Dual Stage Research Articles

Replacement of native sugars of apples by isomaltulose in dual stages

The present study aimed to substitute the native sugars of apples for isomaltulose. In the first stage, native sugars were partially removed from the fruit tissue, and in the second stage, isomaltulose was incorporated. The use of ultrasound was evaluated at both process stages. Water loss (WL), solid gain (SG), water activity (aw), soluble solids content (°Brix), shrinkage (Sh), and color parameters (total color difference (ΔE), L* a*, b*, Cr*, and h°) were evaluated. The temperature of 35 °C stood out for its contribution to the mass transfer parameters (SG = 8.196%; WL = 21.699%—maximum values). There seems to be a threshold regarding the use of ultrasound in the process stages, indicating that its excessive use does not favor impregnation. The ideal process to promote the dehydration of apples and incorporation of the greatest amount of isomaltulose consisted of a treatment in which samples were immersed in water in the first stage, followed by impregnation of isomaltulose for 40 min assisted by ultrasound in the second stage. In addition, there was a significant reduction of aw (0.971 ± 0.001) and a slight change in color parameters (2.91 ± 0.45). Only concerning Sh (28.7 ± 3.6%), this treatment was not advantageous.Graphical

Dual stage MRI image restoration based on blind spot denoising and hybrid attention

BackgroundMagnetic Resonance Imaging (MRI) is extensively utilized in clinical diagnostics and medical research, yet the imaging process is often compromised by noise interference. This noise arises from various sources, leading to a reduction in image quality and subsequently hindering the accurate interpretation of image details by clinicians. Traditional denoising methods typically assume that noise follows a Gaussian distribution, thereby neglecting the more complex noise types present in MRI images, such as Rician noise. As a result, denoising remains a challenging and practical task.MethodThe main research work of this paper focuses on modifying mask information based on a global mask mapper. The mask mapper samples all blind spot pixels on the denoised image and maps them to the same channel. By incorporating perceptual loss, it utilizes all available information to improve performance while avoiding identity mapping. During the denoising process, the model may mistakenly remove some useful information as noise, resulting in a loss of detail in the denoised image. To address this issue, we train a generative adversarial network (GAN) with adaptive hybrid attention to restore the detailed information in the denoised MRI images.ResultThe two-stage model NRAE shows an improvement of nearly 1.4 dB in PSNR and approximately 0.1 in SSIM on clinical datasets compared to other classic models. Specifically, compared to the baseline model, PSNR is increased by about 0.6 dB, and SSIM is only 0.015 lower. From a visual perspective, NRAE more effectively restores the details in the images, resulting in richer and clearer representation of image details.ConclusionWe have developed a deep learning-based two-stage model to address noise issues in medical MRI images. This method not only successfully reduces noise signals but also effectively restores anatomical details. The current results indicate that this is a promising approach. In future work, we plan to replace the current denoising network with more advanced models to further enhance performance.

Dual-stage semantic segmentation of endoscopic surgical instruments.

Endoscopic instrument segmentation is essential for ensuring the safety of robotic-assisted spinal endoscopic surgeries. However, due to the narrow operative region, intricate surrounding tissues, and limited visibility, achieving instrument segmentation within the endoscopic view remainschallenging. This work aims to devise a method to segment surgical instruments in endoscopic video. By designing an endoscopic image classification model, features of frames before and after the video are extracted to achieve continuous and precise segmentation of instruments in endoscopicvideos. Deep learning techniques serve as the algorithmic core for constructing the convolutional neural network proposed in this study. The method comprises dual stages: image classification and instrument segmentation. MobileViT is employed for image classification, enabling the extraction of key features of different instruments and generating classification results. DeepLabv3+ is utilized for instrument segmentation. By training on distinct instruments separately, corresponding model parameters are obtained. Lastly, a flag caching mechanism along with a blur detection module is designed to effectively utilize the image features in consecutive frames. By incorporating specific parameters into the segmentation model, better segmentation of surgical instruments can be achieved in endoscopicvideos. The classification and segmentation models are evaluated on an endoscopic image dataset. In the dataset used for instrument segmentation, the training set consists of 7456 images, the validation set consists of 829 images, and the test set consists of 921 images. In the dataset used for image classification, the training set consists of 2400 images and the validation set consists of 600 images. The image classification model achieves an accuracy of 70% on the validation set. For the segmentation model, experiments are conducted on two common surgical instruments, and the mean Intersection over Union (mIoU) exceeds 98%. Furthermore, the proposed video segmentation method is tested using videos collected during surgeries, validating the effectiveness of the flag caching mechanism and blur detection module. Experimental results on the dataset demonstrate that the dual-stage video processing method excels in performing instrument segmentation tasks under endoscopic conditions. This advancement is significant for enhancing the intelligence level of robotic-assisted spinal endoscopic surgeries.

