Algorithm Parameters Research Articles

Faster parameterized algorithm for r-pseudoforest deletion

Faster parameterized algorithm for r-pseudoforest deletion

Evaluation of the Effect of Nurse-Driven Algorithm in Prevention of Central Catheter-Related Bloodstream Infections in Intensive Care Units.

Infection control in intensive care units is important for both patients' quality of life and institutions. This study was conducted to evaluate the effect of a nurse-driven algorithm in preventing central catheter-related bloodstream infections in intensive care units. This intervention research was implemented in a training and research hospital in Turkey between July 1, 2021, and December 31, 2021. The research was carried out in 3 stages after the creation of the algorithm. The data were collected using the Descriptive Characteristics Form for Nurses, the Descriptive and Medical Characteristics Form for Patients, the algorithm knowledge test, and the Algorithm Parameters Follow-up form. After obtaining the necessary permissions for the study, consent was obtained from the nurses participating in the study. Number, percentage, Wilcoxon, Kruskal-Wallis, and Mann-Whitney U test values were used in the study. With the algorithm, a decrease was found in the rate of infections in the intensive care units. As a result, it was observed that the nurse-driven, central catheter-related bloodstream infection prevention algorithm, prepared using evidence-based clinical guidelines, reduced the rates of infection. Therefore, it is recommended to use algorithm studies as a long-term guide in intensive care units.

Investigation of damage effects on circular air-backed clamped plates under repeated underwater explosions

ABSTRACT Underwater weapon explosions are a primary cause of structural damage to ships, warranting thorough investigation. Experiments and numerical analysis were conducted on a simplified circular air-backed clamped plate after four repeated damage tests in a water tank. The numerical algorithm and material parameters were verified by comparing simulations with experimental and theoretical data. The plate's damage accumulated with repeated explosions, and central deflection decreased with increased thickness, aligning with Taylor's theory. Deformation under various conditions showed that long-distance explosions after short-range ones, and small-charge explosions after large-charge ones, caused greater damage. Increasing plate yield strength up to 850 MPa didn't further reduce deformation from a single explosion but did so under multiple explosions.

P-1891. What’s the Reason for the Consult? Identifying Clusters of Infectious Disease Consultations in an Academic Health System

Abstract Background In many institutions, infectious diseases (ID) consultations are requested via electronic order entry and the reason for consultation is included as free text in the order. We employed an unsupervised clustering algorithm to determine whether the consult order text can be used to ascertain clinically meaningful groups among patients for whom ID consultation is requested. Figure 1. Example Word Cloud ‘Clusters’ of Free-Text Reasons for Infectious Disease Consultation and Corresponding Subject Matter Expert Interpretation Methods We examined the initial ID consultation for all Duke University Health System inpatients receiving consultation from 1/1/2014 - 12/31/2023. The free text “Reason for Consult?” field in the consultation order was extracted, transformed into a weighted vector format using Term-Frequency Inverse-Document Frequency, and categorized using an unsupervised K-means clustering algorithm; Algorithm parameters were adjusted to optimize clinical relevance of the clusters. Within each patient cluster, we evaluated mortality and hospital length of stay, with patients censored at their time of last contact with the health system.Figure 2.Kaplan-Meier Survival Estimates for Each Cluster Results 37,848 unique patients with an ID consultation during the study period were evaluated, and 35,197 had free-text reasons associated with the electronic consult order. A combination of the K-means Elbow Method, silhouette score, and subject matter expert evaluation identified that approximately 10 clusters were optimal to categorize the dataset. Word clouds were generated for each cluster (Figure 1) and expert assessment identified the central theme of each cluster (Table 1). We identified unique patient characteristics or syndromes in nine of the patient clusters, while the final cluster was characterized as a ‘generic’ ID consult. One-year mortality varied according to cluster (Figure 2) with the highest mortality seen in the ‘Hematologic Malignancy’ category, and lowest mortality in the ‘Complicated Wound Infection’ and ‘HIV Management’ categories. Conclusion An unsupervised clustering algorithm allowed for the identification of clinically meaningful groups of ID consultations. If validated in other settings, this approach could present an accessible way to understand qualitative and prognostic trends in ID clinical practice.Table 1.Cluster Characteristics*Institutional policy at the end of study period requires Infectious Disease consultation for patients with HIV, new lung transplant recipients, patients with candidemia or Staphylococcus aureus bacteremia, and patients for whom restricted antibiotics are requested.**Infinite median survival times indicate that the Kaplan-Meier survival curve did not cross the 50% threshold through the evaluated period. Disclosures Kristen Dicks, MD, UpToDate: Advisor/Consultant Nicholas A. Turner, MD, MHSc, PDI: Research contract|Purio Labs: Research contract

