Regularization Method Research Articles

Therapeutic peptides identification via kernel risk sensitive loss-based k-nearest neighbor model and multi-Laplacian regularization.

Therapeutic peptides are therapeutic agents synthesized from natural amino acids, which can be used as carriers for precisely transporting drugs and can activate the immune system for preventing and treating various diseases. However, screening therapeutic peptides using biochemical assays is expensive, time-consuming, and limited by experimental conditions and biological samples, and there may be ethical considerations in the clinical stage. In contrast, screening therapeutic peptides using machine learning and computational methods is efficient, automated, and can accurately predict potential therapeutic peptides. In this study, a k-nearest neighbor model based on multi-Laplacian and kernel risk sensitive loss was proposed, which introduces a kernel risk loss function derived from the K-local hyperplane distance nearest neighbor model as well as combining the Laplacian regularization method to predict therapeutic peptides. The findings indicated that the suggested approach achieved satisfactory results and could effectively predict therapeutic peptide sequences.

Regularization method and a posteriori error estimates for the bilateral obstacle problem

In this paper, we present a relatively complete analysis of the regularization method for of the bilateral obstacle problem with a non-homogeneous condition on the boundary. We provide both the convergence result and a priori estimates. Then by the duality theory from the convex analysis, we determine the dual problem associated with the of the bilateral obstacle problem. Based on this dual problem, we provide a posteriori error estimates for the continuous and the discrete problems. The a posteriori error estimates are desired for the practical implementation of the regularized problem.

INVESTIGATION OF THE EFEECT OF TBLT-BASED DIGITAL STORYBOOK ON STUDENTS’ READING SKILL IN SMP NEGERI 6 SINGARAJA

The purpose of this study is to look at the major impact that TBLT-based digital storybooks have on the reading abilities of ninth-grade students at SMP Negeri 6 Singaraja. This study employed a quasi-experimental methodology and was quantitative in nature. Two classes were assigned to this study: a control class with 31 students and an experimental class with 33 students. The two classrooms received different treatments: the Experimental class used a digital storybook based on TBLT, while the Control class used the teacher's regular approach. The reading score was calculated using a reading test. The data was analyzed using the t-test formula. The outcome demonstrated that the TBLT-based digital storybook had a significant effect on the reading proficiency of the students. The Sig. (2-tailed) value of 0.036 demonstrated this. As a result, the tests' significance value was less than 0.05. This demonstrates that, as compared to students taught using the teacher's regular method, there is a significant influence on the reading skills of students who are taught using TBLT-based digital storybooks. Furthermore, 0.537 was the effect size value as determined by Cohen's d. This significant impact indicates that, in comparison to the teacher's typical approach, the use of TBLT-based digital storybooks can have a very tangible impact on students' reading skill development.

A kind regularization method with Poisson kernel to solve Cauchy problem of Schrödinger equation

In this work, a precise regularization technique based on the mollification method is proposed to solve the ill-posed Schr o ¨ dinger Cauchy problem with potential-free field. In the sense of Hadamard, this problem is severely ill-posed. Using the mollification regularization method based on Poisson kernel, some new error estimates are obtained under the priori and posteriori parameter selection rules. The regular approximate solutions are created, and the convergence evidences are provided. The good performance and high accuracy of this technique are demonstrated through various examples. The solution works well and is resistant to data disruption noise.

Stabilizing Dynamic Backscatter for Swift and Accurate Object Tracking

Accurate and high-speed object movement tracking systems often face significant challenges due to signal instability caused by the object’s movement and rotation. To address these issues, we present STABack, a novel object tracking system using backscatter tags and accelerometer sensors, designed for high-accuracy, high-speed movement tracking. We developed an amplitude stabilization algorithm which uses envelope detection to reduce the impact of high-frequency movement on the signal, and uses dynamic threshold output to lower the BER in backscatter demodulation. Our method reduces the BER by 0.3757 compared to the regular demodulation method, resulting in a final BER of 0.07. Our evaluation of the STABack prototype shows that it achieves a median distance measurement accuracy of 6.45 cm with a standard deviation of 6.95 cm, under the condition of a speed of 120 cm. The attitude angle estimation’s mean error is under 7 degrees. The system’s accuracy in detecting the target object’s trajectory is as high as 99%, and it can still decode with a bit error rate of no more than 0.034 at a speed of 166 cm/s. The power consumption of our system prototype is only 38.54 μW based on our experimental results. Overall, our results demonstrate that STABack can accurately estimate the movement and rotation of target objects in unstable backscatter channels.

