Assessment of noise pollution-prone areas using an explainable geospatial artificial intelligence approach.

This research aims to use the power of geospatial artificial intelligence (GeoAI), employing the categorical boosting (CatBoost) machine learning model in conjunction with two metaheuristic algorithms, the firefly algorithm (CatBoost-FA) and the fruit fly optimization algorithm (CatBoost-FOA), to spatially assess and map noise pollution prone areas in Tehran city, Iran. To spatially model areas susceptible to noise pollution, we established a comprehensive spatial database encompassing data for the annual average Leq (equivalent continuous sound level) from 2019 to 2022. This database was enriched with critical spatial criteria influencing noise pollution, including urban land use, traffic volume, population density, and normalized difference vegetation index (NDVI). Our study evaluated the predictive accuracy of these models using key performance metrics, including root mean square error (RMSE), mean absolute error (MAE), and receiver operating characteristic (ROC) indices. The results demonstrated the superior performance of the CatBoost-FA algorithm, with RMSE and MAE values of 0.159 and 0.114 for the training data and 0.437 and 0.371 for the test data, outperforming both the CatBoost-FOA and CatBoost models. ROC analysis further confirmed the efficacy of the models, achieving an accuracy of 0.897, CatBoost-FOA with an accuracy of 0.871, and CatBoost with an accuracy of 0.846, highlighting their robust modeling capabilities. Additionally, we employed an explainable artificial intelligence (XAI) approach, utilizing the SHAP (Shapley Additive Explanations) method to interpret the underlying mechanisms of our models. The SHAP results revealed the significant influence of various factors on noise-pollution-prone areas, with airport, commercial, and administrative zones emerging as pivotal contributors.

Impact of elevated CO2 on soil microbiota: A meta-analytical review of carbon and nitrogen metabolism.

In the face of 21st-century challenges driven by population growth and resource depletion, understanding the intricacies of climate change is crucial for environmental sustainability. This review systematically explores the interaction between rising atmospheric CO2 concentrations and soil microbial populations, with possible feedback effects on climate change and terrestrial carbon (C) cycling through a meta-analytical approach. Furthermore, it investigates the enzymatic activities related to carbon acquisition, gene expression patterns governing carbon and nitrogen metabolism, and metagenomic and meta-transcriptomic dynamics in response to elevated CO2 levels. The study reveals that elevated CO2 levels substantially influence soil microbial communities, increasing microbial biomass C and respiration rate by 15 % and upregulating genes involved in carbon and nitrogen metabolism by 12 %. Despite a 14 % increase in C-acquiring enzyme activity, there is a 5 % decrease in N-acquiring enzyme activity, indicating complex microbial responses to CO2 changes. Additionally, fungal marker ratios increase by 14 % compared to bacterial markers, indicating potential ecosystem changes. However, the current inadequacy of data on metagenomic and meta-transcriptomic processes underscores the need for further research. Understanding soil microbial feedback mechanisms is crucial for elucidating the role of rising CO2 levels in carbon sequestration and climate regulation. Consequently, future research should prioritize a comprehensive elucidation of soil microbial carbon cycling, greenhouse gas emission dynamics, and their underlying drivers.

Clinical characteristics and long-term outcome of patients with bioprosthetic mitral valve- Experience from a South Asian country.

