Straightforward Algorithm Research Articles

Pitfalls in the analysis conditions of canine brain perfusion computed tomography.

This study aimed to investigate the impact of selected analysis conditions on blood flow values and color maps in canine brain perfusion computed tomography (PCT) and to propose optimal analysis conditions. Dynamic computed tomography imaging was performed on six beagle dogs. Color maps were generated using a combination of analysis algorithms (box-modulation transfer function (Box-MTF) and singular value deconvolution plus (SVD+) methods), slice thicknesses (4.0 and 8.0 mm), analysis matrix sizes (512 × 512, 256 × 256, and 128 × 128), and noise reduction levels (strong and weak). Cerebral blood flow (CBF) and cerebral blood volume (CBV) were calculated for gray matter, white matter, basal ganglia, hippocampus, thalamus, and cerebellum in each map. CBF and CBV values obtained using SVD+ were significantly higher than those obtained using Box-MTF. Noise reduction was more effective with larger matrix sizes; however, excessive noise reduction led to the blurring of anatomical structures in the color map. Across all analysis algorithms, anatomical structures were challenging to visualize at 8.0 mm. For canine brain PCT, it is essential to choose a straightforward algorithm that remains unaffected by circulatory velocity or intracranial bone structure. Given the brain's size, the slice thickness should be minimal, noise reduction level should be suitable for the targeted area, and matrix size should be maximized.

Stripe Embedding: Efficient Maps with Exact Numeric Computation

We consider the fundamental problem of injectively mapping a surface mesh with disk topology onto a boundary constrained convex domain. We start from the basic observation that mapping a strip of triangles onto a rectangular shape always yields a valid embedding, if the vertices that bound the strip are sorted coherently along the sides of the rectangle. Based on this intuition, we propose a straightforward algorithm, called Stripe Embedding, that operates by decomposing the input mesh into a set of triangle strips and then embeds each strip into the target domain by means of linear interpolation between two previously embedded vertices. Thanks to its simplicity, Stripe Embedding is extremely efficient and permits to switch to an exact implementation without almost increasing its running times. Stripe Embedding is up to three orders of magnitude faster than the Tutte embedding for same numerical model and, even when implemented with costly rational numbers, it is faster than any floating point implementation of prior methods at any scale.

Personalized decision-making for vascular access creation in hemodialysis.

Selecting the appropriate kidney replacement therapy (KRT) is crucial in order to secure optimal care for chronic kidney disease (CKD) patients with end-stage renal disease (ESRD). Next to renal transplantation, the choice of dialysis modality directly affects patient well-being, treatment effectiveness, and long-term outcomes.1 Therefore, clinical decision-making must take into account a range of factors to tailor decisions to each patient's unique needs. Previously, when the dialysis population was less diverse and resources more limited, straight-forward algorithms could be effectively implemented both for selection of KRT as well as for hemodialysis (HD) vascular access (VA). Recently, the growing ESRD population with more elderly being considered for dialysis care together with improved treatment opportunities have resulted in more challenging decision-making where on-size-fits-all strategies are being replaced by more tailored and personalized strategies aimed to ensure "the right dialysis access - to the right patient - at the right time - for the right reason." In this review, recent trends enforcing a more personalized approach in the selection of VA for HD are summarized and discussed, where these concerns have become especially relevant.

An algorithm for the incorporation of relevant FVM boundary conditions in the Eulerian SPH framework

The finite volume method (FVM) is widely recognized as a computationally efficient and accurate mesh-based technique. However, it has notable limitations, particularly in mesh generation and handling complex boundary interfaces or conditions. In contrast, the smoothed particle hydrodynamics (SPH) method, a popular meshless alternative, inherently circumvents the challenges of mesh generation and yields smoother numerical outcomes. Nevertheless, this approach comes at the cost of reduced computational efficiency. Consequently, researchers have strategically combined the strengths of both methods to investigate complex flow phenomena, producing precise and computationally efficient outcomes. However, algorithms involving the weak coupling of these two methods tend to be intricate and face challenges regarding versatility, implementation, and mutual adaptation to hardware and coding structures. Thus, achieving a robust and strong coupling of FVM and SPH within a unified framework is essential. A mesh-based FVM has recently been integrated into the SPH-based library SPHinXsys. However, due to the differing boundary algorithms between these methods, the crucial step for establishing a strong coupling of both methods within a unified SPH framework is to incorporate the FVM boundary algorithm into the Eulerian SPH method. In this paper, we propose a straightforward algorithm within the Eulerian SPH method, which is algorithmically equivalent to that in FVM and based on the principle of zero-order consistency. Moreover, several numerical examples, including compressible and incompressible flows with various boundary conditions in the Eulerian SPH method, demonstrate the stability and accuracy of the proposed algorithm.

