Optimization Procedure Research Articles

Long-term outcomes of intravascular ultrasound-guided drug-eluting stents implantation in patients with acute coronary syndrome: ULTIMATE ACS subgroup

Objective: To explore the long-term effects of intravascular ultrasound (IVUS) guidance on patients with acute coronary syndrome (ACS) undergoing drug-eluting stents (DES) implantation. Methods: Data used in this study derived from ULTIMATE trial, which was a prospective, multicenter, randomized study. A total of 1 448 all-comer patients were enrolled between 2014 August and 2017 May. Primary endpoint of this study was target vessel failure (TVF) at 3 years, including cardiac death, target-vessel-related myocardial infarction, and clinically-driven target vessel revascularization. Results: ACS was present in 1 136 (78.5%) patients, and 3-year clinical follow-up was available in 1 423 patients (98.3%). TVF in the ACS group was 9.6% (109/1 136), which was significantly higher than 4.5% (14/312) in the non-ACS group (log-rank P=0.005). There were 109 TVFs in the ACS patients, with 7.6% (43/569) TVFs in the IVUS group and 11.6% (66/567) TVFs in the angiography group (log-rank P=0.019). Moreover, patients with optimal IVUS guidance were associated with a lower risk of 3-year TVF compared to those with suboptimal IVUS results (5.4% (16/296) vs. 9.9% (27/273),log-rank P=0.041). Conclusions: This ULTIMATE-ACS subgroup analysis showed that ACS patients undergoing DES implantation were associated with a higher risk of 3-year TVF. More importantly, the risk of TVF could be significantly decreased through IVUS guidance in patients with ACS, especially in those who had an IVUS-defined optimal procedure.

3D printing of potassium sodium niobate by binder jetting: Printing parameters optimisation and correlation to final porosity

Binder Jetting (BJT) is a non-fusion-based Additive Manufacturing (AM) technique. It consists of the selective deposition of a liquid binder to join powder particles, thereby enabling the creation of near-net-shaped parts.In this study, the main printing parameters correlated to the binder distribution and infiltration (binder saturation, binder set time, drying time, and target bed temperature) were optimised to improve the precision of green parts printed with potassium sodium niobate (KNN) powder. The optimisation procedure was conducted using the Taguchi statistical method. An L9 orthogonal array with four factors of control at three levels each was employed. The analysis showed that the drying time had the greatest influence on the precision of green parts, followed by binder saturation and target bed temperature. Binder set time did not seem to affect the results.Dimensional analysis, microstructural and piezoelectric characterisation of parts densified by pressureless sintering were conducted. The highest average relative density exceeded 80% for the specimens printed with the lower binder saturation. Piezoelectric properties exhibit more complex behaviour. The prolonged infiltration of larger binder volumes is correlated to higher g33, thus FOMh and FOM33, and lower values for ε33T. On the other hand, d33 does not display a specific dependence on density or printing parameters.The results of this study indicate that BJT can be used to fabricate high-precision KNN components with good piezoelectric properties. The optimisation of printing parameters is essential to achieve the desired results.

Surgical and Functional Outcome of Laparoscopic Vaginal Suspension Combined with Suture Rectopexy for Management of Pelvic Organ Prolapse

Abstract Purpose Laparoscopic techniques to treat pelvic organ prolapse are gaining popularity around the globe due to their low recurrence rates and better functional results compared to perineal techniques. However, the optimum surgical procedures are not yet determined. In the current research, we suggest a novel surgical approach, laparoscopic vaginal suspension with suture rectopexy, to treat multiorgan pelvic prolapse. Methods This prospective cohort trial was conducted from March 2018 to March 2022 and comprised 35 females with multiorgan pelvic organ prolapse with obstructed defecation symptoms. A residual rectal prolapse was still present despite the manual reduction of uterine prolapse. Patients' conditions before and after the operation were monitored regarding the obstructed defecation score, sexual function, need for laxatives, anorectal manometry pressures, anorectal sensations, and recurrence. The mean follow-up duration was one year. Results Modified Longo score for obstructed defecation significantly decreased at six and twelve months after surgery. Additionally, a significant reduction was reported in the number of patients who needed laxatives at six and twelve months after surgery. Anorectal manometry pre- and post-surgery showed a significant elevation in the mean squeeze pressure and a decline in all rectal sensations. All parameters of the female sexual function scoring system increased postoperatively. No recurrence was reported during follow-up. Conclusion For multiorgan pelvic prolapse, laparoscopic vaginal suspension combined with suture rectopexy has excellent functional outcomes, minimal morbidity, and low cost.

