To address the problems of insufficient semantic fusion between text and questions and the lack of consideration of global semantic information encountered in machine reading comprehension models, we proposed a machine reading comprehension model called BERT_hybrid based on BERT and hybrid attention mechanism. In this model, BERT is utilized to separately map the text and questions into the feature space. Through the integration of Bi-LSTM, attention mechanism, and self-attention mechanism, the proposed model achieves comprehensive semantic fusion between text and questions. The probabilities distribution of answers is computed using Softmax. Experimental results on the public dataset DuReader demonstrate that the proposed model achieves improvements in BLEU-4 and ROUGE-L scores compared to existing models. Furthermore, to validate the effectiveness of the proposed model design, we analyze the factors influencing the model’s performance.

Hybrid electric vehicles are certainly one of the key solutions for improving fuel efficiency and reducing emissions, especially in special vehicle application and with the use of CO2 neutral fuels. Determining the energy management strategy and finding the optimal solution with regard to the aforementioned goals re-mains the one of the main challenges in the design of HEV. This paper presents a new vehicle modeling method, with an emphasis on HEVs, which is based on the frequency analysis of emissions and consumption according to the current specific traction power of the vehicle. The evaluation of the newly introduced model in the RDE, NEDC and WLTP cycle was performed and the results were compared with the standard verified vehicle model that was created in AVL's CruiseM software package. Positive traction energies have positive deviations of between 0.35% and 2.85%. The largest deviation in CO2 emissions was recorded for the HEV model in the RDE cycle and in the non-hybrid model in the WLTP cycle of 3.79% and 4.4% respectively, all other combinations of cycle and vehicles had deviations of up to about 1%. As expected, the largest relative deviations were recorded for NOx emissions from 0.13% to 9.62% for HEV in the WLTP cycle.

Waste management is an unavoidable technological operation in the process of raw material extraction. The main characteristic of this technological operation is the handling of large quantities of waste material, which can amount to several hundred million cubic metres. Working with this amount of material usually requires high-capacity systems for excavation and loading, a large fleet of trucks for haulage, construction, and maintenance of a complex roads network, use of a significant area of land in order to achieve the required capacities, etc. At the same time, this operation must comply with all administrative and environmental standards. Therefore, optimising waste rock management (particularly haulage and dumping) has the potential to significantly improve the overall value of the project. This paper presents a hybrid model for the optimisation of waste dump design and site selection. The model is based on different mathematical methods (genetic algorithm, analytic hierarchy process and heuristic methods) adapted to different aspects of the problem. The main objective of the model is to provide a solution (in analytical and graphical form) for the draft waste dump design, on the basis of which the final waste dump design can be defined.

The ever-growing student population and limited administrative resources strain traditional communication channels within educational institutions. Chatbot technology offers a promising solution, but current limitations can impede effective student-administration interaction. This paper proposes a hybrid chatbot model to enhance reliability in educational administration by addressing the limitations of generative models like ChatGPT. We investigate three individual chatbot approaches (rule-based, retrieval-based, and generative-based) to handle educational inquiries. An evaluation assessed their strengths, weaknesses, and user experience. Based on these findings, a hybrid model with an intelligent classifier for query routing was developed. This model leverages each approach's strengths and addresses uncertainty in the generative model to achieve increased accuracy and reliability. Our results demonstrate an accuracy improvement compared to individual models. This work contributes to developing adaptable chatbot systems capable of addressing a wider range of user inquiries, which is particularly beneficial in resource-constrained educational settings. Additionally, the proposed model demonstrates competitive performance when benchmarked against existing state-of-the-art administrative educational chatbots. Furthermore, by automating routine inquiries and offering 24/7 access to information and support, this model has the potential to reduce administrative workload and improve the overall student experience significantly. Moreover, the modular design of our hybrid model offers a foundation for future research on personalizing chatbot interactions and integrating domain-specific knowledge for enhanced communication within educational environments.

Towards the investigation and further understanding of the development and propagation of Medicanes, this study explores the forecasting capability of WRF model in case of cyclone “Cleopatra” which affected with extreme rainfall and strong winds Sardinia and Calabria, Italy, in November 2013. This cyclone was unusual in that it developed a warm core but did not fulfill its transformation into a tropical-like cyclone because its core did not expand high enough in the tropospnere. The ERA5 reanalysis dataset was dynamically downscaled from 31 km spatial horizontal resolution to 9 km using WRF model. The methodology consists of; firstly, an extensive physical parameterization schemes sensitivity test and consequently, a short-range ensemble forecasting implementation based on the highest statistical scored physics configuration. All simulation results were validated against surface observations and remote sensing products. Subsequently, the modeled cyclone trajectories are compared to satellite imagery derived from EUMETSAT-SEVIRI gridded data. The findings of the conducted analysis illustrate that ensemble average displays significant difference in performance compared to any of the deterministic runs individually, suggesting that ensemble forecasts will be beneficial in studies assessing cyclonic events in the Mediterranean region.

We designed a bidirectional reflection distribution function (BRDF) measurement system to research the characteristics of space target materials. This system measures the BRDF data of an aluminum plate and gold foil. A hybrid biogeography-based optimization-firefly (BBO-Firefly) algorithm was proposed to optimize the five-parameter BRDF model. To check the performance of the hybrid BBO-Firefly algorithm, we set up a contrast experiment to optimize the BRDF parameters using the BBO algorithm, Firefly algorithm, and hybrid BBO-Firefly algorithm. The experimental results prove that the hybrid BBO-Firefly algorithm surpasses the BBO algorithm and Firefly algorithm in convergence speed and precision in the same situation and has a better optimization effect. Finally, we substitute the optimization results of the hybrid BPO-Firefly algorithm into the BRDF model to analyze the reflection characteristics of an aluminum plate and gold foil.

CAMx-LPiG (Comprehensive Air Quality Model with Extensions – Linear Plume in Grid) is an online hybrid model, based on the Chemistry and Transport Model (CTM) CAMx, that includes a sub-grid scale module to simulate the dispersion of linear road traffic emissions, called LPiG. LPiG is a plume in grid module specifically developed by extending the capabilities of the Lagrangian puff sub-grid model available in CAMx. The online integration of the local scale model within the Eulerian CTM allows for a multiscale simulation of air quality from the regional scale to the urban scale, preserving a coherent description of the chemical state of the atmosphere at all spatial scales and avoiding any double counting of the emissions simulated by the sub-grid module. In this work the model is presented and evaluated against measured NO2 concentrations for the city of Milan for the month of January 2017. The model is able to introduce road traffic induced gradient in NO2 concentration at sub-grid resolution. Moreover, CAMx-LPiG is shown to reduce the bias compared to CAMx stand-alone simulations.

The critical systems, which the failure and malfunction may result in human, environment and financial severe damages, are essential components in various sectors and particularly in energy domains. Although unwished, integration error problems in supervision of critical systems occurs, incurring significant expenses because of operator’s subjective analysis and on hardware topology failures. In this work, a malleable model design approach is proposed to formulate and solve the integration error problem in the critical systems supervision in terms of reliability. A real Hybrid Power Plant (HPP) case is considered as a requirement for a study case with simulated data. A method framework with informal approach (C4 diagram) and formal approach (Hierarchical Coloured Petri Nets) in radial spectrum is applied to the HPP supervision design. Using formal methods, a formulation and solution to this problem through structured, scalable and compact mathematical representations are possible. This malleable model intends to guarantee functional correctness and then reliability of the plant supervision system based on system software architecture. Outcomes suggest corroboration that the malleable model is appropriate for energy domain, and can be used for other types of critical systems, bringing all the benefits of this methodology to the context in which it will be applied.

This paper describes a case study for applying of hybrid-electric propulsion system for a general aviation aircraft. The work was performed by a joint team of CIRA and the Department of Industrial Engineering of the University of Naples “Federico II”. Electric and hybrid electric propulsion for aircraft has gained widespread and significant attention over the past decade. The driver for industry interest has principally been the need to reduce emissions of combustion engine exhaust products and noise, but increasingly studies revealed potential for overall improvement in energy efficiency and mission flexibility of new aircraft types. The project goal was to demonstrate feasibility of aeronautic parallel hybrid-electric propulsion for a Light aircraft varying the mission profiles and the electric configuration. Through a creation, and application, of a global model, with software AMESim^®, in which it can be represented everything about the components chosen by the industrial partners, some interesting considerations are carried out. In particular, it was confirmed that with the only integration of state of the art technologies, for some particular missions, the advantages of aircraft hybrid-electric propulsion, for light aircraft, are notable.

Wind power as a renewable source of energy, has numerous economic, environmental and social benefits. In order to enhance and control the renewable wind power, it is vital to utilize models that predict wind speed with high accuracy. Due to neglecting of requirement and significance of data preprocessing and disregarding the inadequacy of using a single predicting model, many traditional models have poor performance in wind speed prediction. In the current study, for predicting wind speed at target stations in the north of Iran, the combination of a multi-layer perceptron model (MLP) with the Whale Optimization Algorithm (WOA) used to build new method (MLP-WOA) with a limited set of data (2004-2014). Then, the MLP-WOA model was utilized at each of the ten target stations, with the nine stations for training and tenth station for testing (namely: Astara, Bandar-E-Anzali, Rasht, Manjil, Jirandeh, Talesh, Kiyashahr, Lahijan, Masuleh and Deylaman) to increase the accuracy of the subsequent hybrid model. Capability of the hybrid model in wind speed forecasting at each target station was compared with the MLP model without the WOA optimizer. To determine definite results, numerous statistical performances were utilized. For all ten target stations, the MLP-WOA model had precise outcomes than the standalone MLP model. The hybrid model had acceptable performances with lower amounts of the RMSE, SI and RE parameters and higher values of NSE, WI and KGE parameters. It was concluded that WOA optimization algorithm can improve prediction accuracy of MLP model and may be recommended for accurate wind speed prediction.

This paper proposes a model to evaluate business projects to get into an incubator, allowing to rank them in order of selection priority. The model combines the Momentum method to build prospective scenarios and the Multiple Criteria Decision Making (MCDM) method TOPSIS-2N to rank the alternatives. Six business projects were evaluated to be incubated. The Momentum method made it possible for us to create an initial core of criteria for the evaluation of incubation projects. The TOPSIS-2N method supported the decision to choose the company to be incubated by ranking the alternatives in order of relevance. Our evaluation model has improved the existing models used by incubators. This model can be used and / or adapted by any incubator to evaluate the business projects to be incubated. The set of criteria for the evaluation of incubation projects is original and the use of prospective scenarios with a MCDM method to evaluate companies to be incubated does not exist in the literature.

Automatic speech recognition of native languages plays a crucial role in fostering inclusivity and preserving linguistic diversity. The Kashmiri language, an underrepresented Indo-Aryan dialect primarily spoken in the Kashmir Valley, poses substantial challenges for existing AI models due to its phonetic diversity and scant linguistic resources. This study addresses these hurdles by developing a robust spoken Kashmiri recognition system that employs dual feature extraction with spectrogram augmentation and a hybrid Convolutional Neural Network (CNN) and Gated Multi-Layer Perceptron (gMLP) model. Key to this endeavour is the creation of a high-fidelity dataset that captures the phonetic variations across Kashmiri dialects, focusing on twelve specific words. Through the integration of dual feature extraction, spectrogram augmentation, and the innovative hybrid modelling approach, our system attains an impressive 96% accuracy on the test dataset for classifying these twelve spoken words. This research not only enhances the generalization and resilience of spoken Kashmiri recognition systems but also represents a critical step towards advancing technology and safeguarding the Kashmiri language within this underrepresented linguistic domain.

The present work is focused on functionalizing Montmorillonite (Mt) with poly dimethyl siloxane (PDMS) to develop a hybrid membrane reactor for the removal of Co (II) and Ni (II) ions from model wastewater samples. The specific surface area of the novel material was found to be 444 m2 /g. X-Ray Diffraction (XRD) and Fourier Transform Infra-Red (FTIR) spectra reveal the formation of exfoliated composite material. The efficiency of this membrane material was determined by subjecting it to continuous filtration experiments. It was able to remove 50% of Co (II) and 75% of Ni (II) ions, respectively, in one cycle of filtration in less than three minutes of operation with a permeate flux rate of 675 L/m2/H and 975 L/m2/H respectively, for both the ions.

