Time Series Electrical Motor Drives Forecasting Based on Simulation Modeling and Bidirectional Long-Short Term Memory.

Accurately forecasting electrical signals from three-phase Direct Torque Control (DTC) induction motors is crucial for achieving optimal motor performance and effective condition monitoring. However, the intricate nature of multiple DTC induction motors and the variability in operational conditions present significant challenges for conventional prediction methodologies. To address these obstacles, we propose an innovative solution that leverages the Fast Fourier Transform (FFT) to preprocess simulation data from electrical motors. A Bidirectional Long Short-Term Memory (Bi-LSTM) network then uses this altered data to forecast processed motor signals. Our proposed approach is thoroughly examined using a comparative examination of cutting-edge forecasting models such as the Recurrent Neural Network (RNN), Long Short-Term Memory (LSTM), and Gated Recurrent Unit (GRU). This rigorous comparison underscores the remarkable efficacy of our approach in elevating the precision and reliability of forecasts for induction motor signals. The results unequivocally establish the superiority of our method across stator and rotor current testing data, as evidenced by Mean Absolute Error (MAE) average results of 92.6864 and 93.8802 for stator and rotor current data, respectively. Additionally, compared to alternative forecasting models, the Root Mean Square Error (RMSE) average results of 105.0636 and 85.7820 underscore reduced prediction loss.

Flamingo-Optimization-Based Deep Convolutional Neural Network for IoT-Based Arrhythmia Classification.

Cardiac arrhythmia is a deadly disease that threatens the lives of millions of people, which shows the need for earlier detection and classification. An abnormal signal in the heart causing arrhythmia can be detected at an earlier stage when the health data from the patient are monitored using IoT technology. Arrhythmias may suddenly lead to death and the classification of arrhythmias is considered a complicated process. In this research, an effective classification model for the classification of heart disease is developed using flamingo optimization. Initially, the ECG signal from the heart is collected and then it is subjected to the preprocessing stage; to detect and control the electrical activity of the heart, the electrocardiogram (ECG) is used. The input signals collected using IoT nodes are collectively presented in the base station for the classification using flamingo-optimization-based deep convolutional networks, which effectively predict the disease. With the aid of communication technologies and the contribution of IoT, medical professionals can easily monitor the health condition of patients. The performance is analyzed in terms of accuracy, sensitivity, and specificity.

RealPrice: Blockchain-Powered Real-Time Pricing for Software-Defined Enabled Edge Network.

With the limited Internet bandwidth in a given area, unlimited data plans can create congestion because there is no retribution for transmitting many packets. The real-time pricing mechanism can inform users of their Internet consumption to limit congestion during peak hours. However, implementing real-time pricing is opex-heavy from the network provider side and requires high-integrity operations to gain consumer trust. This paper aims to leverage the software-defined network to solve the opex issues and blockchain technology to solve trust issues. First, the network congestion level in a given area is analyzed. Then, the price is adjusted accordingly. Devices that send a lot of traffic during congestion will be charged more expensive bills than if transmitting traffic during an off-peak period. To prevent over-charging, the consumers can pre-configure a customized Internet profile stating how many data bytes they are willing to send during congestion. The software-defined controller also authenticates consumers and checks whether they have enough token deposits in the blockchain as Internet usage fees. We implement our work using Ethereum and POX controllers. The experiment results show that the proposed real-time pricing can be performed seamlessly, and the network provider can reap up to 72.91% more profits than existing approaches, such as usage-based pricing or time-dependent pricing. The fairness and trustability of real-time pricing is also guaranteed through the proof-of-usage mechanism and the transparency of the blockchain.

Classification and Explanation for Intrusion Detection System Based on Ensemble Trees and SHAP Method.