Knowledge transfer accelerated turbine blade optimization via an sample-weighted variational autoencoder

The aerodynamic shape optimization (ASO) of turbine blades is a typical expensive black-box problem. With the ever increasingly strict design requirements, high-fidelity simulations are demanded, and the related simulation cost increases sharply, which means fewer simulation calls are allowed within a design optimization cycle. Then, the problem as “how to achieve better solution with fewer simulations” becomes rather crucial. Inspired by the concept of transfer learning, we propose to leverage the samples of related completed tasks (also known as source samples) to address the above problem. Specifically, an sample-weighted variational autoencoder (VAE) is proposed, which includes the source samples as part of the training dataset to boost the proportion of high-scoring solution candidates in the learnt design space of target problem, and thus increasing the probability of attaining substantially better solutions. The including of source samples in VAE training also helps to increase the accuracy of reparameterizing the source samples in target design space. Furthermore, a multi-fidelity surrogate is used to guide the optimization search, which extracts information from the source samples to help the algorithm to more quickly arrive at the small neighborhood of the true optimal of the target problem. With the above, a knowledge transfer accelerated aerodynamic shape optimization procedure is proposed for the turbine blade design, labeled as KT-ASO. Through test on a highly-loaded low-pressure turbine blade and compared against the ASO procedures without knowledge transfer, the proposed KT-ASO is shown to reduce at least 50% function calls when achieving similar optimal solutions. In the meantime, under the same sample budget, our proposed KT-ASO is shown to attain much better solutions than the compared ASO procedures.

Comprehensive comparison of a new technology with traditional methods for extracting Ougan (Citrus reticulata cv. Suavissima) seed oils: Physicochemical properties, fatty acids, functional components, and antioxidant activities

Citrus seed waste material provides a new solution for addressing the shortage of edible oils in China. supercritical CO2 fluid extraction (SFE), sub-critical butane fluid extraction (SCE), cold press (CP), soxhlet extraction (SXE), and ultrasonic-assisted extraction (UAE) were preformed to obtain Ougan (Citrus reticulata cv. Suavissima) seed oil. Ougan seed oils were tested for physicochemical properties, fatty acid composition, limonoids, polyphenols, total carotenoids, tocopherols, and in vitro antioxidant activities. The results indicated that SCE Ougan seed oil has the most abundant fatty acid composition, and higher in vitro antioxidant capacity, which is mainly positively related to the polyphenol, carotenoid and tocopherol contents. Therefore, SCE may be the optimum procedure for extracting a high content of bioactive substances from Ougan seed oils, providing a theoretical foundation for developing high-quality and nutritional value Ougan seed oils.

Optimal control of entanglement in atom pairs with dipole-dipole interaction by quantum phase space formalism

We investigate the entanglement in a two-particle system with continuous degree of freedom constituted by trapped neutral atoms interacting via tunable dipole-dipole interaction. We describe the system in the quantum Wigner phase space representation. The atom entanglement is measured in terms of violation of the non separability condition or of some Bell inequalities. By an optimal control procedure, we design the control parameters that steer the system toward a maximally entangled state. We estimate the robustness of the control by considering the presence of an external source of noise weakly interacting with the atoms.

Empirical Bayes factors for common hypothesis tests.

Bayes factors for composite hypotheses have difficulty in encoding vague prior knowledge, as improper priors cannot be used and objective priors may be subjectively unreasonable. To address these issues I revisit the posterior Bayes factor, in which the posterior distribution from the data at hand is re-used in the Bayes factor for the same data. I argue that this is biased when calibrated against proper Bayes factors, but propose adjustments to allow interpretation on the same scale. In the important case of a regular normal model, the bias in log scale is half the number of parameters. The resulting empirical Bayes factor is closely related to the widely applicable information criterion. I develop test-based empirical Bayes factors for several standard tests and propose an extension to multiple testing closely related to the optimal discovery procedure. When only a P-value is available, an approximate empirical Bayes factor is 10p. I propose interpreting the strength of Bayes factors on a logarithmic scale with base 3.73, reflecting the sharpest distinction between weaker and stronger belief. This provides an objective framework for interpreting statistical evidence, and realises a Bayesian/frequentist compromise.

