Dual Method Research Articles

DSCA: A Dual Semantic Correlation Alignment Method for domain adaptation object detection

In self-driving cars, adverse weather (e.g., fog, rain, snow, and cloud) or occlusion scenarios result in domain shift being unavoidable in object detection. Researchers have recently proposed Domain Adaptive Object Detection (DAOD), i.e., aligning the source and target domains at the image and instance levels distribution by utilizing the Unsupervised Domain Adaptation (UDA) method. However, the semantic correlation information is ignored leading to the effect of aligning not good, and low detection accuracy of objects in adverse weather or occlusion scenarios. Here, we propose a Dual Semantic Correlation Alignment (DSCA) method for DAOD to address the problem. The core idea behind DSCA is to make full use of semantic correlation information including context correlation semantic information and class correlation semantic information to align object semantic information in source and target domains, which supplement and enhance the missing information for target domains. It consists of a two-level semantic alignment: (1) context correlation semantic alignment is developed to obtain the context correlation semantic information of the object to align context semantic information at the image level; (2) class correlation semantic alignment is proposed to obtain the class correlation semantic information of the object to align class semantic information at the instance level. The two-level semantic alignment can effectively decrease negative transfer and complete object information to improve the detection accuracy of objects in different domains. Experiments on four challenging benchmarks show that our proposed DSCA method outperforms state-of-the-art DAOD methods.

Enhancing fuel cell performance through a dual MPC strategy for coordinated temperature management

Ensuring the optimal operating temperature is imperative for achieving efficient performance in proton exchange membrane fuel cells. Consequently, this study introduces a dual-model predictive control strategy to regulate the water pump and cooling fan in a cooling system. Initially, we establish an electrochemical and thermal model for fuel cell stacks and validate the model’s accuracy through experimental data. The system model is linearized, and the model predictive control (MPC) controller is formulated using the MATLAB/Simulink toolbox. Subsequently, it is collaboratively simulated with the electrochemical model of the fuel cell stack and the temperature model. To evaluate the effectiveness of the MPC controller, we conducted a comparative analysis with the traditional proportional–integral–derivative (PID) control and water pump MPC under step load, uniform load increase, and variable target scenarios. The findings indicate that in contrast to the PID control, the MPC controller significantly decreases the stack temperature difference fluctuation by more than 50%, maintaining the stack temperature within ±0.6 K of the set value. Furthermore, we independently assessed the performance of the MPC controller under varying ambient temperatures. The findings illustrate that the dual MPC method proficiently adapts cooling parameters across different ambient temperature ranges (288.15 K–308.15 K), ensuring the stable performance of the fuel cell. The model is linearized, and the simulation work is explained mainly on the MATLAB/Simulink platform. In order to compare the effectiveness of the MPC controller, the comparison with the MPC controller strategy of the water pump is added, which can better reflect the effectiveness of the proposed collaborative MPC controller strategy.

Structure Preserving Primal Dual Methods for Gradient Flows with Nonlinear Mobility Transport Distances

Structure Preserving Primal Dual Methods for Gradient Flows with Nonlinear Mobility Transport Distances

Near-optimal tensor methods for minimizing the gradient norm of convex functions and accelerated primal–dual tensor methods

Motivated, in particular, by the entropy-regularized optimal transport problem, we consider convex optimization problems with linear equality constraints, where the dual objective has Lipschitz pth order derivatives, and develop two approaches for solving such problems. The first approach is based on the minimization of the norm of the gradient in the dual problem and then the reconstruction of an approximate primal solution. Recently, Grapiglia and Nesterov [Tensor methods for finding approximate stationary points of convex functions, Optim. Methods Softw. (2020), pp. 1–34] showed lower complexity bounds for the problem of minimizing the gradient norm of the function with Lipschitz pth order derivatives. Still, the question of optimal or near-optimal methods remained open as the algorithms presented in [Grapiglia and Nesterov, Tensor methods for finding approximate stationary points of convex functions, Optim. Methods Softw. (2020), pp. 1–34] achieve suboptimal bounds only. We close this gap by proposing two near-optimal (up to logarithmic factors) methods with complexity bounds O ~ ( ε − 2 ( p + 1 ) / ( 3 p + 1 ) ) and O ~ ( ε − 2 / ( 3 p + 1 ) ) with respect to the initial objective residual and the distance between the starting point and solution, respectively. We then apply these results (having independent interest) to our primal–dual setting. As the second approach, we propose a direct accelerated primal–dual tensor method for convex problems with linear equality constraints, where the dual objective has Lipschitz pth order derivatives. For this algorithm, we prove O ~ ( ε − 1 / ( p + 1 ) ) complexity in terms of the duality gap and the residual in the constraints. We illustrate the practical performance of the proposed algorithms in experiments on logistic regression, entropy-regularized optimal transport problem, and the minimal mutual information problem.

