Efficient Implementation Research Articles

An explainable machine learning-based probabilistic framework for the design of scaffolds in bone tissue engineering.

Recently, 3D-printed biodegradable scaffolds have shown great potential for bone repair in critical-size fractures. The differentiation of the cells on a scaffold is impacted among other factors by the surface deformation of the scaffold due to mechanical loading and the wall shear stresses imposed by the interstitial fluid flow. These factors are in turn significantly affected by the material properties, the geometry of the scaffold, as well as the loading and flow conditions. In this work, a numerical framework is proposed to study the influence of these factors on the expected osteochondral cell differentiation. The considered scaffold is rectangular with a 0/90 lay-down pattern and a four-layered strut made of polylactic acid with a 5% steel particle content. The distribution of the different types of cells on the scaffold surface is estimated through a scalar stimulus, calculated by using a mechanobioregulatory model. To reduce the simulation time for the computation of the stimulus, a probabilistic machine learning (ML)-based reduced-order model (ROM) is proposed. Then, a sensitivity analysis is performed using the Shapley additive explanations to examine the contribution of the various parameters to the framework stimulus predictions. In a final step, a multiobjective optimization procedure is implemented using genetic algorithms and the ROM, aiming to identify the material parameters and loading conditions that maximize the percentage of surface area populated by bone cells while minimizing the area corresponding to the other types of cells and the resorption condition. The results of the performed analysis highlight the potential of using ROMs for the scaffold design, by dramatically reducing the simulation time while enabling the efficient implementation of sensitivity analysis and optimization procedures.

Threats, Attacks, and Cryptography Frameworks of Cybersecurity in Critical Infrastructures

Critical Infrastructures (CIs), such as healthcare facilities, power grids, transportation systems, and financial institutions, are vital components of a functioning society, with the economy and safety being dependent on them. Nevertheless, they have become increasingly vulnerable to cyber threats and attacks in recent years. The main reason is their inability to quickly adapt to technological changes, employ updated cryptographic frameworks, and implement a thoroughly secure architecture based on their characteristics. In this study, the unique complexities of these systems are highlighted. Various verified cyberattacks that were executed against CIs in recent years are analyzed. Moreover, the general framework of CIs is demonstrated together with the employed technologies and cryptographic primitives. A thorough architecture of said technologies is developed to better understand the targeted components and easily identify potentially hidden threats. Afterwards, threat, adversary, and attack models that target critical systems and services are designed. The purpose is a better comprehension of the systems’ vulnerabilities, attack structures, motives, and targets for assisting CIs’ designers in creating secure frameworks and mechanisms, with the ability to mitigate such threats. Lastly, security controls and cryptography frameworks are demonstrated together with efficient mitigation architectures and implementations from the research community.

Adoption of New Oral Health Interventions in Primary Care: Qualitative Findings

Introduction: This is the first study to use the Common-Sense Model of Self-Regulation (CSM) theory for oral health (OH) interventions in pediatric practices. The objective of this qualitative study was to assess adoption and implementation of theory-based multi-level OH interventions, by clinicians (pediatricians and nurse practitioners) participating in a cluster-randomized clinical trial (cRCT), to create an OH toolkit for widespread dissemination into pediatric practices. Methods: Semi-structured interviews were conducted at the conclusion of the cRCT with 21 clinicians from 9 practices participating in the intervention arm. Clinicians in this arm received CSM theory-based education and resources to deliver OH interventions to parents/caregivers and document in EMR. Semi-structured interview questions were based on the Diffusion of Innovations Theory, assessing adoption and implementation. The interviews were coded using NVivo (QRS International) software. Main themes were identified using a thematic analysis approach. Results: Five themes identified from the interviews included: strengths of theory-based OH training for clinicians, OH resources to improve quality of care, considerations for efficient future implementation, financial considerations, and parent benefits and challenges. Clinicians found that the theory-based training and resources increased knowledge and confidence when addressing OH with parents and required only ≤ 2 minutes in their workflow with no financial consequences. Clinicians reported an increase in OH awareness among parents but suggested an overall need for more pediatric dentists. Conclusions: The CSM theory-based education and resources were well received by clinicians and perceived to be beneficial without adverse impact on workflow or practice finances. An online toolkit is planned, as these OH interventions can be successfully implemented and delivered in medical settings.