A Method for Estimating the State of Charge and Identifying the Type of a Lithium-Ion Cell Based on the Transfer Function of the Cell

This paper proposes a new method for assessing the state of charge (SoC) and identifying the types of different lithium-ion cells used in the battery systems of light electric vehicles. A particular challenge in the development of this method was the SoC estimation time, as the method is intended for implementation in the control system of a bicycle charging station, where the state of charge must be determined immediately after the bicycle is plugged in in order to start the charging process as quickly as possible according to the appropriate charging algorithm. The method is based on the identification of the transfer function, i.e., the dynamic response of the battery voltage to the current pulse. In the learning phase of this method, a database of reference transfer functions and corresponding SoCs for a specific type of battery cell is created. The transfer functions are described by coefficients determined through the optimization procedure. The algorithm for estimating the unknown battery cell SoCs is based on the comparison of the measured voltage response with the responses of the reference transfer functions from the database created during the learning process to the same current signal. The comparison is made by calculating the integral of the square error (ISE) between the response of the specific reference transfer function and the measured voltage response of the battery cell. Each transfer function corresponds to a specific SoC and cell type. The specific SoC of the unknown battery is determined by quadratic interpolation of the SoC near the reference point with the smallest ISE for each battery type. The cell type detection algorithm is based on the fact that the integral squared error criterion near the actual SoC for the actual cell type changes less than the squared error criterion for any other battery cell type with the same SoC. An algorithm for estimating the SoC and cell type is described and tested on several different cell types. The relative error between the estimated SoC and the actual SoC was used as a measure of the accuracy of the algorithm, where the actual SoC was calculated using the Coulomb counting method.

Implementasi Pemahaman Radiografer terhadap Exposure Index dalam Optimasi Prosedur Pemeriksaan Radiografi Computed Radiography

Background: Increasing the exposure factor is often carried out by radiographers to reduce noise and maintain image quality in computer radiography (CR). Therefore, there is a need for awareness and understanding of the exposure index (EI) to implement optimization procedures in radiographic examinations. Methods: The research method is descriptive and analytical by providing questionnaires to radiographers in PA chest radiographic examinations. This research was carried out at hospitals A, B, and C in the Jakarta-Bogor area with the research carried out between August – October 2023. Statistical analysis used SPSS 27 with data coding previously. After that, the data is processed with descriptive statistics to determine the frequency, distribution, percentage, average, and standard deviation. Then, the test chi-square to determine the relationship between gender, age, education, and work experience in understanding CR optimization procedures. Next, to find out the relationship between age and weight and EI, a test was carried out by Pearson test to determine the level of relationship between the two variablesResults: was found that the level of awareness and understanding of radiographers regarding optimization procedures using EI is still low. Test results chi-square age-understanding 0.309, gender-understanding 0.772, education-understanding 0.036, experience-understanding 0.201. next are the test results Pearson for Hospital-A: age-EI 0.09, weight-EI -0.49, Hospital-B: age-EI -0.21, weight-EI 0.06, Hospital-C: age-EI -0.37, weight-EI 0.56. Conclusions: implementing EI in the optimization procedure, it needs to be confirmed by measuring the patient's radiation dose so that there are no errors in implementing the optimization.

Optimal estimation of reliability parameter for inverse Pareto distribution with application to insurance data

PurposeThe purpose of this paper is to develop optimal estimation procedures for the stress-strength reliability (SSR) parameter R = P(X > Y) of an inverse Pareto distribution (IPD).Design/methodology/approachWe estimate the SSR parameter R = P(X > Y) of the IPD under the minimum risk and bounded risk point estimation problems, where X and Y are strength and stress variables, respectively. The total loss function considered is a combination of estimation error (squared error) and cost, utilizing which we minimize the associated risk in order to estimate the reliability parameter. As no fixed-sample technique can be used to solve the proposed point estimation problems, we propose some “cost and time efficient” adaptive sampling techniques (two-stage and purely sequential sampling methods) to tackle them.FindingsWe state important results based on the proposed sampling methodologies. These include estimations of the expected sample size, standard deviation (SD) and mean square error (MSE) of the terminal estimator of reliability parameters. The theoretical values of reliability parameters and the associated sample size and risk functions are well supported by exhaustive simulation analyses. The applicability of our suggested methodology is further corroborated by a real dataset based on insurance claims.Originality/valueThis study will be useful for scenarios where various logistical concerns are involved in the reliability analysis. The methodologies proposed in this study can reduce the number of sampling operations substantially and save time and cost to a great extent.

