Proximity Relationship Research Articles

Graph-based spatial co-location pattern mining: integrate geospatial analysis and logical reasoning

ABSTRACT Spatial co-location patterns reflect the inherent correlations among geographical elements. Mining co-location patterns of POIs can provide valuable insights for urban planning and resource management. Generally, co-location mining comprises two steps: proximity relationship determination (geospatial analysis) and frequent pattern recursion (logical reasoning). Previous methods often separate these two steps: serializing proximity relationships to enumerate frequent sequences. However, this approach suffers from limited flexibility and intuitiveness: as continuous spatial contexts are segmented into numerous small parts, it fails to adequately represent geographic correlations and hinders the effective visualization of logical reasoning. Facing these challenges, this study proposes a novel graph-based spatial co-location mining method (GSCM), which leverages graphs to integrate geospatial analysis and logical reasoning. Initially, to establish adjacency relationships, GSCM constructs the adaptive neighborhood graph, which dynamically adjusts proximity thresholds to accommodate geographic heterogeneity. Subsequently, the Apriori logical recursive process is realized on the graph structure. By leveraging graph searching, pruning, and growing, the potential growth directions of co-location patterns are identified, enhancing both the efficiency and intuition of frequent pattern recursion. Through experiments conducted on large-scale POI datasets from Wuhan, GSCM is compared with existing baseline methods, verifying its potential to uncover co-location patterns in complex spatial contexts.

Perceptions of informal caregivers about motivations, needs, and benefits of care for dependent older adults.

The informal caregiver provides non-remunerated permanent or regular care to dependent older adults. This qualitative study aimed to identify the perceptions of informal caregivers about motivations, needs, and benefits of caring for dependent older adults. It was conducted with ten Portuguese informal caregivers, based on an instrument with questions about the care provided to older adults and their perceptions about performing this role. The results revealed the following motivations for care: proximity and trust relationship, duty of care, more available family members, home proximity, lack of vacancies, high cost of shelter institutions, and older adults' desire to remain in their homes. The primary care activities for older adults are hydration, hygiene, food, therapeutic administration, companionship, emotional support, comfort, entertainment, and promoting autonomy and dignity. The needs identified by the caregivers were home, social security, and the caregiver's employer support, financial help, psychological support, and training to care for the older adults. The benefits of informal care for dependent older adults were prompt family support, physical and emotional security, affection, and companionship. This study gives voice to crucial citizens.

Molecular mechanism of the treatment of lung adenocarcinoma by Hedyotis Diffusa: an integrative study with real-world clinical data and experimental validation.

With a variety of active ingredients, Hedyotis Diffusa (H. diffusa) can treat a variety of tumors. The purpose of our study is based on real-world data and experimental level, to double demonstrate the efficacy and possible molecular mechanism of H. diffusa in the treatment of lung adenocarcinom (LUAD). Phenotype-genotype and herbal-target associations were extracted from the SymMap database. Disease-gene associations were extracted from the MalaCards database. A molecular network-based correlation analysis was further conducted on the collection of genes associated with TCM and the collection of genes associated with diseases and symptoms. Then, the network separation SAB metrics were applied to evaluate the network proximity relationship between TCM and symptoms. Finally, cell apoptosis experiment, Western blot, and Real-time PCR were used for biological experimental level validation analysis. Included in the study were 85,437 electronic medical records (318 patients with LUAD). The proportion of prescriptions containing H. diffusa in the LUAD group was much higher than that in the non-LUAD group (p < 0.005). We counted the symptom relief of patients in the group and the group without the use of H. diffusa: except for symptoms such as fatigue, palpitations, and dizziness, the improvement rate of symptoms in the user group was higher than that in the non-use group. We selected the five most frequently occurring symptoms in the use group, namely, cough, expectoration, fatigue, chest tightness and wheezing. We combined the above five symptom genes into one group. The overlapping genes obtained were CTNNB1, STAT3, CASP8, and APC. The selection of CTNNB1 target for biological experiments showed that the proliferation rate of LUAD A549 cells in the drug intervention group was significantly lower than that in the control group, and it was concentration-dependent. H. diffusa can promote the apoptosis of A549 cells, and the apoptosis rate of the high-concentration drug group is significantly higher than that of the low-concentration drug group. The transcription and expression level of CTNNB1 gene in the drug intervention group were significantly decreased. H. diffusa inhibits the proliferation and promotes apoptosis of LUAD A549 cells, which may be related to the fact that H. diffusa can regulate the expression of CTNNB1.

