The presented research paper proposes a novel integrated technique combining LeNet-5 with Continuous Wavelet Transform (CWT) along with Long Short-Term Memory (LSTM). The purpose of this integration is to improve the performance of mechanisms used for the detection of defects in rotatory machines across various operating conditions. The Convolutional Neural Networks (CNN) assists the presented CWT-LeNet-5-LSTM technique in finding the complex characteristics in the data, while LSTM learns the trends in the dataset and performs the necessary analysis of vibrations occurring in faulty machines. The developed model was examined for various loads and faults to extract results having accuracies of 99.6%, 96.9%, 92.5% and 96.6% for load conditions 3, 2, 1, and 0, respectively. These results demonstrate the ability of the proposed model to adapt according to varying load conditions while having the necessary levels of accuracy. This validates the model to perform precise fault detection and diagnosis, offering capabilities of predictive maintenance in industrial settings.

In the current work, the dynamic and buckling properties of hybrid fiber-reinforced polymer composite beams are investigated. The hybrid composite beam is made of polyester matrix, carbon fiber, and jute fiber. The mechanical characteristics of carbon/jute/polyester composites were examined using ASTM standards. Experimental dynamic analysis was performed on jute, carbon, and hybrid carbon/jute composite beams. The validity of a finite element (FE) model based on higher-order theory was examined, and it exhibits outstanding agreement for composite beams when compared to experiments and literature. The hybrid composite beams have a considerable impact on structural stiffness when compared to neat jute/polyester composite beams. In this work, the effects of end conditions, taper angles, and composite layers are also investigated in relation to the dynamic and buckling properties of uniform and tapered hybrid carbon/jute/polyester composite beams. The findings imply that hybrid composites can be a superior substitute material for automotive applications.

Motion planning and controller design are challenging tasks for highly coupled and nonlinear dynamical systems such as autonomous vehicles and robotic applications. Nonlinear model predictive control (NMPC) is an emerging technique in which sampling-based methods are used to synthesize the control and trajectories for complex systems. In this study, we have developed the sampling-based motion planning algorithm with NMPC through Bayesian estimation to solve the online nonlinear constrained optimization problem. In the literature, different filtration techniques have been applied to extract knowledge of states in the presence of noise. Due to the detrimental effects of linearization, the Kalman filter with NMPC only achieves modest effectiveness. Moving horizon estimation (MHE), on the other hand, frequently relies on simplifying assumptions and lacks an effective recursive construction. Additionally, it adds another optimization challenge to the regulation problem that has to be solved online. To address this problem, particle filtering is implemented for Bayesian filtering in nonlinear and highly coupled dynamical systems. It is a sequential Monte Carlo method that involves representing the posterior distribution of the state of the system using a set of weighted particles that are propagated through time using a recursive algorithm. For nonlinear and strongly coupled dynamical systems, the novel sampling-based NMPC technique is effective and simple to use. The efficiency of the suggested method has been assessed using simulated studies.

AIMS:Cognitive impairment poses a considerable health challenge in the context of type 2 diabetes mellitus (T2DM), emphasizing the need for effective interventions. This study delves into the therapeutic efficacy of quercetin, a natural flavonoid, in mitigating cognitive impairment induced by T2DM in murine models.MATERIALS AND METHODS:Serum exosome samples were obtained from both T2DM-related and healthy mice for transcriptome sequencing, enabling the identification of differentially expressed mRNAs and long noncoding RNAs (lncRNAs). Subsequent experiments were conducted to ascertain the binding affinity between mmu-miR-129-5p, NEAT1 and BDNF. The structural characteristics and dimensions of isolated exosomes were scrutinized, and the expression levels of exosome-associated proteins were quantified. Primary mouse hippocampal neurons were cultured for in vitro validation, assessing the expression of pertinent genes as well as neuronal vitality, proliferation, and apoptosis capabilities. For in vivo validation, a T2DM mouse model was established, and quercetin treatment was administered. Changes in various parameters, cognitive ability, and the expression of insulin-related proteins, along with pivotal signaling pathways, were monitored.KEY FINDINGS:Analysis of serum exosomes from T2DM mice revealed dysregulation of NEAT1, mmu-miR-129-5p, and BDNF. In vitro investigations demonstrated that NEAT1 upregulated BDNF expression by inhibiting mmu-miR-129-5p. Overexpression of mmu-miR-129-5p or silencing NEAT1 resulted in the downregulation of insulin-related protein expression, enhanced apoptosis, and suppressed neuronal proliferation. In vivo studies validated that quercetin treatment significantly ameliorated T2DM-related cognitive impairment in mice.SIGNIFICANCE:These findings suggest that quercetin holds promise in inhibiting hippocampal neuron apoptosis and improving T2DM-related cognitive impairment by modulating the NEAT1/miR-129-5p/BDNF pathway within serum exosomes.