Optimization and validation of an inverse method for elastic constant estimation in resonant ultrasound spectroscopy

This article introduces an inverse method using a particle swarm optimization algorithm enhanced by fuzzy logic to determine elastic constants in resonant ultrasound spectroscopy. The method adaptively adjusts algorithm parameters and incorporates frequency pairings into the error function, effectively addressing the issue of missing frequencies. Notably, it achieves precise mode matching with a fitting error of 0.163%, even when up to 50% of the first 30 eigenfrequencies are absent in polycrystalline Ti-6Al-4V studies. Under the assumption of Ti-6Al-4V orthotropic symmetry, the method iterates over grouped sets of the nine independent elastic constants, reducing the fitting error to 41.7% compared to the isotropic assumption. When applied to cortical bone with one-third of measured frequencies missing, the algorithm successfully obtained both frequency pairings and transverse isotropic elastic constants. This approach is anticipated to overcome limitations on the number of measured resonant frequencies, expanding the applicability of the method to low-symmetry materials.

A Deformation Prediction Model for Concrete Dams Based on RSA-VMD-AttLSTM

This paper presents a deformation prediction model for concrete dams that integrates a reptile search algorithm (RSA), a Variational Mode Decomposition (VMD) algorithm, and a long short-term memory network model with attention mechanism (AttLSTM). This model utilizes the RSA to optimize the parameters K and α of the VMD algorithm. It combines the variance of the modified mode with the sample entropy of these data as the objective function, effectively converting monitoring data into a stable signal while retaining essential characteristic variation. Data are reformatted into a three-dimensional structure and partitioned into training and testing sets. The AttLSTM network was applied to forecast deformation, and results were validated using practical engineering cases. The performance of the proposed model was compared against that of four other models: LSTM, VMD-LSTM, attention LSTM, and VMD-AttLSTM models. Analysis of the five evaluation criteria revealed that the RSA can better optimize the parameters of the VMD algorithm. Consequently, the proposed model demonstrates superior noise reduction capabilities and improved prediction accuracy.

Enhancing evapotranspiration estimates in composite terrain through the integration of satellite remote sensing and eddy covariance measurements.

Accurate evaluation of water resource systems is essential for informed planning and decision-making. Evapotranspiration (ET), a key component of water resource management, is often estimated using remote sensing techniques; however, such estimates can be subject to significant uncertainties under certain conditions. In this study, we present a novel approach to improving the accuracy of ET estimates in composite terrains. The methodology involves optimizing the Surface Energy Balance Algorithm for Land (SEBAL-OPT) by integrating ground-based eddy covariance (EC) flux tower data into the satellite-based ET retrieval process. The approach was evaluated at four sites in California, each representing different land uses. Parameter optimization was achieved through Bayesian inference using the Differential Evolution Adaptive Metropolis (DREAM) algorithm, which minimized discrepancies between ET estimates derived from Landsat 8 and 9 imagery and the observed ET from EC measurements. Results from the global sensitivity analysis identified solar radiation and hot/cold pixel selection as the most sensitive parameters in the SEBAL algorithm, highlighting their critical role in reducing uncertainty in ET estimates. SEBAL-OPT demonstrated significantly improved accuracy, with root mean square error (RMSE) values ranging from 0.72mm to 1.33mm, compared to the original SEBAL parameterization (SEBAL-ORG), which produced RMSE values between 1.03mm and 2.14mm. This approach highlights that, when properly calibrated, the model can be effectively applied across diverse agricultural landscapes, regardless of the specific land use at individual sites. These findings have significant implications for water resource planning, agricultural water management, and water rights adjudication and could be applied to other remote sensing of ET models.

