Tuning Process Research Articles

Automation of quantum dot measurement analysis via explainable machine learning

Abstract The rapid development of quantum dot (QD) devices for quantum computing has necessitated more efficient and automated methods for device characterization and tuning. Many of the measurements acquired during the tuning process come in the form of images that need to be properly analyzed to guide the subsequent tuning steps. By design, features present in such images capture certain behaviors or states of the measured QD devices. When considered carefully, such features can aid the control and calibration of QD devices. An important example of such images are so-called triangle plots, which visually represent current flow and reveal characteristics important for QD device calibration. While image-based classification tools, such as convolutional neural networks (CNNs), can be used to verify whether a given measurement is good and thus warrants the initiation of the next phase of tuning, they do not provide any insights into how the device should be adjusted in the case of bad images. This is because CNNs sacrifice prediction and model intelligibility for high accuracy. To ameliorate this trade-off, a recent study introduced an image vectorization approach that relies on the Gabor wavelet transform [1]. Here we propose an alternative vectorization method that involves mathematical modeling of synthetic triangles to mimic the experimental data. Using explainable boosting machines, we show that this new method offers superior explainability of model prediction without sacrificing accuracy. This work demonstrates the feasibility and advantages of applying explainable machine learning techniques to the analysis of quantum dot measurements, paving the way for further advances in automated and transparent QD device tuning.

Active Vibration Control of a Cantilever Beam Structure Using Pure Deep Learning and PID with Deep Learning-Based Tuning

Vibration is a major problem that can cause structures to wear out prematurely and even fail. Smart structures are a promising solution to this problem because they can be equipped with actuators, sensors, and controllers to reduce or eliminate vibration. The primary objective of this paper is to explore and compare two deep learning-based approaches for vibration control in cantilever beams. The first approach involves the direct application of deep learning techniques, specifically multi-layer neural networks and RNNs, to control the beam’s dynamic behavior. The second approach integrates deep learning into the tuning process of a PID controller, optimizing its parameters for improved control performance. To activate the structure, two different input signals are used, an impulse signal at time zero and a random one. Through this comparative analysis, the paper aims to evaluate the effectiveness, strengths, and limitations of each method, offering insights into their potential applications in the field of smart structure control.

Deep interval type-2 generalized fuzzy hyperbolic tangent system for nonlinear regression prediction

Recently, due to the rapid rise of artificial intelligence (AI), considerable progress has been made in the field of nonlinear regression prediction. However, many existing methods suffer from the issues of rule and parameter explosion and poor accuracy, particularly for high-dimensional data with uncertainty. To address these limitations, this paper proposes a deep interval type-2 generalized fuzzy hyperbolic tangent system (DIT2GFHS). First, a novel neural network-based implementation of the interval type-2 fuzzy generalized fuzzy hyperbolic tangent system (IT2GFHS) is introduced to improve the efficiency of system parameter updates and optimization. Then, using a hierarchical and block-based framework, multiple IT2GFHSs are stacked layer by layer from bottom to top to construct the DIT2GFHS, with each layer’s fuzzy subsystems being independent of the others. Additionally, DIT2GFHS incorporates optimization algorithms and the Adam optimizer for training, thereby avoiding the tedious manual parameter tuning process. The detailed analysis of the construction manner and internal mechanisms for DIT2GFHS indicates that it features a reduced number of parameters, a transparent and clear structure, strong capability in handling uncertainty, and favorable accuracy. Notably, the small number of parameters and the explicit structure reduce computational and hardware burdens while maintaining interpretability. Finally, extensive experimental studies on both relatively low-dimensional and high-dimensional datasets are conducted. The results demonstrate that DIT2GFHS achieves excellent performance with fewer parameters compared to many deep-structured models, including deep fuzzy systems and deep learning models. This highlights its potential impact in addressing practical nonlinear regression problems.

