Euclidean Distance Method Research Articles

Dynamic liquid level prediction for multiple oil wells based on transfer learning and multidimensional feature fusion network

Abstract Accurate prediction of the dynamic liquid level (DLL) in oil wells is crucial for the intelligent optimization of pumping systems. It provides real-time insights into the operational conditions of the pumping system but also supports the optimization of operational parameters with data. However, due to the long-term operation of oil wells and their complex internal environments, direct measurement of the DLL is challenging, leading to low reliability of the obtained data. Therefore, this paper conducts an in-depth analysis of the parameters involved in the pumping process, identifies the model’s input features, and develops a DLL prediction model for multiple wells based on multidimensional feature fusion (MFF). This model captures the characteristics of DLL changes and the diversity of input features. To address the issues of slow model training and low prediction accuracy caused by insufficient datasets in practical applications, this paper integrates transfer learning (TL) techniques. It proposes a new model, the DLL model for multiple wells based on TL and multidimensional feature fusion (TMFF). Initially, the Euclidean distance and maximum mean discrepancy methods are employed to verify the feature similarity between the source and target domains, using highly similar DLL data as experimental data. By combining TL techniques with the MFF model, the TMFF model is established. The model’s capabilities are validated using field-collected data with broad representativeness. Experimental results demonstrate that the proposed MFF model possesses high accuracy and generalization capability. Additionally, the TMFF model effectively resolves the issue of insufficient data during model training. In summary, the methods proposed in this paper can provide accurate DLL data for practical applications in intelligent oilfields.

Insights into the genetic divergence in Asiatic cotton (Gossypium arboreum L.) germplasm for fibre-quality traits

Abstract Asiatic cotton (Gossypium arboreum L.) has evolved in the Indian subcontinent and is known for its adaptability to low-input management conditions. In the present study, 300 diverse G. arboreum lines, including 100 Nandyal arboreum breeding lines (NAB), 132 Arboreum germplasm collections (AGC) and 68 long-linted arboreum genotypes (LLA), were evaluated for fibre quality to assess the diversity among them and to identify promising genotypes with desirable fibre traits. Significant variations were observed among the genotypes for the studied fibre-quality traits. Principal component analysis showed that the traits micronaire (Mic) and elongation percentage (E%) followed by upper half mean length (UHML) and bundle tenacity (tenacity) were the most significant contributors to variation. Cluster analysis based on the Euclidian distance method showed 16 clusters among 300 G. arboreum genotypes. The genotypes in cluster 4 have desirable UHML, tenacity and UI (uniformity index) traits, and cluster 12 has Mic and E% traits. Furthermore, the number of genotypes with desirable fibre-quality traits was found to be higher in the AGC group than in the LLA and NAB groups. The trait tenacity followed by the UHML showed relatively higher Shannon–Weiner diversity index values across different genotypic groups. Based on the superior performance, the genotypes PA 847, PA 809, PA 837, PA 863, NDLA 3147-2, NDLA 2974 and NDLA 3081 were found to be having desirable fibre traits. The identified promising genotypes are valuable genetic resources for improving fibre quality in G. arboreum cotton.

Implementation of K-Means and Single Linkage on Types of Disabilities in East Java Province

The high number of people with disabilities is one of the problems faced by the Indonesian government, especially in Java Province. After West Java Province, East Java Province is in second place as the province with the highest rate of people with disabilities in Indonesia. Disabled people are people with physical disabilities such as not being able to walk, not being able to talk, not being able to see, and so on. The aim of this research is to group districts in East Java Province based on types of disabilities with the hope of facilitating activities in fulfilling the rights of people with disabilities in East Java Province. The grouping was carried out in order to determine the characteristics of each cluster using so that the optimal k-means method was used for clustering using the Euclidean distance method with cluster 1 in 29 districts and cluster 2 in 9 districts. The most optimal single linkage uses the Euclidean distance method with cluster 1 having 8 districts and cluster 2 having 30 districts. From the results of the validity index values, it was found that the single linkage method had the smallest validity value of the icdrate method compared to the k-means method.

