Development Of Algorithms Research Articles

Exploring Zig-Zag Triangles: H-Coverings and Practical Applications

Graph theory is widely used to analyze the structure models in chemistry, biology, computer science, operations research and sociology. Molecular bonds, species movement between regions, development of computer algorithms, shortest spanning tree in a weighted graphs, aircraft scheduling and exploration of diffusion mechanism are some of these structure models. A computing system C can execute an algorithm A if A is graph-isomorphic to a subgraph of CG. This is equivalent of defining a graph isomorphism between A and subgraph of CG. This research article gives a small description of graph theory applications and one of graph model (zig-zag triangle) to define a suitable computer algorithm. Let Γ be a connected, simple graph with finite vertices v and edges e. A family {Γ1, Γ2, . . . , Γp} ⊂ Γ of subgraphs such that for all e ∈ E, e ∈ Γl, for some l, l = 1, 2, . . . , p is an edge-covering of Γ. If Γl ∼= Γ′, ∀l, then Γ has an Γ′-covering. Graph Γ with Γ′-covering is an (ad, d)-Γ′-antimagic if f : V ∪ E → {1, 2, . . . , |V | + |E|} a bijection exists and the sum over all vertex-weights and edge-weights of Γ′ form a set {ad, ad + d, . . . , ad + (p − 1)d}. The labeling ξ is super for ξ(VΓ) = {1, 2, 3, . . . , |VΓ|} and graph Γ is Γ′-supermagic for d = 0. This manuscript investigates super (ad, d)-Γ′-antimagic labelings of zig-zag triangles for differences d = 1, 2, . . . , 8.

A Survey on Self-Supervised Learning: Algorithms, Applications, and Future Trends.

Deep supervised learning algorithms typically require a large volume of labeled data to achieve satisfactory performance. However, the process of collecting and labeling such data can be expensive and time-consuming. Self-supervised learning (SSL), a subset of unsupervised learning, aims to learn discriminative features from unlabeled data without relying on human-annotated labels. SSL has garnered significant attention recently, leading to the development of numerous related algorithms. However, there is a dearth of comprehensive studies that elucidate the connections and evolution of different SSL variants. This paper presents a review of diverse SSL methods, encompassing algorithmic aspects, application domains, three key trends, and open research questions. First, we provide a detailed introduction to the motivations behind most SSL algorithms and compare their commonalities and differences. Second, we explore representative applications of SSL in domains such as image processing, computer vision, and natural language processing. Lastly, we discuss the three primary trends observed in SSL research and highlight the open questions that remain.

Potential of new 250-nautical mile concentric circle allocation system for improving the donor/recipient HLA matching: Development of new matching algorithm

BackgroundHigh-resolution typing of human leukocyte antigen (HLA) may revolutionize the field of kidney transplantation by selection of low immunogenic grafts. The new 250-nautical mile circle allocation system offers a unique opportunity to find low HLA immunogenic donors for eligible recipients. Methods501 transplant candidates from the University of Toledo Medical Center (UTMC) between 2015 and 2019, registered at the Scientific Registry of Transplant Recipients (SRTR) were virtually matched to 4812 donors procured within 250-nautical miles using an in-house-developed simulation algorithm. Immunogenicity of HMS (hydrophobic mismatch score) ≤10 was measured based on imputed high-resolution HLAs. Simulated “optimal” matches with a KDPI≤50 % were compared with the transplant cohort between 2000 and 2010 with their kidney allograft survivals. ResultsOut of 501 recipients 500 (99.8 %) were matched with donors ≤10 HMS and KDPI ≤50 %. The average HMS value for simulated transplants was 1.4 (range 0–10) versus 6.3 (range 0–75) in the retrospective cohort (p < 0.001). The simulated model had had a median mismatch number of 3/6, while the reference cohort 4/6 among HLA-A/B/DR antigens (p < 0.001). The estimated median graft survival was 18.2 years for the simulated cohort vs. 13.4 years in the real-life cohort (p < 0.001), gaining 4.9 years per transplant and 2450 survival years for all patients. For year 2014, out of 98 patients and 659 donors, each recipient had a median number of 141 donors (HMS < 10; range 8–378). Similar values were found for patients between 2015 and 2019. ConclusionDonors within 250-nautical miles proffers excellent and multiple options for finding well-matched low immunogenic HLA kidney donors for UTMC patients, thus significantly improving their chances for long-term allograft survival.

