Single Machine Research Articles

High-resolution estimation of PM2.5 concentrations across China using multiple machine learning approaches and model fusion

Accurate estimation of fine particulate matter (PM2.5) concentrations can provide necessary data sources for epidemiological studies and environmental management decisions. However, the different sources and resolutions of satellite data and the limited number of monitoring stations decrease the accuracy of PM2.5 concentration estimation, which subsequently complicates the continuous estimation of high-resolution PM2.5. In this study, we evaluate and optimize five different machine learning methods, including cubist, random forest (RF), support vector machine (SVM), the generalized additive model (GAM), and Extreme Gradient Boosting (XGBoost), to perform spatial high-resolution (1 km) estimations of PM2.5 concentrations. Based on the optimization, two model fusion methods, i.e., the Bayesian model averaging and Granger–Ramanathan averaging algorithms, are applied and compared for estimating PM2.5 concentrations. Among the five single machine learning models, the RF model performed the best, with a coefficient of determination (R2) of 0.84, root mean square error (RMSE) of 5.09 μg/m3, and ratio of performance to interquartile distance (RPIQ) of 3.40. The cubist model ranks second in terms of performance (R2 = 0.82, RMSE = 5.37 μg/m3, and RPIQ = 3.23). The estimation accuracies of the five models decrease in the following order: RF > cubist > XGBoost > SVM > GAM. The fusion model performed better than the single model and reliably estimated the PM2.5 concentration (R2 = 0.86, RMSE = 5.04 μg/m3, and RPIQ = 3.44). The results show that the model fusion algorithms improved the overall performance of fine-level and long-term PM2.5 concentration estimation, and that the accuracies of the fusion models are significantly higher than those of the five machine learning models used individually. Fusion model is used to generate an annual PM2.5 concentration distribution map of China from 2017 to 2021. This study provides a reference for estimating PM2.5 concentrations and contributes to the formulation of targeted policies.

Starling: An I/O-Efficient Disk-Resident Graph Index Framework for High-Dimensional Vector Similarity Search on Data Segment

High-dimensional vector similarity search (HVSS) is gaining prominence as a powerful tool for various data science and AI applications. As vector data scales up, in-memory indexes pose a significant challenge due to the substantial increase in main memory requirements. A potential solution involves leveraging disk-based implementation, which stores and searches vector data on high-performance devices like NVMe SSDs. However, implementing HVSS for data segments proves to be intricate in vector databases where a single machine comprises multiple segments for system scalability. In this context, each segment operates with limited memory and disk space, necessitating a delicate balance between accuracy, efficiency, and space cost. Existing disk-based methods fall short as they do not holistically address all these requirements simultaneously. In this paper, we present Starling, an I/O-efficient disk-resident graph index framework that optimizes data layout and search strategy within the segment. It has two primary components: (1) a data layout incorporating an in-memory navigation graph and a reordered disk-based graph with enhanced locality, reducing the search path length and minimizing disk bandwidth wastage; and (2) a block search strategy designed to minimize costly disk I/O operations during vector query execution. Through extensive experiments, we validate the effectiveness, efficiency, and scalability of Starling. On a data segment with 2GB memory and 10GB disk capacity, Starling can accommodate up to 33 million vectors in 128 dimensions, offering HVSS with over 0.9 average precision and top-10 recall rate, and latency under 1 millisecond. The results showcase Starling's superior performance, exhibiting 43.9x higher throughput with 98% lower query latency compared to state-of-the-art methods while maintaining the same level of accuracy.

Combination prediction method of students' performance based on ant colony algorithm.

Students' performance is an important factor for the evaluation of teaching quality in colleges. The prediction and analysis of students' performance can guide students' learning in time. Aiming at the low accuracy problem of single model in students' performance prediction, a combination prediction method is put forward based on ant colony algorithm. First, considering the characteristics of students' learning behavior and the characteristics of the models, decision tree (DT), support vector regression (SVR) and BP neural network (BP) are selected to establish three prediction models. Then, an ant colony algorithm (ACO) is proposed to calculate the weight of each model of the combination prediction model. The combination prediction method was compared with the single Machine learning (ML) models and other methods in terms of accuracy and running time. The combination prediction model with mean square error (MSE) of 0.0089 has higher performance than DT with MSE of 0.0326, SVR with MSE of 0.0229 and BP with MSE of 0.0148. To investigate the efficacy of the combination prediction model, other prediction models are used for a comparative study. The combination prediction model with MSE of 0.0089 has higher performance than GS-XGBoost with MSE of 0.0131, PSO-SVR with MSE of 0.0117 and IDA-SVR with MSE of 0.0092. Meanwhile, the running speed of the combination prediction model is also faster than the above three methods.

