Current Algorithms Research Articles

Does Adding Single-Nucleotide Polymorphisms to Risk Algorithms Improve Cardiovascular Disease Risk Prediction in Rheumatoid Arthritis? An Internal and External Validation of a Clinical Risk Score.

Current risk algorithms do not accurately predict cardiovascular disease (CVD) risk in rheumatoid arthritis (RA). An area of interest is that of single-nucleotide polymorphisms (SNPs), of which several have been associated with CVD in the general population. We investigated whether these SNPs are associated with CVD in RA and whether SNPs could improve CVD risk prediction in RA. Sixty SNPs were genotyped in 353 patients with RA. Logistic and Cox regression analyses were performed to identify SNPs that were associated with CVD (n = 99). A prediction model with clinical variables was made. SNPs were added to investigate the additional predictive value. Both models were internally validated. External validation was done in a separate cohort (n = 297). rs3184504, rs4773144, rs12190287, and rs445925 were significantly associated with new CVD. The clinical prediction model consisted of age, sex, body mass index, systolic blood pressure, high-density lipoprotein cholesterol (HDLc), and creatinine, with an area under the curve (AUC) of 0.74 (P = 0.03). Internal validation resulted in an AUC of 0.76 (P < 0.01). A new model was made including SNPs and resulted in a model with rs17011666 and rs801426, age, total cholesterol, and HDLc, which performed slightly better with an AUC of 0.77 (P < 0.01). External validation resulted in a good fit for the clinical model, but a poor fit for the SNP model. Several SNPs were associated with CVD in RA. Risk prediction slightly improved after adding SNPs to the models, but the clinical relevance is debatable. However, larger studies are needed to determine more accurately the additional value of these SNPs to CVD risk prediction algorithms.

Quantum Lego Expansion Pack: Enumerators from Tensor Networks

We provide the first tensor-network method for computing quantum weight enumerator polynomials in the most general form. If a quantum code has a known tensor-network construction of its encoding map, our method is far more efficient, and in some cases exponentially faster than the existing approach. As a corollary, it produces decoders and an algorithm that computes the code distance. For non-(Pauli)-stabilizer codes, this constitutes the current best algorithm for computing the code distance. For degenerate stabilizer codes, it can be substantially faster compared to the current methods. We also introduce novel weight enumerators and their applications. In particular, we show that these enumerators can be used to compute logical error rates exactly and thus construct (optimal) decoders for any independent and identically distributed single qubit or qudit error channels. The enumerators also provide a more efficient method for computing nonstabilizerness in quantum many-body states. As the power for these speedups rely on a quantum Lego decomposition of quantum codes, we further provide systematic methods for decomposing quantum codes and graph states into a modular construction for which our technique applies. As a proof of principle, we perform exact analyses of the deformed surface codes, the holographic pentagon code, and the two-dimensional Bacon-Shor code under (biased) Pauli noise and limited instances of coherent error at sizes that are inaccessible by brute force. Published by the American Physical Society 2024

The painful knee in children

Knee pain is one of the most common reasons for medical consultation for musculoskeletal problems in the paediatric population. The aetiology is of very variable origin and necessitates aprecise assessment. In addition to athorough anamnesis, clinical and imaging examination methods, as well as laboratory diagnostics are of utmost importance to establish aproper diagnosis and an adequate treatment regimen. This chapter summarises the current diagnostic algorithm for dealing with knee pain in children and adolescents.

Intelligent Decision-Making Algorithm for UAV Swarm Confrontation Jamming: An M2AC-Based Approach