In recent years, many methods for intrusion detection systems (IDS) have been designed and developed in the research community, which have achieved a perfect detection rate using IDS datasets. Deep neural networks (DNNs) are representative examples applied widely in IDS. However, DNN models are becoming increasingly complex in model architectures with high resource computing in hardware requirements. In addition, it is difficult for humans to obtain explanations behind the decisions made by these DNN models using large IoT-based IDS datasets. Many proposed IDS methods have not been applied in practical deployments, because of the lack of explanation given to cybersecurity experts, to support them in terms of optimizing their decisions according to the judgments of the IDS models. This paper aims to enhance the attack detection performance of IDS with big IoT-based IDS datasets as well as provide explanations of machine learning (ML) model predictions. The proposed ML-based IDS method is based on the ensemble trees approach, including decision tree (DT) and random forest (RF) classifiers which do not require high computing resources for training models. In addition, two big datasets are used for the experimental evaluation of the proposed method, NF-BoT-IoT-v2, and NF-ToN-IoT-v2 (new versions of the original BoT-IoT and ToN-IoT datasets), through the feature set of the net flow meter. In addition, the IoTDS20 dataset is used for experiments. Furthermore, the SHapley additive exPlanations (SHAP) is applied to the eXplainable AI (XAI) methodology to explain and interpret the classification decisions of DT and RF models; this is not only effective in interpreting the final decision of the ensemble tree approach but also supports cybersecurity experts in quickly optimizing and evaluating the correctness of their judgments based on the explanations of the results.

Background Load Denoising across Complex Load Based on Generative Adversarial Network to Enhance Load Identification.

Non-Intrusive Load Monitoring (NILM) allows load identification of appliances through a single sensor. By using NILM, users can monitor their electricity consumption, which is beneficial for energy efficiency or energy saving. In advance NILM systems, identification of appliances on/off events should be processed instantly. Thus, it is necessary to use an extremely short period signal of appliances to shorten the time delay for users to acquire event information. However, acquiring event information from a short period signal raises another problem. The problem is target load feature to be easily mixed with background load. The more complex the background load has, the noisier the target load occurs. This issue certainly reduces the appliance identification performance. Therefore, we provide a novel methodology that leverages Generative Adversarial Network (GAN) to generate noise distribution of background load then use it to generate a clear target load. We also built a Convolutional Neural Network (CNN) model to identify load based on single load data. Then we use that CNN model to evaluate the target load generated by GAN. The result shows that GAN is powerful to denoise background load across the complex load. It yields a high accuracy of load identification which could reach 92.04%.

Convenient one-pot access to 2H-3-nitrothiochromenes from 2-bromobenzaldehydes, sodium sulfide and ß-nitrostyrenes.

An efficient synthesis of 2H-3-nitrothiochromenes via a cascade reaction was established. Starting from commercially available o-bromobenzaldehydes and ß-nitrostyrenes with sodium sulfide nonahydrate as an inexpensive sulfur source, various substituted thiochromenes were synthesized with high functional group tolerance without any added transition metal catalyst or additive.

Exploring different strategies for imbalanced ADME data problem: case study on Caco-2 permeability modeling.

In many absorption, distribution, metabolism, and excretion (ADME) modeling problems, imbalanced data could negatively affect classification performance of machine learning algorithms. Solutions for handling imbalanced dataset have been proposed, but their application for ADME modeling tasks is underexplored. In this paper, various strategies including cost-sensitive learning and resampling methods were studied to tackle the moderate imbalance problem of a large Caco-2 cell permeability database. Simple physicochemical molecular descriptors were utilized for data modeling. Support vector machine classifiers were constructed and compared using multiple comparison tests. Results showed that the models developed on the basis of resampling strategies displayed better performance than the cost-sensitive classification models, especially in the case of oversampling data where misclassification rates for minority class have values of 0.11 and 0.14 for training and test set, respectively. A consensus model with enhanced applicability domain was subsequently constructed and showed improved performance. This model was used to predict a set of randomly selected high-permeability reference drugs according to the biopharmaceutics classification system. Overall, this study provides a comparison of numerous rebalancing strategies and displays the effectiveness of oversampling methods to deal with imbalanced permeability data problems.

Physico-Chemical and Structural Interpretation of Discrete Derivative Indices on N-Tuples Atoms.

This report examines the interpretation of the Graph Derivative Indices (GDIs) from three different perspectives (i.e., in structural, steric and electronic terms). It is found that the individual vertex frequencies may be expressed in terms of the geometrical and electronic reactivity of the atoms and bonds, respectively. On the other hand, it is demonstrated that the GDIs are sensitive to progressive structural modifications in terms of: size, ramifications, electronic richness, conjugation effects and molecular symmetry. Moreover, it is observed that the GDIs quantify the interaction capacity among molecules and codify information on the activation entropy. A structure property relationship study reveals that there exists a direct correspondence between the individual frequencies of atoms and Hückel's Free Valence, as well as between the atomic GDIs and the chemical shift in NMR, which collectively validates the theory that these indices codify steric and electronic information of the atoms in a molecule. Taking in consideration the regularity and coherence found in experiments performed with the GDIs, it is possible to say that GDIs possess plausible interpretation in structural and physicochemical terms.