VLSI Architecture for Energy-Efficient and Accurate Pre-Processing Pan-Tompkins Design

This work proposes an energy-efficient and accurate VLSI architecture of the pre-processing Pan-Tompkins algorithm (PTA) for filtering electrocardiogram (ECG) signals. The PTA is among the most straightforward algorithms to detect the QRS complex in ECG signals. This work proposes a fully-parallel PTA architecture with a reduced amount of registers, directly impacting energy reduction. Furthermore, we implement a unified band-pass filter exploring shift-add arrangement to reduce the noises in the ECG signal. This strategy contributes to the savings of 46.49% and 34.64% in the area and energy per operation (EPO), respectively, compared to the literature, keeping the average sensitivity and positive prediction for examined samples at 99.73% and 99.71%, respectively.

A global approach to detecting and characterizing water leakage in a concrete bridge deck: Parametric study to validate an adapted full-waveform inversion method

The objective of this study is to address issues related to defects in waterproofing membranes through the use of Ground Penetration Radar to detect water leakage into the concrete layer of bridge decks. Given that processing radar signals based solely on temporal data introduces significant estimation errors, an advanced method optimized from Full-Waveform Inversion (FWI) is employed. This method accounts for several unknown factors, including boundary conditions, antenna positioning inaccuracies, and approximations inherent in 2D modeling during the inversion process. The method is adaptable and capable of reconstructing both the dielectric and geometric parameters of a multilayered structure with any number of layers and unknown parameters using just a few A-scans (up to 10, depending on the number of parameters and layers). In contrast, other methods typically require several hundred A-scans. This efficiency is achieved due to the two-dimensional nature of the layer system. Additionally, the simplicity of the structure facilitates a much faster and more straightforward inversion algorithm, hence making these features especially advantageous for practical applications. The optimized Full-Waveform Inversion approach allows for an accurate determination of unknown parameters within a multilayered medium. The high accuracy of this method is validated through a direct comparison with experiments, wherein the exact parameters are known. Such an approach enables the attainment of a low relative error using the currently available measurement device.

Structural Parameterizations for Two Bounded Degree Problems Revisited

We revisit two well-studied problems, Bounded Degree Vertex Deletion and Defective Coloring , where the input is a graph G and a target degree Δ, and we are asked either to edit or partition the graph so that the maximum degree becomes bounded by Δ. Both problems are known to be parameterized intractable for the most well-known structural parameters, such as treewidth. We revisit the parameterization by treewidth, as well as several related parameters and present a more fine-grained picture of the complexity of both problems. In particular, we present the following: — Both problems admit straightforward DP algorithms with table sizes \((\Delta +2)^\mathrm{tw}\) and \((\chi _\mathrm{d}(\Delta +1))^{\mathrm{tw}}\) , respectively, where tw is the input graph’s treewidth and \(\chi _\mathrm{d}\) the number of available colors. We show that, under the SETH, both algorithms are essentially optimal, for any non-trivial fixed values of \(\Delta , \chi _\mathrm{d}\) , even if we replace treewidth by pathwidth. Along the way, we obtain an algorithm for Defective Coloring with complexity quasi-linear in the table size, thus settling the complexity of both problems for treewidth and pathwidth. — Given that the standard DP algorithm is optimal for treewidth and pathwidth, we then go on to consider the more restricted parameter tree-depth. Here, previously known lower bounds imply that, under the ETH, Bounded Vertex Degree Deletion and Defective Coloring cannot be solved in time \(n^{o(\sqrt [4]{\mathrm{td}})}\) and \(n^{o(\sqrt {\mathrm{td}})}\) , respectively, leaving some hope that a qualitatively faster algorithm than the one for treewidth may be possible. We close this gap by showing that neither problem can be solved in time \(n^{o(\mathrm{td})}\) , under the ETH, by employing a recursive low tree-depth construction that may be of independent interest. — Finally, we consider a structural parameter that is known to be restrictive enough to render both problems FPT: vertex cover. For both problems the best known algorithm in this setting has a super-exponential dependence of the form \(\mathrm{vc}^{\mathcal {O}(\mathrm{vc})}\) . We show that this is optimal, as an algorithm with dependence of the form \(\mathrm{vc}^{o(\mathrm{vc})}\) would violate the ETH. Our proof relies on a new application of the technique of d -detecting families introduced by Bonamy et al. [ToCT 2019]. Our results, although mostly negative in nature, paint a clear picture regarding the complexity of both problems in the landscape of parameterized complexity, since in all cases we provide essentially matching upper and lower bounds.