Multi-view structural twin support vector machine with the consensus and complementarity principles and its safe screening rules

Nowadays, numerous multi-view algorithms are proposed to achieve better performance in classification tasks. In this article, we propose a multi-view structural twin support vector machine (MvSTSVM), which is guided by both complementarity and consensus principles. In order to keep the consensus information, structural information of inter-class and intra-class is explored in two hyperplanes by clustering methods, which significantly enhances the classification performance. Besides, MvSTSVM introduces the weights to multi-view datasets to dig the complementarity information among different views. In addition, our model achieves both intra-class compactness and inter-class separability. To deal with the optimization problems, an iterative strategy is adopted for the solution of MvSTSVM. Moreover, we derive the safe screening rules for MvSTSVM, which speed up the tuning process obviously without sacrificing accuracy. A series of numerical experiments confirm the effectiveness of MvSTSVM and the safety of proposed screening rules.

Elevating fraud detection: machine learning models with computational intelligence optimization

<span lang="EN-US">The amount of crimes committed online has undoubtedly increased as more people use the internet for e-commerce and other financial transactions. Machine learning algorithms have been created to detect payment fraud in online purchasing in order to address the issue. This study performs a thorough comparative examination of different metaheuristic optimizations as hyperparameter tuning methods; these are Particle Swarm Optimization (PSO) and Genetic Algorithm (GA). They are used to optimize the ROC AUC of the three machine learning algorithms, namely X-Gradient Boost, Random Forest Classifier, and Light Gradient Boost Machine. Since the study's data are unbalanced, the determined metrics were ROC AUC. PSO offers consistent conditions for finding the best solution, according to our experiment. Without the inclusion of population annihilation strategies, PSO can achieve the greatest results in various situations which are different from GA, a consistent condition for finding the best solution, according to our experiment. Without the inclusion of population annihilation strategies, PSO can achieve the greatest results in various situations. The findings indicate that Random Forest Classifier provided the highest ROC AUC value both before and after the hyperparameter tuning process, with a score of 88.69% attained while utilizing PSO.</span>

Sarcasm detection on social data: heuristic search and deep learning

<span lang="EN-US">Due to the significant surge in online activity, sarcasm detection (SD) has attracted major attention in social media networks. Sarcasm is a lexical item of negative sentiments or dislikes by utilizing exaggerated language constructs. SD has created a natural language processing (NLP) procedure focused on the intricate and unclear aspects of sarcasm, primarily used in sentiment analysis (SA), human-computer interaction, and various NLP applications. Concurrently, advancements in machine learning (ML) approaches facilitate the creation of effective SD systems. This manuscript presents the future search algorithm with deep learning assisted sarcasm detection and classification on social networking data (FSADL-SDCSND) approach. The major intent of the FSADL-SDCSND approach is in the effective and automated recognition of sarcastic text. In the presented FSADL-SDCSND technique, several data pre-processing stages are achieved to transform the data into a compatible format. Besides, the FSADL-SDCSND approach applies a bidirectional serial-parallel long short-term memory (BS-PLSTM) approach for SD and classification. The hyperparameter tuning process is accomplished by employing the future search algorithm for improving the recognition of the BS-PLSTM model. For superior output of the FSADL-SDCSND model, a sequence of simulations can be applied. The investigational outputs highlighted the improved solutions of the FSADL-SDCSND model with other approaches under diverse performance measures.</span>

A GWO-Based Indirect IMC-PID Controller for DC-DC Boost Converter

A PID controller design using an internal model control (IMC) approach is a well-established method for controller tuning in a DC-DC boost converter. This study introduces an innovative implementation of a novel indirect Internal Model Control (IMC) strategy for PID controller design, tailored specifically for a DC-DC boost converter. While the indirect IMC approach has been documented in prior research, its application to boost converters signifies a substantial contribution to the field. The proposed method simplifies the tuning process by focusing exclusively on the plant shifting parameter ψ, thereby eliminating the need for an IMC filter. Optimal tuning is achieved through the Grey Wolf Optimization (GWO) method, which enhances the controller’s stability, robustness, and transient response in the presence of disturbances commonly encountered in boost converter operation. Extensive simulations are performed in a MATLAB Simulink environment to compare the performance of the GWO-based indirect IMC-PID controller with traditional PID and IMC-PID designs. Performance is assessed based on transient response parameters and performance indices, such as IAE, ISE, ITAE, and ITSE. Results reveal that the GWO-optimized indirect IMC-PID controller significantly outperforms conventional controllers, demonstrating enhanced servo and regulatory behaviors.