Identifikasi Kematangan Buah Pisang Berdasarkan Variasi Jarak Menggunakan Metode K-Nearest Neighbor

This research aims to identify the level of ripeness of kepok bananas based on the color of their skin using the K-Nearest Neighbor (K-NN) method. Bananas are an important commodity in Indonesia, and various ripeness levels need to be identified. The current process of identifying banana ripeness is still done manually, which requires a lot of labor and tends to be subjective. The K-NN method is used to classify bananas based on their skin color. This research involves the collection of banana images with three ripeness levels (raw, ripe, and overripe) and the extraction of RGB color features from these images. Three distance methods, namely Euclidean, Minkowski, and Manhattan, are also employed to compare accuracy results. The evaluation results of this research show that the accuracy value for the Euclidean distance method is 84%, the Minkowski distance method is 82%, and the Manhattan distance method is 80%. Thus, the findings indicate that the K-NN method and the Euclidean distance method provide good results in identifying the ripeness level of bananas. By implementing the K-NN algorithm, this research attempts to address the weaknesses of the time-consuming and subjective manual identification process, with the hope of providing a more accurate and efficient solution for the banana industry. The results of this research can be used to automate the identification process of banana ripeness levels and improve efficiency in banana sorting. It is expected that this research can provide practical benefits to the community and serve as a basis for further research in this field.

Evaluation of Complex Shear Modulus of Recycled Asphalt Mortars Based on Euclidean Distance Method in the Mixing Process

Evaluation of Complex Shear Modulus of Recycled Asphalt Mortars Based on Euclidean Distance Method in the Mixing Process

The associations between self-construal, emotional expressivity and acculturation among Arab immigrants.

This contribution investigates differences in self-construal and emotional expressivity among Arab immigrants and non-immigrants. Furthermore, it examines the role of acculturation styles and perceived emotional acculturation in predicting these outcomes among Arab immigrants. Using a sample of 1249 self-identified Arabs (629 immigrants in Western Europe and North America; 620 non-immigrant Arabs in the Mashriq and Maghrib regions), we found that collectivist self-construal was significantly lower, and positive emotional expressivity was significantly higher among immigrant, than non-immigrant, Arabs. High home country acculturation (also in combination with high host country acculturation) was the strongest predictor of collectivist self-construal. Immigrants' perception of the positive emotional expressivity of people in their host culture was the strongest predictor of their personal positive emotional expressivity. These results were replicated using the Euclidean distance method to measure acculturation. Hence, the study provides valuable insights into the relationships between self-construal, emotional expressivity and acculturation styles, specifically among Arab immigrants.

Three-dimensional ecological drought identification and evaluation method considering eco-physiological status of terrestrial ecosystems

Ecological drought is a complex process in terrestrial ecosystems where vegetation's eco-physiological functions are impaired due to water stress. However, there is currently a lack of long-term assessment of ecological drought from an eco-physiological perspective. In this study, the standardized ecological drought index (SESNDI) was developed using actual evaporation, root soil moisture, and kernel normalized difference vegetation index via the Euclidean distance method, reflecting ecosystem physiology, water supply capacity, and vegetation status. Solar-induced chlorophyll fluorescence validated SESNDI by reflecting vegetation photosynthesis. Using China as an example, severely impacted by climate change and ecological restoration, ecological drought's spatio-temporal variation and propagation characteristics was evaluated using clustering algorithms. The results demonstrated that (1) SESNDI showed superior performance over several other drought indices. (2) During 1982–2020, ecological drought was prevalent from 1990 to 2010, especially in the central and northeastern regions. (3) Compared to 1982–2000, the median duration and affected area of ecological drought events during 2001–2020 reduced by four months and 1.51 × 105 km2, respectively, while the median intensity increased by 0.06. (4) Decreased precipitation and increased temperature were the primary factors contributing to the frequent occurrence of ecological drought in China from 1990 to 2010. This study offers a crucial methodology for evaluating ecological drought, serving as a reference for developing effective terrestrial restoration strategies.