Multi-output model with Box-Jenkins operators of linear indices to predict multi-target inhibitors of ubiquitin-proteasome pathway.

The ubiquitin-proteasome pathway (UPP) plays an important role in the degradation of cellular proteins and regulation of different cellular processes that include cell cycle control, proliferation, differentiation, and apoptosis. In this sense, the disruption of proteasome activity leads to different pathological states linked to clinical disorders such as inflammation, neurodegeneration, and cancer. The use of UPP inhibitors is one of the proposed approaches to manage these alterations. On other hand, the ChEMBL database contains >5,000 experimental outcomes for >2,000 compounds tested as possible proteasome inhibitors using a large number of pharmacological assay protocols. All these assays report a large number of experimental parameters of biological activity like EC50, IC50 percent of inhibition, and many others that have been determined under many different conditions, targets, organisms, etc. Although this large amount of data offers new opportunities for the computational discovery of proteasome inhibitors, the complexity of these data represents a bottleneck for the development of predictive models. In this work, we used linear molecular indices calculated with the software TOMOCOMD-CARDD and Box-Jenkins moving average operators to develop a multi-output model that can predict outcomes for 20 experimental parameters in >450 assays carried out under different conditions. This generated multi-output model showed values of accuracy, sensitivity, and specificity above 70% for training and validation series. Finally, this model is considered multi-target and multi-scale, because it predicts the inhibition of the UPP for drugs against 22 molecular or cellular targets of different organisms contained in the ChEMBL database.

Organ and tissue-dependent effect of resveratrol and exercise on antioxidant defenses of old mice.

BACKGROUND: Oxidative stress has been considered one of the causes of aging. For this reason, treatments based on antioxidants or those capable of increasing endogenous antioxidant activity have been taken into consideration to delay aging or age-related disease progression. AIM: In this paper, we determine if resveratrol and exercise have similar effect on the antioxidant capacity of different organs in old mice. METHODS: Resveratrol (6 months) and/or exercise (1.5 months) was administered to old mice. Markers of oxidative stress (lipid peroxidation and glutathione) and activities and levels of antioxidant enzymes (SOD, catalase, glutathione peroxidase, glutathione reductase and transferase and thioredoxin reductases, NADH cytochrome B5-reductase and NAD(P)H-quinone acceptor oxidoreductase) were determined by spectrophotometry and Western blotting in different organs: liver, kidney, skeletal muscle, heart and brain. RESULTS: Both interventions improved antioxidant activity in the major organs of the mice. This induction was accompanied by a decrease in the level of lipid peroxidation in the liver, heart and muscle of mice. Both resveratrol and exercise modulated several antioxidant activities and protein levels. However, the effect of resveratrol, exercise or their combination was organ dependent, indicating that different organs respond in different ways to the same stimulus. CONCLUSIONS: Our data suggest that physical activity and resveratrol may be of great importance for the prevention of age-related diseases, but that their organ-dependent effect must be taken into consideration to design a better intervention.

Stixis scandens leaf extract-loading ZnO nanoparticles for porcine epidemic diarrhea virus (PEDV) treatment.

Porcine epidemic diarrhea (PED) is one of the diseases that causes great losses for livestock farmers. Because vaccines against the disease are not very effective, there is a great demand for biological products with effective resistance to PED virus (PEDV). One of the most important trends today is the use of active ingredients from nature in animal husbandry. This study aimed to create an effective agent against PEDV from the extract of Stixis scandens, which has been shown to inhibit PEDV. The aqueous (denoted as TCN) and ethanolic extracts (denoted as TCC) of Stixis scandens leaves were first prepared and then qualitatively analyzed for their chemical compositions. The TCN was used to synthesize ZnO nanoparticles (NPs) at various sizes from 20 to 120 nm. Subsequently, TCC was loaded on ZnO NPs to form ZnO-extract nanoformulations with an extract loading content of 5.8-7.6%. Total polyphenols (TP) and total alkaloids (TA) in TCC were 38.51 ± 0.25 µg GAE per mg and 22.37 ± 0.41 µg AtrE per mg, respectively. TP was less loaded but more released from the nanoformulations than TA. The A1T nanoformulation, containing only 7.6% extract, had a minimum PEDV inhibitory concentration of 3.9 µg mL-1, which was comparable to that of TCC. The experiments confirmed that the nanoformulations are promising for PEDV inhibition applications.