In the quest for sustainable energy transformation, the integration of renewable distributed generation (IRDG) within smart grids (SG) presents a promising avenue, yet it is fraught with multifaceted challenges that impede its full potential. The study delves into the intricacies of SG-IRDG, shedding light on the inherent issues that currently stymie its efficacy and widespread adoption. Chief among these is the inherent intermittency and variability of renewable energy sources, such as solar and wind, which pose significant challenges to grid stability and reliability. This unpredictability necessitates advanced grid management and energy storage solutions to ensure a consistent and reliable energy supply. Additionally, the integration of RDG into existing grid infrastructures demands substantial technological and infrastructural upgrades, alongside the development of sophisticated grid management systems capable of accommodating the decentralized nature of renewable energy sources. The study also highlights the regulatory and policy barriers that further complicate the SG-IRDG landscape, including outdated regulatory frameworks that are ill-suited to the dynamic and distributed nature of renewable energy generation. Financial constraints also emerge as a critical bottleneck, with the high initial investment required for SG-IRDG technologies and infrastructure posing a significant hurdle for both public and private stakeholders. Through a comprehensive analysis, this research underscores the imperative for a holistic approach that encompasses technological innovation, regulatory reform, and financial mechanisms to overcome these challenges.

In recent times, digital transformations and Industry 4.0 have revolutionized real-time bridge monitoring and its inspection. The use of smart Structural Health Monitoring (SHM) techniques is becoming powerful with the competencies of Building Information Modeling (BIM) tools, Artificial Intelligence (AI), Internet of Things (IoT), and Virtual/Augmented (VR/AR) technologies. However, the lack of interconnectivity between these tools limits their functionality. This research has addressed this problem by developing an integrated framework to assess serviceability and implement a smart SHM for a newly constructed extradosed bridge. Using Finite Element Analysis (FEA), the study proposes an integrated SHM system that utilizes various IoT sensors, including Wired Strain Gauges (WSG), Liquid Levelling Sensors (LLS), MEMS accelerometers, and a Weather Monitoring Station (WMS) to monitor concrete deformations, vertical displacements, structural vibrations, and weather conditions. BIM tool is used to develop the virtual replica of the proposed SHM system which is then used in the 3D Game Engine (GE) to develop an AR application. This application is then successfully deployed and tested in the AR headset (HoloLens) where its capabilities for onsite bridge health monitoring are discovered. This approach overcomes the limitations of HoloLens devices by providing real-time access to SHM data through a web platform, enabling on-site or remote AR-based bridge health monitoring. Conclusively, this paper emphasizes the numerical modeling of bridges for the design of a health monitoring system, that highlights the importance of robust SHM techniques in assessing bridge conditions. Moreover, it introduces a novel approach for smart bridge inspection and onsite visualization of structural defects in an AR environment.