Unmanned aerial vehicle (UAV) swarm confrontation jamming offers a cost-effective and long-range countermeasure against hostile swarms. Intelligent decision-making is a key factor in ensuring its effectiveness. In response to the low-timeliness problem caused by linear programming in current algorithms, this paper proposes an intelligent decision-making algorithm for UAV swarm confrontation jamming based on the multi-agent actor–critic (M2AC) model. First, based on Markov games, an intelligent mathematical decision-making model is constructed to transform the confrontation jamming scenario into a symbolized mathematical problem. Second, the indicator function under this learning paradigm is designed by combining the actor–critic algorithm with Markov games. Finally, by employing a reinforcement learning algorithm with multithreaded parallel training–contrastive execution for solving the model, a Markov perfect equilibrium solution is obtained. The experimental results indicate that the algorithm based on M2AC can achieve faster training and decision-making speeds, while effectively obtaining a Markov perfect equilibrium solution. The training time is reduced to less than 50% compared to the baseline algorithm, with decision times maintained below 0.05 s across all simulation conditions. This helps alleviate the low-timeliness problem of UAV swarm confrontation jamming intelligent decision-making algorithms under highly dynamic real-time conditions, leading to more effective and efficient UAV swarm operations in various jamming and electronic warfare scenarios.

A deep reinforcement learning-based intelligent QoS optimization algorithm for efficient routing in vehicular networks

With the rapid development of Telematics, Vehicle Self-Organizing Networks (VANETs) play an increasingly critical role in Intelligent Transportation Systems (ITS). Especially in the environment without roadside assistance units (RSUs), how to effectively manage inter-vehicle communication and improve the stability and communication efficiency of the network has become a hot topic of current research. In this paper, a Deep Reinforcement Learning-based Intelligent QoS-optimized efficient routing algorithm for vehicular networks (DRLIQ) is proposed for VANETs with/without RSU environments, and routing methods are proposed respectively. Among them, in RSU-free environment, the DRLIQ algorithm utilizes the powerful processing capability of deep reinforcement learning to intelligently select the optimal data transmission path by dynamically learning and adapting to the changes in the vehicular network, thus effectively reducing communication interruptions and delays, and improving the accuracy of data transmission. The performance of the DRLIQ algorithm under different vehicle densities is evaluated in simulation experiments and compared with current popular algorithms. The experimental results show that the DRLIQ algorithm outperforms the comparison algorithms in reducing the number of communication interruptions, BER and network delay, especially in vehicle-dense environments. In addition, the DRLIQ algorithm shows higher adaptability and stability in coping with network topology changes and vehicle dynamics.

Head-to-head studies on connective tissue diseases and vasculitides

Head-to-head (H2H) studies enable the direct comparison of several alternative therapeutic approaches and thus provide the evidence-based foundation for the relative position of one treatment as compared to others for a specific indication. These trials constitute an important addition to placebo-controlled clinical trials. Among the controlled clinical trials not performed by the pharmaceutical industry, there are arelevant number of H2H trials for connective tissue diseases (CTDs) and vasculitides, particularly for systemic lupus erythematosus (SLE), systemic sclerosis (SSc), and anti-neutrophil cytoplasmic antibody (ANCA)-associated vasculitis (AAV). This article encompasses a review of the H2H trials for CTDs and vasculitides and discusses their relevance for current treatment algorithms. For SLE the H2H trials were predominantly performed for the treatment of lupus nephritis, demonstrating the impact of low-dose cyclophosphamide and mycophenolate as well as azathioprine for maintenance therapy. In recent H2H trials rituximab could be established as induction and maintenance therapy for AAV, which has now been incorporated into current treatment guidelines. Further comparative trials will be necessary in order to select the most effective and safest treatment for every patient, in the sense of personalized medicine.

CLOCI: unveiling cryptic fungal gene clusters with generalized detection.

Gene clusters are genomic loci that contain multiple genes that are functionally and genetically linked. Gene clusters collectively encode diverse functions, including small molecule biosynthesis, nutrient assimilation, metabolite degradation, and production of proteins essential for growth and development. Identifying gene clusters is a powerful tool for small molecule discovery and provides insight into the ecology and evolution of organisms. Current detection algorithms focus on canonical 'core' biosynthetic functions many gene clusters encode, while overlooking uncommon or unknown cluster classes. These overlooked clusters are a potential source of novel natural products and comprise an untold portion of overall gene cluster repertoires. Unbiased, function-agnostic detection algorithms therefore provide an opportunity to reveal novel classes of gene clusters and more precisely define genome organization. We present CLOCI (Co-occurrence Locus and Orthologous Cluster Identifier), an algorithm that identifies gene clusters using multiple proxies of selection for coordinated gene evolution. Our approach generalizes gene cluster detection and gene cluster family circumscription, improves detection of multiple known functional classes, and unveils non-canonical gene clusters. CLOCI is suitable for genome-enabled small molecule mining, and presents an easily tunable approach for delineating gene cluster families and homologous loci.

