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Examining the influence of land use/land cover transformation on meteorological variables has become imperative for maintaining long-term climate sustainability. Rapid growth and haphazard expansion have caused the conversion of prime agricultural land into a built-up area. This study used multitemporal Landsat data to analyze land use/land cover (LULC) changes, and Terra Climate monthly data to examine the impact of land transformation on precipitation, minimum and maximum temperature, wind speed, and soil moisture in the Aurangabad district of Maharashtra state in India during 1999–2019. Multiple linear regression and correlation analysis were performed to determine the association among LULC classes and climatic variables. This study revealed rapid urbanization in the study area over the years. The built-up area, water bodies, and barren lands have recorded a steep rise, while the agricultural area has decreased in the district. Drastic changes were observed in the climatic variables over the years. The precipitation and wind speed have shown decreasing trends during the study period. A positive relationship between soil moisture and agricultural land was found through a correlation analysis. Conspicuous findings about the positive relationship between the agricultural land and maximum temperature need further investigation. A multiple linear regression analysis demonstrated a negative relationship between the built-up area and precipitation. The intensity of the precipitation has reduced as a consequence of the developmental activities in the study area. Moreover, a positive relationship was observed between the built-up area and maximum temperature. Thus, this study calls for policy implications to formulate a futuristic land-use plan considering climate change projection in the district.

Most of the mechanical systems in industries are made to run through induction motors (IM). To maintain the performance of the IM, earlier detection of minor fault and continuous monitoring (CM) are required. Among IM faults, bearing faults are considered as indispensable because of its high probability incidence nature. CM mainly depends upon signal processing and fault detection techniques. In recent decades, various methods have been involved in detecting the bearing fault using machine learning (ML) algorithms. Additionally, the role of artificial intelligence (AI), a growing technology, has also been used in fault diagnosis of IM. Taking the necessity of minor fault detection and the detailed study about the role of ML and AI to detect the bearing fault, the present study is performed. A comprehensive study is conducted by considering various diagnosis methods from ML and AI for detecting a minor bearing fault (hole and scratch). This study helps in understanding the difference between the diagnosis approach and their effectiveness in detecting an IM bearing fault. It is accomplished through FFT (fast Fourier transform) analysis of the load current and the extracted features are used to train the algorithm. The application is extended by comparing the result of ML and AI, and then explaining the specific purpose of use.

The escalating annual death toll attributed to Cholangiocarcinoma (CCA) is, in part, a consequence of delayed diagnosis. This study developed an optimal CCA diagnostic model through the application of 11 machine-learning algorithms. Initially, 105 differentially expressed genes (DEGs) were identified by analyzing gene expression profiles from 307 CCA tumor tissues and 124 adjacent non-tumor tissues. WGCNA, F-test, characteristic importance, and Lasso regression analysis were employed to identify key DEGs, including APOF, DIO1, APOM, and OTC. Subsequently, diagnostic models were constructed based on APOF, DIO1, and OTC using 11 machine-learning algorithms. The LightGBM algorithm was determined as the optimal model through ROC curve analysis and machine learning performance evaluation, achieving an AUC of 0.84, with accuracy, precision, and recall values of 0.80, 0.83, and 0.90, respectively. Subsequent analyses included gene enrichment, protein-protein interaction (PPI), and CCA-related drug assessments. Additionally, the study revealed an imbalance in immune cell infiltration in CCA and identified CCL16 as a chemokine involved in immunoregulation. RT-qPCR confirmed that APOF, DIO1, and OTC were significantly downregulated in CCA tumor tissues. In conclusion, this research provides new directions for the diagnosis and immunotherapy of this disease.

The mortality rate of oesophageal squamous cell carcinoma (ESCC) remains high, and conventional TNM systems cannot accurately predict its prognosis, thus necessitating a predictive model. In this study, a 17‐gene prognosis‐related gene signature (PRS) predictive model was constructed using the random survival forest algorithm as the optimal algorithm among 99 machine‐learning algorithm combinations based on data from 260 patients obtained from TCGA and GEO. The PRS model consistently outperformed other clinicopathological features and previously published signatures with superior prognostic accuracy, as evidenced by the receiver operating characteristic curve, C‐index and decision curve analysis in both training and validation cohorts. In the Cox regression analysis, PRS score was an independent adverse prognostic factor. The 17 genes of PRS were predominantly expressed in malignant cells by single‐cell RNA‐seq analysis via the TISCH2 database. They were involved in immunological and metabolic pathways according to GSEA and GSVA. The high‐risk group exhibited increased immune cell infiltration based on seven immunological algorithms, accompanied by a complex immune function status and elevated immune factor expression. Overall, the PRS model can serve as an excellent tool for overall survival prediction in ESCC and may facilitate individualized treatment strategies and predction of immunotherapy for patients with ESCC.