Development of preferred sizes for building-integrated lighting units (BILUs)

Building-Integrated Lighting Unit (BILU) is a new type of LED lighting product that is organically integrated with building elements and components. To promote the digital design, standardized production, and assembly construction of new lighting forms, it is necessary to establish Preferred Sizes (PSs) of BILU that are coordinated with building modules. With the aim of reduction in quantity and strength in universality, PSs were selected from BILU modular arrays in this paper. Based on the preliminary survey, a set of target sizes involving building, furniture, and home decoration elements was established to cover all application scenarios of PSs. Through a traversal search of feasible splicing methods of all PSs, the distribution of alternative PSs was analyzed with their universality quantified. Then, multidimensional metrics of alternative PSs were established, including the average feasible scheme proportion and feasible PS proportion of Line-BILU, as well as the feasible coverage-rate proportion and average coverage rate of Surface-BILU. A weighted Euclidean distance method was applied in the multi-criterion decision-making, realizing a quantitative optimization of PSs in multiple scenarios and under multiple objectives. A total of nineteen Line-BILU preferred sizes and sixty-five Surface-BILU preferred sizes were finally obtained, with their feasibility and advantages validated based on existing modular coordination standards and actual lighting project examples. These results can provide data support for establishing product specifications and design standards of BILUs, promote building industrialization from the lighting perspective, and stimulate the sustainable construction of architectural lighting.

Exploratory Study on Distinguishing Dendrobium Stem and Five Species of Dendrobium Using Heracles Neo Ultra-Fast Gas Phase Electronic Nose

Dendrobium stem is a valuable food with medicinal and edible properties. Due to its high medicinal value and price, closely related Dendrobium varieties are often sold as imitations on the market. Therefore, there is an urgent need to develop new methods that can quickly identify Dendrobium stem and its closely related species. The Heracles Neo ultra-fast gas phase electronic nose was used in this study to determine and analyze the composition and contents of volatile organic compounds (VOCs) in Dendrobium stem and samples of five other species closely related to it. A total of 20 VOCs were identified, and a fingerprint map of the VOCs was constructed. Principal component analysis (PCA), Euclidean distance, and other methods were used to comprehensively process and analyze the obtained VOC information. The AroChemBase database was also used for qualitative analysis of the compounds. The results showed that there are significant differences in the odor fingerprint spectra of Dendrobium stem and the five other closely related species. The main types of compounds in Dendrobium stem and its five closely related species were organic esters, aldehydes, ketones, and olefins. Among them, 3-methylbutanal and n-butanol were characteristic compounds of the Dendrobium stem sample, while the VOCs acetonitrile and trometamol were present in the five related Dendrobium species samples. The Heracles Neo ultra-fast gas phase electronic nose can quickly and accurately identify Dendrobium stem and its five closely related species. It can also be used for the quality evaluation of Dendrobium stem, providing a theoretical reference for reducing the phenomenon of medicinal confusion in the Dendrobium stem market.

Karakter Kuantitatif dan Hubungan Kekerabatan Genotipe Padi (Oryza sativa L.) Beras Merah dan Beras Hitam yang Ditanam secara Gogo

This study aims to determine the appearance of quantitative characters and kinship relationships of the genotype of red rice and black rice grown in gogo. The research was conducted in February – May 2024 on a medium plain land with an altitude of 370 meters above sea level, in Tampak Siring Village, Central Lombok, NTB. The materials used were 21 rice genotypes consisting of 11 red rice strains, 4 black rice strains, 2 red rice elders, 2 black rice elders and 2 comparison varieties (Inpago Unram I and IR64). The analysis used is the Analysis of Variance (ANOVA) of the Group Random Design (GRD) model with a real level of 5%, if there are real different characters, then the Duncan Multiple Range Test (DMRT) will be continued at a real level of 5%. Cluster analysis was carried out based on the average value of quantitative characters using the euclidean distance method using the R-Studio program. The results of the study showed that between strains all characters had different appearances except for the number of non-productive saplings. G-2 strain; G-6; and G-18 had the same results as Inpago Unram I. Kinship analysis conducted based on the average value of quantitative characters obtained four clusters, namely cluster I G-21, G-9, G-20, G-11, G-17; cluster II G-19, G-2, G-18, G-6, G-7; cluster III G-1, G-5, G-8, G10, G-3, G-4, G-16; and cluster IV G-12, G-15, G-13, G-14.

