabled energy prediction in WSN data communication.

Nowadays, wireless sensor networks (WSN) have gained huge attention worldwide due to their wide applications in different domains. The limited amount of energy resources is considered as the main limitations of WSN, which generally affect the network life time. Hence, a dynamic clustering and routing model is designed to resolve this issue. In this research work, a deep-learning model is employed for the prediction of energy and an optimization algorithmic technique is designed for the determination of optimal routes. Initially, the dynamic cluster WSN is simulated using energy, mobility, trust, and Link Life Time (LLT) models. The deep neuro-fuzzy network (DNFN) is utilized for the prediction of residual energy of nodes and the cluster workloads are dynamically balanced by the dynamic clustering of data using a fuzzy system. The designed Flamingo Jellyfish Search Optimization (FJSO) model is used for tuning the weights of the fuzzy system by considering different fitness parameters. Moreover, routing is performed using FJSO model which is used for the identification of optimal path to transmit data. In addition, the experimentation is done using MATLAB tool and the results proved that the designed FJSO model attained maximum of 0.657J energy, a minimum of 0.739 m distance, 0.649 s delay, 0.849 trust, and 0.885 Mbps throughput.

An intelligent interval forecasting system based on fuzzy time series and error distribution characteristics for air quality index.

Due to the emergency environment pollution problems, it is imperative to understand the air quality and take effective measures for environmental governance. As a representative measure, the air quality index (AQI) is a single conceptual index value simplified by the concentrations of several routinely monitored air pollutants according to the proportion of various components in the air. With the gradual enhancement of awareness of environmental protection, air quality index forecasting is a key point of environment management. However, most of the traditional forecasting methods ignore the fuzziness of original data itself and the uncertainty of forecasting results which causes the unsatisfactory results. Thus, an innovative forecasting system combining data preprocessing technique, kernel fuzzy c-means (KFCM) clustering algorithm and fuzzy time series is successfully developed for air quality index forecasting. Concretely, the fuzzy time series that handle the fuzzy set is used for the main forecasting process. Then the complete ensemble empirical mode decomposition and KFCM are respectively developed for data denoising and interval partition. Furthermore, the interval forecasting method based on error distribution is developed to measure the forecasting uncertainty. Finally, the experimental simulation and evaluation system verify the great performance of proposed forecasting system and the promising applicability in a practical environment early warning system.

Tracing vulnerable communities to ambient air pollution exposure: A geodemographic and remote sensing approach.

Most studies analyzing the effects of air pollution on disadvantaged populations use ground air quality measurements. However, ground stations are generally limited, with nearly 40% of countries having no official PM2.5 stations, not allowing air quality analysis for a significantly large share of the world's population. Furthermore, limited studies analyze community data from a geodemographic perspective, in other words, to delineate the sociodemographic profiles and geographically locate the socioeconomic groups more exposed to ambient air pollution. Therefore, a significant question arises: How can we trace vulnerable communities to air pollution in areas lacking air-quality ground data? Here, we propose a novel methodology to respond to this question. We use NO2, SO2, CO, and HCHO tropospheric column air-quality data from Sentinel-5P, a satellite that quantifies concentrations of atmospheric species from space operationally. We integrate them with census and environmental data and apply the local fuzzy geographically weighted clustering spatial machine learning method for segmentation analysis. Our findings for Bali, Indonesia, provide quantitative evidence for the benefits of this methodology in tracing and delineating the profiles of the communities most exposed to air pollution. For example, results show that communities with highly disadvantaged populations, such as unemployed (over 27.8%), low educated (over 27.9%), and children (over 22.1%) (mainly located around Bali's south and north coast touristic areas), exhibit very high values (over the 75th quartile) across the pollutants studied. The proposed method is reproducible easily, quickly, and at low cost, as it is based on freely available satellite data and not on costly ground station measurements. This will hopefully assist decision-makers in tracing the most vulnerable subpopulations, even in areas with inadequate air-quality monitoring networks, thus allowing local governments around the globe (even those that are financially weak) to achieve environmental justice and their sustainable development goals.