This study focuses on assessing the toxicity levels in fruits and vegetables based on the presence of polycyclic aromatic hydrocarbons (PAHs), particularly in regions affected by industrial and vehicular pollution where the particulate matter deposits on the plant surfaces. Traditional methods, including Gas Chromatography/Mass Spectrometry (GC/MS) and HighPerformance Liquid Chromatography (HPLC), are used to measure PAH levels in fruits and vegetables, which are found to be valuable but expensive and time-consuming. However, the detection of toxicity relies on either expert knowledge or experimental analysis when compared with the limitations set by EFSA (European Food Safety Authority). Therefore, in this study, artificial intelligence techniques have been employed to evaluate the toxicity levels based on 16 PAHs. The PAH concentrations in fruits and vegetables were collected from different articles corresponding to safe and unsafe datasets and then validated through statistical analysis. The validated dataset is classified using different machine learning algorithms. Based on the output from the neural network, the level of toxicity is also scaled and compared with the targeted outputs. The promising results of the classification of toxicity using artificial intelligence methods are substantiated by an experimental study and validated through statistical methods. From the results, it can be observed that the machine learning algorithm has given classification accuracy of more than 90% along with their degree of harmfulness. This research holds implications for food safety and public health, offering a novel approach to the interdisciplinary understanding of climate change by addressing the impact of environmental contaminants on the edibility of fruits and vegetables.

The validity of results in race walking is often questioned due to subjective decisions in the detection of faults. This study aims to compare machine-learning algorithms fed with data gathered from inertial sensors placed on lower-limb segments to define the best-performing classifiers for the automatic detection of illegal steps. Eight race walkers were enrolled and linear accelerations and angular velocities related to pelvis, thighs, shanks, and feet were acquired by seven inertial sensors. The experimental protocol consisted of two repetitions of three laps of 250 m, one performed with regular race walking, one with loss-of-contact faults, and one with knee-bent faults. The performance of 108 classifiers was evaluated in terms of accuracy, recall, precision, F1-score, and goodness index. Generally, linear accelerations revealed themselves as more characteristic with respect to the angular velocities. Among classifiers, those based on the support vector machine (SVM) were the most accurate. In particular, the quadratic SVM fed with shank linear accelerations was the best-performing classifier, with an F1-score and a goodness index equal to 0.89 and 0.11, respectively. The results open the possibility of using a wearable device for automatic detection of faults in race walking competition.

In the most recent report published by the World Health Organization concerning people with visual disabilities it is highlighted that by the year 2020, worldwide, the number of completely blind people will reach 75 million, while the number of visually impaired (VI) people will rise to 250 million. Within this context, the development of dedicated electronic travel aid (ETA) systems, able to increase the safe displacement of VI people in indoor/outdoor spaces, while providing additional cognition of the environment becomes of outmost importance. This paper introduces a novel wearable assistive device designed to facilitate the autonomous navigation of blind and VI people in highly dynamic urban scenes. The system exploits two independent sources of information: ultrasonic sensors and the video camera embedded in a regular smartphone. The underlying methodology exploits computer vision and machine learning techniques and makes it possible to identify accurately both static and highly dynamic objects existent in a scene, regardless on their location, size or shape. In addition, the proposed system is able to acquire information about the environment, semantically interpret it and alert users about possible dangerous situations through acoustic feedback. To determine the performance of the proposed methodology we have performed an extensive objective and subjective experimental evaluation with the help of 21 VI subjects from two blind associations. The users pointed out that our prototype is highly helpful in increasing the mobility, while being friendly and easy to learn.