In bioprocess engineering the Qualtiy by Design (QbD) initiative encourages the use of models to define design spaces. However, clear guides on how models for QbD are validated are still missing. In this review we provide a comprehensive overview about validation methods, mathematical approaches and metrics currently applied in bioprocess modeling. The methods cover analytics for data used for modeling, model training and selection, measures for predictiveness and model uncertainties. We point out general issues in model validation and calibration for different types of models and put this into context of existing health authority recommendations. This review provides the start-point for developing a guidance for model validation approaches. There is no one-fits-all approach but this review shall help to identify the best fitting validation method or combination of methods for the specific task and type of bioprocess models that is developed.

Quantitative structure-activity relationship (QSAR) model is adopted to study the relationship between the chemical and physical properties of various substances and the structure. Through QSAR studies, the internal relationship between the invisible structure and the activity can be obtained. In this paper, a novel chaos-enhanced accelerate particle swarm algorithm (CAPSO) is proposed, which is used to molecular descriptors screening and optimization of the weights of back propagation artificial neural network (BP ANN). Then, the QSAR model based on CAPSO and BP ANN is put forward, hereinafter referred to as CAPSO BP ANN model. The prediction experiment showed that the CAPSO algorithm is a reliable method for screening molecular descriptors and the five molecular descriptors obtained by CAPSO algorithm could well characterize the molecular structure of each compound in pKa prediction. The experimental results also showed the CAPSO BP ANN model has a good performance in predicting the pKa values of various compounds, the absolute mean relative error, root mean square error, and square correlation coefficient are respectively 0.5364, 0.0632, and 0.9438, indicating the higher prediction accuracy and correlation. The proposed hybrid intelligent model can be applied in all kinds of engineering design, prediction of physical and chemical properties and intelligent calculation.

In recent years, the utilization of machine learning algorithms and advancements in Unmanned Aerial Vehicle (UAV) technology have marked significant shifts in remote sensing practices. In particular, the integration of machine learning with physical models and their application in UAV-satellite data fusion have emerged as two prominent approaches for estimating vegetation biochemistry. This study evaluates the performance of five machine learning regression algorithms (MLRAs) for mapping crop canopy chlorophyll at the Kellogg Biological Station (KBS) in Michigan, USA, across three scenarios: (1) application to Landsat 7, RapidEye, and PlanetScope satellite images; (2) application to UAV-satellite data fusion; and (3) integration with the PROSAIL radiative transfer model (hybrid methods PROSAIL + MLRAs). The results indicate that the majority of the five MLRAs utilized in UAV-satellite data fusion perform better than the five PROSAIL + MLRAs. The general trend suggests that the integration of satellite data with UAV-derived information, including the normalized difference red-edge index (NDRE), canopy height model, and leaf area index (LAI), significantly enhances the performance of MLRAs. The UAV-RapidEye dataset exhibits the highest coefficient of determination (R2) and the lowest root-mean-square errors (RMSE) when employing kernel ridge regression (KRR) and Gaussian processes regression (GPR) (R2 = 0.89 and 0.89 and RMSE = 8.99 µg/cm2 and 9.65 µg/cm2, respectively). Similar performance is closely observed for the UAV-Landsat and UAV-PlanetScope datasets (R2 = 0.86 and 0.87 for KRR, respectively). For the hybrid models, maximum performance is attained with Landsat data using KRR and GPR (R2 = 0.77 and 0.51 and RMSE = 33.10 µg/cm2 and 42.91 µg/cm2, respectively), followed by R2 = 0.75 and RMSE = 39.78 µg/cm2 for PlanetScope data upon integrating partial least squares regression (PLSR) into the hybrid model. Across all hybrid models, RapidEye data yielded the most stable performance, with R2 ranging from 0.45 to 0.71 and RMSE ranging from 19.16 µg/cm2 to 33.07 µg/cm2. The study highlights the importance of synergizing UAV and satellite data which enables effective monitoring of canopy chlorophyll in small agricultural lands.

The requirements for accurate models of renewable energy power plant are urgent for power system operation analysis. Most existing model research in this area is for wind turbine and photovoltaic (PV) power generation unit, a rare renewable power plant model validation mainly adopts the single-machine infinite-bus system. The single equivalent machine method is always used, and the interactions between the power plant and the grid are ignored. The voltage at the interface bus is treated as constant, although this is not consistent with its actual characteristics. The phase shifter method of hybrid dynamic simulation has been applied in the model validation of wind farm. However, this method is heavily dependent on phasor measurement units (PMU) data, resulting in limited application scope, and it is difficult to realize the model error location step by step. In this paper, the fast-responding generator method is used for renewable power plant model validation. The complete scheme comprising model validation, error localization, parameter sensitivity analysis, and parameter correction is proposed. Model validation is conducted based on measured records from a large-scale PV power plant in northwest China. The comparison of simulated and measured data verifies the feasibility and accuracy of the proposed scheme.

Off-road vehicles and transportation are vital for agricultural economics, yet the transition to green energies is challenging. To make this application easier, a tool that enables the testing of heavy-duty off-road vehicles in various scenarios was created. Based on the methods of the World Harmonized Transient Cycle, a new Hybrid Operational Cycle that reflects the features of agricultural work was created and applied in a graphical model simulation. This was a newly developed methodology. The cycle and the model were based on gathered research data. A numerical model of a medium-power tractor with an internal combustion engine and a series-hybrid setup was created, and simulations were performed in Matlab and AVL Cruise. The research compared both diesel and hybrid vehicles in terms of their power production, fuel consumption, and efficiency in fieldwork and transportation scenarios. The research showed that a series-hybrid transmission can achieve an efficiency similar to that of tractors with a continuously variable transmission (CVT), but with the use of an electric powertrain, there is still an opportunity to exploit energy regeneration in transportation and under low-load conditions. The designed model also may be used for the development of control algorithms for hybrid drives and the improvement of the efficiency thereof.

This article presents a forecast model that uses a hybrid architecture of recurrent neural networks (RNN) with surface neural networks (ANN), based on historical records of exported active energy (EAE) and weather data. Two types of models were developed: the first type includes six models that use EAE records and weather variables as inputs, while the second type includes eight models that use only weather variables. Different metrics were applied to assess the performance of these models, and the best model of each type was selected. Finally, a comparison of the performance between the selected models of both types is presented, and they are validated with real data provided by a solar plant, achieving acceptable levels of accuracy. The selected model of the first type has an RMSE of 0.19, MSE of 0.03, MAE of 0.09, a correlation coefficient of 0.96, and a determination coefficient of 0.93. The other selected model of the second type showed lower precision in the metrics (RMSE = 0.24, MSE = 0.06, MAE = 0.10, Corr. Coef. = 0.95, and Det. Coef. = 0.90). Both models demonstrated good performance and acceptable accuracy in forecasting the weekly photovoltaic energy production of the solar plant.

Novel applications of artificial intelligence for tuning the parameters of industrial machines for optimal performance are emerging at a fast pace. Tuning the combine harvesters and improving the machine performance can dramatically minimize the wastes during harvesting, and it is also beneficial to machine maintenance. Literature includes several soft computing, machine learning and optimization methods that had been used to model the function of harvesters of various crops. Due to the complexity of the problem, machine learning methods had been recently proposed to predict the optimal performance with promising results. In this paper, through proposing a novel hybrid machine learning model based on artificial neural networks integrated with particle swarm optimization (ANN-PSO), the performance analysis of a common combine harvester is presented. The hybridization of machine learning methods with soft computing techniques has recently shown promising results to improve the performance of the combine harvesters. This research aims at improving the results further by providing more stable models with higher accuracy.

In this paper, the forecast horizon and solar variability influences on MHFM model based on multiscale decomposition, AR and NN models, are studied. This article follows the works published in [1] showing the performance of the MHFM using 3 multiscale decomposition methods and a forecast horizon equal to 1 hour. Several forecast horizon strategies and his influence on the MHFM performances are investigated. We show that the best strategy for a rRMSE variying from $4.43\%$ to $10.24\%$ is obtained for forecast horizons from $5$ minutes to $6$ hours. In a second part, the solar variability influence on the MHFM is studied. A classification based on a shows that the best performance of MHFM is obtained for clear sky days with a rRMSE of $2.91\%$ and worst for cloudy sky days with a rRMSE of $6.73\%$.

Increasing demand for food production with limited available water resources pose the threat to agricultural activities. The conjunctive allocation of water resources maximizes the net benefit of farmers efficiently. In this study, a novel hybrid optimization model was developed based on a genetic algorithm (GA), bacterial foraging optimization (BFO) and ant colony optimization (ACO) to maximize the net benefit of water deficit Sathanur reservoir command. The GA-based opti-mization model considered crop-related physical and economical parameters to derive optimal cropping patterns for three different conjunctive use policies and further allocation of surface and groundwater for different crops are enhanced with the BFO. The allocation of surface and groundwater for the head, middle and tail reach obtained from BFO is considered as input to ACO as a guiding mechanism to attain an optimal cropping pattern. Comparing the average produc-tivity values Policy 3 (3.665 Rs/m3) has better values relating to Policy 1 (3.662 Rs/m3) and Policy 2 (3.440 Rs/m3). Thus, the developed novel hybrid optimization model (GA-BFO-ACO) is very promising to enhance the farmer's net income as well as for the command area water conservation and can be replicated in other irrigated regions of the globe to overcome chronic land and water problems.

Climate change effect on human-living in verities of way such as health and food security. This study presents predicting crop yields, and production risk in the near future (2020-2029) in northern Thailand using coupling 1 km resolution of regional climate model which is downscaled using a conservative remapping method and the Decision Support System for the Transfer of Agrotechnology (DSSAT) modeling system. The accuracy of the climate and agricultural model was appropriate compared to the observations with Index of Agreement (IOA) in ranges of 0.65 - 0.89. The DSSAT modeling system predicts that rice, and maize production will decrease by 5% and 4% in northern Thailand. In addition, a short-term risk analysis of rice and maize production has shown that, in the context of climate change, maize production appears to be at a high risk of low production in the near future, while rice cultivation might be a low risk.

In this paper, we propose an adaptive duty-cycled hybrid X-MAC (ADX-MAC) protocol for energy-efficient forest fire prediction. The X-MAC protocol acquires the additional environmental status collected by each forest fire monitoring sensor for a certain period. And, based on these values, the length of sleep interval of duty-cycle is changed to efficiently calculate the risk of occurrence of forest fire according to the mountain environment. The performance of the proposed ADX-MAC protocol was verified through experiments the proposed ADX-MAC protocol improves throughput by 19% and was more energy-efficient by 24% compared to X-MAC protocol. As the probability of forest fires increases, the length of the duty cycle is shortened, confirming that the forest fires are detected at a faster cycle.

Computer Vision using Deep Learning algorithms has served numerous human activity identification applications, particularly those linked to safety and security. However, despite the fact that autistic children are frequently exposed to danger as a result of their activities, many Computer Vision experts have shown little interest in their safety. High-grade autistic children frequently experience the Meltdown Crisis condition, characterized by hostile behaviors and loss of control. This study aims to introduce a monitoring system capable of predicting the Meltdown Crisis condition early and alerting the children’s parents or caregivers before entering more difficult settings. For this endeavor, the suggested system was constructed using a combination of a pre-trained Vision Transformer (ViT) model (Swin-3D-b) and a Residual Network (ResNet) architecture to extract robust features from video sequences in order to extract and learn the spatial and temporal features of the Stereotyped Motor Movements made by autistic children at the beginning of the Meltdown Crisis state, which is referred to as the Pre-Meltdown Crisis state. In order to attain a 92% recall and F1 Score, the final decisions made for data preparation, model construction, and training parameters were tweaked and established experimentally. The best loss value obtained was 0.08. The MeltdownCrisis dataset, which includes realistic scenarios of autistic children’s behaviors in the Pre-Meltdown Crisis state and Normal state the Normal state data being used as a negative class was utilized for evaluation.

Dengue fever remains one of the most significant public health challenges globally, particularly in tropical and subtropical regions. Traditional early warning systems (EWS) for dengue outbreaks, based on retrospective epidemiological data, suffer from delays and limited predictive accuracy. Modern systems, leveraging real-time data, artificial intelligence (AI), and machine learning (ML), improve accuracy but can be costly and difficult to implement in resource-constrained settings. This paper proposes a novel hybrid model that integrates the strengths of both traditional and modern EWS, with real-world examples from Brazil, and Malaysia. The hybrid model balances accuracy, scalability, and cost-effectiveness, making it a feasible option for low- and middle-income countries.

