MPCNN: A Novel Matrix Profile Approach for CNN-based Sleep Apnea Classification

Sleep apnea (SA) is a significant respiratory condition that poses a major global health challenge. Previous studies have investigated several machine and deep learning models for electrocardiogram (ECG)-based SA diagnoses. Despite these advancements, conventional feature extractions derived from ECG signals, such as R-peaks and RR intervals, may fail to capture crucial information encompassed within the complete PQRST segments. In this study, we propose an innovative approach to address this diagnostic gap by delving deeper into the comprehensive segments of the ECG signal. The proposed methodology draws inspiration from Matrix Profile algorithms, which generate an Euclidean distance profile from fixed-length signal subsequences. From this, we derived the Min Distance Profile (MinDP), Max Distance Profile (MaxDP), and Mean Distance Profile (MeanDP) based on the minimum, maximum, and mean of the profile distances, respectively. To validate the effectiveness of our approach, we use the modified LeNet-5 architecture as the primary CNN model, along with two existing lightweight models, BAFNet and SE-MSCNN, for ECG classification tasks. Our extensive experimental results on the PhysioNet Apnea-ECG dataset revealed that with the new feature extraction method, we achieved a per-segment accuracy up to 92.11 \% and a per-recording accuracy of 100\%. Moreover, it yielded the highest correlation compared to state-of-the-art methods, with a correlation coefficient of 0.989. By introducing a new feature extraction method based on distance relationships, we enhanced the performance of certain lightweight models, showing potential for home sleep apnea test (HSAT) and SA detection in IoT devices. The source code for this work is made publicly available in GitHub: https://github.com/vinuni-vishc/MPCNN-Sleep-Apnea

Inverse kinematic control algorithm for a welding robot - positioner system to trace a 3D complex curve

The welding robots equipped with rotary positioners have been widely used in several manufacturing industries. However, for welding a 3D complex weld seam, a great deal of points should be created to ensure the weld path smooth. This is a boring job and is a great challenge - rotary positioner system since the robot and the positioner must move simultaneously at the same time. Therefore, in this article, a new inverse kinematics solution is proposed to generate the movement codes for a six DOFs welding robot incorporated with a rotary positioner. In the algorithm, the kinematic error is minimized, and the actual welding error is controlled so that it is always less than an allowable limit. It has shown that the proposed algorithm is useful in developing an offline CAD-based programming tool for robots when welding complex 3D paths. The use of the algorithm increases the accuracy of the end-effector positioning and orientation, and reduces the time for teaching a welding robot - positioner system. Simulation scenarios demonstrate the potency of the suggested method

Differentiable Physics-based Greenhouse Simulation

We present a differentiable greenhouse simulation model based on physical processes whose parameters can be obtained by training from real data. The physics-based simulation model is fully interpretable and is able to do state prediction for both climate and crop dynamics in the greenhouse over very a long time horizon. The model works by constructing a system of linear differential equations and solving them to obtain the next state. We propose a procedure to solve the differential equations, handle the problem of missing unobservable states in the data, and train the model efficiently. Our experiment shows the procedure is effective. The model improves significantly after training and can simulate a greenhouse that grows cucumbers accurately.Comment: Accepted at the Machine Learning and the Physical Sciences workshop, NeurIPS 2022. 7 pages, 2 figure

Synthesis and Photocatalytic Activity for Toluene Removal of CDs/TiO2 - Zeolite Y

Hydrothermally synthesized carbon nanodots (CDs) were impregnated on TiO2. The product (CDs/TiO2) was mechanically mixed with zeolite Y for application in toluene photocatalytic oxidation reaction under UV radiation. Material properties of the samples were investigated by different methods. Toluene vapor was chosen as a typical volatile organic compound to investigate the performance of CDs/TiO2 – zeolite Y photocatalyst when these technological parameters were changed: toluene concentration, gas flow rate, humidity and UV light intensity. In each reaction, only one parameter was changed and the remaining conditions were fixed. The toluene concentrations at the beginning and the end of each reaction were analyzed with the use of gas chromatography (GC). The results of different reaction conditions show the trends for toluene treatment of the CDs/TiO2 – zeolite Y catalyst, thereby providing specific explanations for these trends. The experiments also show that toluene removal is highest when the toluene concentration in the inlet gas is 314 ppmv, the flow rate is 3 L/h, the humidity is 60%, and the catalyst (CDs/TiO2 – zeolite Y composite with 70% zeolite in weight) is illuminated by 4 UV lamps. Copyright © 2022 by Authors, Published by BCREC Group. This is an open access article under the CC BY-SA License (https://creativecommons.org/licenses/by-sa/4.0).

