Network Algorithm Research Articles

Machine learning-assisted assessment of municipal solid waste thermal treatment efficacy via rapid image recognition and visual analysis.

Decentralized thermal treatment is a common method for municipal solid waste (MSW) disposal in rural areas. However, evaluating the effect of incineration has always been challenging owing to the difficult and time-consuming measurements involved. Herein, this study presented a rapid image recognition method for assessing the effects of thermal treatment on MSW using a neural network algorithm and a BAEVA 1.0 software based on the relation between the ignition loss of the incinerated bottom ash and its color properties. Through Pearson correlation analysis, the results demonstrated a strong correlation (R2>0.80) between the ignition loss and the R, G, and B color values. To enhance evaluation accuracy, we introduced the backpropagation artificial neural network (BPANN) algorithm, which exhibited an average evaluation error of only 3.21 in crossvalidation, 27.9% lower than that of the linear regression model. Building upon the BPANN, we developed BAEVA 1.0 as a software tool for thermal treatment effect evaluation. This tool exhibited advantages in functionality, convenience, and accuracy compared to existing methods. Overall, this research provides an important rapid assessment approach for evaluating the effects of MSW incineration when measurement conditions are unavailable.

Drug toxicity prediction model based on enhanced graph neural network.

Prediction of drug toxicity remains a significant challenge and an essential process in drug discovery. Traditional machine learning algorithms struggle to capture the full scope of molecular structure features, limiting their effectiveness in toxicity prediction. Graph Neural Network offers a promising solution by effectively extracting drug features from their molecular graphs. However, existing graph learning algorithms fail to account for the interaction features between graph nodes and the indirect edges connecting them. This paper proposes an enhanced graph Neural Network algorithm that employs multi-view features for each node, capturing the feature interactions between each node and its neighbors. Additionally, the adjacency matrix is preprocessed to handle indirect edge interactions. A pooling technique is then applied to aggregate node features, followed by normalization and an activation layer. To further enhance the proposed algorithm, multi-scale attention is applied to learn graph features at different scales, utilizing weights to understand intricate relationships among node feature vectors. The proposed algorithm is evaluated using eight toxicity datasets, covering binary classification, multi-task multi-class, and regression tasks. For binary classification, the Tox21, AMES, Skin reaction, Carcinogens, and DILI datasets are tested. For multi-task multi-class, the ToxCast dataset is applied, and for regression, the LD50 and hREG datasets are tested. The proposed algorithm is compared with four well-known algorithms including Graph Convolution Network, Graph Attention Network, Graph Isomorphism Network, Enhanced Graph Isomorphism Network, and Graph Total Variation. For the classification task, the proposed algorithm achieves ROC-AUC scores of 0.752 for Tox21, 0.775 for AMES, 0.707 for Skin reaction, 0.845 for Carcinogens, 0.92 for DILI, and 0.691 for the ToxCast dataset. For the regression task, the algorithm attains mean square errors of 0.896 for the LD50 dataset and 0.766 for the hREG dataset. These results demonstrate an improvement over the compared algorithms across all evaluated datasets.

Multi-modal multi-sensor feature fusion spiking neural network algorithm for early bearing weak fault diagnosis

Multi-modal multi-sensor feature fusion spiking neural network algorithm for early bearing weak fault diagnosis

Online monitoring of Haematococcus lacustris cell cycle using machine and deep learning techniques.

Optimal control and process optimization of astaxanthin production from Haematococcuslacustris is directly linked to its complex cell cycle ranging from vegetative green cells to astaxanthin-rich cysts. This study developed an automated online monitoring system classifying four different cell cycle stages using a scanning microscope. Decision-tree based machine learning and deep learning convolutional neural network algorithms were developed, validated, and evaluated. SHapley Additive exPlanations was used to examine the most important system requirements for accurate image classification. The models achieved accuracies on unseen data of 92.4 and 90.9%, respectively. Furthermore, both models were applied to a photobioreactor culturing H.lacustris, effectively monitoring the transition from a green culture in the exponential growth phase to a stationary red culture. Therefore, online image analysis using artificial intelligence models has great potential for process optimization and as a data-driven decision support tool during microalgae cultivation.