Unsteady Two-Dimensional Drift of a Viscoelastic Fluid with Magnetic and Thermal Effects: A Perturbation Approach

This study explores the unsteady, two-dimensional flow of an electrically conductive, viscoelastic, and incompressible fluid past a uniformly moving, infinite, non-conducting vertical plate, in the presence of a uniform first-order chemical process involving mass and heat transfer. A uniform magnetic field is applied perpendicular to the flow, with the magnetic Reynolds number assumed small to neglect the induced magnetic field. The governing equations are solved using a regular perturbation method, yielding approximate solutions for concentration, velocity, temperature, and shear stress at the plate. Additionally, mass and heat transfer rates are determined, with the influence of viscoelasticity and other physical parameters emphasized. Where applicable, results are graphically represented.

Time‐periodic solutions to heated ferrofluid flow models

AbstractIn this work, we prove the existence of time‐periodic solutions to a model describing a ferrofluid flow heated from below. Navier–Stokes equations satisfied by the fluid velocity are coupled to the temperature equation and the magnetostatic equation satisfied by the magnetic potential. The magnetization is assumed to be parallel to the magnetic field and is given by a nonlinear magnetization law generalizing the Langevin law. The proof is based on a semi‐Galerkin approximation and regularization methods together with the fixed point method.

Fading regularization method for an inverse boundary value problem associated with the biharmonic equation

In this paper, we propose a numerical algorithm that combines the fading regularization method with the method of fundamental solutions (MFS) to solve a Cauchy problem associated with the biharmonic equation. We introduce a new stopping criterion for the iterative process and compare its performance with previous criteria. Numerical simulations using MFS validate the accuracy of this stopping criterion for both compatible and noisy data and demonstrate the convergence, stability, and efficiency of the proposed algorithm, as well as its ability to deblur noisy data.

IDENTIFICATION OF AN ARBITRARY MOVING AXISYMMETRIC LOAD ACTING ON A CYLINDRICAL SHELL

Various types of external non-stationary loads can act on various structural elements and cylindrical shells of finite length in particular distributed and concentrated, stationary and moving load. When using various external load identification methods, the type of external load is usually known. In prac- tice this is not always the case. The purpose of the study is to develop a method for identifying an arbitrary axisymmetric load acting on an elastic cy- lindrical shell of finite length, which can be used to identify a moving load. To model the non-stationary load of a cylindrical shell, a system of differ- ential equations of the refined Timoshenko theory for medium thickness shells was used. The solution to this system of differential equations is ob- tained by expanding the unknown functions into Fourier series and applying the integral Laplace transform. The solution of the inverse problem was obtained using the theory of integral equations and the Tikhonov regularization method. As a result of the study, a solution of the inverse problem of solid mechanics to identify an arbitrary axisymmetric non-stationary load was obtained. A numerical experiment was carried out to use the developed method to identify a moving non-stationary load acting on a simply supported cylindrical shell of medium thickness. The simulation results indicate a fairly accurate identification of both the change in time and the distribution along the shell of a non-stationary axisymmetric moving load. A method has been developed for identifying an external non-stationary load randomly distributed along a cylindrical shell, which makes it possible to identify a moving load without preliminary information about the type of this load. The developed method allows us to reproduce moving non-stationary loads, which are often encountered in practice, and expand it to other types of structural elements.

Is Seeing Believing? A Practitioner's Perspective on High-Dimensional Statistical Inference in Cancer Genomics Studies.