A REFINED CLASSIFICATION OF THE SUBSET SUM PROBLEM IN POLYNOMIAL TIME COMPLEXITY

The problem considered is the selection of at least one subset from a set (array) of distinct positive integers, such that the sum of the subset's elements exactly matches a given target sum (target certificate). According to R. M. Karp, this problem belongs to the class of NP-complete problems. Diophantine equations and an auxiliary problem, which facilitates the solution of the original problem and has independent scientific interest, have been introduced. A novel method has been developed, which includes proven lemmas and theorems. These results enable the development of efficient and straightforward algorithms for solving the subset sum problem. The time and space complexity for selecting the required subsets do not exceed the square of the length of the original set. An analytical framework has been proposed for managing indices within the original set. These algorithms are applicable to solving problems related to the independent set of cardinality k and the k-vertex cover problem. Additionally, we present examples to confirm claimed results. It should be noted that the time complexity of sorting an array of integers is proportional to the square of the array's size, and this problem belongs to class P. Therefore, based on the newly developed method, it can be inferred that the subset sum problem, originally classified as NP-complete within the NP class, also belongs to P.

Deciding if a hyperbolic group splits over a given quasiconvex subgroup

We present an algorithm which decides whether a given quasiconvex residually finite subgroup H of a hyperbolic group G is associated with a splitting. The methods developed also provide algorithms for computing the number of filtered ends \tilde{e}(G,H) of H in G under certain hypotheses and give a new straightforward algorithm for computing the number of ends e(G,H) of the Schreier graph of H . Our techniques extend those of Barrett via the use of labelled digraphs, the languages of which encode information on the connectivity of \partial G - \Lambda H .

Targeted materials discovery using Bayesian algorithm execution

Rapid discovery and synthesis of future materials requires intelligent data acquisition strategies to navigate large design spaces. A popular strategy is Bayesian optimization, which aims to find candidates that maximize material properties; however, materials design often requires finding specific subsets of the design space which meet more complex or specialized goals. We present a framework that captures experimental goals through straightforward user-defined filtering algorithms. These algorithms are automatically translated into one of three intelligent, parameter-free, sequential data collection strategies (SwitchBAX, InfoBAX, and MeanBAX), bypassing the time-consuming and difficult process of task-specific acquisition function design. Our framework is tailored for typical discrete search spaces involving multiple measured physical properties and short time-horizon decision making. We demonstrate this approach on datasets for TiO2 nanoparticle synthesis and magnetic materials characterization, and show that our methods are significantly more efficient than state-of-the-art approaches. Overall, our framework provides a practical solution for navigating the complexities of materials design, and helps lay groundwork for the accelerated development of advanced materials.