Analisis Pengujian Website Desa Ketapanrame Berdasarkan Model FURPS Menggunakan Metode Euclidean Distance

The importance of software testing is very prominent in the software development stage. Software testing is an important aspect that cannot be ignored to ensure the quality of an application. One example is testing dynamic web pages such as the one that will be the main focus of the research, namely the Ketapanrame Village Website. Testing of the Ketapanrame Village Website was carried out thoroughly on various aspects, including feature functionality and web appearance aesthetics, to ensure compliance with the specified specifications. In order to achieve satisfactory results from testing, there are feature elements on the web page that will be tested using the FURPS model standard. Each indicator from the FURPS model has a special role in ensuring the overall quality of the software by involving the participation of respondents to find the average value. The challenge arises in finding an appropriate average value for testing such web pages. To overcome this problem, this research is distributing questionnaires to find values from respondents and will calculate the average value using the Euclidean Distance method. This concept was chosen because it has been proven to provide optimal average values. The test results show that the combination of the FURPS model and the Euclidean Distance calculation method provides satisfactory results. Combination suitability is not easy to do, especially in the part of finding the average value which must involve many parties to achieve optimal results.

Modeling and optimization of the ratio of fluorophores: a step towards enhancing the sensitivity of ratiometric probes.

In the ratiometric fluorescent (RF) strategy, the selection of fluorophores and their respective ratios helps to create visual quantitative detection of target analytes. This study presents a framework for optimizing ratiometric probes, employing both two-component and three-component RF designs. For this purpose, in a two-component ratiometric nanoprobe designed for detecting methyl parathion (MP), an organophosphate pesticide, yellow-emissive thioglycolic acid-capped CdTe quantum dots (Y-QDs) (analyte-responsive), and blue-emissive carbon dots (CDs) (internal reference) were utilized. Mathematical polynomial equations modeled the emission profiles of CDs and Y-QDs in the absence of MP, as well as the emission colors of Y-QDs in the presence of MP separately. In other two-/three-component examples, the detection of dopamine hydrochloride (DA) was investigated using an RF design based on blue-emissive carbon dots (B-CDs) (internal reference) and N-acetyl L-cysteine functionalized CdTe quantum dots with red/green emission colors (R-QDs/G-QDs) (analyte-responsive). The colors of binary/ternary mixtures in the absence and presence of MP/DA were predicted using fitted equations and additive color theory. Finally, the Euclidean distance method in the normalized CIE XYZ color space calculated the distance between predicted colors, with the maximum distance defining the real-optimal concentration of fluorophores. This strategy offers a more efficient and precise method for determining optimal probe concentrations compared to a trial-and-error approach. The model's effectiveness was confirmed through experimental validation, affirming its efficacy.

Estimating Spatio-Temporal Building Power Consumption Based on Graph Convolution Network Method

Buildings are responsible for around 30% and 42% of the consumed energy at the global and European levels, respectively. Accurate building power consumption estimation is crucial for resource saving. This research investigates the combination of graph convolutional networks (GCNs) and long short-term memory networks (LSTMs) to analyze power building consumption, thereby focusing on predictive modeling. Specifically, by structuring graphs based on Pearson’s correlation and Euclidean distance methods, GCNs are employed to discern intricate spatial dependencies, and LSTM is used for temporal dependencies. The proposed models are applied to data from a multistory, multizone educational building, and they are then compared with baseline machine learning, deep learning, and statistical models. The performance of all models is evaluated using metrics such as the mean absolute error (MAE), mean squared error (MSE), R-squared (R2), and the coefficient of variation of the root mean squared error (CV(RMSE)). Among the proposed computation models, one of the Euclidean-based models consistently achieved the lowest MAE and MSE values, thus indicating superior prediction accuracy. The suggested methods seem promising and highlight the effectiveness of GCNs in improving accuracy and reliability in predicting power consumption. The results could be useful in the planning of building energy policies by engineers, as well as in the evaluation of the energy management of structures.