Comparative analysis of single and hybrid machine learning models for daily solar radiation

This study investigates the estimation of daily solar radiation (SR) through various machine learning (ML) models, including the k-nearest neighbor algorithm (KNN), support vector regression (SVR), and random forest (RF), both individually and in combination with the wavelet transform (WT). The assessment of these models is based on meteorological data spanning three decades (1981–2010) from the province of Kütahya in Türkiye. To address the inherent uncertainty in these data-driven models, the quantile regression method is employed for uncertainty analysis. Statistical metrics, such as mean absolute error (MAE), root mean square error (RMSE), coefficient of determination (R2), mean prediction interval (MPI), and prediction interval coverage probability (PICP), are utilized to evaluate the effectiveness and uncertainties of the models. The SVR and KNN models exhibit comparable performances concerning both predictive accuracy and uncertainty levels. However, hybrid models, such as KNN-WT, RF-WT, and SVR-WT, display enhanced accuracy compared to individual ML models, as indicated by statistical performance criteria. Notably, the SVR-WT model, incorporating inputs such as sunshine duration, air temperature, wind speed, and relative humidity, outperforms other models in terms of RMSE (2.174 MJ/m2), MAE (1.721 MJ/m2), R2 (0.923), MPI (28.55), and PICP (0.80) for the testing dataset. In conclusion, the integration of WT significantly improves the performance of ML models, providing valuable insights for the design and operation of solar energy systems, where precise daily SR estimation is critical for optimal and cost-efficient operation.

Preparation of tofacitinib sustained-release tablets using hot melt extrusion technology

This study aimed to develop a tablet that shows a drug release profile similar to the tofacitinib sustained-release tablet (Xeljanz XR®; OROS™) using hot melt extrusion technology. Tofacitinib citrate was selected as the drug. HPMCAS, HPMCP, and Kollidon VA64 were used as thermoplastic polymers to prepare a hot-melt extrudate. The extrudate was obtained from a twin screw extruder and pelletizer. The granules were compressed using a single punch press machine and then coated. TGA, DSC, XRD, FT-IR, and SEM were performed on the hot melt extrudate to understand its physicochemical properties. Dissolution tests were performed using the paddle method (USP Apparatus II). The results showed that the crystallinity state of tofacitinib changed to amorphous after the hot melt extrusion process; however, no chemical change was observed. The drug release profile was similar to that of Xeljanz XR®, which has an initial lag time owing to its OROS™ formulation; a coating process was performed to obtain a similar drug release profile. The lag time was controlled by adjusting the thickness of the coating layer. Moreover, the extrudate size and compression force during tableting did not significantly affect drug release. In conclusion, the new tofacitinib sustained-release tablet prepared using hot melt extrusion showed a drug release behavior similar to that of Xeljanz XR®.

An Empirical Evaluation of a Novel Ensemble Deep Neural Network Model and Explainable AI for Accurate Segmentation and Classification of Ovarian Tumors Using CT Images.

Ovarian cancer is one of the leading causes of death worldwide among the female population. Early diagnosis is crucial for patient treatment. In this work, our main objective is to accurately detect and classify ovarian cancer. To achieve this, two datasets are considered: CT scan images of patients with cancer and those without, and biomarker (clinical parameters) data from all patients. We propose an ensemble deep neural network model and an ensemble machine learning model for the automatic binary classification of ovarian CT scan images and biomarker data. The proposed model incorporates four convolutional neural network models: VGG16, ResNet 152, Inception V3, and DenseNet 101, with transformers applied for feature extraction. These extracted features are fed into our proposed ensemble multi-layer perceptron model for classification. Preprocessing and CNN tuning techniques such as hyperparameter optimization, data augmentation, and fine-tuning are utilized during model training. Our ensemble model outperforms single classifiers and machine learning algorithms, achieving a mean accuracy of 98.96%, a precision of 97.44%, and an F1-score of 98.7%. We compared these results with those obtained using features extracted by the UNet model, followed by classification with our ensemble model. The transformer demonstrated superior performance in feature extraction over the UNet, with a mean Dice score and mean Jaccard score of 0.98 and 0.97, respectively, and standard deviations of 0.04 and 0.06 for benign tumors and 0.99 and 0.98 with standard deviations of 0.01 for malignant tumors. For the biomarker data, the combination of five machine learning models-KNN, logistic regression, SVM, decision tree, and random forest-resulted in an improved accuracy of 92.8% compared to single classifiers.