A multi-physical coupling NURBS-based isogeometric analysis for nonlinear electrodynamics of three-directional poroelastic functionally graded circular nanoplate: Introducing deep neural network algorithm for nonlinear electrodynamics problems

For the right design of nanosystems with integrated piezoelectric transducers and materials, it is crucial to understand the electro-mechanical coupling factor. Considering this, this work determines the influence of placement and dimension of piezoelectric patch on the vibrations of circular sandwich sector nanoplate coupled with an electrically layer. The core of the current nanostructure is made of three-directional poroelastic functionally graded material (3D-PFGM). The quasi-3D sinusoidal shear deformation theory (Q-3DSSDT) considering the effect of thickness stretching, compatibility conditions, and Hamilton's principle are coupled with each other regarding discovering the general motion equations and boundary domains related to the circular sector sandwich nanoplate. For considering the size effect, nonlocal quasi-3D sinusoidal strain gradient theory (NQSSGT) by employing both hardening and softening effects is considered. The NURBS-based isogeometric analysis is applied to answer the partial differential coupled equations (PDCE). In addition, the finite element method is implemented for more verification and presenting important outcomes. The novelties of this work are considering the effects of NQSSGT, placement, and dimension of the piezoelectric patch in addition to considering 3D-PFGM of the circular sector sandwich nanoplate. After obtaining the datasets of the mathematics simulation, a deep neural network algorithm is presented to test, train, and validate the presented nonlinear electrodynamics response of the current circular sector sandwich nanoplate. The results of the current nanostructure can be used in related industries of nano-robots and nano-electronic devices for future works. The findings highlight the significant influence of material gradation, poroelastic effects, and piezoelectric coupling on the vibration characteristics, offering valuable insights for the design and optimization of smart materials and structures in microelectromechanical systems (MEMS), sensors, and actuators. This research paves the way for future innovations in the field of advanced functional materials and their applications in high-precision technologies.

Research on Resource Allocation Algorithm for Non-Orthogonal Multiple Access Backscatter-Based Cognitive Radio Networks

Research on Resource Allocation Algorithm for Non-Orthogonal Multiple Access Backscatter-Based Cognitive Radio Networks

Regeneration sites selection in survivable translucent elastic optical networks

Quality of transmission of optical signals, which are propagated beyond their transparent reach, is ensured by re-amplification, re-timing and re-shaping (3R-regeneration) at certain node(s) designated as regeneration site(s). Inclusion of regeneration sites increases capital expenditure (CAPEX) of Translucent Elastic Optical Networks (T-EONs) and so Regeneration Sites Selection (RSS) is an important problem to address. Survivability of T-EONs is a vital issue as a fiber-cut (link failure) can cause disruption of huge traffic. In this paper, we address the RSS problem for survivable T-EONs (ST-EONs). We formulate RSS as an offline problem for ST-EON design. We propose an Integer Linear Program (ILP) formulation for small networks and also heuristic algorithms for large networks. Results from simulation experiments performed on three network topologies suggest that the proposed algorithms can minimize CAPEX for ST-EON design.

Investigation of the Effect of Alloying Elements on the Density of Titanium-Based Biomedical Materials Using Explainable Artificial Intelligence

Titanium alloys are widely preferred in the healthcare sector as biocompatible materials due to their superior properties such as low density and exceptional mechanical strength. Their low density provides lightweight solutions, and their density is closer to that of human bone compared to other metallic alloys with similar strength. This similarity facilitates a balanced load distribution between the bone and the implant, enhancing biomechanical compatibility. This study investigates the effects of alloying elements on the density of titanium-based biomedical materials using a computational materials science approach. A total of 72 different compositions of Ti-Al-V alloys were modeled using JMatPro software, and their densities were simulated at room temperature (25°C). The simulation produced a comprehensive dataset, which was utilized to train an explainable artificial intelligence (XAI) model. Advanced interpretability techniques, including SHAP (SHapley Additive exPlanations), LIME (Local Interpretable Model-agnostic Explanations), and Partial Dependence Plots (PDP), were employed to elucidate the influence of each alloying element on the density. The dataset was analyzed using an XAI-based regression model implemented with the Artificial Neural Network (ANN) algorithm. The interpretability graphs provided insights into the individual contributions of the alloying elements, revealing their positive or negative effects on the density. The findings offer a deeper understanding of the role of alloying elements in optimizing the performance of titanium-based biomedical materials, particularly in achieving lightweight designs. This study highlights the potential of integrating computational material modeling with explainable AI to advance the design and development of high-performance lightweight materials for biomedical applications.