APNet: Adaptive projection network for medical image denoising.

In clinical medicine, low-dose radiographic image noise reduces the quality of the detected image features and may have a negative impact on disease diagnosis. In this study, Adaptive Projection Network (APNet) is proposed to reduce noise from low-dose medical images. APNet is developed based on an architecture of the U-shaped network to capture multi-scale data and achieve end-to-end image denoising. To adaptively calibrate important features during information transmission, a residual block of the dual attention method throughout the encoding and decoding phases is integrated. A non-local attention module to separate the noise and texture of the image details by using image adaptive projection during the feature fusion. To verify the effectiveness of APNet, experiments on lung CT images with synthetic noise are performed, and the results demonstrate that the proposed approach outperforms recent methods in both quantitative index and visual quality. In addition, the denoising experiment on the dental CT image is also carried out and it verifies that the network has a certain generalization. The proposed APNet is an effective method that can reduce image noise and preserve the required image details in low-dose radiographic images.

Method of Fundamental Solutions without Fictitious Boundary in Elastodynamic Behavior Using Dual Reciprocity Method

Method of Fundamental Solutions without Fictitious Boundary in Elastodynamic Behavior Using Dual Reciprocity Method

Design of dual loop controller for boost converter based on PI controller

Boost converters are widely used in industry for many applications, such as electrical vehicles, wind energy systems, and photovoltaic energy systems, to step up the low voltages. Using the topology structure of the DC–DC boost circuit, this paper studied and designed a dual loop control method based on proportional integral controllers for improving the converter efficiency. The inner loop and outer loop controls of the traditional boost circuit are adopted in MATLAB/Simulink software to make the output of the system more stable. The input voltage is set to 24 V DC, and the desired output voltage varies from 36 to 48 V. Through simulation verification, the influence of a 1 kW sudden load connection by using a switch at a nominal output voltage of 48 V DC is studied, and the results show that it reduces the transient output voltage dips during the sudden load connection. Simulation analysis verifies the design scheme of the system, reduces the fluctuation in output voltage and power, reduces the output current ripple, minimizes the dip in voltage to a minimum possible value, and improves the dynamic characteristics and overall efficiency of the converter.

Test antagonism of several Trichoderma species in suppressing the growth of Alternaria sp. in vitro

Alternaria sp. is a pathogenic fungus that causes purple blotch disease in onion plants. Losses from this disease lead to a decrease in yield by more than 70% and seed damage to 100%. For this reason, a study was conducted to test the antagonistic ability of several Trichoderma species in suppressing the growth of Alternaria sp. in-vitro as an alternative to plant disease control that is safer and more environmentally friendly. Alternaria sp. isolate. derived from onion plants located in Bale Atu, Bukit District, Bener Meriah Regency, Aceh Province. The Trichoderma species used are T. asperellum, T. harzianum, T. virens, and Trichoderma sp. Antagonism testing was carried out using a dual culture method in a 9 cm diameter petri dish. The results showed that, T. asperellum, T. harzianum, T. virens, and Trichoderma sp. able to suppress the growth of Alternaria sp. on observation of 5 HSI. The antagonistic mechanism that occurs is competition against space, nutrition, antibiosis and mycoparasites. The best percentage of resistance occurred in the treatment of T. virens, which was 77.78%. next followed by the inhibitory power of Trichoderma sp. by 72.39%, T. harzianum by 64.80%, and T. asperellum by 60.77%.

Dual excitation self-balancing capacitance measurement method based on frequency adjustment

The double-ended excitation self-balancing bridge excitation method is a capacitance measurement method with rather a high measurement accuracy currently. However, the reference impedance of the electric bridge limits the traditional self-balancing excitation measurement method, making it difficult to achieve high accuracy and a wide range of capacitance sensor measurements. In the proposed paper, frequency adjustment method is introduced to enable impedance matching between the measured capacitance and the reference resistance. The measurement range is broadened as well as maintaining the accuracy of the capacitance measurement. The measurement circuit is composed of a reference resistor, a bridge in series with the measured capacitance and an operational amplifier follower circuit. First, we roughly measure the input impedance of the operational amplifier and the capacitive reactance of the capacitance under test using a two-step excitation method. Then, the best excitation frequency is calculated to match the reference resistor according to the capacitive reactance under test, and the input impedance of the operational amplifier is re-estimated with the best frequency. Finally, we complete the accurate measurement of the capacitive reactance under test through the use of dual excitation. The experimental results demonstrate the method’s high accuracy measurement within the range of 1.6 pF–16 nF, with the reference resistance set at 1 MΩ and the excitation frequency adjusted between 10 and 100 kHz. Additionally, dual excitation stability testing verifies relative standard deviations in the range of 0.01%–0.12%. Experiments on filter capsule sensing detection show that the proposed frequency adjustment measurement method can perform highly sensitive capacitive sensing measurements under the influence of parasitic effects.

