Search Results (56)

Background: Atrial fibrillation (AF) patients represent a clinically complex, heterogeneous population comprising multiple homogeneous cohorts. Purpose: We aimed to identify the common clinical phenotypes of AF patients and compare clinical outcomes between these subgroups. Methods: A 1% representative sample of all AF patients hospitalized between 2010 and 2019 was identified from the French national database. Agglomerative hierarchical cluster analysis was performed using Ward’s method and squared Euclidian distance to derive the clusters of patients. Cox regression analyses were used to evaluate outcomes including all-cause death, cardiovascular death, non-cardiovascular death, ischemic stroke, hospitalization for heart failure (HF) and composite of ventricular tachycardia, ventricular fibrillation and cardiac arrest (VT/VF/CA) over a mean follow-up period of 2.0 ± 2.3 years. Results: Four clusters were generated from the 12,688 patients included. Cluster 1 (n = 2375) was younger, low cardiovascular disease (CVD)-risk group with a high cancer prevalence. Clusters 2 (n = 6441) and 3 (n = 1639) depicted moderate-risk groups for CVD. Cluster 3 also had the highest degree of frailty and lung disease while Cluster 4 (n = 2233) represented a high-risk cohort for CVD. After adjusting for confounders, with cluster 1 as the reference, cluster 3 had the highest risk of all-cause death, HR 1.24 (1.09–1.41), ARD (10.3%), cardiovascular death, HR 1.56 (1.19–2.06), ARD (3.3%), non-cardiovascular death, HR 1.20 (1.04–1.38), ARD (6.9%), hospitalization for HF, HR 2.07 (1.71–2.50), ARD (9.1%) and VT/VF/CA, HR 1.74 (1.20–2.53), (ARD 1.3%). Conclusions: Four distinct clusters of AF patients were identified, discriminated by the differential presence of comorbidities. Our findings suggest that hospitalized AF patients with moderate CVD risk may have a poorer prognosis compared to hospitalized AF patients with high CVD risk in the presence of lung pathology and frailty. This subgroup of patients may require more stringent management of existing comorbidities such as chronic obstructive pulmonary disease and sleep apnea, alongside their AF. Full article

In the past two decades, Islamabad has experienced significant urbanization. As a result of inadequate urban planning and spatial distribution, it has significantly influenced land use–land cover (LULC) changes and green areas. To assess these changes, there is an increasing need for reliable and appropriate information about urbanization. Landsat imagery is categorized into four thematic classes using a supervised classification method called the support vector machine (SVM): built-up, bareland, vegetation, and water. The results of the change detection of post-classification show that the city region increased from 6.37% (58.09 km²) in 2000 to 28.18% (256.49 km²) in 2020, while vegetation decreased from 46.97% (428.28 km²) to 34.77% (316.53 km²) and bareland decreased from 45.45% (414.37 km²) to 35.87% (326.49 km²). Utilizing a land change modeler (LCM), forecasts of the future conditions in 2025, 2030, and 2035 are predicted. The artificial neural network (ANN) model embedded in IDRISI software 18.0v based on a well-defined backpropagation (BP) algorithm was used to simulate future urban sprawl considering the historical pattern for 2015–2020. Selected landscape morphological measures were used to quantify and analyze changes in spatial structure patterns. According to the data, the urban area grew at a pace of 4.84% between 2015 and 2020 and will grow at a rate of 1.47% between 2020 and 2035. This growth in the metropolitan area will encroach further into vegetation and bareland. If the existing patterns of change persist over the next ten years, a drop in the mean Euclidian Nearest Neighbor Distance (ENN) of vegetation patches is anticipated (from 104.57 m to 101.46 m over 2020–2035), indicating an accelerated transformation of the landscape. Future urban prediction modeling revealed that there would be a huge increase of 49% in urban areas until the year 2035 compared to the year 2000. The results show that in rapidly urbanizing areas, there is an urgent need to enhance land use laws and policies to ensure the sustainability of the ecosystem, urban development, and the preservation of natural resources. Full article

This study presents a method for determining the optimal gear ratio in electric trains by examining the effects of motor efficiency, wheel wear, and relative damage to the input and output shafts of the reduction gear. In electric trains, reduction gears and wheels are critical for converting the driving motor’s torque and determining the motor’s operational point, which in turn affects efficiency and durability. Over time, wheel wear from regular use and periodic profiling reduces the wheel radius, causing an effective increase in the gear ratio, which impacts the motor efficiency and load distribution across drivetrain components. This study models the dynamic behavior of the vehicle’s drivetrain system using MATLAB/Simulink and incorporates real-world data on wheel wear to address the problem. Through simulations with varying gear ratios, it analyzes changes in motor efficiency and uses Miner’s rule to assess the relative damage on the reduction gear’s input and output shafts. The results enable the identification of a gear ratio that balances motor efficiency and reduces cumulative fatigue damage, which is especially important for maintaining long-term drivetrain durability. This approach provides a systematic way to enhance the overall performance and lifespan of electric train systems by selecting a gear ratio that optimally aligns efficiency and durability. Full article

