Nearest-neighbor Algorithm Research Articles

BIOM-39. GENOMIC AND CLINICAL ASSOCIATIONS WITH RADIATION NECROSIS IN PATIENTS WITH DIFFUSE INFILTRATING GLIOMAS

Abstract BACKGROUND Radiation necrosis (RN) is a complication of radiotherapy (RT) that can significantly affect quality of life and outcomes in patients with gliomas. Clinical and molecular factors predisposing patients to RN are not clearly established. We hypothesize that clinicogenomic data can be leveraged to identify patients at high risk for RN. METHODS We identified patients from 2014-2024 with diffuse gliomas and RN diagnosed based on Advanced-Brain-Tumor-Imaging which includes conventional MRI, 3 perfusion parameters, spectroscopy and/or pathology at MD Anderson. Inclusion criteria included diagnosis of RN 12-weeks after RT and concordance of 4 ABTI parameters. For each case, we identified 2 controls who were gender-,diagnosis-, and age-matched using a nearest neighborhood algorithm. We evaluated the correlation between 15 most mutated genes in baseline tissue and occurance of RN. A logistic regression model was employed to assess the multivariable relationship between patient characteristics, gene mutations, and RN. RESULTS We identified 62 patients with RN; 50 glioblastoma (80.6%), 8 diffuse astrocytoma (12.9%) and 4 oligodendroglioma (6.5%). Time from RT to RN diagnosis was 254 days (IQR 154-483). 18(29%) of RN cases and 29(24.4%) of controls had received more than one course of RT. Patients treated with bevacizumab before RT had significantly lower odds of developing RN compared to those who did not receive bevacizumab (OR 0.103 [0.011 – 0.960], p=0.0459). BRAF mutations were more frequent in the RN cohort (8.1% vs. 2.4%) and patients with BRAF mutation were more likely to have RN than those without BRAF mutation (OR 6.842 [1.2, 39.01], p=0.0304). Age, gender, diagnosis, IDH, MGMT or 1p19q status were not associated with RN. CONCLUSION We found that bevacizumab administration was associated with a significant reduction in RN development. We also found a novel association between presence of BRAF mutations and increased risk of RN in glioma patients, which warrants further investigation.

Machine Learning in Agriculture: KNN-Based Classification of Fruits and Vegetables

In modern agriculture and the food industry, it is essential to classify fruits and vegetables accurately and efficiently to meet growing consumer demand and reduce post-harvest losses. Traditional manual methods are often labor-intensive and error prone, highlighting the need for automated solutions. This paper discusses the application of the k Nearest Neighbor (KNN) algorithm to fruit and vegetable recognition using image processing technology. In this study, image data sets are used for feature extraction using Directional Gradient Histogram (HOG) and dimensionality reduction using Principal Component Analysis (PCA). The accuracy of KNN model on verification set and test set reaches 97%, which proves its validity. Confusion matrix analysis and F1 score evaluation further revealed the strengths and areas of improvement of the model, particularly in distinguishing visually similar categories. The results show that the integration of artificial intelligence (especially KNN) offers great potential for the automation of agricultural classification tasks. Future studies could combine more advanced models, such as CNNS, with larger datasets to improve accuracy and robustness.

Exploring LGE areas in the atria: Insights from electroanatomical voltage mapping in AF patients

