Projection Method Research Articles

Peritoneal infection after colorectal cancer surgery induces substantial alterations in postoperative protein levels: an exploratory study

PurposePeritoneal infection, due to anastomotic leakage, after resection for colorectal cancer have been shown to associate with increased cancer recurrence and mortality, as well as cardiovascsular morbidity. Alterations in circulating protein levels could help shed light on the underlying mechanisms, prompting this exploratory study of 64 patients operated for colorectal cancer with anastomosis.MethodsThirty-two cases who suffered a postoperative peritoneal infection were matched with 32 controls who had a complication-free postoperative stay. Proteins in serum samples at their first postoperative visit and at one year after surgery were analysed using proximity extension assays and enzyme-linked immunosorbent assays. Multivariate projection methods, adjusted for multiple testing, were used to compare levels between groups, and enrichment and network analyses were performed.ResultsSeventy-seven proteins, out of 270 tested, were differentially expressed at a median sampling time of 41 days postoperatively. These proteins were all normalised one year after surgery. Many of the differentially expressed top hub proteins have known involvement in cancer progression, survival, invasiveness and metastasis. Over-represented pathways were related to cardiomyopathy, cell-adhesion, extracellular matrix, phosphatidylinositol-3-kinase/Akt (PI3K-Akt) and transforming growth factor beta (TGF-β) signaling.ConclusionThese affected proteins and pathways could provide clues as to why patients with peritoneal infection might suffer increased cancer recurrence, mortality and cardiovascular morbidity.

Positivity Preserving and Mass Conservative Projection Method for the Poisson–Nernst–Planck Equation

Positivity Preserving and Mass Conservative Projection Method for the Poisson–Nernst–Planck Equation

An improved Dai‐Liao‐style hybrid conjugate gradient‐based method for solving unconstrained nonconvex optimization and extension to constrained nonlinear monotone equations

In this work, for unconstrained optimization, we introduce an improved Dai‐Liao‐style hybrid conjugate gradient method based on the hybridization‐based self‐adaptive technique, and the search direction generated fulfills the sufficient descent and trust region properties regardless of any line search. The global convergence is established under standard Wolfe line search and common assumptions. Then, combining the hyperplane projection technique and a new self‐adaptive line search, we extend the proposed conjugate gradient method and obtain an improved Dai‐Liao‐style hybrid conjugate gradient projection method to solve constrained nonlinear monotone equations. Under mild conditions, we obtain its global convergence without Lipschitz continuity. In addition, the convergence rates for the two proposed methods are analyzed, respectively. Finally, numerical experiments are conducted to demonstrate the effectiveness of the proposed methods.

An implicit DG solver for incompressible two‐phase flows with an artificial compressibility formulation

SummaryWe propose an implicit discontinuous Galerkin (DG) discretization for incompressible two‐phase flows using an artificial compressibility formulation. The conservative level set (CLS) method is employed in combination with a reinitialization procedure to capture the moving interface. A projection method based on the L‐stable TR‐BDF2 method is adopted for the time discretization of the Navier‐Stokes equations and of the level set method. Adaptive mesh refinement (AMR) is employed to enhance the resolution in correspondence of the interface between the two fluids. The effectiveness of the proposed approach is shown in a number of classical benchmarks. A specific analysis on the influence of different choices of the mixture viscosity is also carried out.

Decentralized dynamic system for optimal power dispatch in wind farms based on node-dependence nature

Meeting the power demand from the transmission system operator is an important objective for power dispatch, which introduces a power supply-demand equality constraint coupling all the wind turbines among the wind farm into the optimization problem. For a large-scale wind farm, processing the global equality constraint in a centralized or distributed framework is time-consuming and computationally complex. Here we considered the fast and localized execution issue of the power optimal dispatch problems. A completely decentralized dynamic system was designed to optimize power flow while satisfying the electricity supply constraints. A voltage optimization problem with the global power constraints was decoupled into local wind turbine controllers based on the node-dependence nature, which is an inherent characteristic of wind farms and was fitted to the power sensitivity matrix in this paper. The local optimization problem was solved iteratively using the gradient projection method, and the system converged linearly to the equilibrium point. The simulations for the case studies performed in Simulink demonstrate that the proposed method achieves a near-global optimal performance using only local measurements.