A Regularization-Patching Dual Quaternion Optimization Method for Solving the Hand-Eye Calibration Problem

The hand-eye calibration problem is an important application problem in robot research. Based on the 2-norm of dual quaternion vectors, we propose a new dual quaternion optimization method for the hand-eye calibration problem. The dual quaternion optimization problem is decomposed to two quaternion optimization subproblems. The first quaternion optimization subproblem governs the rotation of the robot hand. It can be solved efficiently by the eigenvalue decomposition or singular value decomposition. If the optimal value of the first quaternion optimization subproblem is zero, then the system is rotationwise noiseless, i.e., there exists a “perfect” robot hand motion which meets all the testing poses rotationwise exactly. In this case, we apply the regularization technique for solving the second subproblem to minimize the distance of the translation. Otherwise we apply the patching technique to solve the second quaternion optimization subproblem. Then solving the second quaternion optimization subproblem turns out to be solving a quadratically constrained quadratic program. In this way, we give a complete description for the solution set of hand-eye calibration problems. This is new in the hand-eye calibration literature. The numerical results are also presented to show the efficiency of the proposed method.

A ratiometric dual-fluorescent paper-based synthetic biosensor for visual detection of tetracycline on-site

The excessive use of tetracycline poses a threat to human health, making it essential to monitor and regulate its usage. While whole-cell biosensors offer a simple and cost-effective method, their utility is constrained by limitations in sensitivity, portability, and robustness, hindering real-time measurements within complex environmental contexts. In this study, a ratiometric i/cTetR synthetic biosensing test strip with an engineered modified dual-fluorescence reporting was developed for detecting Tet antibiotics in water and food. First, the standardized unidirectional promoter PtetR by tailoring and screening TetR transcription factor binding sites and verified by molecular docking, shortening the detection time. Secondly, decoupling the sensing and reporting modules enhances the biosensor's performance, eliminating genetic background leakage and tripling the output signal. Thirdly, a ratiometric dual fluorescence signal i/cTetR biosensing test strip was designed. Under the light box LED/UV light source, the dual signal output method significantly reduced false negative results and enhanced the anti-interference capability of the biosensor. The i/cTetR strips can detect Tet in tap water (5–1280 μg/mL) and milk (50–3200 μg/kg) within 45 min in high volume on-site without separation and purification. This study provides a standardized and universal sensing method for the field detection of antibiotic contaminants.

Prospecting of Bioagents and Screening of Varieties against Anthracnose of Green Gram

Aims: Anthracnose poses a significant threat to green gram cultivation in India. This study focused on evaluating various bioagents and cultivars efficacy in combating Collectotrichum lindemuthianum through in vitro bioassays and glasshouse investigations.
 Study Design: Dual culture technique and screening of varieties
 Place and Duration of Study: The laboratory studies were conducted in the Department of Plant Pathology, N. M. College of Agriculture, Navsari Agricultural University, Navsari, Gujarat, India during 2020–2021.
 Methodology: The dual culture method was employed to gauge the potency of biocontrol agents, while variety screening helped to identify resistant and susceptible green gram varieties against C. lindemuthianum.
 Results: In vitro assessments revealed that among the five biocontrol agents tested, Trichoderma virens (87.63%) and T. viride (85.41%) exhibited significant suppression of mycelial growth compared to the untreated control. However, in greenhouse pot culture experiments involving eight different genotypes, none were found to be immune to green gram anthracnose. Nevertheless, Pusa 1431, KM-2328, TARM-18, and GM 6 exhibited a resistant response against anthracnose, while BPMR-145 and Vaibhav proved susceptible to the disease.
 Conclusion: Biocontrol agents are cost-effective and safe for disease management, while integrating resistant varieties is a solid strategy. These high-yielding, environmentally safe, and economically viable methods benefit farmers. Recognizing resistant genotypes is crucial for breeding disease-resistant varieties, as they limit field spread and reduce conidia production of C. lindemuthianum causing green gram anthracnose.

