Nearest Neighbor Research Articles

Gaseous Air Pollutants and Lung Function in Fibrotic Interstitial Lung Disease (fILD): Evaluation of Different Spatial Analysis Approaches.

Gaseous pollutants such as CO, NO2, O3, and SO2 are linked to adverse clinical outcomes in patients with fibrotic interstitial lung diseases (fILDs), particularly idiopathic pulmonary fibrosis. However, the effect of various exposure estimation methods on these findings remains unclear. This study aims to evaluate three spatial approaches─nearest neighbor (NN), inverse distance weighting (IDW), and Kriging─for estimating gaseous pollutant exposures and to assess how these methods affect health outcome estimates in fILD patients. A 10-fold cross-validation showed that Kriging had the lowest prediction error compared to NN and IDW, with RMSE for CO = 0.43 ppm (11%), O3 = 5.9 ppb (14%), SO2 = 2.7 ppb (12%), and NO2 = 7.6 ppb (9%), respectively. Kriging also excelled over other methods across wide spatial and temporal ranges, showing the highest spatial R2 for CO and O3 and the highest temporal R2 for SO2 and NO2. In a large cohort of patients with fILD, higher levels of CO, SO2, and NO2 exposure were associated with lower pulmonary function. The magnitude of association and its precision were higher in SO2 and CO estimated by the Kriging method. This study underscores Kriging as a robust method for estimating gaseous pollutant levels and offers valuable insights for future epidemiological studies.

Diffusion-based Facial Aesthetics Enhancement with 3D Structure Guidance.

Facial Aesthetics Enhancement (FAE) aims to improve facial attractiveness by adjusting the structure and appearance of a facial image while preserving its identity as much as possible. Most existing methods adopted deep feature-based or score-based guidance for generation models to conduct FAE. Although these methods achieved promising results, they potentially produced excessively beautified results with lower identity consistency or insufficiently improved facial attractiveness. To enhance facial aesthetics with less loss of identity, we propose the Nearest Neighbor Structure Guidance based on Diffusion (NNSG-Diffusion), a diffusion-based FAE method that beautifies a 2D facial image with 3D structure guidance. Specifically, we propose to extract FAE guidance from a nearest neighbor reference face. To allow for less change of facial structures in the FAE process, a 3D face model is recovered by referring to both the matched 2D reference face and the 2D input face, so that the depth and contour guidance can be extracted from the 3D face model. Then the depth and contour clues can provide effective guidance to Stable Diffusion with ControlNet for FAE. Extensive experiments demonstrate that our method is superior to previous relevant methods in enhancing facial aesthetics while preserving facial identity.

Global phase diagram of D-wave superconductivity in the square-lattice t-J model

The Hubbard and closely related t-J models are exciting platforms for unconventional superconductivity (SC). Through state-of-the-art density matrix renormalization group calculations using the grand canonical ensemble, we address open issues regarding the ground-state phase diagram of the extended t-J model on a square lattice in the parameter regime relevant to cuprate superconductors. On large 8-leg cylinders, we demonstrate that the pure t-J model with only nearest-neighbor hoppings and superexchange interactions, for a wide range of doping ([Formula: see text]), hosts robust d-wave superconductivity possibly coexisting with weak unidirectional pair density wave. Furthermore, a small next nearest neighbor hopping [Formula: see text] suppresses pair and charge density waves, resulting in a uniform d-wave SC phase in both electron- and hole-doped cuprate model systems. Our work validates the t-J model as a proper minimum model for the emergence of superconductivity in cuprate superconductors.

Improving Breast Cancer Diagnosis Using Grammatical Evolution-Based Feature Selection

Machine learning has significantly advanced breast cancer diagnosis, yet challenges such as high-dimensional data, severe class imbalance, and limited interpretability persist. To address these issues, we proposed a Grammatical Evolution (GE)-based Feature Selection (FS) approach, integrated with a class-balancing technique called STEM, which combines Synthetic Minority Oversampling Technique, Edited Nearest Neighbour and Mixup, effectively handling both inter-class and intra-class imbalance. Our study evaluates the performance of the GE-based FS method against other FS models, including Logistic Regression (LR) and Extreme Gradient Boosting (XGBoost), in identifying critical features for breast cancer diagnosis. The results demonstrate that the GE-based FS method effectively identifies critical features and achieves superior Area Under the Curve (AUC) scores, particularly with smaller subsets of features, unlike LR and XGBoost, which perform optimally with the full feature set. The analysis was conducted on the Digital Database for Screening Mammography and Wisconsin Breast Cancer datasets, which originally contained 52 and 30 features, respectively. The GE-based FS produces the highest AUC with subsets of 10 and 15 features, while LR and XGBoost achieve their best results using the entire feature set, underscoring the superiority of the GE-based FS method.

