Electrical and galvanomagnetic properties of AuAl2+6%Cu intermetallic compounds at low temperatures

The AuAl2 intermetallic compounds are of substantial interest in view of their application potential. The investigated intermetallics AuAl 2+6%Cu were prepared from fine powders of AuAl2 and Cu by vacuum sputtering on a glass substrate and consisted of films with a thickness of about one micrometer. The films were annealed. The temperature and field dependence of the electroresistivity, the magnetoresistivity and the Hall effect of AuAl2+6%Cu films were measured in the temperature interval from 4.2 to 100 K and at magnetic fields of up to 15 T. We demonstrate that the temperature dependence of the electroresistivity has a minimum at T = 20 K and a metallic behavior above this temperature. The magnetoresistivity is very small (less then 1%), positive at low temperatures and negative above 12 K. The Hall coefficient is positive, which corresponds to the holes in a one zone model with a charge carrier concentration of about 1.6 1020 cm-3. © Published under licence by IOP Publishing Ltd

Mononuclear Cu(II) complexes of novel salicylidene Schiff bases: synthesis and mesogenic properties

Two new Schiff base ligands 1 and 2 (where 1 = 4-(2-hydroxybenzilidenamino)-phenyl-4-(decyloxy)-2-(pent-4-enyloxy)benzoate, 2 = 4-(4-(decyloxy)-2-hydroxybenziliden amino)-phenyl-4-(decyloxy)-2-(pent-4-enyloxy)benzoate) and their copper (Cu)(II) complexes have been synthesised and characterised. The derivatives were fully characterised structurally, and their mesomorphic behaviour was investigated by polarised optical microscopyand differential scanning calorimetry. The structure of Cu(II) complex having 1 as ligand (3) was determined by X-ray diffraction. The Schiff base ligands exhibit enantiotropic nematic phases, the Cu(II) complex 4 shows monotropic nematic phase behaviour, while compound 3 does not show mesomorphism

Reliable Multimodal Heartbeat Classification using Deep Neural Networks

Copyright © 2023 Authors. Arrhythmias are deviations from the normal heart rhythm with impact on the cardiovascular health. Their prompt detection plays an important role in mitigating potential negative outcomes, particularly in patients in the intensive care units (ICU). Heartbeat detection has mainly been focused on electrocardiogram (ECG) signals. However, ICU patient mobility frequently leads to disconnection of certain ECG leads, potentially compromising the accurate heartbeat classification. Arterial line blood pressure (ABP) and central venous pressure (CVP) signals are routinely monitored in ICU patients. Changes in the ABP and CVP suggest alterations in the haemodynamic status and cardiac function of the patients. Thus, leveraging these signals for heartbeat classification, either independently or in conjunction with ECG data, present a viable approach to ensure that even in scenarios where ECG signals are unavailable, alarm systems alerting healthcare providers of arrhythmias remain functional. Moreover, while many researchers have successfully created methodologies to accurately classify heartbeats including paced beats, none were able to distinguish various sub-classes of paced heartbeats. A more comprehensive distinction is crucial as it not only aids in the identification of pacing settings but also facilitates the detection of inadequate pacing settings, a critical aspect in patient care. In this paper, we employ a hybrid model using long-short term memory networks (LSTM) and convolutional neural network (CNN), along with different residual CNN (ResNet) models for multimodal arrhythmia classification and for comprehensive paced heartbeats classification. When using all three channels, ResNet50 achieved the best accuracy of 99.58% on 5 different arrhythmia classes, whereas ResNet34 achieved an accuracy of 93.82% on 12 paced classes. The significant efficiency of utilizing ABP and CVP signals independently for classification, was also highlighted. ResNet50 was trained with ABP and CVP signals independently and correctly identified arrhythmias with an accuracy of 98.79% and 96.67%, respectively. For classifying 12 different paced heartbeats, ResNet34 achieved 74.04% accuracy with ABP signals and 74.38% with CVP signals. Moreover, the same ResNet50 model was trained on the MIT-BIH arrhythmia database, achieving an accuracy, sensitivity, and precision of 98.78%, 98.77% and 98.80%, which demonstrates the scalability of the proposed model.British Heart Foundation for sponsoring this project (No.FS/19/73/34690