Modeling of Bayesian machine learning with sparrow search algorithm for cyberattack detection in IIoT environment

With the fast-growing interconnection of smart technologies, the Industrial Internet of Things (IIoT) has revolutionized how industries work by connecting devices and sensors and automating regular operations via the Internet of Things (IoTs). IoT devices provide seamless diversity and connectivity in different application domains. This system and its transmission channels are subjected to targeted cyberattacks due to their round-the-clock connectivity. Accordingly, a multilevel security solution is needed to safeguard the industrial system. By analyzing the data packet, the Intrusion Detection System (IDS) counteracts the cyberattack for the targeted attack in the IIoT platform. Various research has been undertaken to address the concerns of cyberattacks on IIoT networks using machine learning (ML) and deep learning (DL) approaches. This study introduces a new Bayesian Machine Learning with the Sparrow Search Algorithm for Cyberattack Detection (BMLSSA-CAD) technique in the IIoT networks. The proposed BMLSSA-CAD technique aims to enhance security in IIoT networks by detecting cyberattacks. In the BMLSSA-CAD technique, the min-max scaler normalizes the input dataset. Additionally, the method utilizes the Chameleon Optimization Algorithm (COA)-based feature selection (FS) approach to identify the optimal feature set. The BMLSSA-CAD technique uses the Bayesian Belief Network (BBN) model for cyberattack detection. The hyperparameter tuning process employs the sparrow search algorithm (SSA) model to enhance the BBN model performance. The performance of the BMLSSA-CAD method is examined using UNSWNB51 and UCI SECOM datasets. The experimental validation of the BMLSSA-CAD method highlighted superior accuracy outcomes of 97.84% and 98.93% compared to recent techniques on the IIoT platform.

Mathematical modelling-based blockchain with attention deep learning model for cybersecurity in IoT-consumer electronics

Security in the Internet of Things (IoT)-consumer electronics is decisive in safeguarding connected devices from possible vulnerabilities and threats. Strong security measures are required to protect against unauthorized access, data breaches, and cyberattacks as these smart devices gather, transfer, and save sensitive information. Employing regular software updates, secure authentication protocols, and strong encryption are indispensable approaches to guarantee the integrity and privacy of user details. Drones offer users a bird's-eye view that can be started and implemented anywhere and anytime. But, the malicious use of drones has developed among criminals and cyber-criminals. The possibility and frequency of these attacks are maximum, and their effect is highly unsafe and devastating. Thus, the desire for protective, preventive counter-measures and detective are needed. Intrusion detection utilizing deep learning (DL) drones control advanced neural network (NN) structures to improve security surveillance in dynamic outdoor environments. Prepared with sophisticated sensors and onboard processing abilities, these drones autonomously examine aerial imagery to identify and classify possible risks like suspicious activities, unauthorized personnel, or vehicles. DL approaches allow drones to learn complex patterns and anomalies in real-time, enabling quick response and proactive security procedures. This study introduces an enhanced Mathematical Modeling-based Blockchain with Mountain Gazelle Optimization and Attention to Deep Learning for Cybersecurity (MGOADL-CS) technique in the drone's platform. The MGOADL-CS method aims to improve cybersecurity using BC technology in the drone's environment by detecting attacks using optimal DL models. In the initial stage, the MGOADL-CS technique uses a linear scaling normalization (LSN) approach to normalize the input data. The MGOADL-CS technique uses an improved tunicate swarm algorithm (ITSA) based feature selection approach for dimensionality reduction. Besides, the attention long short-term memory neural network (ALSTM-NN) model is employed to detect and classify cyberattacks. Finally, the MGO-based hyperparameter tuning process is performed to adjust the hyperparameter values of the ALSTM-NN model. To highlight the enhanced attack detection results of the MGOADL-CS technique, a detailed simulation set is accomplished under the NSL dataset. The performance validation of the MGOADL-CS method portrayed a superior accuracy value of 99.71 % over existing approaches.