In this research work the study of Buongiorno Model Darcy Forchheimer flow of Hybrid nanofluid flow on isothermal permeable stretch/shrink sheet with homogeneous and heterogeneous reaction is investigated. The effects of MHD, thermal radiation, viscous dissipation, Joule heating, and heat generation/absorption are taken into account. Hybrid nanofluids can act as a medium for heterogeneous-homogeneous reactions. The nanoparticles dispersed in the fluid can influence reaction kinetics and species transport, potentially, altering reaction rates and pathways. The applications of HNF in reactions, both homogeneous and heterogeneous, lie in leveraging their enhanced thermal properties, catalytic potential, and improved mixing capabilities to optimize reaction efficiency and control reaction conditions. Similarity transformation is used to convert a set of PDEs into a set of ODEs. HAM is used for the solution of the obtained ODEs system. In a heterogeneous reaction, the concentration gradient is amplified; in a homogeneous reaction, the impact is opposite. Moreover, at higher compositions of HNPs, the concentration and velocity rise while the temperature falls. Furthermore, as the values of homogeneous and heterogeneous reactions increase, the concentration falls. It is reliable with the idea that a greater reaction rate lowers the diffusion rate. Nevertheless, these parameters do not affect temperature or velocity.

Ultrasonic-guided lamb wave approach is an effective non-destructive testing (NDT) method used for detecting localized mechanical damage, corrosion, and welding defects experienced in metallic pipelines. Signal processing of the guided waves is often challenged due to the complexity of operational conditions and environments in the pipelines. Machine learning approaches in recent years, including convolutional neural networks (CNN) and long short-term memory (LSTM), have exhibited their advantages to overcome these challenges for signal process and data classification of complex systems, thus great potential for damage detection of critical oil/gas pipeline structures. In this study, a CNN-LSTM hybrid model is utilized for decoding ultrasonic guided waves for damage detection of metallic pipelines, and twenty-nine features are extracted as input to classify different types of defects from metallic pipes. The prediction capacity of the CNN-LSTM model is assessed by comparing it to the CNN and LSTM. The results demonstrate that the CNN-LSTM hybrid model exhibits much higher accuracy, with 94.8%, as compared to those of CNN and LSTM. Interestingly, the results also reveal that predetermined features, including time-, frequency-, and time-frequency domains, could significantly improve the robustness of the deep learning approaches, even though the deep learning approaches are often believed they include automated feature extraction, without the hand-crafted steps as the shallow learning do. Furthermore, the CNN-LSTM model displays higher performance when the noise level is relatively low (e.g., SNR=9 or higher), as compared to the other two models, but its prediction drops gradually with the increase of the noise.

Accurate forecasting of wind power generation plays a key role in improving the operation and management of a power system network and thereby its reliability and security. However, predicting wind power is complex due to the existence of high non-linearity in wind speed that eventually relies on prevailing weather conditions. In this paper, a novel hybrid deep learning model is proposed to improve the prediction accuracy of very short-term wind power generation for the Bodangora Wind Farm located in New South Wales, Australia. The hybrid model consists of convolutional layers, gated recurrent unit (GRU) layers and a fully connected neural network. The convolutional layers have the ability to automatically learn complex features from raw data while the GRU layers are capable of directly learning multiple parallel sequences of input data. The data sets of five-minute intervals from the wind farm are used in case studies to demonstrate the effectiveness of the proposed model against other advanced existing models, including long short-term memory (LSTM), GRU, autoregressive integrated moving average (ARIMA) and support vector machine (SVM), which are tuned to optimise outcome. It is observed that the hybrid deep learning model exhibits superior performance over other forecasting models to improve the accuracy of wind power forecasting, numerically, up to 1.59 per cent in mean absolute error, 3.73 per cent in root mean square error and 8.13 per cent in mean absolute percentage error.

Global navigation satellite system (GNSS) array antenna receivers are widely used to suppress wideband interference in navigation countermeasures. However, existing array antenna receivers all adopt digital array structure and digital beamforming technique, and it has limited analog-front-end (AFE) dynamic range. In strong interference scenarios, AFE saturation will occur, which limits the maximum interference suppression ability of the array receiver. Aiming at this issue, this paper proposes a robust wideband interference suppression method for GNSS array antenna receivers based on hybrid beamforming technique. Firstly, a novel fully connected hybrid array receiver structure is proposed; Secondly, the corresponding hybrid beamforming method is proposed at the same time, and it realizes the complete elimination of strong interference by jointly suppression in analog domain and digital domain. After mathematical simulation experiments, it is verified that compared to the digital beamforming-based anti-jamming technique, the proposed method can effectively suppress strong wideband interference, and the maximum interference suppression ability is improved by 36 dB.

Agricultural tractors are high fuel-consuming and polluting machines, thus the introduction of new sustainable technologies, as hybridization, can be very impactful for the development of electric hybrid agricultural tractors. These vehicles combine the classic internal combustion engine with an electric machine, to reduce the environmental impact and improve the efficiency. This paper reports the modeling and the simulation conducted, using a simulation software typically used for on-road vehicles, of a two wheel-drive agricultural tractor in three different configurations: the conventional one, the series and the parallel electric hybrid powertrain. The simulated task is the trailing of a big square baler during the process of straw wrapping and baling. The evaluation and the comparison of the fuel consumption, CO2 emissions and the depth of discharge of the different configurations has been carried out to determine if it is possible to downsize the ICE, maintaining the same performances. This study points out that the hybridization of agricultural tractors powertrains is one of the most promising approaches to reduce pollutant emissions and fuel consumption.

Lung nodule segmentation in CT images and its subsequent volume analysis can help determinethe malignancy status of a lung nodule. While several efficient segmentation schemes have beenproposed, only a few studies evaluated the segmentation’s performance for large nodules. In thisresearch, we contribute a semi-automatic system which is capable of performing robust 3-D segmen-tations on both small and large nodules with good accuracy. The target CT volume is de-noisedwith an anisotropic diffusion filter and a region of interest is selected around the target nodule ona reference slice. The proposed model performs nodule segmentation by incorporating a mean in-tensity based threshold in Geodesic Active Contour model in level sets. We also devise an adaptivetechnique using image intensity histogram to estimate the desired mean intensity of the nodule.The proposed system is validated on both lung nodules and phantoms collected from publicly avail-able diverse databases. Quantitative and visual comparative analysis of the proposed work withthe Chan-Vese algorithm and statistic active contour model of 3D Slicer platform is also presented.The resulting mean spatial overlap between segmented nodules and reference nodules is 0.855, themean volume bias is 0.10±0.2 ml and the algorithm repeatability is 0.060 ml. The achieved resultssuggest that the proposed method can be used for volume estimations of small as well as large-sizednodules.

Recent measurements in the MRX laboratory plasma [J.Yoo et al, Phys. Rev. Lett. 132, 145101 (2024)] have evidenced the role of lower hybrid drift waves produced during magnetic reconnections in producing an effective anomalous resistivity and heating of electrons. A detailed modelization of the energy transfer from the waves to electrons is missing, though. In this work, we attempt such an exercise using a Hamiltonian test-particle model for wave-electron interaction, valid for low-β weakly collisional plasmas. Quantitative agreement with MRX results is found.

The impact of instructional guidance on learning outcomes in higher biomedical education is subject of intense debate. There is the teacher-centered or traditional way of teaching (TT) and, on the other side, the notion that students learn best under minimal guidance (problem-based learning, PBL). Although the benefits of PBL are well-known, there are aspects susceptible to improvement. Hence, a format merging TT and PBL (hybrid-PBL, h-PBL) may advance education in biomedical sciences. Here, we systematically reviewed studies that employed h-PBL in higher biomedical education compared to TT and/or pure PBL. We found that h-PBL resulted in better overall students’ performance and perception than TT or pure PBL. These findings encourage more research on investigating the pedagogical benefits of h-PBL and posit an eclectic system in which the pedagogical tools from TT and PBL are used cooperatively in the best interest of the education and satisfaction of the students.

In Electrified Vehicles, the cost, efficiency, and durability of electrified vehicles are dependent on the energy storage system (ESS) components, configuration and its performance. This paper, pursuing a minimal size tactic, describes a methodology for quantitatively and qualitatively investigating the impacts of a full bandwidth load on the ESS in the HEV. However, the methodology can be extended to other electrified vehicles. The full bandwidth load, up to the operating frequency of the electric motor drive (20 kHz), is empirically measured which includes a frequency range beyond the usually covered frequency range by published standard drive cycles (up to 0.5 Hz). The higher frequency band is shown to be more efficiently covered by a Hybrid Energy Storage System (HESS) which in this paper is defined as combination of a high energy density battery, an Ultra-Capacitor (UC), an electrolytic capacitor, and a film capacitor. In this paper, the harmonic and dc currents and voltages are measured through two precision methods and then the results are used to discuss about overall HEV efficiency and durability. More importantly, the impact of the addition of high-band energy storage devices in reduction of power loss during transient events is disclosed through precision measurement based methodology.

Mammography is often used as the most common laboratory method for the detection of breast cancer, yet associated with the high cost and many side effects. Machine learning prediction as an alternative method has shown promising results. This paper presents a method based on a multilayer fuzzy expert system for the detection of breast cancer using an extreme learning machine (ELM) classification model integrated with radial basis function (RBF) kernel called ELM-RBF, considering the Wisconsin dataset. The performance of the proposed model is further compared with a linear-SVM model. The proposed model outperforms the linear-SVM model with RMSE, R², MAPE equal to 0.1719, 0.9374 and 0.0539, respectively. Furthermore, both models are studied in terms of criteria of accuracy, precision, sensitivity, specificity, validation, true positive rate (TPR), and false-negative rate (FNR). The ELM-RBF model for these criteria presents better performance compared to the SVM model.

Heterosis or hybrid vigour is a phenomenon in which hybrid progeny exhibit superior yield and biomass to parental lines and has been used to breed F₁ hybrid cultivars in many crops. A similar level of heterosis in all F₁ individuals is expected as they are genetically identical. However, we found variation of rosette size in individual F₁ plants from a cross between C24 and Columbia-0 accessions of Arabidopsis thaliana. Big sized F₁ plants had 26.1% larger leaf area in the 1^st and 2^nd leaves than medium sized F₁ plants at 14 days after sowing in spite of the identical genetic background. We identified differentially expressed genes between big and medium sized F₁ plants by microarray; genes involved in the category of stress response were overrepresented. We made transgenic plants overexpressing 21 genes, which were differentially expressed between the two size classes, some lines had increased plant size at 14 or 21 days after sowing but not at all time points. Change of expression levels in stress responsive genes among individual F₁ plants, implying epigenetic changes, could generate the variation in plant size of individual F₁ plants in A. thaliana.

An experimental study was carried out to evaluate the ductility of reinforced concrete beams longitudinally reinforced with hybrid FRP-Steel bars. The specimens were fourteen reinforced concrete beams with and without hybrid reinforcement. The test variables were bars position, the ratio of longitudinal reinforcement, and the type of FRP bars. The beams were loaded up to failure using a four-point bending test. The performance of the tested beams was observed using the load-deflection curve obtained from the test. Numerical analysis using the fiber element model was used to examine the growth of neutral axis depth due to the effect of test variables. The neutral axis curves were then used to further estimate the neutral axis angle and neutral axis displacement index. The test results show that the position of the reinforcement greatly influences the flexural behavior of the beam with hybrid reinforcement. It was observed from the test that the flexural capacity of beams with hybrid reinforcement is 4% to 50% higher than that of the beams with conventional steel bars depending on bars position and the ratio of longitudinal reinforcement. The ductility decreases as the hybrid reinforcement ratio (Af/As) increases. This study also showed that a numerical model developed can predict the flexural behavior of beams with hybrid reinforcement with reasonable accuracy.

In this paper, we introduce hybrid numbers with Fibonacci and Lucas hybrid number coefficients. We give the Binet formulas, generating functions, exponential generating functions for these numbers. Then we define an associate matrix for these numbers. In addition, using this matrix, we present two different versions of Cassini identitiy of these numbers.

