Optimal Vector Research Articles

Pilot-scale process development for recombinant adeno-associated virus (rAAV) production based on high-density Sf9 cell culture

BackgroundIn recent years, gene therapy drugs have been widely marketed, and their effectiveness and potential have been confirmed. Thus, increasing their production on an industrial scale is critical. Recombinant adeno-associated viruses (rAAVs) are optimal vectors for gene therapy applications, and the baculovirus expression vector system (BEVS), which is based on Sf9 cell culture, is a common tool for rAAV production.MethodsIn this work, an Sf9 cell fed-batch process was developed using shake flasks. In the laboratory-scale bioreactor, four processes were selected as the key factors when carrying out the orthogonal experiment. On the basis of the equal P/V principle and considering the problem posed by air bubbles, a pilot-scale level bioreactor process was established.ResultsHere, we describe a method in which a BEVS was used to produce rAAV vectors, with the cell density increasing to 22.8 × 106 cells/mL and the rAAV titre increasing to 20 × 1011 VG/mL upon adding feed material. By resolving the problems associated with high-density cell culture and air bubbles, this process was successfully scaled to a 50 L pilot-scale level.ConclusionsThis successful experiment not only provides a technological basis for further scale-up but also guarantees product capacity. We hope that this development process can provide reference data for studying cell culture-based drug production.

Intelligent Inversion Analysis of Surrounding Rock Parameters and Deformation Characteristics of a Water Diversion Surge Shaft

The water diversion surge shaft is vital for a hydropower station. However, the complex geological properties of the surrounding rock make it challenging to obtain its mechanical parameters. A method combining particle swarm optimization (PSO) and support vector machine (SVM) algorithms is proposed for estimating these parameters. According to the engineering geological background and support scheme, a three-dimensional model of the water diversion surge shaft is established by FLAC3D. An orthogonal test is designed to verify the accuracy of the numerical model. Then, the surrounding rock mechanical parameter database is established. The PSO-SVM intelligent inversion algorithm is used to invert the optimal values of the mechanical parameters of the surrounding rock. The support for excavating the next layer depends on the mechanical parameters of the current rock layer. An optimized design scheme is then compared and analyzed with the original support scheme by considering deformation and plastic characteristics. The research results demonstrate that the PSO-SVM intelligent inversion algorithm can effectively improve the accuracy and efficiency of the inversion of rock mechanical parameters. Under the influence of excavation, the surrounding rock in the plastic zone mainly fails in shear, with maximum deformation occurring in the middle and lower parts of the excavation area. The maximum deformation of the surrounding rock under support with long anchor cables is 0.6 cm less than that of support without long anchor cables and 4.07 cm less than that of support without an anchor. In the direction of the maximum and minimum principal stress, the maximum depth of the plastic zone under the support with long anchor cables is 1.3 m to 2.6 m less than that of the support without long anchor cables and the support without an anchor. Compared with the support without long anchor cables and support without an anchor, the support with long anchor cables can effectively control the deformation of the surrounding rock and limit the development of the plastic zone.

An interval-valued carbon price prediction model based on improved multi-scale feature selection and optimal multi-kernel support vector regression

An interval-valued carbon price prediction model based on improved multi-scale feature selection and optimal multi-kernel support vector regression

Assessing Willingness to Accept a Compensation for Evicted Land in Peri-urban Areas: A Contingent Valuation Method

Urban expansion in developing countries like Ethiopia is causing political turmoil in peri-urban areas due to inadequate compensation for evicted farmers because of the absence of a land market, which is prohibited by Ethiopia's land policy. This paper applies the contingent valuation method to measure the compensation value considered acceptable to those who are evicted from their farm land. The west Gojjam and Awi zones of Amhara region, Ethiopia are chosen for the case study based on 393 completed questionnaires. It has been demonstrated that a willingness to accept compensation is essential for maintaining sustainable livelihoods. The study applied single-bounded contingent valuation and double-bounded contingent valuation methods to estimate the mean willingness to accept compensation value. Based on the finding, double-bounded contingent valuation could be more optimal for the welfare of the evictees in detecting and correcting respondent’s bias due to its narrow confidence interval. Pilot survey was employed to ensure reliability and validity of the methods employed. Therefore, the optimal bid vector for multiplying the average land output preceding three years of eviction is 29.27. Additionally, variables such as initial bid, age, education, and total annual income significantly affect the willingness to accept compensation. The study recommends that federal and local governments should provide adequate compensation offers that prioritize the well-being of peri-urban communities during urban expansion programs, as farmers are losing their most valuable asset. This is crucial for sustainable living and poverty reduction among those who have been evicted.