Implementation of Deep Learning Models on an SoC-FPGA Device for Real-Time Music Genre Classification

Data Availability Statement: https://www.kaggle.com/datasets/andradaolteanu/gtzan-dataset-music-genre-classification (accessed on 30 June 2023).Copyright © 2023 by the authors. Deep neutral networks (DNNs) are complex machine learning models designed for decision-making tasks with high accuracy. However, DNNs require high computational power and memory, which limits such models to fitting on edge devices, resulting in unnecessary processing delays and high energy consumption. Graphical processing units (GPUs) offer reliable hardware acceleration, but their bulky sizes prevent their utilization in portable equipment. System-on-chip field programmable gated arrays (SoC-FPGAs) provide considerable computational power with low energy consumption, making them ideal for edge computing applications, owing to their innovative, flexible, and small design. In this paper, we implement a deep-learning-based music genre classification system on a SoC-FPGA board, evaluate the model’s performance, and provide a comparative analysis across different platforms. Specifically, we compare the performance of long short-term memory (LSTM), convolutional neural networks (CNNs), and a hybrid model (CNN-LSTM) on an Intel Core i7-8550U by Intel Cooperation. The models are fed an acoustic feature called the Mel-frequency cepstral coefficient (MFCC) for training and testing (inference). Then, by using the advanced Vitis AI tool, a deployable version of the model is generated. The experimental results show that the execution speed is increased by 80%, and the throughput rises four times when the CNN-based music genre classification system is implemented on SoC-FPGA.British Heart Foundation (The development of a sophisticated cardiac pacing simulator: a training tool to enhance the management of post cardiac surgical patient care) under grant number FS/19/73/34690

How robust are recommended waiting times to pacing after cardiac surgery that are derived from observational data?

AIMS: For bradycardic patients after cardiac surgery, it is unknown how long to wait before implanting a permanent pacemaker (PPM). Current recommendations vary and are based on observational studies. This study aims to examine why this variation may exist. METHODS AND RESULTS: We conducted first a study of patients in our institution and second a systematic review of studies examining conduction disturbance and pacing after cardiac surgery. Of 5849 operations over a 6-year period, 103 (1.8%) patients required PPM implantation. Only pacing dependence at implant and time from surgery to implant were associated with 30-day pacing dependence. The only predictor of regression of pacing dependence was time from surgery to implant. We then applied the conventional procedure of receiver operating characteristic (ROC) analysis, seeking an optimal time point for decision-making. This suggested the optimal waiting time was 12.5 days for predicting pacing dependence at 30 days for all patients (area under the ROC curve (AUC) 0.620, P = 0.031) and for predicting regression of pacing dependence in patients who were pacing-dependent at implant (AUC 0.769, P < 0.001). However, our systematic review showed that recommended optimal decision-making time points were strongly correlated with the average implant time point of those individual studies (R = 0.96, P < 0.001). We further conducted modelling which revealed that in any such study, the ROC method is strongly biased to indicate a value near to the median time to implant as optimal. CONCLUSION: When commonly used automated statistical methods are applied to observational data with the aim of defining the optimal time to pacing after cardiac surgery, the suggested answer is likely to be similar to the average time to pacing in that cohort

Behavior of bipyridine derivative Cu(I) complexes in donor solvents

Cu(I) complexes are known as highly emissive compounds having interesting fluorescence applications[1].Theluminescence is generated by more intense metal to ligand charge transfer (MLCT) electronic transitions for Cu(I), affording longer excited-statelifetimes compared to transient d-d excited state of Cu(II)[2].Herein we report the behavior of two bipyridine derivative Cu(I) complexes containing phenanthroline and biquinoline ligands, respectively, in donor solvents as dimethylsulfoxide and acetonitrile.The Cu(I) phenanthroline complex (1) is unstable in solution,due to oxidation of Cu(I) to Cu(II) in time, accompanied by change in coordination geometry from tetrahedral to trigonal bipyramidal. The Cu(I) biquinoline complex (2) is more stable in donor solvents,the stability increasing at low temperatures with the stabilization of tetragonal geometry of Cu(I).In case of biquinoline ligand, this kind of geometry is stabilized by the bulky aryl substituentsatαposition with respect to the pyridine nitrogen