Abstract Introduction Late gadolinium enhancement MRI (LGE MRI) is a common method for the evaluation of the atrial substrate in atrial fibrillation (AF) patients. The extent of LGE is associated with atrial cardiomyopathy and ablation outcomes. However, previous studies have yielded inconsistent results in detecting low voltage areas (LVA) using bipolar electroanatomical mapping (EAM) in LGE regions. Objective Conduct a thorough analysis of the bipolar and unipolar voltage in LGE areas and its relationship with the voltage in non-LGE areas, utilizing high-density (HD) EAM generated by a multipolar HD-mapping catheter. Methods In this study, 20 patients undergoing AF ablation were examined using LGE-MRI. A 3D mesh integrating LGE data was generated. HD-EAM (> 4000 points) was performed in sinus rhythm. The EAM map and MRI mesh underwent manual alignment and registration. Using a nearest neighbor algorithm, EAM points nearest to LGE areas were identified, and the corresponding unipolar and bipolar voltage values, along with LGE voxel intensity, were exported for analysis. Results LGE areas exhibited a median bipolar voltage amplitude of 0.93 mV, median unipolar voltage of 1.73 mV. In contrast, areas without enhancement displayed median bipolar voltage of 2.33 mV, unipolar voltage of 3.58 mV. We observed a variation in voltage amplitudes across the different LGE areas: bipolar voltage ranged from 0.04 to 2.51 mV and unipolar voltage from 0.22 to 3.75 mV. This variation strongly correlated with voxel intensity in LGE areas (correlation coefficient r = -0.86 [p <0.001]). Irrespective of the voltage amplitude in the examined LGE areas, a significant voltage drop was observed, with an average reduction of 54% in bipolar and 51% in unipolar voltages, compared to the amplitudes in non-LGE areas within the same atria. Conclusion Variations in bipolar and unipolar voltage within LGE areas are significantly correlated with local voxel intensity. Despite the inconsistent overlap with the predefined bipolar threshold for LVA, LGE regions exhibit a pronounced and consistent reduction in both bipolar and unipolar voltage compared to non-LGE areas. This pattern suggests notable electrophysiological changes in these areas. These insights underscore the need for further investigation to better understand the implications and mechanisms underlying these alterations in LGE regions.

Cardiovascular mortality with oral potassium-binders in patients with CKD and heart failure

Abstract Introduction Hyperkalemia is an inherent consequence of advancing chronic kidney disease (CKD) as a result of the kidney’s decreased ability to excrete potassium. Heart failure patients with concomitant CKD may not tolerate maximum doses of guideline-directed medical therapies (GDMT) like ACE inhibitors (ACEi), angiotensin receptor blockers (ARB), angiotensin receptor/neprilysin inhibitors (ARNI) and mineralocorticoid receptor antagonists (MRA) due to dose-limiting hyperkalemia. Many randomized controlled trials in heart failure exclude patients with advanced CKD. Two oral therapies exist to manage hyperkalemia - patiromer sorbitex calcium and sodium zirconium cyclosilicate. This study aims to identify the potassium-binder with better outcomes as defined by less readmissions, hyperkalemia episodes, and death. Purpose This project was undertaken to better understand the impact of using potassium-binders in CKD patients with heart failure. Improved management of hyperkalemia will be an avenue for optimization of GDMT in this population. Methods and materials The TriNetX research network was used for this study. Two cohorts of patients were created by using International Classification of Disease 10 (ICD-10) codes and CPT codes. Both cohorts consisted of patients with no prior history of hyperkalemia within 30 days of hospital discharge at which time one cohort was prescribed patiromer and the other received sodium zirconium cyclosilicate. Each cohort was prohibited from exposure to the other medication. The cohorts were balanced by propensity score matching and the greedy nearest neighbor algorithm for age, race, gender, ethnicity. They were also balanced for systolic and diastolic heart failure diagnoses, and for each individual stage of CKD (1 through 5 and end stage). They were also balanced for use of ACEi, ARBs, and MRAs. This resulted in 8,921 patients in each arm. They were then evaluated for the outcomes of hospital readmission, hyperkalemia episodes (>6.1 mmol/L), and death within 12 months. Results Patients treated with patiromer were more likely to be hospitalized over the following twelve months (44% vs 40%, RR 1.11, 95% CI (1.07,1.15), p value <0.0001). They were also more likely to experience hyperkalemia (11.7% vs 9.2%, RR 1.27, 95% CI (1.16,1.38), p value <0.0001). Patiromer was also associated with slightly greater all cause mortality (27% vs 25%, RR 1.08, 95% CI (1.03,1.13), p value 0.0021). Discussion This study demonstrates patiromer has more readmissions and hyperkalemia episodes along with greater mortality. In conclusion, sodium zirconium cyclosilicate was found to have better outcomes compared to that of patiromer. Further studies are necessary to better understand the utility of using sodium zirconium cyclosilicate as maintenance therapy for hyperkalemia in order to maximize GDMT in CKD patients with heart failure.