An efficient algorithm for data transmission certainty in IIoT sensing network: A priority-based approach.

This paper proposes a novel cache replacement technique based on the notion of combining periodic popularity prediction with size caching. The popularity, size, and time updates characteristics are used to calculate the value of each cache item. When it comes to content replacement, the information with the least value is first eliminated. Simulation results show that the proposed method outperforms the current algorithms in terms of cache hit rate and delay. The hit rate of the proposed scheme is 15.3% higher than GDS, 17.3% higher than MPC, 20.1% higher than LRU, 22.3% higher than FIFO, and 24.8% higher than LFU when 350 different categories of information are present. In real-world industrial applications such as including supply chain management, smart manufacturing, automation energy optimization, intelligent logistics transportation, and e-healthcare applications, it offers a foundation for the selection of caching algorithms.

Multimodal multi-objective optimization algorithm based on hierarchical environment selection strategy.

The article proposes an optimization algorithm using a hierarchical environment selection strategyto solve the deficiencies of current multimodal multi-objective optimization algorithms in obtaining the completeness and convergence of Pareto optimal Sets (PSs). Firstly, the algorithm in this article is framed by a differential evolutionary algorithm (DE) and uses a special crowding distance to design a neighborhood-based individual variation strategy, which also ensures the diversity, and then special crowding distance is used to help populations with non-dominated sorting. In the stage of environmental selection, a strategy of hierarchical selection of individuals was designed, which selects sorted non-dominant ranked individual layer by layer according to the ratio, which allows potential individuals tobe explored. Finally, in the stage of evolution of individuals, the convergence and diversity of populations were investigated, anddifferent mutation strategies were selectedaccording to the characteristics of individuals. DE reproduction strategies are used for iteration, preventing individuals from avoiding premature convergence and ensuring the algorithm's searchability. These strategies help the algorithm to obtain more diverse and uniformly distributed PSs and Pareto Front (PF). The algorithm of this article compares with several other excellent algorithms on 13 test problems, and the test results show that all the algorithms of this article exhibit superior performance.

Addition of Social Determinants of Health to Coronary Heart Disease Risk Prediction: The Multi-Ethnic Study of Atherosclerosis.

Social determinants of health (SDoH) are associated with cardiovascular risk factors and outcomes; however, they are absent from risk prediction models. We aimed to assess if the addition of SDoH improves the predictive ability of the MESA (Multi-Ethnic Study of Atherosclerosis) Risk Score. This was a community-based prospective population cohort study that enrolled 6286 men and women, ages 45-84 years, who were free of clinical coronary heart disease (CHD) at baseline. Data from 10-year follow-up were examined for CHD events, defined as myocardial infarction, fatal CHD, resuscitated cardiac arrest, and revascularization in cases of anginal symptoms. Participants included 53% women with average age of 62 years. When adjusting for traditional cardiovascular risk factors, SDoH, and coronary artery calcium, economic strain, specifically low family income, was associated with a greater risk of CHD events (hazard ratio [HR], 1.42 [95% CI, 1.17-1.71], P value<0.001). Area under the curve of risk prediction with SDoH was 0.822, compared with 0.816 without SDoH. The calibration slope was 0.860 with SDoH and 0.878 in the original model. Significant associations were found between economic/financial SDoH and CHD risk factors and outcomes. Incorporation of SDoH into the MESA Risk Score did not improve predictive ability of the model. Our findings do not support the incorporation of SDoH into current risk prediction algorithms.