Variable selection methods have been extensively developed for and applied to cancer genomics data to identify important omics features associated with complex disease traits, including cancer outcomes. However, the reliability and reproducibility of the findings are in question if valid inferential procedures are not available to quantify the uncertainty of the findings. In this article, we provide a gentle but systematic review of high-dimensional frequentist and Bayesian inferential tools under sparse models which can yield uncertainty quantification measures, including confidence (or Bayesian credible) intervals, p values and false discovery rates (FDR). Connections in high-dimensional inferences between the two realms have been fully exploited under the "unpenalized loss function + penalty term" formulation for regularization methods and the "likelihood function × shrinkage prior" framework for regularized Bayesian analysis. In particular, we advocate for robust Bayesian variable selection in cancer genomics studies due to its ability to accommodate disease heterogeneity in the form of heavy-tailed errors and structured sparsity while providing valid statistical inference. The numerical results show that robust Bayesian analysis incorporating exact sparsity has yielded not only superior estimation and identification results but also valid Bayesian credible intervals under nominal coverage probabilities compared with alternative methods, especially in the presence of heavy-tailed model errors and outliers.

Conditional stability and regularization method for inverse source for distributed order time-fractional diffusion equation

Conditional stability and regularization method for inverse source for distributed order time-fractional diffusion equation

A quasi-reversibility method for solving nonhomogeneous sideways heat equation

Abstract We consider solving a severely ill-posed problem for determining the surface temperature and heat flux distribution of the nonhomogeneous sideways heat equation by a quasi-reversibility regularization method. An analytical solution is deduced based on the Fourier transform, and then, the logarithmic-type error estimate for the regularized solution is achieved. Finally, three numerical examples, including smooth and non-smooth functions, validate the feasibility and effectiveness of the proposed approach.

DeepASD: a deep adversarial-regularized graph learning method for ASD diagnosis with multimodal data

Autism Spectrum Disorder (ASD) is a prevalent neurological condition with multiple co-occurring comorbidities that seriously affect mental health. Precisely diagnosis of ASD is crucial to intervention and rehabilitation. A single modality may not fully reflect the complex mechanisms underlying ASD, and combining multiple modalities enables a more comprehensive understanding. Here, we propose, DeepASD, an end-to-end trainable regularized graph learning method for ASD prediction, which incorporates heterogeneous multimodal data and latent inter-patient relationships to better understand the pathogenesis of ASD. DeepASD first learns cross-modal feature representations through a multimodal adversarial-regularized encoder, and then constructs adaptive patient similarity networks by leveraging the representations of each modality. DeepASD exploits inter-patient relationships to boost the ASD diagnosis that is implemented by a classifier compositing of graph neural networks. We apply DeepASD to the benchmarking Autism Brain Imaging Data Exchange (ABIDE) data with four modalities. Experimental results show that the proposed DeepASD outperforms eight state-of-the-art baselines on the benchmarking ABIDE data, showing an improvement of 13.25% in accuracy, 7.69% in AUC-ROC, and 17.10% in specificity. DeepASD holds promise for a more comprehensive insight of the complex mechanisms of ASD, leading to improved diagnosis performance.

A hybrid, nonlinear programming approach for optimizing passive shimming in MRI.

In magnetic resonance imaging (MRI), maintaining a highly uniform main magnetic field (B0) is essential for producing detailed images of human anatomy. Passive shimming (PS) is a technique used to enhance B0 uniformity by strategically arranging shimming iron pieces inside the magnet bore. Traditionally, PS optimization has been implemented using linear programming (LP), posing challenges in balancing field quality with the quantity of iron used for shimming. In this work, we aimed to improve the efficacy of passive shimming that has the advantages of balancing field quality, iron usage, and harmonics in an optimal manner and leads to a smoother field profile. This study introduces a hybrid algorithm that combines particle swarm optimization with sequential quadratic programming (PSO-SQP) to enhance shimming performance. Additionally, a regularization method is employed to reduce the iron pieces' weight effectively. The simulation study demonstrated that the magnetic field was improved from 462 to 3.6ppm, utilizing merely 1.2kg of iron in a 40cm diameter spherical volume (DSV) of a 7T MRI magnet. Compared to traditional optimization techniques, this method notably enhanced magnetic field uniformity by 96.7% and reduced the iron weight requirement by 81.8%. The results indicated that the proposed method is expected to be effective for passive shimming.