Multilayer immobilizing of denitrifying Bacillus sp. and TiO2-AgNPs on floating expanded clay carrier for co-treatment of nitrite and pathogens in aquaculture.

Nitrite contamination and the spread of pathogens can seriously degrade water quality. To simultaneously control these factors, an innovative approach of fabricating a remediation agent that contained denitrifying bacteria and TiO2-AgNPs co-immobilized on floating expanded clay (EC) was proposed in this study. The EC was fabricated from a mixture of clay and rice husk through pyrolysis at a high temperature of 1200 °C, followed by a rapid cooling step to create a porous structure for the material. TiO2NPs were modified with Ag to shift the absorbance threshold of TiO2-AgNPs into the visible region of 700-800 nm. The experimental results showed that the stirring speed of 250 rpm was suitable for immobilizing TiO2-AgNPs on EC and achieved the highest Ti and Ag content of 639.38 ± 3.04 and 200.51 ± 3.71 ppm, respectively. Coating TiO2-Ag/EC with chitosan (0.5%) significantly reduced the detachment level of immobilized TiO2-AgNPs compared to that of the material with no coating. In particular, this functionalized material inhibited 99.93 ± 0.1% of Vibrio parahaemolyticus pathogen but did not adversely affect the denitrifying bacteria after 2 h of visible light irradiation. Based on the electrostatic bond between oppositely charged polymers, the denitrifying bacteria, Bacillus sp., in alginate solution was successfully immobilized on the chitosan-coated TiO2-Ag/EC with a bacteria density of (76.67 ± 9.43) × 107 CFU g-1, retaining its nitrite removal efficiency at 99.0 ± 0.27% through six treatment cycles. These findings provide solid evidence for further investigating the combination of biodegradation and photodegradation in wastewater treatment.

High Biocompatibility, MRI Enhancement, and Dual Chemo- and Thermal-Therapy of Curcumin-Encapsulated Alginate/Fe3O4 Nanoparticles.

(1) Background: Magnetite (Fe3O4) nanoparticles have great potential for biomedical applications, including hyperthermia and magnetic resonance imaging. In this study, we aimed to identify the biological activity of nanoconjugates composed of superparamagnetic Fe3O4 nanoparticles coated with alginate and curcumin (Fe3O4/Cur@ALG) in cancer cells. (2) Methods: The nanoparticles were evaluated for the biocompatibility and toxicity on mice. The MRI enhancement and hyperthermia capacities of Fe3O4/Cur@ALG were determined in both in vitro and in vivo sarcoma models. (3) Results: The results show that the magnetite nanoparticles exhibit high biocompatibility and low toxicity in mice at Fe3O4 concentrations up to 120 mg/kg when administered via intravenous injection. The Fe3O4/Cur@ALG nanoparticles enhance the magnetic resonance imaging contrast in cell cultures and tumor-bearing Swiss mice. The autofluorescence of curcumin also allowed us to observe the penetration of the nanoparticles into sarcoma 180 cells. In particular, the nanoconjugates synergistically inhibit the growth of sarcoma 180 tumors via magnetic heating and the anticancer effects of curcumin, both in vitro and in vivo. (4) Conclusions: Our study reveals that Fe3O4/Cur@ALG has a high potential for medicinal applications and should be further developed for cancer diagnosis and treatment.

Modification of expanded clay carrier for enhancing the immobilization and nitrogen removal capacity of nitrifying and denitrifying bacteria in the aquaculture system.