Optimization of Video Heart Rate Detection Based on Improved SSA Algorithm.

A solution to address the issues of environmental light interference in Remote Photoplethysmography (rPPG) methods is proposed in this paper. First, signals from the face's region of interest (ROI) and background noise signals are simultaneously collected, and the two signals are processed by a differential to obtain a more accurate rPPG signal. This method effectively suppresses background noise and enhances signal quality. Secondly, the singular spectrum analysis algorithm (SSA) is enhanced to further improve the accuracy of heart rate detection. The algorithm's parameters are adaptively optimized by integrating the spectral and periodic characteristics of the heart rate signal. Experimental results demonstrate that the method proposed in this paper effectively mitigates the effects of lighting changes on heart rate detection, thereby enhancing detection accuracy. Overall, the experiments indicate that the proposed method significantly improves the effectiveness and accuracy of heart rate detection, achieving a high level of consistency with existing contact-based detection methods.

Performance evaluation of perceptible impulsive noise detection methods based on auditory models

Reference-free audio quality assessment is a valuable tool in many areas, such as audio recordings, vinyl production, and communication systems. Therefore, evaluating the reliability and performance of such tools is crucial. This paper builds on previous research by analyzing the performance of four additional algorithms in detecting perceptible impulsive noise (clicks) based on auditory models. We compared the results of eight algorithms, hypothesizing that computationally simpler algorithms could perform as well as more complex ones. We obtained a set of audio signals, with and without clicks, annotated by human subjects from a publicly available dataset. Audio signal sets are categorized based on the obtained annotation results to train the algorithms for different levels of the experiments. Experiments containing cross-validation are done for multiple parameters of algorithms. The algorithm training is based on maximizing a discriminability metric (A′). Evaluation criteria of the algorithms included the hit rate, false alarm rate, A′, and computational time. Our findings indicate that computationally simpler auditory models have performed as well as computationally more complex ones, while conventional models exhibit lower performance. Conclusively, the ERBlet transform based algorithm demonstrated superior performance in terms of A′ and robustness. This paper provides insights into the capabilities of auditory models in a practical use case of perceptible click detection. The results presented here can help research and develop such algorithms for vinyl production, audio archiving, podcasting, music production, and telecommunications.

Spatial analysis and classification of land use patterns in Lucknow district, UP, India using GIS and random forest approach

Mapping land use and land cover (LULC) is essential for comprehending changes in the environment and promoting sustainable planning. To achieve accurate and effective LULC mapping, this work investigates the integration of Geographic Information Systems (GIS) with Machine Learning (ML) methodology. Different types of land covers in the Lucknow district were classified using the Random Forest (RF) algorithm and Landsat satellite images. Since the research area consists of a variety of landforms, there are issues with classification accuracy. These challenges are met by combining supplementary data into the GIS framework and adjusting algorithm parameters like selection of cloud free images and homogeneous training samples. The result demonstrates a net increase of 484.59 km2 in built-up areas. A net decrement of 75.44 km2 was observed in forest areas. A drastic net decrease of 674.52 km2 was observed for wetlands. Most of the wastelands have been converted into urban areas and agricultural land based on their suitability with settlements or crops. The classifications achieved an overall accuracy near 90%. This strategy provides a reliable way to track changes in land cover, supporting resource management, urban planning, and environmental preservation. The results highlight how sophisticated computational methods can enhance the accuracy of LULC evaluations.

Improvements to monoscopic analysis for imaging atmospheric Cherenkov telescopes: Application to H.E.S.S.