A real-world pharmacovigilance analysis for Demethylation drug: findings from the FDA adverse event reporting database.

The real-world safety profiles of the demethylating agents Azacitidine and Decitabine remain inadequately characterized despite their widespread clinical use. Both drugs are extensively employed for the treatment of hematologic malignancies such as myelodysplastic syndromes (MDS) and acute myeloid leukemia (AML). This study aims to evaluate their adverse event (AE) profiles by leveraging data from the U.S. Food and Drug Administration (FDA) Adverse Event Reporting System (FAERS) database. All adverse drug event (ADE) data related to Azacitidine and Decitabine were collected from the FAERS database from its inception through the second quarter of 2024 (Q2). After standardizing the data, four disproportionality methods were applied to evaluate the association between Azacitidine, Decitabine, and ADEs. The Weibull shape parameter (WSP) was used to analyze the time-to-onset curves. Among the 15,538 ADEs where Azacitidine was the primary suspect drug, a total of 439 preferred terms (PTs) and two system organ classes (SOCs) showed significant disproportionality across all four algorithms. These SOCs included INFECTIONS AND INFESTATIONS (n=7328, ROR 3.78) and BLOOD AND LYMPHATIC SYSTEM DISORDERS (n=5613, ROR 8.92). Compared with the Azacitidine label, 52 previously unreported ADEs were identified at the PT level. Among the 3,064 ADEs where Decitabine was the primary suspect drug, a total of 200 PTs and two SOCs exhibited significant disproportionality across all four algorithms. These SOCs included BLOOD AND LYMPHATIC SYSTEM DISORDERS (n=1284, ROR 6.53) and SURGICAL AND MEDICAL PROCEDURES (n=571, ROR 3.41). Compared with the Decitabine label, 29 previously unreported ADEs were identified at the PT level. Furthermore, the Bayesian Confidence Propagation Neural Network (BCPNN) algorithm revealed that the highest IC025 values for both Azacitidine and Decitabine were concentrated in SOCs related to benign, malignant, and unspecified tumors. In summary, using the FAERS database, we compared the real-world safety profiles of two demethylating agents, Azacitidine and Decitabine. The results indicate that adverse drug reactions (ADRs) related to these two agents are concentrated in the hematologic, respiratory, circulatory, and digestive systems, as well as in neoplasms of unspecified nature, warranting close clinical attention.

Signals of propaganda-Detecting and estimating political influences in information spread in social networks.

Social networks are a battlefield for political propaganda. Protected by the anonymity of the internet, political actors use computational propaganda to influence the masses. Their methods include the use of synchronized or individual bots, multiple accounts operated by one social media management tool, or different manipulations of search engines and social network algorithms, all aiming to promote their ideology. While computational propaganda influences modern society, it is hard to measure or detect it. Furthermore, with the recent exponential growth in large language models (L.L.M), and the growing concerns about information overload, which makes the alternative truth spheres more noisy than ever before, the complexity and magnitude of computational propaganda is also expected to increase, making their detection even harder. Propaganda in social networks is disguised as legitimate news sent from authentic users. It smartly blended real users with fake accounts. We seek here to detect efforts to manipulate the spread of information in social networks, by one of the fundamental macro-scale properties of rhetoric-repetitiveness. We use 16 data sets of a total size of 13 GB, 10 related to political topics and 6 related to non-political ones (large-scale disasters), each ranging from tens of thousands to a few million of tweets. We compare them and identify statistical and network properties that distinguish between these two types of information cascades. These features are based on both the repetition distribution of hashtags and the mentions of users, as well as the network structure. Together, they enable us to distinguish (p - value = 0.0001) between the two different classes of information cascades. In addition to constructing a bipartite graph connecting words and tweets to each cascade, we develop a quantitative measure and show how it can be used to distinguish between political and non-political discussions. Our method is indifferent to the cascade's country of origin, language, or cultural background since it is only based on the statistical properties of repetitiveness and the word appearance in tweets bipartite network structures.

Construction and evaluation of machine learning-based predictive models for early-onset preeclampsia.

