Efficient Implementation Research Articles

Damage identification in sandwich structures using Convolutional Neural Networks

The increasing complexity of structures and materials, coupled with ever more stringent demands for safety and cost reduction in maintenance operations, has driven the need to develop advanced techniques for structural integrity monitoring, known as Structural Health Monitoring (SHM). In this context, this study investigates the use of image processing techniques of mode shapes in a composite sandwich panel, employing various Convolutional Neural Network (CNN) models, with the purpose of identifying damage. The main objective is to classify the type of damage, whether it is in the core, at the interface, or in the laminate, followed by the precise location of the flaw and determination of damage dimensions in terms of length and width. To achieve these goals, the finite element method was used to create a representative database, and subsequently, an efficient data management system and model implementation were established to optimize computational costs. The results obtained show that, in the classification task, a high accuracy in damage type identification (99%) was achieved, even when images had high levels of noise. Regarding the regression task for damage localization, satisfactory results were obtained, while the dimensioning of damage length proved acceptable, albeit with some limitations, and inclination identification presented challenges. This study represents a significant advancement in the field of SHM and explores both the advantages and limitations of using Convolutional Neural Networks for this purpose. The results obtained provide valuable insights for the practical application of these techniques in the detection and assessment of damage in sandwich structures, thus contributing to enhancing the safety and efficiency of maintaining such structures.

Efficient colorectal polyp segmentation using wavelet transformation and AdaptUNet: A hybrid U-Net

The prevalence of colorectal cancer, primarily emerging from polyps, underscores the importance of their early detection in colonoscopy images. Due to the inherent complexity and variability of polyp appearances, the task stands difficult despite recent advances in medical technology. To tackle these challenges, a deep learning model featuring a customized U-Net architecture, AdaptUNet is proposed. Attention mechanisms and skip connections facilitate the effective combination of low-level details and high-level contextual information for accurate polyp segmentation. Further, wavelet transformations are used to extract useful features overlooked in conventional image processing. The model achieves benchmark results with a Dice coefficient of 0.9104, an Intersection over Union (IoU) coefficient of 0.8368, and a Balanced Accuracy of 0.9880 on the CVC-300 dataset. Additionally, it shows exceptional performance on other datasets, including Kvasir-SEG and Etis-LaribDB. Training was performed using the Hyper Kvasir segmented images dataset, further evidencing the model's ability to handle diverse data inputs. The proposed method offers a comprehensive and efficient implementation for polyp detection without compromising performance, thus promising an improved precision and reduction in manual labour for colorectal polyp detection.

Efficient realization of quantum algorithms with qudits

The development of a universal fault-tolerant quantum computer that can solve efficiently various difficult computational problems is an outstanding challenge for science and technology. In this work, we propose a technique for an efficient implementation of quantum algorithms with multilevel quantum systems (qudits). Our method uses a transpilation of a circuit in the standard qubit form, which depends on the characteristics of a qudit-based processor, such as the number of available qudits and the number of accessible levels. This approach provides a qubit-to-qudit mapping and comparison to a standard realization of quantum algorithms highlighting potential advantages of qudits. We provide an explicit scheme of transpiling qubit circuits into sequences of single-qudit and two-qudit gates taken from a particular universal set. We then illustrate our method by considering an example of an efficient implementation of a 6-qubit quantum algorithm with qudits. In this particular example, we demonstrate how using qudits allows a decreasing amount of two-body interactions in the qubit circuit implementation. We expect that our findings are of relevance for ongoing experiments with noisy intermediate-scale quantum devices that operate with information carriers allowing qudit encodings, such as trapped ions and neutral atoms, as well as optical and solid-state systems.