Atomic thermal fluctuation reduction method for robust local lattice structure identification in finite-temperature molecular dynamics

Classical molecular dynamics (MD) is extensively employed to explore the properties, deformations, and fractures of materials at the atomic scale. Identifying local structures is crucial for understanding the mechanisms behind material deformation and fracture. Nevertheless, analyzing the local lattice structure at high temperatures poses challenges due to atomic thermal fluctuations, which act as noise and potentially lead to misjudgment of the local lattice structure. To date, various strategies have been implemented to circumvent this issue. However, they cannot be a solution because it is unable to reproduce phenomena unique to high temperatures, whereas others require significant computational resources. This paper introduces an innovative method to reduce atomic thermal fluctuations using a straightforward algorithm, thereby facilitating accurate identification of local lattice structures even at high temperatures. Our approach incorporates novel degrees of freedom, termed ‘Markers,’ that are linked to atoms. By reducing the thermal fluctuation of these Markers, precise analysis of the local lattice structure becomes feasible. The efficacy of this method is validated through its thermal reducibility and Markers trackabilities to atoms. Utilizing common neighbor analysis, the error rate for structure identification with our method is nearly 0% at temperatures up to 1200 K in Fe, in contrast to approximately 5% without it. Furthermore, the average distance between atoms and Markers remains below 0.1 Å. Applying our method to phase transformations, we successfully observed the transition from face-centered cubic to body-centered cubic structure in Fe at 1200 K. This method holds promise for expanding the capabilities of MD simulations at high temperatures.

Performance Assessment of Object Detection Models Trained with Synthetic Data: A Case Study on Electrical Equipment Detection.

This paper explores a data augmentation approach for images of rigid bodies, particularly focusing on electrical equipment and analogous industrial objects. By leveraging manufacturer-provided datasheets containing precise equipment dimensions, we employed straightforward algorithms to generate synthetic images, permitting the expansion of the training dataset from a potentially unlimited viewpoint. In scenarios lacking genuine target images, we conducted a case study using two well-known detectors, representing two machine-learning paradigms: the Viola-Jones (VJ) and You Only Look Once (YOLO) detectors, trained exclusively on datasets featuring synthetic images as the positive examples of the target equipment, namely lightning rods and potential transformers. Performances of both detectors were assessed using real images in both visible and infrared spectra. YOLO consistently demonstrates F1 scores below 26% in both spectra, while VJ's scores lie in the interval from 38% to 61%. This performance discrepancy is discussed in view of paradigms' strengths and weaknesses, whereas the relatively high scores of at least one detector are taken as empirical evidence in favor of the proposed data augmentation approach.

Intelligently Counting Agricultural Pests by Integrating SAM with FamNet

The utilization of the large pretrained model (LPM) based on Transformer has emerged as a prominent research area in various fields, owing to its robust computational capabilities. However, there remains a need to explore how LPM can be effectively employed in the agricultural domain. This research aims to enhance agricultural pest detection with limited samples by leveraging the strong generalization performance of the LPM. Through extensive research, this study has revealed that in tasks involving the counting of a small number of samples, complex agricultural scenes with varying lighting and environmental conditions can significantly impede the accuracy of pest counting. Consequently, accurately counting pests in diverse lighting and environmental conditions with limited samples remains a challenging task. To address this issue, the present research suggests a unique approach that integrates the outstanding performance of the segment anything model in class-agnostic segmentation with the counting network. Moreover, by intelligently utilizing a straightforward TopK matching algorithm to propagate accurate labels, and drawing inspiration from the GPT model while incorporating the forgetting mechanism, a more robust model can be achieved. This approach transforms the problem of matching instances in different scenarios into a problem of matching similar instances within a single image. Experimental results demonstrate that our method enhances the accuracy of the FamNet baseline model by 69.17% on this dataset. Exploring the synergy between large models and agricultural scenes warrants further discussion and consideration.

Quantifying the expansion rates of aftershock zones for magnitude-7 class earthquakes around the Japanese archipelago

Earthquakes (mainshocks) trigger sequences of aftershocks, the frequency of which diminishes following a power-law decay, while the spatial domain of these aftershocks extends logarithmically over time. The delineation of the aftershock zone can be modulated by variables beyond the magnitude of the mainshock, encompassing the location of the fault (whether the fault is at a plate boundary), the depth at which the event occurs, and the prevailing local stress conditions. Here, we evaluate the expansion rate of aftershock zones by analyzing earthquakes of magnitude-7 class in the vicinity of the Japanese archipelago. Prior studies have offered approximate assessments of expansion rates; however, our approach involves the utilization of a straightforward algorithm for the automated estimation of this metric, facilitating the compilation of a catalog. Across the dataset, no pronounced correlations were discerned between the expansion rate and other examined parameters. Yet, an inverse relationship is identified between the expansion rate and the b value of aftershocks for mainshocks occurring at plate boundaries. This observation suggests that the expansion rate of aftershock zones predominantly mirrors the stress field following the mainshock. Such a pattern is not detected in mainshocks occurring within the plate's interior. While the expansion rate of aftershock zones is likely influenced by various factors, aftershock zones may expand more rapidly with higher differential stress in areas surrounding hypocenters of major interplate earthquakes of magnitude 8 or 9.