Construction health indicator using physically-informed 1D-WGAN-GP joint attention LSTM-DenseNet method

In data-driven prognosis methods, the accuracy of predicting the remaining useful life (RUL) of mechanical systems is predominantly contingent upon the efficacy of system health indicators (HI), typically amalgamated from statistical features derived from collected signals. Nevertheless, the majority of extant HI are beset by two principal shortcomings: (1) during traditional data denoising processes, degradation information from raw data is prone to loss owing to the lack of incorporation of the true physical properties of the data; and (2) the performance evaluation of constructed HI is imbalanced due to the influence of network structures on single models, often resulting in strong performance in only one or two indicators. To overcome such shortcomings, a mechanical health indicator construction method based on physical properties was proposed, termed 1D-WGAN-GP Joint attention LSTM-DenseNet. Firstly, artificial sample data is generated by analyzing the physical properties of the original dataset, which is then used to train the 1D-WGAN-GP model to achieve data denoising. Subsequently, the fusion of the attention LSTM (A-LSTM) network and DenseNet network is utilized to extract crucial feature vectors of HI under varying health conditions from the denoised data. Finally, the extracted feature vectors are used to construct system HI using the Euclidean distance method, and these indicators are used for predicting the system’s RUL. The results indicate that the proposed method outperformed traditional methods in terms of denoising effectiveness. Further, through ablation experiment analysis, the HI constructed by the proposed method demonstrated obvious complementarity in terms of monotonicity, correlation, robustness, and comprehensive evaluation. In RUL prediction applications, the proposed method also exhibited good performance, thereby validating its effectiveness.

DNA hypomethylation patterns and their impact on the tumor microenvironment in colorectal cancer.

Recent research underscores the pivotal role of immune checkpoints as biomarkers in colorectal cancer (CRC) therapy, highlighting the dynamics of resistance and response to immune checkpoint inhibitors. The impact of epigenetic alterations in CRC, particularly in relation to immune therapy resistance, is not fully understood. We integrated a comprehensive dataset encompassing TCGA-COAD, TCGA-READ, and multiple GEO series (GSE14333, GSE37892, GSE41258), along with key epigenetic datasets (TCGA-COAD, TCGA-READ, GSE77718). Hierarchical clustering, based on Euclidean distance and Ward's method, was applied to 330 primary tumor samples to identify distinct clusters. The immune microenvironment was assessed using MCPcounter. Machine learning algorithms were employed to predict DNA methylation patterns and their functional enrichment, in addition to transcriptome expression analysis. Genomic mutation profiles and treatment response assessments were also conducted. Our analysis delineated a specific tumor cluster with CpG Island (CGI) methylation, termed the Demethylated Phenotype (DMP). DMP was associated with metabolic pathways such as oxidative phosphorylation, implicating increased ATP production efficiency in mitochondria, which contributes to tumor aggressiveness. Furthermore, DMP showed activation of the Myc target pathway, known for tumor immune suppression, and exhibited downregulation in key immune-related pathways, suggesting a tumor microenvironment characterized by diminished immunity and increased fibroblast infiltration. Six potential therapeutic agents-lapatinib, RDEA119, WH.4.023, MG.132, PD.0325901, and AZ628-were identified as effective for the DMP subtype. This study unveils a novel epigenetic phenotype in CRC linked to resistance against immune checkpoint inhibitors, presenting a significant step toward personalized medicine by suggesting epigenetic classifications as a means to identify ideal candidates for immunotherapy in CRC. Our findings also highlight potential therapeutic agents for the DMP subtype, offering new avenues for tailored CRC treatment strategies.

Identifying Protein Phosphorylation Site-Disease Associations Based on Multi-Similarity Fusion and Negative Sample Selection by Convolutional Neural Network.