Exploring the role of machine learning models in risk assessment models for developed organizations’ management decision policies

The goal of this work was to create and assess machine-learning models for estimating the risk of budget overruns in developed projects. Finding the best model for risk forecasting required evaluating the performance of several models. Using a dataset of 177 projects took into account variables like environmental risks employee skill level safety incidents and project complexity. In our experiments, we analyzed the application of different machine learning models to analyze the risk for the management decision policies of developed organizations. The performance of the chosen model Neural Network (MLP) was improved after applying the tuning process which increased the Test R2 from −0.37686 before tuning to 0.195637 after tuning. The Support Vector Machine (SVM), Ridge Regression, Lasso Regression, and Random Forest (Tuned) models did not improve, as seen when Test R2 is compared to the experiments. No changes in Test R2’s were observed on GBM and XGBoost, which retained same Test R2 across different tuning attempts. Stacking Regressor was used only during the hyperparameter tuning phase and brought a Test R2 of 0. 022219.Decision Tree was again the worst model among all throughout the experiments, with no signs of improvement in its Test R2; it was −1.4669 for Decision Tree in all experiments arranged on the basis of Gender. These results indicate that although, models such as the Neural Network (MLP) sees improvements due to hyperparameter tuning, there are minimal improvements for most models. This works does highlight some of the weaknesses in specific types of models, as well as identifies areas where additional work can be expected to deliver incremental benefits to the structured applied process of risk assessment in organizational policies.

A Simulation-Assisted Field Investigation on Control System Upgrades for a Sustainable Heat Pump Heating

Heat pump-based renewable energy and waste heat recycling have become a mainstay of sustainable heating. Still, configuring an effective control system for these purposes remains a worthwhile research topic. In this study, a Smith-predictor-based fractional-order PID cascade control system was fitted into an actual clean heating renovation project and an advanced fireworks algorithm was used to tune the structural parameters of the controllers adaptively. Specifically, three improvements in the fireworks algorithm, including the Cauchy mutation strategy, the adaptive explosion radius, and the elite random selection strategy, contributed to the effectiveness of the tuning process. Simulation and field investigation results demonstrated that the fitted control system counters the adverse effects of time lag, reduces overshoot, and shortens the settling time. Further, benefiting from a delicate balance between heating demand and supply, the heating system with upgraded management increases the average exergetic efficiency by 11.4% and decreases the complaint rate by 76.5%. It is worth noting that the advanced fireworks algorithm mitigates the adverse effect of capacity lag and simultaneously accelerates the optimizing and converging processes, exhibiting its comprehensive competitiveness among this study’s three intelligent optimization algorithms. Meanwhile, the forecast and regulation of the return water temperature of the heating system are independent of each other. In the future, an investigation into the implications of such independence on the control strategy and overall efficiency of the heating system, as well as how an integral predictive control structure might address this limitation, will be worthwhile.

Budget-aware Query Tuning: An AutoML Perspective

Modern database systems rely on cost-based query optimizers to come up with good execution plans for input queries. Such query optimizers rely on cost models to estimate the costs of candidate query execution plans. A cost model represents a function from a set of cost units to query execution cost, where each cost unit specifies the unit cost of executing a certain type of query processing operation (such as table scan or join). These cost units are traditionally viewed as constants, whose values only depend on the platform configuration where the database system runs on top of but are invariant for queries processed by the database system. In this paper, we challenge this classic view by thinking of these cost units as variables instead. We show that, by varying the cost-unit values one can obtain query plans that significantly outperform the default query plans returned by the query optimizer when viewing the cost units as constants. We term this cost-unit tuning process 'query tuning' (QT) and show that it is similar to the well-known hyper-parameter optimization (HPO) problem in AutoML. As a result, any state-of-the-art HPO technologies can be applied to QT. We study the QT problem in the context of anytime tuning, which is desirable in practice by constraining the total time spent on QT within a given budget-we call this problem budgetaware query tuning. We further extend our study from tuning a single query to tuning a workload with multiple queries, and we call this generalized problem budgetaware workload tuning (WT), which aims for minimizing the execution time of the entire workload. WT is more challenging as one needs to further prioritize individual query tuning within the given time budget. We propose solutions to both QT and WT and experimental evaluation using both benchmark and real workloads demonstrates the efficacy of our proposed solutions.