Simultaneous determination of meloxicam and bupivacaine via a novel modified dual wavelength method and an advanced chemometric approach

This study presents two spectrophotometric methods; a novel dual wavelength—derivative spectrophotometry and multivariate curve resolution-alternating least squares (MCR-ALS) for the simultaneous determination of a fixed dose combination of bupivacaine (BUP) and meloxicam (MEL) in a ratio of 30:1. The extended UV spectrum of MEL enables its direct determination at λmax 360 nm with no interference from BUP. The determination of BUP was unfeasible directly because the UV spectra of both drugs are moderately overlapped over the wavelength range of 250–450 nm, thus new chemometric based spectrophotometric methods should be developed for its determination. Dual wavelength-derivative method was employed based on using first derivative spectra. The selected dual wavelengths for determination BUP were 274.6 nm and 374.6 nm where the dA/dλ amplitudes differences for MET are equal to zero. MCR-ALS is advanced chemometric tool that enables analysis of multicomponent samples in complex matrices with high resolution based on the decomposition of signal/spectral data into the pure spectra and corresponding concentration profile. The figures of merits for MCR model show that there is a good agreement between the actual and predicted concentrations for MEL and BUP. The methods were validated and statistically compared with a reported HPLC method.

DGEKT: A Dual Graph Ensemble Learning Method for Knowledge Tracing

Knowledge tracing aims to trace students’ evolving knowledge states by predicting their future performance on concept-related exercises. Recently, some graph-based models have been developed to incorporate the relationships between exercises to improve knowledge tracing, but only a single type of relationship information is generally explored. In this article, we present a novel Dual Graph Ensemble learning method for Knowledge Tracing (DGEKT), which establishes a dual graph structure of students’ learning interactions to capture the heterogeneous exercise–concept associations and interaction transitions by hypergraph modeling and directed graph modeling, respectively. To combine the dual graph models, we introduce the technique of online knowledge distillation. This choice arises from the observation that, while the knowledge tracing model is designed to predict students’ responses to the exercises related to different concepts, it is optimized merely with respect to the prediction accuracy on a single exercise at each step. With online knowledge distillation, the dual graph models are adaptively combined to form a stronger ensemble teacher model, which provides its predictions on all exercises as extra supervision for better modeling ability. In the experiments, we compare DGEKT against eight knowledge tracing baselines on three benchmark datasets, and the results demonstrate that DGEKT achieves state-of-the-art performance.

High-performance solar-blind photodetector based on amorphous BN in harsh environment operations

High-performance solar-blind photodetectors capable of operating in extreme environments are desirable for a wide range of applications, such as engine control, down hole drilling, space exploration, and environmental monitoring. Boron nitride (BN), with an ultrawide bandgap and high band edge absorption coefficient, is especially suitable for such application scenarios owing to its chemical and structural stability at high temperatures and radiative conditions, which, however, in turn, brings difficulties in synthesis of large-area continuous single crystalline BN. Here, we report highly robust solar-blind photodetectors based on amorphous BN (a-BN) films that can operate at high temperatures and high electric fields. The a-BN films are dense and uniform, grown at 500 °C using dual beam-assisted deposition method. The a-BN-based photodetectors exhibit high performance with a responsivity of 0.56 mA/W at 20 V under UV illumination of 222 nm and a high rejection ratio (R222 nm/R295 nm > 200 and R222 nm/R315 nm > 500). More importantly, these photodetectors demonstrate excellent responsivity and stability at high temperatures up to 500 K and high bias of 200 V without breakdown. The photocurrent mechanism at elevated temperatures is analyzed by temperature-dependent decay time of the temporal response, showing the electron–phonon interaction and self-trapped holes are dominant. Our work suggests that the deposition of such a-BN films offers a promising strategy toward highly environment-resistant solar-blind photodetectors.

The effects of the dual control method on the aerodynamic characteristics of the wind turbine blade

ABSTRACT In order to improve the aerodynamic characteristics of small wind turbine blades, a new form of flow control was investigated in this paper. Which was the addition of an S809 airfoil profile slat on the leading edge of blade and a Gurney flap on the trailing edge of the blade. The effects of dual control method on the aerodynamic characteristics were discussed through numerical simulations method. The findings showed that this dual control method increased the fluid velocity on the suction surface of the blade. The fluid could receive higher energy to suppress the inverse pressure gradient in the boundary layer. The blade torque had a significant influence by the dual control method, the total torque of the blade was greatly increased compared to the original blade. The main blade torque of blade-1 was increased by 180.21 N⋅m compared to the original blade at 15.1 m/s. The pressure difference was also increased and the flow separation was restrained, especially near r/R = 0.6. At 10 m/s, the torque coefficients of the r/R = 0.5–0.7 spanwise cross-section were improved by the dual control method. When wind speed was 20.1 m/s, blade-3 increased the torque coefficient from 0.84 to 1.89 at r/R = 0.3 compared to the blade-0, an increase of 125%.