Objectives: Due to rheumatic heart disease, young people are more likely to develop valvular heart disease in developing countries. In countries like Pakistan, surgeons implant more bioprosthetic mitral valves (MVs) in younger patients. However, bioprosthetic valves degenerate rapidly in younger people, leading to bioprosthetic MV dysfunction (BMVD). This study aims to evaluate the clinical characteristics and long-term outcomes of patients with bioprosthetic MV replacement (MVR) at a tertiary care hospital in a South Asian country. Material and Methods: This is a retrospective observational study, conducted at a tertiary care hospital. We included a total of 502 patients who underwent bioprosthetic MVR from the year 2006 to 2020. Clinical and surgical characteristics along with transthoracic echocardiographic findings (pre-surgery and recent most follow-up studies) were noted. Follow-up data were also collected. Results: Out of 502 patients, 322 (64%) were female, mean age at the time of surgery was 49.42 ± 14.56 years. Mitral regurgitation was more common, found in 279 (55.6%) patients followed by mitral stenosis in 188 (37.5%) patients. MVR was done as an elective procedure due to the New York Heart Association (NYHA) II to IV symptoms at the time of surgery in 446 (88.8%) patients. In the mean follow-up of 6.59 ± 2.99 years, BMVD was observed in 183 (36.5%) patients. However, re-do MV surgery was done in only 49 (9.8%) patients. Patients were divided into two groups based on normal functioning bioprosthetic MV and BMVD. Comparing the two groups, individuals with normal functioning bioprosthetic MV had a mean age of 51.6 ± 14.27 years, while those with BMVD had a mean age of 45.639 ± 14.33 years at the time of index surgery (P = 0.000). There were more long-term complications including heart failure (n = 16, 8.74%), atrial fibrillation (n = 11, 6.01%), and death (n = 6, 3.28%) in the BMVD group which were statistically significant. Conclusion: This study is distinct because it demonstrates the outcomes of bioprosthetic valve replacement in a relatively younger South Asian population. Due to rapid degeneration of bioprosthetic valve in younger patients, significant number of patients developed BMVD along with poor long-term clinical outcomes, even at a short follow-up period of <10 years. These findings are similar to international data and signify that mechanical MVR may be a more reasonable alternative in younger patients.

Recent deep learning-based brain tumor segmentation models using multi-modality magnetic resonance imaging: a prospective survey.

Radiologists encounter significant challenges when segmenting and determining brain tumors in patients because this information assists in treatment planning. The utilization of artificial intelligence (AI), especially deep learning (DL), has emerged as a useful tool in healthcare, aiding radiologists in their diagnostic processes. This empowers radiologists to understand the biology of tumors better and provide personalized care to patients with brain tumors. The segmentation of brain tumors using multi-modal magnetic resonance imaging (MRI) images has received considerable attention. In this survey, we first discuss multi-modal and available magnetic resonance imaging modalities and their properties. Subsequently, we discuss the most recent DL-based models for brain tumor segmentation using multi-modal MRI. We divide this section into three parts based on the architecture: the first is for models that use the backbone of convolutional neural networks (CNN), the second is for vision transformer-based models, and the third is for hybrid models that use both convolutional neural networks and transformer in the architecture. In addition, in-depth statistical analysis is performed of the recent publication, frequently used datasets, and evaluation metrics for segmentation tasks. Finally, open research challenges are identified and suggested promising future directions for brain tumor segmentation to improve diagnostic accuracy and treatment outcomes for patients with brain tumors. This aligns with public health goals to use health technologies for better healthcare delivery and population health management.

Corn leaf disease: insightful diagnosis using VGG16 empowered by explainable AI.

The agricultural sector is pivotal to food security and economic stability worldwide. Corn holds particular significance in the global food industry, especially in developing countries where agriculture is a cornerstone of the economy. However, corn crops are vulnerable to various diseases that can significantly reduce yields. Early detection and precise classification of these diseases are crucial to prevent damage and ensure high crop productivity. This study leverages the VGG16 deep learning (DL) model to classify corn leaves into four categories: healthy, blight, gray spot, and common rust. Despite the efficacy of DL models, they often face challenges related to the explainability of their decision-making processes. To address this, Layer-wise Relevance Propagation (LRP) is employed to enhance the model's transparency by generating intuitive and human-readable heat maps of input images. The proposed VGG16 model, augmented with LRP, outperformed previous state-of-the-art models in classifying corn leaf diseases. Simulation results demonstrated that the model not only achieved high accuracy but also provided interpretable results, highlighting critical regions in the images used for classification. By generating human-readable explanations, this approach ensures greater transparency and reliability in model performance, aiding farmers in improving their crop yields.

The Diels-Alder Cross-Linked Gelatin/Dextran Nanocomposite Hydrogels with Silver Nanoparticles for Wound Healing Applications: Synthesis, Characterization, and In Vitro Evaluation.