Extracting multilane roads from OpenStreetMap through graph convolutional neural network and road mesh relationship analysis

Multilane roads in vector data are comprised of parallel line features that represent the same road. The extraction of these features is crucial for updating maps and cartographic generalization. This article addresses previous object-level extraction limitations by introducing a mesh-based method that employs a GCNN model to classify them based on geometric features and proximity relationship. A bidirectional region growing clustering method (BRGC) is designed to cluster the identified multilane road meshes belonging to the same road into groups considering their spatial relationships. The object-level multilane roads are extracted based on the clustering results. Validation using road data from OpenStreetMap in Chengdu and Zhengzhou achieved a classification f1-score >90% for multilane road mesh. Moreover, the object-level multilane detection precision and recall were 88.05% and 92.72% on the Chengdu dataset, and 86.99% and 91.37% on the Zhengzhou dataset, which demonstrates the proposed method effectively extracts multilane roads at the object level, aligning with real-world scenarios and human cognition.

A new network representation for time series analysis from the perspective of combinatorial property of ordinal patterns

Revealing system behavior from observed time series is a fundamental problem worthy of in-depth study and exploration, and has attracted extensive attention in a wide range of fields due to its wide application values. In this paper, we propose a novel network construction method for time series analysis, which is different from the existing ordinal network method concerning the transition probability of ordinal patterns in transition networks. The proposed network representation is based on the combinatorial property concerning the inversion number of ordinal patterns from the ordinal partitions of time series. For the proposed network construction method, the network nodes are represented by each ordinal partition of time series and the edge weight between network nodes is determined by a novel proximity relationship of ordinal patterns which is a newly defined metric based on the inversion number of ordinal patterns. Using random signals and chaotic signals as examples, we demonstrate the potential of the proposed network construction method for the network representation of time series. We also employ the proposed network construction method in quantitative EEG for the identification of three different physiological and pathological brain states. According to the results of AUC values, one can observe that the discriminating power of the AND of the proposed network construction method is slightly stronger than that of the available ordinal network. The experimental results illustrate that our proposed network construction method opens up a new pathway for network representation of time series, which is capable of quantifying time series for feature extraction and pattern learning for time series analysis.

Discriminative projection fuzzy K-Means with adaptive neighbors

Fuzzy K-Means (FKM) based on fuzzy theory is a classic method to effectively handle overlapping regions between clusters. However, redundant features and noises brought by increasing data dimensions affect the effectiveness of FKM. To cope with this issue, we propose a Discriminative Projection Fuzzy K-Means with adaptive neighbors (DPFKM) model, which embeds a discriminative subspace into FKM to facilitate learning of global structure and the most discriminative information. Firstly, a novel projection space with uncorrelated constraints are adopted to promote statistical independence among the data in the subspace as well as to enhance the ability of FKM to discern and utilize discriminative information. Secondly, the Frobenius norm is introduced as the regularization term to eliminate discrete solutions, while preserving the fuzziness and enhancing the sparsity of FKM. Finally, we propose a novel optimization method to finetune the model, with a particular focus on adaptive adjustment of the regularization parameter based on the proximity relationship between the samples and clusters. Comprehensive experiments are conducted on multiple data sets, and the results can prove the superiority of the proposed model.

Radiographic evaluation of mandibular third molars: an ex vivo comparative study between multilayer and conventional panoramic radiography.

