The lack of labeled data and variable working conditions brings challenges to the application of intelligent fault diagnosis. Given this, extracting labeled information and learning distribution-invariant representation provides a feasible and promising way. Enlightened by metric learning and semi-supervised architecture, a triplet-guided path-interaction ladder network (Tri-CLAN) is proposed based on the aspects of algorithm structure and feature space. An encoder–decoder structure with path interaction is built to utilize the unlabeled data with fewer parameters, and the network structure is simplified by CNN and an element additive combination activation function. Metric learning is introduced to the feature space of the established algorithm structure, which enables the mining of hard samples from extremely limited labeled data and the learning of working condition-independent representations. The generalization and applicability of Tri-CLAN are proved by experiments, and the contribution of the algorithm structure and the metric learning in the feature space are discussed. As one of the most important components of intelligent manufacturing equipment, the health status of rotating machinery may affect the overall operation status of the equipment. For instance, the faults of bearings are prone to reducing the processing quality of the workpiece, and even result in considerable economic losses and potential safety hazards. Each fault of rotating machinery will eventually be embodied in the external excitation caused by mechanical structure defects, which produces mechanical vibration signals which differ from the healthy state. Due to the exceptional performance in solving the nonlinear feature extraction for machine vibration data, deep learning has witnessed remarkable success in the field of rotating machinery fault diagnosis . In pracRecent advances in supervised learning methods have been widely employed to overcome the challenge of variable working conditions. Xing et al.  proposedResearchers have mainly made great efforts to alleviate the problem of insufficient labeled data from these three aspects: the feature learning-based strategy, the algorithm structure-based strategy and the data augmentation-based strategy . From thTaking the considerable fault information of unlabeled data into account, which is the most inexpensive data available in industrial scenarios, designing a semi-supervised algorithm structure appears to be a viable solution to address the issue above. The graph-based semi-supervised learning method ,12,13 coRegarding aspects of the data augmentation-based strategy, some researchers attempt to extract more sensitive fault features based on signal processing. Zhang et al.  input thThese two challenges are usually overcome individually, and few works in the literature have studied these two issues simultaneously.Closer attention is paid to expanding labeled data for supervised learning, while considerable fault information contained in unlabeled data is ignored and wasted.More than ten labeled training samples are chiefly required; however, the available labeled samples are fewer in real industrial scenarios.Taken together, the research described above has the following shortcomings when facing the lack of labeled data and the variable working conditions:Recent advances in face recognition are attributed to the rise of metric learning. Unlike generative networks, which need to pay attention to each detail of the labeled data distribution, metric learning shows its promising potential to learn discriminative embeddings that can distinguish from other samples. Typically, contrastive loss  and tripCLAN, a novel CNN-based ladder network, replaces the vanilla ladder network (LAN) backbone with a CNN and integrates the structure of the vanilla ladder network. Thus, the classification error of labeled samples and the reconstruction error of unlabeled samples can be obtained, and the parameters of the training process can be reduced by a simplified combination activation function and a path-interaction strategy.To further alleviate the feature distribution shifting problem under variable working conditions, the triplet loss with the hard sample mining strategy is utilized to enlarge the margin among the embeddings of the limited labeled samples under different working conditions, which enables the proposed method to emphasize the fault-related features.The proposed method is evaluated on two datasets: the first is the public bearing dataset from Case Western Reserve University (CWRU) for comparison with other state-of-the-art algorithms and the second is the experimental bearing dataset from our laboratory test rig of the motorized spindle to illustrate its extensive applicability. A few labeled data are selected randomly to verify the effectiveness of the proposed method. Moreover, variable working conditions are able to prove the ability of the learning distribution-invariant features.Given the shortcomings of the above methods and enlightened by metric learning, both algorithm structure-level and feature-level aspects are considered in this paper. In terms of the algorithm’s structure-level, a CNN-based ladder network (CLAN) with path interaction is established to extract features from the most readily available unlabeled data and the limited labeled data. From the aspects of the feature-level, the similarity among anchor, positive and negative samples are calculated in the embedding space based on metric learning, in which extremely limited labeled samples can be regarded as hard samples to mine fault-related information and eliminate the working condition shifting effect. Therefore, the acquired classification error, reconstruction error and triplet loss are jointly defined as the objective function for the proposed method. The main contributions of this study, as well as the acquisition of the objective function, are listed as follows:The remaining part of the paper is organized as follows. In In the field of unsupervised learning, a two-stage strategy of “unsupervised pre-training + supervised fine-tuning”  is adoptThe structure of the vanilla LAN was proposed by Rasmus et al. , which cGiven a sequence of sample set BN) and rectified linear units (ReLU) are applied to each layer, including the top-level layer. The corrupted noise Generally, the vanilla LAN is based on a fully connected autoencoder network, and batch normalization  as a semi-supervised model for extremely limited labeled samples under variable working conditions. Compared to conventional semi-supervised deep learning methods, the innovation of Tri-CLAN is mainly reflected in two aspects: on the one hand, a CNN is utilized to substitute for the fully connected (FC) layer in the vanilla LAN, and reasonable model simplification is implemented to prevent over-fitting; on the other hand, triplet loss is introduced to further mine hard samples from limited labeled data, which forces the model to learn distinctive and working condition-independent embeddings among samples. The architecture of Tri-CLAN is displayed in Conv(∙) followed by the batch normalization operation BN(∙) and the non-linear activation operation ReLU(∙) as follows:Conv(∙) multiplies the local input data by the shared weight of the convolution kernel, and thus the feature matrix can be obtained and the corresponding output of Conv(∙) at layer l can be described as:BN(∙) is represented by ReLU(∙) is formulated by As depicted in Path 1 of BN(∙) and an element-wise activation operation l, andIn the decoder, the corrupted representation So far, the corrupted representation The acquisition path of the classification error consists of a corrupted encoder sharing parameters with the corrupted encoder of the acquisition path for reconstruction loss, which is a branch followed by fully connected layers for the prediction of fault types, as depicted in Path 2 of The acquisition path of triplet loss, another branch of labeled data, is composed of pairwise distance calculation, hard labeled sample mining and hardest triplet training. From the aspect of mapping space, the fault information is mined to the greatest extent according to the similarity among triple samples. Intuitively, the large amount of possible generating triplets may result in uninformative representation, rendering a slow convergence and an overlong training duration. With this realization, selecting the hardest positive and negative samples provides an organizational modification for the triplet loss, which dramatically helps learning essential fault-related representation of limited labeled data. As depicted in Path 3 of Profiting from the joining of semi-supervised learning and metric learning, the proposed method can not only excavate discriminative features from unlabeled and labeled samples, but it can also optimize the embedding space of limited labeled samples to further obtain semantically meaningful fault features. As the illustration of the information flow shown in Unlike the fixed “pre-trained + fine-tuning” mode, lgorithm . When TrSeveral experiments on two case studies are conducted to evaluate the effectiveness and applicability of the proposed method. One is the public bearing dataset from CWRU, which is regarded as the standard dataset for objective comparison with the state-of-the-art algorithms, and the other is the laboratory test rig of the motorized spindle.BN and ReLU operation. In the CE-FC and DE-FC, a fully connected form with three hidden layers is adopted for a fair comparison. Likewise, the predicting module with FC is used for dimension reduction and fault classification. Put slightly differently, the predicting #1 requires an additional flattening operation to integrate the features of several channels into a 1D form. The detailed kernel size and neuron number of each module are listed in In experimental studies, the raw data are usually 1D, and fast Fourier transform (FFT) is used to highlight more frequency-domain information. In order to facilitate the subsequent description of the architecture of the proposed method and the corresponding comparison methods, several basic modules are built, including a corrupted encoder module based on 1D CNN (CE-CNN), a decoder module based on 1D CNN (DE-CNN), a corrupted encoder module based on FC (CE-FC), a decoder module based on FC (DE-FC), a predicting module and a metric learning module. In the CE-CNN and DE-CNN, three 1D convolutional layers and three 1D transposed convolutional layers are separately constructed followed by This study applies comparison to conduct a systematic and intensive study, and several deep learning methods based on basic modules are employed to verify the effectiveness of the proposed method, which contains a conventional CNN and vanilla LAN. Additionally, methods without the metric learning module are employed to discuss the constructive contribution for limited labels and variable working conditions. The detailed architecture of these methods can be shown in The experiments are carried out on a computer with NVIDIA Quadro RTX 6000 GPU, and PyTorch platform is implemented as the backend for programming. For the experimental error reduction, each case study experiment is repeated with ten trials to avoid contingency.As is well known, the public bearing dataset of CWRU is