Accurate prediction of mercury content emitted from fossil-fueled power stations is of utmost importance for environmental pollution assessment and hazard mitigation. In this paper, mercury content in the output gas of power stations’ boilers was predicted using an adaptive neuro-fuzzy inference system (ANFIS) method integrated with particle swarm optimization (PSO). The input parameters of the model include coal characteristics and the operational parameters of the boilers. The dataset has been collected from 82 power plants and employed to educate and examine the proposed model. To evaluate the performance of the proposed hybrid model of ANFIS-PSO model, the statistical meter of MARE% was implemented, which resulted in 0.003266 and 0.013272 for training and testing, respectively. Furthermore, relative errors between acquired data and predicted values were between -0.25% and 0.1%, which confirm the accuracy of the model to deal nonlinearity and representing the dependency of flue gas mercury content into the specifications of coal and the boiler type.

: Leveraging the sustainability of the power system market, researchers have developed various ML models for forecasting electricity demand. The LSSVM is well suited to handle complex non-linear power load series. However, the less optimal regularization parameter and the Gaussian kernel function in the LSSVM model have contributed to flawed forecasting accuracy and random generalization ability. Thus, these parameters of LSSVM need to be chosen appropriately using intelligent optimization algorithms. This study proposes a hybrid model based on the LSSVM optimized by the IBFOA for forecasting the daily electricity load in Peninsular Malaysia. The IBFOA for parameter optimization of LSSVM is introduced. The IBFOA based on the sine cosine equation is proposed to adjust the constant step size in the BFOA, which creates an imbalance between the exploration and exploitation during optimization. Finally, the load forecasting model based on LSSVM-IBFOA is constructed using MAPE as the objective function. Comparative analysis demonstrates the model, achieving the highest R2 (0.9880) and significantly reducing error metrics (MAPE, MAE, RMSE, MSE, NRMSE) compared to the baseline LSSVM (average reduction of 27.72% to 47.72%). Additionally, IBFOA exhibits faster convergence and higher accuracy compared to BFOA, highlighting the accuracy of LSSVM-IBFOA for short-term load forecasting.

Large language models (LLMs) are known for their exceptional performance across a range of natural language processing tasks, but their deployment comes at a high computational and financial cost. On the other hand, smaller language models (SLMs), which can be deployed on lower-cost edge devices, struggle to match the performance of their larger counterparts. This paper presents a novel hybrid inference approach that leverages the strengths of both model types while minimizing reliance on costly cloud-based LLMs. Unlike existing methods that route entire queries to either an SLM or a cloud LLM, our approach introduces a reward-based mechanism to dynamically determine the involvement of the cloud LLM during token generation. Specifically, each token predicted by the SLM is evaluated against a reward score, and only when this score falls below a certain threshold is the cloud LLM consulted for assistance in the next token prediction. This method not only reduces the traffic to the cloud LLM, thereby lowering costs, but also allows for flexible control over response quality depending on the reward score threshold. Experimental results demonstrate that our approach significantly reduces cloud LLM usage with minimal impact on overall response quality, offering a cost-effective solution for deploying high-performance language models.

In the present study, an attempt was made to induce rooting from single-node cuttings of hybrid aspen (Populus tremula L. ×P. tremuloides Michx.) with different concentrations of IAA, IBA and NAA during rooting. Among the three auxins used, NAA showed more effective induction on rooting as compared to IAA and IBA at the whole level. Thereafter, NAA was used further in experiments for anatomical and biochemical investigation. The results showed that it took 12 days from the differentiation of primordium to the appearance of young adventitious roots with NAA application. It was found that endogenous IAA, ZR and GA3 levels increased, but ABA decreased in cuttings with 0.54 mM NAA treatment. In contrast to the endogenous IAA level, NAA had negative effect on IAA-oxidase (IAAO) activity. Similarly, the decreased peroxidase (POD) activity, consistent with down-regulation of expressed levels of POD1 and POD2, was observed in NAA-treated cuttings. Whereas, NAA resulted in a higher activity in polyphenol oxidase (PPO) compared to the control cuttings. Collectively, the study highlighted that 0.54 mM NAA is efficient on rooting in hybrid aspen, and its effect on metabolic changes during rooting is discussed, which provide valuable information for propagating hybrid aspen.

Deep learning is emerging in many industrial sectors in hand with big data analytics to streamline production. In the biomanufacturing sector, big data infrastructure is lagging comparatively to other industries. A promising approach is to combine Deep Neural Networks (DNN) with prior knowledge in Hybrid Neural Network (HNN) workflows that are less dependent on the quality and quantity of data. This paper reviews published articles over the past 30 years on the topic of HNN applications to bioprocesses. It revealed that HNNs were applied to various bioprocesses, including microbial cultures, animal cells cultures, mixed microbial cultures, and enzyme biocatalysis. HNNs were mainly applied for process analysis, process monitoring, development of software sensors, open- and closed-loop control, batch-to-batch control, model predictive control, intensified design of experiments, quality-by-design, and recently for the development of digital twins. Most previous HNN studies combined shallow Feedforward Neural Networks (FFNNs) with physical laws, such as macroscopic material balance equations, following the semiparametric design principle. Only recently, deep HNNs based on deep FFNNs, Convolution Neural Networks (CNN), Long Short-Term Memory (LSTM) networks and Physics Informed Neural Networks (PINNs) have been reported. The biopharma sector is currently a major driver but applications to biologics quality attributes, new modalities, and downstream processing are significant research gaps.

Hybrid machine tools combining additive and subtractive processes have arisen as a solution to the increasing manufacture requirements, boosting the potentials of both technologies, while compensating and minimizing their limitations. Nevertheless, the idea of hybrid machines is relatively new and there is a notable lack of knowledge in the field. Therefore, in the present paper, an insight into the advancements of hybrid machines is given, identifying their real capabilities, together with the latest developments from an industrial context. In addition, the current situation and future perspectives of hybrid machines from the point of view of process planning, monitoring and inspection are discussed. Finally, the challenges that must be overcome and the opportunities that the hybrid machines will provide in the forthcoming years are presented.

Nowadays, according to spectacular improvement in health care and biomedical level, a tremendous amount of data is recorded by hospitals. In addition, the most effective approach to reduce disease mortality is to diagnose it as soon as possible. As a result, data mining by applying machine learning in the field of diseases provides good opportunities to examine the hidden patterns of this collection. An exact forecast of the mortality after heart surgery will cause Successful medical treatment and fewer costs. This research wants to recommend a new stacking predictive model after utilizing the random forest feature importance method to foresee the mortality after heart surgery on a highly unbalanced dataset by using the most practical features. To solve the unbalanced data problem, a combination of the SVM-SMOTE over-sampling algorithm and the Edited-Nearest-Neighbor under-sampling algorithm is used. This research compares the introduced model with some other machine learning classifiers to ensure efficiency through shuffle hold-out and 10-fold cross-validation strategies. In order to validate the performance of the implemented machine learning methods in this research, both shuffle hold-out, and 10-fold cross-validation results indicated that our model had the highest efficiency compared to the other models. Furthermore, the Friedman statistical test is applied to survey the differences between models. The result demonstrates that the introduced stacking model reached the most accurate predicting performance after Logistic Regression.

This work utilizes the particle swarm optimization (PSO) for the optimal sizing of a solar – wind – battery hybrid renewable energy system (HRES) for a rural community in Rivers State, Nigeria (Okorobo-Ile Town). The objective is to minimize the total economic cost (TEC), the total annual system cost (TAC) and the levelized cost of energy (LCOE). A two – step approach is used. The algorithm first determines the optimal number of solar panels and wind turbines. Based on the results obtained in the first step, the optimal number of batteries and inverters is computed. The overall results obtained is then compared with results from the NGSA-II and hybrid GA – PSO optimization algorithm. An energy management system monitors the energy balance and ensures the load management is adequate using the battery state of charge as a control strategy. Results obtained showed that the optimal configuration consist of solar panels (154), wind turbine (3), inverter (136) and batteries (29). This results in a minimized TEC, TAC and LCOE of 476, 731USD, 301, 947USD and 0.011USD/kWh respectively. The optimal configuration when simulated under various climatic scenarios was able to meet the energy needs of the community irrespective of ambient condition.

The article presents data on phylogeny, genome size, and ploidy of species of the genus Kalidium Moq. in the flora of Kazakhstan. Genus Kalidium belongs to the tribe Salicornieae of the subfamily Salicornioideae of the family Chenopodiaceae and unites 8 species, the main range of which covers the Iranian-Turanian and Central Asian deserts. There are 4 species in the flora of Kazakhstan: K. foliatum, K. caspicum, K. schrenkianum and the recently described K. juniperinum. Populations of species of the genus Kalidium in the saline deserts of Kazakhstan occupy large areas, often forming monodominant communities. Sometimes there is a joint growth of 2 and very rarely 3 species of the genus. During the period of fieldwork (2021-2022), populations were identified in which these species grew together with a predominance, in most cases, of K. caspicum. Samples of representatives from 15 populations were collected for research. Selected plant samples were studied by flow cytometry to determine plant ploidy. Sequencing of nrITS and two chloroplast fragments were used to build a phylogenetic tree, including sequences from the NCBI database., A phylogenetic tree of species of the genus Kalidium was compiled which considers previously published data. In the valley of the middle reaches of the Syrdarya River tetraploid populations of K. caspicum were found. A hybrid between K. foliatum and K. caspicum was found in the Ili River valley (Almaty region, Uigur district). To identify phylogenetic processes at the intraspecific level, the SCoT (Start codon targeted) fingerprinting method was used.

Eco-friendly betanin/clay minerals hybrid materials with good stability were synthesized combining natural betanin molecules extracted from beetroot with 2:1 type palygorskite (Pal) and 1:1 type halloysite (Hal), respectively. It was found that the adsorption, grinding and heating treatment played a key role to enhance the interaction between betanin and clay minerals during preparation process, which favored improving the thermal stability and solvent resistance of natural betanin. The L* and a* values of the betanin/Pal and betanin/Hal hybrid materials were 64.94 and 14.96, 62.55 and 15.48, respectively, indicating that betanin/Hal exhibited the better color performance. The structural characterizations confirmed that betanin was mainly adsorbed on the outer surface of Pal or Hal through hydrogen-bond interaction, and part of them also were entered into the inner surface of Hal via electrostatic interaction. Therefore, Hal preferred to improve the color properties, heating stability and solvent resistance of natural betanin due to its structural features compared with Pal.

Epithelial-mesenchymal transition (EMT) has been well recognized for its essential role in cancer progression as well as normal tissue development. In cancer cells, activation of EMT permits the cells to acquire migratory and invasive abilities and stem-like properties. However, simple categorization of cancer cells into epithelial and mesenchymal phenotypes misleads the understanding of the complicated metastatic process, and contradictory results from different studies also indicate the limitation of application of EMT theory in cancer metastasis. Nowadays, growing evidence suggests the existence of an intermediate status between epithelial and mesenchymal phenotypes, i.e., the “hybrid epithelial-mesenchymal (hybrid E/M)”state, provides a possible explanation for those conflicting results. Appearance of hybrid E/M phenotype offers a more plastic status for cancer cells to adapt the stressful environment for proceeding metastasis. In this article, we review the biological importance of the dynamic changes between the epithelial and the mesenchymal states. The regulatory mechanisms encompassing the translational, post-translational, and epigenetic control for this complex and plastic status are also discussed .

Directional Modulation (DM) technique, as an emerging promising approach to secure wireless communications, has been attracting increasing attention for its unique characteristic in the past decade. The baseband signal is synthesized at the antenna level for maintaining the standard constellation format along the prescribed direction(s) while simultaneously distorting the signal constellations in other undesired directions. In this paper, we present a novel DM signal synthesis method based on phased array using a hybrid genetic algorithm (GA) and particle swarm optimization (PSO) algorithm, which takes advantages of both GA and PSO algorithm. Then, the bit error rate of the proposed method is analyzed and simulated. Simulation results are presented to verify that the proposed DM signal have a narrower information beam-width. Therefore, it can offer a better physical layer security (PLS) performance compared with the conventional directional modulation signal.