Enhanced CAD Detection Using Novel Multi-Modal Learning: Integration of ECG, PCG, and Coupling Signals

Early and highly precise detection is essential for delaying the progression of coronary artery disease (CAD). Previous methods primarily based on single-modal data inherently lack sufficient information that compromises detection precision. This paper proposes a novel multi-modal learning method aimed to enhance CAD detection by integrating ECG, PCG, and coupling signals. A novel coupling signal is initially generated by operating the deconvolution of ECG and PCG. Then, various entropy features are extracted from ECG, PCG, and its coupling signals, as well as recurrence deep features also encoded by integrating recurrence plots and a parallel-input 2-D CNN. After feature reduction and selection, final classification is performed by combining optimal multi-modal features and support vector machine. This method was validated on simultaneously recorded standard lead-II ECG and PCG signals from 199 subjects. The experimental results demonstrate that the proposed multi-modal method by integrating all signals achieved a notable enhancement in detection performance with best accuracy of 95.96%, notably outperforming results of single-modal and joint analysis with accuracies of 80.41%, 86.51%, 91.44%, and 90.42% using ECG, PCG, coupling signal, and joint ECG and PCG, respectively. This indicates that our multi-modal method provides more sufficient information for CAD detection, with the coupling information playing an important role in classification.

A Hybrid Multiobjective Control Strategy Based on Combination of Modulated Model Predictive Control and Phase‐Shifted Modulation for a Unidirectional Five‐Level Rectifier

ABSTRACTThe unidirectional multilevel rectifier has immense potential in high‐power medium‐voltage industrial applications. This paper proposes a multiobjective control method based on the combination of optimal‐vector‐pair modulated model predictive control (OVP‐M2PC) and a phase‐shifted modulation strategy for a unidirectional five‐level rectifier. This strategy takes both of the inductor current tracking and capacitor voltage balancing as the control objectives. First, an optimal vector pair is determined based on the AC‐side current variation rate, and then, through combining the calculated action duration of OVP with carrier phase‐shifted modulation, inductor current tracking is achieved. Second, capacitor voltage balancing control is carried out by compensating for the duty cycles which also decouples the input current tracking and output capacitor voltage balance control. Compared with traditional FCS‐MPC, under the proposed control strategy, complex cost function calculation and weighting factor tuning are not required, which much reduced computational burden. Multiobjective control is achieved with better performance in both steady‐state and dynamic conditions. The superiority of the proposed strategy over traditional FCS‐MPC are validated through simulations and hardware experiments.

Arch dam point cloud segmentation based on deep feature learning and normal vector data optimization

Separating the dam body, spillway, and other structures from the point cloud in the dam area is an important step in dam deformation monitoring. Manual segmentation is time consuming and inaccurate. This study proposes a point cloud segmentation neural network model based on normal vector optimization suitable for dam environment: (1) This model utilizes the voxel uniform sampling method of equal length cubes to solve the problem of uneven point cloud density caused by wide range and long-distance measurement during point cloud measurement in dam areas. (2) Designed block input and combined output modules in the model, achieving efficient input of large volume point cloud and eliminating the impact of interpolation points offset during seq2seq model decoding process. (3) In response to the diverse characteristics of point cloud normal vectors presented by vegetation, rock mass, and complex dam structures in the dam area, this paper proposes an adaptive radius plane fitting vector estimation method based on eigenvalue method to improve the accuracy of segmentation. The experiment on the prototype arch dam shows that the proposed normal estimation method improves the classification accuracy of PointNet + + from 96.26 to 98.27%. Compared with the other three normal estimation methods (2-jets, Hough CNN, iterative PCA), the overall accuracy is improved by 0.82%, 1.22%, and 0.22%, and the joint average intersection is improved by 0.0293, 0.0325, and 0.0104. The prototype arch dam experiment shows that our proposed method has a segmentation accuracy of 98.27%. Compared with 2-jets, Hough CNN, and iterative PCA, the overall accuracy has been improved by 0.82%, 1.22%, and 0.22%. This study provides a high-precision segmentation scheme for applications such as deformation detection of dam components based on point clouds.