most commonly used to verify the effectiveness of diagnostic algorithms. As shown in Before beginning the experiments, the datasets under different working conditions need to be preprocessed uniformly, and a sample consisting of 2048 points is intercepted from the original vibration data of each fault label. Then, FFT and zero-mean normalization processing are introduced into each sample to obtain samples with a length of 1024. Hereafter, 3000 and 1000 samples are generated for training and test datasets, respectively. According to different experimental purposes, the training data will be subsequently divided into labeled training data and unlabeled training data in different proportions.1, B1, C1) with different labeled sample numbers are established by randomly selecting from working condition C1; the exact number of training samples and testing samples for each category is listed in 2, B2, C2, D2) on the foundation of experiment 1, and the number of training samples and testing samples is listed in Two experiments were conducted to verify the superiority of the proposed Tri-CLAN. Firstly, to preliminarily demonstrate the diagnostic performance, three datasets . It is worth adding that the confusion matrix diagram shown in As shown in On the premise of preliminarily proving the effectiveness of the proposed algorithm with extremely limited labeled samples, we further discuss the contribution of the ladder-shaped semi-supervised network structure and triplet loss for the proposed Tri-CLAN. Therefore, the methods listed in According to the ranking in Based on the comparisons of architectures with different modules, we systematically select some excellent references from recent years with the same diagnostic purpose and compared the experimental results with the proposed Tri-CLAN in this paper. The classification category, the number of training samples and the test accuracy of the comparative experiment are all listed in On the whole, experiment 1 focuses on the performance comparison of limited labeled samples, and from the above discussion of the experimental results, it can be proved that the proposed Tri-CLAN can effectively utilize the unlabeled samples to assist extremely limited labeled samples and improve the accuracy of the method. In a follow-up experiment, we decided to increase the difficulty of experiment 1 by training with extremely limited labeled samples under one working condition and testing under other working conditions. Herein, the actual manufacturing operation is simulated, in which it is difficult to obtain data under the same working condition.To better understand the working condition shifting effect with extremely limited samples, Probing into the experimental group with the most significant difference degree and the least labeled sample is meaningful to prove the effectiveness and ability for learning distribution-invariant features of the proposed Tri-CLAN. As can be seen from the above heatmap, the average accuracy of C1 → C4 and C4 → C1 is 83.05% and 86.58%, respectively, which are the worst diagnosis test results in experiment 2. To further observe the specific situation of the classification, confusion matrices and visualization features which are closest to the average accuracy are shown in We review experiment 1 and prove that triplet loss plays a crucial role when the labeled samples are extremely few. To reduce unnecessary comparisons, we directly compare the proposed Tri-CLAN with the top two algorithms in experiment 1, which are the methods adding metric-learning . The comparative experimental results are presented in the form of three-dimensional histograms in The motor fault dataset is collected and organized by Shenzhen Technology University (SZTU). The test system is shown in In order to verify the diagnostic performance of the proposed Tri-CLAN under different working conditions, four working conditions are listed in We directly select one labeled sample for experimental verification under variable conditions, which is the most extreme case. The heatmaps of experimental results for one labeled sample are displayed in Compared to the publicly available CWRU dataset, the motor fault dataset has more fault categories and more complex working conditions, which can sufficiently demonstrate the applicability of the proposed method in more complex practical scenarios.Here, to further illustrate the extensive applicability of the proposed method, a laboratory bearing of the motorized spindle test rig (MSTR)  is separTo align with the experiment of case study 1, the sampling frequency of the vibration data is set to 12 kHz. The rotation speed of the bearing is consistent with that of the motorized spindle, and two working conditions with a large difference are selected for experimental verification according to the reliability test conditions in the literature , which aWe carried out two typical working conditions including four kinds of identification tasks, respectively, as the most demanding data condition, and the heatmaps of the experimental results for one labeled sample are displayed in Given the actual industrial data, it is urgent and necessary to carry out end-to-end intelligent fault diagnosis. This paper innovatively proposes a triplet-guided path-interaction CNN-based ladder network, which realizes the intelligent fault diagnosis with extremely limited labeled samples under variable working conditions. To accommodate the data requirements, the proposed method can be elaborated from the aspects of algorithm structure and feature space. First, to integrate the advantage of the convolution operation and achieve information fusion with fewer parameters, this paper replaces the vanilla LAN backbone with a CNN and constructs a path-interaction semi-supervised architecture with a simplified combination activation function. Furthermore, benefiting from the contribution to the triplet loss with a hard sample mining strategy, the feature distribution shifting problem between variable working conditions is alleviated by learning fault-related representation at the feature level. The public CWRU bearing dataset is utilized to verify the effectiveness of the proposed method, and two experimental datasets are applied to illustrate its extensive applicability. In future research, we will collect data from more engineering application scenarios to verify the proposed algorithm. Furthermore, we will further study the intelligent fault diagnosis method for the purpose of its application to other extreme circumstances in the industry.


Nowadays, infection diseases are one of the most significant threats to humans all around the world. An encouraging strategy for solving this issue and fighting resistant microorganisms is to develop drug carriers for a prolonged release of the antibiotic to the target site. The purpose of this work was to obtain metronidazole-encapsulated chitosan nanoparticles using an ion gelation route and to evaluate their properties. Due to the advantages of the ionic gelation method, the synthesized polymeric nanoparticles can be applied in various fields, especially pharmaceutical and medical. Loading capacity and encapsulation efficiency varFied depending on the amount of antibiotic in each formulation. Physicochemical characterization using scanning electron microscopy revealed a narrow particle size distribution where 90% of chitosan particles were 163.7 nm in size and chitosan-loaded metronidazole nanoparticles were 201.3 nm in size, with a zeta potential value of 36.5 mV. IR spectra revealed characteristic peaks of the drug and polymer nanoparticles. Cell viability assessment revealed that samples have no significant impact on tested cells. Release analysis showed that metronidazole was released from the chitosan matrix for 24 h in a prolonged course, implying that antibiotic-encapsulated polymer nanostructures are a promising drug delivery system to prevent or to treat various diseases. It is desirable to obtain new formulations based on drugs encapsulated in nanoparticles through different preparation methods, with reduced cytotoxic potential, in order to improve the therapeutic effect through sustained and prolonged release mechanisms of the drug correlated with the reduction of adverse effects. For many years, nanotechnology significantly contributed to biomedicine regarding prevention, diagnosis and treatment of various diseases ,2,3. NanMetronidazole (MET) is a broad-spectrum antibiotic used to combat anaerobic bacteria and some parasites. It is a nitro-imidazole  derived Administration of MET can cause certain side effects such as nausea, mouth dryness, epigastric pain and others. A major disadvantage regarding conventional therapy refers to the accumulation of high drug concentrations in the liver and kidneys. Among the side effects caused by the administration of systemic antibiotics, gastrointestinal intolerances, hypersensitivity reactions and the establishment of bacterial resistance can be noted. There are studies that reveal that if the active substance does not reach the desired concentration at the site of action, systems for the prolonged release of drugs can be developed, reaching the desired concentration at the site of action and reducing adverse effects.In order to combat adverse effects of metronidazole it is necessary to reformulate the antibiotic by developing a different drug delivery nanosystem which yields better targeted transport of the active molecule. Nowadays, researchers are focused on reducing antibiotic side effects as well as delivering the active molecule to the target site, enhancing drug efficiency ,14,15,16Although many types of nanoparticles were studied as drug delivery systems, ample researches were developed on polymeric nanostructures for various active molecule transports. Polymeric nanosized architectures own excellent stability, reproducibility and biodegradability properties, making them suitable for the application in several pharmaceutical formulations .Chitosan is a natural polymer obtained with the deacetylation of chitin. It is a biocompatible cationic polysaccharide metabolized with specific human enzymes, mainly lysozyme, which attributes chitosan to be a possibly efficient drug carrier ,20,21. CThe significant properties of chitosan determined researchers to use it in various bio-medical formulations. Studies on the synthesis of nanoparticles with the ionic gelation method have attracted the attention of researchers in recent years. This is because various compounds can be loaded into the polymer structure, thus constituting the drug delivery system and being able to be used later in various medical fields. Therefore, this biopolymer has been involved in several trials, namely drug delivery, with applications in many medical areas, such as dental medicine, tissue engineering, epidemiology, pulmonology, neurosurgery, neonatology, cardiology, emergency, regenerative medicine (including hard tissue and soft tissue due to its ability to protect unstable biomolecules), biodegradation, biocompatibility, mucosal tissue adhering, nontoxicity and antimicrobial activity. The use of such systems requires small amounts of the active substance as the absorption of the medicine is carried out in a sustained and controlled manner, thus reducing the side effects of antibiotics ,29,30,31Metronidazole active substance was offered by a research institute for free . Chitosan (75–85% de-acetylated) and sodium tri-polyphosphate was acquired from Sigma–Aldrich, Darmstadt, Germany. The other reagents used to obtain antibiotic-loaded chitosan nanoparticles were of analytical grade of purity.v/v glacial acetic acid) resulting in positively charged chitosan. Then, this solution is added to a TPP solution (0.1% w/v in ultrapure water) under stirring conditions and forms a positive–negative complex. Chitosan nanoparticles (Chi) were obtained with ionic gelation method  using trFor preparation of antibiotic-loaded polymer nanoparticles, metronidazole (in different concentrations) was added to chitosan solution, and adjustment of the pH of each product at 5.0 was performed by using solutions of 0.1 N HCl and 1 N NaOH. The as prepared nanocomposites were centrifuged at 9000 rpm for 1 h at room temperature. After this step, the supernatant was kept at 2–6 °C until further assessment. Finally, the antibiotic-loaded chitosan nanoparticles were lyophilized for 48 h and analyzed to achieve the aims of this study. The ionic gelation method requires cheap and easy-to-use materials and equipment, and having the advantage of the electrostatic interaction mechanism, instead of chemical synthesis, leads to the avoidance of possible toxicity of the reagents; however, its disadvantage is that nanoparticles are not produced on a large scale with a uniform size distribution. This technique is widely used because it has proven to be useful for obtaining various formulations of polymeric nanoparticles loaded with drugs. Each formulation code is presented in After preparing the nanostructures, their characterization included particle size distribution (PSD), zeta potential, morphology , encapsulation efficiency (EE), loading capacity (LC), Fourier transform infrared spectroscopy (FTIR) and spectrometry (UV-Vis). The characteristics of nanocomposites using these advanced techniques depended on the synthesis conditions.Characterization of the obtained samples was based on antibiotic-loaded polymer nanoparticles.Loading capacity and encapsulation efficiency of each composite was established using a Cary 60 UV-Vis spectrometer at 319.5 nm to measure the absorption of each supernatant after centrifugation. Loading capacity and encapsulation efficiency are expressed by the following formula:-Wt is the total content of metronidazole encapsulated initially into the polymer matrix;-Wf represents the amount of drugs in the supernatant;-Wn represents the weight of dried nanoparticles after lyophilization process.Loading capacity is an indicator that reveals the amount of drugs that can be loaded into a quantity of nanocomposite; encapsulation efficiency is an index that shows the yield of the process of obtaining nanostructures.Particles’ size distribution was established with DLS (dynamic light scattering) using a Zetasizer Nano ZS , which evaluates the particle size, electrophoretic mobility and zeta potential. Morphological features of metronidazole-loaded chitosan nanoparticles were analyzed using scanning electron microscope  equipped with an energy dispersive spectrometer , which allows high-resolution morphological investigations of these types of nanocomposites. Nanostructured samples were dried at room temperature by spreading the suspensions on a glass plate; then, they were coated with gold under vacuum before examination. −1) that were obtained by compressing a mixture containing small amounts of KBr and antibiotic-loaded polymer nanoparticles. FTIR (Fourier transform infrared spectroscopy) characterization implied scanning of KBr tablets , allowed to attach and grow overnight. Treatment with the polymeric nanoparticles anticipated the replacement of growth medium with new complete medium containing the NPs in doses ranging from 100 µg/mL to 0.01 µg/mL. After 24 h of treatment, the cells were washed and covered with 100 μL of fresh DMEM 10% FBS. An amount of 10 μL of MTT (5 mg/mL) was added into medium, and cells were incubated for another 3 h. DMSO  was used to dissolve the formazan that was formed, and the absorbance was recorded at 570 nm. HeLa cells were used for testing cytotoxic effect of metronidazole-encapsulated chitosan nanoparticles. After 24 h of incubation, in presence of the tested compounds, cell viability was evaluated with MTT assay. HeLa cell line was maintained in DMEM , and supplemented with 10% FSB , 100 IU/mL penicillin and 100 µg/mL streptomycin at 37 °C in a humidified atmosphere of 5% COThe loading capacity and encapsulation efficiency  reveals The results of particle size and zeta potential evaluation showed that the concentration of the antibiotic did not intercede with the size and the positive zeta potential of the metronidazole-loaded chitosan nanoparticles.SEM micrographs, By comparing the morphology of antibiotic-loaded polymeric nanoparticles and non-loaded nanoparticles, it was observed that loading the drug into the chitosan structure did not significantly change the textural properties of the nanoparticles.−1 attributed to the C-O stretching vibration, a N-O stretching at 1370 cm−1, a C-N stretching vibration at 1540 cm−1 and a O-H bond at 3230 cm−1. The IR spectrum of chitosan showed characteristic peaks at 3450 cm−1 assigned to the –OH and NH2 stretching vibration, and peaks at 1650 cm−1 attributed to the amide group.−1 corresponding to the amide group in chitosan, and peaks at 1490 cm−1 attributed to the –NO3 group in the metronidazole molecule and peaks at 1420 cm−1 assigned to the intermolecular hydrogen stretching vibration. IR spectrum of ChiMet_2.2 sample presented peaks at 1670 cmFrom the analysis of the IR spectra, it can be assumed that the efficiency of the incorporated active principle depends on the technique of loading the drug into the polymer structure. The in vitro release profiles of the antibiotic from the polymeric nanoparticles in different dissolution media A,B: phosThe release of metronidazole from the polymeric nanostructures shows that these drug carriers allow sustained and prolonged release of the drug at the target site, increasing the bioavailability of the drug and minimizing its adverse effects.The release profile of metronidazole with different concentrations in the formulation showed that the release rate of the drug tended to increase as the amount of the drug in the sample increased, which lead to differences in the diffusion mechanism.Cellular viability (%)  was calcNo significant differences were recorded between the different concentrations used in terms of the cytotoxic impact. However, even if the value of the cytotoxic impact is around 30%, from the point of view of the antitumor response, it has no therapeutic significance.−1 to evaluate chemical interactions between metronidazole and polymer molecules. FTIR spectral investigations, performed to analyze the alteration of chemical structure, revealed no significant physicochemical interaction between metronidazole and chitosan, concluding that these compounds are compatible with each other. The small changes in the absorption spectra of the new formulation of chitosan nanoparticles loaded with metronidazole possibly occurred due to the antibiotic molecule that changes the functional groups in the polymer. The dissolution assay indicated that the antibiotic was immobilized in the polymer structure due to the crosslinked gelation technique. The release profile suggests that the active substance was released in a controlled, prolonged manner from the polymer matrix with a non-Fickian behavior, indicating a transport of the active principle via diffusion, which is associated with polymer relaxation. Changing the metronidazole concentration did not significantly change the release profile of the drug from the tested samples and reached 80% for a 24 h period. The swelling and relaxation of the polymer could have a significant role in the release mechanism of the embedded substance. This could be due to the rapid dissolution of the drug on the surface of the chitosan nanoparticles that created pores, and its release from the polymeric nanostructure would have occurred through those pores. Towards the end of the delivery time, a decrease in the release speed could be observed due to the increase in the diffusion path of the drug. MTT testing is a quantitative method used on a large scale to determine, in vitro, the cytotoxic effects of some products  on cell lines. The cytotoxicity test was used to evaluate the possibility of the new formulation based on chitosan nanoparticles loaded with metronidazole having a therapeutic action without toxic effects on the cells. These findings presume promising applications of drug carriers in pharmacological and medical areas. Nowadays, there is a particular interest in obtaining polymeric nanoparticles through the ionic gelation method, which can be later loaded with new active ingredients and used both to prevent and to treat serious infections caused by various pathogens. This research aimed to highlight the possibility of expanding the field of applications of drug carriers based on polymeric nanoparticles loaded with different active principles to prevent and treat dangerous infections but also to reduce the side effects of drugs. An important aspect to consider refers to the chemical structure of the active molecule; therefore, the chemical affinity between the polymer and the drug can reveal surface interactions. Considering this factor in the design of new formulations could lead to the functionalization of the surface of polymeric nanoparticles to improve their performance as controlled drug release systems. This work aimed to obtain a new formulation of polymeric nanoparticles loaded with an antibiotic in which the structural and morphological characteristics were evaluated according to the conditions and methods of preparation and thus obtaining nanoparticulate systems with prolonged release of the drug without significant cytotoxicity. Further research should focus on testing chitosan nanoparticles loaded with metronidazole in different medical fields, and is expected to obtain a special performance of the transport and release of the drug at the target site. The prepared nanoparticles were characterized for their use in the delivery of active principles in biological systems, focusing on new formulations that reduce the adverse effects of drugs.The continuous development and improvement of drug delivery nanosystems is of major importance for the technologies used in the pharmaceutical field. Thus, obtaining nanostructures that act as a matrix for different active principles is significant due to the stability and control of drug release. Recently, the use of drug delivery systems in nanomedicine enhanced prevention, diagnosis and treatment of some threatening diseases by targeting the affected site while minimizing adverse effects. By using the ionic gelation method, bioactive molecules can be encapsulated in the polymer matrix to improve their effectiveness. Changing the amount of the drug in nanocomposites determined a loading capacity in the range of 70–62% and an entrapment efficiency in the range of 26–35%. The encapsulation capacity variation occurs due to the fact that chitosan nanoparticles precipitate faster, inhibiting