Search Results (1,606)

An algebraic graph is defined in terms of graph theory as a graph with related algebraic structures or characteristics. If the vertex set of a graph G is a group, a ring, or a field, then G is called an algebraic structure graph. This work uses an algebraic structure graph based on the modular ring Zn, known as a hyper-chordal ring network. The lower and upper bounds of the local fractional metric dimension are computed for certain families of hyper-chordal ring networks. Utilizing the cardinalities of local fractional resolving sets, local fractional resolving (LFR)M-polynomials are computed for hyper-chordal ring networks. Further, new topological indices based on (LFR)M-polynomials are established for the proposed networks. The local fraction entropies are developed by modifying the first three kinds of Zagreb entropies, which are calculated for the chosen hyper-chordal ring networks. Furthermore, numerical and graphical comparisons are discussed to observe the order between newly computed topological indices. Full article

In plasticulture production systems, the conventional practice involves broadcasting pre-emergent herbicides over the entire surface of raised beds before laying plastic mulch. However, weed emergence predominantly occurs through the transplant punch-holes in the mulch, leaving most of the applied herbicide beneath the plastic, where weeds cannot grow. To address this issue, we developed and evaluated a precision spraying system designed to target herbicide application to the transplant punch-holes. A dataset of 3378 images was manually collected and annotated during a tomato experimental trial at the University of Florida. A YOLOv8x model with a p2 output layer was trained, converted to TensorRT^® to improve the inference time, and deployed on a custom-built computer. A Python-based graphical user interface (GUI) was developed to facilitate user interaction and the control of the smart sprayer system. The sprayer utilized a global shutter camera to capture real-time video input for the YOLOv8x model, which activates or disactivates a TeeJet solenoid for precise herbicide application upon detecting a punch-hole. The model demonstrated excellent performance, achieving precision, recall, mean average precision (mAP), and F1score exceeding 0.90. Field tests showed that the smart sprayer reduced herbicide use by up to 69% compared to conventional broadcast methods. The system achieved an 86% punch-hole recognition rate, with a 14% miss rate due to challenges such as plant occlusion and variable lighting conditions, indicating that the dataset needs to be improved. Despite these limitations, the smart sprayer effectively minimized off-target herbicide application without causing crop damage. This precision approach reduces chemical inputs and minimizes the potential environmental impact, representing a significant advancement in sustainable plasticulture weed management. Full article

The creation of digital surface models (DSMs) from aerial and satellite imagery is often the starting point for different remote sensing applications. For this task, the two main used approaches are stereo matching and multi-view stereo (MVS). The former needs stereo-rectified pairs as inputs and the results are in the disparity domain. The latter works with images from various perspectives and produces a result in the depth domain. So far, both approaches have proven to be successful in producing accurate DSMs, especially in the deep learning area. Nonetheless, an assessment between the two is difficult due to the differences in the input data, the domain where the directly generated results are provided and the evaluation metrics. In this manuscript, we processed synthetic and real optical data to be compatible with the stereo and MVS algorithms. Such data is then applied to learning-based algorithms in both analyzed solutions. We focus on an experimental setting trying to establish a comparison between the algorithms as fair as possible. In particular, we looked at urban areas with high object densities and sharp boundaries, which pose challenges such as occlusions and depth discontinuities. Results show in general a good performance for all experiments, with specific differences in the reconstructed objects. We describe qualitatively and quantitatively the performance of the compared cases. Moreover, we consider an additional case to fuse the results into a DSM utilizing confidence estimation, showing a further improvement and opening up a possibility for further research. Full article

Background/Objectives: This study assessed the feasibility and security of remote surgical wound monitoring using the RedScar© smartphone app, which employs automated diagnosis for early visual detection of infections without direct healthcare personnel involvement. Additionally, patient satisfaction with telematic care was evaluated as a secondary aim. Surgical site infection (SSI) is the second leading cause of healthcare-associated infections (HAIs), leading to prolonged hospital stays, heightened patient distress, and increased healthcare costs. Methods: The study employed a prospective paired-cohort and single-blinded design, with a sample size of 47 adult patients undergoing abdominal surgery. RedScar© was used for remote telematic monitoring, evaluating the feasibility and security of this approach. A satisfaction questionnaire assessed patient experience. The study protocol was registered at ClinicalTrials.gov under the identifier NCT05485233. Results: Out of 47 patients, 41 successfully completed both remote and in-person follow-ups. RedScar© demonstrated a sensitivity of 100% in detecting SSIs, with a specificity of 83.13%. The kappa coefficient of 0.8171 indicated substantial agreement between the application’s results and human observers. Patient satisfaction with telemonitoring was high: 97.6% believed telemonitoring reduces costs, 90.47% perceived it prevents work/school absenteeism, and 80.9% found telemonitoring comfortable. Conclusions: This is the first study to evaluate an automatic smartphone application on real patients for diagnosing postoperative wound infections. It establishes the safety and feasibility of telematic follow-up using the RedScar© application for surgical wound assessment. The high sensitivity suggests its utility in identifying true cases of infection, highlighting its potential role in clinical practice. Future studies are needed to address limitations and validate the efficacy of RedScar© in diverse patient populations. Full article

