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Parking space management has become a critical challenge in urban areas due to increasing vehicle numbers and limited parking infrastructure. This paper presents a comprehensive study of machine learning (ML) models in IoT-enabled environments focusing on proposing an ML-based model for predicting available parking space. The study evaluates the performance of various models including K-nearest neighbors (KNNs), support vector machines (SVMs), random forest (RF), decision tree (DT), logistic regression (LR), and Naïve Bayes (NB) based on “precision, recall, accuracy, and F1-score performance metrics”. The results obtained by implementing ML models on the data with 65% and 85% threshold values are compared to draw meaningful conclusions regarding their performance in predicting parking space availability. Among the evaluated models, random forest (RF) demonstrates superior performance with high precision, recall, accuracy, and F1-score values. It showcases its effectiveness in accurately predicting parking space availability in the IoT-enabled environment. On the other hand, models such as K-nearest neighbors (KNNs), decision tree (DT), logistic regression (LR), and Naïve Bayes (NB) show relatively lower performance in complex parking scenarios. The paper concludes that the use of advanced predictive models, particularly random forest, significantly enhances the accuracy and reliability of IoT-enabled parking management systems and also reduces the waiting time of the vehicles, leading to more efficient resource utilization, reduced traffic congestion in real-time scenarios, and better user satisfaction in the IoT-enabled environment., Author(s): Anchal Dahiya [1]; Pooja Mittal [1]; Yogesh Kumar Sharma [2]; Umesh Kumar Lilhore (corresponding author) [3,4]; Sarita Simaiya [4]; Ehab Ghith [5]; Mehdi Tlija [6] 1. Introduction As more [...]

In this paper, we introduce a comprehensive theoretical study to obtain an optimal highly sensitive fluidic sensor based on the one-dimensional phononic crystal (PnC). The mainstay of this study strongly depends on the high impedance mismatching due to the irregularity of the considered quasi-periodic structure, which in turn can provide better performance compared to the periodic PnC designs. In this regard, we performed the detection and monitoring of the different concentrations of lead nitrate (Pb(NO3)2) and identified it as being a dangerous aqueous solution. Here, a defect layer was introduced through the designed structure to be filled with the Pb(NO3)2 solution. Therefore, a resonant mode was formed within the transmittance spectrum of the considered structure, which in turn shifted due to the changes in the concentration of the detected analyte. The numerical findings demonstrate the role of the different sequences such as Fibonacci, Octonacci, Thue–Morse, and double period on the performance of the designed PhC detector. Meanwhile, the findings of this study show that the double-period quasi-periodic sequence provides the best performance with a sensitivity of 502.6 Hz/ppm, a damping rate of 5.9 × 10 − 5 , a maximum quality factor of 8463.5, and a detection limit of 2.45. [ABSTRACT FROM AUTHOR]

In this study, we demonstrate the reflectance spectrum of one-dimensional photonic crystals comprising two different types of metamaterials. In this regard, the designed structure can act as a simple and efficient detector for fat concentrations in milk samples. Here, the hyperbolic and gyroidal metamaterials represent the two types of metamaterials that are stacked together to construct the candidate structure; meanwhile, the designed 1D PCs can be simply configured as [G(ED)m]S. Here, G refers to the gyroidal metamaterial layers in which Ag is designed in a gyroidal configuration form inside a hosting medium of TiO2. In contrast, (ED) defines a single unit cell of the hyperbolic metamaterials in which two layers of porous SiC (E) and Ag (D) are combined together. It is worth noting that our theoretical and simulation methodology is essentially based on the effective medium theory, characteristic matrix method, Drude model, Bruggeman's approximation, and Sellmeier formula. Accordingly, the numerical findings demonstrate the emergence of three resonant peaks at a specified wavelength between 0.8 μm and 3.5 μm. In this context, the first peak located at 1.025 μm represents the optimal one regarding the detection of fat concentrations in milk samples due to its low reflectivity and narrow full bandwidth. Accordingly, the candidate detector could provide a relatively high sensitivity of 3864 nm/RIU based on the optimal values of the different parameters. Finally, we believe that the proposed sensor may be more efficient compared to other counterparts in monitoring different concentrations of liquid, similar to fats in milk. [ABSTRACT FROM AUTHOR]