Direct application of chemically enhanced primary treatment in a municipal wastewater treatment plant: A case study

A feasible and efficient implementation of an Fe-based chemically enhanced primary treatment (CEPT) in a large conventional wastewater treatment plant (WWTP) was thoroughly evaluated. The influence of the velocity gradient and hydraulic retention time in the coagulation and flocculation stages, as well as the coagulant (FeCl3) dosage, were evaluated at lab-scale. Standard Jar Tests were performed to simulate CEPT, and the results were compared with those obtained by primary settling without coagulant. The increase in FeCl3 dosage and flocculation hydraulic retention time (HRTF) increased the primary settling COD removal capacity between 6.6% and 18.1%, highlighting its effect on the easily biodegradable COD fraction. The COD removal capacity increased up to 16.8% when working with an HRTF of less than 4 min, facilitating CEPT direct application in conventional WWTPs, avoiding the need to incorporate flocculation chamber, with a corresponding decrease in the associated implementation cost. The presence of residual Fe in the primary settling effluent and an increase in the primary sludge volume were the main drawbacks of the process. The coagulation and flocculation velocity gradients or the coagulation hydraulic retention time did not significantly influence the process.

Treatment Efficiency of Maxillary and Mandibular Orovestibular Tooth Expansion and Compression Movements with the Invisalign® System in Adolescents and Adults.

Aligners are an effective and esthetic orthodontic treatment option for permanent and mixed dentition. There are only a few studies dealing with the effectiveness of orovestibular tooth movement using aligners and applying adequate examination methods. In the present retrospective study, the aligner efficiency of orovestibular movements for the entire dentition was systematically evaluated using 3D superimposition, taking into account the influence of jaw, tooth type and Invisalign® system. Group 1 (n = 18 adults, Invisalign®) and Group 2 (n = 17 adolescents, Invisalign® Teen) were treated with Invisalign® Ex30 aligner material and Invisalign® specific auxiliary means. In this non-interventional retrospective study, pre- and post-treatment maxillary and mandibular plaster cast models were scanned and superimposed with ClinChecks® via Surface-Surface Matching Algorithm on unmoved teeth providing stable references. Effectivity of planned versus clinically realized movements was evaluated for each tooth. Statistics were performed with a t-test and Bonferroni-Holm correction (α = 0.05). Orovestibular movement efficiency was excellent without statistical significance regarding jaw, tooth type or Invisalign® system. Mandibular translational tooth movements were highly effective, and outstanding for premolars (91-98%). Maxillary translational tooth movements were successful for incisors and premolars, but less effective for canines and molars. Almost all teeth were moderately or very effectively corrected by crown tipping, performing better for mandibular (70-92%) than maxillary (22-31%) canines as much as for adolescent upper front teeth (81-85%) and lower canines (92%). Aligners are able to effectively implement translational orovestibular movements, supported by tilting the crowns for even more efficient implementation of the movements. This phenomenon was observed in our studies for all teeth in both jaws, regardless of the Invisalign® system used. Treatment planning should nevertheless take into account the individual patient parameters with regard to the movements to be performed in order to make the aligner therapy as successful as possible in terms of realizing the desired therapeutic goal.

A Holonic Construction Management System for the Efficient Implementation of Building Energy Renovation Actions

In the architecture, engineering and construction (AEC) industry, many efforts have provided remarkable contributions to construction planning and control processes during work execution. Nevertheless, frequent coordination issues among stakeholders and difficulties in dealing with unexpected events can be explained by the complexity featuring the construction sector. Several approaches to deal with this issue were investigated in the manufacturing area, among which this paper looks at the holonic approach as one of the most promising strategies. This study first analyzes the more fragmented and dynamic nature of the construction industry as compared with the manufacturing one. Secondly, it suggests developing a process-based holonic construction management system based on building information modeling (BIM) and a conceptual architecture for manufacturing control called Product Resource Order Staff Architecture (PROSA). The process-based paradigm ensures exploiting the benefits of BIM towards the development of sustainable and efficient regeneration methods of the built environment. Subsequently, a first management system prototype was developed and tested for the purpose of renovation works management. For the first time, results from an actual implementation of PROSA were applied to a real construction site, and its feasibility was assessed using the data on the field. Key performance indicators (KPIs) evaluated during the onsite demonstration confirmed a good performance of PROSA and the presented holonic approach, which contributed to the overall success of the energy efficient refurbishment project.

Highly efficient implementation of analytic nonadiabatic derivative couplings within the pseudospectral method.