In aquaculture systems, the treatment of nitrogen pollution has always been a center of attention due to its impact on productiveness. The bioremediation method based on simultaneous nitrification and denitrification was often used to effectively remove ammonium, nitrite, and nitrate compounds. In addition, the attachment and biofilm formation of the nitrogen-converting bacteria on carriers had superior removal efficiency over the suspended bacteria. Thus, this study focused on the fabrication of a porosity floatable expanded clay (EC) carrier that provided the basic structure for the immobilization of the nitrifiers Nitrosomonas sp., Nitrobacter sp., and the denitrifier Bacillus sp. The EC was also coated with alginate and essential nutrient to support the cohesion and growth of bacteria. Especially, the selected Bacillus sp. previously proved was able to reduce nitrite/nitrate in aerobic conditions. The co-immobilization of these three aerobic bacteria on the prepared carrier would simply the treatment process in practical use. Initial results showed that the integration of essential nutrients (N, P, K) on alginate coated EC (EC_Alg_N) increased bacterial density to (57 ± 3) × 107 - (430 ± 30) × 108 CFU/g, which then led to the enhancement of removal efficiency up to 91.62 ± 0.67% in the medium containing initial nitrogen content of 60 mg-N/L. The nitrogen removal efficacy of bacterial immobilized EC_Alg_N remained at 83.95 ± 0.15% after being reused for 6 cycles. In conclusion, the bacterial immobilized EC_Alg_N could be a potential material for nitrogen polluted wastewater treatment in aquaculture systems.

Allium sativum@AgNPs and Phyllanthus urinaria@AgNPs: a comparative analysis for antibacterial application.

Although medicinal herbs contain many biologically active ingredients that can act as antibiotic agents, most of them are difficult to dissolve in lipids and absorb through biofilms in the gastrointestinal tract. Besides, silver nanoparticles (AgNPs) have been widely used as a potential antibacterial agent, however, to achieve a bactericidal effect, high concentrations are required. In this work, AgNPs were combined into plant-based antibiotic nanoemulsions using biocompatible alginate/carboxyl methylcellulose scaffolds. The silver nanoparticles were prepared by a green method with an aqueous extract of Allium sativum or Phyllanthus urinaria extract. The botanical antibiotic components in the alcoholic extract of these plants were encapsulated with emulsifier poloxamer 407 to reduce the particle size, and make the active ingredients both water-soluble and lipid-soluble. Field emission scanning electron microscopy (FESEM) and energy-dispersive X-ray (EDX) analysis showed that the prepared nanosystems were spherical with a size of about 20 nm. Fourier transform infrared spectroscopy (FTIR) confirmed the interaction of the extracts and the alginate/carboxyl methylcellulose carrier. In vitro drug release kinetics of allicin and phyllanthin from the nanosystems exhibited a retarded release under different biological pH conditions. The antimicrobial activity of the synthesized nanoformulations were tested against Escherichia coli. The results showed that the nanosystem based on Allium sativum possesses a significantly higher antimicrobial activity against the tested organisms. Therefore, the combination of AgNPs with active compounds from Allium sativum extract is a good candidate for in vivo infection treatment application.

Novel coumarin-based tyrosinase inhibitors discovered by OECD principles-validated QSAR approach from an enlarged, balanced database.

The present work is devoted to the development and application of a multi-agent Quantitative Structure-Activity Relationship (QSAR) classification system for tyrosinase inhibitor identification, in which the individual QSAR outputs are the inputs of a fusion approach based on the voting mechanism. The individual models are based on TOMOCOMD-CARDD (TOpological Molecular COMputational Design-Computer Aided Rational Drug Design) atom-based bilinear descriptors and Linear Discriminant Analysis (LDA) on a novel enlarged, balanced database of 1,429 compounds within 701 greatly dissimilar molecules presenting anti-tyrosinase activity. A total of 21 adequate models are obtained taking into account the requirements of the Organization for Economic Cooperation and Development (OECD) principles for QSAR validation and present global accuracies (Q) above 84.50 and 79.27% in the training and test sets, respectively. The resulted fusion system is used for the in silico identification of synthesized coumarin derivatives as novel tyrosinase inhibitors. The 7-hydroxycoumarin (compound C07) shows potent activity for the inhibition of monophenolase activity of mushroom tyrosinase giving a value of inhibition percentage close to 100% in vitro assays, by means of spectrophotometric analysis. The current report could help to shed some clues in the identification of new chemicals that inhibit tyrosinase enzyme, for entering in the pipeline of drug discovery development.

Dual Loading of Doxorubicin and Magnetic Iron Oxide into PLA-TPGS Nanoparticles: Design, in vitro Drug Release Kinetics, and Biological Effects on Cancer Cells.