Characterization of Extracellular Chitinase from Bacillus cereus SAHA 12.13 and Its Potency as a Biocontrol of Curvularia affinis

Bacillus cereus SAHA 12.13 can produce chitinase, an enzyme that digests chitin in the main compounds of cell walls, mycelia, and spores in pathogenic fungi that cause leaf spots on oil palm plants such as Curvularia affinis. This study aims to determine the properties of the chitinase enzyme B. cereus SAHA 12.13 that can inhibit the growth of C. affinis. Chitinase enzyme production and characterization were measured using the Spindler method. Antagonism test against pathogenic fungi using dual culture method by testing cell culture and enzyme crude extract. This result showed that the isolate produced a high level of specific chitinase activity at 37 °C for 45 h of incubation with 8.45 U mg-1 proteins with a growth rate (k) of 0.25 generation/h, and the generation time was 3.96 h/generation. The optimum chitinase activity was achieved at pH 7.0 and 45 °C and was stable for 3 h with a half-life (t1/2) of 770 min. The crude enzyme and cell culture of strain can inhibit the growth of C. affinis by 36.27±0.043% and 34.25±0.041%, respectively. These characteristics indicate that B. cereus strain SAHA12.13 can be used to inhibit C. affinis, which causes leaf blight of oil palm, under varying pH and temperature conditions.

Free vibration and buckling analysis of functionally graded beams using the DMCDM

In this paper, the Dual Mesh Control Domain Method (DMCDM) is used to carry out the free vibration and buckling analysis of two-constituent Functionally Graded Beams (FGBs) with through-thickness material variation. The material composition (modulus and density) is varied continuously through the beam thickness according to a power-law. The Euler-Bernoulli beam model consisting of the displacements and bending moment as the primary nodal degrees of freedom (i.e., mixed model) and the Timoshenko beam model with the displacement degrees of freedom (i.e., displacement model) are formulated as eigenvalue problems using the DMCDM. The numerical examples presented consider typical beam boundary conditions and various power-law exponents and slenderness ratios. The accuracy of this method is demonstrated by comparing the numerical results with those available in the literature.

Antagonistic Test of Bacteria Producing Siderophore and Protease Enzymes from The Rhizosfer of Peanut Plants on The Growth of Pathogenic Fungus Colletotrichum gloeosporioides

This study aims to determine the inhibitory effect of bacterial isolates producing siderophores and protease enzymes on the growth of the pathogenic fungus Colletotrichum gloeosporioides. The initial stage of research begins with the isolation of pathogenic bacteria and fungi, and is followed by testing the production of siderophores and protease enzymes. Bacteria were isolated from the rhizosphere of peanut plants, while pathogenic fungi were isolated from large chili fruit infected with anthracnose disease taken from Jelantik Village, Central Lombok Regency. Characterization of isolates for siderophore production used the Arnow method, while the protease enzyme production test used SKIM Milk Agar media. Next, the inhibition test of bacterial isolates against pathogenic fungi was carried out using the dual culture method. Characterization of potential isolates was carried out by observing bacterial colony morphology, gram staining and biochemical tests. The results of the siderophore production test showed that there was one isolate capable of producing siderophores with the isolate code RKT2. Meanwhile, the protease enzyme production test showed that all bacterial isolates produced protease enzymes, where isolate RKT9 had the highest Proteolytic Index, namely 1.57. The two isolates showed different inhibitory test results, namely isolate RKT2 had high inhibition, while RKT9 had low inhibition. The results of the research showed that a bacterial isolate (RKT2) from the rhizosphere of peanut plants was able to inhibit the growth of the pathogenic fungus Colletotrichum gloeosporioides in the high category.

Effective field theory bootstrap, large-N χPT and holographic QCD

We review the effective field theory (EFT) bootstrap by formulating it as an infinite-dimensional semidefinite program (SDP), built from the crossing symmetric sum rules and the S-matrix primal ansatz. We apply the program to study the large-N chiral perturbation theory (χPT) and observe excellent convergence of EFT bounds between the dual (rule-out) and primal (rule-in) methods. This convergence aligns with the predictions of duality theory in SDP, enabling us to analyze the bound states and resonances in the ultra-violet (UV) spectrum. Furthermore, we incorporate the upper bound of unitarity to uniformly constrain the EFT space from the UV scale M using the primal method, thereby confirming the consistency of the large-N expansion. In the end, we translate the large-N χPT bounds to constrain the higher derivative corrections of holographic QCD models.