To compare the multilayer panoramic radiography (MPAN) and conventional panoramic radiography (CPAN) in the evaluation of mandibular third molars using cone-beam computed tomography (CBCT) as a reference. CPAN, MPAN, and CBCT scans from 33 dry human mandibles were acquired using the OP300 Maxio unit, totalizing 56 mandibular third molars to be evaluated. Three examiners evaluated each third molar according to their position, depth of impaction in the mandibular ramus, proximity between the dental root apexes and the mandibular canal, and the presence of radiographic signs of proximity to the mandibular canal. In addition, when there was a distance between the root apexes and the mandibular canal, it was measured. As a reference, these same parameters were assessed in the CBCT scans by a fourth examiner. For the statistical analysis, the weighted Kappa, Bland Altman, and Wilcoxon tests were performed (α = 0.05). The agreement between the assessments performed in the panoramic modalities with the CBCT ranged from 66.1% to 100.0% for the categorical variables. Overall, the agreement values of CPAN and MPAN with CBCT were similar. The distances between the dental root apex and the mandibular canal for both CPAN and MPAN were significantly underestimated compared to CBCT (p < 0.05). The intra- and interexaminer agreements of the examiners ranged from poor to almost perfect; in general, the agreements were higher in the evaluation performed in the MPAN than in the CPAN. The MPAN performs similarly to CPAN for evaluating mandibular third molars and their proximity relationship to the mandibular canal. Preoperative evaluation of lower mandibular third molars is usually performed using CPAN. Recently, a new tool, MPAN, was developed, which has not yet been tested for the evaluation of mandibular third molars and showed similar performance to CPAN in the present study. Future studies using MPAN are encouraged to evaluate other diagnostic tasks.

Evaluation of porosity and permeability of sandstones within the Oti Group of the Volta Basin using petrophysical and petrographic techniques

Evaluation of porosity and permeability of sandstones within the Oti Group of the Volta Basin using petrophysical and petrographic techniques

Is panoramic radiography reliable to evaluate the relationship between maxillary molar and premolar roots and the maxillary sinus?

Aim: To evaluate the radiographic signs of proximity relationship between maxillary molar roots and maxillary sinus in panoramic radiographs, using CBCT as control. Methods: 81 examinations of patients who had panoramic radiographs and CBCT of the maxillary molars and pre molars region were used. Pathological situations were excluded from this study. Panoramic radiographs and CBCT were evaluated randomly and separately by an experienced dental radiology examiner. 1,055 root apices were evaluated individually. When assessing the relationship between maxillary molar and pre molar apices, and maxillary sinus, the examiner rated the images, both in panoramic radiography and CBCT, according to a scale of 0 to 3, where 0–Without relationship or distant; 1-Root apex projection or overlapping; 2-Maxillary sinus circumventing the tooth root; 3-Interruption of the continuity of maxillary sinus floor. Tabulated data were statistically analyzed using Kappa test and interclass correlation coefficient (ICC) respectively, with a significance level of 5%. A second analysis of the sample was performed after 15 days to analyze reproducibility. Results: Kappa test indicated near perfect reproducibility (Kw=0.973). The highest prevalence ratio, when comparing the classification in panoramic radiographs and CBCT, was for type 1 (52,7%). There was no difference between the type 1 signal and the gold standard observed on CBCT (ρ=0.2152). Conclusion: Panoramic radiography can be used to evaluate the relationship between roots of maxillary molars and premolars with the maxillary sinus. For cases where there is overlapping between apices and maxillary sinus, CBCT remains the indicated exam for better evaluation.

Latency Minimization for Mobile Edge Computing Enhanced Proximity Detection in Road Networks

In road networks, mobile users (including vehicles and pedestrians) need to know the proximity relationship with other users in real time, referred to as the problem of proximity detection which is very significant for autonomous driving. However, due to limited computing and storage resources of mobile users and real-time changes of road network status, it becomes a difficult task to calculate and update the proximity relationship between users in real time. Therefore, in this paper, we first propose a computation offloading scheme and a dynamic road network state update model for proximity detection in dynamic road networks based on Mobile Edge Computing (MEC), and formulate the latency optimization problem for proximity detection in the dynamic road network as a nonlinear optimization problem. Then we use the Sequential Least Squares Programming (SLSQP) algorithm to solve the latency optimization problem. In addition, to reduce the running time, we also use the deep reinforcement learning approach, i.e., the Deep Deterministic Policy Gradient (DDPG) algorithm, to address the latency optimization problem. Simulation results show that, compared with the SLSQP algorithm, the DDPG algorithm can effectively and efficiently reduce the computational time of the optimal latency each time by continuously adjusting the task allocation strategy, and the computational time of the DDPG algorithm is two orders of magnitude lower than the SLSQP algorithm.