The multifunctional nano drug delivery system (MNDDS) has much revolutionized in cancer treatment, aiming to eliminate many disadvantages of conventional formulations. This paper herein proposes and demonstrates MNDDS inspired by poly(lactide)-tocopheryl polyethylene glycol succinate (PLA-TPGS) copolymer co-loaded Doxorubicin and magnetic iron oxide nanoparticles (MIONs) with a 1 : 1 (w/w) optimal ratio. In vitro drug release kinetics of Doxorubicin from this nanosystem fitted best to the Weibull kinetic model and can be described by the classical Fickian diffusion mechanism under acidic pH conditions. The combination of MIONs and Doxorubicin in the PLA-TPGS copolymer has maintained the fluorescence properties of Doxorubicin and good cell penetration, especially inside the nucleus and its vicinity. Moreover, different cell cycle profiles were observed in HeLa cell lines treated with MNDDSs.

Beyond model interpretability using LDA and decision trees for α-amylase and α-glucosidase inhibitor classification studies.

In this report are used two data sets involving the main antidiabetic enzyme targets α-amylase and α-glucosidase. The prediction of α-amylase and α-glucosidase inhibitory activity as antidiabetic is carried out using LDA and classification trees (CT). A large data set of 640 compounds for α-amylase and 1546 compounds in the case of α-glucosidase are selected to develop the tree model. In the case of CT-J48 have the better classification model performances for both targets with values above 80%-90% for the training and prediction sets, correspondingly. The best model shows an accuracy higher than 95% for training set; the model was also validated using 10-fold cross-validation procedure and through a test set achieving accuracy values of 85.32% and 86.80%, correspondingly. Additionally, the obtained model is compared with other approaches previously published in the international literature showing better results. Finally, we can say that the present results provided a double-target approach for increasing the estimation of antidiabetic chemicals identification aimed by double-way workflow in virtual screening pipelines.

Design, Synthesis and Biological Evaluation of Novel N-hydroxyheptanamides Incorporating 6-hydroxy-2-methylquinazolin-4(3H)-ones as Histone Deacetylase Inhibitors and Cytotoxic Agents.

BACKGROUND: Target-based approach to drug discovery currently attracts a great deal of interest from medicinal chemists in anticancer drug discovery and development worldwide, and Histone Deacetylase (HDAC) inhibitors represent an extensive class of targeted anti-cancer agents. Among the most explored structure moieties, hydroxybenzamides and hydroxypropenamides have been demonstrated to have potential HDAC inhibitory effects. Several compounds of these structural classes have been approved for clinical uses to treat different types of cancer, such as vorinostat and belinostat. AIMS: This study aims at developing novel HDAC inhibitors bearing quinazolinone scaffolds with potential cytotoxicity against different cancer cell lines. METHODS: A series of novel N-hydroxyheptanamides incorporating 6-hydroxy-2 methylquinazolin-4(3H)-ones (14a-m) was designed, synthesized and evaluated for HDAC inhibitory potency as well as cytotoxicity against three human cancer cell lines, including HepG-2 (liver cancer), MCF-7 (breast cancer) and SKLu-1 (lung cancer). Molecular simulations were finally carried out to gain more insight into the structure-activity relationships. ADME-T predictions for selected compounds were also performed to predict some important features contributing to the absorption profile of the present hydroxamic derivatives. RESULTS: It was found that the N-hydroxyheptanamide 14i and 14j were the most potent, both in terms of HDAC inhibition and cytotoxicity. These compounds displayed up to 21-71-fold more potent than SAHA (suberoylanilide hydroxamic acid, vorinostat) in terms of cytotoxicity, and strong inhibition against the whole cell HDAC enzymes with IC50 values of 7.07-9.24µM. Docking experiments on HDAC2 isozyme using Autodock Vina showed all compounds bound to HDAC2 with relatively higher affinities (from -7.02 to -11.23 kcal/mol) compared to SAHA (-7.4 kcal/mol). It was also found in this research that most of the target compounds seemed to be more cytotoxic toward breast cancer cells (MCF-7) than liver (HepG2), and lung (SKLu-1) cancer cells.

Corrigendum to "Nonintrusive Load Monitoring Based on Advanced Deep Learning and Novel Signature".

[This corrects the article DOI: 10.1155/2017/4216281.].