AUTOMATED INDOOR ACTIVITY MONITORING FOR ELDERLY AND VISUALLY IMPAIRED PEOPLE USING QUANTUM SALP SWARM ALGORITHM WITH MACHINE LEARNING

Elderly and vision-impaired indoor activity monitoring uses sensor technology to monitor interaction and movement in the living area. This system can identify deviations from regular patterns, provide alerts, and ensure safety in case of emergencies or potential risks. These solutions enhance quality of life by promoting independent living while offering peace of mind to loved ones and caregivers. Visual impairments have become the chief part of these problems over the past few years. Usually, visually impaired people seek help from others to keep doing their daily tasks. An automated human activity recognition (HAR) technique could enhance a vision-impaired person’s transaction activity and safe movement. HAR using deep learning (DL) includes training neural networks to automatically detect and classify various activities carried out by humans based on sensor information. By leveraging DL approaches, the HAR system could accurately identify and classify activities, including sitting, standing, walking, or running, from the information captured by sensors or wearable devices. This system is integral to smart home automation, healthcare, and sports analytics. This study develops an Automated Fractal Quantum Salp Swarm Algorithm with Machine Learning based HAR (QSSA-MLHAR) for assisting elderly and visually impaired people. The QSSA-MLHAR technique uses sensory input data to identify human activities in diverse classes. In the QSSA-MLHAR technique, the min–max scalar is primarily used to scale the input data. Besides, the QSSA-MLHAR technique applies a mixed extreme learning machine (M-ELM) model to classify various human actions. The QSSA-based hyperparameter tuning process is used to improve the detection results of the M-ELM system. A wide range of simulations was performed to ensure the better performance of the QSSA-MLHAR method. The comprehensive outcomes assured that the QSSA-MLHAR technique performs well compared to other models.

Ocean wave prediction using Long Short-Term Memory (LSTM) and Extreme Gradient Boosting (XGBoost) in Tuban Regency for fisherman safety

The fishing industry has a large role in the Indonesian economy, with potential profits in 2020 of around US$ 1.338 billion. Tuban Regency is one of the regions in East Java that contributes to the fisheries sector. Fisheries relate to the work of fishermen. Accidents in shipping are still a major concern. One of the natural factors that influence shipping accidents is the height of the waves. Fisherman safety regulations have been established by the Ministry of Maritime Affairs and Fisheries and the Meteorology, Climatology and Geophysics Agency. Apart from regulations, the results of wave height predictions using the Long Short-Term Memory (LSTM) and Extreme Gradient Boosting (XGBoost) methods can help fishermen determine shipping departures, thereby reducing the risk of accidents. In this study, the Grid Search hyperparameter tuning process was used for both methods which were carried out on four location coordinates. Based on the analysis results, LSTM is superior in predicting wave height for the next 30 days because it can predict wave height at all three locations, with results at the first location (RMSE 0.045; MAE 0.029; MAPE 8.671 %), second location (RMSE 0.051; MAE 0.035; MAPE 10.64 %), and third location (RMSE 0.044; MAE 0.027; MAPE 7.773 %), while XGBoost only has the best value at fourth location (RMSE 0.040; MAE 0.025; MAPE 7.286 %).•Hyperparameter tuning with gridsearch is used in LSTM and XGBoost to obtain optimal accuracy•LSTM outperforms in three locations, while XGBoost outperforms in the fourth location.•Advanced prediction techniques such as LSTM and XGBoost improve fishermen's safety by providing accurate wave height estimates, thereby reducing the possibility of shipping accidents.

Accuracy estimation of a CFD multiphysics approach to study a mixed parallel and serpentine flow channels PEM fuel cell