Considering Games with the broad definition proposed by Juul (Juul, 2010), consequences outside of the magic circle can be negotiated. This definition opens up the possibility to define serious games, games developed with an utilitarian goal in mind, in addition to fun. The entertaining and utilitarian objectives may however be contradictory, leading serious games to be, more often than not, less than optimal in at least one of the two dimensions. Another way to play with the boundaries of games is to consider pervasive games, which include alternate reality games, and crossmedia games (Montola, 2005). We question here the limit between game, play and toy in the context of a mixed reality serious game. ‘Pangu’ is a game designed for bachelor students, with biochemistry as the utilitarian objective, and the origin of life as a game theme. The students are asked to play the game on their smartphone, which in turn ask them to build molecules with a tangible balls-and-sticks model typically used in chemistry classes. Pictures taken from the models allow users to ‘scan’ these models and progress in the game. The use of the game was observed in four opportunities. An unanticipated observation is that, in addition to expected behaviors, some students used briefly the models like a toy rather than in the context of the game. It is therefore tempting to speculate that the pervasive nature of the game is blurring the game/non game boundary and, in the context of this serious game, opens a door for fun.

The predictability of wind information in a given location is essential for the evaluation of a wind power project. Predicting wind speed accurately improves the planning of wind power generation, reducing costs and improving the use of resources. This paper seeks to predict the mean hourly wind speed in anemometric towers (at a height of 50 meters) at two locations: a coastal region and one with complex terrain characteristics. To this end, the Holt-Winters (HW), Artificial Neural Networks (ANN) and Hybrid time-series models were used. Observational data evaluated by the Modern-Era Retrospective analysis for Research and Applications-Version 2 (MERRA-2) reanalysis at the same height of the towers. The results show that the hybrid model had a better performance in relation to the others, including when compared to the evaluation with MERRA-2. For example, in terms of statistical residuals, RMSE and MAE were 0.91 and 0.62 m/s, respectively. As such, the hybrid models are a good method to forecast wind speed data for wind generation.

The challenge of this work is to re-define the concept of intelligent agent as a building block of social networks by presenting it as a physical particle with additional non-Newtonian properties. The proposed model of an intelligent agent described by a system of ODE coupled with their Liouville equation has been introduced and discussed. Following the Madelung equation that belongs to this class, non-Newtonian properties such as superposition, entanglement, and probability interference typical for quantum systems have been described. Special attention was paid to the capability to violate the second law of thermodynamics, which makes these systems neither Newtonian, nor quantum. It has been shown that the proposed model can be linked to mathematical models of livings as well as to models of AI. The model is presented in two modifications. The first one is illustrated by the discovery of a stochastic attractor approached by the social network; as an application, it was demonstrated that any statistics can be represented by an attractor of the solution to the corresponding system of ODE coupled with its Liouville equation. It was emphasized that evolution to the attractor reveals possible micro-mechanisms driving random events to the final distribution of the corresponding statistical law. Special attention is concentrated upon the power law and its dynamical interpretation: it is demonstrated that the underlying micro- dynamics supports a “violent reputation” of the power-law statistics. The second modification of the model of social network associated with a decision-making process and applied to solution of NP-complete problems known as being unsolvable neither by classical nor by quantum algorithms. The approach is illustrated by solving a search in unsorted database in polynomial time by resonance between external force representing the address of a required item and the response representing the location of this item.

Yeast two-hybrid approaches, which are based on fusion proteins that must co-localise to the nucleus to reconstitute the transcriptional activity of GAL4 have greatly contributed to understand the nitrogen interaction network of cyanobacteria, whose main hubs are the trimeric PII and the monomeric PipX regulators. The bacterial two-hybrid system, based on the reconstitution in the E. coli cytoplasm of the adenylate cyclase of Bordetella pertussis, should provide a relatively faster and presumably more physiological assay for cyanobacterial proteins than the yeast system. Here we use the bacterial two-hybrid system to gain additional insights into the cyanobacterial PipX interaction network while simultaneously assessing the advantages and limitations of the two most popular two-hybrid systems. Comprehensive mutational analysis of PipX and bacterial two-hybrid assays were performed to compare outcomes between yeast and bacterial systems. We detected interactions previously recorded in the yeast two-hybrid system as negative as well as a “false positive”, the self-interaction of PipX, which is rather an indirect interaction dependent on PII homologs from the E. coli host, a result confirmed by Western analysis with relevant PipX variants. This is, to our knowledge, the first report of the molecular basis of a false positive in the bacterial two-hybrid system.

This work targets the development of a mobility control system for high-speed series hybrid electric tracked vehicles, which operate with independent traction motors for each track. The scope of the research encompasses the modeling of the series hybrid powertrain specific to military tracked vehicles, as well as an in-depth analysis of their dynamic behavior. Subsequently, the study conducts a critical review of the mobility control approaches sourced from literature, identifying key techniques relevant to high-inertia vehicular applications. Building on the foundational models, the study puts forward a robust closed-loop mobility control system aimed at ensuring precise and stable off-road vehicle operations. The system's resilience and adaptability to a variety of driving conditions are emphasized, with a particular focus on handling maneuvers such as steering and pivoting, which are challenging operations for tracked vehicle agility. The performance of the proposed mobility control system is tested through a series of simulations, covering a spectrum of operational scenarios. These tests are carried out in both offline simulation settings, which permit meticulous fine-tuning of system parameters, and real-time environments that replicate actual field conditions. The simulation results demonstrate the system's capacity to improve vehicular response and highlight its potential impact on the future designs of mobility control systems for the heavy-duty vehicle sector, particularly in defense applications.

This paper proposes a novel approach to transitioning the classical TLS 1.3 protocol to a post-quantum version with minimal changes to the existing infrastructure. By integrating a hybrid KEMTLS protocol with Identity-Based Encryption (IBE), the solution achieves post-quantum security without incorporating post-quantum keys into TLS certificates. This enables the protocol to function with Certificate Authorities that do not support post-quantum keys, ensuring its security while simplifying the transition. The main challenge addressed is the immediate need for support of post-quantum algorithms by Certificate Authorities and servers. The proposed solution leverages existing certificates and keys, facilitating a smoother transition.

This study addresses the formidable challenge of solving large-scale Max-Cut problems (MCP). We introduce a rapid computational procedure utilizing a hybrid 1-flip/r-flip local search heuristic. This innovative strategy significantly reduces the computational time required for MCP problems while consistently generating solutions of exceptional quality. The paper presents substantial computational insights, showcasing the effectiveness of our approach on large-scale Max-Cut instances with varying densities. Our proposed heuristic is rigorously evaluated by comparing its performance against a quantum annealing solver, leveraging a multi-start Tabu Search framework. The results underscore the potency of this unique combination as an efficient and effective solution for large-scale QUBO problems. Notably, our hybrid heuristic consistently delivers high-quality solutions within the stringent CPU time limits of 600 seconds, demonstrating its efficacy across Max-Cut instances ranging from 10,000 to 40,000 variables. This research contributes to advancing the state-of-the-art in large-scale QUBO problem-solving, offering a powerful and time-efficient approach with broad applicability.

Graphene hybrid-filler polymer composites have emerged as prominent materials that revolutionize heavy industries. This study encapsulates the in-depth analysis of different influential factors such as filler/graphene type, aspect ratios, dispersion methods, filler-matrix compatibility, fiber orientation, synergistic effects, different processing techniques, and post-curing conditions that affect the processing, properties and determine the resultant applications of graphene hybrid polymer composites. Additionally, it discusses the substantial role of graphene collaboration with other fillers such as carbon nanotubes, silica, nano-clays and metal oxides to produce functionalized hybrid polymer composites with synergistically enhanced tailored properties to offer solutions for heavy industries like aerospace, automotive, electronics, and energy harvesting. The review concludes with some suggestions and outlook on the future of these composite materials by emphasizing the need for continued research to fully optimize their potential.

The main purpose of tissue engineering is to fabricate and exploit engineered constructs suitable for the effective replacement of damaged tissues and organs, and able to perfectly integrate with the host’s organism without eliciting any adverse reaction. Ideally, autologous materials represent the best option, but they are often limited due to the low availability of compatible healthy tissues. So far, one therapeutic approach relies on the exploitation of synthetic materials: they exhibit good features in terms of impermeability, deformability and flexibility, but present chronic risks of infections and inflammations. Alternatively, biological materials, including naturally derived ones and acellular tissue matrices of human or animal origin, can be used to induce cells growth and differentiation, which are needed for tissue regeneration: however, this kind of materials lacks satisfactory mechanical resistance and reproducibility, affecting their clinical application. In order to overcome the above-mentioned limitations, hybrid materials, which can be obtained by coupling synthetic polymers and biological materials, have been investigated with the aim to improve biological compatibility and mechanical features. Currently, the interest in these mate-rials is growing, but the ideal ones have not been found yet. The present review aims at exploring some applications of hybrid materials, with particular mention to urological and cardiovascular fields: in the first case, the efforts to find a construct that can guarantee impermeability, mechanical resistance and patency will be herein illustrated; in the second case, the search for impermeability, hemocompatibility and adequate compliance will be been disclosed.

Human and Computer interaction has been a part of our day-to-day life. Speech is one of the essential and comfortable ways of interacting through devices as well as a human being. The device, particularly smartphones have multiple sensors in camera and microphone, etc. speech recognition is the process of converting the acoustic signal to a smartphone as a set of words. The efficient performance of the speech recognition system highly enhances the interaction between humans and machines by making the latter more receptive to user needs. The recognized words can be applied for many applications such as Commands & Control, Data entry, and Document preparation. This research paper highlights speech recognition through ANN (Artificial Neural Network). Also, a hybrid model is proposed for audio-visual speech recognition of the Tamil and Malay language through SOM (Self-organizing map0 and MLP (Multilayer Perceptron). The Effectiveness of the different models of NN (Neural Network) utilized in speech recognition will be examined.

Selecting a feature in data mining is one of the most challenging and important activities in pattern recognition. The issue of feature selection is to find the most important subset of the main features in a specific domain, the main purpose of which is to remove additional or unrelated features and ultimately improve the accuracy of the categorization algorithms. As a result, the issue of feature selection can be considered as an optimization problem and to solve it, meta-innovative algorithms can be used. In this paper, a new hybrid model with a combination of whale optimization algorithms and flower pollination algorithms is presented to address the problem of feature selection based on the concept of opposition-based learning. In the proposed method, we tried to solve the problem of optimization of feature selection by using natural processes of whale optimization and flower pollination algorithms, and on the other hand, we used opposition-based learning method to ensure the convergence speed and accuracy of the proposed algorithm. In fact, in the proposed method, the whale optimization algorithm uses the bait siege process, bubble attack method and bait search, creates solutions in its search space and tries to improve the solutions to the feature selection problem, and along with this algorithm, Flower pollination algorithm with two national and local search processes improves the solution of the problem selection feature in contrasting solutions with the whale optimization algorithm. In fact, we used both search space solutions and contrasting search space solutions, all possible solutions to the feature selection problem. To evaluate the performance of the proposed algorithm, experiments are performed in two stages. In the first phase, experiments were performed on 10 sets of data selection features from the UCI data repository. In the second step, we tried to test the performance of the proposed algorithm by detecting spam emails. The results obtained from the first step show that the proposed algorithm, by running on 10 UCI data sets, has been able to be more successful in terms of average selection size and classification accuracy than other basic meta-heuristic algorithms. Also, the results obtained from the second step show that the proposed algorithm has been able to perform spam emails more accurately than other similar algorithms in terms of accuracy by detecting spam emails.