An Analysis of Traditional Methods and Deep Learning Methods in SSVEP-Based BCI: A Survey

The brain–computer interface (BCI) is a direct communication channel between humans and machines that relies on the central nervous system. Neuroelectric signals are collected by placing electrodes, and after feature sampling and classification, they are converted into control signals to control external mechanical devices. BCIs based on steady-state visual evoked potential (SSVEP) have the advantages of high classification accuracy, fast information conduction rate, and relatively strong anti-interference ability, so they have been widely noticed and discussed. From k-nearest neighbor (KNN), multilayer perceptron (MLP), and support vector machine (SVM) classification algorithms to the current deep learning classification algorithms based on neural networks, a wide variety of discussions and analyses have been conducted by numerous researchers. This article summarizes more than 60 SSVEP- and BCI-related articles published between 2015 and 2023, and provides an in-depth research and analysis of SSVEP-BCI. The survey in this article can save a lot of time for scholars in understanding the progress of SSVEP-BCI research and deep learning, and it is an important guide for designing and selecting SSVEP-BCI classification algorithms.

The Performance of the Current Algorithm of HIV Diagnosis.

We aimed to evaluate the performance of the current algorithm the HIV diagnosis that has been performed for four years. Results of HIV Ag/Ab tests, anti-HIV 1/2 confirmatory tests, HIV-1 RNA tests and the time for official results to be approved were evaluated. The relationship of HIV Ag/Ab tests, anti-HIV 1/2 confirmation tests and HIV-1 RNA PCR tests, their result times and suitability to the algorithm were examined at Izmir Dokuz Eylül University between May 2017 and June 2021. HIV Ag/Ab ELISA was reactive repetitively in 165/54628 (0.30%) serum samples. Anti-HIV 1/2 confirmation test was reactive in 54.42% (80/147) of samples. The most common pattern (18.2%) in the confirmation tests was the positivity of the antibodies against gp160 - gp41 envelope glycoproteins together. The mean reporting time of the confirmation test result was 3h 50 min after the ELISA test. The mean reporting time of the HIV-1 RNA PCR was 12.79 d (±10.22) after the ELISA test and 12.63 (± 10.12) day after the confirmation test. In ROC analysis, the estimated rate of the ELISA test for the confirmation test was highest when S/CO was >13.16 (sensitivity: 97.59 %, specificity: 97.59%). The confirmation test in the current algorithm enabled the rapid test results, early diagnosis of HIV and early antiretroviral therapy. To use the new algorithm effectively, decentralization of the validation tests would be appropriate.

Task Offloading in Real-Time Distributed Energy Power Systems

The distributed energy power system needs to provide sufficient and flexible computing power on demand to meet the increasing digitization and intelligence requirements of the smart grid. However, the current distribution of the computing power and loads in the energy system is unbalanced, with data center loads continuously increasing, while there is a large amount of idle computing power at the edge. Meanwhile, there are a large number of real-time computing tasks in the distributed energy power system, which have strict requirements on execution deadlines and require reasonable scheduling of multi-level heterogeneous computing power to meet real-time computing demands. Based on the aforementioned background and issues, this paper studies the real-time service scheduling problem in a multi-level heterogeneous computing network of distributed energy power systems. Specifically, we consider the divisibility of tasks in the model. This paper presents a hierarchical real-time task-scheduling framework specifically designed for distributed energy power systems. The framework utilizes an orchestrating agent (OA) as the execution environment for the scheduling module. Building on this, we propose a hierarchical selection algorithm for choosing the appropriate network layer for real-time tasks. Further, we develop two scheduling algorithms based on greedy strategy and genetic algorithm, respectively, to effectively schedule tasks. Experiments show that the proposed algorithms have a superior success rate in scheduling compared to other current algorithms.