Quantizing Galilean spacetime: a reconstruction of Maxwell’s equations in empty space

As was recently shown, non-relativistic quantum theory can be derived by means of a projection method from a continuum of classical solutions for (massive) particles. In this paper, we show that Maxwell’s equations in empty space can be derived using the same method. In this case, the starting point is a continuum of solutions of equations of motion for massless particles describing the structure of Galilean space-time. As a result of the projection, the space-time structure itself is changed by the appearance of a new fundamental constant c with the dimension of a velocity. This maximum velocity c, derived here for massless particles, is analogous to the accuracy limit ħ\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\hbar $$\\end{document} derived earlier for massive particles. The projection method can thus be interpreted as a generalized quantization. We suspect that all fundamental fields can be traced back to continuous sets of particle trajectories, and that in this sense, the particle concept is more fundamental than the field concept.

Институт проектного офиса в организациях с бимодальным управлением

Today a lot of attention is paid to project management. This is due both to the special importance of supporting strategic advantage (rapid changes, an objective reduction in the life cycle of modern products and an increase in dependence on customer needs can critically devalue the assets and potential of the organization), and to the need to find solutions to problems without disrupting the overall functioning and taking into account the risks associated with resistance to change. In management practice, projects are often equated with programs and interpreted as a set of pre-planned activities, which requires maintaining the "top" management, providing resources for the implementation of these activities. Meanwhile, the key in projects is budgeting tasks, results of operations, and not activities, individual operations. At the same time, within the framework of the tasks set, the set of their constituent operations should not be administered. If this condition is not met, then there will be no positive effects of the project associated with novelty (discoveries), changes, development. Another innovation of the 21st century for managing the development of organized systems is bimodal management. This article discusses the features of project management in private and public organizations when they use modern bimodal management, as well as the problems of functioning of a special project support structure — the project office. The author's approach to projects as a partnership is proposed, in contrast to a special organization of resource management and operations with a bias towards coordination and control activities, in principle alien to the idea of projects as activities of personally interested persons outside the framework of official or functional duties and powers. The article shows the restrictive conditions of the project method and proposals for overcoming them in the field of public administration.

Numerical Algorithms for Divergence-Free Velocity Applications

This work focuses on the well-known issue of mass conservation in the context of the finite element technique for computational fluid dynamic simulations. Specifically, non-conventional finite element families for solving Navier–Stokes equations are investigated to address the mathematical constraint of incompressible flows. Raviart–Thomas finite elements are employed for the achievement of a discrete free-divergence velocity. In particular, the proposed algorithm projects the velocity field into the discrete free-divergence space by using the lowest-order Raviart–Thomas element. This decomposition is applied in the context of the projection method, a numerical algorithm employed for solving Navier–Stokes equations. Numerical examples validate the approach’s effectiveness, considering different types of computational grids. Additionally, the presented paper considers an interface advection problem using marker approximation in the context of multiphase flow simulations. Numerical tests, equipped with an analytical velocity field for the surface advection, are presented to compare exact and non-exact divergence-free velocity interpolation.

Analysis of project work using earned value management and precedence diagram method in manufacturing system projects

PT. Weiss Tech has received a project order for the fabrication and installation of coffee packaging production machines from PT.XYZ, scheduled to start on June 19, 2023, with a deadline of June 24, 2024, spanning 54 weeks. The project has been ongoing for 16 weeks with an update indicating that only 20% of the planned progress has been achieved, which suggests that it may not be completed on time. To evaluate the project's performance, the EVM method will be used to identify critical tasks and reduce the project duration using the Precedence Diagram Method with the help of MS Project software. The evaluation shows negative values for CV and SV variances, as well as performance index SPI<1 and CPI<1, indicating poor project performance. The critical tasks identified include electrical & instrument installation, machine & utility installation, trial work, assembly, quality control, polishing, milling, turning, cutting, and design. Design work has been identified as the most influential on the critical path. By rescheduling the project plan, positive values for CV and SV have been achieved. With the SPI value of 1.13 and CPI value of 1, it can be concluded that future project performance will be completed faster and within budget.