Dual Stage Bionic Spatial Convolutional Restoration Model for Image De-noising and De-mosaicking

The first two important steps in the pipeline for processing picture signals are de-noising and de-mosaicking. The joint solution of the highly uncertain inverse problem of de-noising and de-mosaicking has garnered increased attention in research today. It is difficult to restore high-quality images from raw data in low light because of a variety of disturbances brought on by a low photon count and a complex image signal processing scheme. Even if some restoration and improvement techniques have been used, they might not work in harsh situations, including raw data imaging with brief exposure. Therefore, this research focuses on developing a de-mosaicking and de-noising model with effective end to end manner outcomes. Initially, the pre-processing is conducted using Gaussian filtering to eliminate artifacts from the input image, thereby enhancing the image quality. Then, the proposed method incorporates an Enhanced Spatial Convolutional Residual Net (EnConvResNet) for image de-mosaicking and an Adaptive U-net restoration model for image de-noising. An enhanced gazelle optimization (EnGa) algorithm is used to fine-tune the hyper-parameters of the model in order to maximize its performance and improve its generalization capacity. The proposed method accomplished peak signal to noise ratio (PSNR) and structural similarity index measure (SSIM) of 46.65 and 98.89, respectively.

Impact of Artificial Intelligence on Team Creativity: A Dual Stage Perspective

Impact of Artificial Intelligence on Team Creativity: A Dual Stage Perspective

From circular to square‐based section spouted beds: Scale‐up and design overview of a multistage thermal unit

AbstractThis work summarizes the results of several research projects to demonstrate the possibility of scaling‐up spouted beds by assembling a number of square‐based jet units. Cylindrical vessels with a bottom cone and parallelepiped ones with a frustum base do not differ to a large extent in terms of hydrodynamic features. The existing correlations can be used to predict these square‐based spouted beds. Spouted beds can be very reasonably described as well‐mixed reactors. By assembling several units, it is possible to approach a plug flow state for solids residence time distribution (RTD) in continuous processes. Spouting stability is fully reached when the modules do not interfere each other: this goal is obtained by positioning the downstream units at a certain lower level and having the solids move by overflow; placing internal vertical baffles between contiguous units is the solution. Two hydrodynamical models are proposed to describe single and multiple spouted beds; their predictions help in choosing the best geometrical configuration for the assemblage. A possible application of a multiple spouted bed reactor was envisaged for gasifying pelletized textile residues to generate syngas which can be directly used in situ as a co‐fuel. Two units were built: a single 0.2 m side square‐based spouted bed furnace and the consequent dual stage scale‐up apparatus suitable for an auto thermal process. In the bottom stage, four units work independently to run combustion/gasification of low‐quality solid residues, while the upper stage had a cascade of four modules to run pyrolysis/gasification of selected biomass and obtain valuable secondary products.

Exploring the Recent Pioneering Developments of Small Molecules in Antimalarial Drug Armamentarium: A Chemistry Prospective Appraisal.

Malaria is a very destructive and lethal parasitic disease that causes significant mortality worldwide, resulting in the loss of millions of lives annually. It is an infectious disease transmitted by mosquitoes, which is caused by different species of the parasite protozoan belonging to the genus Plasmodium. The uncontrolled intake of antimalarial drugs often employed in clinical settings has resulted in the emergence of numerous strains of plasmodium that are resistant to these drugs, including multidrug-resistant strains. This resistance significantly diminishes the effectiveness of many primary drugs used in the treatment of malaria. Hence, there is an urgent need for developing unique classes of antimalarial drugs that function with distinct mechanisms of action. In this context, the design and development of hybrid compounds that combine pharmacophoric properties from different lead molecules into a single unit gives a unique perspective towards further development of malaria drugs in the next generation. In recent years, the field of medicinal chemistry has made significant efforts resulting in the discovery and synthesis of numerous small novel compounds that exhibit potent antimalarial properties, while also demonstrating reduced toxicity and desirable efficacy. In light of this, we have reviewed the progress of hybrid antimalarial agents from 2021 up to the present. This manuscript presents a comprehensive overview of the latest advancements in the medicinal chemistry pertaining to small molecules, with a specific focus on their potential as antimalarial agents. As possible antimalarial drugs that might target both the dual stage and multi-stage stages of the parasite life cycle, these small hybrid molecules have been studied. This review explores a variety of physiologically active compounds that have been described in the literature in order to lay a strong foundation for the logical design and eventual identification of antimalarial drugs based on lead frameworks.