Competitive Influence Maximization within Time and Budget Constraints in Online Social Networks: An Algorithmic Approach

Competitive Influence Maximization ( CIM ) problem, which seeks a seed set nodes of a player or a company to propagate their product’s information while at the same time their competitors are conducting similar strategies, has been paid much attention recently due to its application in viral marketing. However, existing works neglect the fact that the limited budget and time constraints can play an important role in competitive influence strategy of each company. In addition, based on the the assumption that one of the competitors dominates in the competitive influence process, the majority of prior studies indicate that the competitive influence function (objective function) is monotone and submodular.This led to the fact that CIM can be approximated within a factor of 1 − 1 / e − ϵ by a Greedy algorithm combined with Monte Carlo simulation method. Unfortunately, in a more realistic scenario where there is fair competition among competitors, the objective function is no longer submodular. In this paper, we study a general case of CIM problem, named Budgeted Competitive Influence Maximization ( BCIM ) problem, which considers CIM with budget and time constraints under condition of fair competition. We found that the objective function is neither submodular nor suppermodular. Therefore, it cannot admit Greedy algorithm with approximation ratio of 1 − 1 / e . We propose Sandwich Approximation based on Polling-Based Approximation ( SPBA ), an approximation algorithm based on Sandwich framework and polling-based method. Our experiments on real social network datasets showed the effectiveness and scalability of our algorithm that outperformed other state-of-the-art methods. Specifically, our algorithm is scalable with million-scale networks in only 1.5 min

Minimizing cost for influencing target groups in social network: A model and algorithmic approach

Stimulated by practical applications arising from economics, viral marketing, and elections, this paper studies the problem of Groups Influence with Minimum cost (GIM), which aims to find a seed set with the smallest cost that can influence all target groups in a social network, where each user is assigned a cost and a score and a group of users is influenced if the total score of influenced users in the group is at least a certain threshold. As the group influence function, defined as the number of influenced groups or users, is neither submodular nor supermodular, theoretical bounds on the quality of solutions returned by the well-known greedy approach may not be guaranteed.In this work, two efficient algorithms with theoretical guarantees for tackling the GIM problem, named Groups Influence Approximation (GIA) and Exact Groups Influence (EGI), are proposed. GIA is a bi-criteria polynomial-time approximation algorithm and EGI is an (almost) exact algorithm; both can return good approximate solutions with high probability. The novelty of our approach lies in two aspects. Firstly, a novel group reachable reverse sample concept is proposed to estimate the group influence function within an error bound. Secondly, a framework algorithmic is designed to find serial candidate solutions with checking theoretical guarantees at the same time. Besides theoretical results, extensive experiments conducted on real social networks show our algorithms' performance. In particular, both EGI and GIA provide the solution quality several times better, while GIA is up to 800 times faster than the state-of-the-art algorithms.Web of Science21219718

Kinetics of Thermal Unimolecular Decomposition of Acetic Anhydride: An Integrated Deterministic and Stochastic Model

An integrated deterministic and stochastic model within the master equation/­Rice–Ramsperger–­Kassel–Marcus (ME/RRKM) framework was first used to characterize temperature- and pressure-dependent behaviors of thermal decomposition of acetic anhydride in a wide range of conditions (i.e., 300–1500 K and 0.001–100 atm). Particularly, using potential energy surface and molecular properties obtained from high-level electronic structure calculations at CCSD­(T)/CBS, macroscopic thermodynamic properties and rate coefficients of the title reaction were derived with corrections for hindered internal rotation and tunneling treatments. Being in excellent agreement with the scattered experimental data, the results from deterministic and stochastic frameworks confirmed and complemented each other to reveal that the main decomposition pathway proceeds via a 6-membered-ring transition state with the 0 K barrier of 35.2 kcal·mol^–1. This observation was further understood and confirmed by the sensitivity analysis on the time-resolved species profiles and the derived rate coefficients with respect to the ab initio barriers. Such an agreement suggests the integrated model can be confidently used for a wide range of conditions as a powerful postfacto and predictive tool in detailed chemical kinetic modeling and simulation for the title reaction and thus can be extended to complex chemical reactions