Existence and density theorems of Henig global proper efficient points

In this work, we provide some novel results that establish both the existence of Henig global proper efficient points and their density in the efficient set for vector optimization problems in arbitrary normed spaces. Our results do not require the assumption of convexity, and in certain cases, can be applied to unbounded sets. However, it is important to note that a weak compactness condition on the set (or on a section of it) and a separation property between the order cone and its conical neighbourhoods remains necessary. The weak compactness condition ensures that certain convergence properties hold. The separation property enables the interpolation of a family of Bishop-Phelps cones between the order cone and each of its conic neighbourhoods. This interpolation, combined with the proper handling of two distinct types of conic neighbourhoods, plays a crucial role in the proofs of our results, which include as a particular case other results that have already been established under more restrictive conditions.

Optimization of a novel expression system for recombinant protein production in CHO cells

Chinese hamster ovary (CHO) cells are common mammalian cell lines for expressing recombinant proteins, yet the expression level of recombinant proteins is still hindered. Vector optimization and cell line modification are the key factors to improve the expression of recombinant proteins. In this study, the vector was optimized by adding the regulatory elements Kozak and Leader to the upstream of target gene to detect the transient and stable expression of recombinant proteins. Results indicated that the expression level of target proteins with the addition of regulatory elements was significantly increased compared with the control group. In addition, the inhibition of apoptotic pathway has great potential to increase recombinant protein production, and Apaf1 protein dependent on the mitochondrial apoptosis pathway plays an important role in this respect. The knockout of apoptotic gene Apaf1 in CHO cells can also increase recombinant protein production. Therefore, the vector was optimized by adding regulatory elements, and the cell line was modified by using CRISPR/Cas9 technology to establish a novel CHO cell expression system, which remarkably improved the expression level of recombinant proteins and laid the foundation for the large-scale production of recombinant proteins.

Research on an improved deadbeat model predictive current control of double three-phase open-winding permanent magnet motor

Model predictive current control has high control accuracy and good control performance, and has been widely applied in the control strategy of dual three-phase permanent magnet synchronous motors. This article proposes an improved deadbeat model predictive current control (DMPCC) method by analyzing the mathematical model of a dual three-phase open-winding permanent magnet synchronous motor. One inverter predicts and calculates the optimal voltage vector through the deadbeat model, while the other inverter calculates the optimal voltage vector through the value function rolling optimization. This method reduces the computational workload of the controller, avoids the delay problem of the traditional model predictive current control (MPCC) method, improves the system's response speed, effectively suppresses the current in the harmonic sub-plane, and reduces torque ripple. The simulation results have verified the accuracy and speed of the above control method.

Unboundedness of the images of set-valued mappings having closed graphs: application to vector optimization

AbstractIn this paper, we propose criteria for unboundedness of the images of set-valued mappings having closed graphs in Euclidean spaces. We focus on mappings whose domains are non-closed or whose values are connected. These criteria allow us to see structural properties of solutions in vector optimization, where solution sets can be considered as the images of solution mappings associated to specific scalarization methods. In particular, we prove that if the domain of a certain solution mapping is non-closed, then the weak Pareto solution set is unbounded. Furthermore, for a quasi-convex problem, we demonstrate two criteria to ensure that if the weak Pareto solution set is disconnected then each connected component is unbounded.

