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This paper presents new general mathematical models of normalized hysteresis curves, which define major‐hysteresis‐loop and minor‐hysteresis‐loop trajectories with several degrees of freedom. These mathematical models may be integrated into models of circuits containing nonlinear inductances for application in simulation studies. Also, the models presented can be applied to the description of hysteresis of different physical nature in other areas of science where the hysteresis phenomenon is encountered, for example: dielectric hysteresis, mechanical hysteresis, adsorption hysteresis, optical hysteresis, and so forth.

Classification of remote sensing images (RSI) is a challenging task in computer vision. Recently, researchers have proposed a variety of creative methods for automatic recognition of RSI, and feature fusion is a research hotspot for its great potential to boost performance. However, RSI has a unique imaging condition and cluttered scenes with complicated backgrounds. This larger difference from nature images has made the previous feature fusion methods present insignificant performance improvements.

This work proposed a two-convolutional neural network (CNN) fusion method named main and branch CNN fusion network (MBC-Net) as an improved solution for classifying RSI. In detail, the MBC-Net employs an EfficientNet-B3 as its main CNN stream and an EfficientNet-B0 as a branch, named MC-B3 and BC-B0, respectively. In particular, MBC-Net includes a long-range derivation (LRD) module, which is specially designed to learn the dependence of different features. Meanwhile, MBC-Net also uses some unique ideas to tackle the problems coming from the two-CNN fusion and the inherent nature of RSI.

Extensive experiments on three RSI sets prove that MBC-Net outperforms the other 38 state-of-the-art (STOA) methods published from 2020 to 2023, with a noticeable increase in overall accuracy (OA) values. MBC-Net not only presents a 0.7% increased OA value on the most confusing NWPU set but also has 62% fewer parameters compared to the leading approach that ranks first in the literature.

MBC-Net is a more effective and efficient feature fusion approach compared to other STOA methods in the literature. Given the visualizations of grad class activation mapping (Grad-CAM), it reveals that MBC-Net can learn the long-range dependence of features that a single CNN cannot. Based on the tendency stochastic neighbor embedding (t-SNE) results, it demonstrates that the feature representation of MBC-Net is more effective than other methods. In addition, the ablation tests indicate that MBC-Net is effective and efficient for fusing features from two CNNs.

This paper aims to propose a classification method for steel strip surface defects based on a mixed attention mechanism to achieve fast and accurate classification performance. The traditional method of classifying surface defects of hot-rolled steel strips has the problems of low recognition accuracy and low efficiency in the industrial complex production environment.

The authors selected min–max scaling comparison method to filter the training results of multiple network models on the steel strip surface defect data set. Then, the best comprehensive performance model EfficientNet-B0 was refined. Based on this, the authors proposed two mixed attention addition methods, which include squeeze-excitation spatial mixed module and multilayer mixed attention mechanism (MMAM) module, respectively.

With these two methods, the authors achieved 96.72% and 97.70% recognition accuracy on the steel strip data set after data augmentation for adapting to the complex production environment, respectively. Using the transfer learning method, the EfficientNet-B0 based on MMAM obtained 100% recognition accuracy.

This study not only focuses on improving the recognition accuracy of the network model itself but also considers other performance indicators of the network, which are rarely considered by many researchers. The authors further improve the intelligent production technique and address this issue. Both methods proposed in this paper can be applied to embedded equipment, which can effectively improve steel strip factory production efficiency and reduce material and time loss.

The purpose of this paper is to investigate the salivary fluoride retention as fluoride concentration, amount of soluble fluoride, half-life (t_1/2) and salivary flow rate of different amounts of toothpaste and rinsing procedures.

A randomized crossover study of 21 healthy volunteers was designed to compare pharmacokinetic parameters of 1 g (B1) and 0.3 g (B0.3) of toothpaste without rinsing and brushing with 1 g of toothpaste with expectoration followed by water rinsing (B1R). Unstimulated saliva was collected before brushing as a baseline and at 0, 5, 10, 30, 60 and 90 min after the completion of the tooth brushing procedure.

The salivary fluoride concentration and amount of soluble fluoride of the B1 group were significantly higher than the B0.3 and B1R groups. The B1 and B1R groups prolonged the remineralizing level up to 60 min while the B0.3 group retained their remineralizing levels for 30 min. The initial t_1/2 (rapid phase) of B1 and B1R groups were significantly longer than the B0.3 group. The late t_1/2 (slow phase) of the B0.3 group was significantly longer than the B1 group. This is called the two-compartment open pharmacokinetics model. There was no statistical difference of salivary flow rates between all groups.

Non-rinsing and the amount of fluoride toothpaste play an important role in raising salivary fluoride levels and prolonging the remineralizing level of the oral cavity.