As one of the most important post-translational modifications (PTMs), protein phosphorylation plays a key role in a variety of biological processes. Many studies have shown that protein phosphorylation is associated with various human diseases. Therefore, identifying protein phosphorylation site-disease associations can help to elucidate the pathogenesis of disease and discover new drug targets. Networks of sequence similarity and Gaussian interaction profile kernel similarity were constructed for phosphorylation sites, as well as networks of disease semantic similarity, disease symptom similarity and Gaussian interaction profile kernel similarity were constructed for diseases. To effectively combine different phosphorylation sites and disease similarity information, random walk with restart algorithm was used to obtain the topology information of the network. Then, the diffusion component analysis method was utilized to obtain the comprehensive phosphorylation site similarity and disease similarity. Meanwhile, the reliable negative samples were screened based on the Euclidean distance method. Finally, a convolutional neural network (CNN) model was constructed to identify potential associations between phosphorylation sites and diseases. Based on tenfold cross-validation, the evaluation indicators were obtained including accuracy of 93.48%, specificity of 96.82%, sensitivity of 90.15%, precision of 96.62%, Matthew's correlation coefficient of 0.8719, area under the receiver operating characteristic curve of 0.9786 and area under the precision-recall curve of 0.9836. Additionally, most of the top 20 predicted disease-related phosphorylation sites (19/20 for Alzheimer's disease; 20/16 for neuroblastoma) were verified by literatures and databases. These results show that the proposed method has an outstanding prediction performance and a high practical value.

Segmenting Chinese wine consumers on the basis of wine knowledge and consumption behavior

Purpose This study aims to provide insights into wine consumers in Hong Kong and Mainland China, and the diversity of their behavior, by investigating their motivations, consumption and purchasing behavior and segmenting the sample based on wine knowledge and frequency of consumption to identify three distinct clusters that are then profiled. Design/methodology/approach Data was collected through an online survey of wine consumers of ethnic Chinese origin residing in Mainland China and Hong Kong, using two methods of recruitment (a convenience sample from wine-related databases, a panel survey undertaken by an international market research firm). The final sample comprised 477 respondents, about equally divided among Mainland Chinese and Hong Kong wine consumers. Analysis of the data using the Statistical Package for Social Sciences for Windows (SPSS) 29 included a series of descriptive analyses, followed by a two-step cluster analysis using an Euclidean distance method. Findings Wine knowledge and frequency of wine consumption were relatively higher within the sample as a whole than previous studies, but three distinct consumer segments are evident. There is evidence of a democratization of wine consumption, and a greater focus on wine consumption for enjoyment in relaxed and informal settings. rather than consumption driven by health and status, occurring primarily in commercial or business settings. The dominance of red wine in this market is also declining, and intrinsic cues – specifically, prior experience of the wine – are becoming important in wine purchase decisions. Originality/value This study uses a broad sample of wine consumers to identify trends in wine consumption patterns and motivations, and purchase decision-making behavior, of Mainland Chinese and Hong Kong wine consumers. The identification of three clusters of wine consumers offers both points of comparison with previous segmentation research globally and within China, whilst also identifying scope for further research.

Feature Extraction of Flow Sediment Content of Hydropower Unit Based on Voiceprint Signal

The hydropower turbine parts running in the sand-bearing flow will experience surface wear, leading to a decline in the hydropower unit’s stability, mechanical performance, and efficiency. A voiceprint signal-based method is proposed for extracting the flow sediment content feature of the hydropower unit. Firstly, the operating voiceprint information of the hydropower unit is obtained, and the signal is decomposed by the Ensemble Empirical Mode Decomposition (EEMD) algorithm, and a series of intrinsic mode functions (IMFs) are obtained. Combined with correlation analysis, more sensitive IMF components are extracted and input into a convolutional neural network (CNN) for training, and the multi-dimensional output of the fully connected layer of CNN is used as the feature vector. The k-means clustering algorithm is used to calculate the eigenvector clustering center of the hydropower unit with a clean flow state and a high sediment content state, and the characteristic index of the hydropower unit sediment content is constructed based on the Euclidean distance method. We define this characteristic index as SI, and the change in the SI value can reflect the degree of sediment content in the flow of the unit. A higher SI value indicates a lower sediment content, while a lower SI value suggests a higher sediment content. Combined with the sediment voiceprint data of the test bench, when the water flow changed from clear water to high sediment flow (1.492 × 105 mg/L), the SI value decreased from 1 to 0.06, and when the water flow with high sediment content returned to clear water, the SI value returned to 1. The experiment proves the effectiveness of the method. The extracted feature index can be used to detect the flow sediment content of the hydropower unit and give early warning in time, so as to improve the maintenance level of the hydropower unit.