Generalized Nash Equilibrium Seeking for Noncooperative Game With Different Monotonicities by Adaptive Neurodynamic Algorithm.

This article proposes a novel adaptive neurodynamic algorithm (ANA) to seek generalized Nash equilibrium (GNE) of the noncooperative constrained game with different monotone conditions. In the ANA, the adaptive penalty term, which acts as trajectory-dependent penalty parameters, evolves based on the degree of constraints violation until the trajectory enters the action set of noncooperative game. It is shown that the trajectory of the ANA enters the action set in finite time benefited from the adaptive penalty term. Moreover, it is proven that the trajectory exponentially (or polynomially) converges to the unique GNE when the pseudo-gradient of cost function in noncooperative game satisfies strong (or "generalized" strong) monotonicity. To the best of our knowledge, this is the first time to study the polynomial convergence of GNE seeking algorithm. Furthermore, when the pseudo-gradient mentioned above satisfies monotonicity in general, based on Tikhonov regularization method, a new ANA for finding its ε -generalized Nash equilibrium ( ε -GNE) is proposed, and the related exponential convergence of the algorithm is established. Finally, the river basin pollution game and 5G base station location game are given as examples to showcase the algorithm's effectiveness.

Bifurcations and Homoclinic Orbits of a Model Consisting of Vegetation-Prey-Predator Populations.

This study provides a comprehensive analysis of the dynamics of a three-level vertical food chain model, specifically focusing on the interactions between vegetation, herbivores, and predators in a Snowshoe hare-Canadian lynx system. By simplifying the model through dimensional analysis, we determine conditions for equilibrium existence and identify various types of bifurcations, including Saddle-Node and Hopf bifurcations. Additionally, the study explores codimension-two bifurcations such as Bogdanov-Takens (BT) and zero-Hopf bifurcations. Coefficient formulas of normal forms are derived through the use of center manifold reduction and normal form theory. The study also presents an approximation of homoclinic orbits near a BT bifurcation of the system by computing explicit asymptotics based on regular perturbation methods. Utilizing the MATLAB package MATCONT, a family of limit cycles and their associated bifurcations are computed, including limit point cycles, period-doubling bifurcations, cusp points of cycles, fold-flip bifurcations, and various resonance bifurcations (R1, R2, R3, and R4). The biological implications of the findings are discussed in detail, highlighting how the identified bifurcations and dynamics can impact the population dynamics of vegetation, herbivores, and predators in real-world ecosystems. Numerical experiments validate the theoretical results and provide further support for the conclusions.

Boron/Fe2+/H2O2 combined with resins process cost-effectively remove Ni(II) in wastewater containing Ni-EDTA: Performance and mechanism

This study proposes a green purification strategy in which mild decomplexation of Ni-EDTA into other Ni complexes facilitates the efficient removal and recovery of nickel through resin adsorption. Decomplexation of Ni-EDTA by boron/Fe2+/H2O2 (B/Fe2+/H2O2) followed by resins adsorption was conducted for Ni(II) removal in Ni-EDTA. The Ni(II) removal efficiency could only achieve 1.3 % and 6.2 % while Ni(II) coexisted with EDTA but more than 10 times of higher when Ni(II) coexisted with EDTA-relatives via the single adsorption of commercial heavy metal-affined resins (D001 and D463), which attributed to the increased binding energies of these Ni-EDTA’s degradation products complexes than Ni-EDTA based on density functional theory (DFT) calculation. In comparing to precipitation, the resin adsorption required much less mineralization of EDTA to have a better removal efficiency of Ni(II). B improved the efficiency of Ni-EDTA decompexation by accelerating the recycle of Fe3+/Fe2+. The EDTA (and TOC) removal enhanced from 48.7 % (4.7 %) to 94.7 % (11.9 %) by B/Fe2+/H2O2 process comparing to Fe2+/H2O2 process. With the high EDTA but low TOC removal efficiency after B/Fe2+/H2O2 treatment, Ni(II) removal efficiency via resins adsorption could maximum achieve 80.5 %, 2–3 times of that via regular precipitation method. In addition, B and resins exhibited good reusability in five cycles, and B/Fe2+/H2O2-resins process had excellent adaptability to different heavy metal complexes. B/Fe2+/H2O2-D463 system not only could reduce 78.4 % reagents cost compared with that of Fe2+/H2O2-precipitation process, but also could save much cost than other processes. The results provide new insights for the cost-effective treatment of EDTA-complexed heavy metal wastewater.