Gamifying Cybersecurity Education: A CTF-based Approach to Engaging Students in Software Security Laboratories

CTF (Capture the Flag) competitions are increasingly being used in information technology and software engineering education to teach students about secure coding best practices. This paper presents an approach to improve engagement of software engineering students during software security laboratory assessments by using CTF-style challenges that are fun to solve and reflect modern security threats. The approach involves gamifying laboratory exercises by providing CTF-like challenges that are not only enjoyable but also reflect real-world security vulnerabilities. Additionally, students are given a single virtual machine bundled with everything needed for the entire semester, eliminating unnecessary logistic problems. Feedback from students confirms the effectiveness of this approach in reaching course objectives. Students were keen to learn more about the topic and showed greater interest in the laboratories. This approach provides a more dynamic and interactive learning experience that fosters critical thinking skills and encourages students to pursue careers in software security.

Fully automated learning and predict price of aquatic products in Taiwan wholesale markets using multiple machine learning and deep learning methods

The aquatic market price is an essential indicator for fishermen in making decisions regarding conveyance, fish-catching, and wholesale strategies. As a guide for the aquaculture industry, understanding market prices, the geographical distribution of fish prices, and the prediction of fish prices are vital. In Taiwanese fisheries, however, predicting aquatic prices is challenging due to their drastic fluctuations caused by tropical and subtropical climate variations, export/import fish quantity changes, and political and economic situation uncertainty. Therefore, instead of using a single machine learning model to address the price fluctuations, we propose a hybrid fish price prediction model integrating multiple machine learning and deep learning models to identify and predict fish price dynamics in various wholesale markets. The extensive experimental results on actual market data from the Taiwan Council of Agriculture (COA) show that the proposed models can achieve >90% accuracy in predicting future aquatic prices. Additionally, we developed a fully automated learning and prediction system architecture on the cloud, allowing us to acquire data automatically and fine-tune the AI models continuously to achieve better performance with long-term operability.

Magnetic Shielding Design and Performance Improvement of HTS Coil for Single HTS-Excitation Claw-Pole Field-Modulation Machine

Magnetic Shielding Design and Performance Improvement of HTS Coil for Single HTS-Excitation Claw-Pole Field-Modulation Machine

Minimum current stress control strategy for three degree of freedom three-phase dual active bridge DC-DC converter

Compared to single-phase dual active bridge (1p-DAB) DC-DC converters, three-phase dual active bridge (3p-DAB) DC-DC converters have advantages such as high single machine power, high power density, small filtering capacitance, and are more suitable for high-voltage situations. Therefore, they have good application prospects in DC transmission and industrial high-power DC-DC converters. However, under traditional single-phase shift (SPS) control, especially in the low power range, there is a large reactive current and severe soft switch loss. This article adopts an asymmetric modulation strategy for its larger reactive power, which reduces its reactive power in the low power range with more degrees of freedom and improves the efficiency of power transmission. Taking current stress as the optimization objective, the KKT algorithm is used to solve, and the corresponding analytical solution is ultimately obtained. Finally, a simulation is built by using MATLAB software to verify the feasibility of the proposed solution.

Go-Explore Complex 3-D Game Environments for Automated Reachability Testing

Modern AAA video games feature huge game levels and maps which are increasingly hard for level testers to cover exhaustively. As a result, games often ship with catastrophic bugs such as the player falling through the floor or being stuck in walls. We propose an approach specifically targeted at reachability bugs in simulated 3D environments based on the powerful exploration algorithm, Go-Explore, which saves unique checkpoints across the map and then identifies promising ones to explore from. We show that when coupled with simple heuristics derived from the game's navigation mesh, Go-Explore finds challenging bugs and comprehensively explores complex environments <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">without the need for human demonstration or knowledge of the game dynamics</i> . Go-Explore vastly outperforms more complicated baselines including reinforcement learning with intrinsic curiosity in both covering the navigation mesh and number of unique positions across the map discovered. Finally, due to our use of parallel agents, our algorithm can fully cover a vast 1.5km x 1.5km game world within 10 hours on a single machine making it extremely promising for continuous testing suites.

A multifunctional reference torque standard machine

In response to the problem of using different single machines to achieve measurement and detection of existing torque instruments, this article introduces a multifunctional reference torque standard and proposes specific steps for torque measurement and detection. This standard machine can be used to detect various existing torque instruments and can solve the problem of using a single machine to achieve the detection of a certain type of torque equipment. After adopting this standard machine and its detection method, experimental data shows that the measurement of the torque transducer can meet the requirements of JJG995-2005 “Static Torque Measuring Machines” and the measurement of the reference torque wrench can meet the requirements of JJG1103-2014 “Reference torque wrench Verification Regulations”.