This comparative review explores the pivotal role of hydrogen in the global energy transition towards a low-carbon future. The study provides an exhaustive analysis of hydrogen as an energy carrier, including its production, storage, distribution, and utilization, and compares its advantages and challenges with other renewable energy sources. The study emphasizes the potential of hydrogen, produced via electrolysis powered by conventional and renewables, as a promising solution for abate sectors such as heavy industries, transport, and heating applications. Despite its potential, the study recognizes significant obstacles, such as high production costs, lack of adequate infrastructure for transport and storage, and the need for supportive policies and regulatory frameworks. A key insight is that hydrogen is not in competition with other renewable technologies, but rather, is a vital complement, especially for addressing renewable intermittency and energy storage issues. A regional analysis shows varied levels of readiness and adoption, with Europe, United States and, East Asia leading in policy support and infrastructure development. The study concludes with the broader social and economic implications of a hydrogen economy, including regional development opportunities. The review study underscores the substantial role of hydrogen in the energy transition, while acknowledging the challenges that need to be addressed through technology advancements, policy support, and international collaboration.

The study delves into the potential application of Germany energy transition methodologies to Iraq, emphasizing the industrial sector role, the imperativeness of adaptability, and the crucial role of demand response management. Germany achievements in evolving towards a greener and more efficient energy framework offer vital lessons for nations, such as Iraq, aiming for similar objectives. However, the contrasting socio-economic and energy landscapes of both countries call for distinct approaches. The study delves into these variances, exploring how Germany energy transition strategies can be tailored to Iraq unique situation. The discussion underscores the integration of the industrial sector in the transition and the requisite for a malleable strategy. Additionally, the article sheds light on the significance of adept demand response management, a cornerstone of Germany energy shift. Included is a case study that illustrates the customization of German methodologies for Iraq, exploring possible advantages and challenges. The outcomes act as a guide for Iraqi journey towards sustainable energy, drawing inspiration from Germany successful trajectory. The insights and suggestions presented might also resonate with other countries eyeing or initiating their energy metamorphosis, advancing the collective vision of a sustainable tomorrow.

The study evaluates the integration of solar, wind, and biomass energy systems in Iraq, targeting 88 locations to optimize electricity production for the building sector, which accounts for 45 % of the country energy consumption. The study reveals significant geographical variations in costs and efficiency, highlighting the necessity for tailored regional strategies. Three scenarios have been evaluated: Biomass-Photovoltaic (PV) and Biomass-Wind systems, and Biomass-PV-Wind hybrid scenario. The results highlight the economic superiority of southwest regions identified as optimal for Biomass-PV and Biomass-Wind applications. Transitioning to a Biomass-PV-Wind hybrid system yields a cost reduction of 61 % to 83 %. Economic assessments across scenarios reveal spatial complexities and underscore the importance of region-specific strategies. The Biomass-PV scenario indicates northern territories have the highest cost, while shorter distances to the grid are observed in middle and eastern regions. The Biomass-Wind scenario demonstrates the cost-effectiveness of wind turbines in relation to grid distance, and the hybrid Biomass-PV-Wind scenario shows potential in combining solar and wind energies, especially for stations with longer distances from the grid. Environmentally, the study assesses the CO2 emissions and surplus electricity across scenarios, noting the significant contribution of solar and wind energies to electricity production. The Biomass-PV scenario results in 105.5 kg/year of CO2 emissions, with a notable variance in surplus electricity. The Biomass-Wind scenario generates 115.2 kg/year of CO2, with a strong performance in surplus electricity production. The Hybrid Biomass-PV-Wind scenario showcases a 41.15 % average surplus electricity across stations, demonstrating its potential efficiency, but also contributes to 55.8 kg/year of CO2 emissions due to the usage of generators.

Vehicle engine vibration signals acquired using MEMS sensors are crucial in the diagnosis of engine malfunctions, notably misfires due to unwanted signals and external noises in the recorded vibration dataset. In this study, the ADXL1002 accelerometer interfaced with the Beaglebone Black microcontroller is employed to capture vibration signals emitted by the vehicle engine across various operational states, including unloaded, loaded, and misfire conditions at 1500 RPMs, 2500 RPMs, and 3000 RPMs. In conjunction with the acquisition of this raw vibration data, frequency-domain signal processing techniques are employed to meticulously analyze and diagnose the distinct signatures of misfire occurrences across various engine speeds and loads. These techniques encompass the fast Fourier transform (FFT), envelope spectrum (ES), and empirical mode decomposition (EMD), each tailored to discern and characterize the nuanced vibration patterns associated with misfire events at different operational conditions.