Imaging atmospheric Cherenkov telescopes (IACTs) detect rays by measuring the Cherenkov light emitted by secondary particles in the air shower when the rays hit the atmosphere of the Earth. Given usual distances between telescopes in IACT arrays, at low energies (lesssim 100 GeV), the limited amount of Cherenkov light produced typically implies that the event is registered by one IACT only. Such events are called monoscopic events, and their analysis is particularly difficult. Challenges include the reconstruction of the event's arrival direction, energy, and the rejection of background events due to charged cosmic rays. Here, we present a set of improvements, including a machine-learning algorithm to determine the correct orientation of the image in the camera frame, an intensity-dependent selection cut that ensures optimal performance across all energies, and a collection of new image parameters. To quantify these improvements, we make use of simulations and data from the 28-m diameter central telescope of the IACT array. Knowing the correct image orientation, which corresponds to the arrival direction of the photon in the camera frame, is especially important for the angular reconstruction, which could be improved in resolution by 57% at 100,GeV. The event selection cut, which now depends on the total measured intensity of the events, leads to a reduction of the low-energy threshold for source analyses by ∼50%. The new image parameters characterize the intensity and time distribution within the recorded images and complement the traditionally used Hillas parameters in the machine learning algorithms. We evaluate their importance to the algorithms in a systematic approach and carefully evaluate associated systematic uncertainties. We find that including subsets of the new variables in machine-learning algorithms improves the reconstruction and background rejection, resulting in a sensitivity improved by 41% at the low-energy threshold. Finally, we apply the new analysis to data from the Crab Nebula and estimate systematic uncertainties introduced by the new method.

Enhancing Quantum Algorithms for Quadratic Unconstrained Binary Optimization via Integer Programming

To date, research in quantum computation promises potential for outperforming classical heuristics in combinatorial optimization. However, when aiming at provable optimality, one has to rely on classical exact methods like integer programming. State-of-the-art integer programming algorithms can compute strong relaxation bounds even for hard instances, but may have to enumerate a large number of subproblems for determining an optimum solution. If the potential of quantum computing realizes, it can be expected that in particular finding high-quality solutions for hard problems can be done fast. Still, near-future quantum hardware considerably limits the size of treatable problems. In this work, we go one step into integrating the potentials of quantum and classical techniques for combinatorial optimization. We propose a hybrid heuristic for the weighted maximum-cut problem and for quadratic unconstrained binary optimization. The heuristic employs a linear programming relaxation, rendering it well-suited for integration into exact branch-and-cut algorithms. For large instances, we reduce the problem size according to a linear relaxation such that the reduced problem can be handled by quantum machines of limited size. Moreover, we improve the applicability of depth-1 QAOA, a parameterized quantum algorithm, by deriving a parameter estimate for arbitrary instances. We present numerous computational results from real quantum hardware.

Genetic Algorithm based Routing in Wireless Sensor Networks with Various Distance Metrics

The study highlights the operational characteristics of wireless sensor networks (WSNs). It describes genetic operators and parameters that serve as the foundation for the genetic algorithm's functionality. The optimal values for population size and the number of generations required for data routing in WSNs were determined. The mathematical framework and application aspects of distance metrics such as Euclidean, Chebyshev, Manhattan, and Minkowski were analyzed. A block diagram of the proposed genetic algorithm for data transmission between sensor nodes is presented. The effectiveness of the developed genetic algorithm was investigated for route formation using different distance metrics in a network with nodes characterized by three operational radii. Experimental results indicate that, for finding the shortest route with minimal computational time in a network of 25 sensor nodes, the optimal genetic algorithm parameters are 150 generations and a population size of 300. Simulation results demonstrate the superiority of the proposed solution over the greedy algorithm in terms of route length.