Roughness measurement results evaluation of 6082 aluminium alloy specimens after fatigue bending tests

In this paper, the topography of 6082 aluminium alloy specimens after fatigue bending tests was studied with a comprehensive evaluation of measurement noise caused by vibration. Roughness results were acquired by contactless Focus Variation Microscopy (FVM). Studied data were pre-processed, removing the non-measured points and outliers with regular methods, respectively, and high-frequency noise was considered. The variations in ISO 25178 roughness parameters were studied. Based on the previous studies, it was found that surfaces after fatigue bending tests can be difficult to consider when analyzing the measurement noise in a selected bandwidth. Some advantages of profile data extraction in selected directions, like horizontal, vertical or crack, were found deficient, even in studies by various functions, like autocorrelation, power spectral density, or texture direction ratio. When noise suppression methods depend on the details studied, boundary areas were extracted to compare and highlight the presence of high-frequency data characteristics. The proposed method was validated when contrasting standardised Gaussian or median filtering techniques with the spline filtering approach. A proper filter for the reduction of vibrational noise from the results of FVM topography measurements was suggested based on the proposed procedure. Finally, it was proposed how use the new method for reducing errors caused by high-frequency measurement noise in the surface topography of specimens after fatigue bending tests.

Particle swarm optimization method for parameter selecting in Tikhonov regularization method for solving inverse problems

In this article the Tikhonov method used as a regularization technique for solving the inverse problem liner operator equation of the first kind with noisy and noise-free data. We also provide the Tikhonov method's essential analysis for tackling inverse problems. Following that, we go over how to choose the Tikhonov regularization parameter by implementing the Particle Swarm Optimization (PSO) technique. The effectiveness of combining these two approaches particle swarm optimization and Tikhonov is demonstrated to be highly practicable.

Pemberdayaan Mayarakat Melalui Kuliah Kerja Nyata (KKN) Reguler Unkriswina Sumba Dibidang UMKM, Pangan Lokal, Pertanian, dan Pendidikan di Desa Mata Lombu

Kuliah Kerja Nyata (KKN) is one of the lecture programs that must be provided and implemented by every university in order to achieve one of the goals and tri dharma, namely Pengabdian Kepada Masyarakat (PKM). Mata Lombu is one of the villages selected to carry out the KKN program, but as a village in Indonesia, it is generally not free from several problems. Based on observations and social analysis carried out, there are various problems that need to be addressed including: (1) lack of community understanding regarding local food processing, (2) lack of understanding of farmers regarding preventing pests in crops, (3) lack of literacy and numeracy among students. Elementary and junior high schools in the village, (5) limited access to clean water, and (6) limitations of MSMEs (Micro, Small and Medium Enterprises) in creating jobs. Based on a number of problems found during observations and social analysis, and the description in the background, the main objective of all these programs is to fulfill community service and empowerment in Mata Lombu Village through a project action planning framework or Project Action Plan (PAP). The series of Unkriswina Sumba Regular KKN activity methods includes the stages of program planning, implementation and evaluation. The results of this activity show that this activity can increase insight, understanding and new knowledge regarding local food, making bokashi fertilizer, making natural pesticides, and tutoring, and can increase crop yields, food security, reduce the impact of environmental pollution, and increase value. increase the economy of village communities.