The effects of solute and deformation on the mobility of screw dislocation cores in bcc molybdenum

Abstract In body-centered cubic metals such as molybdenum, screw dislocations critically govern the plastic deformation behavior of alloys. The presence of solute atoms in alloys can effectively alter the formation and movement of screw dislocations. In this study, we employed first-principles calculations to delve into the electronic origins of these influences. Initially, we constructed single atomic column and triple atomic column models to simulate the formation of screw dislocations with solute atoms. Our investigation revealed that tantalum (Ta) and tungsten (W) increase the formation energy of solute-dislocation interactions, while osmium (Os), iridium (Ir), and platinum (Pt) have the opposite effect. Subsequently, utilizing a screw dislocation dipole model under shear deformation, we explored the combined effects of solute atoms and deformation on dislocation core movement. We found that Os, Ir, and Pt, located as the first nearest neighbors of the dislocation core, exhibit an attractive effect on the dislocation core. Solute atoms at specific positions can alter the direction of dislocation slip, inducing cross-slip and enhancing material ductility. In contrast, under the same stress, Ta and W exhibit repulsion towards the dislocation core and cannot change the direction of dislocation slip, only altering the energy barrier for dislocation core movement. This work provides atomic-scale insights into solute-induced dislocation dynamics, offering guidelines for advanced Mo alloy design.

Quantum correlations in a cluster spin model with three-spin interactions

Abstract An exactly solvable cluster spin model with three-spin interaction couplings Jx (for XZX spin
components) and Jy (for YZY spin components) in the presence of a transverse magnetic field h
for a spin chain is investigated. For h = 0, and with only one nonzero interaction strength, the
ground state is the cluster state. Through the Jordan-Wigner fermion mapping, the odd sites and
the even sites form two separate transverse-XY chains, connected only through the boundary terms.
Consequently, all measures of quantum correlations for nearest neighbour spins, the concurrence,
the quantum mutual information and the quantum discord are all zero in the ground state. The
dynamics is spin conserving for Jy = Jx, exhibiting a line of critical points for |h/Jx|≤2, with
an uncorrelated direct product ground state for |h/Jx|> 2. There are several quantum critical
points in the parameter space, with multi-fold degenerate ground states. The magnetisation and
the global entanglement measure exhibit strong singular features for the spin conserving case. The
next-neighbour quantum correlation measures are investigated analytically, which exhibit singular
features in the vicinity of degeneracy critical points. The Jy - and h- derivatives of the concurrence
exhibit singular peak behaviour near the degeneracy critical points, except in the spin conserving
case where the derivatives are zero.

Probabilistic analysis of data structures for locality-sensitive hashing functions

For the nearest neighbor search problem in high-dimensional spaces, Locality-Sensitive Hashing (LSH)has shown excellent performance in terms of query cost and disk space utilization. Under the traditional analysis model, LSH is considered a randomized algorithm, with the only uncertainty being the choice of the hash function. In this research, the collision probability obtained under this model is referred to as the hash-function-based collision probability. In this paper, a different analysis model is used to conduct a theoretical analysis of LSH. The motivation for this work is twofold:1) Under the existing analysis model, users must generate random data structures for each query point to achieve the theoretical effect, which is impractical in real applications.2) The performance metric that users are concerned with is the expected collision probability of a random query point in a single data structure. Based on this, this paper derives the collision probability of random point pairs under the Hamming distance for any single hash function. The collision probability derived under this model is referred to as the query-based collision probability. It is also proven that in the Hamming space, the two types of collision probabilities are identical.