Comparison of ventricular tachyarrhythmias in HF patients on dapagliflozin versus empagliflozin

Abstract Background Ventricular tachycardia (VT) and ventricular fibrillation (VF) are devastating tachyarrhythmias that can cause sudden cardiac death in patients with heart failure (HF) if not anticipated or treated promptly. Although post-hoc analysis in DAPA-HF showed promise in their benefit, Sodium-glucose transport protein 2 inhibitors (SGLT2i) have not yet been evaluated for their effect on these tachyarrhythmias. This retrospective analysis aims to compare the 2 most commonly used SGLT2i agents, dapagliflozin and empagliflozin, and the incidence of VT in patients with HF. Methods The TriNetX Research network was used for this study. The two initial patient cohorts were created using ICD-10 codes and medication codes from the TriNetX platform. Both cohorts consisted of patients over age 60 with a history of heart failure and either VF or VT. One cohort was on treatment with dapagliflozin and the other with empagliflozin. The cohorts were balanced by propensity score matching and the greedy nearest neighbor algorithm for age, gender, race, ethnicity, hyperkalemia, and the current use of spironolactone, amiodarone, metoprolol, and carvedilol, resulting in 9,841 patients in each arm. The cohorts were then evaluated for the development of VF or VT over the subsequent five years as well as all-cause mortality. Additional cohorts were created to evaluate dapagliflozin vs empagliflozin in diabetics, nondiabetics, and patients with ejection fractions less than 35% Results Dapagliflozin was associated with an increased risk of ventricular arrhythmia in heart failure patients compared to empagliflozin (46.0% vs. 40.8%, RR 1.13, 95% CI (1.09,1.16), P value < 0.0001). It was also associated with higher mortality at five years (RR 1.10, 95% CI (1.03,1.18), P value 0.0034). Conclusions In comparisons between dapagliflozin and empagliflozin in heart failure patients with previously documented ventricular arrhythmias, empagliflozin had 13% less risk of recurrence of ventricular arrhythmias. Secondarily, the empagliflozin cohort also exhibited lower all-cause mortality (14.8% vs 16.3%) at five years from starting the drug. Our analysis does not disprove findings in DAPA-HF, but rather may support the potential class effect of SGLT2 inhibitors in their ability to suppress VT.(1) Importantly, the overall incidence of ventricular tachyarrhythmias is high in both cohort, given we selected a high-risk population. Notably, our documented recurrence rate is similar to past reports of recurrence after VT ablation, the gold standard for treatment of VT. (2) SGLT2 inhibitors have shown clear benefit in multiple placebo-controlled studies in terms of heart failure outcomes like mortality and readmission, but their effect on arrhythmias is one more additional benefit this class may have. (3) Given these associations, empagliflozin may be a more favorable options for HF patients who have already had VT or VF.

Comparison of cardiovascular outcomes in patients with diabetes treated with tirzepatide versus semaglutide: a multi-institutional analysis

Abstract Introduction Glucagon-like-peptide-1 (GLP-1) receptor agonists have been demonstrated to have cardiovascular benefits in patients with diabetes. Two of the most prescribed medications in this class are tirzepatide and semaglutide. Tirzepatide is a dual glucose-dependent insulinotropic polypeptide and GLP-1 receptor agonist, while semaglutide is a selective GLP-1 receptor agonist. Tirzepatide and semaglutide have previously been directly compared to assess efficacy in glycemic control as the primary endpoint. However, there is limited data directly comparing cardiovascular outcomes between the two medications in patients with diabetes. This retrospective cohort study compares the incidence of major adverse cardiovascular events (MACE) in patients with diabetes treated with tirzepatide or semaglutide. Purpose The purpose of this study is to identify differences in cardiovascular outcomes between patients with diabetes treated with tirzepatide as compared to semaglutide. Materials and Methods The TriNetX research network was used for this study. Two patient cohorts were generated using International Classification of Disease 10 (ICD-10) codes and medication codes in the TriNetX system. Both cohorts of patients had a history of diabetes (E08-E13) and no prior history of cerebral vascular accident (I63) or myocardial infarction (I21). One cohort received semaglutide and the other tirzepatide. Each cohort was prohibited from receiving the other agent. The cohorts were balanced for age, race, gender, and ethnicity by propensity score matching and the greedy nearest neighbor algorithm resulting in 36,212 patients in each arm. They were then evaluated for the 3-year outcomes of major adverse cardiovascular events (myocardial infarction, cerebral vascular accident, and death). Results Tirzepatide was associated with statistically significantly fewer MACE outcomes compared to semaglutide for each of the individual endpoints as well as the overall composite endpoint over the 3 year follow up period. Table 1. MACE events for semaglutide and tirzepatide. Table 2. Relative risks for semaglutide MACE endpoints compared to tirzepatide. Conclusions In this retrospective cohort study, tirzepatide was associated with a lower incidence of major adverse cardiovascular events compared to semaglutide in patients with diabetes. These findings suggest that tirzepatide offers superior cardiovascular protection for primary prevention compared to semaglutide and warrant further investigation in future studies and clinical trials.Table 1Table 2