Development of machine learning algorithms in student performance classification based on online learning activities

Development of machine learning algorithms in student performance classification based on online learning activities

Quality Improvement Initiative Using Predictive Swallowing Score to Guide Nutritional Support for Patients With Post-Stroke Dysphagia.

Decisions on enteral nutrition for patients with dysphagia after acute ischemic stroke (AIS) are often not evidence based. We sought to determine whether development of a nutritional support algorithm leveraging the Predictive Swallowing Score (PRESS) could improve process times without placement of unnecessary gastrostomies. This is a quality improvement study conducted at an academic medical center comparing a 6-month cohort of adults with AIS and dysphagia prepathway (PRE, July 1, 2019-December 31, 2019) and a 6-month cohort postpathway (POST, January 1, 2020-June 30, 2020). Gastrostomy recommendation, time to gastrostomy decision (TTD), discharge with gastrostomy, discharge with a nasogastric tube (NGT), and length of stay (LOS) were compared between groups. Among 121 patients with AIS and dysphagia, 58 (48%) were hospitalized prealgorithm and 63 (52%) postalgorithm. PRE TTD was longer than POST TTD (4.5 vs 1.5 days, p = 0.004). Frequency of gastrostomy was similar between PRE and POST (12% vs 8%, p = 0.58). LOS for patients recommended gastrostomy was longer in PRE (14.5 vs 6.5 days, p = 0.03). Frequency of discharge with NGT was numerically higher in POST but not significantly different (0.7% vs 6%, p = 0.4). Overall, LOS was the same in both groups (5 days). Development of a structured nutritional support algorithm incorporating PRESS may help facilitate sooner gastrostomy placement without increasing gastrostomy placement frequency and encourage more discharges to inpatient rehabilitation facilities with NGTs.

ScQA: A dual-perspective cell type identification model for single cell transcriptome data

Single-cell RNA sequencing technologies have been pivotal in advancing the development of algorithms for clustering heterogeneous cell populations. Existing methods for utilizing scRNA-seq data to identify cell types tend to neglect the beneficial impact of dropout events and perform clustering focusing solely on quantitative perspective. Here, we introduce a novel method named scQA, notable for its ability to concurrently identify cell types and cell type-specific key genes from both qualitative and quantitative perspectives. In contrast to other methods, scQA not only identifies cell types but also extracts key genes associated with these cell types, enabling bidirectional clustering for scRNA-seq data. Through an iterative process, our approach aims to minimize the number of landmarks to approximately a dozen while maximizing the inclusion of quasi-trend-preserved genes with dropouts both qualitatively and quantitatively. It then clusters cells by employing an ingenious label propagation strategy, obviating the requirement for a predetermined number of cell types. Validated on 20 publicly available scRNA-seq datasets, scQA consistently outperforms other salient tools. Furthermore, we confirm the effectiveness and potential biological significance of the identified key genes through both external and internal validation. In conclusion, scQA emerges as a valuable tool for investigating cell heterogeneity due to its distinctive fusion of qualitative and quantitative facets, along with bidirectional clustering capabilities. Furthermore, it can be seamlessly integrated into border scRNA-seq analyses. The source codes are publicly available at https://github.com/LD-Lyndee/scQA.

Development of Deep Learning-based Subcomponent Classification Algorithm for Automatic Structural Assembly Process in Ship Sub-assembly

Development of Deep Learning-based Subcomponent Classification Algorithm for Automatic Structural Assembly Process in Ship Sub-assembly

Mathematical modeling and simulations using software like MATLAB, COMSOL and Python

This study explores the application of MATLAB, COMSOL, and Python in mathematical modeling and simulation within precision engineering. These tools are analyzed for their strengths in handling various engineering challenges, from control systems to multiphysics simulations and custom algorithm development. The study also investigates the role of artificial intelligence (AI), in supporting mathematical modeling tasks by automating coding, providing concept explanations, and aiding model structuring. By comparing computational performance, accuracy, and usability, the research aims to identify the best-suited software for different simulation types, such as thermal-fluid dynamics and structural analysis. The findings underscore the significance of choosing appropriate software for optimizing computational resources, validating models, and achieving reliable, efficient simulations. This study contributes practical guidelines for bridging the gap between theoretical models and practical applications, enhancing productivity, and fostering innovation in precision engineering.