A novel coupled ergodic sequential logic-based cochlear model: nonlinear hearing characteristics of two-tone responses and efficient hardware implementation

A novel coupled ergodic sequential logic-based cochlear model: nonlinear hearing characteristics of two-tone responses and efficient hardware implementation

End-to-end codesign of Hessian-aware quantized neural networks for FPGAs

We develop an end-to-end workflow for the training and implementation of co-designed neural networks (NNs) for efficient field-programmable gate array (FPGA) hardware. Our approach leverages Hessian-aware quantization of NNs, the Quantized Open Neural Network Exchange intermediate representation, and the hls4ml tool flow for transpiling NNs into FPGA firmware. This makes efficient NN implementations in hardware accessible to nonexperts in a single open sourced workflow that can be deployed for real-time machine-learning applications in a wide range of scientific and industrial settings. We demonstrate the workflow in a particle physics application involving trigger decisions that must operate at the 40-MHz collision rate of the CERN Large Hadron Collider (LHC). Given the high collision rate, all data processing must be implemented on FPGA hardware within the strict area and latency requirements. Based on these constraints, we implement an optimized mixed-precision NN classifier for high-momentum particle jets in simulated LHC proton-proton collisions.

Finding eigenvectors with a quantum variational algorithm

This paper presents a hybrid variational quantum algorithm that finds a random eigenvector of a unitary matrix with a known quantum circuit. The algorithm is based on the SWAP test on trial states generated by a parametrized quantum circuit. The eigenvector is described by a compact set of classical parameters that can be used to reproduce the found approximation to the eigenstate on demand. This variational eigenvector finder can be adapted to solve the generalized eigenvalue problem, to find the eigenvectors of normal matrices and to perform quantum principal component analysis on unknown input mixed states. These algorithms can all be run with low-depth quantum circuits, suitable for an efficient implementation on noisy intermediate-scale quantum computers and, with some restrictions, on linear optical systems. In full-scale quantum computers, where there might be optimization problems due to barren plateaus in larger systems, the proposed algorithms can be used as a primitive to boost known quantum algorithms. Limitations and potential applications are discussed.

Protocol-in-a-Day Workshop: Expediting IRB Approval for Junior and Senior Faculty.

Delays in research protocol development may be a single factor that hinders the career progression of academic faculty. Structured educational guidance during this phase proves crucial in mitigating setbacks in Institutional Review Board (IRB) approval and expediting trial implementation. To address this, the Protocol-in-a-Day (PIAD) workshop, a comprehensive 1-day event involving members from six critical facets of RO clinical trial implementation, was established, offering significant input to individual protocols. Efficacy and satisfaction of the PIAD workshop were assessed through a 5-question survey and the average time from submission to IRB initial approval. The normality of the data was analyzed using the Shapiro-Wilk Test. Nonparametric data was analyzed using a Mann-Whitney U test for significance. A total of 18 protocols that went through the PIAD workshop were activated. The mean time to IRB approval for protocols that went through PIAD was 39.8 days compared to 58.4 days for those that did not go through the PIAD workshop. Based on survey results, 100% of PIAD participants said the PIAD workshop was useful and 94% of participants stated that the PIAD workshop improved the overall quality of their protocol. Participant surveys further highlighted substantial improvements in trial quality, language, and statistical design and revealed that all participants found the workshop helpful. Therefore, both junior and senior faculty benefitted from this educational program during protocol development, as both groups demonstrated shorter times to IRB approval than non-participants. This acceleration not only fosters efficient trial implementation but also supports academic faculty in their career development.

Efficient implementation of majority-inverter graph logic and arithmetic functions with memristor arrays

Efficient implementation of majority-inverter graph logic and arithmetic functions with memristor arrays

The Challenges of Poverty Implementation Policy and Programmes in Nigeria

Poverty alleviation has been a recurrent decimal in the anals of development planning in Nigeria since post-colonial era. The various interventions of successive governments have unanimously been agreed and observed to be a monumental failure in respect of the objectives for which they were focused. Consequently, this paper examines the challenges confronting the implementation of these programmes with a view to proffer recommendations for effective and efficient implementation. A total of 720 respondents consisting of men and women between the ages of 20 and 60 years above were drawn from two local governments areas in Lagos and Ogun states, Nigeria respectively; using simple random and convenient sampling. Questionnaires were administered and responses collated and analysed. The chi-square statistical techniques and percentages were used in testing the hypothesis and in analyzing the collated data. Results revealed that the implementation of alleviation policy and programmes of Nigerian successive governments since independence faced the challenges of corruption, lack of transparency and political will on the part of government to make the programme a success. The findings also revealed that the rural dwellers which formed the original focus of the programmes benefited less due to wrong focus. The study therefore concluded by recommending that government should enthrone high levels of transparency, accountability and political will in the implementation process. It also recommended that the community which constituted the highest percentage of poverty ridden people should be the focus of government and government should therefore adhere to faithful implementation of these policies and programmes as a means of achieving reults.