GPU accelerated sparse curvelet-constrained wavefield reconstruction inversion with source estimation: Application to Chevron Benchmark 2014 blind test data set

Full-waveform inversion (FWI) is a pivotal tool in seismic exploration and seismology but encounters a persistent challenge known as “cycle-skipping,” causing it to frequently converge to local minima. Wavefield reconstruction inversion (WRI) has emerged as a potential solution to mitigate the risk of cycle skipping. Researchers have provided numerical examples indicating that WRI is less prone to being ensnared in local minima caused by the absence of low-frequency component data and the absence of a well-defined initial model compared with conventional FWI. Despite its potential, the computational demands of WRI have hindered its widespread application, especially in scenarios involving ocean towed streamer seismic acquisition and unknown sources, where the augmented systems differ from source to source. In our study, we introduce a novel approach — sparse curvelet-constrained WRI with source estimation (WRI-SE-CC) — accelerated by graphics processing unit (GPU). Real-time source function estimation is achieved through the variable projection method, and noise-related artifacts are suppressed using sparse curvelet constraints. By optimizing the utilization of hundreds of computation processors within a GPU for parallel computing of matrix-vector multiplications, we present a GPU-based grouped conjugate gradient method to accelerate the computation of WRI-SE-CC. Numerical experiments demonstrate a significant 240-fold acceleration compared with the preconditioned conjugate gradient using one CPU core for computations involving multiple sources. Inversion experiments with the overthrust model demonstrate the capability of our method in mitigating local minima and suppressing noise-related artifacts. Furthermore, we validate the framework on the Chevron 2014 blind test data set, showcasing its effectiveness in addressing practical challenges in the field.

A compound planning algorithm considering both collision detection and obstacle avoidance for intelligent demolition robots

This paper presents a compound planning algorithm considering both collision detection and obstacle avoidance for intelligent demolition robots working safely in high-radiation environments. Firstly, configurations and kinematics of the intelligent demolition robot are detailed to detect the possible obstacles in its workspace. A collision detection function based on the improved dual vector method is proposed to detect the different distances between the robot and obstacles in three cases: a point and a line segment, two line segments, and a line segment and a geometric shape. This function can also be applied to detect collisions with various obstacles of different shapes reasonably and efficiently. Furthermore, an obstacle avoidance function based on modified gradient projection method considering multi-task transformation is proposed. According to the different distances between the robot and the obstacle, it can be used in three situations: end obstacle avoidance task, end effector operation task, and end trajectory tracking task. This function can be applied to avoid obstacles both in the workspace and on the desired path. Finally, a simulation system is established to verify the collision detection and obstacle avoidance algorithms of the intelligent demolition robot. An experiment was conducted on the intelligent demolition robot. This robot can successfully achieve the expected trajectory with the method described in this article. Results of simulation and experiment demonstrate that obstacles both in workspace and on desired path can be detected and avoided properly.

ISLAMIC DONATIONS-BASED CROWDFUNDING PLATFORMS IN MALAYSIA: AN EVALUATION OF REGULATORY AND SHARIAH ISSUES

The emergence of innovative digital financial technologies has made donation-crowdfunding viable as a method that can supplement traditional fundraising mechanisms. Crowdfunding has recently gained worldwide attention as an alternative financing mechanism to the traditional fundraising methods for business ventures or social projects. While donation-crowdfunding which utilise platforms has demonstrated promising growth, there is no established legal framework available to accommodate its operation, especially for Islamic ones. Therefore, this study provides intuitive information pertaining to legal and Shariah issues in governing Islamic donation-based crowdfunding in Malaysia. Drawing from the conceptual framework of Islamic economics and qualitative data collection through semi-structured with six representatives of the Islamic donation-crowdfunding platforms, this study concludes that the issues associated with Malaysian regulations regimes and Shariah issues revolve around the donation- based crowdfunding platforms business model should be considered when devising a Shariah framework for donation-crowdfunding schemes. The findings will help policymakers to formulate and put in place the appropriate regulatory framework to facilitate Islamic donation-based crowdfunding practice.