A pseudospectral implementation of nonadiabatic derivative couplings in the Tamm-Dancoff approximation is reported, and the accuracy and efficiency of the pseudospectral nonadiabatic derivative couplings are studied. Our results demonstrate that the pseudospectral method provides mean absolute errors of 0.2%-1.9%, while providing a significant speedup. Benchmark calculations on fullerenes (Cn, n up to 100) using B3LYP achieved 10- to 15-fold, 8- to 17-fold, and 43- to 75-fold speedups for 6-31G**, 6-31++G**, and cc-pVTZ basis sets, respectively, when compared to the conventional spectral method.

Deep Latent Factor Model for Spatio-Temporal Forecasting

Latent factor models can perform spatio-temporal forecasting (i.e., predicting future responses at unmeasured as well as measured locations) by modeling temporal dependence using latent factors and considering spatial dependence using a spatial prior on factor loadings. However, they may fail to capture complex spatio-temporal dependence because the latent factors are typically assumed to follow a classical linear time series model, such as a vector autoregressive model. In this paper, we propose a deep latent factor model for spatio-temporal forecasting that can model complex spatio-temporal dependence more flexibly by leveraging the high expressive power of a deep neural network. Specifically, the latent factors are modeled using a recurrent neural network and the factor loadings are modeled using a distance-based Gaussian process. The proposed model allows the number of latent factors to be inferred from the data using a beta-Bernoulli process, which enables computationally more efficient implementation compared to previous methods. We derive a stochastic variational inference algorithm for scalable inference of the proposed model and validate the model using simulated and real data examples.

A service-based approach to cryoEM facility processing pipelines at eBIC.

Electron cryo-microscopy image-processing workflows are typically composed of elements that may, broadly speaking, be categorized as high-throughput workloads which transition to high-performance workloads as preprocessed data are aggregated. The high-throughput elements are of particular importance in the context of live processing, where an optimal response is highly coupled to the temporal profile of the data collection. In other words, each movie should be processed as quickly as possible at the earliest opportunity. The high level of disconnected parallelization in the high-throughput problem directly allows a completely scalable solution across a distributed computer system, with the only technical obstacle being an efficient and reliable implementation. The cloud computing frameworks primarily developed for the deployment of high-availability web applications provide an environment with a number of appealing features for such high-throughput processing tasks. Here, an implementation of an early-stage processing pipeline for electron cryotomography experiments using a service-based architecture deployed on a Kubernetes cluster is discussed in order to demonstrate the benefits of this approach and how it may be extended to scenarios of considerably increased complexity.

Multidisciplinary aspects of the notion of efficiency: a discussion under applicability in the judiciary

This article aimed to discuss the multidisciplinary aspects of the notion of efficiency in relation to the decision maker's actions when aiming to meet speed, transparency, planning and economy. The principle of efficiency was included in the Brazilian Constitution in art. 37 by Constitutional Amendment nº 19, of 06/04/1998, defining the way public administration operates in its spheres of power to optimize management and achieve better results. This is qualitative research, with a descriptive nature, in which the interpretation and explanation of the theme and its social relations with aspects of reality prevail. As for technical procedures, it is characterized as bibliographic and documentary, in which it made use of elaborate material (books, scientific articles) and various materials (legislation, reports, manuals and others). It was found that the notion of efficiency has always been, in general, approached as the cost-benefit relationship observing the combination between available resources and their results (products and/or services). In Brazil, the applicability of efficiency in public management has gained emphasis over the years, both due to the evolution of legislation and the improvement of the way public entities are managed. In the judiciary, speed is more associated with procedural rites (time variable); transparency, good examples are the Justice in Numbers Report and the Socio-Environmental Report; planning, the National Council of Justice is responsible for improving work, control and administrative and procedural transparency; economy, the notion of performance with quality, achieving the best possible result. The concept of eco-efficiency has gained strength in recent years due to the gap in the development and implementation of efficiency as sustainability strategies with the premise of combining economic and environmental efficiencies.

VFL-Chain: Bulletproofing Federated Learning in the V2X environments

Federated Learning (FL) has gained significant traction as a promising approach to enable collaborative machine learning (ML) while safeguarding data privacy across diverse applications, with the Vehicle-to-Everything (V2X) environment being a notable use case. However, conventional FL systems remain susceptible to model poisoning attacks, wherein malicious participants can introduce inaccurate or deliberately misleading local model updates to the central aggregator. In the context of V2X, such attacks could spawn catastrophic outcomes and jeopardize safety-critical applications. To tackle this pressing concern, we introduce a privacy-preserving and verifiable FL framework, dubbed VFL-Chain, designed to facilitate secure and efficient collaboration among intelligent connected vehicles (ICVs). VFL-Chain aspires to deliver a privacy-centric, verifiable FL experience complemented by a robust incentive mechanism tailored for ICVs. Our proposed solution leverages Bulletproofs to enable efficient verification of model update integrity, which are assimilated within smart contracts on an underlying permissioned blockchain. Furthermore, VFL-Chain presents a fair incentive mechanism that rewards honest participation, fostering a resilient and efficient FL implementation. We conduct an exhaustive security analysis and performance assessment of our proposed system, which underscores its efficacy in countering data poisoning attacks and augmenting the accuracy of FL.