We used cluster analysis on 200-year-old tree-ring chronologies to examine the patterns that emerge from self-organization, driven by environmental heterogeneity, that might drive diversification in ponderosa pine (Pinus ponderosa). We determined the natural patterns on the landscape and then tested these groups against historically separated varieties within this species that could be evidence of diversification. We used 178 previously collected tree-ring chronologies from the western United States that were archived in the International Tree-Ring Databank. We explored a variety of clustering techniques, settling on Ward’s clustering with Euclidian distance measures as the most reasonable clustering process. These techniques identified two (p = 0.005) to ten (p = 0.01) potential natural clusters in the ponderosa pine chronologies. No matter the number of clusters, we found that the ponderosa pine varieties ponderosa and benthamiana always cluster together. The variety scopulorum differentiates clearly on its own, but brachyptera is a mix of diverse groups, based on the environmental driving factors that control tree-ring chronology variability. Cluster analysis is a useful tool to examine natural grouping on the landscape using long-term tree-ring chronologies, enabling the researcher to examine the patterns of environmental heterogeneity that should lead to speciation. From this analysis, we suggest that the brachyptera variety should be more varied genetically. Full article

Twenty Opuntia accessions from Tenerife (Canary Islands, Spain) were classified according to 52 quantitative and qualitative descriptors, including the traits of the cladodes, flowers, fruits, and spines, as described by the International Union for the Protection of New Varieties of Plants (UPOV) guidelines. A database composed of 20 accessions and 52 traits was used to perform a cluster analysis based on the Euclidian distance and Ward’s method and a canonical discriminant analysis. In terms of the analyzed characteristics, cactus pears with orange flesh showed less variability than cactus pears with white or purple flesh. Good classifications according to fruit flesh color were obtained using discriminant analysis. As a result of the cluster analysis, Opuntia plant accessions with white, orange, or purple-fleshed fruits were classified into four homogeneous groups according to the cubic clustering criteria. This study proves that it was possible to make a preliminary classification of Opuntia varieties from the Canary Islands based on a few main morphological characteristics. To improve the classification, a molecular analysis of the different Opuntia plants is necessary. Full article

Brain tumor needle biopsies are performed to retrieve tissue samples for neuropathological analysis. Although preoperative images guide the procedure, there are risks of hemorrhage and sampling of non-tumor tissue. This study aimed to develop and evaluate a method for frameless one-insertion needle biopsies with in situ optical guidance and present a processing pipeline for combined postoperative analysis of optical, MRI, and neuropathological data. An optical system for quantified feedback on tissue microcirculation, gray–whiteness, and the presence of a tumor (protoporphyrin IX (PpIX) accumulation) with a one-insertion optical probe was integrated into a needle biopsy kit that was used for frameless neuronavigation. In Python, a pipeline for signal processing, image registration, and coordinate transformation was set up. The Euclidian distances between the pre- and postoperative coordinates were calculated. The proposed workflow was evaluated on static references, a phantom, and three patients with suspected high-grade gliomas. In total, six biopsy samples that overlapped with the region of the highest PpIX peak without increased microcirculation were taken. The samples were confirmed as being tumorous and postoperative imaging was used to define the biopsy locations. A 2.5 ± 1.2 mm difference between the pre- and postoperative coordinates was found. Optical guidance in frameless brain tumor biopsies could offer benefits such as quantified in situ indication of high-grade tumor tissue and indications of increased blood flow along the needle trajectory before the tissue is removed. Additionally, postoperative visualization enables the combined analysis of MRI, optical, and neuropathological data. Full article

Most ecologists have used climate change, as an omnipresent pressure, to support their findings in researching the vulnerability of specific taxa, communities, or ecosystems. However, there is a widespread lack of long-term biological, biocoenological, or community data of periods longer than several years to ascertain patterns as to how climate change affects communities. Since the 1950s, southern Europe has faced an ongoing trend of drying and loss of precipitation. A 13-year research program in the Dinaric karst ecoregion of Croatia aimed to comprehensively track emergence patterns of freshwater insects (true flies: Diptera) in a pristine aquatic environment. Three sites, spring, upper, and lower tufa barriers (calcium carbonate barriers on a barrage lake system that act as natural damns), were sampled monthly over 154 months. This coincided with a severe drought event in 2011/2012. This was the most significant drought (very low precipitation rates for an extended period of time) in the Croatian Dinaric ecoregion since the start of detailed records in the early 20th century. Significant shifts in dipteran taxa occurrence were determined using indicator species analysis. Patterns of seasonal and yearly dynamics were presented as Euclidian distance metrics of similarity in true fly community composition compared at increasing time intervals, to ascertain the degree of temporal variability of similarity within the community of a specific site and to define patterns of similarity change over time. Analyses detected significant shifts in community structure linked to changes in discharge regimes, especially to the drought period. Full article