The hybrid seeds of several important crops with supreme qualities, including yield, biotic and abiotic stress tolerance, have been cultivated from decades. Thus far, a major challenge with hybrid seed, it does not hold ability to produce plants with same qualities over subsequent generations. Apomixis exist naturally an asexual mode of reproduction in flowering plants via avoiding meiosis and ultimately leads to seed production. Apomixis possess potential to preserve hybrid vigor for multiple generations for economically important plant genotypes. The evolution and genetics of asexual seed production is unclear and need much more efforts to find its genetic architecture. To fix hybrid vigor synthetic apomixis has been suggested an alternative. The development of MiMe (Mitosis instead of Meiosis) genotypes are utilized further for clonal gametes production. However, the identification and parental origin of genes responsible for synthetic apomixis are less known and need further understanding. Genome modifications utilizing genome editing technologies (GETs) like clustered regularly interspaced short palindromic repeats (CRISPR)/CRISPR-associated protein 9 (cas9) a reverse genetics tool has paved way to utilize emerging technologies in plant molecular biology. From the last decade, several genes in important crops have been successfully edited. The vast availability of GETs has made the functional genomics studies easy to conduct in crops important for food security. The disruption of expression of genes specific to egg cell MATRILINEAL (MTL) or BABY BOOM1 (BBM1) through CRISPR/Cas genome editing system can promote haploid plants. The establishment of synthetic apomixis by engineering MiMe genotype by genome editing BBM1 expression or disruption of MTL leads toward clonal seed production. In present review, we discussed the current development in plants by utilizing CRISPR/Cas9 technology and its possibility of promoting apomixis in crops to preserve hybrid vigour. In addition to this, genetics, evolution, epigenetic modifications and strategy for MiMe genotype development has been discussed in detail.

Plant male sterility refers to the failure in the production of fertile pollen. It occurs spontaneously in natural populations and may be caused by genes encoded in the nuclear (genic male sterility; GMS) or mitochondrial (cytoplasmic male sterility; CMS) genomes. This feature has great agronomic value for the production of hybrid seeds and has been widely used in crops, such as corn, rice, wheat, citrus, and several species of the family Solanaceae. Mitochondrial genes determining CMS have been uncovered in a wide range of plant species. The modes of action of CMS have been classified in terms of the effect they produce in the cell, which ultimately leads to a failure in the production of pollen. Male fertility can be restored by nuclear-encoded genes, termed restorer-of-fertility (Rf) factors. CMS from wild plants has been transferred to species of agronomic interest through somatic hybridization. Somatic hybrids have also been produced to generate CMS de novo upon recombination of the mitochondrial genomes of two parental plants or by separating the CMS cytoplasm from the nuclear Rf alleles

Diatom microalgae are the most outstanding natural source of porous silica. Diatom cell is enclosed in 3-D ordered nanopatterned silica cell wall, called frustule. The unique properties of diatoms frustule, including high specific surface area, thermal stability, biocompatibility, tailorable surface chemistry, make them really promising for biomedical applications. Moreover, diatoms are easy to cultivate in artificial environment and there is a huge availability of diatom frustules as fossil material (diatomite) in several areas of the world. For all these reasons, diatoms are an intriguing alternative to synthetic materials for the development of low-cost drug delivery systems. This review article focuses on the possible use of diatoms derived silica as drug carrier systems. The functionalization strategies of diatom micro-/nanoparticles for improving their biophysical properties, such as cellular internalization and drug loading/release kinetics, are described. In addition, the realization of hybrid diatom-based devices with advanced properties for theranostics and targeted or augmented drug delivery applications, are also discussed.

Energy storage systems play a crucial role in the overall performance of hybrid electric vehicles. Therefore, the state of the art in energy storage systems for hybrid electric vehicles has been discussed in this paper along with appropriate background information for facilitating future research in this domain. Specifically, we have compared the key parameters such as cost, power density, energy density, cycle life, and response time for various energy storage systems. For the energy storage systems employing ultra capacitors, we have presented the characteristics such as cell voltage, cycle life, power density and energy density. Furthermore, we have discussed and evaluated the interconnection topologies for the existing energy storage systems. We have also discussed the hybrid battery-flywheel energy storage system as well as the mathematical modeling of the battery-ultracapacitor energy storage system. Toward the end, we have discussed energy efficient powertrain for hybrid electric vehicles.

In the present work we study the existence of solutions for hybrid nonlinear fractional integrodifferential equations. We developed an algorithm by using the operator theoretical techniques in order to obtain the approximate solutions. The main results depend on the Dhage iteration method that were incorporated with the modern hybrid fixed point theorems. The approximate solutions were obtained by using Lipschitz conditions and weaker form of mixed partial continuity. Further, we provide some examples to explain the hypotheses and the related results.

The combination of artificial intelligence algorithms and numerical methods has recently become popular in the prediction of macroscopic and microscopic hydrodynamics parameters of bubble column reactors. The multi inputs and outputs machine learning can cover small phase interactions or large fluid behavior in industrial domains. This numerical combination can develop the smart multiphase bubble column reactor with the ability of low-cost computational time. It can also decrease case studies for the optimization process when big data is appropriately used during learning. There are still many model parameters that need to be optimized for a very accurate artificial algorithm, including data processing and initialization, the combination of inputs and outputs, number of inputs and model tuning parameters. For this study, we aim to train four inputs big data during learning process by an adaptive neuro-fuzzy inference system or adaptive-network-based fuzzy inference system (ANFIS) method, and we consider the superficial gas velocity as one of the input variables, while for the first time, one of the computational fluid dynamics (CFD) outputs named gas velocity is used as an output of the artificial algorithm. The results show that the increasing number of input variables improves the intelligence of the ANFIS method up to , and the number of rules during learning process has a significant effect on the accuracy of this type of modeling. The results also show that propper selection of model parameters results in more accuracy in prediction of the flow characteristics in the column structure.

The paper demonstrates the analysis of Corona Virus Disease based on a probabilistic model. It involves a technique for classification and prediction by recognizing typical and diagnostically most important CT images features relating to Corona Virus. The main contributions of the research include predicting the probability of recurrences in no recurrence (first time detection) cases at applying our proposed approach for feature extraction. The combination of the conventional statistical and machine learning tools is applied for feature extraction from CT images through four images filters in combination with proposed composite hybrid feature extraction (CHFS). The selected features were classified by the stack hybrid classification system(SHC). Experimental study with real data demonstrates the feasibility and potential of the proposed approach for the said cause.

Remaining service life (RSL) of pavement, as a sign of future pavement performance, has always received growing attention from pavement engineers. The RSL describes the time from the moment of pavement inspection until such a time when a major repair or reconstruction is required. The conventional approach to determining RSL involves using non-destructive tests. These tests, in addition to being costly, interfere with traffic flow and compromise users' safety. In this paper, surface distresses of pavement have been used to estimate the pavement’s RSL in order to eliminate the aforementioned problems and challenges. To implement the proposed theory, 105 flexible pavement segments were taken from Shahrood-Damghan Highway (Highway 44) in Iran. For each pavement segment, the type, severity, and extent of surface damage and pavement condition index (PCI) were determined. The pavement RSL was then estimated using non-destructive tests include Falling Weight Deflectometer (FWD) and Ground Penetrating Radar (GPR). After completing the dataset, the modeling was conducted to predict RSL using three techniques include Support Vector Regression (SVR), Support Vector Regression Optimized by Fruit Fly Optimization Algorithm (SVR-FOA), and Gene Expression Programming (GEP). All three techniques estimated the RSL of the pavement by selecting the PCI as input. The Correlation Coefficient (CC), Nash-Sutcliffe efficiency (NSE), Scattered Index (SI), and Willmott’s Index of agreement (WI) criteria were used to examine the performance of the three techniques adopted in this study. In the end, it was found that GEP with values of 0.874, 0.598, 0.601, and 0.807 for CC, SI, NSE, and WI criteria, respectively, had the highest accuracy in predicting the RSL of pavement.

The fuel prices are increasing nowadays, causing a burden on the power system. From this, diesel generators that are still in use, must be hybridized with renewable energy (RE) to levelized the overall projects costs. This study aims to schedule an operation of a hybrid system photovoltaic (PV)/ diesel in a remote area which located in Wadi Rum district in Jordan to serve factory load in addition to obtain the best economical system combination that can serve the needs of a continuous electrical load without any interruption. Based on the results of the analysis using HOMER software, the configuration in the system will be a combination between PV modules, and diesel generator. The hybrid system has met the target for electricity production which is documented in upcoming sections.

Carbon fiber reinforced plastic (CFRP) is one of the representative lightweight material. The automotive industry has focused on producing the steel/CFRP hybrid part to reduce the weight. After manufacturing, delamination can occur at the interface between the CFRP and steel owing to the hybrid part constituting dissimilar materials. However, most studies have focused only on designing the manufacturing processes for hybrid part or evaluating the adhesive used at the interface. Therefore, it is necessary to predict the behavior of the interface after demolding the hybrid part. This study aimed to predict the interface behavior of a steel/CFRP hybrid part by considering its forming and cohesive properties. First, double cantilever beam (DCB) and end-notched flexure (ENF) tests were performed to obtain cohesive parameters, such as energy release rate of modes Ⅰ and Ⅱ (GⅠ, GⅡ). The experimentally obtained properties were applied to the bonding area of the hybrid part. Subsequently, a forming simulation was performed to obtain the stress of the steel blank in the hybrid part. The stress distribution after forming was utilized as the initial condition for spring-back simulation. Finally, the interface behavior of the hybrid part was predicted by a spring-back simulation, and the results were compared with the experimental results for verification.

Droughts have negative impacts on agricultural productivity and economic growth. Effective monitoring and accurate forecasting of drought occurrences and trends are crucial for minimizing drought losses and mitigating their spatial and temporal effects. In this study, trend dynamics in monthly total rainfall time series measured at Cape Town International Airport were analyzed using Mann-Kendall (MK) test, Modified Mann-Kendall (MMK) and innovative trend analysis (ITA) were examined. Additionally, we utilized hybrid prediction method that combined the model with the complementary ensemble empirical mode decomposition with adaptive noise (CEEMDAN) technique, the autoregressive integrated moving average (ARIMA) model, the long short-term memory (LSTM) network (i.e. CEEMDAN-ARIMA-LSTM) to forecast SPI values of 6-, 9-, and 12-months using rainfall data between 1995 and 2020 from Cape Town International Airport meteorological rainfall stations. The results demonstrate the following essential points: (1) The trend analysis results of the total monthly rainfall values indicate a significant decreasing trend with the negative z-score (MK= -3.7541 and MMK= - 4.0773). ITA also indicated a significant downwards trend of rainfall in the study area. (2) As the forecasting horizon increases, the prediction accuracy of the models and CEEMDAN-combined models gradually improves, reaching their optimum performance at the 12-month horizon. (3) CEEMDAN efficiently stabilizes time-series data, resulting in greater prediction accuracy in the hybrid model compared to individual models at all timescales. (4) The RMSE and R2 values for the hybrid CEEMDAN-ARIMA-LSTM model at a 12-month forecasting horizon are 0.042 and 0.995, demonstrating the applicability of this hybrid approach for drought forecasting.

Hybrid electric vehicles (HEVs) are perceived as a first step towards the future of sustainable transport. Of course, battery electric vehicles (EVs) are currently ideal for what is wanted to stand for transport in the future. The lack of infrastructure for these vehicles makes many potential users choose hybrid vehicles in these conditions. This paper analyzes the influence of the engine and electric motor of hybrid vehicles on their performances. Three engines with slightly different power and close characteristics were considered for configured models. Also, two electric motors with very different power but having a very close maximum continuous torque have been used in models. One is an induction motor, and the other is a permanent magnet synchronous motor. ADVISOR software has been used for vehicles configuration and simulation. Series and parallel hybrid vehicles have been considered. The main dynamic performances and the fuel consumption in two driving cycles were compared for the configured models. Three conventional models with the same engines used in HEVs have also been simulated for reference. The results highlight that in the case of HEVs, the choice of combination engine/electric motor is crucial in obtaining the best report between dynamic performances and low fuel consumption, and implicitly a low negative impact on the environment.

Time evolution of the quantum correlation of the hybrid qubit-qutrit system in multilocal and global dephasing under the effect of a classical noises environment is presented. We considered the qutrit in z direction and the qubit x direction. In addition, our system presents two configurations: the different and the common environment and our discussion involves three types of noises: A Static Noise (SN), Random Telegraph Noise (RTN) and Ornstein- Uhlenbeck noise (OU). By using the density matrix approach to determine the evolution of the state of the system, the found that the static noise is fatal for the quantum entanglement, the quantum correlation and for RTN and OU noises the quantum entanglement, the quantum correlation decreases monotonically with a sufficiently long time. Therefore, we can say that, entanglement and the quantum correlation are more robust to the noise precisely those Random telegraph noise and Ornstein-Uhlenbeck noise.