the incorporation of the drug, which is followed by an increase in the diffusion of the drug when its amount in the formulation increases. Particle size distribution and zeta potential indicated no considerable modification when the antibiotic molecule was encapsulated into the polymer structure. The method used for the preparation of chitosan nanoparticles loaded with metronidazole led to a narrow size distribution of the nanocomposites. The complexity of the formation mechanism of the new nanostructures is probably due to the presence of phenomena, such as nucleation, growth, self-assembly and aggregation, that take place at the same time, thus determining the characteristics of the newly prepared formulations. If the predominant phenomenon is nucleation, a larger number of nanoparticles with smaller sizes are obtained; if it is molecular growth, self-assembly or aggregation predominates, and larger nanoparticles are formed. The conditions predetermined with the preparation method of the new compounds determine the predominant phenomenon, with them being the factors that influence the morphology and size distribution of the obtained nanoarchitectures. It was observed that an increase in the amount of the drug in the formulation implies a slight increase in the size of chitosan nanoparticles loaded with metronidazole from 163.7 nm to 201.3 nm, thus suggesting that the incorporation of the active molecule in the polymer matrix does not significantly change the size of the nanostructure. Morphology analysis performed with scanning electron microscopy indicated a porous surface of pure chitosan nanoparticles and spherical-shaped particles of the tested metronidazole-immobilized chitosan nanoparticles. A difference in the degree of agglomeration in nanocomposites compared to chitosan nanoparticles can also be observed. The IR spectra were recorded at the wavenumber range of 4000–400 cmThis study presents the synthesis and characterization of metronidazole-loaded chitosan nanoparticles as carriers for the antibiotic target site. The ionic gelation technique was used to prepare polymer nanoparticles. Chitosan is a natural polymer obtained with the deacetylation of chitin in an alkaline environment which is then metabolized by a human enzyme such as lysozyme. It is a cheap and easy to obtain, biocompatible, biodegradable and non-toxic compound that acts as a matrix for the inclusion of active principles. They also have a prolonged release at the site of action, which is followed by the dissolution of the host. Structural and morphological properties of as prepared delivery systems were performed using FTIR, SEM techniques and UV-VIS spectroscopy in order to investigate the release behavior. FTIR and SEM analyses indicate that the loading of the polymer matrix with an active principle does not significantly change its structural and textural characteristics. Moreover, the prolonged and controlled release of the molecule helps modern medicine in the diagnosis, prevention and treatment of many diseases. The results of the particle size distribution indicated a slight increase from 163.7 nm, in the case of uncharged polymer nanoparticles, to 210.3 nm, in the case of chitosan nanoparticles loaded with metronidazole. The cytotoxicity profile revealed that the cytotoxic impact was insignificant. Based on the obtained results, drug carriers can be successfully used for enhancing the encapsulation and biodistribution of metronidazole at the target site. Chitosan possesses low toxicity, indicating that it can be used in the biomedical field as a drug delivery nanosystem, thus promising various formulations which enhance drug therapy and limit side effects. Incorporating the antibiotic into the structure of chitosan nanoparticles can offer multiple advantages in the creation of transport systems and the localization of the active substance. The characteristics of nanocarriers such as the shape, size, release profile and cytotoxicity are significant in the design of drug delivery systems. Moreover, a promising strategy to combat antibiotic-resistant microorganisms and to reduce nosocomial infections is to use drug-loaded nanocarriers.


This paper discusses the challenges in characterizing electromagnetic (EM) waves propagating through inhomogeneous media, such as reinforced cement concrete and hot mix asphalt. Understanding the EM properties of materials, including their dielectric constant, conductivity, and magnetic permeability, is crucial to analyzing the behavior of these waves. The focus of this study is to develop a numerical model for EM antennas using the finite difference time domain (FDTD) method, and to gain a deeper understanding of various EM wave phenomena. Additionally, we verify the accuracy of our model by comparing its results with experimental data. We analyze several antenna models with different materials, including the absorber, high-density polyethylene and perfect electrical conductors, to obtain an analytical signal response that is verified against the experimental response. Furthermore, we model the inhomogeneous mixture of randomly distributed aggregates and voids within a medium. We verify the practicality and reliability of our inhomogeneous models using experimental radar responses on an inhomogeneous medium. Ground-penetrating radar (GPR) is a non-destructive investigative tool that uses electromagnetic (EM) waves and is commonly utilized in the fields of geophysics, structures, and transport systems ,2,3. HowEM wave simulations can be conducted using various numerical methods, including the finite difference time domain (FDTD) method, the finite element method (FEM), and the method of moments (MoM). In the FDTD method, the simulation domain is partitioned into a grid of small cells and the electric and magnetic fields at each cell are calculated at discrete time steps . The FEMIn a study conducted by Bai and Sinfield (2014), it was concluded that FEM is better suited to accurately describing complex geometries and the representing field. On the other hand, FDTD is better suited to spectroscopic studies when a large number of wavelengths are of interest and/or when a non-regular and/or non-Cartesian mesh refinement is implemented. Hence, the FDTD method is preferable for the simulation of EM waves under the same conditions, as well as for GPR simulations. Furthermore, recent computational advancements have significantly benefited volume-based numerical techniques such as the FDTD method and enabled larger and more complex problems to be investigated. This, combined with the need to obtain quantitative information from GPR, necessitates the creation and use of detailed 3D FDTD models of realistic GPR antennas .In their recent study,  employedTo validate the results obtained through the use of random and irregular materials, it is crucial to match the various conditions during the experimental study of the inhomogeneous model. This paper presents works that were conducted to overcome the key technical barriers for a numerical analysis of the inhomogeneous model, with a model of the EM wave using a 1600 MHz antenna to conduct the verification experiment. The antenna model was constructed by conducting an experiment where the 1600 MHz antenna emitted EM waves between the antenna and a steel plate that was placed at a distance of 230–506 mm. The analysis model was then fine-tuned to match the experimental results obtained under the same conditions. This approach ensured that the experimental and numerical analyses were conducted under identical conditions, thus validating the accuracy of the results obtained through the numerical analysis. The findings indicate that the newly designed antenna model has potential for the analytical simulation of inhomogeneous random distributed particles. This model serves as a valuable reference for analysis, complementing the experimental results and improving the accuracy of GPR signal analyses in such media.The FDTD method is a widely used numerical technique for simulating EM fields. It discretizes both the time and space domains and solves Maxwell’s equations at discrete points in the time and space to simulate the wave propagation. The governing equations for Maxwell’s time-varying EM fields are expressed in Equations (1)–(4).The electric flux density (By solving Equation (7), we can obtain the three-dimensional spatial effect resulting from the changes in the electric field over time, while taking into account the permittivity and permeability of the medium, as shown in Equation (8).The FDTD method, which employs the second-order central difference algorithm in the Yee cell, is commonly used to simulate EM wave propagation. This numerical method involves discretizing both the space and time and replacing the derivatives in Ampere’s and Faraday’s laws with finite differences. By doing so, the electric and magnetic fields can be staggered in both space and time. The difference equations are then solved to obtain updated equations that express the future EM fields in terms of the past EM fields. The magnetic fields are evaluated one time-step into the future, effectively becoming past fields, followed by the evaluation of the electric fields one time-step into the future, also becoming past fields. These steps are repeated until the fields have been obtained over the desired duration. The FDTD method is a powerful numerical tool for simulating EM wave propagation and has applications in various fields, including optics, electromagnetics, and microwave engineering. Considering the Yee cell theory, the Maxwell equation with the applying curl can be rewritten, as shown in Equations (9)–(14).In the Yee cell numerical simulation, the parameters affecting the medium are taken into consideration. These parameters include the permittivity  modeling approach and verify its accuracy through experimentation with aggregate specimens.To achieve the first task, we will begin by conducting a comprehensive review of the existing literature on EM waves and antenna modeling. We will then design and create simulation models using different antenna designs to evaluate their effectiveness in generating and receiving these EM waves. These simulation models will be tested using a testing configuration that we will establish to enable a comparative analysis. To accomplish the second task, we will develop the RDP modeling approach based on its established principles and theory. We will create simulation models of the EM waves through the inhomogeneous medium using the RDP approach. To verify the accuracy of the RDP approach, we will perform experiments with aggregate specimens, comparing the results obtained from the simulation modeling with those obtained from the experimentation. Throughout both tasks, we will use analytical tools such as a numerical analysis and statistical analysis to analyze the data obtained from the experimentation and simulation modeling. This will enable us to draw conclusions and make recommendations based on our findings.The methodology involved in this study utilizes the FDTD numerical method for EM wave propagation simulations. To accurately simulate the wave propagation, it is important to carefully consider the input sources used in the simulation. Therefore, various input source signals were studied in different setups and