A computable quantification of multi-mode Gaussian coherence and complete monogamy relation

We propose a quantification CνGn of n-mode Gaussian coherence. The value of CνGn only depends on the covariance matrices and displacement vectors of continuous-variable states without any optimization procedures, and thus is easily calculated. For n=1, CνG1 is a proper Gaussian coherence measure of single-mode CV system. For n≥2, CνGn is invariant under any permutation of submodes, nonincreasing under any n-mode local incoherent Gaussian channels, vanishes at incoherent Gaussian states, and, satisfies the unification condition and the hierarchy condition that a multi-partite quantum correlation measure should obey. Thus CνGn is a multi-partite Gaussian correlation measure, which reveals, though the quantum coherence lives in single-partite systems, that the multi-mode coherence for continuous-variable systems can be regarded as a multipartite Gaussian correlation between modes, and such multi-partite Gaussian correlation is also a quantum resource. Moreover, we show that CνGn is completely monogamous as a multipartite Gaussian correlation measure. This means that the n-mode Gaussian coherence subjects to the principles of resource allocation. In addition, CνGn is an upper bound of the geometric-based single-mode Gaussian coherence measure CBu by the Bures distance at pure Gaussian states of mode ≤2 and can be used to detect coherence in any n-mode Gaussian states more efficiently.

Operational modal analysis, seismic vulnerability assessment and retrofit of a degraded RC bell tower

This paper presents damage assessment through Operational Modal Analysis (OMA) and Finite Element (FE) model updating of the bell tower of the church of Castro in Bergamo, Italy. The tower is a 39 m high reinforced concrete structure with hollow cross-section and double-curved shape. The research was dictated by the need to identify the actual damage state of the structure, which was found through visual inspections. Piezoelectric accelerometers were used to record the ambient vibrations in subsequent test setups, using the roving technique for system identification. A detailed FE model was created with shell elements and calibrated to match the system identification results. A simplified beam model was then developed based on the modal analysis results of the detailed model. A sensitivity analysis was performed to identify the most influential model parameters on the modal characteristics of the system. Subsequently, the optimal values of these parameters were determined by an optimisation procedure carried out using a typical global optimization algorithm. The updating results allowed assessment of the actual condition of the bell tower and its seismic vulnerability. Finally, a seismic strengthening solution was recommended.

Gold-Deposited Graphene Nanosheets for Self-Cleaning Graphene Surface-Enhanced Raman Spectroscopy with Superior Charge-Transfer Contribution.

The interaction of graphene with metals initiates charge-transfer interaction-induced chemical enhancements, which critically depend on the doping effect from deposited metallic configurations. In this paper, we have explored the gold nanoparticle-decorated monolayer graphene nanosheets for the large graphene-induced Raman enhancement of adsorbed analytes, indicating the surface-enhanced Raman spectroscopy (SERS) capabilities of metal-doped graphene (G-SERS). Here, the systematically sputtered Au thickness optimization procedure revealed noticeable modifications in the graphene Raman spectra and photoluminescence (PL) background quenching, which indicated favorable charge transfer through n-type doping of chemical vapor deposition-grown graphene nanosheets. The highly consistent, individually distributed morphology of the gold nanoislands over graphene nanosheets depicted a reproducibly uniform G-SERS signal with excellent relative standard deviation values (<5%), resulting in the strongest Raman intensity enhancement factors of ∼108 (MB) (methylene blue) and 107 (DPA) (2,6-pyridinedicarboxylic acid) composed of the weakest PL background. The combined charge-transfer-induced chemical enhancement and electromagnetic enhancement from individual Au nanoislands result in a lowering of detectability down to 10-16 M (MB) and 10-11 M (DPA) concentrations with efficient time-dependent signal stability. Additionally, the GAu demonstrated its effective (∼94.4%) photocatalytic degradation capabilities by decomposing MB dye molecules from a concentration of 1 μM to 2.52 fM within 60 min. Therefore, the prominent charge-transfer contribution through controlled Au decoration over graphene nanosheets provides a potential strategy for fabricating superior SERS sensors and photocatalysts exhibiting adequate signal consistency, stability, and photodegradation efficiency through overcoming the limitations of the traditional sensing platforms.