Optimization of recycled rubber self-compacting concrete: Experimental findings and machine learning-based evaluation

This research aims to assess the rheological and mechanical characteristics of Self-compacting concrete (SCC) incorporating waste tire rubber aggregates (WRTA) as an interim substitute for coarse aggregates. However, the standard experimental modeling approach has significant obstacles when it comes to overcoming the nonlinearity and environmental susceptibility of concrete parts. Therefore, linear regression (LR) and extreme gradient boosting (XGBoost) were used as two standard single machine learning (ML) models to predict the aforementioned rubberized SCC features. In this study, conventional coarse aggregates were supplanted with WRTA at 0%, 5%, 10%, and 20% to uncover the optimal proportion of coarse aggregates substituting rubber. To find the optimum amount of WRTA to use as a substitute, the study follows the impacts of rubber on the self-compacting rubberized concrete's (SCRC) rheological and mechanical characteristics. The consequences on fresh properties were investigated by the slump flow, J-ring, and V-funnel tests, while compressive and splitting tensile strengths tests were conducted to assess mechanical properties. Increasing WRTA test outputs indicated a deterioration in workability and hardened qualities. While a 10% swapping ratio is deemed feasible for producing SCRC, optimal results were achieved by reducing environmental impacts and efficiently managing a significant volume of rubber tire waste with a 5% substitution of rubber within the coarse aggregates. The research findings indicated a noticeable decrease in fresh properties as the WRTA content increased. Notably, after 28 days, a 10% WRTA substitution led to a 34% reduction in compressive strength and a 28% decrease in splitting tensile strength, satisfying ACI standards. Furthermore, XGBoost demonstrated superior predictive performance with the highest R2 values, outperforming the LR model and affirming its efficacy in delivering more accurate predictions.

Solving Maximum Early Jobs Time and Range of Lateness Jobs Times Problem Using Exact and Heuristic Methods

In this paper, a solution to one of the Bicriteria Machine Scheduling Problems (BCMSP) is proposed. This problem focuses on the maximum early jobs time and range of lateness jobs time on a single machine (1//(E_max,R_L )). First, we derive a subproblem 1//(E_max+R_L ) from the main problem which is a special case for the suggested problem. Secondly, both exact complete enumeration and Branch and Bound (BAB) with two new lower bounds with some heuristic methods to solve the problems are proposed. The results prove the accuracy of BAB to solve the problem for n≤110 jobs in a reasonable time. In addition, the accuracy of the suggested heuristic methods is compared with the results of the exact methods.

Distributed Bootstrap Simultaneous Inference for High-Dimensional Quantile Regression

Modern massive data with enormous sample size and tremendous dimensionality are usually impossible to process with a single machine. They are typically stored and processed in a distributed manner. In this paper, we propose a distributed bootstrap simultaneous inference for a high-dimensional quantile regression model using massive data. Meanwhile, a communication-efficient (CE) distributed learning algorithm is developed via the CE surrogate likelihood framework and ADMM procedure, which can handle the non-smoothness of the quantile regression loss and the Lasso penalty. We theoretically prove the convergence of the algorithm and establish a lower bound on the number of communication rounds ιmin that warrant statistical accuracy and efficiency. The distributed bootstrap validity and efficiency are corroborated by an extensive simulation study.

Research Trends in the Directed Energy Deposition Method of Heterogeneous Materials

Metal Additive Manufacturing (AM) or 3D printing garners attention for economically producing components with superior strength, durability, and corrosion resistance. The microstructure, influenced by factors like heat input, scanning speed, and layer thickness, shapes metal materials intricately in 3D printing.Unlike traditional manufacturing with separate fabrication and post-processing, Multi Materials AM (MM-AM) streamlines the construction of composite structures in a single step using a single machine. Although initial MM-AM, especially inpolymer 3D printing, was simpler, the shift to metal-based MM-AM presents challenges. The distinctive bonding style in MM-AM facilitates robust connections between different metals, minimizing stress concentration and simplifying the joining of diverse metals. Challenges like intermetallic compound formation, residual stress, and material-specific issues leading to cracks persist. Ongoing research focuses on metallurgy, process optimization,and computational simulation to overcome these hurdles. This review delves into current research trends in multi-material AM, identifying pathways for technological advancements. It discusses the application of Directed Energy Deposition (DED)in stacking austenitic and ferritic metals for nuclear power plant cooling system piping's gradient-form safe-ends. The study emphasizes advanced manufacturing techniques for developing functionally graded materials and dissimilar metal joints, highlighting the transformative potential of additive manufacturing across industries.