Low Complexity Speech Secure Hash Retrieval Algorithm Based on KDTree Nearest Neighbor Search

With the continuous growth of dimension in retrieval system, only a few data points are distributed near the center (empty space phenomenon), and the distance between data points in high-dimensional space is nearly equal (dimensional effect), resulting in high complexity and low accuracy in retrieval. Aiming at the above problems, this paper designs a speech secure hash retrieval scheme. In this scheme, the spectral subband centroids(SSCS) of speech is extracted to generate the feature vector, then the biometric template index is established by KDTree classification, and the specific SHA256-Ushiki chaotic encryption algorithm key is allocated to each index. The security framework is constructed according to the cancelable biometric template generated by the combination of classification and distribution key, and the binary hash vector is generated, then hash vector is encrypted. Experimental results show that through the establishment of KDTree cancelable biometric template index, the super rectangular region of the K dimensional space is constructed, which effectively solves the empty space phenomenon and the dimensional effect. Through the KDTree nearest neighbor search, the algorithm reduces the number of matches between classes, which effectively reduces computational complexity and accuracy problems. The tampering comparison of mobile terminal realizes the content verifiable retrieval. The speech encryption effectively prevents the leakage of plaintext and ensures security of the speech storage and transmission process.

Characterization and differentiation of aluminum powders used in improvised explosive devices-Part 3: Comparison of statistical analysis methods.

Determining the extent to which sources of aluminum (Al) powders, often used as fuel in improvised explosive devices (IEDs), can be differentiated is important for forensic investigations and gathering intelligence. Previous work developed effective methods of characterizing Al powders using micromorphometric features of the Al particles and a multistage sampling approach. Since then, ~100 additional samples from Al powder sources representing five powder types used in IEDs and 33 product distributors have been added to the dataset. Using this large dataset, a study confirmed that 200 randomly selected fields of view (FOV) are needed to accurately characterize the powder. Three different statistical methods, each using a different method of summarizing the large volumes of data, are used: a modified Wasserstein distance score nearest neighbor classifier for the FOV means, an ASTM-style match interval for means of the FOV means, and a linear discriminant analysis for the means of means of means. Two of the methods classify each questioned subsample to an Al powder sample, whereas the ASTM-style method classifies questioned/known-source subsample pairs as matching or non-matching. All three classifiers show that Al powder sources can be discriminated, although samples of the same powder type or made of Al products from the same distributor are often confused. Analysis of Al powder samples from three casework IEDs shows they were likely made using Al powder from Al-containing paint products. These results are integral to closed-set classification of Al powders where the source of a questioned subsample is contained in the database.

Spatial Distribution and Growth of Islamic Places of Worship in Sokoto Metropolis

This study wishes to explore the effect of religious sub-groupings on the proliferation and distribution of places of worship on the religious landscape of Sokoto metropolis. Geographical Information System (GIS) environment using a geographical positioning system for ground-truthing was employed in mapping the religious places of worship and their actual location/position on the landscape of the study area. The places identified were 32 Hallaras centres, 35 Zawiyas, 81 Jumu’at mosques and other relevant preaching grounds. Two (2) participants were chosen from each of the sect’s orders particularly leaders and imams for interview. The research revealed that the subgroupings are the yardstick for the proliferation of religious places of worship in the study area. Hallaras, Hallaqas and zawiyas and Jumu’at mosques are found to be the major places of worship in the metropolis. It was also concluded that job creation, seeking popularity, desire for leadership and misinterpretation of religious teachings are the other contributing factors to the proliferation of places of worship. Nearest neighbour analysis (NNA) of Sufi order sect, JIBWIS and Jumu’at places of worship have less than 1% given the z-scores of -4.506 strongly indicating the random pattern of distribution in Sokoto metropolis. The work recommends among others the need for spatial planning policies of religious places of worship in the metropolis for unity and better sustainability of the religious landscape in Sokoto metropolis.