Design and Analysis of High Gain Dual Stage Op-amp in Cadence 180 nm Technology

Abstract: Operational amplifier contains basic current mirror and dual input differential amplifier. Basic Current mirror is an electronic circuit which contains two transistors. The operation of the current mirror is that whenever current is applied to drain of one of the transistors with some magnitude same current will produce at the output of another transistor, hence the name mirror of the input signal. Operational amplifier contains two legs- inverting leg, non- inverting leg. If signals are applied to inverting and non-inverting legs , operational amplifier used as an amplifier and comparator or adder or subtractor. A two stage operational amplifier consists of a differential amplifier at the input stage.While the second stage is a high gain stage biased by the output of differential amplifier.

Comparative Analysis of Single Stage and Dual Stage PV Based Generation System for Pollution Reduction in Asian Nations

The growing need for sustainable and clean energy solutions has elevated photovoltaic (PV) systems to the forefront of competitive options. In order to evaluate the performance and viability of single-stage and dual-stage single-phase PV voltage source inverter systems within the context of renewable energy, this study undertakes a thorough comparative analysis. MATLAB Simulink has been used to run the simulations, and the results produced show that the selected configurations are robust and closely match theoretical expectations. The study explores the subtle distinctions, benefits, and drawbacks that come with single- and dual-stage systems. By extensively evaluating the consequences on system stability, grid synchronisation, and power quality, useful insights emerge. The simplicity and efficiency of the single-stage system are demonstrated by the direct connectivity between the PV MPPT output and the inverter. In contrast, the dual-stage system addresses the issue of low PV output voltage by incorporating a boost converter with an MPPT algorithm, albeit at the expense of more parts and complexity. The research described here adds a great deal to the current discussion on PV system optimization. The study notably clarifies methods for improving dependability and efficiency in applications involving renewable energy. With the increasing worldwide trend towards sustainable energy, it is critical to comprehend the subtle dynamics of various PV setups. In addition to offering a comprehensive grasp of the complexities involved in single- and dual-stage single-phase PV voltage source inverter systems, this research paves the way for future developments in the planning and execution of effective renewable energy solutions especially for Asian nations that still rely on coal-based power plants. Increased contribution of solar based electricity generation will lead to a significant drop in pollution contributed by the ash and gases released by thermal power plants that employ fossil fuels. The ultimate goal of this study is to provide information to stakeholders in the renewable energy industry so that they may make more informed decisions and create PV systems that are both sustainable for the environment and efficient for the utility.

Combustion and particulate I/SVOC characteristics of an aero-engine combustor with dual-stage under operational power and injection pressure

The combustion and particulate intermediate-volatile/semi-volatile organic compound (I/SVOC) emission characteristics of a technology of low emission stirred swirl aero-engine combustor, which was operated under dual combustion stages of diffusion and premixed-dominant combustion have been investigated. The operational conditions of the combustor were combustor power (7 %–100 %) and injection pressure (IP, 1.5–6.0 MPa). Initially, the ignition and extinction boundary regions were identified, and within the ignition region, the emitted I/SVOCs were comprehensively resolved via a two-dimensional gas chromatography-time of flight mass spectrometer (GC × GC-ToFMS). It is found that I/SVOCs were remarkable under diffusion combustion, and reduced significantly by 61 % under premixed-dominant combustion (at 3.0 MPa IP). Besides, I/SVOCs were suppressed prominently by 49 % as IP increased from 1.5 MPa to 6.0 MPa (at 30 % combustor power). Based on the Response Surface Method (RSM), a model has been established to simulate the particulate I/SVOCs. The simulation model reveals that high Power-IP (100 % power, 6 MPa IP) could reduce I/SVOCs maximally by 84 %, 78 % and 56 % for n-alkanes, cycloalkanes and PAHs, respectively in comparison with low Power-IP (30 % power, 1.5 MPa IP). The essence of the findings reveals that domination of premixed combustion with promotional atomization inhibits aviation-emitted organic particles effectively.