Parallel GEMM-based convolution for deep learning on multicore RISC-V processors

AbstractWe address the efficient implementation of the convolution operator on the GAP8 parallel ultra-low power platform (PULP), a heterogeneous multi-core processor equipped with a fabric controller (FC); a cluster of eight compute cores; and a four-level memory hierarchy with scratchpads instead of conventional, hardware-assisted cache memories. Our solution for this platform transforms the convolution into a general matrix–matrix multiplication (gemm) via the lowering approach, demonstrating that it is possible to attain reasonable performance on the GAP8 by carefully adapting techniques such as tiling and loop parallelism, which are mainstream in the multi-threaded, cache-aware realization of gemm.

The Impact of China’s New Infrastructure Development on Urban Innovation Quality—A Quasi-Natural Experiment of Smart City Pilots

Currently, human society is in the era of the digital economy, driven by a new wave of digital technology revolution. Against this backdrop, China actively draws on global development concepts, accelerating the advancement of new infrastructure construction. This initiative aims to stabilize current economic demands while laying a material foundation for long-term development. Therefore, the efficient implementation of this new infrastructure has become a pressing issue for China, as unlocking its empowering role in the national economy is of paramount importance. This study, based on balanced panel data from China’s initial smart city pilot projects from 2008 to 2018, employs both two-way fixed effects and mediation effect models to empirically examine the impact of new infrastructure construction on urban innovation quality, considering endogeneity issues. The research findings reveal that new infrastructure construction enhances urban innovation quality by expediting industrial structural upgrades and enhancing total factor productivity. Furthermore, due to variations in geographical location and population density, there is heterogeneity in the impact of new infrastructure on urban innovation quality, with investments in new infrastructure exerting a more pronounced positive effect in cities with high population density.

A multi-domain spectral collocation method for the Fokker–Planck equation in an infinite channel

In this paper, we propose a multi-domain spectral collocation method for partial differential equations on two-dimensional unbounded domains. Some approximation results on the composite generalized Laguerre-Legendre interpolation and quasi-orthogonal projecting are established, respectively. These results play a significant role in related spectral collocation method. As an application, a multi-domain spectral collocation scheme is provided for the Fokker–Planck equation with absorption or non-homogeneous boundary conditions. The convergence of the proposed algorithm is performed. An efficient implementation is presented. Numerical experiments demonstrate the effectiveness and high accuracy of the algorithm.

Machine Learning K-Means Clustering of Interpolative Separable Density Fitting Algorithm for Accurate and Efficient Cubic-Scaling Exact Exchange Plus Random Phase Approximation within Plane Waves.

The exact-exchange plus random-phase approximation (EXX+RPA) method has emerged as a crucial tool for precisely characterizing electronic structures in molecular and solid systems. We present an accurate and efficient implementation of EXX+RPA calculations that scale cubically and are conducted within plane waves. Our approach incorporates the interpolative separable density fitting (ISDF) algorithm, effectively mitigating the computational challenges associated with the plane wave basis set. To overcome the constraints of the conventional ISDF algorithm, characterized by the exceptionally high prefactor in QR factorization for interpolation point selection, we introduce an enhanced machine learning K-means method. This method incorporates a novel empirical weight function called "SSM+" for more precise interpolation point selection, capturing physical information more accurately across diverse systems. Our machine learning approach offers a quasiquadratic scaling alternative, effectively replacing the computationally demanding cubic-scaling QRCP algorithm in plane-wave-based EXX+RPA calculations. Furthermore, we enhance the method's capabilities by optimizing GPU acceleration using MATLAB's integrated GPU toolkit. In particular, our approach reduces the computational scaling of χ0 from 3.80 to 2.13 and the overall computational scaling of EXX from 2.74 to 2.10. We achieve a remarkable GPU acceleration speedup of up to 35×. Regarding CPU computation time, the standard quartic-scaling method requires 22 h to compute Si128, while QRCP completes the calculation in only around 1 h, achieving a speedup up to 20×. However, the utilization of the K-means algorithm reduces the time to 800 s, a substantial improvement of 100× compared to the standard algorithm. By employing the K-means algorithm, the computational time for interpolative point calculation using QRCP decreases from 1 h to 1 min, resulting in a 55× speed increase. With this improved algorithm, we successfully computed the dissociation curve of H2 and the equilibrium polyynic geometry of C18 molecules.