Developing an advanced safety device for bridge-type cranes

Introduction. Experimentally revealed disadvantages of domestic complex safety devices in the part of load capacity limitation and parameters registration are described. The reasons why it is inadmissible to re-equip cranes under the control of foreign safety systems to the existing domestic devices are explained. It is suggested to achieve the safety of such cranes through the development of safety devices that take into account the dynamic characteristics of a particular crane and control algorithms of its drives.Materials and methods. The study was done using the developed safety device realizing the functions of load capacity limitation, automatic determination of the parameters of the load capacity limiter algorithm, determination of the crane operation intensity. The description of these algorithms in relation to cranes equipped with frequency control system is given. Experimental determination of operational parameters of the device is carried out on an overhead crane equipped with the ControlPro safety system (KoneCranes).Results. The use of the device allowed to decrease the dynamic coefficient in the whole range of lifted loads masses. A twofold reduction of the dynamic component was obtained when lifting near-nominal loads. The accuracy of characteristic number determination was 1.3%.Discussion and conclusion. It is demonstrated that the developed algorithm of load capacity limitation allowed not only to increase crane security, but also to reduce the loading on the crane, in comparison with the standard safety system. The accuracy of operating parameters determination in conditions of real technological process satisfies the requirements of normative documentation. Thus, the possibility of duplicating or replacing some functions of the standard safety system by using the developed device is shown.

Optimized KNN Algorithm for Diabetic Retinopathy Classification with PCA-Based Data Fusion and Cuckoo Search Optimization

Diabetes is a disease that occurs when the body is unable to use the insulin it produces effectively or the body fails to produce enough insulin. One of the most important complications of this disease is diabetic retinopathy (DR), which is considered the main cause of severe visual impairment and blindness. Previous studies have proven that the KNN algorithm is an effective algorithm for solving classification and prediction problems, as the performance of this algorithm rely on determining the value of the K parameter because the inappropriate choice of this value can negatively affect the accuracy of classification. On the other hand, adjusting this value manually is very difficult because this value depends on the state of determining the solution to the problem each time. Therefore, there is still an urgent need to use smart algorithms to adjust this value and obtain an ideal value that ultimately leads to obtaining a very high classification accuracy. In this paper, the Cuckoo Search algorithm was used, which is considered one of the smart and modern algorithms in the field of diagnosis, in addition to applying more than one technique and algorithm to build an integrated system to enhance the accuracy of diagnosis and obtain competitive diagnostic accuracy. The proposed work was implemented using the Debrecen diabetic retinopathy dataset and competitive results were obtained for recall, sensitivity, precision, F1 score, accuracy and specificity (98.05%), (97.30%), (99.01%), (98.70%), (99.70%), and (99.08%), respectively. Our results demonstrate that the Cuckoo Search algorithm is an effective and suitable choice for optimizing the parameters in the KNN algorithm, in addition to enhancing this algorithm to diagnose the disease early and support direct intervention and treatment, and this method lays the foundation for diagnosing other diseases and thus improving patient care in most related fields.

Industrial Design of Type-1 and Interval Type-2 Fuzzy Logic Control

This paper focuses on the design of type-1 and interval type-2 (IT2) PID fuzzy logic controllers (FLC) for ensuring - by a programmable logic controller (PLC) - a high-performance real-time liquid level control in a carbonization column (CCl) for soda production. Firstly, Takagi-Sugeno-Kang models - derived via genetic algorithms parameter optimizations, experimental data and simulations for the basic and the worst CCl loads - are studied at different operation points, and the worst Ziegler-Nichols (ZN) model is assessed. Next, two-input fuzzy units are designed - assuming various membership functions (MF) and uncertainties - and the greatest linearization gain is computed. Based on it and the ZN worst plant model, the parameters of the FLC input pre-processing differentiator and the PI post-processing are empirically tuned. Finally, a PLC oriented analytical description of the IT2 MF, fuzzy rules and type-reduction is suggested. The designed FLC systems are studied via simulation to determine the factors that have the greatest impact on the system performance improvement. The obtained results unveil that the tuned FLC outperforms the tuned linear PI. Better system performance is achieved by a small number of MF with large support ensuring economical PLC presentation.

Interdisciplinary role of statistics in energy consumption prediction within smart grid big data analytics

The study of energy consumption in buildings is crucial due to environmental and financial impacts. Smart metering is suggested to improve energy efficiency, but predicting electrical consumption accurately remains challenging, considering various factors. To tackle this, we propose a Multi-objective Golden Eagle Optimization (MGE) based Hybrid Deep Dynamic Convolutional Fuzzy Network (HDDCFN) method. This approach optimizes algorithm parameters and prediction models using multi-objective optimization, considering historical usage and weather conditions. The research is conducted using Matlab.