locations. These signals were then cross-verified with a reference signal obtained from a 1600 MHz antenna over an air medium. However, achieving an exact or even a similar match between the FDTD signal and the experimental signal is a challenging task. To address this issue, the study focuses on obtaining a signal match within the wave propagating medium. In order to validate the wave propagation within the air medium at specific depths, a reference metal plate was placed at the respective depths to obtain a perfect reflection of the EM wave. The placement and reflection of the reference metal plate can be observed.To validate the antenna model under varying conditions, the simulations were conducted at different depths of air medium, with a metal reference plate placed below each depth. The EM waves were propagated through the experimental setups at depths of 305, 356, 406, 457, and 508 mm , as depicted in To achieve a random distribution of the particles without overlap, a Python code was developed to determine the 3D locations of the particles. Initially, equally spaced particles were modeled with the Python code, using Equations (15) and (16) to understand their patterns and locations.To achieve a random distribution of the particles, the percentage of the total volume was taken into account, as shown in Equation (17), to determine the number of particles to be randomly placed in the model, with a tolerance distance between each particle, as specified in Equation (19). Once this tolerance distance was maintained, the respective particle sphere was added to the input file and the volume of the particle was reduced accordingly, as seen in Equation (20). An example of these randomly distributed particles is shown in The random particle experiment was conducted at three different depths of 305, 356, and 405 mm , which were made of plywood and had two aggregate sieve size ranges of 12–25 mm and 25–50 mm (0.5–1 inch and 1–2 inches), as shown in Various model parametric values were simulated to verify the accuracy of the input source and their results were compared with the experimental response, as shown in The objective of this study was to investigate the response of EM waves under different parameters, in order to understand their effects and variability with each antenna design component. In The parameter studies conducted in this paper demonstrate that a simple source and transmitter cannot accurately represent a GPR signal response, and instead, the structure of the antenna must be taken into consideration. In this study, the internal components of the antenna module were considered from a cross-sectional perspective , with thTo validate the accuracy of the designed antenna, simulation models were created with varying depths of air medium (ranging from 305–506 mm or 12–20 inches), as shown in The experimental setup for testing the inhomogeneous aggregate–void medium is illustrated in The simulation model was analyzed under the same conditions as the experimental setup shown in In comparing the results of the random particle simulation and the experimental study for the different sizes of aggregates, a similar trend is observed, but there is a delay in the arrival time at each depth due to the random distribution of the aggregate models and variable sizes . HoweverThe dielectric constant and dimensions of the material through which an EM wave propagates can affect the signal’s travel time and energy attenuation. However, for a more comprehensive analysis of the wet- and magnetic-related phenomena, other parameters such as conductivity and magnetic permeability must also be taken into account.A simple voltage source or receiver cannot be considered to be equivalent to an experimental antenna. To achieve a similar response to that of the experimental results, a well-designed antenna model should include absorbers, PCBs, and HDPE covers.The 1600 MHz antenna model that was designed showed reasonable analysis results when compared to the arrival time of the metal reflection in relation to the experimental propagation of the EM waves through the air medium.The simulation models that were developed using the randomly generated particle algorithm demonstrated a good comparison with the experimental results of the aggregate–void inhomogeneous medium when analyzed with the antenna model.Additionally, the randomly generated particle algorithm only considered a sphere shape for its analysis. Further study on the generation of irregular inhomogeneous mediums for a better analysis of the EM propagation needs to be considered.The use of the FDTD method has led to a better understanding of EM propagation, with the identification of the various factors that affect the medium, including the dielectric constant, dimensions, and sensor location. The development of a newly designed antenna model, similar to an experimental 1600 MHz antenna, has aided in investigating the EM wave propagation in response to different mediums. Based on our findings, the following conclusions can be drawn:


The flow of water through a horizontal small-scale Venturi tube of rectangular cross-section is simulated using a modified version of the open-source code DualSPHysics, which is based on Smoothed Particle Hydrodynamics (SPH) methods. Water is simulated using the Murnaghan-Tait equation of state so that weak compressibility is allowed. The hydrodynamics is coupled to a Large-Eddy Simulation (LES) turbulence model. The convergence properties of SPH are improved by adopting a C When the fluid enters the convergent section, its velocity increases as the pipe diameter decreases, resulting in an effective pressure drop according to Bernoulli’s equation2. This differential pressure across the convergent-divergent sections is considered to be the most important parameter to determine the performance of the Venturi tube3. This pressure reduction is called the Venturi effect4.Venturi tubes are obstruction meters employed in many industrial applications and laboratory experiments to measure the volumetric flow rate of a fluid in a pipe. A typical Venturi meter is a tubular device of varying cross-sectional area, consisting of a convergent conical section that leads to a constricted pipe section, or throat, which is then followed by a divergent conical section, or diffuser, in which the diameter increases again to that of the main pipeline9. In addition to being one of the most popular and highly efficient gas cleaning devices, Venturi injectors have also found important applications in sprinkler and drip irrigation systems for intensive farming and industrial agriculture10. They have also been used in applications of liquid flow metering11, mixing of liquid/liquid and gas/liquid systems as, for example, in the injection of ozone in water and in water aeration systems14, and in the enhancement of the chemical reaction of hydrodynamic cavitating flows15. Efforts have also focused on studying the instability of cavitating flows in a Venturi meter, whose control can help to limit structural damage16. In general, Venturi injectors operate over a wide range of pressures, are simple in design, have no moving parts and are almost free of maintenance. Due to all these advantages and their widespread applications in the industry, a wide range of models of varying complexity have been presented in the literature, which have been frequently validated experimentally.Over the past decades, Venturi tubes have been widely used as wet scrubbing systems in numerous industrial applications and in nuclear power plants to remove particles and gaseous pollutants from waste gas streams prior to their emission into the environment17 to investigate the pressure drop characteristics of Venturi scrubbers, while the hydrodynamics of a liquid/liquid phase distribution through a Venturi meter in a vertical pipe was examined by Jana et al.18. Numerical simulations of flow behavior within a Venturi meter have been performed to determine air injection rates19, pressure differences on a bluff body object for water flow20 and on turbine blades placed within the throat section for airflow21, as well as to determine the effects of varying the geometry of the convergent section on pressure drop22 and study the behavior of two-phase flow and mixing27. More recently, cavitating flows in a small-sized Venturi tube have been studied by Razali et al.28 Further numerical simulations have been carried out to measure discharge coefficients and pressure drops in industrial-sized Venturi meters for oxygen-free nitrogen29 and water flow30. The effects of varying the structural parameters of a Venturi tube, including the contraction ratio, the ratio of the throat section length to diameter, the diffusion angle and the inlet and outlet pressure difference, on fluid flow were analyzed by Zhang31 using the Fluent v6.3 software. Finite element methods have been used for design optimization analysis of a Venturi tube for medium conveying in strengthen grinding processes32, while investigation of large over-reading of the Venturi flow rate in the feed water flow control system in the second circuit of a nuclear power plant was recently performed by Wang et al.9 with the aid of the software package ANSYS Fluent 17.0.In addition to the large number of existing experiments, a great deal of effort has also been devoted to the numerical modeling of Venturi flows in order to reproduce the experimental data for laboratory-scale and industrial-size devices. Theoretical studies on the hydrodynamics of Venturi meters range from simple correlations to more complex numerical simulations. For instance, a boundary layer growth model was developed by Gamisans et al.33, which is based on SPH theory for solving the equations of fluid dynamics. Water is simulated using the Murnaghan-Tait equation of state so that a weakly compressible SPH scheme is proposed34. The Large-Eddy Simulation (LES) turbulence model is implemented to filter the equations of fluid dynamics. The convergence properties of SPH are improved in three different ways. First, a C36 is used as the interpolation kernel. In contrast to most conventional kernels, Wendland functions support large numbers of neighboring particles without becoming unstable and do not allow particle motions on a sub-resolution scale . Therefore, they maintain quasi-ordered particle distributions even in highly dynamical test problems. Second, approximate volume partitioning is achieved using a large number of neighbors38. Third, non-reflecting outlet boundary conditions are implemented by allowing the particles that leave the Venturi diffuser and enter the outflow zone to move according to an outgoing wave equation for the velocity field. This effectively reduces feedback noises from the outlet boundary, improving the accuracy of the solution. The Venturi tube was designed for experimental purposes and the numerically obtained flow behavior is compared with calibrated experimental flow measurements. The motivation for this study is twofold. First, understand the flow measurement device and second, explore the efficiency and accuracy of the proposed SPH method for recalibrating flow meters at moderately high Reynolds numbers. In particular, the flow structure along the convergent, throat and divergent sections of the Venturi as well as the differential pressure and