The exponential increase in population has led to a shortage of land for constructing tall buildings, resulting in the need to design irregular structures due to the limited availability of land. Assessing the impact of wind-generated effects can be achieved utilizing the Computational Fluid Dynamics (CFD) method, specifically employing ANSYS. This involves resolving the intricate fluid dynamics problem through numerical analysis using the ANSYS software. The validation study is performed on a standard shape-building model where the result is compared with experimental values and other international standards. The outcomes are presented in a graphical format, such as mean pressure, streamline, and pressure distribution in the vertical and horizontal planes. This research has studied four building models with equal area and height. Models A and B have regular shapes, while Models C and D exhibit an irregular ‘Y’ shape. The wind incidence angle was adjusted between 0 and 180 degrees at every 15-degree interval. The results were validated to ensure the accuracy of the numerical techniques employed. This involved performing validation and grid sensitivity analyses, which showed consistent results comparable to experimental data and established international standards. Model-C irregular-shaped buildings demonstrated the highest efficiency in minimizing wind loads among the building models examined in this study. Full article

The nucleus-independent chemical shift (NICS) criterion plays a significant role in evaluating (anti-)aromaticity. While being readily accessible even for non-computational chemists, adding ghost atoms for multi-points NICS evaluations poses a significant challenge. In this article, I introduce py.Aroma 4, a freely available and open-source Python package designed specifically for analyzing (anti-)aromaticity. Through its user-friendly graphical interface, py.Aroma simplifies and enhances aromaticity analyses by offering key features such as HOMA/HOMER index computation, Gaussian-type input file generation for diverse NICS calculations and corresponding output processing, NMR spectra plotting, and computational supporting information (SI) generation for scientific manuscripts. Additionally, NICS_⊥ is suggested for evaluating (anti-)aromaticity for non-planar or tilted rings. Pre-compiled executables for macOS and Windows are freely available online. Facilitate accessibility for users lacking programming experience or time constraints. Full article

Massive image datasets are often required for the proper functioning of Machine Learning (ML) and Computer Vision (CV) applications. This paper offers a solution to computational challenges in the Image Processing of satellite imagery, by proposing an optimization procedure. The presented approach is verified by an exemplary Python implementation, constituting a standalone tool for automating the dataset creation and labeling, including the extraction of road network data from the national satellite cartography provider. The collected data include detailed road maps along with the parcel information obtained via WebMapService endpoints. The method presented in this paper involves three basic steps: road segmentation (using the Shapely module) to facilitate handling high-resolution orthoimagery, and then a modified Region-of-Interest approach, i.e., removing irrelevant areas, with only roads remaining. This results in obtaining file sizes that are significantly smaller. The presented algorithm also involves asynchronous tile downloading, which, combined with the masking of irrelevant areas, improves not only the efficiency but surprisingly also the accuracy of subsequent ML/CV procedures. The research results of the paper reveal substantial file size reduction, and improved processing efficiency, thus making the optimized geospatial graphical data more practical for ML/CV applications, while still maintaining the original data quality and relevance of the analyzed parcels or infrastructure. Full article

As an effective solution to the first- and last-mile logistics of door-to-door intermodal container transportation, inland container transportation involves transporting containers by truck between terminals, depots, and customers within a local area. This paper is the first to focus specifically on the inland container transportation problem with limited depot capacity, where the storage of empty containers is constrained by physical space limitations. To reflect a more realistic scenario, we also consider the initial stock levels of empty containers at the depot. The objective of this problem is to schedule trucks to fulfill inland container transportation orders such that the overall cost is minimum and the depot is neither out of stock or over stocked at any time. A novel graphical representation is introduced to model the constraints of empty containers and depot capacity in a linear form. This problem is then mathematically modeled as a mixed-integer linear programming formulation. To avoid discretizing the time horizon and effectively achieve the optimal solution, we design a tailored branch-and-price-and-cut algorithm where violated empty container constraints for critical times are dynamically integrated into the restricted master problem. The efficiency of the proposed algorithm is enhanced through the implementation of several techniques, such as a heuristic label-setting method, decremental state-space relaxation, and the utilization of high-quality upper bounds. Extensive computational studies are performed to assess the performance of the proposed algorithm and justify the introduction of enhancement strategies. Sensitivity analysis is additionally conducted to investigate the implications of significant influential factors, offering meaningful managerial guidance for decision-makers. Full article