The performance of the one-sided truncated exponentially weighted moving average X ¯ control chart in the presence of measurement errors

When the direction of a potential mean shift can be anticipated, the one-sided exponentially weighted moving average (EWMA) X ¯ control chart using the truncation method (namely, the one-sided TEWMA X ¯ chart) is more efficient than those conventional one- and two-sided EWMA X ¯ schemes for process monitoring. Although attractive, there are no studies on designing the one-sided TEWMA X ¯ chart by taking measurement errors into account. In this context, we investigate the effect of measurement errors on the performance of the one-sided TEWMA X ¯ chart based on the linear covariate error model. Additionally, a Markov chain model is established to evaluate the run length properties of the scheme in the presence of measurement errors. Then, an optimal design procedure is developed for searching the optimal design parameters of the scheme. Based on these mentioned studies, several tables and figures are presented to evaluate the detecting performance of the scheme under different parameters of the linear covariate error model, and then a conventional one-sided EWMA X ¯ chart with reflecting boundary is introduced to further study the effect of the presence and absence of measurement errors on control chart comparison studies. Simulation results show that although the detecting performance of the proposed scheme is significantly affected by measurement errors, its performance is still superior to the classic competing chart under the same comparison conditions. Finally, an illustrative example is given to show the implementation of the recommended scheme.

Performance‐based optimum seismic design of self‐centering steel moment frames with SMA‐based connections

AbstractShape memory alloys (SMAs) have found several applications in earthquake‐resilient structures. However, because of high material costs, their implementation on industry projects is still limited. Developing design approaches that minimize the use of expensive SMAs is critical to facilitating their widespread adoption in real structures. This paper proposes a performance‐based seismic design optimization procedure for self‐centering steel moment‐resisting frames (SC‐MRFs) with SMA‐bolted endplate connections. The topology optimization uses a metaheuristic algorithm to minimize the frame's total cost, including the initial construction and expected repair costs. The design variables are the steel beam and column sections, SMA connection properties, and the topology of the SMA connections. Different constraints are considered, such as the constructability of the chosen steel sections, member strengths, performance‐based design, Park‐Ang damage index, and strong‐column weak‐beam requirements. Furthermore, the seismic safety of optimal designs is assessed by calculating adjusted collapse margin ratios according to FEMA‐P695. An illustrative optimization study using three‐ and nine‐story SC‐MRFs is presented. The optimal SC‐MRFs are then assessed in terms of cost and seismic performance. The results confirm the effectiveness of the proposed optimum design, which minimizes the use of SMAs while ensuring improved seismic performance. The case studies show that the optimal placement of SMA connections can reduce the total cost by up to 71% and 61% for the three‐ and nine‐story SC‐MRFs, respectively, compared to nonoptimal frames. Moreover, the optimal SC‐MRFs exhibit more uniform drift distributions, lower residual story drifts by up to 96%, and increase collapse capacity by up to 102%.

Physics-infused deep neural network for solution of non-associative Drucker–Prager elastoplastic constitutive model

In the present work, a physics-informed deep learning-based constitutive modeling approach has been introduced, for the first time, to solve non-associative Drucker–Prager elastoplastic solid governed by a linear isotropic hardening rule. A purely data-driven surrogate modeling approach for representing complex and highly non-linear elastoplastic constitutive response prevents accurate predictions due to the absence of prior physical information. To mitigate this, we design an efficient physics-constrained training approach leveraging prior physics-driven optimization procedures. It has been achieved by formulating a highly physics-augmented multi-objective loss function that includes elastoplastic constitutive relations, Drucker–Prager yield criterion, non-associative flow rule, Kuhn–Tucker consistency conditions, and various boundary conditions. Utilizing multiple densely connected independent feed-forward deep neural networks fed with high-fidelity numerical solutions in a data-driven loss function, the model obtains the accurate elastoplastic solution by minimizing the proposed loss function. The strength and robustness of the approach have been demonstrated by accurately solving the benchmark problem where a plastically deformed isotropic shallow stratum has been subjected to compressive pressure under plane strain Drucker–Prager yield condition. To optimize the performance and trainability of the model, extensive experiments on network architecture and various degrees of data-driven estimate shed light on significant improvement in terms of the accuracy of the elastoplastic solution, particularly, that exhibits sharp, or very localized features. Moreover, we propose a transfer learning-based PINNs modeling approach that elucidates the possibility of predicting solutions for different sets of applied stress and material parameters. Requiring significantly less training data, the framework can simultaneously enhance the accuracy of the solution and adaptability of training by demonstrating rapid convergence in critical loss components. The current study highlights a systematic development of a novel physics-informed deep learning approach which is quite generic in nature, yet robust and highly physics-augmented for transferability of known knowledge for vastly accelerated convergence with improved accuracy of predicting an accurate description of non-associative elastoplastic solution in the regime of continuum mechanics.