The potential effects of methotrexate on cardiovascular outcomes in rheumatoid arthritis patients: a multi-institutional analysis

Abstract Introduction and Purpose The association between rheumatoid arthritis (RA) and increased risk of developing cardiovascular disease has been previously described. Recent studies have also shown that methotrexate may reduce the risk of cardiovascular events in patients with RA but have been limited by small sample size and/or demographic constraints. Methotrexate is a first-line drug treatment for RA and works by suppressing multiple anti-inflammatory and immune reactions, thereby reducing the inflammation that promotes progression of cardiovascular disease in these patients. The goal of this study is to evaluate the potential benefit of methotrexate use on cardiovascular outcomes in patients with RA on a larger scale by utilizing a large international medical record database. Methods The TriNetX Diamond network was used for this study. Two cohorts were generated for comparison by using International Classification of Disease 10 (ICD-10) codes. Both cohorts included adult patients with a diagnosis of rheumatoid arthritis (M06) and both were excluded from being treated with the following biologic therapies: etanercept, infliximab, adalimumab, golimumab, and certolizumab pegol. One cohort was on active treatment with methotrexate while the other was excluded from being on it. The cohorts were then balanced for age, gender, race, ethnicity, presence of known coronary artery disease (I20-I25) and history of prior cardiovascular diseases (Z86.7) by propensity score matching and use of the greedy nearest neighbor algorithm. This resulted in 471,054 patients in each arm. They were then compared for the outcomes of acute myocardial infarction, chronic ischemic heart disease, congestive heart failure, and death at 5 years. Results Untreated RA patients were 11% more likely to develop myocardial infarction (RR 1.11, 95% CI (1.09,1.14), P value <0.0001) and 14% more likely to develop chronic ischemic heart disease (RR 1.14, 95% CI (1.13,1.15), P value <0.0001). CHF incidence was 21% higher (RR 1.21, 95% CI (1.19,1.22), P value <0.0001) in the untreated group, but 5-year mortality in the two groups was comparable at around 8.2% (RR 0.98, 95% CI (0.97,0.997), P value 0.016). Conclusion This study confirms that methotrexate is associated with fewer cardiovascular events in RA patients, especially congestive heart failure. Although the use of methotrexate in patients with RA did not result in a mortality benefit, treatment may offer substantial improvements in patient morbidity and healthcare system cost. These benefits were seen in a sample of greater than 900,000 patients representing every adult demographic. These data reaffirm the previously described association between RA and cardiovascular disease and support the initiation of treatment with methotrexate for any adult patient with RA. Watchful waiting is associated with a higher incidence of adverse events in this population.

A Three-Dimensional Modeling Approach for Carbon Nanotubes Filled Polymers Utilizing the Modified Nearest Neighbor Algorithm.