Wound healing involves a sophisticated biological process that relies on ideal conditions to advance through various stages of repair. Modern wound dressings are designed to imitate the natural surroundings around cells and offer properties such as moisture regulation, strength, and antimicrobial defense to boost healing. A recent research project unveiled a new type of gelatin (Gel)/dextran (Dex) hydrogels, linked through Diels-Alder (D-A) reactions, loaded with silver nanoparticles (Ag-NPs) for cutting-edge wound treatment. Gel and Dex were chemically modified to form the hydrogels via the D-A reaction. The hydrogels were enriched with Ag-NPs at varying levels. Thorough analyses of the hydrogels using methods like NMR, FT-IR, and SEM were carried out to assess their structure and nanoparticle integration. Rheological tests displayed that the hydrogels had favorable mechanical attributes, particularly when Ag-NPs were included. The hydrogels demonstrated controlled swelling, responsiveness to pH changes, and were non-toxic. Testing against E. coli showcased the strong antibacterial activity of the nanocomposite hydrogels in a concentration-dependent manner. This investigation showcased the promise of these bioactive nanocomposite hydrogels in promoting speedy wound healing by maintaining a moist environment, offering an antimicrobial shield, and ensuring mechanical support at the wound site.

Revolutionizing tumor detection and classification in multimodality imaging based on deep learning approaches: Methods, applications and limitations.

BACKGROUND: The emergence of deep learning (DL) techniques has revolutionized tumor detection and classification in medical imaging, with multimodal medical imaging (MMI) gaining recognition for its precision in diagnosis, treatment, and progression tracking. OBJECTIVE: This review comprehensively examines DL methods in transforming tumor detection and classification across MMI modalities, aiming to provide insights into advancements, limitations, and key challenges for further progress. METHODS: Systematic literature analysis identifies DL studies for tumor detection and classification, outlining methodologies including convolutional neural networks (CNNs), recurrent neural networks (RNNs), and their variants. Integration of multimodality imaging enhances accuracy and robustness. RESULTS: Recent advancements in DL-based MMI evaluation methods are surveyed, focusing on tumor detection and classification tasks. Various DL approaches, including CNNs, YOLO, Siamese Networks, Fusion-Based Models, Attention-Based Models, and Generative Adversarial Networks, are discussed with emphasis on PET-MRI, PET-CT, and SPECT-CT. FUTURE DIRECTIONS: The review outlines emerging trends and future directions in DL-based tumor analysis, aiming to guide researchers and clinicians toward more effective diagnosis and prognosis. Continued innovation and collaboration are stressed in this rapidly evolving domain. CONCLUSION: Conclusions drawn from literature analysis underscore the efficacy of DL approaches in tumor detection and classification, highlighting their potential to address challenges in MMI analysis and their implications for clinical practice.

Revolutionizing agriculture with artificial intelligence: plant disease detection methods, applications, and their limitations.

Accurate and rapid plant disease detection is critical for enhancing long-term agricultural yield. Disease infection poses the most significant challenge in crop production, potentially leading to economic losses. Viruses, fungi, bacteria, and other infectious organisms can affect numerous plant parts, including roots, stems, and leaves. Traditional techniques for plant disease detection are time-consuming, require expertise, and are resource-intensive. Therefore, automated leaf disease diagnosis using artificial intelligence (AI) with Internet of Things (IoT) sensors methodologies are considered for the analysis and detection. This research examines four crop diseases: tomato, chilli, potato, and cucumber. It also highlights the most prevalent diseases and infections in these four types of vegetables, along with their symptoms. This review provides detailed predetermined steps to predict plant diseases using AI. Predetermined steps include image acquisition, preprocessing, segmentation, feature selection, and classification. Machine learning (ML) and deep understanding (DL) detection models are discussed. A comprehensive examination of various existing ML and DL-based studies to detect the disease of the following four crops is discussed, including the datasets used to evaluate these studies. We also provided the list of plant disease detection datasets. Finally, different ML and DL application problems are identified and discussed, along with future research prospects, by combining AI with IoT platforms like smart drones for field-based disease detection and monitoring. This work will help other practitioners in surveying different plant disease detection strategies and the limits of present systems.