Supervisor incivility and service employee creativity: a moderated mediation model of negative affect state and core self-evaluation

PurposeThe present study examines the effect of supervisor incivility on service employee creativity and the mediating mechanism of negative affect state on the said relationship with the aid of Affective Events Theory (AET) and Social Power Theory. Additionally, the study explores the mitigating role of core self-evaluation in the dual stage of the moderated mediation model.Design/methodology/approachSurvey data were collected from 420 frontline employees working in four-star and five-star hotels across India and PROCESS macro in SPSS 23.0 was employed to test the hypotheses proposed in the study.FindingsThe study findings observe that frontline employees experiencing supervisor incivility face a negative affect state and it further diminishes service employee creativity. Also, the indirect effect gets attenuated at dual stages when frontline employees are equipped with a high level of core self-evaluation.Practical implicationsThe study findings provide various insights to the managers to understand the deleterious effect of supervisor incivility at work and to develop interventions to manage supervisor incivility and the negative affect state among frontline hotel employees.Originality/valueThe present study is the first and unique attempt to investigate the role of supervisor incivility experienced by frontline hotel employees in India with reference to their creativity towards customers and mediating mechanism of negative affect state. The study adds value to the literature by establishing the role of core self-evaluation in the moderated mediation model. Further, the study also provides a unique contribution to the management of frontline hospitality employees.

Enhancing Medium Term Wind Power Forecasting Accuracy With Dual Stage Attention Based TCN-GRU Model and White Shark Optimization

This research introduces a creative strategy for addressing the challenges of wind power predicting, crucial for effective renewable energy integration into the power grid. We propose a dual-stage attention-based Temporal Convolutional Network and Gated Recurrent Units (ATCN-AGRU) method for predicting wind energy over the medium term. In the first stage, a local attention mechanism captures fine-grained details and local dependencies, while the second stage employs a global attention mechanism to emphasize broader context and long-range dependencies within the data sequence. Our experimentation employs wind turbine data at 1-hr resolution, examining various time horizons from 24 hr to 1 week to assess multi-step forecasting precision and computational efficiency. Through rigorous statistical assessments, we demonstrate the model’s validity, with the mean absolute percentage errors (MAPE) consistently below 9% for week-ahead forecasting. Model parameters were fine-tuned using white shark optimization (WSO), enhancing convergence and overall performance. The proposed hybrid model significantly outperforms standard forecasting methods, achieving a maximum MAPE of 8.07% for week-ahead forecasting and a minimal 4.44% for day-ahead forecasting. To test the model’s stability, we extend the experiment to month-ahead forecasting, with the mean absolute error (MAE) ranging from 2.42 to 6.2% for weekly predictions.

Modeling and Experimental Validation of Broad Input-Output Range Three-Voltage-Level Rectifier

A new type of single–conversion–step wide–input–range versatile step–up/down three–voltage–level power–factor correction stage is presented in this manuscript. The rectifier can operate both in continuous–conduction mode and discontinuous–conduction mode. First, the rectifier’s principle of operation is described, and then the innovative rectifier is analyzed in continuous and discontinuous–conduction modes. After, an average model for the innovative rectifier is developed. Lastly, the proposed theory is experimentally validated using a multiplier–less dual–control–loop mode at discontinuous–conduction modes. It is shown that although no multiplier is used in the control circuitry, the power factor is near unity. It is revealed that the rectifier can swing the output voltage from 50 V to 900 V while the input voltage is 230 Vrms. Although the rectifier output has a split DC bus with three voltage levels, the required control effort is low, and the output voltage is balanced. The innovative topology suits any standard power–factor correction rectifier application, dual–stage low–voltage power supply, and three–level voltage supplement for low–harmonic inverters. Since the rectifier’s output–voltage swing is extremely wide, energy storage systems and electric vehicle batteries are suitable applications.

Design and Control of a Flexure-Based Dual Stage Piezoelectric Micropositioner

In the field of advanced manufacturing technology, there is a growing need for high-precision micro/nano positioners. The traditional single stage actuated positioners have encountered performance limitation in achieving longer travel range and higher precision. This motivates us to develop a novel dual stage piezoelectric-actuated micropositioner presented in this paper. The micropositioner incorporates displacement amplification mechanisms to overcome the limited range of piezoelectric actuators. Design considerations such as flexure characteristics and material selection are discussed, and structural analysis is performed using finite element analysis (FEA). For precise positioning, the dual stage control strategy is investigated and compared with the conventional proportional-integral-derivative (PID) single stage control method. In the proposed positioner, a combination of parallelogram and bridge mechanisms is utilized. The bridge mechanism works to amplify the piezoelectric actuator displacement output. The parallelogram mechanism, integrated within the system, helps mitigate resonance modes and contributes to the achievement of linearized motion. The characteristics of the micropositioner were evaluated using analytical modelling and FEA. Multiple analysis was used to optimise the positioner’s design parameters. Furthermore, experimental studies were carried out to validate the characteristics of the micropositioner performance in terms of achievable output travel range and sustained positioning accuracy.