Carbon nanotubes (CNTs) are extensively utilized in the fabrication of high-performance composites due to their exceptional mechanical, electrical, and thermal characteristics. To investigate the mechanical properties of CNTs filled polymers accurately and effectively, a 3D modeling approach that incorporates the microstructural attributes of CNTs was introduced. Initially, a representative volume element model was constructed utilizing the modified nearest neighbor algorithm. During the modeling phase, a corresponding interference judgment method was suggested, taking into account the potential positional relationships among the CNTs. Subsequently, stress-strain curves of the model under various loading conditions were derived through finite element analysis employing the volume averaging technique. To validate the efficacy of the modeling approach, the stress within a CNT/epoxy resin composite with varying volume fractions under different axial strains was computed. The resulting stress-strain curves were in good agreement with experimental data from the existing literature. Hence, the modeling method proposed in this study provides a more precise representation of the random distribution of CNTs in the matrix. Furthermore, it is applicable to a broader range of aspect ratios, thereby enabling the CNT simulation model to more closely align with real-world models.

Rapid method for identifying diacylglycerol edible oils using Raman spectroscopy combined with the “oil microscopy” method

This study aims to identify diacylglycerol (DAG) edible oils using Raman spectroscopy to protect consumers. Raman spectral data from 28 samples were collected, including DAG (1,3-diolein), triacylglycerol (triolein), DAG edible oils, adulterated oils, and ordinary edible oils. These samples were analyzed using a combined linear discriminant analysis–k nearest neighbors algorithm, characteristic band ratios, peak area ratios, and oil microscopy. The results of Raman spectroscopy analysis demonstrated DAG edible oils, adulterated oil, and ordinary cooking oil at 1746 cm−1 with Iordinary edible oil >Iadulterated oil >IDAG edible oil. Further analyses revealed differences in esterification between DAG edible oils and triacylglycerol. Moreover, the results demonstrated that the combination of Raman spectroscopy and oil microscopy can quickly and accurately detect DAG edible oil adulteration, which is expected to be an effective tool for food safety regulation to protect consumers’ rights.

Multi-level multi-view network based on structural contrastive learning for scRNA-seq data clustering.

Clustering plays a crucial role in analyzing scRNA-seq data and has been widely used in studying cellular distribution over the past few years. However, the high dimensionality and complexity of scRNA-seq data pose significant challenges to achieving accurate clustering from a singular perspective. To address these challenges, we propose a novel approach, called multi-level multi-view network based on structural consistency contrastive learning (scMMN), for scRNA-seq data clustering. Firstly, the proposed method constructs shallow views through the $k$-nearest neighbor ($k$NN) and diffusion mapping (DM) algorithms, and then deep views are generated by utilizing the graph Laplacian filters. These deep multi-view data serve as the input for representation learning. To improve the clustering performance of scRNA-seq data, contrastive learning is introduced to enhance the discrimination ability of our network. Specifically, we construct a group contrastive loss for representation features and a structural consistency contrastive loss for structural relationships. Extensive experiments on eight real scRNA-seq datasets show that the proposed method outperforms other state-of-the-art methods in scRNA-seq data clustering tasks. Our source code has already been available at https://github.com/szq0816/scMMN.

Comparative Analysis of Machine Learning Algorithms for Used Car Price Prediction

After 2021, over 90 million passenger automobiles were produced, marking a significant increase in auto production. This growth has led to a flourishing used car market, which has become a highly lucrative sector. One of the most critical and fascinating areas of research within this market is automobile price prediction. Accurate price prediction models can greatly benefit buyers, sellers, and businesses in the used car industry. This paper presents a detailed comparative analysis of two supervised machine learning models: K-Nearest Neighbour and Support Vector Machine regression techniques, to predict used car prices. We utilized a comprehensive dataset of used cars sourced from the Kaggle website for training and testing our models. The K Nearest Neighbour algorithm is known for its simplicity and effectiveness in regression tasks. On the other hand, the Support Vector Machine regression technique uses a different approach, finding the optimal hyperplane that best fits the data. Both methods have their strengths and weaknesses, which we explored in this study. Our results indicated that both KNN and SVM models performed well in predicting used car prices, but with slight variations in accuracy. Consequently, the suggested models fit as the optimum models and have an accuracy of about 83 percent for KNN and 80 percent for SVM. The results indicate that the KNN model slightly outperforms the SVM model in predicting used car prices.