Integrated Waterflooding Effect Evaluation Methodology for Carbonate Fractured–Vuggy Reservoirs Based on the Unascertained Measure–Mahalanobis Distance Theory

The waterflooding effect evaluation of carbonate fractured–vuggy reservoirs constitutes a comprehensive multiple-information decision-making process involving quantitative unascertained measure theory. This paper establishes a novel comprehensive methodology to evaluate the waterflooding effects of carbonate fractured–vuggy reservoirs for the first time. A new evaluation grading criteria is proposed using the Mahalanobis distance method based on the multi-index comprehensive unascertained measure theory derived from a modified five-scale analytical hierarchy process–entropy weight method. The actual field data from the carbonate fractured–vuggy reservoirs and the nine evaluation indices are specifically applied to demonstrate the calculation process for the construction of the grading system model on the waterflooding effects and to verify the accuracy of the Mahalanobis distance method by comparing the calculation results with the Minkowski and Euclidean distance methods. The proposed methodology facilitates the effective evaluation of the waterflooding strategies implemented in carbonate fractured–vuggy reservoirs with three categories; the ones with outstanding performance usually demonstrated favorable index characteristics, with substantial contributions to the enhanced oil recovery, manifesting with complete well patterns, a balance in the injection–production dynamics, excellent waterflooding utilization, and control competence. In contrast, fractured–vuggy units with fair waterflooding performance revealed limitations in the enhanced oil recovery. It can also be inferred that the mediocre waterflooding performance of the fractured–vuggy units is associated with incomplete well patterns, an imbalance in the injection–production dynamics, low waterflooding utilization, and a negligible waterflooding effect. The results in this study show that this newly proposed integrated model can effectively assess the waterflooding effects quantitatively and provide a more precise scientific basis for evaluating the waterflooding effects in carbonate fractured–vuggy reservoirs, with potential applicability in other fields.

Navigating the Maps: Euclidean vs. Road Network Distances in Spatial Queries

Using spatial data in mobile applications has grown significantly, thereby empowering users to explore locations, navigate unfamiliar areas, find transportation routes, employ geomarketing strategies, and model environmental factors. Spatial databases are pivotal in efficiently storing, retrieving, and manipulating spatial data to fulfill users’ needs. Two fundamental spatial query types, k-nearest neighbors (kNN) and range search, enable users to access specific points of interest (POIs) based on their location, which are measured by actual road distance. However, retrieving the nearest POIs using actual road distance can be computationally intensive due to the need to find the shortest distance. Using straight-line measurements could expedite the process but might compromise accuracy. Consequently, this study aims to evaluate the accuracy of the Euclidean distance method in POIs retrieval by comparing it with the road network distance method. The primary focus is determining whether the trade-off between computational time and accuracy is justified, thus employing the Open Source Routing Machine (OSRM) for distance extraction. The assessment encompasses diverse scenarios and analyses factors influencing the accuracy of the Euclidean distance method. The methodology employs a quantitative approach, thereby categorizing query points based on density and analyzing them using kNN and range query methods. Accuracy in the Euclidean distance method is evaluated against the road network distance method. The results demonstrate peak accuracy for kNN queries at k=1, thus exceeding 85% across classes but declining as k increases. Range queries show varied accuracy based on POI density, with higher-density classes exhibiting earlier accuracy increases. Notably, datasets with fewer POIs exhibit unexpectedly higher accuracy, thereby providing valuable insights into spatial query processing.