Morphological and molecular characterization of Hysterothylacium spp. parasitizing Pomatomus saltatrix and Pagrus pagrus of the State of São Paulo, Brazil.

Raphidascarid nematodes have been the focus of several studies, mainly due to the zoonotic potential of some species, even though the cases are underreported. Due to the difficulty in identifying their larvae, the use of diagnostic techniques involving morphological and molecular analyses has grown in the last 20 years. The present study had as objective the morphological and molecular characterization of the L3 larval types of Hysterothylacium collected in Pomatomus saltatrix and Pagrus pagrus from the Brazilian coast, close to the municipality of Santos, State of São Paulo. Twenty specimens of P. saltatrix were necropsied and Hysterothylacium type V (n = 257) and Hysterothylacium type X (n = 5) larvae were found. Five specimens of P. pagrus were necropsied and all were parasitized by Hysterothylacium type V larvae. The analyses showed a genetic proximity relationship between Hysterothylacium types V with other Hysterothylacium V and with H. deardorffoverstreetorum, although this is a species inquirenda. Haplotypes for Hysterothylacium type X found in the present study formed a monophyletic group with other Hysterothylacium X, H. amoyense, and H. zhoushanense. Through this study, new hosts and localities were registered for Hysterothylacium type V and Hysterothylacium type X.

Testing for Local Spatial Association Based on Geographically Weighted Interpolation of Geostatistical Data with Application to PM2.5 Concentration Analysis

Using local spatial statistics to explore local spatial association of geo-referenced data has attracted much attention. As is known, a local statistic is formulated at a particular sampling unit based on a prespecific proximity relationship and the observations in the neighborhood of this sampling unit. However, geostatistical data such as meteorological data and air pollution data are generally collected from meteorological or monitoring stations which are usually sparsely located or highly clustered over space. For such data, a local spatial statistic formulated at an isolate sampling point may be ineffective because of its distant neighbors, or the statistic is undefinable in the sub-regions where no observations are available, which limits the comprehensive exploration of local spatial association over the whole studied region. In order to overcome the predicament, a local-linear geographically weighted interpolation method is proposed in this paper to obtain the predictors of the underlying spatial process on a lattice spatial tessellation, on which a local spatial statistic can be well formulated at each interpolation point. Furthermore, the bootstrap test is suggested to identify the locations where local spatial association is significant using the interpolated-value-based local spatial statistics. Simulation with comparison to some existing interpolation and test methods is conducted to assess the performance of the proposed interpolation and the suggested test methods and a case study based on PM2.5 concentration data in Guangdong province, China, is used to demonstrate their applicability. The results show that the proposed interpolation method performs accurately in retrieving an underlying spatial process and the bootstrap test with the interpolated-value-based local statistics is powerful in identifying local patterns of spatial association.

A new mesoscale eddy tracking methodology based on fast normalized cross-correlation and its validation in the Northwest Pacific

Most mesoscale eddy tracking methodologies used prior to this study evaluated eddy features using a distance-based proximity relationship, rather than considering similarities between eddies. This study applies a fast normalized cross-correlation methodology in the field of image registration to propose a novel mesoscale eddy tracking methodology that can rapidly and comprehensively calculate the similarities between two eddies and judge their relationship through the correlation coefficient, thus facilitating a more accurate mesoscale eddy trajectory tracking. The sea level anomaly data field is employed to identify the positions of eddies over time. The tracking methodology is then used to track the mesoscale eddy trajectories. After comparing the local nearest neighbor methodology (LNN) with our proposed new methodology in the Northwest Pacific Ocean, we conclude that the proposed methodology can address issues of discontinuity in tracking; especially in cases involving eddies with long lifespans. The tracking trajectories utilized in the proposed methodology achieve superior continuity and integrity and a higher degree of characterization than LNN, with the tracking results showing greater consistency with real eddy motion. The new methodology proposed in this paper has great significance for more widespread use.