Multi-scale spatiotemporal attention network for neuron based motor imagery EEG classification.

BACKGROUND: In recent times, the expeditious expansion of Brain-Computer Interface (BCI) technology in neuroscience, which relies on electroencephalogram (EEG) signals associated with motor imagery, has yielded outcomes that rival conventional approaches, notably due to the triumph of deep learning. Nevertheless, the task of developing and training a comprehensive network to extract the underlying characteristics of motor imagining EEG data continues to pose challenges. NEW METHOD: This paper presents a multi-scale spatiotemporal self-attention (SA) network model that relies on an attention mechanism. This model aims to classify motor imagination EEG signals into four classes (left hand, right hand, foot, tongue/rest) by considering the temporal and spatial properties of EEG. It is employed to autonomously allocate greater weights to channels linked to motor activity and lesser weights to channels not related to movement, thus choosing the most suitable channels. Neuron utilises parallel multi-scale Temporal Convolutional Network (TCN) layers to extract feature information in the temporal domain at various scales, effectively eliminating temporal domain noise. RESULTS: The suggested model achieves accuracies of 79.26%, 85.90%, and 96.96% on the BCI competition datasets IV-2a, IV-2b, and HGD, respectively. COMPARISON WITH EXISTING METHODS: In terms of single-subject classification accuracy, this strategy demonstrates superior performance compared to existing methods. CONCLUSION: The results indicate that the proposed strategy exhibits favourable performance, resilience, and transfer learning capabilities.

Nano guardians of the heart: A comprehensive investigation into the impact of silver nanoparticles on cardiovascular physiology.

Globally, cardiovascular diseases (CVDs) constitute the leading cause of death at the moment. More effective treatments to combat CVDs are urgently required. Recent advances in nanotechnology have opened the door to new avenues for cardiovascular health treatment. Silver nanotechnology's inherent therapeutic powers and wide-ranging applications have made it the center of focus in recent years. This review aims to analyze the chemical, physical, and biological processes ofproducing AgNPs and determine their potential utility as theranostics. Despite significant advances, the precise mechanism by which AgNPs function in numerous biological systems remains a mystery. We hope that at the end of this review, you will better understand how AgNPs affect the cardiovascular system from the research done thus far. This endeavor thoroughly investigates the possible toxicological effects and risks associated with exposure to AgNPs. The findings shed light on novel applications of these versatile nanomaterials and point the way toward future research directions. Due to a shortage of relevant research, we will limit our attention to AgNPs as they pertain to CVDs. Future research can use this opportunity to investigate the many medical uses of AgNPs. Given their global prevalence, we fully endorse academics' efforts to prioritize nanotechnological techniques in pursuing risk factor targeting for cardiovascular diseases. The critical need for innovative solutions to this widespread health problem is underscored by the fact that this technique may help with the early diagnosis and treatment of CVDs.

An AI-Enabled Bias-Free Respiratory Disease Diagnosis Model Using Cough Audio.

Cough-based diagnosis for respiratory diseases (RDs) using artificial intelligence (AI) has attracted considerable attention, yet many existing studies overlook confounding variables in their predictive models. These variables can distort the relationship between cough recordings (input data) and RD status (output variable), leading to biased associations and unrealistic model performance. To address this gap, we propose the Bias-Free Network (RBF-Net), an end-to-end solution that effectively mitigates the impact of confounders in the training data distribution. RBF-Net ensures accurate and unbiased RD diagnosis features, emphasizing its relevance by incorporating a COVID-19 dataset in this study. This approach aims to enhance the reliability of AI-based RD diagnosis models by navigating the challenges posed by confounding variables. A hybrid of a Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) networks is proposed for the feature encoder module of RBF-Net. An additional bias predictor is incorporated in the classification scheme to formulate a conditional Generative Adversarial Network (c-GAN) that helps in decorrelating the impact of confounding variables from RD prediction. The merit of RBF-Net is demonstrated by comparing classification performance with a State-of-The-Art (SoTA) Deep Learning (DL) model (CNN-LSTM) after training on different unbalanced COVID-19 data sets, created by using a large-scale proprietary cough data set. RBF-Net proved its robustness against extremely biased training scenarios by achieving test set accuracies of 84.1%, 84.6%, and 80.5% for the following confounding variables-gender, age, and smoking status, respectively. RBF-Net outperforms the CNN-LSTM model test set accuracies by 5.5%, 7.7%, and 8.2%, respectively.