Using clinician and patient input to assess utility, accuracy, efficiency, and therapeutic implementation of a new data-driven digital therapeutic for personalized clinical eating disorder treatment: Awaken digital guide

Objective Eating disorders (EDs) take a life every 52 minutes and treatments are ineffective for ∼50% of individuals. Though EDs are heterogeneous illnesses, current evidence-based treatments take a “one-size-fits-all” approach. Network-Informed Personalized Treatment is a new promising treatment for EDs, but clinician-patient-friendly software tools are needed to integrate this guidance system into routine treatment. Adoption is key for impact, necessitating the inclusion of clinicians in the software development. The current pilot assessed a new data-driven clinician-guidance therapeutic. Method A two-part pilot was analyzed for quantitative (0-not at all to 10-extremely) and qualitative input on user perception through quantitative and open-ended prompted questions evaluating using personalizing ED treatment with the Awaken Digital Guide therapeutic. Results Results demonstrated that clinicians in a focus group (N = 9) and clinician/patient dyads within implementation (N = 10) endorsed improved efficiency, effectiveness, self-awareness, and accuracy using Awaken Digital Guide compared to current treatment as suggested by quantitative and qualitative results. Both clinicians and patients rated the tool positively (6.8-9.6/5.8-8.6, respectively) with an average rating of good and excellent. Conclusion Findings suggest that ED-specialized clinicians desire data-driven guidance on personalizing ED treatment. Users perceive Awaken Digital Guide therapeutic with potential to increase collaboration, motivation, efficiency, and effectiveness of ED personalized treatment.

Using the Quemada rheology model for drilling fluids — Practical considerations

A mathematical description of the Quemada rheological model is given, focusing on realistic drilling fluids. The purpose of the work described is to derive results that are required for robust, accurate, and efficient implementations of the model. Challenges related to non-monotonic shear stress vs. shear rate for fluids with high yield stress are discussed and solutions are suggested. A brief discussion of the Quemada model’s connection with chemistry and physics, which is an important motivation for using it, is included.

Toward the Next Generation of Density Functionals: Escaping the Zero-Sum Game by Using the Exact-Exchange Energy Density.

ConspectusKohn-Sham density functional theory (KS DFT) is arguably the most widely applied electronic-structure method with tens of thousands of publications each year in a wide variety of fields. Its importance and usefulness can thus hardly be overstated. The central quantity that determines the accuracy of KS DFT calculations is the exchange-correlation functional. Its exact form is unknown, or better "unknowable", and therefore the derivation of ever more accurate yet efficiently applicable approximate functionals is the "holy grail" in the field. In this context, the simultaneous minimization of so-called delocalization errors and static correlation errors is the greatest challenge that needs to be overcome as we move toward more accurate yet computationally efficient methods. In many cases, an improvement on one of these two aspects (also often termed fractional-charge and fractional-spin errors, respectively) generates a deterioration in the other one. Here we report on recent notable progress in escaping this so-called "zero-sum-game" by constructing new functionals based on the exact-exchange energy density. In particular, local hybrid and range-separated local hybrid functionals are discussed that incorporate additional terms that deal with static correlation as well as with delocalization errors. Taking hints from other coordinate-space models of nondynamical and strong electron correlations (the B13 and KP16/B13 models), position-dependent functions that cover these aspects in real space have been devised and incorporated into the local-mixing functions determining the position-dependence of exact-exchange admixture of local hybrids as well as into the treatment of range separation in range-separated local hybrids. While initial functionals followed closely the B13 and KP16/B13 frameworks, meanwhile simpler real-space functions based on ratios of semilocal and exact-exchange energy densities have been found, providing a basis for relatively simple and numerically convenient functionals. Notably, the correction terms can either increase or decrease exact-exchange admixture locally in real space (and in interelectronic-distance space), leading even to regions with negative admixture in cases of particularly strong static correlations. Efficient implementations into a fast computer code (Turbomole) using seminumerical integration techniques make such local hybrid and range-separated local hybrid functionals promising new tools for complicated composite systems in many research areas, where simultaneously small delocalization errors and static correlation errors are crucial. First real-world application examples of the new functionals are provided, including stretched bonds, symmetry-breaking and hyperfine coupling in open-shell transition-metal complexes, as well as a reduction of static correlation errors in the computation of nuclear shieldings and magnetizabilities. The newest versions of range-separated local hybrids (e.g., ωLH23tdE) retain the excellent frontier-orbital energies and correct asymptotic exchange-correlation potential of the underlying ωLH22t functional while improving substantially on strong-correlation cases. The form of these functionals can be further linked to the performance of the recent impactful deep-neural-network "black-box" functional DM21, which itself may be viewed as a range-separated local hybrid.