Keratin 7 (KRT7), also known as cytokeratin-7 (CK-7) or K7, constitutes the principal constituent of the intermediate filament cytoskeleton and is primarily expressed in the simple epithelia lining the cavities of the internal organs, glandular ducts, and blood vessels. Various pathological conditions, including cancer, have been linked to the abnormal expression of KRT7. KRT7 overexpression promotes tumor progression and metastasis in different human cancers, although the mechanisms of these processes caused by KRT7 have yet to be established. Studies have indicated that the suppression of KRT7 leads to rapid regression of tumors, highlighting the potential of KRT7 as a novel candidate for therapeutic interventions. This review aims to delineate the various roles played by KRT7 in the progression and metastasis of different human malignancies and to investigate its prognostic significance in cancer treatment. Finally, the differential diagnosis of cancers based on the KRT7 is emphasized.

This study aimed to diagnose healthy and misfire conditions in vehicle engines using vibration data recorded by a low-cost ADXL1002 accelerometer interfaced with a BeagleBone Black controller. Vibration signals were acquired from a vehicle engine using a cost-effective microelectromechanical system (MEMS) accelerometer. The data was analyzed using artificial neural network (ANN) and convolutional neural networks (CNN) models. The experimental results demonstrated that the two-dimensional (2D) CNN and 2D DCNN models significantly outperformed the ANN and one-dimensional (1D) CNN models in terms of prediction accuracy. Specifically, the validation accuracies were 90.12%, 92.42%, 96.52%, and 98.21% for ANN, 1D CNN, 2D CNN, and 2D DCNN, respectively. Furthermore, detailed accuracy analysis revealed that the 2D DCNN model achieved the highest prediction accuracy of 99% with healthy and 97% with misfire conditions. The findings indicate that transforming 1D vibration signals into 2D grayscale images enhances the model’s ability to distinguish between different engine conditions. Thus, employing 2D neural network architectures in conjunction with low-cost ADXL1002 accelerometer proves to be a highly effective approach for diagnosing complex systems such as vehicle engines.

During the last decade, DC microgrids have been extensively researched due to their simple structure compared to AC microgrids and increased penetration of DC loads in modern power networks. The DC microgrids consist of three main components, that is, distributed generation units (DGU), distributed non-linear load, and interconnected power lines. The main control tasks in DC microgrids are voltage stability at the point of common coupling (PCC) and current sharing among distributed loads. This paper proposes a distributed control algorithm using the higher-order multi-agent system for DC microgrids. The proposed control algorithm uses communication links between distributed multi-agents to acquire information about the neighbors’ agents and perform the desired control actions to achieve voltage balance and current sharing among distributed DC loads and DGUs. In this research work, non-linear ZIP loads and dynamical RLC lines are considered to construct the model. The dynamical model of the power lines and DGU are used to construct the control objective for each distributed DGU that is improved using the multi-agent system-based distributed current control. The closed-loop stability analysis is performed at the equilibrium points, and control gains are derived. Finally, simulations are performed using MATLAB/Simulink environment to verify the performance of the proposed control method.

The present study employs a finite element (FE) formulation to examine the free vibration and bending analyses of sandwich shells made of a glass fiber reinforced polymer (GFRP) composite honeycomb core and graphene decorated with graphene quantum dots (GDGQD) reinforced by GFRP composite face layers. The mechanical characteristics of the GFRP composites with GDGQD reinforcement were evaluated using experimental tests. The FE formulation was developed to compute the vibration and bending responses of the sandwich shells via MATLAB software. The Lagrange method is used for dynamic analysis, whereas the principle of potential energy is used for static analysis. The FE model's validity is compared to numerical data reported in the literature in terms of frequencies and deflection, and the results demonstrate excellent agreement. The impacts of the end conditions, core-to-facesheet thickness ratio, curvature ratios, and weight percent of GDGQD are studied further in relation to the bending and dynamic properties of GFRP sandwich shells.