In smart cities, sustainable development depends on energy load prediction since it directs utilities in effectively planning, distributing and generating energy. This work presents a novel hybrid deep learning model including components of the Improved-convolutional neural network (CNN), bidirectional long short-term memory (Bi-LSTM), Graph neural network (GNN), Transformer and Fusion Layer architectures for precise energy load forecasting. Better feature extraction results from the Improved-CNN's dilated convolution and residual block accommodation of wide receptive fields reduced the vanishing gradient problem. By capturing temporal links in both directions, Bi-LSTM networks help to better grasp complicated energy use patterns. Graph neural networks improve predictive capacities across linked systems by characterizing the spatial relationships between energy-consuming units in smart cities. Emphasizing critical trends to guarantee reliable forecasts, transformer models use attention methods to manage long-term dependencies in energy consumption data. Combining CNN, Bi-LSTM, Transformer and GNN component predictions in a Fusion Layer synthesizes numerous data representations to increase accuracy. With Root Mean Square Error of 5.7532 Wh, Mean Absolute Percentage Error of 3.5001%, Mean Absolute Error of 6.7532 Wh and R 2 of 0.9701, the hybrid model fared better than other models on the 'Electric Power Consumption' Kaggle dataset. This work develops a realistic model that helps informed decision-making and enhances energy efficiency techniques, promoting energy load forecasting in smart cities. [ABSTRACT FROM AUTHOR]

Tool steels such as AISI D2 are famous in the manufacturing industry because of their engineering applications. The precise interplay of improved hardness and toughness makes the machining of complex geometries challenging through conventional machining options. Therefore, non-conventional processes such as wire electric discharge machining (WEDM) are preferred because of their simultaneous machining and surface modification actions. To investigate the complex process parameters and their sensitivity, material removal rate (MRR) and cutting surface roughness (SR) are the corresponding performance measure characteristics for WEDM machining on AISI D2 tool steel. The L18 mixed-level Taguchi technique has been used for obtaining combinations of experiments on two levels of thickness and three levels of other remaining factors (21 × 33). Analysis of variance (ANOVA) and signal-to-noise ratio have been applied to measure the magnitude of effects on each control factor, to investigate the optimum levels of input process parameters on machining characteristics, and to identify their significance. ANOVA analysis revealed that, for both responses, all main effect variables are highly significant, with p-values equal to zero. Moreover, the coefficient of determination (R2) value in the ANOVA findings for both responses is above 97%, indicating the high reliability of the model. In addition, the composite desirability (dG) is considered to maximize MRR and minimize the SR during WEDM of D2; the better combination of optimum levels of machining parameters (T = 25.4 mm, Pon = 4 µs, SV = 95 V, and WT = 5 kg-f) has a dG of 0.5614. [ABSTRACT FROM AUTHOR]

This study treats the problem of Finite Time Stability Analysis (FTSA) and Finite Time Feedback Control (FTFC), using a Linear Matrix Inequalities Approach (LMIA). It specifically focuses on Takagi–Sugeno fuzzy Time Delay Fractional-Order Systems (TDFOS) that include nonlinear perturbations and interval Time Varying Delays (ITVDs). We consider the case of the Caputo Tempered Fractional Derivative (CTFD), which generalizes the Caputo Fractional Derivative (CFD). Two main results are presented: a two-step procedure is provided, followed by a more relaxed single-step procedure. Two examples are presented to show the reduction in conservatism achieved by the proposed methods. The first is a numerical example, while the second involves the FTFC of an inverted pendulum, which exhibits both symmetrical dynamics for small angular displacements and asymmetrical dynamics for larger deviations. [ABSTRACT FROM AUTHOR]

This paper investigates the problem of observer-based control for a class of nonlinear systems described by the Caputo–Hadamard fractional-order derivative. Given the growing interest in fractional-order systems for their ability to capture complex dynamics, ensuring their practical stability remains a significant challenge. We propose a novel concept of practical stability tailored to nonlinear Hadamard fractional-order systems, which guarantees that the system solutions converge to a small ball containing the origin, thereby enhancing their robustness against perturbations. Furthermore, we introduce a practical observer design that extends the classical observer framework to fractional-order systems under an enhanced One-Sided Lipschitz (OSL) condition. This extended OSL condition ensures the convergence of the proposed practical observer, even in the presence of significant nonlinearities and disturbances. Notably, the novelty of our approach lies in the extension of both the practical observer and the stability criteria, which are innovative even in the integer-order case. Theoretical results are substantiated through numerical examples, demonstrating the feasibility of the proposed method in real-world control applications. Our contributions pave the way for the development of robust observers in fractional-order systems, with potential applications across various engineering domains. [ABSTRACT FROM AUTHOR]