To analyze the influencing factors of early-onset preeclampsia (EOPE). And to construct and validate the prediction model of EOPE using machine learning algorithm. Based on Python system, the data profile of 1040 pregnant women was divided into 80% training set and 20% test set. Logistic regression algorithm, XGBoost algorithm, random forest algorithm, support vector machine algorithm and artificial neural network algorithm were used to construct the EOPE prediction model, respectively, and the resulting model was validated by resampling method. Accuracy, sensitivity, specificity, F1 score, and area under the ROC curve were used to evaluate the resulting models and screen the optimal models. EOPE in pregnant women. The results of binary logistic regression showed that the influencing factors of EOPE included six indicators: pre-pregnancy BMI, number of pregnancies, mean arterial pressure, smoking, alpha-fetoprotein, and methods of conception. Among them, the prediction model of EOPE constructed based on the XGBoost algorithm performed the best in the training and test sets, with an F1 score of 0.554±0.068 and an AUC of 0.963 (95% CI: 0.943∼0.983) in the training set, and an F1 score of 0.488±0.082 and an AUC of 0.936 (95% CI: 0.887∼0.983). Our prediction model for EOPE constructed based on the XGBoost algorithm has superior disease prediction ability and can provide assistance in predicting the disease risk of EOPE.

Revolutionizing colorectal cancer detection: A breakthrough in microbiome data analysis.

The emergence of Next Generation Sequencing (NGS) technology has catalyzed a paradigm shift in clinical diagnostics and personalized medicine, enabling unprecedented access to high-throughput microbiome data. However, the inherent high dimensionality, noise, and variability of microbiome data present substantial obstacles to conventional statistical methods and machine learning techniques. Even the promising deep learning (DL) methods are not immune to these challenges. This paper introduces a novel feature engineering method that circumvents these limitations by amalgamating two feature sets derived from input data to generate a new dataset, which is then subjected to feature selection. This innovative approach markedly enhances the Area Under the Curve (AUC) performance of the Deep Neural Network (DNN) algorithm in colorectal cancer (CRC) detection using gut microbiome data, elevating it from 0.800 to 0.923. The proposed method constitutes a significant advancement in the field, providing a robust solution to the intricacies of microbiome data analysis and amplifying the potential of DL methods in disease detection.

Atomic Adsorption Energies Prediction on Bimetallic Transition Metal Surfaces Using an Interpretable Machine Learning-Accelerated Density Functional Theory Approach.

In this study, we identified features with the largest contributions and property trends in predicting the adsorption energies of carbon, hydrogen, and oxygen adsorbates on transition metal (TM) surfaces by performing Density Functional Theory (DFT)-based calculations and Machine Learning (ML) regression models. From 26 monometallic and 400 bimetallic fcc(111) TM surfaces obtained from Catalysis-hub.org, three datasets consisting of fourteen elemental, electronic, and structural properties were generated using DFT calculations, site calculations, and online databases. The number of features was reduced using feature selection and then finely-tuned random forest regression (RFR), gaussian process regression (GPR), and artificial neural network (ANN) algorithms were implemented for adsorption energy prediction. Finally, model-agnostic interpretation methods such as permutation feature importance (PFI) and shapely additive explanations (SHAP) provided rankings of feature contributions and directional trends. For all datasets, RFR and GPR demonstrated the highest prediction accuracies. In addition, interpretation methods demonstrated that the largest contributing features and directional trends in the regression models were consistent with structure-property-performance relationships of TMs like the d-band model, the Friedel model, and higher-fold adsorption sites. Overall, this interpretable ML-DFT approach can be applied to TMs and their derivatives for atomic adsorption energy prediction and model explainability.

An Adaptive Neural Network Algorithm with Quasi Opposition-Based Learning for Numerical Optimization Problems

An Adaptive Neural Network Algorithm with Quasi Opposition-Based Learning for Numerical Optimization Problems

P-785. Construction and Validation of a Clinical Diagnostic Model for Active Tuberculosis in Patients with Systemic Lupus Erythematosus