Clustering Of Sapodilla Fruit (Manilkara Zapota) Maturity Levels Based On Color Using K-Means Clustering Method

The study aimed to evaluate the accuracy of determining sapodilla fruit maturity using the K-Means Clustering algorithm, a method that partitions data into clusters of similar characteristics; by applying K-Means Clustering on data samples obtained from Kaggle, and using Matlab for pixel value calculation, the algorithm effectively classified 150 sapodilla fruit samples into two clusters—75 mature and 75 raw—with an accuracy of 92.2% for mature sapodilla and 94.5% for raw sapodilla, demonstrating that K-Means Clustering, a straightforward and user-friendly algorithm, is highly effective in distinguishing sapodilla fruit maturity levels.

A Practical Approach to Diagnosing and Managing Chronic Spontaneous Urticaria.

Chronic spontaneous urticaria (CSU) is an unpredictable inflammatory skin condition characterized by the spontaneous onset of itchy wheals, angioedema, or both, which occurs for longer than 6weeks overall. Despite the relatively straightforward diagnostic algorithm for CSU, relying primarily on a detailed medical history and only limited laboratory tests, patients often wait years to be diagnosed, with many cycling through different healthcare practitioners before a diagnosis is made. Even then, current treatment options for CSU are limited, with approximately half of patients resistant to standard-of-care second-generation antihistamines at standard or higher doses. As such, there is an unmet need for improved, streamlined management for patients with CSU. Here, we review the evidence-based diagnostic algorithm for CSU, consider the required steps of the diagnostic workup, and provide practical, real-world advice on the management of CSU to improve the timely diagnosis and care of patients with this debilitating disease.

Power equalization and optimization of photovoltaic module based on forward-flyback converter

This paper focuses on enhancing the energy extraction efficiency of photovoltaic (PV) modules through the use of a straightforward power converter and control algorithm. This research delves into the electrical characteristics of PV modules, explaining the concepts of global maximum power point, and local maximum power points. By integrating maximum power point tracking algorithms and differential power processing technology, an innovative scheme for power equalization and optimization of PV modules is introduced. The scheme is based on a single-switch multi-winding forward-flyback converter. Using the STP-340-72-Vfh-type PV module as a case study, a simulation model is developed with PLECS simulation software. The simulations cover 30 different irradiance scenarios. The findings illustrate the effectiveness of the proposed PV module power optimization system in achieving maximum power output under different irradiance conditions, achieving an average efficiency of 94.61%. This efficiency rate is 13.95% greater than that of existing global maximum power tracking schemes.

Sensorless model-based displacement estimator for piezoelectric actuator through a practical three-stage mechanism

Piezoelectric elements (PEMs) are used in a variety of applications. In this paper, we developed a new simple sensorless method for a Piezoelectric Actuator (PEA), which includes piezostack elements and a three-stage amplification mechanism.This research focuses on a piezoelectric actuator that incorporates a three-stage amplification system, where the outcome of one stage serves as the input for the subsequent one. The actuator receives two types of inputs: the voltage applied to the piezoelectric elements and the mechanical load it carries. Its output is defined by the rotation angle observed at the end of the third amplification stage. To indirectly measure the actuator's displacement, a basic external circuit is utilized.The precise movement of these actuators is essential. To circumvent the high costs and limitations associated with highly accurate displacement sensors, there has been a growing interest in sensorless control methods. Certain electrical signals, when measured, can provide an estimation of displacement. However, induced voltage measurements are not effective for piezoelectric stacks. Two more promising measures are the voltage and current of the piezoelectric material.Given that the electrical charge on these actuators closely reflects their displacement with minimal hysteresis across a broad frequency spectrum, it's proposed that displacement can be effectively gauged through current measurements that assess charge.The core contribution of this paper is the introduction and validation, both theoretically and experimentally, of a hybrid algorithm that leverages these two electrical signals to enhance the accuracy of displacement estimates. This was confirmed using a laboratory setup.The primary benefit of this research is the presentation of a straightforward sensorless control algorithm, poised for further exploration within the realm of piezoelectric actuators. The simplicity of both the theoretical model and the sensorless technique facilitates their application across a diverse range of piezoelectric actuators and amplification systems, thereby streamlining the design, modeling, and control strategy development for various actuators.The innovation of this study stems from the application of an uncomplicated sensorless estimation algorithm, coupled with a system-level perspective on piezoelectric actuators. This approach utilizes a simple, adaptable model suitable for a wide array of applications and operational techniques.

