SYSTEM, METHOD, AND COMPUTER PROGRAM PRODUCT FOR RECOMMENDING A SET OF ITEMS TO A USER

Systems, methods, and computer program products obtain training data and train a neural network based on the training data by concatenating a user identifier to each item of a first set of multiple items in a first set of fused embeddings, concatenating the user identifier to each item of a second set of multiple items in a second set of fused embeddings, determining a first score associated with the first set of multiple items based on the first set of fused embeddings, determining a second score associated with the second set of multiple items based on the second set of fused embeddings, and modifying, using an objective function of the neural network that depends on the first score, the second score, and a margin between the first score and the second score, one or more parameters of the neural network

Optimal Correction of Infeasible System in Linear Equality via Genetic Algorithm

This work is focused on the optimal correction of infeasible system of linear equality. In this paper, for correcting this system, we will make the changes just in the coefficient matrix by using l norm and show that solving this problem is equivalent to solving a fractional quadratic problem. To solve this problem, we use the genetic algorithm. Some examples are provided to illustrate the efficiency and validity of the proposed method

Fault recovery in control systems : a discrete event system approach

Fault recovery is a challenging task that is crucial in achieving stringent reliability and safety goals. In this thesis, the problem of fault recovery is studied in discrete-event systems (DES), assuming permanent failures. A diagnosis system is assumed to be available to detect and isolate faults with a bounded delay. Thus, the combination of the plant and diagnosis system can be thought of having three modes: normal, transient, and recovery. Initially the plant is in the normal mode. Once a failure occurs, the system enters the transient mode. After the failure is diagnosed by the diagnosis system, the system enters the recovery mode. This framework does not depend on the diagnosis technique used, as long as the diagnosis delay is bounded. As a result, the diagnosis and control problems are almost decoupled. In general, for each mode there is a set of specifications that have to be met. We propose a modular switching supervisory scheme. The proposed framework contains one normal-transient supervisor and multiple recovery supervisors each corresponding to a particular failure mode. Once a fault is detected and isolated by the diagnoser, the normal-transient supervisor is removed from the feedback loop and one of the recovery supervisors will take sole control of the system. The issue of non-blocking is studied and it is shown that essentially if the system under supervision is non-blocking in the normal mode, then it will remain non-blocking during the recovery procedure. Supervisor admissibility is also studied. This approach is developed for untimed DES and then extended to timed DES. In the process, previous results on supervisor design for untimed DES with partial observation are extended to timed DES. Various examples from manufacturing and process control are provided to illustrate the approach

Silver nanoparticles modified titanium carbide MXene composite for RSM-CCD optimised chloride removal from water

Unsafe levels of chloride in drinking water can make it unpalatable, susceptible to infrastructure corrosion and prone to heavy metals mobility. Conventional chloride mitigation strategies are subjected to inefficient performance and costly operation, necessitating innovations for more sustainable, affordable, and scalable technologies. In this study, silver nanoparticles-modified Ti3C2 MXene nanocomposite (AgMX) is synthesised via dry impregnation method for effective removal of chloride ion from water. The composite physicochemical properties were thoroughly characterised using various analytical techniques, including TEM, SEM, XRD, EDS, BET, zeta potential and pHpzc analysis. The experimental testing was optimised using CCD-RSM method in terms of adsorbent dosage (0.2–2 g/L), reaction time (1–17 min), and chloride concentration (10–90 mg/L). Under optimal conditions (adsorbent:1.55 g/L, time: 12.19 min, & concentration: 52.17 mg/L), a promising chloride removal of 91.8 % was achieved. Langmuir model showed the best fit to adsorption isotherm (R2: 0.9852) comparing to Freundlich and Dubinin-Kaganer-Radushkevich (DKR) isotherms, while pseudo-second-order kinetic model offered the closest data to the experimental results (R2: 9893) compared to the pseudo-first-order, Elovich and Intraparticle diffusion models R2: 0.2335,0.1212 and 0.2050, respectively. The composite reusability and regeneration potential after four repeated cycles were found practically efficient as ≥ 68 % and ≥ 84 %, respectively. The outcomes of this study can demonstrate the efficiency of the formulated composite as a promising material for the sustainable treatment of chloride-contaminated water

Combining a convolutional neural network with autoencoders to predict the survival chance of COVID-19 patients.

COVID-19 has caused many deaths worldwide. The automation of the diagnosis of this virus is highly desired. Convolutional neural networks (CNNs) have shown outstanding classification performance on image datasets. To date, it appears that COVID computer-aided diagnosis systems based on CNNs and clinical information have not yet been analysed or explored. We propose a novel method, named the CNN-AE, to predict the survival chance of COVID-19 patients using a CNN trained with clinical information. Notably, the required resources to prepare CT images are expensive and limited compared to those required to collect clinical data, such as blood pressure, liver disease, etc. We evaluated our method using a publicly available clinical dataset that we collected. The dataset properties were carefully analysed to extract important features and compute the correlations of features. A data augmentation procedure based on autoencoders (AEs) was proposed to balance the dataset. The experimental results revealed that the average accuracy of the CNN-AE (96.05%) was higher than that of the CNN (92.49%). To demonstrate the generality of our augmentation method, we trained some existing mortality risk prediction methods on our dataset (with and without data augmentation) and compared their performances. We also evaluated our method using another dataset for further generality verification. To show that clinical data can be used for COVID-19 survival chance prediction, the CNN-AE was compared with multiple pre-trained deep models that were tuned based on CT images