Unifying convolution and transformer: a dual stage network equipped with cross-interactive multi-modal feature fusion and edge guidance for RGB-D salient object detection

Unifying convolution and transformer: a dual stage network equipped with cross-interactive multi-modal feature fusion and edge guidance for RGB-D salient object detection

Power quality enhancement of dual stage three phase grid integrated SPV system using sequential neural network-based algorithm

In this paper, a single-layer sequential neural network (SNN) control algorithm has been proposed for managing the power quality (PQ) in the double-stage three-phase grid-integrated solar photovoltaic (SPV) system. The proposed algorithm efficiently extracts the active and reactive fundamental components of the load current, with a higher convergence rate. The extracted reference currents are matched with the sensed source currents to generate gating pulses for the voltage source converter (VSC). To handle power quality issues in distribution networks such as harmonic imbalances, voltage fluctuations (sags/swells), and the need for active and reactive power compensation, the VSC is controlled very precisely. The control algorithm consists of two-stage processes. In the initial stage, the incremental conductance (INC) based DC-DC converter has been incorporated to extract the peak power from the SPV system. In the later stage SNN-based algorithm is used to control the VSC. A 100 KW solar PV system connected to the utility grid has been designed by using a three-leg VSC in the MATLAB/Simulink environment. The system performance has been evaluated by comparing it to existing techniques. The simulation results comfortably meet the requirements of the IEEE-519–2014 and IEEE 1547–2003 standards for utility grid connection.

Dual stage black-box adversarial attack against vision transformer

Dual stage black-box adversarial attack against vision transformer

Understanding mobile learning continuance after the COVID-19 pandemic: Deep learning-based dual stage partial least squares-structural equation modeling and artificial neural network analysis

The influence of COVID-19 on educational processes has halted physical forms of teaching and learning and initiated online and mobile learning systems in most countries. The provision and usage of online and e-learning systems are becoming the main challenge for many universities during the COVID-19 pandemic. Due to the novelty of this situation, a substantial amount of research has been carried out to investigate the issue of m-learning adoption or acceptance. Nevertheless, little is known about studying to examine the continued use of m-learning, which is still in short supply and calls for further research. Five different theoretical models are integrated into this study to develop an integrated model that overcomes this limitation, including the technology acceptance model, the theory of planned behavior, the expectation-confirmation model, the Delone and McLean Information System Success Model, and the Unified Theory of Acceptance and Utilization of Technology 2. This conceptual framework shows novel relationships between variables by integrating trust, personal innovation, learning value, instructor quality, and course quality. Unlike extant literature, this study utilized a hybrid analysis methodology combining two-stage analysis using partial least squares structural equation modeling (PLS-SEM) and evolving artificial intelligence named deep learning (Artificial Neural Network [ANN]) on 250 usable responses. The sensitivity analysis results revealed that attitude has the most considerable effect on the continued use of m-learning, with 100% normalized importance, followed by perceived usefulness (88%), satisfaction (77%), and habit (61%). This research reveals that a “deep ANN architecture” may determine the non-linear relationships between variables in the theoretical model. Further theoretical and practical implications are also discussed.

Interweaving Power Discourse: An Analysis of Dynamic Interaction Between the Dramatic Monologue and Imago of Confinement in My Last Duchess and Andrea Del Sarto

Robert Browning, based on a story and a painting, reconstructed the inner voices of the story's protagonist and the figures in the painting within his poetry. His skillful use of "dramatic monologue" provided a stage for the characters' inner activities, and the image of confinement set the stage for conflicts between the characters and the flow of power discourse. This interactive and intertwined "dual stage" enhances the power of narrative and dramatic elements in Browning's poetry. The purpose of this paper is to find out the closed imagery employed by Robert Browning in My Last Duchess and Andrea del Sarto combined with Foucault's concept of "power discourse" to analyze how they interact and the change of "power discourse" in protagonist within the dramatic monologues, and further explore "transgression" within two poems. The author finds that the dramatic monologue offers a "big stage" for the conflict. Still, reading Browning's poetry needs to pay more attention to the "small stage" provided by the closed imagery, where the speaker who holds the power of speech changes his identity with the closed imagery and achieves the "transgression" through it.

Design of an efficient energy based dual level co-operative route protocols in multiple hop WSNs

The design and implementation of an energy efficient dual stage cooperative routing protocol (DSCR) in multiple hop wireless nets is discussed in this paper in brief, with the goal of improving network life-time, boosting dependability, and reducing energy consumption. This contribution’s major goal is to propose an enhanced version of the double stage data transfer system that computes the routing path utilizing cooperative mechanisms and the notion of reduced node energy. The work presented here also includes talks, discussion and graphical representations of the various outcomes achieved for all of the test scenarios, as well as the relevant observations and explanations. The research paper comes to a close with the multiple hop networks’ overall conclusions remarks.