The current era of energy production from agricultural by-products comprises numerous criteria such as societal, economical, and environmental concerns, which is thought to be difficult, considering the complexities involved. Making the optimum choice among the various classes of organic waste substances with different physio-chemical characteristics based on their appropriateness for pyrolysis is made possible by a ranking system. By using a feasible model, which combines several attributes of decision-making processes, it is possible to select the ideal biomass feedstock from a small number of possibilities based on relevant traits that have an impact on the pyrolysis. In this study, a multi-criteria decision-making (MCDM) technique model based on the weight calculated from the analytical hierarchy process (AHP) tool has been applied to obtain a ranking of different types of agro-waste-derived biomass feedstock. The technique of order preference by similarity to ideal solution (TOPSIS) is used to examine the possibilities of using/utilizing locally available biomass. From this point of view, multi-criteria are explained to obtain yield maximum energy. The suggested approaches are supported by the experimental findings and exhibit a good correlation with one another. Six biomass alternatives and eight evaluation criteria are included in this study. Sawdust is the highest-ranking agricultural waste product with a closeness coefficient score of 0.9 out of the six biomass components that were chosen, followed by apple bagasse with 0.8. The hybrid approach model that has been built can be evaluated and validated for the ranking method using the Euclidian distance-based approximation. This study offers a unique perspective on decision-making, particularly concerning thermo-chemical conversion. Full article

Big cities affected by intense mobility, traffic and pollution are adopting electrification-based solutions for the reduction of the ${\mathrm{CO}}_2$ emissions of combustion engines. An interesting field in which the transition toward electrification can achieve important benefits is the area of instant deliveries. Instant deliveries deal with the mobility related to commercial trades between suppliers and customers. In this respect, optimal solutions can be considered during route planning based on the minimization of several metrics, such as distance, energy and road slope, among others. To this end, this paper presents an optimal solution to the instant deliveries problem in which the result is the optimal route, in the city under study, that minimizes energy consumption based on road slope and total distance traveled, and that gives higher priority to routes that include cycling infrastructure that the city can provide. The paper uses electric bikes since they are easily transportable and are highly versatile for instant deliveries. The results obtained were compared to a previous version of the optimal algorithm already published by the authors which minimizes the Haversine and Euclidian distances only. It was found that the shortest distance travelled between customers does not necessarily imply the least energy consumption. The latter, in combination with an energy consumption estimation approach, represent the original contribution of the work. Full article

Ranking sports teams generally relies on supervised techniques, requiring either prior knowledge or arbitrary metrics. In this paper, we offer a purely unsupervised technique. We apply this to operational decision-making, specifically, the controversial European Super League for association football, demonstrating how this approach can select dominant teams to form the new league. We first use random forest regression to select important variables predicting goal difference, which we use to calculate the Euclidian distances between teams. Creating a Laplacian eigenmap, we bisect the Fiedler vector to identify the natural clusters in five major European football leagues. Our results show how an unsupervised approach could identify four clusters based on five basic performance metrics: shots, shots on target, shots conceded, possession, and pass success. The top two clusters identify teams that dominate their respective leagues and are the best candidates to create the most competitive elite super league. Full article

Agricultural drought monitoring in Niger is relevant for the implementation of effective early warning systems and for improving climate change adaptation strategies. However, the scarcity of in situ data hampers an efficient analysis of drought in the country. The present dataset was created for agricultural drought characterization in the Sahelian climate zone of Niger. The dataset comprises the three-month scale and monthly time series of a composite drought index (CDI) and their corresponding drought classes at a spatial resolution of 1 km² for the period 2000–2020. The CDI was generated from remote sensing data, namely CHIRPS (Climate Hazards Group InfraRed Precipitation with Stations), normalized difference vegetation index (NDVI) and land surface temperature (LST) from MODIS (Moderate Resolution Imaging Spectroradiometer). A weighing technique combining entropy and Euclidian distance was applied in the CDI derivation. From the present dataset, the extraction of the CDI time series can be performed for any location of the study area using its geographic coordinates. Therefore, seasonal drought characteristics, such as onset, end, duration, severity and frequency can be computed from the CDI time series using the theory of runs. The availability of the present dataset is relevant for the socio-economic assessment of drought impacts at small spatial scales, such as district and household level. This dataset is also important for the assessment of drought characteristics in remote areas or areas inaccessible due to civil insecurity in the country as it was entirely generated from remote sensing data. Finally, by including temperature data, the dataset enables drought modelling under global warming. Full article