Dynamic overmodulation strategy based on torque and flux optimal tracking for DTC‐SVM of surface‐mounted PMSM drives

AbstractDirect torque control with space vector modulation has been increasingly attracting emphasis for permanent‐magnet synchronous machine control, benefiting from a high dynamic response and low torque ripple. However, the rapid tracking performance of torque and stator flux linkage is gradually deteriorating as the machine enters the overmodulation region because of the output‐voltage limitation of the inverter. Therefore, to enhance the system tracking performance in the overmodulation region, an innovative overmodulation method based on torque and flux optimal tracking is proposed. In contrast to the conventional overmodulation method where only one of the torque and stator flux linkage tracking is considered, the proposed strategy first constructs the weightless cost function to achieve a trade‐off control between the torque and stator flux linkage. Then, by using an equivalent geometric path to intuitively express the weightless cost function, the minimum cost function can be easily solved and the optimal output voltage vector can be determined correspondingly, to achieve the fast‐tracking of both the torque and stator flux linkage. Additionally, the voltage utilization of different overmodulation schemes is also analysed. Finally, the experimental results demonstrate the efficiency and practicality of the proposed strategy.

Optimality results for nondifferentiable multiobjective fractional programming problems under E-B-invexity

In this article, we study nonconvex multiobjective fractional programming problems involving E-differentiable functions (MFPE). We establish the E-Karush–Kuhn–Tucker (E-KKT) sufficient E-optimality conditions for nonsmooth vector optimization problems under the assumption of E-B-invexity. To demonstrate the validity of the derived results, we provide an example where the involved functions exhibit E-B-invexity.

Vector optimization problems in near vector space

ABSTRACT The conventional vector optimization problems intend to optimize the objective functions, which are defined on a vector space into another vector space. In this paper, we are going to study the vector optimization problems by minimizing the objective functions, which are defined on a vector space into another so-called near vector space. The concept of near vector space is axiomatically defined, and it does not necessarily contain the additive inverse element. A so-called pointed-like convex cone is introduced to propose the concept of minimizer of this new type vector optimization problem. Its corresponding scalar optimization problem is also formulated. Finally, we show that the optimal solution of the corresponding scalar optimization problem is also a minimizer of the original vector optimization problem based on the near vector space.

An SCA-based classifier for motor imagery EEG classification

Efficient and accurate multi-class classification of electroencephalogram (EEG) signals poses a significant challenge in the development of motor imagery-based brain–computer interface (MI-BCI). Drawing inspiration from the sine cosine algorithm (SCA), a widely employed swarm intelligence algorithm for optimization problems, we proposed a novel population-based classification algorithm for EEG signals in this article. To fully leverage the characteristics contained in EEG signals, multi-scale sub-signals were constructed in terms of temporal windows and spectral bands simultaneously, and the common spatial pattern (CSP) features were extracted from each sub-signal. Subsequently, we integrated the multi-center optimal vectors mechanism into the classical SCA, resulting in the development of a multi-center SCA (MCSCA) classifier. During the classification stage, the label was assigned to the test trials by evaluating the Euclidean distance between their feature vectors and each optimal vector in MCSCA. Additionally, the weights of feature vectors were exploited to select the sub-signal of specific temporal windows and spectral bands for feature reduction, thereby declining computational effort and eliminating data redundancy. To validate the performance of the MCSCA classifier, we conducted four-class classification experiments using the BCI Competition IV dataset 2a, achieving an average classification accuracy of 71.89%. The experimental results show that the proposed algorithm offers a novel and effective approach for EEG classification.

Robust model averaging approach by Mallows-type criterion.

Model averaging is an important tool for treating uncertainty from model selection process and fusing information from different models, and has been widely used in various fields. However, the most existing model averaging criteria are proposed based on the methods of ordinary least squares or maximum likelihood, which possess high sensitivity to outliers or violation of certain model assumption. For the mean regression, no optimal robust methods are developed. To fill this gap, in our paper, we propose an outlier-robust model averaging approach by Mallows-type criterion. The idea is that we first construct a generalized M (GM) estimator for each candidate model, and then build robust weighting schemes by the asymptotic expansion of the final prediction error based on the GM-type loss function. So, we can still achieve a trustworthy result even if the dataset is contaminated by outliers in response and/or covariates. Asymptotic properties of the proposed robust model averaging estimators are established under some regularity conditions. The consistency of our weight estimators tending to the theoretically optimal weight vectors is also derived. We prove that our model averaging estimator is robust in terms of having bounded influence function. Further, we define the empirical prediction influence function to evaluate the quantitative robustness of the model averaging estimator. A simulation study and a real data analysis are conducted to demonstrate the finite sample performance of our estimators and compare them with other commonly used model selection and averaging methods.