Slime mould algorithm with mechanism of leadership and self-phagocytosis for multilevel thresholding of color image

Threshold based segmentation method is widely used because of its simplicity, high efficiency and easy implementation. However, as the number of thresholds increases, the algorithm's search space also increases, increasing computational complexity, which reduces computational efficiency and segmentation accuracy. Additionally, the existing multi-threshold (MT) segmentation methods still struggle with slow convergence speed and low solution accuracy. The optimum threshold for the image is determined in this study using the Slime Mould Algorithm with the Mechanism of Leadership and Self-phagocytosis (SMA-MLS). Firstly, the initial population production method based on Logistic-Tent mapping is introduced to enhance the diversity of algorithm solutions. Secondly, the position update formula with leadership mechanism is proposed to improve the convergence speed and accuracy. In addition, the adaptive combined mutation mechanism balances the exploration and exploitation ability. Finally, the self-phagocytosis mechanism maintains population diversity and heritability. A series of benchmark test suites are employed to evaluate the performance of the algorithm. Experimental results demonstrate that SMA-MLS does show good performance. It is superior in terms of convergence accuracy, convergence speed and stability. In the MT segmentation experiments, by using Kapur as the objective function and working with color images, SMA-MLS proved to be effective in the MT segmentation problem of images. Moreover, SMA-MLS will be applied to mechanical parameter optimization, neural network and other fields in future research.

Non-stationary Anderson acceleration with optimized damping

Anderson acceleration (AA) has a long history of use and a strong recent interest due to its potential ability to dramatically improve the linear convergence of the fixed-point iteration. Most authors are simply using and analyzing the stationary version of Anderson acceleration (sAA) with a constant damping factor or without damping. Little attention has been paid to nonstationary algorithms. However, damping can be useful and is sometimes crucial for simulations in which the underlying fixed-point operator is not globally contractive. The role of this damping factor has not been fully understood. In the present work, we consider the non-stationary Anderson acceleration algorithm with optimized damping (AAoptD) in each iteration to further speed up linear and nonlinear iterations by applying one extra inexpensive optimization. We analyze the convergence rate this procedure and develop an efficient and inexpensive implementation scheme. We show by extensive numerical experiments that the proposed non-stationary Anderson acceleration with optimized damping procedure often converges much faster than stationary AA with constant damping, adaptive damping or without damping, especially in the cases larger window sizes are needed. We also observe that simple strategies like using constant damping factors and adaptive damping factors, sometimes, work very well for some problems while sometimes they are even worse than AA without damping. Our proposed method is usually more robust than AA with constant damping and adaptive damping. Moreover, we also observed from our numerical results that damping can be good, but choosing the wrong damping factors may slow down the convergence rate. Theoretical analysis of the effects of damping factors are needed and important.