Development and validation of a claims-based algorithm to identify incidents and determine the progression phases of gastric cancer cases in Japan.

Although health insurance claims data can address questions that clinical trials cannot answer, the uncertainty of disease names and the absence of stage information hinder their use in gastric cancer (GC) research. This study aimed to develop and validate a claims-based algorithm to identify and determine the progression phases of incident GC cases in Japan. The gold standard for validation in this retrospective observational study was medical records of patients with incident GC who underwent specific treatments, defined by the claim codes associated with GC treatment. The algorithm was developed and refined using a cohort from two large tertiary care medical centers (April-September 2017 and April-September 2019) and subsequently validated using two independent cohorts: one from different periods (October 2017-March 2019 and October 2019-March 2021) and the other from a different institution (a community hospital). The algorithm identified incident cases based on a combination of the International Classification of Diseases, 10th Revision diagnosis codes for GC (C160-169), and claim codes for specific treatments, classifying them into endoscopic, surgical, and palliative groups. Positive predictive value (PPV), sensitivity of incident case identification, and diagnostic accuracy of progression phase determination were evaluated. The developed algorithm achieved PPVs of 90.0% (1119/1244) and 95.9% (94/98), sensitivities of 98.0% (1119/1142) and 98.9% (94/95) for incident case identification, with diagnostic accuracies of 94.1% (1053/1119) and 93.6% (88/94) for progression phase determination in the two validation cohorts, respectively. This validated claims-based algorithm could advance real-world GC research and assist in decision-making regarding GC treatment.

LX-mixers for QAOA: Optimal mixers restricted to subspaces and the stabilizer formalism

We present a novel formalism to both understand and construct mixers that preserve a given subspace. The method connects and utilizes the stabilizer formalism that is used in error correcting codes. This can be useful in the setting when the quantum approximate optimization algorithm (QAOA), a popular meta-heuristic for solving combinatorial optimization problems, is applied in the setting where the constraints of the problem lead to a feasible subspace that is large but easy to specify. The proposed method gives a systematic way to construct mixers that are resource efficient in the number of controlled not gates and can be understood as a generalization of the well-known X and XY mixers and a relaxation of the Grover mixer: Given a basis of any subspace, a resource efficient mixer can be constructed that preserves the subspace. The numerical examples provided show a dramatic reduction of CX gates when compared to previous results. We call our approach logical X-Mixer or logical X QAOA (LX-QAOA), since it can be understood as dividing the subspace into code spaces of stabilizers S and consecutively applying logical rotational X gates associated with these code spaces. Overall, we hope that this new perspective can lead to further insight into the development of quantum algorithms.

Automatic Denoising of Seismograms using Fingerprints: Algorithms, Properties, Limitations

This article describes the new technique for automatically preparing a noisy seismic record for further analysis using expert information systems. The technique is based on seismogram fingerprints, which, due to their concise but informative pattern, allow the development of a reliable algorithm for finding important noise characteristics. The search for the optimal cutoff frequency for a high-pass filter is especially important under conditions of partial overlap of the signal and noise spectra at a high intensity of the latter. It is precisely this difficult case that this study aims to address. The article analyzes the developed methodology on the example of several hundred registrations of regional earthquakes and explosions. The analysis showed that reliable results can be achieved in more than 90% of cases. In addition to all the problems and limitations of the method, which are an extension of its capabilities, are mentioned. Appendix to the article contains detailed description of the algorithm underlying the method.