Redesign of Laboratory Facilities Layout Quality Control at PT. Industri Kimia

PT. The chemical industry is a company that has been producing hydrogen peroxide since 1992. One of the essential roles of this chemical factory is the Quality Control Laboratory, which functions to analyze raw materials and hydrogen peroxide products before distributing them to consumers to ensure product quality. Based on the standard of laboratory quality management system, SNI ISO/IEC 17025:2008 states that in order to facilitate the correctness of test performance, the laboratory must monitor, control and record the temperature and humidity in the testing room because it will have an impact on the validity of the quality of the data produced—however, Procurement of test equipment facilities in the QC laboratory of PT. The chemical industry is not supported by good facility layout planning, so it is necessary to rearrange the layout, including the function of equipment and materials and the conditioning of utility and electricity systems. The laboratory layout redesign parameters used are floor area measurement, ARC (Activity Relationship Chart) analysis, and ARD (Activity Relationship Diagram) analysis. The study results are in the form of calculating the total closeness rating value on the activity proximity relationship diagram, the largest value is the sample space of 6200, and the lowest value is the computer room of 200. From these calculations, a layout template for testing laboratory facilities is created based on the order of the highest total closeness rating value to the smallest value so that a laboratory layout will be obtained according to the SNI ISO/IEC 17025:2008 standard and can create an effective and efficient work environment.

Relational POI recommendation model combined with geographic information.

Point of interest (POI) recommendation is a popular personalized location-based service. This paper proposes a Geographic Personal Matrix Factorization (GPMF) model that makes effective use of geographic information from the perspective of the relationship between POIs and users. This model considers the role of geographic information from multiple perspectives based on the locational relationship among users, the distributional relationship between users and POIs, and the proximity and clustering relationship among POIs. The GPMF mines the influence of geographic information on different objects and carries out unique modeling through cosine similarity, non-linear function, and k nearest neighbor (KNN). This study explored the influence of geographic information on POI recommendation through extensive experiments with data from Foursquare. The result shows that GPMF performs better than the commonly used POI recommendation algorithm in terms of both precision and recall. Geographic information through proximity relations effectively improves the recommendation algorithm.

Joint optimization of latency and energy consumption for mobile edge computing based proximity detection in road networks

In recent years, artificial intelligence and automotive industry have developed rapidly, and autonomous driving has gradually become the focus of the industry. In road networks, the problem of proximity detection refers to detecting whether two moving objects are close to each other or not in real time. However, the battery life and computing capability of mobile devices are limited in the actual scene, which results in high latency and energy consumption. Therefore, it is a tough problem to determine the proximity relationship between mobile users with low latency and energy consumption. In this article, we aim at finding a tradeoff between latency and energy consumption. We formalize the computation offloading problem base on mobile edge computing (MEC) into a constrained multiobjective optimization problem (CMOP) and utilize NSGA-II to solve it. The simulation results demonstrate that NSGA-II can find the Pareto set, which reduces the latency and energy consumption effectively. In addition, a large number of solutions provided by the Pareto set give us more choices of the offloading decision according to the actual situation.

Multigraph-Based Low-Rank Tensor Approximation for Hyperspectral Image Restoration

Low-rank-tensor-approximation (LRTA)-based hyperspectral image (HSI) restoration has drawn increasing attention. However, most of the methods construct a hidden low-rank tensor by utilizing the non-local self-similarity (NLSS) and global spectral correlation (GSC) inherited by HSIs. Although achieving state-of-the-art (SOTA) restoration performance, NLSS and GSC have limitations. NLSS is introduced from natural image denoising to remove spatially independent identically distributed (i.i.d.) Gaussian and impulse noise. While GSC, which is naturally possessed by HSIs, is adopted to maintain the spectral integrity and remove spectrally, i.i.d., degradations. Therefore, NLSS and GSC may not be successfully used for complex HSI restoration tasks, such as destriping, cloud removal and recovery of atmospheric absorption bands. To solve the issue, borrowing the idea from manifold learning, the geometry information characterized by proximity relationship, is integrated with the LRTA to solve the above issue, named as multi-graph-based LRTA (MGLRTA). Different with most of the existing methods, the proposed MGLRTA directly models an HSI as a low-rank tensor and efficiently explores the extra proximity information on the defined graphs that are not only inherited by the low-rank constraints but also naturally possessed in HSIs. A well-posed iterative algorithm is designed to solve the restoration problem. Experimental results on different datasets that cover several severe degradation scenarios demonstrate that the proposed MGLRTA outperforms the SOTA HSI restoration methods.