A novel linear and planar multiband fractal‐shaped antenna arrays with low sidelobes

SummaryFractal antenna arrays are usually used to tune multiband frequencies. However, these types of iteratively constructed antennas are associated with undesirable high sidelobe levels and low directivities. In this paper, an optimization procedure based on the genetic algorithm is used to find the optimal weights of the array elements such that the corresponding array patterns have low sidelobe levels and good directivity. Moreover, the fractal nature in the proposed arrays is maintained regardless of the optimized weights. Thus, the proposed fractal‐shaped array maintains its capability in performing multiband frequency operation. These good radiation features make the proposed fractal‐shaped array more appropriate for the current and future wireless communication applications. Simulation results confirm the superiority of the presented linear and planar fractal‐shaped array structures with compared to the conventional fractal cantor linear array and the standard Sierpinski carpet planar array. For the proposed fractal cantor linear array, the sidelobe level has been reduced to more than −20 dB at different operating frequencies, and the directivity has been improved by more than 8 dB, while the modified Sierpinski carpet planar array has achieved −30 dB depressions in the sidelobe level and 6 dB improvement in the directivity.

Asymptotically optimal procedures for sequential joint detection and estimation

We investigate the problem of jointly testing multiple hypotheses and estimating a random parameter of the underlying distribution in a sequential setup. The aim is to jointly infer the true hypothesis and the true parameter while using on average as few samples as possible and keeping the detection and estimation errors below predefined levels. Based on mild assumptions on the underlying model, we propose an asymptotically optimal procedure, i.e., a procedure that becomes optimal when the tolerated detection and estimation error levels tend to zero. The implementation of the resulting asymptotically optimal stopping rule is computationally cheap and, hence, applicable for high-dimensional data. We further propose a projected quasi-Newton method to optimally choose the coefficients that parameterize the instantaneous cost function such that the constraints are fulfilled with equality. The proposed theory is validated by numerical examples.

Some studies on optimization of process parameters for wire ARC additive manufacturing of Hastelloy C276 using GRA-GWO hybrid techniques

Wire arc additive manufacturing (WAAM) has become an inevitable manufacturing technology in the present decade. This article discusses optimizing process variables for fabricating Hastelloy C276 alloy via WAAM. The controlling parameters are gas flow rate (GFR), deposition speed, and welding current, while the depth of penetration, top reinforcement, and bead width are the output parameters considered for optimization. The magnitude of interaction effects between control parameters is equal to that of individual impact on the weld bead characteristics evident from analysis of variance results. Grey relational analysis revealed the optimal parameters (trial 1 with I = 120 A, S = 200 mm/min, GFR = 15 L/min) chosen through its robust optimization procedure. S/N ratio analysis revealed the most influencing parameters at the factor level (current level 1, speed level 1). The Grey Wolf optimizer (GWO) algorithm was used as a precision and confirmation experiment. The GWO optimization results further confirmed that the GFR of 15 L/min, speed of 200 mm/min, and current of 120 A were the optimized parameters used in this design space. Further, normal probability plots with minimal error of around 1% reinforce that the parameters chosen in this design space are well within the optimal limits. The surface plots reported the relationship and variation between control and resulting factors.

Adaptive Pair-Weight Vector Projection Classifier for Semi-Supervised Classification

Manifold regularization is a popular technology based on graph theory and has been widely applied to nonparallel hyperplane classifiers for semi-supervised learning. However, these classifiers adopt the fixed similarity matrix that may be sensitive to noises and outliers. Thus, we propose a novel nonparallel hyperplane classifier, named the adaptive pair-weight vector projection (APVP) classifier, for semi-supervised classification tasks. The proposed APVP unifies projection learning and graph construction into a general framework. For projection learning, APVP achieves two projection vectors by solving the pair-wise optimization problems in which we need to simultaneously maximize the between-class scatter of data and minimize both the within-class scatter of data and the adaptive manifold regularization. For graph construction, the adaptive similarity matrix is constructed during the procedure of optimization. Thus, APVP can weaken the effect of noises and outliers by the adaptive similarity matrix and strengthen the discriminant capability by the Fisher criterion. To classify unseen data, we also design a new classification rule. Experimental results show that APVP has good performance in semi-supervised scene.