Insights into Segregation and Aggregation in Dilute Atom Alloy Catalysts Using DFT and Machine Learning

AbstractDilute atom alloys (DAAs) are an important class of heterogeneous catalysts due to their ability to precisely tune the activity and selectivity of reactions. DAA catalysts typically consist of a small quantity of metal solute in a metal host. Key considerations in the stability of DAA catalysts are the segregation and aggregation energy. In this work, we report a systematic theoretical study of segregation and aggregation energies of DAA catalysts composed of 3d, 4d, and 5d transition metals. To investigate the nature of DAAs, we analyzed both Bader charge and density of states, as well as formation energies, to identify the most stable DAA configuration for a given alloy. We further applied regression‐based, tree‐based, and neural network machine learning (ML) models to gain physics‐based insights in predicting segregation and aggregation energies based on readily available atomic and bulk features. We found that the d‐band filling of the solute and host, nearest neighbor distance of the host, and d‐band width of the solute determine the segregation energy, whereas the Pauling electronegativity of the host and solute, nearest neighbor distance of the host, and cohesive energy of host determine aggregation energy. Our findings provide crucial insights for DAA catalyst design.

Interlayer magnetic coupling in FePS3 and NiPS3 stacked bilayers from first principles

Abstract Single layers of transition-metal thiophosphates (2D-TMPS$_{3}$) van der Waals magnets are an ideal platform for studying antiferromagnetic interactions in 
two dimensions. However, the magnetic coupling mechanism between two or more individual layers of these materials remains mostly unexplored.
This study presents a density-functional based analysis and analytical models to describe the magnetic configurations of FePS$_{3}$ and NiPS$_{3}$ stacked bilayers. We explore the interplay between magnetic configurations and stacking shift, therefore identifying the mechanisms that result in either ferromagnetic or antiferromagnetic coupling between layers. Our findings indicate that the stacking with the lowest energy is metal-dependent, and the interlayer magnetic configuration (ferromagnetic or antiferromagnetic) varies based on the stacking type and the metal involved. Using an Ising-Hamiltonian model and a tight-binding model based on Wannier functions, we show that interlayer exchange interactions must be considered up to the third nearest neighbor and to elucidate the superexchange mechanism for the NiPS$_{3}$ system.

Outcomes of Transarterial Hepatic Embolization versus Yttrium-90 Radioembolization for Treatment of Patients with Hepatocellular Carcinoma > 7 cm.

Outcomes of Transarterial Hepatic Embolization versus Yttrium-90 Radioembolization for Treatment of Patients with Hepatocellular Carcinoma > 7 cm.

Improved density peak clustering with a flexible manifold distance and natural nearest neighbors for network intrusion detection

Recently, density peak clustering (DPC) has gained attention for its ability to intuitively determine the number of classes, identify arbitrarily shaped clusters, and automatically detect and exclude anomalies. However, DPC faces challenges as it considers only the global distribution, resulting in difficulties with group density, and its point allocation strategy may lead to a domino effect. To expand the scope of DPC, this paper introduces a density peak clustering algorithm based on the manifold distance and natural nearest neighbors (DPC-MDNN). This approach establishes nearest neighbor relationships based on the manifold distance and introduces representative points using local density for distribution segmentation. In addition, an assignment strategy based on representatives and candidates is adopted, reducing the domino effect through microcluster merging. Extensive comparisons with five competing methods across artificial and real datasets demonstrate that DPC-MDNN can more accurately identify clustering centers and achieve better clustering results. Furthermore, application experiments using three subdatasets confirm that DPC-MDNN enhances the accuracy of network intrusion detection and has high practicality.

Electric Field Control of Magnetic Skyrmion Helicity in a Centrosymmetric 2D van der Waals Magnet.

Two-dimensional van der Waals magnets hosting topological magnetic textures, such as skyrmions, show promise for spintronics and quantum computing. Electrical control of these topological spin textures is crucial for enhancing operational performance and functionality. Here, using electron microscopy combined with in situ electric and magnetic biasing, we show that the skyrmion helicity in insulating Cr2Ge2Te6 can be controlled by the direction of the external electric field applied during the field cooling process. The electric field-tuned helicity remains stable at low temperature, even amid variations in magnetic and electric fields. Our theoretical investigation reveals that nonzero Dzyaloshinskii-Moriya interactions between nearest neighbors, induced by the electric field, change their sign upon reversing the electric field direction, thereby facilitating helicity selection. The electrical control of magnetic helicity demonstrated in this study can be extended to other centrosymmetric skyrmion-hosting magnets, paving the way for future device designs in topological spintronics and quantum computing.