In this article, we focus on the association of sound and sense harmony in the collection of sonnets written by Shakespeare in the XVI° beginning of the XVII° century and propose a new four-dimensional representation to visualize them by means of the system called SPARSAR. To compute the degree of harmony and disharmony, we automatically extracted the sound grids of all the sonnets and combined them with the semantics and polarity expressed by their contents. We explain in detail the algorithm and show the representation of the whole collection of 154 sonnets and comment on them extensively. In a second experiment, we use data from the manual annotation of the sonnets for satire detection using the Appraisal Theory Framework, to gauge the system’s accuracy in matching these data with the output of the automatic algorithm for sound–sense harmony. The results obtained with a 94.6% accuracy confirm the obvious fact that the poet has to account for both sound and meaning in the choice of words. Full article

Background: The ankle joint is among the most vulnerable areas for injuries during daily activities and sports. This study focuses on individuals with chronic ankle instability (CAI), comparing the biomechanical characteristics of the lower limb during side-step cutting under various conditions. The aim is to analyze the impact of kinesiology tape (KT) length on the biomechanical properties of the lower limb during side-step cutting, thereby providing theoretical support and practical guidance for protective measures against lower-limb sports injuries. Methods: Twelve subjects with CAI who met the experimental criteria were recruited. Each subject underwent testing without taping (NT), with short kinesiology tape (ST), and with long kinesiology tape (LT), while performing a 45° side-step cutting task. This study employed the VICON three-dimensional motion capture system alongside the Kistler force plate to synchronously gather kinematic and kinetic data during the side-step cutting. Visual 3D software (V6.0, C-Motion, Germantown, MD, USA) was utilized to compute the kinematic and kinetic data, while OpenSim 4.4 software (Stanford University, Stanford, CA, USA) calculated joint forces. A one-way Analysis of Variance (ANOVA) was conducted using SnPM, with the significance threshold established at p < 0.05. The Origin software 2021 was used for data graphic processing. Results: KT was found to significantly affect joint angles, angular velocities, and moments in the sagittal, frontal, and transverse planes. LT increased hip and knee flexion angles as well as angular velocity, while ST resulted in reduced ankle inversion and increased knee internal rotation. Both types of KT enhanced hip abduction moment and knee adduction/abduction moment. Additionally, LT reduced the ankle joint reaction force. Conclusions: These findings suggest that the application of KT over a short duration leads to improvements in the lower-limb performance during side-step cutting motions in individuals with CAI, thus potentially decreasing the risk of injury. Full article

Well-defined amorphous/semi-crystalline statistical copolymers of n-dodecyl isocyanate, DDIC, and allyl isocyanate, ALIC, were synthesized via coordination polymerization using the chiral half-titanocene complex CpTiCl₂(O-(S)-2-Bu) as an initiator. In the frame of the terminal model, the monomer reactivity ratios of the statistical copolymers were calculated using both well-known linear graphical methods and the computer program COPOINT. The molecular and structural characteristics of the copolymers were also calculated. The thermal properties of these samples were studied by differential scanning calorimetry, DSC, measurements. The kinetics of the thermal decomposition of the statistical copolymers was studied by thermogravimetric analysis, TGA, and differential thermogravimetry, DTG, and the activation energy of this process was calculated by employing several theoretical models. Moreover, block copolymers with the structure P[DDIC-b-(DDIC-co-ALIC)] were synthesized by sequential addition of monomers and coordination polymerization methodologies. The samples were characterized by nuclear magnetic resonance, NMR, spectroscopy; size exclusion chromatography, SEC; and DSC. The thermal stability of the blocks was also studied by TGA and DTG and compared to the corresponding statistical copolymers, showing that the macromolecular architecture greatly affects the properties of the copolymers. A thiol-ene click post-polymerization reaction was performed to introduce aromatic groups along the polyisocyanate chain in order to improve the thermal stability of the parent polymers. Evidently, these statistical and block copolymers can be employed as precursors for the synthesis of novel polyisocyanate-based materials. Full article