This research aims to develop an intelligent framework for quality control and fault detection in pre-production and post-production systems in Industry 4.0. In the pre-production system, the health of the manufacturing machine is monitored. In this study, we examine the gear system of induction motors used in industries. In post-production, the product is tested for quality using a machine vision system. Gears are fundamental components in countless mechanical systems, ranging from automotive transmissions to industrial machinery, where their reliable operation is vital for overall system efficiency. A faulty gear system in the induction motor directly affects the quality of the manufactured product. Vibration data, collected from the gear system of the induction motor using vibration sensors, are used to predict the motor's health condition. The gear system is monitored for six different fault conditions. In the second part, the quality of the final product is inspected with the machine vision system. Faults on the surface of manufactured products are detected, and the product is classified as a good or bad product. The quality control system is developed with different deep learning models. Finally, the quality control framework is validated and tested with the evaluation metrics. [ABSTRACT FROM AUTHOR]

A hydrogen-based microgrid (MG) is an energy system that uses hydrogen as a primary energy carrier within a localized grid. Numerous alternative approaches and concepts are found concerning the management of renewable energy systems. This study proposes a novel approach to assess the energy management system (EMS) and optimal hydrogen-based Energy Storage Systems (HBESS) at minimal total cost, employing particle swarm optimization (PSO) and fuzzy control in stand-alone microgrids. Together, these methods effectively address control and management challenges within hybrid microgrids (HMGs). This has been proposed to enhance energy management and to improve power quality. The findings reveal that PSO is the most advantageous and efficient approach. Its utilization proves instrumental in reducing costs, boosting reliability, and optimizing operational schedules within HMGs. Furthermore, the power profile holds considerable importance in this study, significantly enhancing system reliability and stability. This study has achieved an impressive 6.147% improvement in cost-effectiveness compared to traditional methods. This has been put into practice and validated through implementation within a MATLAB (9.13.0 (R2022b))/Simulink framework. [ABSTRACT FROM AUTHOR]

The objective of this research endeavor is to examine the properties of stagnation point flow in the presence of absorption, viscous dissipation, and internal thermal generation with respect to a shrinking surface. The resulting system of differential equations is notoriously challenging to solve analytically. The equations controlling the boundary layer flow were solved using a finite difference method. The analysis includes the examination of important physical quantities through the presentation of plots and tabulated values. Our findings reveal a strong connection between the presence of solutions for high shrinking parameters and the magnetic field that was applied. Temperatures increase when there is an increase in both Ec and α at the same time. These results also suggest a shallowing of the thermal boundary layer. As a result of these findings, it appears that temperature and thermal boundary layer thickness are sensitive to changes in these factors. [ABSTRACT FROM AUTHOR]

The interest in hydrogen is rapidly expanding because of rising greenhouse gas emissions and the depletion of fossil resources. The current work focuses on employing affordable Al alloys for hydrogen production and storage to identify the most efficient alloy that performs best in each situation. In the first part of this work, hydrogen was generated from water electrolysis. The Al alloys that are being examined as electrodes in a water electrolyzer are 1050-T0, 5052-T0, 6061-T0, 6061-T6, 7075-T0, 7075-T6, and 7075-T7. The flow rate of hydrogen produced, energy consumption, and electrolyzer efficiency were measured at a constant voltage of 9 volts to identify the Al alloy that produces a greater hydrogen flow rate at higher process efficiency. The influence of the electrode surface area and water electrolysis temperature were also studied. The second part of this study examines these Al alloys' resistance to hydrogen embrittlement for applications involving compressed hydrogen gas storage, whether they are utilized as the primary vessel in Type 1 pressure vessels or as liners in Type 2 or Type 3 pressure vessels. Al alloys underwent electrochemical charging by hydrogen and Charpy impact testing, after which a scanning electron microscope (SEM) was used to investigate the fracture surfaces of both uncharged and H-charged specimens. The structural constituents of the studied alloys were examined using X-ray diffraction analysis and were correlated to the alloys' performance. Sensitivity analysis revealed that the water electrolysis temperature, electrode surface area, and electrode material type ranked from the highest to lowest in terms of their influence on improving the efficiency of the hydrogen production process. The 6061-T0 Al alloy demonstrated the best performance in both hydrogen production and storage applications at a reasonable material cost. [ABSTRACT FROM AUTHOR]