Phase field modeling of ductile fracture with isotropic hardening and radius return method

Phase field model has been investigated for brittle fracture in many static and dynamic scenarios, but its applications to ductile fracture is not as common as brittle fracture, especially implementing in software LS-DYNA with explicit scheme. In this study, an efficient LS-DYNA implementation of the phase field modeling of ductile fracture is presented and both with and without the split of elastic strain energy have been considered for the damage evolution. In more detail, plasticity formulation of ductile material with isotropic hardening is briefly presented first and then the governing equations of the classical phase field model are derived, which gives the displacement-phase coupled problem. For with the split of elastic strain energy, the shear component of elastic strain energy is considered for the damage evolution. The influence of degradation function on stress–strain curve is also investigated by using three kinds of function (polynomial function, algebraic fraction function and sigmoid function), which leads to linear and nonlinear finite element method (FEM) formulation of the phase field model and Newton–Raphson method is used to solve the nonlinear FEM formulation of the phase field model. A tensile bar test shows the influence of critical energy release rate and degradation function on stress–strain curve. Mode Ⅰ failure of three-point bending test, Mode Ⅱ failure of single-edge notched plate and mixed-mode failure of asymmetrical double-notched plate verify the proposed model in this study. From these simulations, with the split of elastic strain energy shows improvements on plastic deformation than without the split of elastic strain energy.

A Closest Point Method for PDEs on Manifolds with Interior Boundary Conditions for Geometry Processing

Many geometry processing techniques require the solution of partial differential equations (PDEs) on manifolds embedded in \(\mathbb {R}^2 \) or \(\mathbb {R}^3 \) , such as curves or surfaces. Such manifold PDEs often involve boundary conditions (e.g., Dirichlet or Neumann) prescribed at points or curves on the manifold’s interior or along the geometric (exterior) boundary of an open manifold. However, input manifolds can take many forms (e.g., triangle meshes, parametrizations, point clouds, implicit functions, etc.). Typically, one must generate a mesh to apply finite element-type techniques or derive specialized discretization procedures for each distinct manifold representation. We propose instead to address such problems in a unified manner through a novel extension of the closest point method (CPM) to handle interior boundary conditions. CPM solves the manifold PDE by solving a volumetric PDE defined over the Cartesian embedding space containing the manifold, and requires only a closest point representation of the manifold. Hence, CPM supports objects that are open or closed, orientable or not, and of any codimension. To enable support for interior boundary conditions we derive a method that implicitly partitions the embedding space across interior boundaries. CPM’s finite difference and interpolation stencils are adapted to respect this partition while preserving second-order accuracy. Additionally, we develop an efficient sparse-grid implementation and numerical solver that can scale to tens of millions of degrees of freedom, allowing PDEs to be solved on more complex manifolds. We demonstrate our method’s convergence behaviour on selected model PDEs and explore several geometry processing problems: diffusion curves on surfaces, geodesic distance, tangent vector field design, harmonic map construction, and reaction-diffusion textures. Our proposed approach thus offers a powerful and flexible new tool for a range of geometry processing tasks on general manifold representations.

THE FUTURE OF OPEN GOVERNMENT

The article examines the potential and obstacles of adopting open government principles in Vietnam. Utilizing a thorough document analysis of governmental reports, policy directives, and academic documents, the study highlights significant progress through legislative reforms and digital technology adoption in Vietnam. This country is undergoing a shift towards transparent governance, motivated by its strong economic growth and political development. Notwithstanding these progressions, Vietnam encounters various obstacles. These factors encompass discrepancies in technology infrastructure, cultural and institutional opposition, concerns around privacy andsecurity, and limitations in available resources. Furthermore, the challenge of aligning new policies with current legislation and ensuring efficient implementation presents additional complexities. The study suggests that enhancing legal and regulatory frameworks, digital infrastructure, fostering a culture of transparency in public agencies, building inclusive participation methods, and prioritizing anti-corruption measures will help Vietnam overcome obstacles in developing an open government.

Candida auris Updates: Outbreak Evaluation through Molecular Assays and Antifungal Stewardship-A Narrative Review.