Relaxed projection methods for solving variational inequality problems

Relaxed projection methods for solving variational inequality problems

A robust cut‐cell finite element method for Poisson's equation in three dimensions

SummaryThis article documents a cut‐cell finite element method for solving Poisson's equation in smooth three‐dimensional domains using a uniform, Cartesian axis‐aligned grid. Neumann boundary conditions are imposed weakly by way of a Delaunay triangulation, while Dirichlet boundary conditions are imposed strongly using a projection method. A set of numerical simulations demonstrates the proposed method is robust and preserves the asymptotic rate of convergence expected of corresponding body‐fitted methods.

On the multi‐parameters identification of concrete dams: A novel stochastic inverse approach

AbstractThis paper introduces a novel stochastic inverse method that utilizes perturbation theory and advanced intelligence techniques to solve the multi‐parameter identification problem of concrete dams using displacement field monitoring data. The proposed method considers the uncertainties associated with the dam displacement monitoring data, which are comprised of two distinct sources: the first is related to stochastic mechanical properties of the dam, and the second is due to observation errors. The displacements at different measuring points generated by dam mechanical properties exhibit spatial correlation, while the observation errors at different points can be considered statistically random. In this context, the inversion formulas are derived for unknown stochastic parameters of the dam by combining perturbation equations and Taylor expansion methods. An improved meta‐heuristic optimization method is employed to identify the mean of stochastic parameters, while mathematical and statistical methods are used to determine the variance of stochastic parameters. The feasibility of the proposed method is verified through numerical examples of a typical dam section under different conditions. Additionally, the paper discusses and demonstrates the applicability of this method in a practical dam project. Results indicate that this method can effectively capture the uncertainty of dam's mechanical properties and separates them from observation errors.

Crop Water Status Analysis from Complex Agricultural Data Using UMAP-Based Local Biplot

To optimize growth and management, precision agriculture relies on a deep understanding of agricultural dynamics, particularly crop water status analysis. Leveraging unmanned aerial vehicles, we can efficiently acquire high-resolution spatiotemporal samples by utilizing remote sensors. However, non-linear relationships among data features, localized within specific subgroups, frequently emerge in agricultural data. Interpreting these complex patterns requires sophisticated analysis due to the presence of noise, high variability, and non-stationarity behavior in the collected samples. Here, we introduce Local Biplot, a methodological framework tailored for discerning meaningful data patterns in non-stationary contexts for precision agriculture. Local Biplot relies on the well-known uniform manifold approximation and projection method, such as UMAP, and local affine transformations to codify non-stationary and non-linear data patterns while maintaining interpretability. This lets us find important clusters for transformation and projection within a single global axis pair. Hence, our framework encompasses variable and observational contributions within individual clusters. At the same time, we provide a relevance analysis strategy to help explain why those clusters exist, facilitating the understanding of data dynamics while favoring interpretability. We demonstrated our method’s capabilities through experiments on both synthetic and real-world datasets, covering scenarios involving grass and rice crops. Moreover, we use random forest and linear regression models to predict water status variables from our Local Biplot-based feature ranking and clusters. Our findings revealed enhanced clustering and prediction capability while emphasizing the importance of input features in precision agriculture. As a result, Local Biplot is a useful tool to visualize, analyze, and compare the intricate underlying patterns and internal structures of complex agricultural datasets.