NIR-responsive carboxymethyl-cellulose hydrogels containing thioketal-linkages for on-demand drug delivery system.

Near-infrared (NIR) light-responsive hydrogels have emerged as a highly promising strategy for effective anticancer therapy owing to the remotely controlled release of chemotherapeutic molecules with minimal invasive manner. In this study, novel NIR-responsive hydrogels were developed from reactive oxygen species (ROS)-cleavable thioketal cross-linkers which possessed terminal tetrazine groups to undergo a bio-orthogonal inverse electron demand Diels Alder click reaction with norbornene modified carboxymethyl cellulose. The hydrogels were rapidly formed under physiological conditions and generated N2 gas as a by-product, which led to the formation of porous structures within the hydrogel networks. A NIR dye, indocyanine green (ICG) and chemotherapeutic doxorubicin (DOX) were co-encapsulated in the porous network of the hydrogels. Upon NIR-irradiation, the hydrogels showed spatiotemporal release of encapsulated DOX (>96 %) owing to the cleavage of thioketal bonds by interacting with ROS generated from ICG, whereas minimal release of encapsulated DOX (<25 %) was observed in the absence of NIR-light. The in vitro cytotoxicity results revealed that the hydrogels were highly cytocompatible and did not induce any toxic effect on the HEK-293 cells. In contrast, the DOX + ICG-encapsulated hydrogels enhanced the chemotherapeutic effect and effectively inhibited the proliferation of Hela cancer cells when irradiated with NIR-light.

An adaptive ensemble deep learning framework for reliable detection of pandemic patients.

Nurses, often considered the backbone of global health services, are disproportionately vulnerable to COVID-19 due to their front-line roles. They conduct essential patient tests, including blood pressure, temperature, and complete blood counts. The pandemic-induced loss of nursing staff has resulted in critical shortages. To address this, robotic solutions offer promising avenues. To solve this problem, we developed an ensemble deep learning (DL) model that uses seven different models to detect patients. Detected images are then used as input for the soft robot, which performs basic assessment tests. In this study, we introduce a deep learning-based approach for nursing soft robots, and propose a novel deep learning model named Deep Ensemble of Adaptive Architectures. Our method is twofold: firstly, an ensemble deep learning technique detects COVID-19 patients; secondly, a soft robot performs basic assessment tests on the identified patients. We evaluate the performance of various deep learning-based object detectors for patient detection, examining implementations of You Only Look Once (YOLO), Single Shot MultiBox Detector (SSD), Region-Based Convolutional Neural Network (RCNN), and Region-Based Fully Convolutional Network (R-FCN) on a proprietary dataset comprising 32,668 hospital surveillance images. Our results indicate that while YOLO and VGG facilitate rapid detection, Faster-RCNN (Inception ResNet-v2) and our proposed Ensemble-DL achieve the highest accuracy. Ensemble-DL offers accurate results in a reasonable timeframe, making it apt for patient detection on embedded platforms. Through real-world experiments, our method outperforms baseline approaches (including Faster-RCNN, R-FCN variants, CNN+LSTM, etc.) in terms of both precision and recall. Achieving an impressive accuracy of 98.32%, our deep learning-based model for nursing soft robots presents a significant advancement in the identification and assessment of COVID-19 patients, ultimately enhancing healthcare efficiency and patient care.

Facile Fabrication of NIR-Responsive Alginate/CMC Hydrogels Derived through IEDDA Click Chemistry for Photothermal-Photodynamic Anti-Tumor Therapy.