A wireless sensor network for coal mine safety powered by modified localization algorithm.

Coal mining industry is one of the main source for economy of every nations, whereas safety in the underground coal mining area is still doubtful. According to some reports, there is heavy loss of life and money due to the occasional accidents in the coal mining area. Some existing researchers has been addressed this issue and approached their method. But the limitations are the communication range between the anchor nodes are weak because of its complex infrastructure. In coal mines, gas leaks pose a serious safety risk, often leading to explosions or toxic exposure. Temperature control is difficult due to fluctuating underground conditions, potentially causing heat stress or equipment malfunction. Real-time worker tracking is challenging in these environments, making it harder to monitor safety and respond swiftly to emergencies. To overcome the challenges, the proposed research uses modified precise DV (Distance Vector)-Hop localization algorithm. The data is collected from the nodes and it will generate as anchor nodes and sensor nodes. The proposed research includes some parameters to analyze the abnormal environment characteristics such as network parameters, proposed localization algorithm parameters, simulation parameters, safety monitoring parameters. These parameters helps to send the safety alert to the ground center in case of any emergency such as high temperature, gas leakage, changes in air humidity. The proposed model has the ability to detect all the sensor nodes within the threshold value as well as outside the threshold value. This shows that the proposed model communication range and it accuracy is higher than the existing research. The MATLAB simulation tool is used to enhance the localization of the proposed algorithm where, it is compare the traditional DV-Hop localization algorithm. The proposed algorithm attain high performance accuracy than existing method. The efficiency of the proposed modified precise DV-Hop localization algorithm is evaluated by the simulation outcomes.

A reinforcement learning based memetic algorithm for energy-efficient distributed two-stage flexible job shop scheduling problem

Given the increasingly severe environmental challenges, distributed green manufacturing has garnered significant academic and industrial interest. This paper addresses the distributed two-stage flexible job shop scheduling problem (DTFJSP) under time-of-use (TOU) electricity pricing, with the objective of minimizing both makespan and total energy consumption costs (TEC). To tackle the problem, a hybrid memetic algorithm (HMA) is proposed. Initially, a three-tier vector encoding scheme and three population initialization strategies are devised. Subsequently, global search operators are tailored to the problem’s characteristics, and seven local search algorithms based on Q-learning are introduced. Additionally, an energy-saving operator is incorporated. Finally, orthogonal experimental design is employed to set algorithm parameters and validate the efficacy of some components. The numerical experimental results demonstrate that the proposed local search operator and energy-saving strategy are effective. Furthermore, the HMA exhibits superior diversity, breadth, and distribution compared to VNS, CMA, and NSGA-II, thereby validating the efficacy of the specialized designs of the HMA in addressing the DTFJSP.

Performance investigation of MVMD-MSI algorithm in frequency recognition for SSVEP-based brain-computer interface and its application in robotic arm control.

This study focuses on improving the performance of steady-state visual evoked potential (SSVEP) in brain-computer interfaces (BCIs) for robotic control systems. The challenge lies in effectively reducing the impact of artifacts on raw data to enhance the performance both in quality and reliability. The proposed MVMD-MSI algorithm combines the advantages of multivariate variational mode decomposition (MVMD) and multivariate synchronization index (MSI). Compared to widely used algorithms, the novelty of this method is its capability of decomposing nonlinear and non-stationary EEG signals into intrinsic mode functions (IMF) across different frequency bands with the best center frequency and bandwidth. Therefore, SSVEP decoding performance can be improved by this method, and the effectiveness of MVMD-MSI is evaluated by the robot with 6 degrees-of-freedom. Offline experiments were conducted to optimize the algorithm's parameters, resulting in significant improvements. Additionally, the algorithm showed good performance even with fewer channels and shorter data lengths. In online experiments, the algorithm achieved an average accuracy of 98.31% at 1.8s, confirming its feasibility and effectiveness for real-time SSVEP BCI-based robotic arm applications. The MVMD-MSI algorithm, as proposed, represents a significant advancement in SSVEP analysis for robotic control systems. It enhances decoding performance and shows promise for practical application in this field.