Candida auris was reported by the WHO as second to Cryptococcus neoformans, in the list of nineteen fungal priority pathogens, along with two species with a new nomenclature, Nakaseomyces glabrata (Candida glabrata) and Pichia kudriavzevii (Candida krusei). This novel classification was based on antifungal resistance, the number of deaths, evidence-based treatment, access to diagnostics, annual incidence, and complications and sequelae. We assessed which molecular assays have been used to diagnose Candida auris outbreaks in the last five years. Using "Candida auris; outbreak; molecular detection" as keywords, our search in PubMed revealed 32 results, from which we selected 23 original papers published in 2019-2024. The analyzed studies revealed that the detection methods were very different: from the VITEK® 2 System to MALDI TOF (Matrix-Assisted Laser Desorption Ionization-Time of Flight), NGS (Next-Generation Sequencing), WGS (Whole Genome Sequencing), and commercially available real-time PCR (Polymerase Chain Reaction) assays. Moreover, we identified studies that detected antifungal resistance genes (e.g., FKS for echinocandins and ERG11 for azoles). The analyzed outbreaks were from all continents, which confirms the capability of this yeast to spread between humans and to contaminate the environment. It is important that real-time PCR assays were developed for accurate and affordable detection by all laboratories, including the detection of antifungal resistance genes. This will allow the fast and efficient implementation of stewardship programs in hospitals.

Developing a framework for understanding policy decision-making behaviors in the transition of an HIV prevention program towards sustainability: a case study from Zambia’s voluntary medical male circumcision program

Faced with declining donor funding for HIV, low- and middle-income countries must identify efficient and cost-effective ways to integrate HIV prevention programs into public health systems for long-term sustainability. In Zambia, donor support to the voluntary medical male circumcision (VMMC) program, which previously funded non-governmental organizations as implementing partners, is increasingly being directed through government structures instead. We developed a framework to understand how the behaviors of individual decision-makers within the government could be barriers to this transition. We interviewed key stakeholders from the national, provincial, and district levels of the Ministry of Health, and from donors and partners funding and implementing Zambia’s VMMC program, exploring the decisions required to attain a sustainable VMMC program and the behavioral dynamics involved at personal and institutional levels. Using pattern identification and theme matching to analyze the content of the responses, we derived three core decision-making phases in the transition to a sustainable VMMC program: 1) developing an alternative funding strategy, 2) developing a policy for early-infant (0–2 months) and early-adolescent (15–17 years) male circumcision, which is crucial to sustainable HIV prevention; and 3) identifying integrated and efficient implementation models. We formulated a framework showing how, in each phase, a range of behavioral dynamics can form barriers that hinder effective decision-making among stakeholders at the same level (e.g., national ministries and donors) or across levels (e.g., national, provincial and district). Our research methodology and the resulting framework offer a systematic approach for in-depth investigations into organizational decision-making in public health programs, as well as development programs beyond VMMC and HIV prevention. It provides the insights necessary to map organizational development and policy-making transition plans to sustainability, by explaining tangible factors such as organizational processes and systems, as well as intangibles such as the behaviors of policymakers and institutional actors.

Digital Library for the Implementation of Formative Assessment into Teaching

This paper presents information about the development, implementation, and verification of efficiency of “Digilib” – a digital library of formative assessment (FA) tools. A selection of 10 different types of FA tools (e.g., self-assessment card, prediction card, and Frayer model) was conducted based on a literature search and Internet resources. Subsequently, in accordance with the educational standards and in cooperation with teachers, a total of 36 databases covering 774 FA tools for the selected thematic units and topics in science (biology, physics, chemistry), mathematics, and informatics were created for primary schools. “Digilib” application allows teachers to create and save their own FA tools (forms), which can be assigned to students to collect information about their understanding of the subject matter, i.e., to receive real-time feedback. In the 2022/2023 school year, 8 primary schools, 16 teachers, and 46 classes (grades 5 to 9) with a total of 846 students were registered in the digital library. Semi-structured interviews were conducted to collect teachers’ opinions on the implementation of FA provided by the digital library. This paper presents the results of a semi-structured interview performed at the selected primary school. The respective teacher appreciated the simple user experience, quick feedback about students’ current state of knowledge, automated creation of complex overviews of students’ answers, and archiving. The feedback provided by the self-assessment and prediction cards helps students develop an ability to perform objective self-assessment and address possible misunderstanding in learning.