mainstream velocity profiles at different stations along the tube are compared with the experimental data. The results show that the experimental measurements are reproduced with errors less than about 7.1% in the velocity profiles . In this study, we perform three-dimensional calculations of water flow at Reynolds numbers between 13272 and 33180 through a small-scale Venturi tube of rectangular cross-sectional shape, using a modified version of the open-source code DualSPHysicsThe experimental flow measurements were performed using a closed flow loop as shown schematically in the upper part of Fig. M stands for the number of points on the centerline where measured values of the velocity are known. This procedure is repeated by iterating backward from the value of pressure at point PThe Venturi meter is made of transparent acrylic to facilitate flow visualization, while the pipeline upstream and downstream of the Venturi is made of PVC and has a circular cross-section of diameter 25 mm. The procedure undertaken in the experiments is as follows. First, a peripheral pump of 1 HP capacity is switched on to drive water from a 50-liter storage tank open to the atmosphere through an orifice. The flow is controlled by a check valve, which is placed upstream of a flow meter which in turn adjusts the volumetric flow rate at the inlet of the Venturi to be in the range between 10 and 35 liters per minute (lpm). When the circuit is completely filled with water, the pump keeps the circulation going by maintaining a head difference and the desired flow. The dynamic pressure is measured by means of three pressure transducers placed at the inlet   2) couplmentclass2pt{minim33, which is based on SPH methods41. SPH is a fully Lagrangian, mesh-free scheme for the simulation of complex fluid-flows, where the fluid is represented by a discrete number of moving particles that carry all field information of the system as, for example, the density and velocity. In DualSPHysics the density of particle a is calculated using Eq.   6) are ssing Eq. , which i42. Here M is the total fluid mass within the computational domain and a and all other particles, where the summation is taken over all N particles filling the computational domain. The convergence properties of SPH are improved by adopting a C3643. This approach has been successfully applied in the simulation of free-surface flows, including Newtonian and non-Newtonian flows, dam break flows and wave overtopping on different coastal structures47, among many other applications.The particles are moved along the Venturi meter by integrating the equation49. In these simulations, a layer of particles at rest defines the walls of the Venturi and two layers of ghost particles are placed outside the computational domain around the volume occupied by the Venturi meter. Although these particles are updated using Eq.  is  is 11) ising Eq. , they arsing Eq. .Figure 350. Non-reflecting outflow boundary conditions are implemented by defining an outflow zone in front of the outlet  plane , has been used to simulate the flow of water at moderately high Reynolds numbers through a horizontal, small-scale Venturi meter of rectangular cross-sectional shape. The geometry and dimensions of the Venturi tube as well as the initial flow parameters used for the numerical simulations correspond to the experimentally defined values.51. Section varying devices with square or rectangular cross-section are common implementations in controlled irrigation systems within the schemes of precision farming as in open-loop techniques and DWC, due to the square geometry of flooded receptacles for plants. As a low-impact tool, the Venturi meter studied here can be added to the output of the pumping system to inject air into the pond which can aid in keeping high dissolved oxygen levels.As the flow rate is increased within the allowed experimental range, the SPH simulations predict mainstream velocity profiles and differential pressures along the Venturi tube that are in good qualitative agreement with the experimental data. The deviations between the SPH and the experimental mainstream velocity profiles vary from 4.3% for a volumetric flow rate (Q) of 10 liters per minute (lpm) to 7.1% for


Automated deep learning is promising in artificial intelligence (AI). However, a few applications of automated deep learning networks have been made in the clinical medical fields. Therefore, we studied the application of an open-source automated deep learning framework, Autokeras, for detecting smear blood images infected with malaria parasites. Autokeras is able to identify the optimal neural network to perform the classification task. Hence, the robustness of the adopted model is due to it not needing any prior knowledge from deep learning. In contrast, the traditional deep neural network methods still require more construction to identify the best convolutional neural network (CNN). The dataset used in this study consisted of 27,558 blood smear images. A comparative process proved the superiority of our proposed approach over other traditional neural networks. The evaluation results of our proposed model achieved high efficiency with impressive accuracy, reaching 95.6% when compared with previous competitive models. Malaria is a transmissible and life-threatening disease, as professed by the World Health Organization (WHO), and is prevalent in many countries, especially Senegal and those in Asia and Africa . The priIt is worth noting in this report that the proportion of children under the age of five out of the total number of people who died of malaria reached 67% 274,000), which indicates that malaria is one of the most-significant causes of death among children in the world. In the last few years and during the COVID-19 pandemic, the number of malaria patients rose, which is a dire circumstance ,4. There,000, whiIn detecting malaria, thin and thick blood smears are usually taken from possibly infected people and examined on glass slides. These smears are viewed under a light microscope for surveillance. This diagnostic technique requires a high level of expertise to achieve precise results. Moreover, further complications regarding disease detection and medication probably lead to inconsistent and delayed results due to inadequate tools and proficiency in developing regions, as well as many other circumstances .Artificial intelligence comprises machine learning and deep learning neural networks that could be used to handle current problems in healthcare around the world . The curThe automated machine learning methods to detect malaria reduce the human effort in the construction of networks as an alternative to creating classifier networks from scratch and modifying the parameters. This work was based on a novel technique that automatically discovers the best deep learning model to classify cells into parasitized or healthy cells. No experience in coding is required. It depends on a few lines of code to function. Large technology companies have made a respectable effort to disseminate various automated machine learning software, such as Google Cloud AutoML and Azure automated machine learning. The benefit of such services is the ability to build high-performance models with as few experts in machine learning as possible. Moreover, several of the open-source software frameworks also achieve high performance, for example AutoWeka , AutoSKLThis work represents a pioneering work in classifying images of malaria-infected and normal blood cells by the AutoKeras software, one of the auto-machine learning systems.We propose preprocessing on malaria image datasets before applying the Autokeras model.We chose the best model out of 20 trials performed by the Autokeras software to search for the best network that gave the lowest validation loss.We demonstrated the high performance of the Autokeras software in detecting malaria-infected cells and its superiority over the traditional deep learning (DL) models, which require machine learning experts.We sought in this paper to study the efficiency and performance of auto-machine learning systems in the medical field. As far as we know, this is the first paper to test the power of auto-machine learning (AutoML) by implementing Autokeras, an automated deep learning network, to classify cell images as infected or not with the malaria parasite. Autokeras’s deep learning model was preferred over other approaches to automatic machine learning for several reasons mentioned in The results of the proposed method were applied to various medical problems, and the results were compared to other methods. The proposed method obtained better results in solving the test problems than the other methods selected from the literature.The remainder of this paper is organized in the following manner. In this section, the related works that have used deep learning and machine learning techniques are presented as follows. The immense capability of convolution neural networks (CNNs) in image detection in various fields has been demonstrated. Several studies have been conducted on malaria parasite detection, which can be summarized as follows. In , the autIn , the stuIn , the autIn , the PlaIn , the autThe work  was baseIn , the autAnother approach using in an imbalanced dataset can be found in , where tA computer-aided design was proposed in  to recogIn , the worThe work in  adopted In , the autThe authors of  used theThe work in  was baseThe proposed work by  relied oHowever, not all current deep learning (DL) models can be readily implemented for malaria diagnosis systems, as they require extra effort and may be considered as a technical challenge, as in the previous solutions of deep learning works. Moreover, constructing a high-efficiency DL model for a particular task can face issues that make these processes more complex, such as being resource-expensive, time-consuming, and depending heavily on human experience through a trial-and-error method. To this end, Autokeras provides a promising alternative to the manual DL solutions and requires little effort or involvement of the user, as it excels in providing the best-performing deep learning network along with the feature engineering  and network construction (hyperparameter choice and fine-tuning).The AutoML technique can be described as automatically discovering the best classifier for a certain dataset. As such, performing this method on neural networks results in identifying the model’s structure and the hyperparameters required to train the classifier. It relies on a neural architecture search algorithm (NAS). K for the NN architecture, D as the input data, which are split into training data C, the main goal of the NAS technique is to detect the best neural network D. This corresponds to finding the However, the basic problem that the NAS aims to solve is as follows: Assuming a search space C is the metric assessment function such as the accuracy and mean-squared error (MSE). The following formulas explain the mathematical notations for each one:x is the truevalue, n is the size of the data.k, and the following equation explains it:We can identify K includes all the neural architectures that can be derivedfrom the startingarchitectures. The searcher unit is responsiblefor the searching process in the neural architecture search (NAS) algorithm.The search space In our work, we applied the Autokeras technique as our model. However, there are different algorithms for AutoML such as Autokeras , Auto-SkAutokeras is an open-source software library established by DATA Lab at Texas A&M University . The devCloud services are paid services, which may be a problem for many users who intend to use AutoML algorithms, unlike Autokeras, which is free.Usually, cloud-based AutoML requires users to have a good background in computer science, which is the opposite for Autokeras with its ease of use.The Autokeras services are characterized by their availability to all people, which can be used locally on a personal desktop. As such, the Autokeras services solve the problem of the security issues associated with using other AutoML applications.The main advantage of using the Autokeras software on a local machine is the ability to dispense with building Dockers and Kubernetes in the cloud. Unlike other AutoML systems such as TPOT , SMAC 2, Auto-SkSecondly, the model trainer unit is used for the GPU computation. The model trainer is responsible for achieving parallelism by training a particular neural network with the training data in a separate process. The graph module processes the neural network’s computational graphs. The current neural architecture in the graph module is located on the RAM to speed up the access process. The stored models are considered as a trained model collection. Due to the enormous size of the neural networks, in addition to the difficulty of storing them in the memory, all the trained models are savedon the storage devices.True positives (TPs) signify the amount of correctly predicted positive samples.True Negatives (TNs) identify the number of correctly predicted negative samples.False Positives (FPs) are cases where the images were predicted as positive, but were notFalse Negatives (FNs) are cases where the images were predicted as negative, but were not.The confusion matrix (CM) (an error matrix) can be defined as a concise table or tool that reveals how well the model can predict from a particular testing dataset. A CM consists of rows and columns giving the labels of the ground truth and the predicted class. The ground truth is the actual infected and uninfected blood cells. On the other hand, the predicted values specify the number of correct and incorrect classifications made by the model. The evaluation measures used in the confusion matrix are presented as follows:Accuracy is a measure of all correct predictions from all the samples sets and is determined by Equation . We calcPrecision is the number of times the model made a correct prediction out of an actual class, determined by Equation .(6)PrecThe recall is known as the true positive rate (TPR) or the sensitivity. It implies the percentage of accurately predicted infected cells or TPs to all TPs and FNs and is determined by Equation .(7)RecaThe F1-measure is a weighted average between the recall and precision, which can have a maximum score of 1, which is the best case, and the lowest score of 0, and it is determined by Equation .(8)F-meIn this work, the publicly available malaria dataset provided by Lister Hill National Centre for Biomedical Communications (LHNCBC)  was usedIn this work, the Windows 10 system platform with 64 bits was used as the software platform for implementing the experiments of this research, where the hardware consisted of 2 GB VRAM with NVIDIA GeForce MX13, and the Intel(R) also was used as a hardware component with Core(TM), 12 GB RAM, and i5 = 8250U CPU @ 1.60–1.80 GHz. The experiments were performed in the environment of a Jupyter Notebook with Python programming language Version 3.9.Any model’s performance is entirely governed by the data that are fed to it. Therefore, data preprocessing plays a primary role in conducting tests. Therefore, in this work, some preprocessing steps were conducted on the data, such as resizing the images to 32 (to reduce time), converting them to arrays (because the input data in the Autokeras function are expected to be an array), and then shuffling all the training data.In this paper, the “Hold-Out” validation mechanism  was adopIn detail, once the data were ready for training, the Autokeras system started locally working with a workflow as follows. The API conveys the dataset after preprocessing it to the searcher to begin the search process. We trained the Autokeras software with a max of 20 trials to enable the searcher to find the best network architecture with the lowest validation loss. The Bayesian optimizer creates a new CPU architecture in such a searcher. The mission of the Bayesian optimizer is to call the graph module to construct the created neural architecture into an actual neural network in the RAM. The novel neural architecture is used to copy the model trainer to GPU for training the dataset. Feedback on the model’s performance is passed to the searcher to make any necessary modifications to the Gaussian process.As we mentioned earlier, the metrics used to assess the performance of the Autokeras model were the accuracy, precision, recall, and F1-measure, which are illustrated in detail in These four metrics aimed to better describe the adopted model’s evaluation results, which can be derived from the confusion matrix. A complete review of the performance evaluation metrics is offered in Despite the large size of the malaria dataset of the images of the thin blood smear cells, Autokeras’ proposed model proved its robustness in classifying the cell images into infected or uninfected with malaria.The Grad-CAM algorithm was applied in order to show a visualized localization of prominent features appearing on the parasitized blood cell. This approach helped add transparency to our outcomes. Grad-CAM is a generic method that uses a CNN as the final activation layer. As such, a set of high-level features is produced in the shape of a heat map. In As is clear in Moreover, the papers in ,21 demonIn conclusion, this paper applied a novel optimization technique to tune the hyperparameters of the machine learning techniques, called automated machine learning. The proposed method proved its ability to obtain the optimal parameter values, which better reflect the results of the utilized evaluation measures compared to the other methods from the literature.This work adopted an open-source automatic AutoKeras deep learning model to identify malaria parasites in blood cell images. AutoKeras focuses on techniques for automatically determining the highest-performing model for a given dataset. This theme has received attention recently and has been developing quickly ever since. However, the primary motivation for automating the hyperparameters’ tuningin any model is to find an automatic ML solution that can achieve a high degree of performance accuracy and lead to an innovative level of automation in the future using artificial intelligence.The proposed Autokeras malaria classification approach achieved the best overall accuracy of 95.6%. It was evident from the experimental test implemented that the robustness of Autokeras outperformed previous works from some studies. This proved that the proposed method can solve other similar problems efficiently according to the results of the comparisons with the the-stat-of-the-art methods.Other machine learning methods combined with advanced optimization algorithms can be used to solve the hyperparameter problems in any model and find a robust automatic ML method. Moreover, other applications can be addressed using the proposed method, such as brain medical images, patient risk identification, pancreatic cancer, sarcoma, pattern imaging analytics, stomach cancer, clinical trial research, predicting epidemics, kidney cancer, maintaining healthcare records, and others.The limitations of using auto-machine learning are that it requires much time to train with such a limited platform and requires a faster device. We recommend using a faster device in such cases.


Electrolytes have a wide range of technological applications. Despite the recent improvements in characterizing and predicting the phase behavior of microemulsion systems by hydrophilic-lipophilic deviation (HLD) and net-average curvature (NAC) frameworks, they are ineffective in the presence of different salts. This work seeks to bridge this gap by investigating the influence of salt nature on the microemulsion phase formulation. First, a one-dimensional salinity scan on different microemulsion systems consisting of sodium dodecyl benzene sulfonate as a surfactant, hexane as an oil and, several brines was carried out, and the effect of each salt on the phase behavior were precisely evaluated. The results for optimum salinity and solubilization parameter of different salts were consistent with the Hofmeister series. In addition, multiple linear regression model is presented to accurately predicting the optimum salinity of different salts using this research data and all the available experimental data. The results revealed that the values estimated by this model is in significant consistency with the experimental data by correlation coefficient of 0.92. Finally, the effect of salt type on the NAC parameters (length parameter, and characteristic length Four types of microemulsion systems form with changes in formulation variables, Winsor type I, II, III and IV. In Winsor I systems, an excess oil phase and an O/W microemulsion which consists of oil-swollen micelles are in equilibrium3. Among Winsor III microemulsions, the optimum formulation is the one where the amounts of solubilized oil and water are equal and at the maximum amount. At this optimum point, a deep fall happens in the interfacial tension (IFT), resulting in maximum oil/water separation and enhanced oil recovery4.Microemulsions are thermodynamically stable and optically transparent/translucent surfactant–oil–water (SOW) systems5. As a result, understanding the behavior and predicting the extent of the impact of different salts on the phase behavior and properties of microemulsions gives profound insight to design SOW systems such as crude oil dewatering, enhanced oil recovery, drug delivery, nanoparticles and cleaning products synthesis8. It has been demonstrated that O/W microemulsions can be utilized to synthesize a wide range of nanomaterials with controlled characteristics10. O/W microemulsions are used as potential delivery systems since they increase lipophilic drugs' water solubility and absorption rate12.Salts are crucial ingredients in formulating the microemulsions and considerably affect the phase behavior and optimum formulation of SOW systemsIn this respect, Hofmeister ordered ions according to their salting-out capacity as follows:15. It has been suggested that charge density and hydration radius of ions are important parameters that govern whether ions are salting-out or salting-in.Salting-out ions (left hand side) which usually have a larger hydration radius, lowers the solubility of other solutes such as surfactants. From left to right, the salting-out power of ions decreases and at the extreme conditions, this power may be inversed. In this situation, ions may even improve the solute solubility and show the salting-in behavior. In general, the left-sided and right-sided ions in Hofmeister series are known as salting-out and salting-in ions19.According to Liu et al., the presence of cations causes interfacial dehydration of solutes, and micelle transition originates from hydration competition between ions and surfactants. Zavitsas reported that salting-out behavior of ions is due to their water-binding capacity, which is in direct relation with charge density of ions. In addition, Liu provided some evidence that there is a linear relationship between the charge density of ions and their optimum salinity20. The latter can be quantified by the solubilization parameter (SP) which is the ability of a surfactant to solubilize oil and water in the microemulsion phase5.Regarding the salting out phenomenon, some researchers have conducted a variety of experiments to acquire further information about the impact of ions on the phase behavior properties of SOW systems. Accordingly, it can be said that there is strong connection between the nature and concentration of ions and the solubilization capacity of surfactant in SOW systems23. For ionic systems, the HLD equation is presented as:Many researchers have tried to develop a mathematical framework to quantify and predict the phase behavior of SOW systems. Salager and colleagues introduced the semi-empirical hydrophilic-lipophilic deviation (HLD) equation to address this issue. The positive, negative, and zero HLD values reflect Winsor type I, II, and III, respectively25. The term f(A) is an indicator of the type and concentration of cosurfactants in the system. In the absence of any cosurfactant, this parameter is considered to be zero27. The temperature deviation from the reference temperature (25 °C) is known as 28. 