Novel chemically cross-linked hydrogels derived from carboxymethyl cellulose (CMC) and alginate (Alg) were prepared through the utilization of the norbornene (Nb)-methyl tetrazine (mTz) click reaction. The hydrogels were designed to generate reactive oxygen species (ROS) from an NIR dye, indocyanine green (ICG), for combined photothermal and photodynamic therapy (PTT/PDT). The cross-linking reaction between Nb and mTz moieties occurred via an inverse electron-demand Diels-Alder chemistry under physiological conditions avoiding the need for a catalyst. The resulting hydrogels exhibited viscoelastic properties (G' ~ 492-270 Pa) and high porosity. The hydrogels were found to be injectable with tunable mechanical characteristics. The ROS production from the ICG-encapsulated hydrogels was confirmed by DPBF assays, indicating a photodynamic effect (with NIR irradiation at 1-2 W for 5-15 min). The temperature of the ICG-loaded hydrogels also increased upon the NIR irradiation to eradicate tumor cells photothermally. In vitro cytocompatibility assessments revealed the non-toxic nature of CMC-Nb and Alg-mTz towards HEK-293 cells. Furthermore, the ICG-loaded hydrogels effectively inhibited the metabolic activity of Hela cells after NIR exposure.

Federated and Transfer Learning Methods for the Classification of Melanoma and Nonmelanoma Skin Cancers: A Prospective Study.

Skin cancer is considered a dangerous type of cancer with a high global mortality rate. Manual skin cancer diagnosis is a challenging and time-consuming method due to the complexity of the disease. Recently, deep learning and transfer learning have been the most effective methods for diagnosing this deadly cancer. To aid dermatologists and other healthcare professionals in classifying images into melanoma and nonmelanoma cancer and enabling the treatment of patients at an early stage, this systematic literature review (SLR) presents various federated learning (FL) and transfer learning (TL) techniques that have been widely applied. This study explores the FL and TL classifiers by evaluating them in terms of the performance metrics reported in research studies, which include true positive rate (TPR), true negative rate (TNR), area under the curve (AUC), and accuracy (ACC). This study was assembled and systemized by reviewing well-reputed studies published in eminent fora between January 2018 and July 2023. The existing literature was compiled through a systematic search of seven well-reputed databases. A total of 86 articles were included in this SLR. This SLR contains the most recent research on FL and TL algorithms for classifying malignant skin cancer. In addition, a taxonomy is presented that summarizes the many malignant and non-malignant cancer classes. The results of this SLR highlight the limitations and challenges of recent research. Consequently, the future direction of work and opportunities for interested researchers are established that help them in the automated classification of melanoma and nonmelanoma skin cancers.

Effect of exogenous application of biogenic silicon sources on growth, yield, and ionic homeostasis of maize (Zea mays L.) crops cultivated in alkaline soil.

Salinity has emerged as a major threat to food security and safety around the globe. The crop production on agricultural lands is squeezing due to aridity, climate change and low quality of irrigation water. The present study investigated the effect of biogenic silicon (Si) sources including wheat straw biochar (BC-ws), cotton stick biochar (BC-cs), rice husk feedstock (RH-fs), and sugarcane bagasse (SB), on the growth of two consecutive maize (Zea mays L.) crops in alkaline calcareous soil. The application of SB increased the photosynthetic rate, transpiration rate, stomatal conductance, and internal CO2 concentration by 104, 100, 55, and 16% in maize 1 and 140, 136, 76, and 22% in maize 2 respectively. Maximum yield (g/pot) of cob, straw, and root were remained as 39.5, 110.7, and 23.6 while 39.4, 113.2, and 23.6 in maize 1 and 2 respectively with the application of SB. The concentration of phosphorus (P) in roots, shoots, and cobs was increased by 157, 173, and 78% for maize 1 while 96, 224, and 161% for maize 2 respectively over control by applying SB. The plant cationic ratios (Mg:Na, Ca:Na, K:Na) were maximum in the SB applied treatment in maize 1 and 2. The study concluded that the application of SB on the basis of soluble Si, as a biogenic source, remained the best in alleviating the salt stress and enhancing the growth of maize in rotation. The field trials will be more interesting to recommend the farmer scale.