The integration of nonconventional energy sources such as solar, wind, and fuel cells into electrical power networks introduces significant challenges in maintaining frequency stability and consistent tie-line power flows. These fluctuations can adversely affect the quality and reliability of power supplied to consumers. This paper addresses this issue by proposing a Proportional–Integral–Derivative (PID) controller optimized through a hybrid Particle Swarm Optimization–Artificial Hummingbird Algorithm (PSO-AHA) approach. The PID controller is tuned using the Integral Time Absolute Error (ITAE) as a fitness function to enhance control performance. The PSO-AHA-PID controller's effectiveness is evaluated in two networks: a two-area thermal tie-line interconnected power system (IPS) and a one-area multi-source power network incorporating thermal, solar, wind, and fuel cell sources. Comparative analyses under various operational conditions, including parameter variations and load changes, demonstrate the superior performance of the PSO-AHA-PID controller over the conventional PSO-PID controller. Statistical results indicate that in the one-area multi-source network, the PSO-AHA-PID controller achieves a 76.6% reduction in overshoot, an 88.9% reduction in undershoot, and a 97.5% reduction in settling time compared to the PSO-PID controller. In the dual-area system, the PSO-AHA-PID controller reduces the overshoot by 75.2%, reduces the undershoot by 85.7%, and improves the fall time by 71.6%. These improvements provide a robust and reliable solution for enhancing the stability of interconnected power systems in the presence of diverse and variable energy sources. [ABSTRACT FROM AUTHOR]

Protein sequence classification needs to be performed quickly and accurately to progress bioinformatics advancements and the production of pharmaceutical products. Extensive comparisons between large databases of known proteins and unknown sequences are necessary in traditional protein classification methods, which can be time‐consuming. This labour‐intensive and slow manual matching and classification method depends on functional and biological commonalities. Protein classification is one of the many fields in which deep learning has recently revolutionized. The data on proteins are organized hierarchically and sequentially, and the most advanced algorithms, such as Deep Family‐based Method (DeepFam) and Protein Convolutional Neural Network (ProtCNN), have shown promising results in classifying proteins into relative groups. On the other hand, these methods frequently refuse to acknowledge this fact. We propose a novel hybrid model called ProteinCNN‐BLSTM to overcome these particular challenges. To produce more accurate protein sequence classification, it combines the techniques of amino acid embedding with bidirectional long short‐term memory (BLSTM) and convolutional neural networks (CNNs). The CNN component is the most effective at capturing local features, while the BLSTM component is the most capable of modeling long‐term dependencies across protein sequences. Through the process of amino acid embedding, sequences of proteins are transformed into numeric vectors, which significantly improves the precision of prediction and the representation of features. Using the standard protein samples PDB‐14189 and PDB‐2272, we analyzed the proposed ProteinCNN‐BLSTM model and the existing deep‐learning models. Compared to the existing models, such as CNN, LSTM, GCNs, CNN‐LSTM, RNNs, GCN‐RNN, DeepFam, and ProtCNN, the proposed model performed more accurately and better than the existing models. [ABSTRACT FROM AUTHOR]

This study aims to address a variant of the hybrid flow shop problem by simultaneously integrating lag times, unloading times, and transportation times, with the goal of minimizing the maximum completion time, or makespan. With applications in image processing, manufacturing, and industrial environments, this problem presents significant theoretical challenges, being classified as NP-hard. Notably, the problem demonstrates a notable symmetry property, resulting in a symmetric problem formulation where both the scheduling problem and its symmetric counterpart share the same optimal solution. To improve solution quality, all proposed procedures are extended to the symmetric problem. This research pioneers the consideration of the hybrid flow shop scheduling problem with simultaneous attention to lag, unloading, and transportation times, building upon a comprehensive review of existing literature. A two-phase heuristic is introduced as a solution to this complex problem, involving iterative solving of parallel machine scheduling problems. This approach decomposes the problem into manageable sub-problems, facilitating focused and efficient resolution. The efficient solving of sub-problems using the developed heuristic yields satisfactory near-optimal solutions. Additionally, two new lower bounds are proposed, derived from estimating minimum idle time within each stage via solving a polynomial parallel machine problem aimed at minimizing total flow time. These lower bounds serve to evaluate the performance of the developed two-phase heuristic, over measuring the relative gap. Extensive experimental studies on benchmark test problems of varying sizes demonstrate the effectiveness of the proposed approaches. All test problems are efficiently solved within reasonable timeframes, indicating practicality and efficiency. The proposed methods exhibit an average computational time of 8.93 seconds and an average gap of 2.75%. These computational results underscore the efficacy and potential applicability of the proposed approaches in real-world scenarios, providing valuable insights and paving the way for further research and practical implementations in hybrid flow shop scheduling. [ABSTRACT FROM AUTHOR]