Combinatorial leaky probiotic for anticancer immunopotentiation and tumor eradication.

Combination therapies present a compelling therapeutic regimen against the immunosuppressive and heterogeneous microenvironment of solid tumors. However, incorporating separate therapeutic modalities in regimen designs can be encumbered by complex logistical, manufacturing, and pharmacokinetic considerations. Herein, we demonstrate a single-vector combinational anticancer therapy using an lpp gene knockout leaky probiotic for simultaneous secretion of immunotherapeutic and oncolytic effector molecules. Through fusion protein design and vector optimization, a Nissle1917 (EcN) bacteria vector is engineered to secrete Neoleukin-2/15 (Neo-2/15) cytokine-functionalized anti-PDL1 nanobody (aPDL1-Neo2/15) and anti-mesothelin-functionalized hemolysin E (HlyE-aMSLN). The multifunctional leaky probiotic enables synchronous immune activation and tumor-targeted cytolytic activity for effective tumor suppression, elevation of tumor immune cell infiltration, and establishment of anticancer immunological memory. lpp gene knockout is further shown to improve probiotic tolerability and intravenous applicability, offering a therapeutically viable approach for combination regimen development.

A new discrepancy for sample generation in stochastic response analyses of aerospace problems with uncertain parameters

Good distribution of samples and weights can improve the computational accuracy and efficiency in the stochastic response analyses of aerospace problems with uncertain parameters. This work proposes a new generalized L2 discrepancy based on a general point (GL2D-GP) for generating samples and their corresponding weights. The proposed GL2D-GP is an extension of the existing discrepancy by introducing the non-same weights and a smaller box to measure probability errors. Minimizing the GL2D-GP can yield a weight optimization formula that generates a set of optimal non-identical weights for a given sample set. Through minimizing the GL2D-GP assigned to the set of optimal non-same weights, a new sample and weight generation method is developed. In the proposed method, the samples can be easily generated in terms of the generalized Halton formula with a series of optimal permutation vectors which are found by the intelligent evolutionary algorithm. Once the sample set is obtained, the optimal weights can be generated in terms of the weight optimization formula. Five numerical examples are presented to verify the high accuracy, efficiency, and strong robustness of the proposed sample generation method based on GL2D-GP.

Intelligent Evaluation and Dynamic Prediction of Oyster Freshness with Electronic Nose Based on the Distribution of Volatile Compounds Using GC-MS Analysis.

The quality of oysters is reflected by volatile organic components. To rapidly assess the freshness level of oysters and elucidate the changes in flavor substances during storage, the volatile compounds of oysters stored at 4, 12, 20, and 28 °C over varying durations were analyzed using GC-MS and an electronic nose. Data from both GC-MS and electronic nose analyses revealed that alcohols, acids, and aldehydes are the primary contributors to the rancidity of oysters. Notably, the relative and absolute contents of Cis-2-(2-Pentenyl) furan and other heterocyclic compounds exhibited an upward trend. This observation suggests the potential for developing a simpler test for oyster freshness based on these compounds. Linear Discriminant Analysis (LDA) demonstrated superior performance compared to Principal Component Analysis (PCA) in differentiating oyster samples at various storage times. At 4 °C, the classification accuracy of the optimal support vector machine (SVM) and random forest (RF) models exceeded 90%. At 12 °C, 20 °C, and 28 °C, the classification accuracy of the best SVM and RF models surpassed 95%. Pearson correlation analysis of the concentrations of various volatile compounds and characteristic markers with the sensor response values indicated that the selected sensors were more aligned with the volatiles emitted by oysters. Consequently, the volatile compounds in oysters during storage can be predicted based on the response information from the sensors in the detection system. This study also demonstrates that the detection system serves as a viable alternative to GC-MS for evaluating oysters of varying freshness grades.

Second Order Duality Involving Second Order Cone Arcwise Connected Functions and Their Generalizations in Vector Optimization Problem over Cones

In this paper, we introduce second order cone arcwise connected function and its generalizations. Further we study the interrelations among these functions also. Mond Weir type second order dual is formulated and duality results are proved using these functions.