Abstract Background Systemic Lupus Erythematosus (SLE), an autoimmune disease, involves multiple immune dysfunctions. The progression of M.tb infection is closely related to the host's immune status. Abnormal immune functions increase the risk of active tuberculosis (ATB), particularly in patients with autoimmune diseases. Meanwhile, both SLE and ATB’s clinical symptoms includes several overlaps, resulting in a diagnosis challenge when without etiological evidence. Forest plot showing the results of univariate logistic regression in the discovery cohort Methods We included 66 SLE patients with ATB hospitalized at Peking Union Medical College Hospital from 1975 to 2017, and 132 age matched SLE patients without ATB as the discovery cohort. An external validation cohort was drawn from the ETHERTB cohort, which covers rheumatic and immune disease patients from 13 tertiary hospitals in China. Univariate Logistic regression analysis was used in the discovery cohort to identify risk factors for ATB among SLE patients. A neural network algorithm was used to construct the clinical diagnostic model. The external validation cohort was used to validate the diagnostic potential of our model. models developed Importance of all risk factors (A) and variables included in our model (B). Neural network-based model architecture (C). Results In the discovery cohort, fever (OR=47.2), night sweats (OR=5.3), cough (OR=14.5), weight loss (OR=70.1), moderate to severe SLE disease activity (SLEDAI-2000 5-9, OR=5.1; SLEDAI-2000≥10, OR=6.7), close contact with TB (OR=7.8), T-SPOT.TB (OR=1.004), and the use of specific immunosuppressants such as azathioprine (AZA) (OR=3.4), leflunomide (LEF) (OR=2.7), cyclosporine A (CYSA) (OR=3.8), and a maximum daily dose of 30 mg of corticosteroids (OR=14.9) were identified as risk factors for ATB in SLE patients. Variables including fever, night sweats, cough, weight loss, TB contact, T-SPOT.TB, SLEDAI-2000, and maximum daily corticosteroids dose were included in the diagnostic model based on an importance threshold of 0.5. The model's AUC was 0.96(95%Cl: 0.927-0.996), with a sensitivity of 85.3%, specificity of 96.0%, and an accuracy of 93.7%. In the external validation cohort, the AUC was 0.88(95Cl: 0.800-0.963), sensitivity 84.4%, specificity 88.1%, and accuracy 88.0%. Evaluation of diagnosis model Distribution of risk scores in the discovery cohort (A) and validation cohort (B), and ROC curves in the discovery (C) and validation cohorts (D). Conclusion Our study constructed and validated a diagnostic model for ATB in SLE patients. The model demonstrated good diagnostic potential, which could assist in diagnosis in the absence of etiological evidence. Disclosures All Authors: No reported disclosures

Greenhouse environmental monitoring and control system based on improved fuzzy PID and neural network algorithms

Abstract Compared with traditional agriculture, greenhouse planting can more accurately control the growth environment, thereby improving the yield and agricultural product quality. However, traditional greenhouse environments (GhEs) present a number of challenges, including inflexibility in monitoring and wiring, difficulty in management, and high labor costs. To improve the limitations of traditional GhEs and enhance the accuracy of GhE monitoring and control systems, a sensor-based GhE monitoring and control system is designed. In addition, a prediction model for GhE monitoring is constructed using a backpropagation neural network to better predict nonlinear factors such as humidity, temperature, and light intensity in the GhE. Simultaneously, an improved fuzzy proportional integral derivative (PID) controller is utilized to address issues such as fuzziness and uncertainty in GhEs. The results show that the temperature error of the greenhouse environment monitoring and control system based on improved fuzzy PID and neural network algorithms is 0.35–5.04%, the humidity error is −1.3 to 1.65%, and the lighting error is −3.5 to −0.79%. Comprehensive data show that the greenhouse environmental monitoring and control (GEMC) system based on improved fuzzy PID and neural network algorithms effectively improves the accuracy of environmental monitoring and control. The GEMC system, which is based on improved fuzzy PID and neural network algorithms, has facilitated the advancement of agricultural technology in China. It has also provided support and a reference point for GhE monitoring and agricultural production.