In Kyrgyzstan the prevalence of injecting drug behaviour is among the highest found throughout the world. Health promotion training, improved health care and needle/syringe exchange (NSE) programmes have been shown to decrease risk behaviour among injecting drug users. In Kyrgyzstan, an intervention study with training of prison staff and prisoners was performed in one prison. Before and after the training, a random selection of the prisoners answered a questionnaire about drug use, risk behaviour and health care. The survey was carried out in both the intervention prison and in a reference prison. The number of drug users, the use of drugs and risk behaviour were improved significantly within half a year and, especially, the injection and use of drugs decreased in the intervention group. The study clearly shows that increased focus, improved healthcare and training of prisoners and staff on drug use and harm reduction can reduce both use of drugs and risk behaviour.

The purpose of the study is to classify the radiographic images into three categories such as fillings, cavity and implant to identify dental diseases because dental disease is a very common dental health problem for all people. The detection of dental issues and the selection of the most suitable method of treatment are both determined by the results of a radiological examination. Dental x-rays provide important information about the insides of teeth and their surrounding cells, which helps dentists detect dental issues that are not immediately visible. The analysis of dental x-rays, which is typically done by dentists, is a time-consuming process that can become an error-prone technique due to the wide variations in the structure of teeth and the dentist's lack of expertise. The workload of a dental professional and the chance of misinterpretation can be decreased by the availability of such a system, which can interpret the result of an x-ray automatically.

This study uses deep learning (DL) models to identify dental diseases in order to tackle this issue. Four different DL models, such as ResNet-101, Xception, DenseNet-201 and EfficientNet-B0, were evaluated in order to determine which one would be the most useful for the detection of dental diseases (such as fillings, cavity and implant).

Loss and accuracy curves have been used to analyze the model. However, the EfficientNet-B0 model performed better compared to Xception, DenseNet-201 and ResNet-101. The accuracy, recall, F1-score and AUC values for this model were 98.91, 98.91, 98.74 and 99.98%, respectively. The accuracy rates for the Xception, ResNet-101 and DenseNet-201 are 96.74, 93.48 and 95.65%, respectively.

The present study can benefit dentists from using the DL model to more accurately diagnose dental problems.

This study is conducted to evaluate dental diseases using Convolutional neural network (CNN) techniques to assist dentists in selecting the most effective technique for a particular clinical condition.

Our objective was to compare the physical health status of adult prisoners with and without a mental illness. Mental illness was diagnosed in a sample of 557 Australian prisoners using the Composite International Diagnostic Interview (CIDI). Physical health measures included self‐reported chronic health conditions, recent health complaints and symptoms, self‐assessed health using the Short‐Form 36 Health Survey (SF‐36), and markers of infectious diseases known to be highly prevalent among prisoner populations (hepatitis A, hepatitis B, and hepatitis C). Men and women with a mental illness had lower scores on the SF‐36 compared with those without a mental illness indicating poor overall health. Adjusting for age and sex, a diagnosis of any mental illness (symptoms of psychosis, anxiety or affective disorder) was positively associated with a history of head injury, back problems, asthma, peptic ulcers, cancer, and epilepsy/seizures. There was a significant association between post traumatic stress disorder and asthma, a history of head injury, peptic ulcers, and cancer. There was no significant difference in the proportion of current tobacco smokers in the mentally ill and nonmentally ill groups (81% vs. 77%, p = 0.33). However, those with a mental illness were less likely than those with no diagnosis to exercise in the past 4 weeks (79% vs. 89%, p = 0.002). Mentally ill prisoners also have significant physical co‐morbidity compared with the non‐mentally ill. These findings suggest that those providing mental health services to prisoners should adopt a holistic approach embracing both mental and physical health.

HAVING discussed in the standard longhand notation the main ideas and methods for the calculation of redundant structures on the basis of forces as unknowns we now turn our attention to the matrix formulation of the analysis. Consider a system consisting of s structural elements with a total number n of redundancies which may be forces (stresses), moments or any generalized forces. We select a basic system by ‘cutting’ a number r of redundancies where r<n. Thus, the simple idea of a statically determinate basic system (r=n) is but a particular case of our investigations.

The purpose of this paper is to present optimally designed synchronous reluctance machine (SynRM) to demonstrate the feasibility of eliminating the use of rare earth permanent magnet (PM) in electric machine for vehicle traction applications.

A typical rare earth interior permanent magnet (IPM) machine is used as the benchmark to conduct the optimal design study. Based on the flux distribution, major changes are made to the rotor lamination design. Enhanced torque production and lower torque ripple are specifically targeted as the two main objectives of the proposed design approach.

As a result, the optimally designed SynRM can achieve performance very close to that of the benchmark PM machine with a potential for further improvement.

Discussions of IPM replacement by optimally designed SynRM in electrical and hybrid electrical vehicles are given in terms of performance and cost.