Among the electrochemical energy storage (EES) technologies, rechargeable batteries (RBs) and supercapacitors (SCs) are the two most desired candidates for powering a range of electrical and electronic devices. The RB operates on Faradaic processes, whereas the underlying mechanisms of the SC vary as non-Faradaic in the electrical double layer capacitors (EDLCs), Faradaic at the surface of the electrodes in the pseudo capacitors (PCs), and a combination of both non-Faradaic and Faradaic in the hybrid capacitors. The EDLCs offer high power density but low energy density. The hybrid capacitors take advantage of the Faradaic process without compromising their capacitive nature. Unlike batteries, supercapacitors provide high power density and numerous charge-discharge cycles; however, their energy density lags that of batteries. Here, we review recently published critically selected articles on supercapatteries, a generic term that refers to EES devices that can combine the merits of EDLCs and RBs. Also discussed are the properties, design strategies, and future perspectives of supercapatteries.

Antimicrobial resistance is an increasing problem for the global public health. One of the strategies to combat this issue is the synthesis of novel antimicrobials through rational drug design based on extensive structure-activity relationship studies. The thiazole nucleus is a prominent feature in the structure of many authorized antimicrobials, being clubbed with different heterocycles. The purpose of this review is to study the structure-activity relationship in antimicrobial thiazoles clubbed with various heterocycles reported in the literature between 2017 and 2022, in order to offer an overview of the last years in terms of antimicrobial research and provide a helpful instrument for future research in the field.

Developing a biomolecular detection method that minimizes photodamage while maintaining an environment suitable for biological constituents to maintain their physiological state is expected to drive new diagnostic and mechanistic breakthroughs. In addition, ultra-sensitive diagnostic platforms are needed for rapid and point-of-care technologies for various diseases. Considering this, surface-enhanced Raman scattering (SERS) is proposed as a non-destructive and sensitive approach to address the limitations of fluorescence, electrochemical, and other optical detection techniques. However, to advance the applications of SERS, novel approaches to enhance the signal of substrate materials are needed to improve reproducibility and costs associated with manu-facture and scale-up. Due to their physical properties and synthesis, semiconductor-based nanostructures have gained increasing recognition as SERS substrates; however, low signal en-hancements have offset their widespread adoption. To address this limitation and assess the po-tential for use in biological applications, zinc oxide (ZnO) was coated with different concentra-tions (0.01-0.1 M) of gold (Au) precursor. When crystal violet (CV) was used as a model target with the synthesized substrates, the highest enhancement was obtained with ZnO coated with 0.05 M Au precursor. This substrate was subsequently applied to differentiate exosomes derived from three cell types to provide insight into their molecular diversity. We anticipate this work will serve as a platform for colloidal hybrid SERS substrates in future bio-sensing applications.

Titanium alloys are difficult to machine and have poor tribological properties. This paper investigates the lubricating performance of γ-Al2O3/ZnO hybrid nanofluids for Ti-6Al-4V. The pure and hybrid nanofluid are compared, and the effects of γ-Al2O3/ZnO ratios are studied. The results show that γ-Al2O3/ZnO hybrid nanofluids outperform pure nanofluid in terms of lower friction coefficients and better surface quality. Moreover, the hybrid nanofluid with a mass ratio of Al2O3 to ZnO of 2:1 demonstrates the best lubrication performance with a reduced friction coefficient of up to 22.1% compared to the base solution, resulting in improved surface quality. Al2O3 nanoparticles can adhere to the surface of ZnO nanoparticles and work as a coating, which further enhances the lubrication performance of the water-based nanofluid.

Laparoscopic partial nephrectomy (LPN) after hyperselective embolization of tumor vessels (HETV) in a hybrid operating room (HOR) that combines traditional surgical equipment with advanced imaging technology, is a non-clamping surgical approach to treat localized kidney tumors that have shown promising short-term results. The aim of this study was to evaluate the long-term oncological and functional outcomes of this procedure. All consecutive patients treated for a localized kidney tumor by LPN after HETV between May 2015 and October 2022 in a single academic institution were included in the study. Clinical, pathological and biological data were collected prospectively in the uroCCR database. We evaluated perioperative data, postoperative complications, surgical margin and modification of renal function after surgery. We included 245 patients. Median tumor size was 3.2 (2.5-4.4) cm. The R.E.N.A.L. complexity was low, medium and high for 104 (43.5%), 109 (45.6%) and 26 (10.9%) patients, respectively. Median LPN time was 75 (65 -100) min and median blood loss was 100 (50-300) mL. Surgical postoperative complications occurred in 56 (22.9%) patients with 17 (5.7%) major complications. The median preoperative Glomerular Function Rate (GFR) was 90.5 (77-101.8) mL/min and the median GFR variation at 6 months was -7.5 (-15- -2) mL/min. Malignant tumors were present in 211 (86.1%) patients and 12 (4.9%) patients had positive surgical margins. After a median follow-up of 27 (8-49) months, 20 (8.2%) patients had a tumor recurrence and 4 (1.6%) died from cancer. The use of a HOR to perform LPN after HETV is a safe and efficient non-clamping approach to treat localized kidney tumors.

Diverse development models, including waterfall development, iterative development, and agile development, have been put forth and implemented across real-world contexts. When engaging in discussions on project management, the examination and exploration of development models assume paramount importance and are integral. This paper embarks upon an investigation and scrutiny of these development models, culminating in the proposition of "Quasi" Development Models: Quasi-Waterfall and Quasi-Agile.

Sentiment analysis of public opinion expressed in social networks has been developed into various applications, especially in English. Hybrid approaches are potential models for reducing sentiment errors on increasingly complex training data. This paper aims to test some hybrid deep learning models' reliability in some domains' Vietnamese language. Our research questions are to determine whether it is possible to produce hybrid models that outperform the Vietnamese language. Hybrid deep sentiment-analysis learning models are built and tested on reviews and feedback of the Vietnamese language. The hybrid models outperformed the accuracy of Vietnamese sentiment analysis on Vietnamese datasets. It contributes to the growing body of research on Vietnamese NLP, providing insights and directions for future studies in this area.

When the majority of museums and other cultural institutions were shut down due to the pandemic, mixed museum visits became a hot issue. After the epidemic, mixed visits, in the opinion of many experts, would become the new norm for experiencing cultural content. Diverse types of merging online and onsite visits are already being investigated by researchers, not only to avoid the spread of a disease but also to enable visits of people who were previously excluded, such as persons in remote geographic areas or people with mobility challenges. The current work takes into account the Contextual Model of Museum Learning to define potential use scenarios for visits from people of different cultural backgrounds and offers an evaluation of current practices.

In recent years, online learning has become one of the most popular methods of educational delivery due to advances in technology, which has been made even more evident in the COVID-19 lockdown period. Online education has evolved into a distinct field of study within the educational system over the last few years. It is also important to note that parallel with the growth in this field, there has also been an increase in the number of scholarly journals that regularly publish research in this field, reflecting the importance of this field in the modern day. In spite of the fact that online learning offers a wide range of educational options, from short courses to full-time degrees, as well as being accessible, flexible, environmentally friendly, and affordable, there are also certain challenges associated with this educational approach. These challenges include the lack of social interaction, technical errors, a lack of hands-on training, and difficulties in assessing students. It is, therefore, imperative to ask the crucial question of whether online learning can replace traditional classroom learning or whether it can supplement it in hybrid models with it, as well as what factors and conditions are likely to determine this in the short- and long-term, as well as how it will be blended together in the future. The purpose of this commentary is to provide a brief summary of the current status of both learning models, as well as their pros and cons, in order to answer the question that was posed above.

Cooperative non-orthogonal multiple access (CNOMA) has been recently adapted with energy harvesting (EH) to increase energy efficiency and extend the lifetime of energy-constrained wireless networks. In EH, two protocols are usually used for CNOMA, namely, power splitting (PS) and time switching (TS). In order to improve the existing EH protocols, this paper investigates a hybrid EH protocol-assisted CNOMA, which is a combination of the two aforementioned main existing EH protocols. The end-to-end bit error rate (BER) and outage probability (OP) expressions of users in our scheme are derived over Nakagami-m fading channels. For the sake of comparison and to highlight the achievable gains, the performance of the hybrid EH (HEH) protocol is compared with the benchmark schemes (i.e., existing EH protocols and no-EH). Based on extensive simulations, it is shown that the analytical results match perfectly with simulations which demonstrates the correctness of the derivations. Numerical results clearly indicate the superiority of the proposed scheme over all the considered benchmarks. In addition, we discuss the optimum value of EH factors to minimize the error and outage in the proposed scheme and show that an optimum value can be obtained according to channel parameters.

Abstract: Early assessment of crop development is a key aspect of precision agriculture. Shortening the time of response before a deficit of irrigation, nutrients and damage by diseases is one of the usual concerns in agriculture. Early prediction of crop yields can increase profitability in the farmer's economy. In this study we aimed to predict the yield of four maize commercial hybrids (Dekalb7508, Advanta9313, MH_INIA619 and Exp_05PMLM) using remotely sensed spectral vegetation indices (VI). A total of 10 VI (NDVI, GNDVI, GCI, RVI, NDRE, CIRE, CVI, MCARI, SAVI, and CCCI) were considered for evaluating crop yield and plant cover at 31, 39, 42, 46 and 51 days after sowing (DAS). A multivariate analysis was applied using principal component analysis (PCA), linear regression, and r-Pearson correlation. In the present study, highly significant correlations were found between plant cover with VIs at 46 (GNDVI, GCI, RVI, NDRE, CIRE and CCCI) and 51 DAS (GNDVI, GCI, NDRE, CIRE, CVI, MCARI and CCCI). The PCA indicated a clear discrimination of the dates evaluated with VIs at 31, 39 and 51 DAS. The inclusion of the CIRE and NDRE in the prediction model contributed to estimate the performance, showing greater precision at 51 DAS. The use of RPAS to monitor crops allows optimizing resources and helps in making timely decisions in agriculture in Peru.

Organically-coated nanomaterials are intensively studied and find numerous applications in a wide range of areas ranging from optics to biomedicine. One of the recent trends in material science is the application of bio-mimetic polydopamine coatings that can be produced on a variety of substrates in a cost-efficient way under mild conditions. Such coatings not only modify the biocompatibility of the material but also add functional amino groups to the surface that can be further modified by classic conjugation techniques. Here we show an alternative strategy of substrates modification using not dopamine but dopamine conjugates. Compared to the classic scheme the proposed strategy allows for separation of the "organic" and "colloidal" stages and simplifies identification and purification steps. Modification with pre-modified dopamine allowed to achieve high active components loading up to 10.5% wt. A series of organo-inorganic hybrids were synthesized and their bioactivity was analyzed.

Metastasis is the leading cause of cancer death and can be realized through the phenomenon of tumor cell fusion. The fusion of tumor cells with other tumor or normal cells leads to the appearance of tumor hybrid cells (THCs) exhibiting novel properties such as increased proliferation and migration, drug resistance, decreased apoptosis rate and avoiding immune surveillance. Experimental studies showed the association of THCs with a high frequency of cancer metastasis; however, the underlying mechanisms remain unclear. Many other questions also remain to be answered: the role of genetic alterations in tumor cell fusion, the molecular landscape of cells after fusion, the lifetime and fate of different THCs, and the specific markers of THCs, and their correlation with various cancers and clinicopathological parameters. In this review, we discuss the factors and potential mechanisms involved in the occurrence of THCs, the types of THCs, and their role in cancer drug resistance and metastasis, as well as potential therapeutic approaches for the prevention and targeting of tumor cell fusion. In conclusion, we emphasize the current knowledge gaps in the biology of THCs that should be addressed to develop highly effective therapeutics and strategies for metastasis suppression.

(1) Background:A BMI > 25 is the most decisive, albeit modifiable,risk factors for knee osteoarthritis (OA). This study aimed at assessing the efficacy of intra-articular injections of hybrid hyaluronic acid (HA) complexes (Synovial® H-L)for the treatment of kneeOA in overweight patientsin terms ofdisease severity, cardiocirculatory capacity, and quality of life. (2) Materials: In this single-site, open-label, prospective trial, 37 patients with symptomatic knee OA were assessed at baseline and 3 months after ultrasound-guided intra-articular injection of hybrid HA complexes (Synovial® H-L). (3) Results: Primary variables displaying a statistically significant improvement after treatment were pain (NRS), disease severity (WOMAC), and cardiopulmonary capacity a(6-Minute Walk Test). Among secondary variables, quality of life (SF-12) improved significantly, as did analgesic intake for pain control. No statistically significant difference was observed in body fat and muscle mass percentage measured by bioelectrical impedance analysis. (4) Conclusions:Intra-articular hybrid HA injections are significantly effective in improving OA-related disease severity, cardiopulmonary function, and analgesic intake. This supports the role of hybrid HA viscosupplementation as a non-pharmacological treatment to relieve pain, reduce disability and improve quality of life, and limit the risk of polypharmacy in overweight patients with knee OA.