Approximation of the Objective Function of Single-Machine Scheduling Problem

The problem of the approximation of the coefficients of the objective function of a scheduling problem for a single machine is considered. It is necessary to minimize the total weighted completion times of jobs with unknown weight coefficients when a set of problem instances with known optimal schedules is given. It is shown that the approximation problem can be reduced to finding a solution to a system of linear inequalities for weight coefficients. For the case of simultaneous job release times, a method for solving the corresponding system of inequalities has been developed. Based on it, a polynomial algorithm for finding values of weight coefficients that satisfy the given optimal schedules was constructed. The complexity of the algorithm is O(n2(N+n)) operations, where n is the number of jobs and N is the number of given instances with known optimal schedules. The accuracy of the algorithm is estimated by experimentally measuring the function ε(N,n)=1n∑j=1n∣wj−wj0∣wj0, which is an indicator of the average modulus of the relative deviation of the found values wj from the true values wj0. An analysis of the results shows a high correlation between the dependence ε(N,n) and a function of the form α(n)/N, where α(n) is a decreasing function of n.

Towards a Reliable Design of Geopolymer Concrete for Green Landscapes: A Comparative Study of Tree-Based and Regression-Based Models

The design of geopolymer concrete must meet more stringent requirements for the landscape, so understanding and designing geopolymer concrete with a higher compressive strength challenging. In the performance prediction of geopolymer concrete compressive strength, machine learning models have the advantage of being more accurate and faster. However, only a single machine learning model is usually used at present, there are few applications of ensemble learning models, and model optimization processes is lacking. Therefore, this paper proposes to use the Firefly Algorithm (AF) as an optimization tool to perform hyperparameter tuning on Logistic Regression (LR), Multiple Logistic Regression (MLR), decision tree (DT), and Random Forest (RF) models. At the same time, the reliability and efficiency of four integrated learning models were analyzed. The model was used to analyze the influencing factors of geopolymer concrete and determine the strength of their influencing ability. According to the experimental data, the RF-AF model had the lowest RMSE value. The RMSE value of the training set and test set were 4.0364 and 8.7202, respectively. The R value of the training set and test set were 0.9774 and 0.8915, respectively. Therefore, compared with the other three models, RF-AF has a stronger generalization ability and higher prediction accuracy. In addition, the molar concentration of NaOH was the most important influencing factors, and its influence was far greater than the other possible factors including NaOH content. Therefore, it is necessary to pay more attention to NaOH molarity when designing geopolymer concrete.

Estimation of the Region of Attraction of Polynomial Swing Equation Using Sum of Squares Theory

Estimation of the region of attraction of the power system using sum of squares (SOS) programming theory is investigated in this paper. Firstly, the Taylor expansion formula is applied to explore the inherent polynomial structure of the swing equation. The single machine projection equation for a multi-machine system is proposed based on the amplitude characteristic of the rotor angle response in decoupled space. Secondly, the theory of SOS programming originating from the polynomial system is derived, and the theory about estimation of the region of attraction for the differential algebraic system is summarized. Thirdly, the method for estimating the region of attraction for the polynomial swing equation based on a V-S two-stage iterative procedure is proposed. Finally, a single machine infinite bus power system, an IEEE 3-machine power system, and an IEEE 4-machine power system are employed to validate the effectiveness and accuracy of the proposed method and analysis.

A Heuristic Based on LPT rule and a restricted B&amp;B Method to Minimizing the Expected Makespan on a Single Machine

Considering the production and maintenance schedule simultaneously has a great attention for more than two decades. At the beginning, most of these researches treated maintenance activities as a fixed hole in the study period. Later, in the majority of them, the maintenance periods starting time were fixed but their intervals may variate. In the non-preemptive case, only few of articles that deals with the maintenance activities starting time as a decision variable. In this article, the time to starting the planed maintenance activities (PM) is a decision variable and the expected repair time is estimated according to nonhomogeneous Poisson process (NHPP). The failure function is Weibully distributed and the repair due to failure is minimal while the PM repair is perfect. Two cases were considered for the model which is aiming to minimizing the expected makespan (EMS), first, the preemptive case which was solved optimally. Secondly, a heuristic based on longest processing time rule (LPT) and a restricted branch and bound method (B&amp;B) are provided to solve the problem for the non-preemptive case. Each of the proposed procedures uses the optimal solution properties of the preemptive case. The optimal solution for the non-preemptive model can be reached using LPT in case of . Finally, the results showed the robustness for B&amp;B which their lower bound obtained by the optimal solution of the preemptive case and the upper bound is the first solution obtained by LPT.