A novel nearest neighbors algorithm based on power muirhead mean

This paper introduces the innovative Power Muirhead Mean K-Nearest Neighbors (PMM-KNN) algorithm, a novel data classification approach that combines the K-Nearest Neighbors method with the adaptive Power Muirhead Mean operator. The proposed methodology aims to address the limitations of traditional KNN by leveraging the Power Muirhead Mean for calculating the local means of K-nearest neighbors in each class to the query sample. Extensive experimentation on diverse benchmark datasets demonstrates the superiority of PMM-KNN over other classification methods. Results indicate statistically significant improvements in accuracy on various datasets, particularly those with complex and high-dimensional distributions. The adaptability of the Power Muirhead Mean empowers PMM-KNN to effectively capture underlying data structures, leading to enhanced accuracy and robustness. The findings highlight the potential of PMM-KNN as a powerful and versatile tool for data classification tasks, encouraging further research to explore its application in real-world scenarios and the automation of Power Muirhead Mean parameters to unleash its full potential.

Analysis of human ambulation as a chaotic time-series: with nonlinear dynamics tools.

The aim of the present study is to investigate the complexity and stability of human ambulation and the implications on robotic prostheses control systems. Fourteen healthy individuals participate in two experiments, the first group run at three different speeds. The second group ascended and descended stairs of a five-level building block at a self-selected speed. All participants completed the experiment with seven inertial measurement units wrapped around the lower body segments and waist. The data were analyzed to determine the fractal dimension, spectral entropy, and the Lyapunov exponent (LyE). Two methods were used to calculate the long-term LyE, first LyE calculated using the full size of data sets. And the embedding dimensions were calculated using Average Mutual Information (AMI) and the False Nearest Neighbor (FNN) algorithm was used to find the time delay. Besides, a second approach was developed to find long-term LyE where the time delay was based on the average period of the gait cycle using adaptive event-based window. The average values of spectral entropy are 0.538 and 0.575 for stairs ambulation and running, respectively. The degree of uncertainty and complexity increases with the ambulation speed. The short term LyEs for tibia orientation have the minimum range of variation when it comes to stairs ascent and descent. Using two-way analysis of variance we demonstrated the effect of the ambulation speed and type of ambulation on spectral entropy. Moreover, it was shown that the fractal dimension only changed significantly with ambulation speed.

Differences in Advanced Therapeutic Modalities for Overactive Bladder in the United States by Race

ObjectiveTo investigate the impact of race on prescription patterns of therapies for OAB. MethodsThe TriNetX Diamond network was queried to identify adult females with a diagnosis of urinary urgency incontinence (UUI) or OAB, excluding those with stress incontinence or mixed incontinence. Treatments were defined as behavioral, medical, or minimally invasive in accordance with American Urological Association (AUA) guidelines. Propensity score matching for multiple covariates was performed using the greedy nearest-neighbor algorithm. Cox proportional hazards regression analysis was employed to compare the matched cohorts. Rates of treatment by three-digit zip codes were compared through chi-square tests or Fisher’s exact tests and geographic distribution maps were generated via STATA 17.0. ResultsWe identified 2,687,316 adult females diagnosed with OAB; 767,159 identified as White and 108,464 as Black. Within these cohorts, 16.6% of Black patients and 20.4% of White patients received OAB treatment. Twenty-six-point three percent of US zip-codes contained data for both White and Black patients, and in all these zip codes, White patients received significantly higher rates of prescriptions compared to Black patients. After propensity-score matching, significant differences in prescriptions persisted between the two groups with White patients exhibiting higher rates of any prescription. ConclusionsOur results demonstrate a significantly lower rate of prescriptions of medical and minimally invasive therapies for Black women. These racial differences in prescription of advanced therapeutic modalities for OAB may not be secondary to diagnosis but are likely secondary to prescribing disparities. Further research is needed to understand these differences.