Eigen-entropy based time series signatures to support multivariate time series classification

Most current algorithms for multivariate time series classification tend to overlook the correlations between time series of different variables. In this research, we propose a framework that leverages Eigen-entropy along with a cumulative moving window to derive time series signatures to support the classification task. These signatures are enumerations of correlations among different time series considering the temporal nature of the dataset. To manage dataset’s dynamic nature, we employ preprocessing with dense multi scale entropy. Consequently, the proposed framework, Eigen-entropy-based Time Series Signatures, captures correlations among multivariate time series without losing its temporal and dynamic aspects. The efficacy of our algorithm is assessed using six binary datasets sourced from the University of East Anglia, in addition to a publicly available gait dataset and an institutional sepsis dataset from the Mayo Clinic. We use recall as the evaluation metric to compare our approach against baseline algorithms, including dependent dynamic time warping with 1 nearest neighbor and multivariate multi-scale permutation entropy. Our method demonstrates superior performance in terms of recall for seven out of the eight datasets.

Research on identification of nucleus-shaped anomaly regions in space electric field

The presence of nucleus-shaped anomalous regions in the power spectrum image of the electric field VLF frequency band has been discovered in previous studies. To detect and analyze these nucleus-shaped abnormal areas and improve the recognition rate of nucleus-shaped abnormal areas, this paper proposes a new nucleus-shaped abnormal area detection model ODM_Unet (Omni-dimensional Dynamic Mobile U-net) based on U-net network. Firstly, the power spectrum image data used for training is created and labeled to form a dataset of nucleus-shaped anomalous regions; Secondly, the ODConv (Omni-dimensional Dynamic Convolution) module with embedded attention mechanism was introduced to improve the encoder, extracting nucleus-shaped anomaly region information from four dimensions and focusing on the features of different input data; An SDI (Semantics and Detail Infusion) module is introduced between the encoder and decoder to solve the problem of detail semantic loss in high-level images caused by the reduction of downsampling image size; In the decoder stage, the SCSE (Spatial and Channel Squeeze-and-Excitation) attention module is introduced to more finely adjust the feature maps output through the SDI module. The experimental results show that compared with the current popular semantic segmentation algorithms, the ODM_Unet model has the best detection performance in nucleus-shaped anomaly areas. Using this model to detect data from November 2021 to October 2022, it was found that the frequency of nucleus-shaped anomaly areas is mostly between 0 and 12.5KHz, with geographic spatial distribution ranging from 40° to 70° south and north latitudes, and magnetic latitude spatial distribution ranging from 58° to 80° south and north latitudes. This method has reference significance for detecting other types of spatial electromagnetic field disturbances.

Research on Autonomous Manoeuvre Decision Making in Within-Visual-Range Aerial Two-Player Zero-Sum Games Based on Deep Reinforcement Learning

In recent years, with the accelerated development of technology towards automation and intelligence, autonomous decision-making capabilities in unmanned systems are poised to play a crucial role in contemporary aerial two-player zero-sum games (TZSGs). Deep reinforcement learning (DRL) methods enable agents to make autonomous manoeuvring decisions. This paper focuses on current mainstream DRL algorithms based on fundamental tactical manoeuvres, selecting a typical aerial TZSG scenario—within visual range (WVR) combat. We model the key elements influencing the game using a Markov decision process (MDP) and demonstrate the mathematical foundation for implementing DRL. Leveraging high-fidelity simulation software (Warsim v1.0), we design a prototypical close-range aerial combat scenario. Utilizing this environment, we train mainstream DRL algorithms and analyse the training outcomes. The effectiveness of these algorithms in enabling agents to manoeuvre in aerial TZSG autonomously is summarised, providing a foundational basis for further research.

Contemporary Treatment of Pulmonary Arterial Hypertension: A U.S. Perspective.

Pulmonary arterial hypertension (PAH) is a complex fatal condition that requires aggressive treatment with close monitoring. Significant progress has been made over the last three decades in the treatment of PAH, but, despite this progress, survival has remained unacceptably low. In the quest to improve survival, therapeutic interventions play a central role. In the last few years, there have been remarkable attempts to identify novel treatments. Finally, we have had a breakthrough with the discovery of the fourth treatment pathway in PAH. Activin signaling inhibition distinguishes itself as a potential antiproliferative intervention as opposed to the traditional therapies, which mediate their effect primarily by vasodilatation. With this novel treatment pathway, we stand at an important milestone with an exciting future ahead and the natural question of when to use an activin signaling inhibitor for the treatment of PAH. In this state-of-the-art review, we focus on the placement of this novel agent in the PAH treatment paradigm, based on the available evidence, with special focus on the U.S. patient population. This review also provides an expert opinion of the current treatment algorithm in important subgroups of patients with comorbidities from the U.S. perspective.