Timely and accurate information on crop planting structures is crucial for ensuring national food security and formulating economic policies. This study presents a method for high-precision crop classification using time-series UAV (unmanned aerial vehicle) images. Before constructing the time-series UAV images, Euclidian distance (ED) was utilized to calculate the separability of samples under various vegetation indices. Second, co-occurrence measures and the gray-level co-occurrence matrix (GLCM) were employed to derive texture characteristics, and the spectral and texture features of the crops were successfully fused. Finally, random forest (RF) and other algorithms were utilized to classify crops, and the confusion matrix was applied to assess the accuracy. The experimental results indicate the following: (1) Time-series UAV remote sensing images considerably increased the accuracy of crop classification. Compared to a single-period image, the overall accuracy and kappa coefficient increased by 26.65% and 0.3496, respectively. (2) The object-oriented classification method was better suited for the precise classification of crops. The overall accuracy and kappa coefficient increased by 3.13% and 0.0419, respectively, as compared to the pixel-based classification results. (3) RF obtained the highest overall accuracy and kappa coefficient in both pixel-based and object-oriented crop classification. RF’s producer accuracy and user accuracy for cotton, spring wheat, cocozelle, and corn in the study area were both more than 92%. These results provide a reference for crop area statistics and agricultural precision management. Full article

Two indices of activity space measurements using Euclidian distance measurements have been argued to be able to measure specific visited out-of-home activity locations closer to activity space definitions than other methods. However, the Euclidian distance does not consider any barriers or obstacles, such as the existence of public spaces (e.g., army bases, government offices and airports) or natural barriers (e.g., mountains, hills and agricultural fields that have no road infrastructure). Therefore, this study tries to fill the research gap by measuring the two indices using road network distance. Moreover, this study tries to determine whether the activity space of different generations, namely Generations (Gens) X, Y and Z, is significantly different, and whether some socio-demographic and activity pattern variables can help differentiate the activity space measurements. Using the 2019 Yogyakarta Metropolitan Area (YMA) dataset, this study confirms that measuring activity space using road network distance statistically gives different results from activity space measured using Euclidian distance. Moreover, this study confirms that the oldest generation had opposite activity space patterns in comparison to Gens Y and Z. Unlike the younger ones, the oldest generation visited out-of-home activity locations nearer to their home locations on weekdays but expanded to visit farther out-of-home locations on weekends. Trade-off mechanisms were found between weekdays and weekends, by which Gens X and Y significantly visited out-of-home activity locations farther from their home more often on weekends than on weekdays. However, all generations were observed to visit out-of-home activity locations near their out-of-home activity anchors every day, whereas the oldest tended more often to visit the activity locations farther from their out-of-home activity anchors than the younger generations on Fridays and Sundays. Full article

Roll-front uranium deposits are ore mineralizations that occur in sandstones or arkoses downstream from redox fronts or reduced/oxidized geochemical barriers. They are often bounded above and below by impermeable shaly/muddy layers making them ideal for in-situ leaching exploitation. Several stochastic simulations were previously investigated either to characterize the ore grade distribution within roll-front type deposits, or for describing geological processes involved in their formation. This work suggests some modifications/improvements of conventional geostatistical algorithms for honoring hydrodynamic constraints that govern fluid flows in ore bearing layers. In particular, instead of using the classical Euclidian or curvilinear (for Sgrid) distance for computing the variogram, it is proposed to calculate the variogram accounting for the time of flight (TOF) of water particles down the streamlines together with available well data. Non-deterministic streamline-based methods seem to provide more accurate interpolation results and resource estimation compared to a traditional geostatistical approach when applied to roll-front deposits. Full article

The World Health Organization declared COVID-19 as a pandemic disease on 12 March 2020. Currently, this disease caused by the SARS-CoV-2 virus remains one of the biggest public health problems in the world. Thus, it is essential to apply methods that enable a better understanding of the virus diffusion processes, not only at the spatial level but also at the spatiotemporal one. To that end, we tried to understand the spatial distribution of COVID-19 pathology in continental Portugal at the municipal level and to comprehend how mobility influences transmission. We used autocorrelation indices such as Getis-Ord (with Euclidian distance and commuting values), Local Moran, and a new hybrid approach. Likewise, aiming to identify the spatiotemporal patterns of the virus propagation by using Man–Kendall statistics, we found that most hotspots of infected individuals occur in the municipalities of metropolitan areas. The spatiotemporal analysis identified most of the municipalities as oscillating hotspots. Full article