A lightweight joint metric detection approach on YOLO for hot spots in photovoltaic modules

The hot spot effect is one of the primary causes of damage to photovoltaic (PV) modules and a significant factor contributing to the decline in their power generation capacity. Thermal imaging inspection of PV modules is an indispensable aspect of PV plant operation and maintenance. This article introduces a lightweight detection algorithm for hot spots in PV modules based on an enhanced version of YOLOv8. The algorithm incorporates a lightweight convolution operator, Volumetric Grid Spatial Cross Stage Partial, as a replacement for the original C2f operator, resulting in effective reduction of the model's parameter size and improved detection speed. Additionally, the Content-Aware ReAssembly of FEatures upsampling operator is utilized instead of the nearest-neighbor algorithm during the upsampling process, thereby minimizing the loss of hot spot feature information. Furthermore, a joint metric approach combining the Complete Intersection over Union metric and the normalized Wasserstein distance metric is proposed to calculate the localization loss of the target bounding boxes, thereby enhancing the regression accuracy of small hot spot prediction boxes. Experimental results demonstrate that the improved YOLOv8 network enables fast and accurate detection of hot spots. This method achieves an impressive average precision of 98.8% and a high detection speed of 218.3 fps.

Energy-Aware 3D Path Planning by Autonomous Ground Vehicle in Wireless Sensor Networks

Wireless sensor networks are used to monitor the environment, to detect anomalies or any other problems and risks in the system. If used in the transportation network, they can monitor traffic and detect traffic risks. In wireless sensor networks, energy constraints must be handled to enable continuous environmental monitoring and surveillance data gathering and communication. Energy-aware path planning of autonomous ground vehicle charging for sensor nodes can solve energy and battery replacement problems. This paper uses the Nearest Neighbour algorithm for the energy-aware path planning problem with an autonomous ground vehicle. Path planning simulations show that the Nearest Neighbour algorithm converges faster and produces a better solution than the genetic algorithm. We offer robust and energy-efficient path planning algorithms to swiftly collect sensor data with less energy, allowing the monitoring system to respond faster to anomalies. Positioning communicating sensors closer minimizes their energy usage and improves the network lifetime. This study also considers the scenario in which it is recommended to avoid taking direct travelling pathways between particular node pairs for a variety of different reasons. To address this more challenging scenario, we provide an Obstacle-Avoided Nearest Neighbour-based approach that has been adapted from the Nearest Neighbour approach. Within the context of this technique, the direct paths that connect the nodes are restricted. Even in this case, the Obstacle-Avoided Nearest Neighbour-based approach achieves almost the same performance as the the Neighbour-based approach.

Spectral ensemble clustering with doubly stochastic co-association matrix

Although co-association (CA) matrix-based spectral ensemble clustering (SEC) has achieved significant success, it faces two persistent challenges: (1) CA matrix-based SEC may result in imbalanced cluster outcomes, and (2) SEC encounters difficulties due to the high time and space complexity resulting from the quadratic scaling of the CA matrix with the number of data points. To tackle these issues, we propose a doubly stochastic co-association (DSCA) matrix construction method. We have theoretically demonstrated that DSCA matrix-based SEC can achieve a more balanced cluster outcome compared to CA matrix-based SEC. Leveraging the DSCA matrix, we introduce two novel methods: Fast SEC (FSEC) and Approximate SEC (ASEC). FSEC efficiently solves the normalized cut on the DSCA matrix as it is symmetric and doubly stochastic. In ASEC, we employ random samples rather than the entire big dataset for ensemble clustering. After obtaining the ensemble clustering result of benchmark random sample, the approximate ensemble clustering result for all data points is derived using our newly proposed probability nearest neighbors (PNN) algorithm. Experimental results on both real-world and synthetic datasets are evaluated in terms of accuracy (ACC), normalized mutual information (NMI), purity, F1-score, and adjusted rand index (ARI) and confirm the scalability and effectiveness of the proposed FSEC and ASEC.

Complexity of hydrometeorological variables: false nearest neighbour algorithm

ABSTRACT This study examines the complexity of hydrometeorological variables in the Savitri River basin in India. Specifically, the study estimates the dimensionality of daily rainfall, runoff, maximum temperature, minimum temperature, pan evaporation, relative humidity, sunshine duration, and wind speed observed during 2000–2010 at two stations (Kangule and Birwadi). The false nearest neighbour (FNN) algorithm is employed to estimate the dimensionality of each variable. The dimensionality represents the number of variables dominantly governing the system. The FNN dimension values of the eight daily hydrometeorological time series from each station range between 4 and 7, which may be considered as exhibiting a medium level of complexity. Among the eight series, wind speed is found to be the least complex, whereas minimum temperature and sunshine duration are the most complex. An attempt is also made to examine the effect of temporal scale on the complexity of the hydrometeorological variables, by analysing the hourly rainfall and runoff series from the two stations. The results indicate that, for both rainfall and runoff, hourly data (finer scale) exhibits greater complexity, with two to three additional influential variables. The present results have important implications for hydrometeorological modelling, prediction, and disaster management in the small and flood-prone Savitri River basin.