29.In this expression, S represents the salinity of the aqueous phase, EACN is the oil's hydrophilic character. The parameter MW and ℎc are the optimum salinity of sodium chloride obtained by the HLD equation, molecular weight and hydration number of ions, respectively. The lack of a unique methodology to measure the hydration number of ions and overestimation of the optimum salinity of various salts are the major problems of this equation31.Warren and Harwell added a hydration number term to the Eq. , as foll26. These equations are:On the other hand, the NAC model was developed by Acosta et al. to upgrade the applicability of HLD by predicting the properties of SOW systems. The NAC model is based on two statistical descriptions32. 26. The length parameter is a measure of surfactant hydrophobicity. It is proportional to the extended length of the surfactant tail group, which is a function of linear carbon number in the surfactant tail35.27.Characteristic length quantifies the interaction power of surfactant molecules at the interface with the oil/water molecules in the bulk phase. The farther the surfactant molecule can extend its influence, the higher the characteristic length value will be. Both L and Despite the advantages provided by the NAC model, it cannot predict the properties of microemulsion systems containing salts other than NaCl.To fulfill this shortcoming, we designed the present study to evaluate the salt effect on the phase behavior of SOW systems. In this regard, we propose a new robust and convenient correlation to predict the optimum salinity of various salts based on the formulation variables and intrinsic properties of salts. We believe this correlation is more precise and easier to use in comparison with the similar ones. Therefore, a one-dimensional salinity scan was performed for a series of SOW system containing sodium dodecyl benzene sulfonate (SDBS), hexane and the following salts: In addition, we investigate the effect of various salts on the NAC model parameters  were well mixed together by stirring for three minutes. The surfactant concentration in aqueous phase was constant, at 2 37. The first method chooses the tube with equal excess phase volumes as the optimum salinity. The second method selects the tube that shows the fastest phase separation. In this work, we use the first method to find the S* tube and apply the second method when the first condition is not met.Previously, two methods have been presented for the visual selection of the optimum salinity tube (S*)33. In this regard, after the phases in graduated test tubes reached equilibrium, the experimental upper and lower boundaries of each tube were determined by measuring the levels of each phase in the test tube. Then, the corresponding HLD values of test tubes and surfactant parameters such as molecular weight and head area and phase volume fractions were introduced to the HLD-NAC equation of state. Finally, the objective function was minimized by Matlab and NAC parameters were obtained. In the supplementary information file The NAC parameters are fitted by the phase volume fraction data based on the method proposed in previous works38. Figure 39. Potassium chloride, which is not accounted for in the HLD equation, may cause this discrepancy. However, HLD and HLD-NAC can still adequately predict the actual phase behavior. Providing additional information about salinity scans of other salts, supplementary file Salinity scans were conducted to examine how different salts affect the phase behavior of the SDBS–hexane–electrolyte system. As shown in Fig. mentclass2pt{minim26. Therefore, the lower the surfactant solubility in water, the sooner the phase transition happens from Winsor I → III → II in the salinity scan. Although the overall trend in Fig. + and Cl- are the exceptions. Such a reversal trend in the Hofmeister series has been reported several times in the literature, but a clear justification has not been provided yet45. Sögaard et al. reported a reversal trend when divalent ions interacted with silica nanoparticles46. Oechsle et al. reported that the viscosity of collagen in the presence of different salts had a reversal trend according to the Hofmeister series47. Vera et al. observed a reversal between nitrate and chloride anions, while examining the solubility of surfactants in aqueous solutions5.The optimal salinity value of different salts is depicted in Fig. entclass1pt{minima49. Therefore, it is proven that in the presence of salting-out ions, the optimum salinity, which is equal to net-zero-curvature point, moves to the lower values. For example, it is observed that the lowest optimum salinity belongs to the salt with the highest salting-out power, magnesium chloride.In addition, it has been demonstrated that salting-out ions decrease the micelle curvature while salting-in ions increase it. The reason for this phenomenon is attributed to the stronger electrostatic screening of salting-out ions, which reduces the repulsion between negatively charged micellesFigure 19.As can be seen in this figure, salting-in ions enhance the surfactant solubilization ability while salting-out ions reduce it being consistent with previous results52. optimum salinity of sodium chloride is calculated by substituting the formulation variables into the HLD equation. The input data contains 17 samples and 3 features. Influential characteristics on the salting-out behavior of salts were chosen as the feature to improve the prediction capability of correlation. The features are the charge density ratio of cation 53. All the features are dimensionless with respect to sodium chloride. The hydration thickness of cation (Here we used the multiple linear regression (MLR) tool of Matlab software to develop a relationship between the optimal salinity values of different salts obtained experimentally from both the literature and this work, and the influential variables, affecting the optimum salinity of each salt. The target data are the optimum salinity ratios of different salts to that of sodium chlorideTable The following equation is developed using the MATLAB MLR tool:Figure The proposed correlation not only is appropriate for optimum salinity estimation of different salts but also is applicable for predicting the related salinities of Winsor I  and characteristic length (.Here, we have investigated the influence of various salts on the surfactant’s length parameter. In this regard, we have fitted the SDBS length parameter in the SOW samples, which contain different salts using the experimentally measured phase volumes. The supplementary file 54:56.This result can be interpreted in terms of "double-layer thickness”. According to Acosta, changes in the net-curvature (57.Generally speaking, since the salt nature has not been investigated enough in the previous studies, the importance of L is neglected. Therefore, the surfactant length scaling parameter has been only attributed to the number of carbon atoms in the surfactant tail chain. Here we illustrate that the surfactants in SOW systems containing salts with higher optimum ionic strength show higher length parameter and the opposite occurs for salts with the lower optimum ionic strength. It seems that this behavior illustrates the extension/compression of the surfactants tail in presence of different salts. Figures 32. Subsequently, the surfactant will have less control over the bulk molecules, reducing the characteristic length.As previously stated, in this study, both 58. On the other hand, Vera et. al. have reported that in the presence of salting-out ions, the head-area of SDBS increases while in the presence of salting-in ions, it decreases5. As a result, our finding that the characteristic length decreases in the presence of salting-out ions and increases in the presence of salting-in ions is consistent with literature.Previous studies confirm the obtained trend implicitly. It was seen before that changing the type of surfactant with various t head area affects the characteristic length of the system. They showed that the characteristic length decreases as the surfactant head area increases19.Moreover, the obtained trend for the effect of salt on the characteristic length follows the similar trend for the optimum solubilization parameter data on Fig. This work investigated how the type of salt affects the microemulsion formulation parameters and enhances the performance of HLD-NAC by proposing the salt impact on the length parameter and characteristic length, two key NAC parameters, for different salts. Our systematic study using HLD and HLD-NAC was consistent with the Hofmeister series, which showed that salting-out ions had lower optimal salinity and solubilization capacity than salting-in ions. It was shown that salt nature affects both the characteristic length and length parameter. The salts with the higher optimum ionic strength, expand the surfactant length parameter, whereas the salts with the lower optimum ionic strength, compress it. This is a sign of spring-like behavior of the surfactant tail when various salts are present. In terms of characteristic length, we found that the salting-out ions shrink the characteristic length while the salting-in ions do the reverse. Moreover, the MLR model was developed to estimate the optimal salinity of various salts based on more accessible parameters than previous studies. The correlation coefficient of 92.41% for the testing dataset demonstrated that the MLR model had a high prediction ability. We believe that our proposed model with a high degree of accuracy, will help researchers to design the emulsion systems more precisely.Supplementary Information.