An Enhanced Geographically and Temporally Weighted Neural Network for Remote Sensing Estimation of Surface Ozone

Surface ozone (O₃) pollution is a severe environmental problem that endangers human health. It is necessary to obtain high spatio-temporal resolution O₃ data to provide support for pollution monitoring and prevention. For this purpose, this study makes comprehensive use of remote sensing data, reanalysis data, and ground station observations, and develops an enhanced geographically and temporally weighted neural network (EGTWNN) model to acquire high spatial and temporal resolutions of O₃ data. The EGTWNN model is nested by two neural networks. The first neural network automatically learns the spatio-temporal proximity relationship to obtain spatio-temporal weights for the samples, and the spatio-temporal weights are then inputted into the second neural network to conduct weighted modeling of the relationship between O₃ and influencing variables. The contribution of the proposed model is that the first neural network replaces the traditional empirical weighting method, and represents the spatio-temporal proximity relationship more accurately to improve estimation accuracy. Results indicate that the cross-validation R² and RMSE of EGTWNN are 0.81 and 21.24 &#x03BC;g/m³, respectively, which are increased by 0.02 and decreased by &#x007E;1 &#x03BC;g/m³ relative to those of the traditional empirical weighting method based geographically and temporally weighted neural network model. The results also show that compared with the geographically and temporally weighted regression model, the proposed model achieves superior performance. In addition, the spatio-temporal weights obtained by the first neural network of EGTWNN are highly consistent with those obtained by the traditional empirical weighting method, indicating that the results of neural networks are highly interpretable.

Relationship Between Lower Third Molars and Mandibular Alveolar Canal through Cone Beam CT Scans

Background: Third molar extractions are considered one of the most common procedures in oral surgery. Well-known complications of these procedures are lesions to the inferior alveolar nerve because of its proximity to the roots of third molars. Hence, it is important to making an adequate assessment of this relationship to avoid complications. Purpose: To determine the relationship between the inferior alveolar canal (IAC) retained lower third molars, and that relationship with angulation, class and type of lower third molars through cone-beam computed tomographies. Methods: A descriptive and transversal study was conducted with 73 tomographies to 113 lower third molars that were analyzed in the axial, occlusal, and sagittal planes (p = 0.05). Results: 54 % of third molars were in contact with the IAC and more frequently in its lower location (45.9 %) (p = 0.000). Proximity relationships were more common on the left side with a vertical angulation (40.6 %), type A (37.5 %), and class II (75 %) (p = 0.015). On the right side, a mesioangular angulation (34.5 %), type A (48.3 %) (p = 0.004), and class II (58.6 %) (p = 0.000) were the most common. Conclusion: A close proximity relationship of the IAC with the lower third molars was found using cone beam computed tomography, and it is recommended to conduct a careful diagnosis before a surgical procedure.

Factors behind the prevalence of carbapenem-resistant Klebsiella pneumoniae in pediatric wards.

The emergence of carbapenem-resistant Enterobacteriaceae made the treatment difficult, which has become a significant issue of public health. A sharp increase of carbapenem-resistance rate in Klebsiella pneumoniae was observed in a maternity and child health care hospital in Zunyi, China, in 2014.In 2015 to 2016, carbapenem-resistant Klebsiella pneumoniae (CRKp) isolated from all the clinical samples were analyzed to identify the carbapenem-resistance genes. They were then fingerprinted in order to determine their genetic relationship. Clinical data such as usage of imipenem in 2012 to 2016 and the nosocomial infection surveillance data were analyzed.Thirty-five isolates of CRKp out of 4328 various pathogens were obtained, and blaNDM-1 was identified to be the most common resistant gene present in the CRKp isolates. The fingerprint analysis identified 15 major clusters of CRKp isolates. The bacteria with close proximity relationship tended to be from the same wards. However, a few CRKp isolates from different wards were found to be genetically highly related. The clinical data showed a significantly higher usage of carbapenems in 2012 to 2013 before the CRKp rate sharply increased in 2014. The nosocomial infection surveillance showed an unexpectedly high rate of failures to meet the requirement of the hospital environment hygiene and hand hygiene in the neonatal ward.The increasing isolation rate of CRKp was associated with poorly regulated usage of carbapenems, impropriate medical practices, and the poor hospital environmental hygiene and hand hygiene.