Colour difference detection algorithm for warp‐knitted fabric based on image colour appearance model

AbstractDetecting colour differences in warp‐knitting fabric is essential to ensure its quality. Machine vision algorithms are commonly used for automatic colour difference detection. However, the current algorithm directly extracts RGB (red, green, blue) colour stimulus values from the image without considering the image's colour attributes, leading to a significant error in colour difference detection. To solve this issue, a colour appearance model is introduced into the field of colour difference detection for warp‐knitted fabric. Initially, a colour appearance model based on CAM16 is established, and the critical parameters are calculated to obtain the colour appearance attribute of the fabric. The colour difference calculation formula is then constructed in the uniform colour space of CAM16‐UCS. A template selection algorithm based on principal component analysis was designed to select warp‐knitted cloth images with standard colours. Subsequently, a cloth colour difference detection algorithm was developed using the cloth colour profile model. The performance of the colour difference formula based on the colour profile model was evaluated using the PF/3 method. To compare the CIELab, CMC(2:1), and CIEDE2000 colour difference formulas, standardised residual sum of squares was used. The results indicated that the colour difference formula based on the colour‐appearance model is about 5.32% different from the visual colour difference perceived by the human eye. However, it can perform as well as the CMC(2:1) colour difference formula, which is widely used in the textile industry.

Extracting fetal heart signals from Doppler using semi-supervised convolutional neural networks.

Cardiotocography (CTG) measurements are critical for assessing fetal wellbeing during monitoring, and accurate assessment requires well-traceable CTG signals. The current FHR calculation algorithm, based on autocorrelation to Doppler ultrasound (DUS) signals, often results in periods of loss owing to its inability to differentiate signals. We hypothesized that classifying DUS signals by type could be a solution and proposed that an artificial intelligence (AI)-based approach could be used for classification. However, limited studies have incorporated the use of AI for DUS signals because of the limited data availability. Therefore, this study focused on evaluating the effectiveness of semi-supervised learning in enhancing classification accuracy, even in limited datasets, for DUS signals. Data comprising fetal heartbeat, artifacts, and two other categories were created from non-stress tests and labor DUS signals. With labeled and unlabeled data totaling 9,600 and 48,000 data points, respectively, the semi-supervised learning model consistently outperformed the supervised learning model, achieving an average classification accuracy of 80.9%. The preliminary findings indicate that applying semi-supervised learning to the development of AI models using DUS signals can achieve high generalization accuracy and reduce the effort. This approach may enhance the quality of fetal monitoring.

A single-phase grid-tied extendable-level inverter for renewable energy applications

The extraction of power from green energy sources (GES) has rapidly popularized owing to increasing energy demands, depletion of fossil fuels and growing concerns about climatic changes. Grid-tied multilevel inverters are critical components in the integration of green energy sources into the power grid and offer numerous benefits over standard two-level inverters. These benefits include superior power quality, diminished harmonic distortion, and the potential to utilize high power levels efficiently. A single-phase Grid-Tied Extendable-Level Inverter (GTELI) for green energy applications is demonstrated in this paper. The GTELI offers superior power quality and distortion-free output relative to existing grid-tied multilevel inverters and hence is an excellent choice for extracting power from sustainable energy sources. To ensure efficient utilization, the extracted power from GTELI is to be integrated into the supply grid. A new current control algorithm for the GTELI is also proposed in this paper. Using this current control technique a grid control technique is established to modulate the power flow to the grid. The proposed GTELI achieved sinusoidal grid voltage and injected current with minimised harmonic distortions of 1.6% and 2.7% respectively, which comply well with the tolerable range of the IEEE 519 standard.