The design and integration of intelligent energy management systems in hybrid electric vehicle (EV) charging stations, leveraging industry 4.0 and renewable energy sources, is crucial for advancing sustainability, efficiency, and technological development. The innovative hybrid EV charging station described in this study uses a combination of fuel cells, batteries, and solar panels that run at 14 amps a piece at 240 volts. The system consists of five essential components that work together to transfer power wirelessly: an EV battery bank, a boost converter, an HF inverter, transfer coils, and a power supply. Two crucial phases make up the optimization process. In phase 1, the boost converter's maximum power point is tracked and optimized to generate the most power possible by varying the duty cycle between 10% and 90%. In phase 2, the HF uses a class ϕ2 inverter at 30 MHz to synchronize with the resonant frequency of wireless power transfer coils. Zero-voltage switching is used by a digital signal processor card to carry out control for effective operations. By utilizing hybrid sources to optimize power transmission, this design improves the sustainability of EV charging options. [ABSTRACT FROM AUTHOR]

Improving efficiency in multi-stage flexible flowshop operations is essential for modern industrial systems. Optimizing total completion time while simultaneously considering the unloading and transportation times is vital for enhanced productivity and cost-effectiveness. This study delves into the intricate realm of flexible flow shop scheduling, particularly focusing on scenarios prevalent in modern manufacturing processes such as sand casting. Despite limited exploration in prior literature for particular cases, the significance of this problem underscores the pressing need for efficient algorithms and the exploration of new problem properties. Notably, we unveil the symmetry inherent in the problem, where scheduling from the initial stage to the final one, or vice versa, yields identical optimal solutions, thereby augmenting solution quality. Given the strongly NP-Hard nature of the problem, approximate solutions are preferred, especially for medium to large-scale instances. To address this challenge, we propose a novel two-phase heuristic approach, encompassing both a constructive phase and an improvement phase. Leveraging an existing efficient heuristic tailored for parallel machine scheduling, our method extends to efficiently incorporate considerations for unloading and transportation times. The efficacy of the two-phase heuristic lies in its ability to consistently generate high-quality schedules at each stage. Furthermore, we introduce efficient lower bounds derived from estimating the minimum idle time within each stage, drawing from insights in polynomial parallel machine scheduling with a focus on flow time minimization in preceding stages. These lower bounds serve as critical benchmarks for evaluating the performance of the two-phase heuristic against the relative gap performance measure. Extensive experimentation on benchmark test problems underscores the effectiveness of our proposed algorithms, with results demonstrating an average computation time of 9.48 seconds and a mean relative gap of only 2.42% across varying job and stage quantities up to 200 jobs and 10 stages. [ABSTRACT FROM AUTHOR]

Additive Manufacturing (AM) has emerged as an innovative technology that gives designers several advantages, such as geometric freedom of design and less waste. However, the quality of the parts produced is affected by different design and manufacturing parameters, such as the part orientation, the nozzle temperature and speed, the support material, and the layer thickness. In this context, the layer thickness is considered an important AM parameter affecting the part quality and accuracy. Thus, in this paper, a new adaptative slicing method based on the cusp vector and the surface deviation is proposed with the aim of minimizing the dimensional defects of FFF printed parts and investigate the impact on the dimensional part tolerancing. An algorithm is developed to automatically extract data from the STL file, select the build orientation, and detect intersection points between the initial slicing and the STL mesh. The innovation of this algorithm is exhibited via adapting the slicing according to the surface curvature based on two factors: the cusp vector and the surface deviation. The suggested slicing technique guarantees dimensional accuracy, especially for complex feature shapes that are challenging to achieve using a uniform slicing approach. Finally, a preview of the slicing is displayed, and the G-code is generated to be used by the FFF machine. The case study consists of the dimensional tolerance inspection of prototypes manufactured using the conventional and adaptive slicing processes. The proposed method's effectiveness is investigated using RE and CMM processes. The method demonstrates its reliability through the observed potential for accuracy improvements exceeding 0.6% and cost savings of up to 4.3% in specific scenarios. This reliability is substantiated by comparing the resulting dimensional tolerances and manufacturing costs. [ABSTRACT FROM AUTHOR]