Abstract This study presents a comprehensive analysis of a 50 cm2 fuel cell with mixed parallel and serpentine flow channels using Computational Fluid Dynamics modelling. Through rigorous validation against experimental measurements, the numerical model ensures accuracy in capturing convective and diffusive fluid flows, thermodynamic, and electrochemical phenomena and their mutual interaction. The study discusses a systematic procedure for achieving model convergence and identifies key parameters influencing the fitting of numerical polarization curves with experimental data. Operating within a temperature of 333 K, at atmospheric pressure, with a relative humidity of 100% for both the anode and cathode, the model offers crucial insights for advancing our understanding of fuel cell operation and guiding the development of more efficient and reliable technologies. In the activation region, reference exchange current densities and charge transfer coefficients were proved to play a significant role in reproducing the PEM fuel cell behaviour. Increasing the exchange current densities raises the voltage output in the activation polarization region while charge transfer coefficients affect the slope of the curve. In the ohmic region, a proton conduction coefficient of 1.84 and a contact resistance between gas diffusion layers and bipolar plates equal to 4.03e-7 Ω·m2 were used to fit the experimental polarization curve. After the tuning process, the numerical curve closely matches the experimental one with a maximum relative error of 2.40% and an R2 value of 0.9848. Moreover, a detailed analysis of the internal distribution of key variables was conducted for a specific point on the polarization curve (0.25 A/cm2). This analysis provided significant insights into the complex interplay of fluid dynamics, heat transfer, and electrochemical processes. Specifically, it was observed how the flow distribution, pressure drop, and reactant concentrations within the fuel cell channels and gas diffusion layers affect overall performance, highlighting the crucial role of convection and diffusion in reactant transport and the importance of managing heat for optimal cell operation.

Design and Optimization of a Piecewise Flight Controller for VTOL UAVs

The dynamic systems of unmanned aerial vehicles (UAVs) are susceptible to parametric uncertainties, unmodeled dynamics, and external vibrational disturbances during motion control. Although proportional–integral–derivative (PID) controllers are widely used in UAVs, the manual tuning process is time-consuming, and the resultant control performance is vulnerable to uncertainties in varying flight conditions, leading to suboptimal flight control throughout the entire hovering flight envelope. To address this issue, the present study designs and optimizes a novel piecewise flight controller based on a hybrid data-driven approach combining gradient-based methods and pattern search algorithms. The optimization process is validated through comprehensive flight testing, comparing the piecewise controller with the baseline controller across a wide range of flight conditions. The study examines the objective functions and design parameters, demonstrating the robustness of the optimized piecewise controller in the presence of disturbances. The results reveal significant variations in the system model under different flight conditions, and the piecewise optimized controller demonstrates a response that is more than twice as fast as the benchmark, with acceptable overshoots and steady-state errors under the predefined trajectories. The salient features of the proposed piecewise flight controller and design optimization process are verified through flight testing, which is expected to provide a framework for future UAV flight control designs.

An adaptive network with consecutive and intertwined slices for real-world time-series forecasting

Real-world systems are chiefly comprised of multiple sensors and devices, whose instantaneous data and upcoming tendencies are demanded by the decision makers to formulate future schemes. Accuracy and timeliness are both of paramount significance. Thereby, although the deep forecasting models are promising, the space and resources are often limited in reality, rendering their adaptability requisite. Unfortunately, the majority of current deep forecasting models are far from adaptive and acquire heavy hyper-parameter tuning processes to obtain the satisfactory performances. Moreover, their strategies to reduce the model complexities sometimes bring extra hyper-parameters. To tackle these issues, we propose AdaNS: an Adaptive Network with consecutive and intertwined Slices. The data-driven features of time-series in frequency domain are used in AdaNS to adaptively determine the model structure, as well as hyper-parameters, and categorize variables. In accordance with these frequency-based features, input sequences are first sliced in a consecutive way to extract the universal features and then in an intertwined way to extract the local features, for hierarchical and comprehensive forecasting. Extensive experiments show that AdaNS achieves state-of-the-art performances on fourteen benchmarks, virtually without any hyper-parameter tuning. The source code is available at https://github.com/OrigamiSL/AdaNS.