IDS Framework based on ML for Cloud Computing

Introduction: The last few decades have seen a marked increase in individuals' dependence on the internet. Users require considerable internet data and a wide variety of services. Cloud computing offers on-demand services under a "pay-as-you-use" framework. Given its open and distributed nature, security is a paramount concern. An intrusion detection system (IDS) serves the purpose of overseeing activities and identifying any unauthorized access or attacks on the computing system. Machine learning (ML) techniques are effective in identifying both known and unknown threats. In this study, we have suggested an IDS framework that addresses the problem of a single point of failure by utilizing the collaboration between the user and the cloud service provider to operate the IDS. It makes use of clustering followed by classification approaches. Clustering helps in minimizing the data size, time to respond and shorten the training period for classification. We conducted an evaluation of the proposed IDS framework’s suitability using KDD cup 1999 dataset on the cloud platform. In the first experiment, clusters were labelled using k nearest neighbour method on cloud VMs users, and all VMs’s clusters are merged label wise for classification. In the second experiment, all clusters belonging to the same cloud VM user are aggregated before classifying them. The results show that the low frequent attacks are more accurately detected in second experiment than the first one. The first experiment excels in detecting Probe and Denial of service attacks. However, both experiment tend to have a high detection rate for normal data, exceeding 97%. Objectives: The objective of this paper is to design and implement IDS framework using machine leanring techniques for cloud platform. Methods:To evaluate the performance and functionality of the proposed IDS, we have carried out two independent experiments on a cloud platform using the KDD CUP 1999 intrusion dataset. This dataset includes 19.69% normal data, 2% of various attack types such as R2L, U2R, and PROBE and 79.23% DOS attacks. In these experiments, we have utilized 98,804 instances for testing and 395,216 instances for training the models.Labeled clusters of each cloud user – VMs are combined in experment 1. All clusters of a cloud users are combined in experiment 2. Results: The first scenario performs better at detecting DoS and probe attacks, while the second scenario is more effective at identifying low-frequency attacks. In both scenarios, over 97% of normal data is detected. Additionally, the proposed solution improves the detection rate of DoS and regular attacks by 0.1%. ConclusionsThe model is implemented in two stages. In the first stage, a clustering technique is used to reduce reaction time and data volume. This stage helps distinguish between different types of attacks and identify multiclass attacks. The supervised learning approach benefits from reduced training time by utilizing clusters generated from the dataset. Cloud providers and consumers participating in the proposed IDS reduce the risk of a single point of failure. In the event of a virtual machine failure, the IDS remote controller can still detect the intrusion. The applicability of the proposed IDS framework has been verified using the KDD CUP 1999 dataset.

A Cross-Sectoral Telemedicine Network (sekTOR-HF) for Patients with Heart Failure

Objectives: Heart failure is associated with frequent hospital admissions and high mortality. Digital medical technologies could help to improve information exchange between healthcare providers and patients to prevent recurrent cardiac decompensation. Methods: Eligible patients aged between 18 and 85 (mean age 65 ± 12; 35.4% female) with symptomatic heart failure were included in this cross-sectoral telemedicine network (sekTOR-HF) study (n = 79) with a 12-month intervention period. Depending on the severity of heart failure at the time of inclusion, patients in the intervention group were labeled either as inpatients (NYHA III–IV) or outpatients (NYHA I–II). All patients not included served as the control group. Nearest Neighbor Propensity Score Matching was performed to obtain a control group of the same size. Patients in the intervention group received an electronic patient record with all relevant health data in an eHealth portal and the option to use learning modules. A coordinating network office supported all patients in the intervention group. Monitoring included patient self-measurement of blood pressure, weight, heart rate, and oxygen saturation and a digital electrocardiogram. The primary endpoint was all-cause rehospitalization in both groups. Results: The cumulative incidence for all-cause rehospitalization was lower in the intervention group compared to the control group (sHR 1.86; 95% CI: 1.12–3.09). There was no difference in all-cause mortality (HR 1.5; 95% CI: 0.53–4.21). Conclusions: Intervention management in this cross-sectoral telemedicine network led to a lower cumulative incidence of all-cause rehospitalization even in the early phase of intervention.

Thermodynamic Parameter Estimation for Modified Oligonucleotides Using Molecular Dynamics Simulations.