Accurate estimation of the normalized mutual information of multidimensional data.

While the linear Pearson correlation coefficient represents a well-established normalized measure to quantify the inter-relation of two stochastic variables X and Y, it fails for multidimensional variables, such as Cartesian coordinates. Avoiding any assumption about the underlying data, the mutual information I(X, Y) does account for multidimensional correlations. However, unlike the normalized Pearson correlation, it has no upper bound (I ∈ [0, ∞)), i.e., it is not clear if say, I = 0.4 corresponds to a low or a high correlation. Moreover, the mutual information (MI) involves the estimation of high-dimensional probability densities (e.g., six-dimensional for Cartesian coordinates), which requires a k nearest-neighbor algorithm, such as the estimator by Kraskov et al. [Phys. Rev. E 69, 066138 (2004)]. As existing methods to normalize the MI cannot be used in connection with this estimator, a new approach is presented, which uses an entropy estimation method that is invariant under variable transformations. The algorithm is numerically efficient and does not require more effort than the calculation of the (un-normalized) MI. After validating the method by applying it to various toy models, the normalized MI between the Cα-coordinates of T4 lysozyme is considered and compared to a correlation analysis of inter-residue contacts.

Evaluating the protective effectiveness and risk factors of ursodeoxycholic acid on COVID-19 among outpatients.

Objective: This study aimed to assess the chemopreventive effect of ursodeoxycholic acid (UDCA) against COVID-19 and to analyze infection risk factors, symptoms, and recovery in outpatients with UDCA exposure. Methods: The study enrolled outpatients prescribed UDCA from the Second Affiliated Hospital of Chongqing Medical University, China, between 01 July 2022, and 31 December 2022. Data on demographics, comorbidities, and drug combinations were collected using electronic medical records. COVID-19 infection, symptoms, severity, prognosis, vaccinations, and UDCA administration were surveyed by telephone interviews. UDCA non-users served as controls and were matched in a 1:2 ratio with UDCA users using propensity score matching with the nearest neighbor algorithm. Infection rates, symptomatology, severity, and prognosis were compared between matched and control cohorts, and risk factors and infection and recovery symptoms were analyzed in UDCA-exposed outpatients. Results: UDCA-exposed outpatients (n = 778, 74.8%) and matched UDCA users (n = 95, 74.2%) showed significantly lower SARS-CoV-2 infection rates than control patients (n = 59, 92.2%) (p < 0.05). The matched UDCA group exhibited substantially lower fever, cough, sore throat, and fatigue rates than controls (p < 0.05). Participants with UDCA exposure generally experienced mild symptoms, while those without UDCA had moderate symptoms. The matched UDCA group also had significantly shorter durations of fever and cough (p < 0.05). Risk factors such as age over 60, less than 1month of UDCA administration, diabetes mellitus, and coronary artery disease significantly increased SARS-CoV-2 infection rates (p < 0.05), while smoking led to a decrease (p < 0.05). Hypertension was associated with a prolonged COVID-19 recovery (p < 0.05), while smoking, vaccination, and fatty liver disease were associated with shorter recovery periods (p < 0.05). The main symptoms in the full UDCA cohort were fever, cough, and sore throat, with fatigue, cough, and hyposthenia being the most persistent. Conclusion: UDCA demonstrated chemopreventive effect against SARS-CoV-2 in outpatients by significantly reducing infection incidence and mitigating COVID-19 symptoms, severity, and recovery duration. Old age, short UDCA course, and comorbidities such as diabetes mellitus and CAD increased infection rates, while hypertension prolonged recovery. Smoking, vaccination, and fatty liver disease reduced infection rates and shortened recovery. UDCA had minimal impact on symptom types. Larger and longer-term clinical studies are needed further to assess UDCA's effectiveness in COVID-19 prevention or treatment.