Optimal Allocation of Fast Charging Stations on Real Power Transmission Network with Penetration of Renewable Energy Plant

Because of their stochastic nature, the high penetration of electric vehicles (EVs) places demands on the power system that may strain network reliability. Along with increasing network voltage deviations, this can also lower the quality of the power provided. By placing EV fast charging stations (FCSs) in strategic grid locations, this issue can be resolved. Thus, this work suggests a new methodology incorporating an effective and straightforward Red-Tailed Hawk Algorithm (RTH) to identify the optimal locations and capacities for FCSs in a real Aljouf Transmission Network located in northern Saudi Arabia. Using a fitness function, this work’s objective is to minimize voltage violations over a 24 h period. The merits of the suggested RTH are its high convergence rate and ability to eschew local solutions. The results obtained via the suggested RTH are contrasted with those of other approaches such as the use of a Kepler optimization algorithm (KOA), gold rush optimizer (GRO), grey wolf optimizer (GWO), and spider wasp optimizer (SWO). Annual substation demand, solar irradiance, and photovoltaic (PV) temperature datasets are utilized in this study to describe the demand as well as the generation profiles in the proposed real network. A principal component analysis (PCA) is employed to reduce the complexity of each dataset and to prepare them for the k-means algorithm. Then, k-means clustering is used to partition each dataset into k distinct clusters evaluated using internal and external validity indices. The values of these indices are weighted to select the best number of clusters. Moreover, a Monte Carlo simulation (MCS) is applied to probabilistically determine the daily profile of each data set. According to the obtained results, the proposed RTH outperformed the others, achieving the lowest fitness value of 0.134346 pu, while the GRO came in second place with a voltage deviation of 0.135646 pu. Conversely, the KOA was the worst method, achieving a fitness value of 0.148358 pu. The outcomes attained validate the suggested approach’s competency in integrating FCSs into a real transmission grid by selecting their best locations and sizes.

Smart Parking Space Detection with Generative Artificial Intelligence and Large Language Models

CAMERON VU, Dept of Computer Science and Math & ENGR, Shepherd University, Shepherdstown, WV, 25443, and DARIA PANOVA, Dept of Computer Science and Math & ENGR, Shepherd University, Shepherdstown, WV, 25443, and JOSIAH KOWALSKI, Dept of Computer Science and Math & ENGR, Shepherd University, Shepherdstown, WV, 25443, and Dr. W. LIAO (Faculty Advisor), Dept of Computer Science and Math & ENGR, Shepherd University, Shepherdstown, WV, 25443, and Dr. O. Guzide (Faculty Advisor), Dept of Computer Science and Math & ENGR, Shepherd University, Shepherdstown, WV, 25443. Smart Parking Space Detection with Generative Artificial Intelligence and Large Language Models. The increasing relevance of generative AI and large language models is reshaping various sectors of modern society. These advancements have spurred notable progress in fields such as healthcare, finance, and education. Yet, the application of AI extends beyond expert domains, offering simplified solutions to everyday tasks for the general populace. This project harnesses the power of generative artificial intelligence and large language models to develop a practical application: smart parking space detection. By leveraging these technologies, individuals can effortlessly ascertain the availability of parking spots in monitored lots via camera or photographic monitoring, facilitated by a straightforward algorithm. The overarching objective is twofold: to engineer a user-friendly system utilizing generative AI principles and to demonstrate the potential for such technologies to enhance the daily experiences of ordinary individuals.