Multi-target quantum compilation algorithm

Abstract Quantum compilation is the process of converting a target unitary operation into a trainable unitary represented by a quantum circuit. It has a wide range of applications, including gate optimization, quantum-assisted compiling, quantum state preparation, and quantum dynamic simulation. Traditional quantum compilation usually optimizes circuits for a single target. However, many quantum systems require simultaneous optimization of multiple targets, such as thermal state preparation, time-dependent dynamic simulation, and others. To address this, we develop a multi-target quantum compilation algorithm to improve the performance and flexibility of simulating multiple quantum systems. Our benchmarks and case studies demonstrate the effectiveness of the algorithm, highlighting the importance of multi-target optimization in advancing quantum computing. This work lays the groundwork for further development and evaluation of multi-target quantum compilation algorithms.

The development of algorithms for safe control of an autonomous ship

The paper presents the results of a research on the development of algorithms for ship’s safe ship trajectory calculation and automatic control of the ship along the determined trajectory. These methods are intended for use in the autonomous navigation system of an Unmanned Surface Vehicle (USV) or a Maritime Autonomous Surface Ship (MASS). The work presents the consecutive stages of a research that must be carried out in order to develop a system that will perform the task of multidimensional ship control in a port. The safe trajectory is calculated with the use of the Ant Colony Optimization (ACO) method. The ship motion control is based on the Linear Matrix Inequalities (LMI) method, implemented using the Mathworks and Yalmip libraries. The results of controlling the ship’s motion in accordance with the calculated trajectory are presented in the paper. With the use of the developed system, the ship can move autonomously based on the information from the DGPS system and a gyrocompass. The presented results concerned a computer simulation of maneuvers in the port of the Blue Lady ship from the Foundation for Shipping Safety and Environmental Protection.

Evaluating snow depth retrievals from Sentinel-1 volume scattering over NASA SnowEx sites

Abstract. Snow depth retrievals from spaceborne C-band synthetic aperture radar (SAR) backscatter have the potential to fill an important gap in the remote monitoring of seasonal snow. Sentinel-1 (S1) SAR data have been used previously in an empirical algorithm to generate snow depth products with near-global coverage, subweekly temporal resolution and spatial resolutions on the order of hundreds of meters to 1 km. However, there has been no published independent validation of this algorithm. In this work we develop the first open-source software package that implements this Sentinel-1 snow depth retrieval algorithm as described in the original papers and evaluate the snow depth retrievals against nine high-resolution lidar snow depth acquisitions collected during the winters of 2019–2020 and 2020–2021 at six study sites across the western United States as part of the NASA SnowEx mission. Across all sites, we find agreement between the Sentinel-1 snow depth retrievals and the lidar snow depth measurements to be considerably lower than requirements placed for remotely sensed observations of snow depth, with a mean root mean square error (RMSE) of 0.92 m and a mean Pearson correlation coefficient r of 0.46. Algorithm performance improves slightly in deeper snowpacks and at higher elevations. We further investigate the underlying Sentinel-1 data for a snow signal through an exploratory analysis of the cross- to co-backscatter ratio (σVH/σVV; i.e., cross ratio) relative to lidar snow depths. We find the cross ratio increases through the time series for snow depths over ∼ 1.5 m but that the cross ratio decreases for snow depths less than ∼ 1.5 m. We attribute poor algorithm performance to (a) the variable amount of apparent snow depth signal in the S1 cross ratio and (b) an algorithm structure that does not adequately convert S1 backscatter signal to snow depth. Our findings provide an open-source framework for future investigations, along with insight into the applicability of C-band SAR for snow depth retrievals and directions for future C-band snow depth retrieval algorithm development. C-band SAR has the potential to address gaps in radar monitoring of deep snowpacks; however, more research into retrieval algorithms is necessary to better understand the physical mechanisms and uncertainties of C-band volume-scattering-based retrievals.