Ultrasonic crack detection is one of the effective non-destructive methods of structural health monitoring (SHM) of buildings and structures. Despite its widespread use, crack detection in porous and heterogeneous composite building materials is an insufficiently studied issue and in practice leads to significant errors of more than 40%. The purpose of this article is to study the processes occurring in ceramic bricks weakened by cracks under ultrasonic exposure and to develop a method for determining the crack depth based on the characteristics of the obtained ultrasonic response. At the first stage, the interaction of the ultrasonic signal with the crack and the features of the pulse propagation process in ceramic bricks were considered using numerical modeling with the ANSYS environment. The FEM model allowed us to identify the characteristic aspects of wave propagation in bricks and compare the solution with the experimental one for the reference sample. Further experimental studies were carried out on ceramic bricks, as the most common elements of buildings and structures. A total of 110 bricks with different properties were selected. The cracks were natural or artificially created and were of varying depth and width. The experimental data showed that the greatest influence on the formation of the signal was exerted by the time parameters of the response: the time when the signal reaches a value of 12 units, the time of reaching the first maximum, the time of reaching the first minimum, and the properties of the material. Based on the regression analysis, a model was obtained that relates the crack depth to the signal parameters and the properties of the material. The error in the predicted values according to this model was approximately 8%, which was significantly more accurate than the existing approach. Full article

To better process irregular sample images for their image feature extraction and recognition, this essay proposes asymmetric adaptive neural network (AACNN) structures, including dual structures of an adaptive image feature extraction network (AT-CNN) and adaptive image recognition network (AT-ACNN). They both comprise an Adaptive Transform (AT) module and a deep learning network, but the ACNN comprises pixel-adaptive convolutional (PAC) kernels that CNN does not have, reflecting the asymmetry of these network structures. Structural analysis and comparative testing experiments indicated that the proposed method is more appropriate and effective for dealing with irregular sample images with different sizes and views, mainly focusing on their feature extraction accuracy and image recognition efficiency. The proposed method constructs the interaction between asymmetric dual network structures, essential in improving model performance and efficiency. It specifically manifests that the PAC kernels in an ACNN resolves the problem of content-agnostic convolution in image recognition by learning image features from a pre-trained CNN. On the other hand, it improves image recognition efficiency by using feature maps extracted from the pre-trained CNN to train the classifiers in the ACNN. We also found that parameter balance was essential in adaptive neural network structure for better performance in further testing experiments. When setting the Dropout layer parameter at 0.5 and the iteration number was 32, the proposed model achieved adequate recognition accuracy and efficiency. Smaller parameters affect model performance, but more extensive parameters significantly increase computational burden and loss. Comparative testing experiments fully validated its superiority compared with traditional methods based on CNNs. Using traditional carving patterns from Anhui Province as an example, we conducted image recognition and feature graphic application under ideal parameter balance conditions and thereby demonstrated the practicality and value of the proposed method. Full article

The present work describes the development of a graphical environment to represent typical flight instruments on a computer screen. The instruments’ behavior is displayed according to information regarding the aircraft’s flight conditions. Some of the instruments represented in this work, such as the altimeter, the vertical speed indicator, the aircraft speed indicator, and the Mach indicator, use air pressure principles. The algorithms and routines developed for the screen display are created using the C++ programming language and compiled independently to be included as libraries to improve the software performance. The algorithms developed for this purpose also include the corresponding relationship between the physical variables, such as the speed and displacement, and the standard atmosphere to provide an equivalent value. These algorithms are successfully tested using data information to simulate three hypothetical flights, which are divided into maneuvers with the aircraft in a stopped position, running on the ground, taking off and flying away, including some changes in directions. Moreover, the routines for displaying the aircraft path with the instruments’ animation are also successfully tested by comparison. Finally, an approach analysis as a function of the step time (Δt) used for calculation of the aircraft displacement to evaluate the efficiency of the numerical method integrated in the simulator is also described. It is proved that the aircraft instrument representation is appropriate according to the input data of the analyzed flights, and an improvement in the calculation can be easily obtained, making it possible to represent any flight condition on the aircraft instruments. Full article

The aim of this article is to present an overview of the current state of the mathematical and scientific competences in Primary Education in Spain and how their development is assessed in STEAM experiences (with students of 6–12 years of age). To this end, a systematic review based on the PRISMA 2020 protocol was carried out in the Scopus, Web of Sciences, and Dialnet databases, analyzing all articles published from 1993 to 2022 relating to the subject. In total, 53 articles were selected, most published in the last five years and primarily in the Dialnet database. It was found that the majority of the targeted objectives in the experiences were related to STEAM competence, but only 14 evaluated aspects related to mathematical or scientific competence. The mathematical competence was evaluated by the measurement of geometry or number content and their corresponding operations and by how information data (statistics) are graphically represented. In a few cases, the evaluation is also related to computational thinking and orientation skills. In the case of scientific competence, its evaluation was mainly based on the assessment of the understanding of concepts, with those concepts being related to Physics in 80% of the cases. The only evaluated aspect of scientific competence other than concept understanding was hypothesis formulation, which was featured in two articles. These results highlight the need to systematically incorporate assessments that provide a clearer view of mathematical or scientific competence development in implementing STEAM experiences in primary education. Full article