Dust detection and susceptibility mapping by aiding satellite imagery time series and integration of ensemble machine learning with evolutionary algorithms.

To mitigate the impact of dust on human health and the environment, it is crucial to create a model and map that identifies the areas susceptible to dust. The present study focused on identifying dust occurrences in the Bushehr province of Iran between 2002 and 2022 using moderate-resolution imaging spectroradiometer (MODIS) imagery. Subsequently, an ensemble machine learning model was improved to prepare a dust susceptibility map (DSM). The study employed differential evolution (DE), genetic algorithm (GA), and flower pollination algorithm (FPA) - three evolutionary algorithms - to enhance the random forest (RF) ensemble model. A spatial database was created for modeling, including 519 dust occurrence points (extracted from MODIS imagery) and 15 factors affecting dust (Slope, bulk density, aspect, clay, altitude, sand, rainfall, lithology, soil order, distance to river, soil texture, normalized difference vegetation index (NDVI), soil water content, land cover, and wind speed). By utilizing the differential evolution (DE) algorithm, we determined the significance of these factors in impacting dust occurrences. The results indicated that altitude, wind speed, and land cover were the most influential factors, while the distance to the river, bulk density, and soil texture had less impact on dust occurrence. Data were preprocessed using multicollinearity analysis and the frequency ratio (FR) approach. For this research, three RF-based meta-heuristic optimization algorithms, namely RF-FPA, RF-GA, and RF-DE, were created for DSM. The effectiveness prediction of the constructed models by indexes of root-mean-square-error (RMSE), the area under the receiver operating characteristic (AUC-ROC), and coefficient of determination (R2) from best to worst were RF-DE (RMSE = 0.131, AUC-ROC = 0.988, and R2 = 0.93), RF-GA (RMSE = 0.141, AUC-ROC = 0.986, and R2 = 0.919), RF-FPA (RMSE = 0.157, AUC-ROC = 0.981, and R2 = 0.9), and RF (RMSE = 0.173, AUC-ROC = 0.964, and R2 = 0.878). The results showed that combining evolutionary algorithms with an RF model improves the accuracy of dust susceptibility modeling.

Road Feature Detection for Advance Driver Assistance System Using Deep Learning.

Hundreds of people are injured or killed in road accidents. These accidents are caused by several intrinsic and extrinsic factors, including the attentiveness of the driver towards the road and its associated features. These features include approaching vehicles, pedestrians, and static fixtures, such as road lanes and traffic signs. If a driver is made aware of these features in a timely manner, a huge chunk of these accidents can be avoided. This study proposes a computer vision-based solution for detecting and recognizing traffic types and signs to help drivers pave the door for self-driving cars. A real-world roadside dataset was collected under varying lighting and road conditions, and individual frames were annotated. Two deep learning models, YOLOv7 and Faster RCNN, were trained on this custom-collected dataset to detect the aforementioned road features. The models produced mean Average Precision (mAP) scores of 87.20% and 75.64%, respectively, along with class accuracies of over 98.80%; all of these were state-of-the-art. The proposed model provides an excellent benchmark to build on to help improve traffic situations and enable future technological advances, such as Advance Driver Assistance System (ADAS) and self-driving cars.

Review on Propolis Applications in Food Preservation and Active Packaging.

Propolis is a natural hive product collected by honeybees from different plants and trees. The collected resins are then mixed with bee wax and secretions. Propolis has a long history of use in traditional and alternative medicine. Propolis possesses recognized antimicrobial and antioxidant properties. Both properties are characteristics of food preservatives. Moreover, most propolis components, in particular flavonoids and phenolic acids, are natural constituents of food. Several studies suggest that propolis could find use as a natural food preservative. This review is focused on the potential application of propolis in the antimicrobial and antioxidant preservation of food and its possible application as new, safe, natural, and multifunctional material in food packaging. In addition, the possible influence of propolis and its used extracts on the sensory properties of food is also discussed.