A Review of Deep Learning Applications for Sustainable Water Resource Management

Deep learning (DL) techniques and algorithms have the capacity to significantly impact world economies, ecosystems, and communities. DL technologies have been utilized in the development and administration of urban structures. However, there exists a dearth of literature reviewing the present level of these applications and exploring potential directions in which DL can address water challenges. This study aims to review demand projections, leakage detection and localization, drainage defect and blockage, cyber security and wealth surveillance, wastewater recycling and management, water safety prediction, rainfall conversation, and irrigation regulation. The application of DL techniques is currently in its early stages. Most studies have adopted standard networks, simulated information, and experimental or prototype settings to evaluate the efficacy of DL approaches. However, there have been no reported instances of practical adoption. Compared to other reviewed problems, leakage detection is currently being implemented practically in daily operations and handling of water facilities. The major challenges for the practical deployment of DL in water management include algorithmic development, multi-agent platforms, virtual clones, data quality and availability, security, context-aware data analysis, and training efficiency. We validate our study by using several case studies that employ DL for water treatment. Prospective exploration and deployment of DL systems are anticipated to advance water systems toward increased cognition and flexibility. This research aims to encourage further research and development in utilizing DL for feasible water usage and digitalization of the global water sector.

Development and Application of Small Object Visual Recognition Algorithm in Assisting Safety Management of Tower Cranes

This study presents a novel video-based risk assessment and safety management technique aimed at mitigating the risk of falling objects during tower crane lifting operations. The conventional YOLOv5 algorithm is prone to issues of missed and false detections, particularly when identifying small objects. To address these limitations, the algorithm is enhanced by incorporating an additional small object detection layer, implementing an attention mechanism, and modifying the loss function. The enhanced YOLOv5s model achieved precision and recall rates of 96.00%, with average precision (AP) values of 96.42% at an IoU of 0.5 and 62.02% across the range of IoU values from 0.5 to 0.95. These improvements significantly enhance the model’s capability to accurately detect crane hooks and personnel. Upon identifying the hook within a video frame, its actual height is calculated using an interpolation function derived from the hook’s dimensions. This calculation allows for the precise demarcation of the danger zone by determining the potential impact area of falling objects. The worker’s risk level is assessed using a refined method based on the statistical analysis of past accidents. If the risk level surpasses a predetermined safety threshold, the worker’s detection box is emphasized and flagged as a caution on the monitoring display.

Setting the standard for machine learning in phase field prediction: a benchmark dataset and baseline metrics

Phase field models are an important mesoscale method that serves as a bridge between the atomic scale and the macroscale, used for modeling complex phenomena at the microstructure level. Machine learning can be employed to accelerate these simulations, enabling faster and more efficient analyses. However, the development of new machine learning algorithms depends on access to extensive datasets. This work introduces an accessible and well-documented dataset aimed at benchmarking new machine learning algorithms. We validate the dataset with a benchmark using U-Net regression, a widely used neural network architecture. Although direct comparisons are limited by the lack of existing benchmarks, our model’s error metrics are competitive with previous work and generalize across multiple domain sizes. This contribution provides a valuable resource for future efforts in machine learning model development for phase field simulations and demonstrates the potential of U-Net regression, highlighting the scope for novel method development in this area.

A Multivariable Prediction Model for Mild Cognitive Impairment and Dementia: Algorithm Development and Validation.