CPM Analysis on Boarding House Construction Projects in Samarinda City

Population growth increases the need for practical and affordable housing, so boarding houses become the main choice. Boarding house construction projects need to be managed through good project management. This research discusses the use of the Critical Path Method (CPM) in the boarding house project on Jl. Mangkupalas Gg. Padai in Samarinda City. The CPM method is used to determine project duration and optimize costs, with information on work, costs and time for each stage. The results show the critical activity path A – B – C – E – F – G – H – I – K, reducing the project duration from 210 days to 173 days, 37 days faster. Using the CPM method saves IDR 19,050,000 from the initial project cost of IDR 298,939,000 to IDR 279,889,000. This research supports the CPM method for cost and time efficiency in development projects and the results of this research are in accordance with previous studies which also discusses the CPM method of project management.

Gridder-HO: Rapid and efficient parallel software for high-order curvilinear mesh generation

The advancement in high-order computational methods is reshaping the landscape of mesh generation in Computational Fluid Dynamics (CFD), steering the focus towards curvilinear mesh techniques to meet the escalating accuracy demands. Gridder-HO, the software designed to generate high-order curvilinear mesh efficiently and rapidly, has been developed. Gridder-HO supports the elevation of meshes to P2 (quadratic-order) or P3 (cubic-order). It features a layered architecture and utilizes the concurrent hash table and the Alternating Digital Tree (ADT) data structure, supporting thread-level parallelism to convert straight-edge mesh into high-order curvilinear mesh seamlessly. Gridder-HO utilizes the projection method based on a thread pool to precisely preserve geometry, and employs a novel localized RBF method with ADT for volume node interpolation to untangle the mesh, which aims to achieve a satisfactory balance between efficiency and accuracy. Validated through CFD simulations using the GPU-accelerated Python Flux Reconstruction (PyFR) solver, the practicality of Gridder-HO is demonstrated across various Reynolds numbers in typical cases such as sphere, cylinder, and SD7003 airfoil. These results confirm the high-order curvilinear meshes generated by Gridder-HO meet the high-order requirements of emerging computational methods. Moreover, Gridder-HO exemplifies its effectiveness in generating large-scale, high-order curvilinear meshes for the DLR-F6 transport aircraft configuration standard test cases. It elevates a mesh with 5 million elements to P2 in 3 min 39 sec at 68% parallel efficiency on 16 threads, and another with 14 million elements to P3 in 52 min 39 sec at 60% efficiency, illustrating its efficiency and potential in satisfying the demands of complex geometries in engineering applications.

ADVANTAGES OF APPLYING PROJECT-BASED LEARNING TO FOREIGN LANGUAGE CLASSES

The project method is a student-oriented method, which implies the need to pay attention to their needs and abilities. This learning method is based on the principles of cooperation and active participation. The use of the project method in teaching a foreign language allows students to use a foreign language as a cognitive tool, a way to express their ideas, to perceive and understand other people's ideas. This is the most efficient way to shift students' attention from form of expression to content and to introduce the surrounding world into knowledge through foreign language, thus expanding the scope of socio-cultural competence.

Optimizing hybrid beamforming in millimeter‐wave massive multiple‐input multiple‐output systems: A gradient projection approach

AbstractMillimeter waves (mmWave) present an enticing opportunity for wireless communication due to their substantial bandwidth. However, mitigating signal losses within this spectrum necessitates employing numerous antennas for transmission and reception. In practical scenarios, dedicating individual RF chains to each antenna is often unfeasible. In response to this limitation, our research investigates a hybrid beamforming approach, seeking to optimize spectral efficiency through an alternating optimization (AO) technique. Our goal is to develop an algorithm that can be easily integrated into diverse hybrid beamforming configurations. On the other hand the pursuit of optimizing spectral efficiency while adhering to the constraints imposed by phase shifters results in a non‐convex problem. To confront this challenge, we employ a gradient descent framework combined with projection methods. We introduce the gradient prediction method (GPM), which leads to a closed‐form solution for projection. Simulations underscore that this hybrid beamforming structure can achieve the performance of a fully digital beamforming method when the number of RF chains is twice the total number of data streams, regardless of the number of antennas involved. Furthermore, we will conduct a comparative performance analysis of our proposed algorithm against other established benchmark algorithms to ascertain its superiority.