Despite numerous counteracting efforts, antivaccine content linked to delays and refusals to vaccinate has grown persistently on social media, while only a few provaccine campaigns have succeeded in engaging with or persuading the public to accept immunization. Many prior studies have associated the diversity of topics discussed by antivaccine advocates with the public's higher engagement with such content. Nonetheless, a comprehensive comparison of discursive topics in pro- and antivaccine content in the engagement-persuasion spectrum remains unexplored. We aimed to compare discursive topics chosen by pro- and antivaccine advocates in their attempts to influence the public to accept or reject immunization in the engagement-persuasion spectrum. Our overall objective was pursued through three specific aims as follows: (1) we classified vaccine-related tweets into provaccine, antivaccine, and neutral categories; (2) we extracted and visualized discursive topics from these tweets to explain disparities in engagement between pro- and antivaccine content; and (3) we identified how those topics frame vaccines using Entman's four framing dimensions. We adopted a multimethod approach to analyze discursive topics in the vaccine debate on public social media sites. Our approach combined (1) large-scale balanced data collection from a public social media site (ie, 39,962 tweets from Twitter); (2) the development of a supervised classification algorithm for categorizing tweets into provaccine, antivaccine, and neutral groups; (3) the application of an unsupervised clustering algorithm for identifying prominent topics discussed on both sides; and (4) a multistep qualitative content analysis for identifying the prominent discursive topics and how vaccines are framed in these topics. In so doing, we alleviated methodological challenges that have hindered previous analyses of pro- and antivaccine discursive topics. Our results indicated that antivaccine topics have greater intertopic distinctiveness (ie, the degree to which discursive topics are distinct from one another) than their provaccine counterparts (t =2.30, P=.02). In addition, while antivaccine advocates use all four message frames known to make narratives persuasive and influential, provaccine advocates have neglected having a clear problem statement. Based on our results, we attribute higher engagement among antivaccine advocates to the distinctiveness of the topics they discuss, and we ascribe the influence of the vaccine debate on uptake rates to the comprehensiveness of the message frames. These results show the urgency of developing clear problem statements for provaccine content to counteract the negative impact of antivaccine content on uptake rates.

Background Recently, we developed a colorectal cancer (CRC) diagnostic model based on a machine learning algorithm with gut microbiota analysis. In this study, we evaluated the reproducibility of the diagnostic accuracy of the gut microbiota model, compared the diagnostic accuracy of the gut microbiota model with that of the fecal immunochemical test (FIT), and investigated the practical application potential of the gut microbiota model. Methods Fecal samples were collected from both CRC patients and healthy individuals (HI) who underwent FIT. Gut microbiota analysis was performed using the same pipeline as that used in our previous study. Study subjects were diagnosed using the machine‐learning‐based gut microbiota model (ml‐GMM) with the same cut‐off value as in our previous study and by FIT. Results The true positive rates of ml‐GMM and FIT were 53.1% and 86.4%, respectively, among 81 CRC patients, whereas the false positive rates among 245 HI cases were 7.3% and 2.4%, respectively. Evaluation of the proportion of either ml‐GMM or FIT being positive revealed a rate of 91.4% among CRC patients (Stage 0/I 78.3%; Stage II, 95.5%; Stage III, 96.6%; stage IV, 100.0%), whereas it was 9.4% among HI. Furthermore, we demonstrated a possible synergistic effect of ml‐GMM with FIT for detection of more CRC patients. Conclusions The reproducibility of the diagnostic accuracy of ml‐GMM was confirmed. It was suggested that ml‐GMM in combination with FIT could detect more CRC patients than FIT alone.

The accurate classification and 3D mapping of benthic habitats in coastal ecosystems are vital for developing management strategies for these valuable shallow water environments. However, both automatic and semiautomatic approaches for deriving ecologically significant information from a towed video camera system are quite limited. In the current study, we demonstrate a semiautomated framework for high-resolution benthic habitat classification and 3D mapping using Structure from Motion and Multi View Stereo (SfM-MVS) algorithms and automated machine learning classifiers. The semiautomatic classification of benthic habitats was performed using several attributes extracted automatically from labeled examples by a human annotator using raw towed video camera image data. The Bagging of Features (BOF), Hue Saturation Value (HSV), and Gray Level Co-occurrence Matrix (GLCM) methods were used to extract these attributes from 3000 images. Three machine learning classifiers (k-nearest neighbor (k-NN), support vector machine (SVM), and bagging (BAG)) were trained by using these attributes, and their outputs were assembled by the fuzzy majority voting (FMV) algorithm. The correctly classified benthic habitat images were then geo-referenced using a differential global positioning system (DGPS). Finally, SfM-MVS techniques used the resulting classified geo-referenced images to produce high spatial resolution digital terrain models and orthophoto mosaics for each category. The framework was tested for the identification and 3D mapping of seven habitats in a portion of the Shiraho area in Japan. These seven habitats were corals (Acropora and Porites), blue corals (H. coerulea), brown algae, blue algae, soft sand, hard sediments (pebble, cobble, and boulders), and seagrass. Using the FMV algorithm, we achieved an overall accuracy of 93.5% in the semiautomatic classification of the seven habitats.