Quantum-inspired fluid simulation of two-dimensional turbulence with GPU acceleration

Tensor network algorithms can efficiently simulate complex quantum many-body systems by utilizing knowledge of their structure and entanglement. These methodologies have been adapted recently for solving the Navier-Stokes equations, which describe a spectrum of fluid phenomena, from the aerodynamics of vehicles to weather patterns. Within this quantum-inspired paradigm, velocity is encoded as matrix product states (MPS), effectively harnessing the analogy between interscale correlations of fluid dynamics and entanglement in quantum many-body physics. This particular tensor structure is also called quantics tensor train (QTT). By utilizing NVIDIA's cuQuantum library to perform parallel tensor computations on GPUs, our adaptation speeds up simulations by up to 12.1 times. This allows us to study the algorithm in terms of its applicability, scalability, and performance. By simulating two qualitatively different but commonly encountered 2D flow problems at high Reynolds numbers up to 1×107 using a fourth-order time stepping scheme, we find that the algorithm has a potential advantage over direct numerical simulations in the turbulent regime as the requirements for grid resolution increase drastically. In addition, we derive the scaling χ=O(poly(1/ε)) for the maximum bond dimension χ of MPS representing turbulent flow fields, with an error ε, based on the spectral distribution of turbulent kinetic energy. Our findings motivate further exploration of related quantum algorithms and other tensor network methods. Published by the American Physical Society 2025

Insights into the specific capacitance of CNT-based supercapacitor electrodes using artificial intelligence.

In this study, the specific capacitance characteristics of a carbon nanotube (CNT) supercapacitor was predicted using different machine learning algorithms, such as artificial neural network (ANN), random forest regression (RFR), k-nearest neighbors regression (KNN), and decision tree regression (DTR), based on experimental studies. The results of the simulation verified the accuracy of the ANN algorithm with respect to the data derived from the specific capacitance of the supercapacitor module. It was observed that there was a strong correlation between the experimental results and the predictions made by the ANN algorithm. Comparative analysis showed that the developed ANN algorithm was consistently superior over other algorithms in terms of different metrics, as indicated by the lowest root mean square error (RMSE) value of roughly 26.24 and the highest R 2 value of approximately 0.91. In contrast, the DTR model recorded the least reliable results in the accuracy analysis, as indicated by the highest RMSE value of about 53.46 and the lowest R 2 value of roughly 0.63. To further explore the impact of independent input parameters including pore structure, specific surface area, and I D/I G ratio on a single output parameter (particularly, the specific capacitance) the sensitivity analysis was also conducted using the SHapley Additive exPlanations (SHAP) framework. This investigation sheds light on the relative significance and effects of different input variables on the specific capacitance of supercapacitors based on CNTs. The results indicated that the ANN algorithm accurately predicted the capacitance of the CNT-based supercapacitor, demonstrating the feasibility and significance of neural network algorithms in the design of energy storage devices.

Novel Distributed Downlink Resource Allocation Methods for Heterogeneous Networks

ABSTRACTThis paper proposes novel distributed subchannel and power allocation algorithms for heterogeneous networks. First, we introduce a subchannel allocation algorithm that minimizes the maximum effective interference experienced by users in the network. We analytically prove that the proposed subchannel allocation algorithm minimizes the effective interference in the network. Subsequently, two power control algorithms are presented for the power allocation problem, and their convergence analysis is given. Afterward, the improvement in throughput by utilizing the proposed power control algorithm is shown by simulations. Finally, the proposed subchannel and power allocation algorithms are combined to form a joint resource allocation method, which is shown to outperform existing distributed resource allocation methods in the literature.

Prediction of greenhouse gas emissions in Türkiye with long short-term memory (LSTM) and 1-dimensional convolutional neural network (1-DCNN) algorithms

ABSTRACT The sector with the largest share (32%) of total GHG emissions is now electricity and heat production. This means that more fossil fuels have to be burned to meet the ever-increasing demand for electricity and heat. This study aims to estimate the pollutants such as CO2, CH4, N2O, F-gases, and total GHGs produced during electricity generation in Türkiye using LSTM and 1-DCNN. In the training and prediction processes of these algorithms, the 1990-2020 values from the TURKSTAT were used as the data set. Seven of the data were used in the prediction process, while the remaining data were used in the training process of the algorithms. In 1990, a total of 57,543 GWh of electricity was produced in Türkiye, compared to 306,703 GWh in 2020. In 2020, 34.5% of the electricity was produced from coal, 0.1% from liquid fuels, 23.1% from natural gas, 25.5% from hydropower, and 16.8% from renewables and waste. While CO2 emissions comprised 69.05% of Türkiye’s total GHG emissions in 1990, this percentage rose to 78.92% in 2020. It was concluded that the algorithms preferred in this study gave satisfactory results separately for the estimation of GHG emissions released to the atmosphere during electricity production in Türkiye.