Innovative tissue engineering biomimetic hydrogels based on hydrophilic polymers have been investigated for their physical and mechanical properties. 5% to 25% by volume loading PHEMA-nanosilica glassy hybrid samples were equilibrated at 37°C in aqueous physiological isotonic and hypotonic saline solutions (0.15 and 0.05 M NaCl) simulating two limiting possible compositions of physiological extracellular fluids. The glassy and hydrated hybrid materials were characterized both for dynamo-mechanical properties and equilibrium absorptions in the two physiological-like aqueous solutions. Mechanical and the morphological modifications occurring in the samples have been described. The 5% volume nanosilica loading hybrid nanocomposite composition showed mechanical characteristics in the dry and hydrated states that were comparable to those of cortical bone and articular cartilage, respectively, and then chosen for further sorption kinetics characterization. Sorption and swelling kinetics were monitored up to equilibrium. Changes in water activities and osmotic pressures in the water-hybrid systems equilibrated at the two limiting solute molarities of the physiological solutions have been related to the observed anomalous sorption modes using the Flory-Huggins interaction parameter approach. The bulk modulus of the dry and glassy PHEMA-5% nanosilica hybrid at 37°C has been observed to be comparable with the values of the osmotic pressures generated from the sorption of isotonic and hypotonic solutions. The anomalous sorption modes and swelling rates are coherent with the difference between osmotic swelling pressures and hybrid glassy nano-composite bulk modulus: the lower the differences the higher the swelling rate and equilibrium solution uptakes. Bone tissue engineering benefits of use of tuneable biomimetic scaffold biomaterials that can be “designed” to act as biocompatible and biomechanically active hybrid interfaces are discussed.

This paper presents a methodology for predictive and prescriptive analytics of complex engineering systems. The methodology is based on a combination of physics-based and data-driven modeling using machine learning techniques. Combining these approaches results in a set of reliable, fast, and continuously updating models for prescriptive analytics. The methodology is demonstrated with a case study of a jet-engine power plant preventive maintenance and diagnostics of its flame tube.

The availability of rail infrastructure resources is a major driver of rail operations performance. To evaluate the impact of infrastructure provision, network simulation models can be used to accurately represent train traffic behavior in a wide range of scenarios. However, performing this task can result in a problem of high combinatorial nature as the number of factors and their associated levels increase. This requires more sophisticated techniques such as experimental design formulations or optimization modeling in order to yield satisfactory results. Yet the research in network simulation models for rail systems has hitherto been limited to simple what-if analysis, made up from few factors that cannot represent the whole spectrum of interventions. This is especially critical in closed-loop rail systems where trains are subject to various interferences. Local improvements can be misleading as the queues are merely transferred within the network. Considering this, we propose a hybrid simulation-optimization model to aid the strategic decision of minimizing supplementary capital costs in a heavy-haul Brazilian railroad under construction. As soon as the railroad is completed, investments in both loading and unloading rail terminals will be necessary. First, we developed a representative and flexible model capable of dealing with complex relations between variable infrastructure provision and the resulting operational performance. Then, we simulated this system to prove that the current set of proposed infrastructure resources cannot meet the transportation demands. Afterwards, we demonstrate that local improvements can be delusive as the queues are shifted from loading to unloading operations, reciprocally. Then, we solve an optimization model to define the minimal supplementary investment in order to meet the commercial goals of mining companies. This is done by choosing the best trackage configuration, equipment quantity and capacity and fleet sizing in 3 different production scenarios. The best values of the objective function were found by improving both loading and unloading equipment and increasing the number of trains.

The effects of carbon fiber hybridisation on the thermal properties of woven kenaf reinforced epoxy composites kenaf fibre were studied. Woven kenaf hybrid composites at the different weave designs of plain and satin, and fabric count of 5 × 5 and 6 × 6 were manually prepared by vacuum infusion technique. Thermal properties of pure carbon fibre and hybrid composites were conducted by using thermogravimetric analyser (TGA) and differential scanning calorimeter (DSC). It was found that at high kenaf fibre content showed better thermal stability while the highest thermally stable was found in pure carbon fibre composite. The TG and DTG results showed that the amount of residue decreased in plain-designed hybrid composite compared to satin-designed hybrid composite. The DSC data revealed that the presence of woven kenaf increase decomposition temperature.

Inorganic and organic – inorganic (hybrid) perovskite semiconductor materials have attracted the worldwide scientific attention and research effort as the new wonder material in optoelectronics. Their excellent physical and electronic properties have been exploited to boost the solar cells efficiency beyond 23% and captivate their potential as competitors to the dominant silicon solar cells technology. However, the fundamental principles in Physics dictate that an excellent material for photovoltaic applications must be also an excellent light emitter. This has been realized for the case of perovskite based light emitting diodes (LEDs) but much less for the case of the respective laser devices. Here, the strides have been made since 2014 are presented for the first time. The solution processability, low temperature crystallization, formation of nearly defect free, nanostructures, the long range ambipolar transport, the direct energy band gap, the high spectral emission tunability over the entire visible spectrum and the almost 100% external luminescence efficiency show perovskite semiconductors’ potential to transform the nanophotonics sector. The operational principles, the various adopted material and laser configurations along the future challenges are reviewed and presented in this paper.

The resource allocation for tasks in heterogeneous distributed system is a well known NP-hard problem. For the sake of making the makespan is minimized, it is hard to distribute the tasks to proper processors. The problem is even more complex and challenging when the processors have unavailable time and the tasks type are various. This paper investigates a resource allocation problem for hybrid tasks comprising both divisible and bag-of-tasks(BoT) in heterogeneous distributed system when the processors has unavailable time. First, the mathematical model, which minimizes the makespan of the hybrid tasks when the processors have unavailable time, is established. Second, we propose a scheduling algorithm referred to as bag-of-tasks allocate-pull and divisible task allocation (BoTAPDTA) algorithm for handling hybrid tasks on heterogeneous distributed systems. In addition, to solving the optimization model efficiently, a generic algorithm(GA) is proposed. For the sake of reducing the search space and solving the optimization model effectively, a two step scheduling algorithm(TSGA), which first allocate bag-of-tasks(BoT) using generic algorithm and then assign divisible task to processors like BoTAPDTA, is designed. Finally, numerical simulation experiments are conducted, and experimental results indicate the effectiveness of the proposed model and algorithm.

In the present study, a hybrid optimizing algorithm has been proposed using Genetic Algorithm (GA)and Particle Swarm Optimization (PSO) for Artificial Neural Network (ANN) to improve the estimation of electricity demand of the state of Tamil Nadu in India. The GA-PSO model optimizes the coefficients of factors of gross state domestic product (GSDP) , electricity consumption per capita, income growth rate and consumer price index (CPI) that affect the electricity demand. Based on historical data of 25 years from 1991 till 2015 , the simulation results of GA-PSO models are having greater accuracy and reliability than single optimization methods based on either PSO or GA. The forecasting results of ANN-GA-PSO are better than models based on single optimization such as ANN-BP, ANN-GA, ANN-PSO models. Further the paper also forecasts the electricity demand of Tamil Nadu based on two scenarios. First scenario is the "as-it-is" scenario , the second scenario is based on milestones set for achieving goals of "Vision 2023" document for the state. The present research also explores the causality between the economic growth and electricity demand in case of Tamil Nadu. The research indicates that a direct causality exists between GSDP and the electricity demand of the state.

In the present study, a hybrid optimizing algorithm has been proposed using Genetic Algorithm (GA)and Particle Swarm Optimization (PSO) for Artificial Neural Network (ANN) to improve the estimation of electricity demand of the state of Tamil Nadu in India. The GA-PSO model optimizes the coefficients of factors of gross state domestic product (GSDP) , electricity consumption per capita, income growth rate and consumer price index (CPI) that affect the electricity demand. Based on historical data of 25 years from 1991 till 2015, the simulation results of GA-PSO models are having greater accuracy and reliability than single optimization methods based on either PSO or GA. The forecasting results of ANN-GA-PSO are better than models based on single optimization such as ANN-BP, ANN-GA, ANN-PSO models. Further the paper also forecasts the electricity demand of Tamil Nadu based on two scenarios. First scenario is the “as-it-is” scenario, the second scenario is based on milestones set for achieving goals of “Vision 2023” document for the state. The present research also explores the causality between the economic growth and electricity demand in case of Tamil Nadu. The research indicates that a direct causality exists between GSDP and the electricity demand of the state.

Wind-solar power generating and hybrid battery-supercapacitor energy storage complex is used for autonomous power supply of consumers in remote areas. This work uses passivity-based control (PBC) for this complex in accordance with the accepted energy management strategy (EMS). Structural and parametric synthesis of the overall PBC system was carried out that was accompanied by a significant amount of research. In order to simplify this synthesis, a structural decomposition of the overall dynamic system of the object, which was described by a system of differential equations of the seventh order, into three subsystems was applied. These subsystems are a wind turbine, a PV plant and a hybrid battery-supercapacitor system. For each of the subsystems, it is quite simple to synthesize the control influence formers, which locally perform the tasks set by the EMS. The results obtained by computer simulation of the overall and decomposed systems demonstrate the effectiveness of this approach in simplifying synthesis and debugging procedures of complex multiphysical systems.

Hybrid carbon-silicon, carbon-nitrogen, and carbon-boron clathrates are new classes of Type I carbon-based clathrates that have been identified by first-principles computational methods by substituting atoms on the carbon clathrate framework with Si, N, and/or B atoms. The hybrid framework is further stabilized by embedding appropriate guest atoms within the cavities of the cage structure. Series of hybrid carbon-silicon, carbon-boron, carbon-nitrogen, and carbon-silicon-nitrogen clathrates have been shown to exhibit small positive values of the energy of formation, indicating that they may be metastable compounds and amenable to fabrication. In this overview article, the energy of formation, elastic properties, and electronic properties of selected hybrid carbon-based clathrates are summarized. Theoretical calculations that explore the potential applications of hybrid carbon-based clathrates as energy storage materials, electronic materials, or hard materials are presented. The computational results identify compositions of hybrid carbon-silicon and carbon-nitrogen clathrates that may be considered candidate materials for use as either electrode materials for Li-ion batteries or as hydrogen storage materials. Prior processing routes for fabricating selected hybrid carbon-based clathrates are highlighted and difficulties encountered are discussed.

Alzheimer's disease (AD) is a significant regular type of dementia that causes damage in brain cells. Early detection of AD acting as an essential role in global health care due to misdiagnosis and sharing many clinical sets with other types of dementia, and costly monitoring the progression of the disease over time by magnetic reasoning imaging (MRI) with consideration of human error in manual reading. Our proposed model, in the first stage, apply the medical dataset to a composite hybrid feature selection (CHFS) to extract new features for select the best features to improve the performance of the classification process due to eliminating obscures features. In the second stage, we applied a dataset to a stacked hybrid classification system to combine Jrip and random forest classifiers with six model evaluations as meta-classifier individually to improve the prediction of clinical diagnosis. All experiments conducted on a laptop with an Intel Core i7- 8750H CPU at 2.2 GHz and 16 G of ram running on windows 10 (64 bits). The dataset evaluated using an explorer set of weka data mining software for the analysis purpose. The experimental show that the proposed model of ‏(CHFS) feature extraction ‏performs better than principal component analysis (PCA), and lead to effectively reduced the false-negative rate with a relatively high overall accuracy with support vector machine (SVM) as meta-classifier of 96.50% compared to 68.83% which is considerably better than the previous state-of-the-art result. The receiver operating characteristic (ROC) curve was equal to 95.5%. Also, the experiment on MRI images Kaggle dataset of CNN classification process with 80.21% accuracy result. The results of the proposed model show an accurate classify Alzheimer's clinical samples against MRI neuroimaging for diagnoses AD at a low cost.