Mild cognitive impairment (MCI) poses significant challenges in early diagnosis and timely intervention. Underdiagnosis, coupled with the economic and social burden of dementia, necessitates more precise detection methods. Machine learning (ML) algorithms show promise in managing complex data for MCI and dementia prediction. This study assessed the predictive accuracy of ML models in identifying the onset of MCI and dementia using the Korean Longitudinal Study of Aging (KLoSA) dataset. This study used data from the KLoSA, a comprehensive biennial survey that tracks the demographic, health, and socioeconomic aspects of middle-aged and older Korean adults from 2018 to 2020. Among the 6171 initial households, 4975 eligible older adult participants aged 60 years or older were selected after excluding individuals based on age and missing data. The identification of MCI and dementia relied on self-reported diagnoses, with sociodemographic and health-related variables serving as key covariates. The dataset was categorized into training and test sets to predict MCI and dementia by using multiple models, including logistic regression, light gradient-boosting machine, XGBoost (extreme gradient boosting), CatBoost, random forest, gradient boosting, AdaBoost, support vector classifier, and k-nearest neighbors, and the training and test sets were used to evaluate predictive performance. The performance was assessed using the area under the receiver operating characteristic curve (AUC). Class imbalances were addressed via weights. Shapley additive explanation values were used to determine the contribution of each feature to the prediction rate. Among the 4975 participants, the best model for predicting MCI onset was random forest, with a median AUC of 0.6729 (IQR 0.3883-0.8152), followed by k-nearest neighbors with a median AUC of 0.5576 (IQR 0.4555-0.6761) and support vector classifier with a median AUC of 0.5067 (IQR 0.3755-0.6389). For dementia onset prediction, the best model was XGBoost, achieving a median AUC of 0.8185 (IQR 0.8085-0.8285), closely followed by light gradient-boosting machine with a median AUC of 0.8069 (IQR 0.7969-0.8169) and AdaBoost with a median AUC of 0.8007 (IQR 0.7907-0.8107). The Shapley values highlighted pain in everyday life, being widowed, living alone, exercising, and living with a partner as the strongest predictors of MCI. For dementia, the most predictive features were other contributing factors, education at the high school level, education at the middle school level, exercising, and monthly social engagement. ML algorithms, especially XGBoost, exhibited the potential for predicting MCI onset using KLoSA data. However, no model has demonstrated robust accuracy in predicting MCI and dementia. Sociodemographic and health-related factors are crucial for initiating cognitive conditions, emphasizing the need for multifaceted predictive models for early identification and intervention. These findings underscore the potential and limitations of ML in predicting cognitive impairment in community-dwelling older adults.

Artificial intelligence in respiratory pandemics-ready for disease X? A scoping review.

This study aims to identify repeated previous shortcomings in medical imaging data collection, curation, and AI-based analysis during the early phase of respiratory pandemics. Based on the results, it seeks to highlight essential steps for improving future pandemic preparedness. We searched PubMed/MEDLINE, Scopus, and Cochrane Reviews for articles published from January 1, 2000, to December 31, 2021, using the terms "imaging" or "radiology" or "radiography" or "CT" or "x-ray" combined with "SARS," "MERS," "H1N1," or "COVID-19." WHO and CDC Databases were searched for case definitions. Over the last 20 years, the world faced several international health emergencies caused by respiratory diseases such as SARS, MERS, H1N1, and COVID-19. During the same period, major technological advances enabled the analysis of vast amounts of imaging data and the continual development of artificial intelligence algorithms to support radiological diagnosis and prognosis. Timely availability of data proved critical, but so far, data collection attempts were initialized only as individual responses to each outbreak, leading to long delays and hampering unified guidelines and data-driven technology to support the management of pandemic outbreaks. Our findings highlight the multifaceted role of imaging in the early stages of SARS, MERS, H1N1, and COVID-19, and outline possible actions for advancing future pandemic preparedness. Advancing international cooperation and action on these topics is essential to create a functional, effective, and rapid counteraction system to future respiratory pandemics exploiting state of the art imaging and artificial intelligence. Question What has been the role of radiological data for diagnosis and prognosis in early respiratory pandemics and what challenges were present? Findings International cooperation is essential to developing an effective rapid response system for future respiratory pandemics using advanced imaging and artificial intelligence. Clinical relevance Strengthening global collaboration and leveraging cutting-edge imaging and artificial intelligence are crucial for developing rapid and effective response systems. This approach is essential for improving patient outcomes and managing future respiratory pandemics more effectively.

Using human factors methods to mitigate bias in artificial intelligence-based clinical decision support.

To highlight the often overlooked role of user interface (UI) design in mitigating bias in artificial intelligence (AI)-based clinical decision support (CDS). This perspective paper discusses the interdependency between AI-based algorithm development and UI design and proposes strategies for increasing the safety and efficacy of CDS. The role of design in biasing user behavior is well documented in behavioral economics and other disciplines. We offer an example of how UI designs play a role in how bias manifests in our machine learning-based CDS development. Much discussion on bias in AI revolves around data quality and algorithm design; less attention is given to how UI design can exacerbate or mitigate limitations of AI-based applications. This work highlights important considerations including the role of UI design in reinforcing/mitigating bias, human factors methods for identifying issues before an application is released, and risk communication strategies.