This study investigates the thermodynamic parameters of 1300 RNA/DNA hybrid duplexes, including both natural and chemically modified forms, using molecular dynamics (MD) simulations. Modified duplexes consist of phosphorothioate (PS) backbones and 2'-O-methoxyethyl (MOE) modifications, both commonly used in therapeutic oligonucleotides. Hybridization enthalpy and entropy were calculated from MD trajectories using molecular mechanics Poisson-Boltzmann surface area (MMPBSA) and molecular mechanics generalized Born surface area (MMGBSA) approaches. To address discrepancies with experimental data, we established empirical relationships by comparing calculated values with known experimental results of natural hybrid duplexes, then extended these relationships to the entire data set. The corrected parameters were subsequently used to generate nearest-neighbor (NN) models, allowing for experimentally reliable melting temperature predictions. In this process, MMGBSA demonstrated superior predictive performance with high convergence and consistency for both natural and modified duplexes. Specifically, MMGBSA captured the stabilizing effects of the MOE modification with minimal bias, while MMPBSA exhibited greater variability and limited reliability. These findings highlight the potential of MMGBSA for accurate thermodynamic modeling of both natural and modified nucleic acids, providing a robust framework and experimentally meaningful insights for applications in nucleic acid-based therapeutic design and biotechnology.

Breakdown of Magic Numbers in Spherical Confinement.

Magic numbers in finite particle systems correspond to specific system sizes that allow configurations with low free energy, often exhibiting closed surface shells to maximize the number of nearest neighbors. Since their discovery in atomic nuclei, magic numbers have been essential for understanding the number-structure-property relationships in finite clusters across different scales. However, as the system size increases, the significance of magic numbers diminishes, and the precise system size at which magic number phenomena disappear remains uncertain. In this study, we investigate colloidal clusters formed through confined self-assembly. Small magic number clusters display icosahedral symmetry with closed surface shells, corresponding to pronounced free energy minima. Our findings reveal that beyond a critical system size, closed surface shells disappear, and free energy minima become less pronounced. Instead, we observe a distinct type of colloidal cluster, termed a football cluster, which retains icosahedral symmetry but features lower-coordinated facets disconnected by terraces. A sphere packing model demonstrates that forming closed surface shells becomes impossible beyond a critical system size, explaining the breakdown of magic numbers in large confined systems.

Robust Intrusion Detection System Using an Improved Hybrid Deep Learning Model for Binary and Multi-Class Classification in IoT Networks

The rapid expansion of internet of things (IoT) applications has significantly boosted productivity and streamlined daily activities. However, this widespread adoption has also introduced considerable security challenges, making IoT environments vulnerable to large-scale botnet attacks. These attacks have often succeeded in achieving their malicious goals, highlighting the urgent need for robust detection strategies to secure IoT networks. To overcome these obstacles, this research presents an innovative anomaly-driven intrusion detection approach specifically tailored for IoT networks. The proposed model employs an advanced hybrid architecture that seamlessly integrates convolutional neural networks (CNN) with multilayer perceptron (MLP), enabling precise detection and classification of both binary and multi-class IoT network traffic. The CNN component is responsible for extracting and enhancing features from network traffic data and preparing these features for effective classification by the MLP, which handles the final classification task. To further manage class imbalance, the model incorporates the enhanced hybrid adaptive synthetic sampling-synthetic minority oversampling technique (ADASYN-SMOTE) for binary classification, advanced ADASYN for multiclass classification, and employs edited nearest neighbors (ENN) alongside class weights. The CNN-MLP architecture is meticulously crafted to minimize erroneous classifications, enhance instantaneous threat detection, and precisely recognize previously unseen cyber intrusions. The model’s effectiveness was rigorously tested using the IoT-23 and NF-BoT-IoT-v2 datasets. On the IoT-23 dataset, the model achieved 99.94% accuracy in two-stage binary classification, 99.99% accuracy in multiclass classification excluding the normal class, and 99.91% accuracy in single-phase multiclass classification including the normal class. Utilizing the NF-BoT-IoT-v2 dataset, the model attained an exceptional 99.96% accuracy in the dual-phase binary classification paradigm, 98.02% accuracy in multiclass classification excluding the normal class, and 98.11% accuracy in single-phase multiclass classification including the normal class. The results demonstrate that our model consistently delivers high levels of accuracy, precision, recall, and F1 score across both binary and multiclass classifications, establishing it as a robust solution for securing IoT networks.