This work presents the simple synthesis of a green and novel Palladium based magnetic nanocatalyst with effective catalytic properties and reusability. These heterogeneous catalysts were prepared by the anchoring of Pd(0) on the surface of ZrFe 2 O 4 MNPs coated with a di-substituted adenine (Ade) compound as a green linker. The as-synthesized ZrFe 2 O 4 @SiO 2 @Ade-Pd MNPs were methodically characterized over different physicochemical measures like VSM, EDX, Map, SEM, TEM, ICP, and FT-IR analysis. The catalytic activity of ZrFe 2 O 4 @SiO 2 @Ade-Pd was carefully examined for the room-temperature Carbon–Carbon coupling reaction in acetonitrile as a solvent. It is worth noting that the synthesized solid catalyst can be easily recovered with a bar magnet and reused for five cycles without decrease of catalytic activity.

This paper presents a novel adaptive fast-terminal neuro-sliding mode control (AFTN-SMC) for a two-link robot manipulator with unknown dynamics and external disturbances. The proposed controller is chattering-free and adaptive to the time-varying system uncertainties. Furthermore, the radial base function neural network (RBFNN) is employed to approximate the unknown state dynamics. The simulations have been completed in MATLAB, which illustrates the successful implementation of the proposed controller. The results showcased the effectiveness of the AFTN-SMC in achieving accurate tracking and stability, even in the presence of uncertainties and parameter variations. The incorporation of the RBFNN in the controller proved to be a valuable tool for approximating the unknown dynamics, enabling accurate estimation and control of the manipulator’s behavior. The research presented in this paper contributes to the advancement in control techniques for robot manipulators in diverse industrial and automation applications.

This paper presents a Deep Convolutional Neural Network (DCNN) model with a SoftMax classifier for the diagnosis of bearing and gear faults based on 2D grayscale images converted from raw vibration signals. The proposed model is validated and verified using a vibration dataset consisting of gear and bearing vibrations recorded under different operating conditions and fault conditions. The experimental results demonstrate that the DCNN achieves remarkable accuracies for both bearing fault diagnosis under different operating conditions and fault conditions, with high validation accuracy achieved. Furthermore, the performance of the proposed model is evaluated and compared with existing models in the literature for the same datasets, and it is concluded that the proposed DCNN is more efficient in terms of fault diagnosis accuracy, demonstrating its superiority. Therefore, the proposed high-accuracy DCNN can be an effective and accurate tool for fault diagnosis in various industrial applications.

Fault diagnosis and health monitoring of industrial rotating machines are of paramount importance for ensuring the reliability, safety, and efficiency of modern industrial operations. This paper proposes a Short-Time Fourier Transform (STFT)-based fault diagnosis approach for industrial rotating machinery. In this proposed model, the STFT of the reference vibration signals is evaluated and compared with the STFT of the other testing vibration signals to diagnose the fault types. Three different similarity operators: Euclidean distance, cosine similarity, and structural similarity are used to conclude the similarity index between the reference signal and test signal. By using variable speed vibration data with different fault types, the proposed model can better simulate real- world conditions and improve the accuracy and effectiveness of fault diagnosis. The results from the confusion matrices, heat maps, and t-SNE plots demonstrate the effectiveness of the proposed method for fault diagnosis and monitoring of variable-speed rotating machines using vibration signals. It is concluded that the structural similarity index proved to be a promising approach for accurate fault diagnosis in variable-speed rotating machines. The results are also compared with the existing approaches in the literature and it was concluded that the proposed model attains the highest accuracy for the variable speed rotating machines.