Combining antibacterial and wound healing features: Xanthan gum/guar gum 3D-printed scaffold tuned with hydroxypropyl-β-cyclodextrin/thymol and Zinc2+

Biofilm formation on biological and material surfaces represent a heavy health and economic burden for both patient and society. To contrast this phenomenon, medical devices combining antibacterial and pro-wound healing abilities are a promising strategy. In the present work, Xanthan gum/Guar gum (XG/GG)-based scaffolds were tuned with thymol and Zn2+ to obtain wound dressings that combine antibacterial and antibiofilm properties and favour the healing process. The tuning process preserved the 3D extrusion-based printability of the XG/GG ink. Scaffolds swelling profile was assessed in PBS pH 7.4, and the resistance to compressive forces was studied using a texturometer. The scaffolds microarchitectures were analyzed by SEM, while ATR-FTIR spotlighted the chemical modifications of the customized materials. Thymol and Zn2+ release was analyzed in biologically relevant media, showing a burst release in the first hours. The antibacterial properties were confirmed against S. aureus, P. aeruginosa, and S. epidermidis by isothermal microcalorimetry and biofilm viable cell counting. Incorporation of hydroxypropyl-β-cyclodextrin (HPβCD) improved thymol loading (7- and 14-times higher thymol content) and enhanced the antimicrobial and antioxidant performances of the dressing, while the presence of Zn2+ strongly potentiated the antimicrobial activity, showing a potent antibiofilm response in both Gram-positive and Gram-negative strains of clinical concern. The thymol and Zn2+ combination led to a reduction of 99.95 %, 99.99 %, and 98.26 %, of biofilm formation against S. aureus, P. aeruginosa, and S. epidermidis, respectively. Furthermore, the scaffolds demonstrated good hemocompatibility, cytocompatibility, tissue integration and pro-angiogenic features in an in ovo CAM model.

DC Motor Speed Control using Particle Swarm Optimization based on Labview

In Industrial applications, DC motors are commonly applied because of high reliability, ease of control and ability to provide accurate speed. However, to get accurate speed control under several operation conditions such as disturbances and changes in the load is significant challenge. This research explores the implementation of particle swarm optimization (PSO) to tune parameters of proportional-integral (PI) controller. PSO that is a population-based optimization technique, is inspired by the social behavior of swarms. It is a population-based optimization technique. By automation process in the algorithm. Using the tuning process of PSO, it can effectively obtain the parameters of PI controller. experimental hardware using DIGIAC 1750 is used to assess the performance of the proposed method. The parameters of and are 0.7492 and 0.2007, respectively. The results show that the performance of the DC motor using PSO tuned by PI for , , and are 0.3687 s, 0.5106 s, and 0.6051 s, respectively. Furthermore, when the system is given a disturbance, the response can come back again following the setpoint and when the setpoint is changed, the response can follow the setpoint quickly as well. The proposed method can address the challenges associated with DC motor speed control.

Detection of Aspergillus flavus in Figs by Means of Hyperspectral Images and Deep Learning Algorithms

Plant diseases cause economic losses and health risks, such as aflatoxins linked to liver cancer. These toxins, produced by fungi like Aspergillus flavus in figs, are often detected late through invasive methods or visual inspection. Since Spain, particularly Extremadura, is a key fig producer, alternative detection methods are essential to preventing aflatoxins in the food chain. The aim of this research is the early detection of Aspergillus flavus fungus using non-invasive techniques with hyperspectral imaging and applying artificial intelligence techniques, in particular deep learning. The images were taken after inoculation of the microtoxin using 3 different concentrations, related to three different classes and healthy figs (healthy controls). The analysis of the hyperspectral images was performed at the pixel level. Firstly, a fully connected neural network was used to analyze the spectral signature associated with each pixel; secondly, the wavelet transform was applied to each spectral signature. The resulting images were fed to a convolutional neural network. The hyperparameters of the proposed models were adjusted based on the parameter tuning process that was performed. The results are promising, with 83% accuracy, 82.75% recall, and 83.25% F1-measure for the fully connected neural network. The high F1-measure demonstrates that the model’s performance is good. The model has a low incidence of false positives for samples that contain aflatoxin, while a higher number of false positives appears in healthy controls. Due to the presence of false negatives, this class also has a high recall. The convolutional neural network results, accuracy, recall, and F1 are 77.25%, indicating moderate model performance. Only class 3, with higher aflatoxin concentration, achieves high precision and low false positive incidence. Healthy controls exhibit a high presence of false negatives. In conclusion, we demonstrate the effectiveness of pixel-level analysis in identifying the presence of the fungus and the viability of the non-invasive techniques applied in improving food safety. Although further research is needed, in this study, the fully connected neural network model shows good performance with lower energy consumption.