Blockchain Technology in Supply Chain Management

Blockchain is a promising technology with its features such as immutability and decentralized database. It has applications in various fields such as pharmaceutical, finance, & food industry. At the core of its heart lies its feature, traceability which is the most desired key in supply chains. However, supply chains have always been hit rock bottom by scandals and controversies. In this review paper, we have explored the advancement and research gaps, of blockchain technology (BT) in supply chain management (SCM). We have used the Prisma framework for systematic literature review (SLR) and included a minuscule amount of grey literature to reduce publication bias. We found that supply chain traceability and transparency is the most researched objective in SCM. There was hardly any research in supply chain resilience. Further, we found that 40 % of the papers were application based. Most articles have focused on the advantages of BT, rather than analyzing it critically. This study will help identify gaps and, suitable actions to be followed for an efficient implementation of BT in SCM.

Efficient GPU implementation of randomized SVD and its applications

Matrix decompositions are ubiquitous in machine learning, including applications in dimensionality reduction, data compression and deep learning algorithms. Typical solutions for matrix decompositions have polynomial complexity which significantly increases their computational cost and time. In this work, we leverage efficient processing operations that can be run in parallel on modern Graphical Processing Units (GPUs), predominant computing architecture used e.g. in deep learning, to reduce the computational burden of computing matrix decompositions. More specifically, we reformulate the randomized decomposition problem to incorporate fast matrix multiplication operations (BLAS-3) as building blocks. We show that this formulation, combined with fast random number generators, allows to fully exploit the potential of parallel processing implemented in GPUs. Our extensive evaluation confirms the superiority of this approach over the competing methods and we release the results of this research as a part of the official CUDA implementation.11https://docs.nvidia.com/cuda/cusolver/index.html.

Fused GEMMs towards an efficient GPU implementation of the ADER‐DG method in SeisSol

SummaryThis study shows how GPU performance of the ADER discontinuous Galerkin method in SeisSol (an earthquake simulation software) can be further improved while preserving its original design that ensures high CPU performance. We introduce a new code generator (“ChainForge”) that fuses subsequent batched matrix multiplications (“GEMMs”) into a single GPU kernel, holding intermediate results in shared memory as long as necessary. The generator operates as an external module linked against SeisSol's domain specific language YATeTo and, as a result, the original SeisSol source code remains mainly unchanged. In this paper, we discuss several challenges related to automatic fusion of GPU kernels and provide solutions to them. By and large, we gain 60% in performance of SeisSol's wave propagation solver using Fused‐GEMMs compared to the original GPU implementation. We demonstrated this on benchmarks as well as on a real production scenario simulating the Northridge 1994 earthquake.

A density-fitting implementation of the density-based basis-set correction method.

This work reports an efficient density-fitting implementation of the density-based basis-set correction (DBBSC) method in the MOLPRO software. This method consists in correcting the energy calculated by a wave-function method with a given basis set by an adapted basis-set correction density functional incorporating the short-range electron correlation effects missing in the basis set, resulting in an accelerated convergence to the complete-basis-set limit. Different basis-set correction density-functional approximations are explored and the complementary-auxiliary-basis-set single-excitation correction is added. The method is tested on a benchmark set of reaction energies at the second-order Møller-Plesset (MP2) level and a comparison with the explicitly correlated MP2-F12 method is provided. The results show that the DBBSC method greatly accelerates the basis convergence of MP2 reaction energies, without reaching the accuracy of the MP2-F12 method but with a lower computational cost.

A fast and high‐accurate commutation failure identification method for LCC‐HVDC system

AbstractCommutation failure (CF) is one of the most common issues in line‐commuted converter‐based high voltage direct current systems (LCC‐HVDC), leading to critical power system security and stability problems. Accurate and rapid identification of CF is crucial to prevent subsequent CF in HVDC systems. However, the existing CF identification methods are lack of the required accuracy and speed. The relationship between bridge arm current, commutation voltage, and trigger pulse based on the commutation progress mechanism after the AC fault is analysed in this paper. A novel CF identification method is presented, which utilises the CF identification factor, enabling fast and high‐accurate identification without relying on the value of the extinction angle. The proposed method offers a simpler and more efficient implementation in engineering practice. Finally, the effectiveness of the proposed method is verified in both the standard International Council on Large Electric systems HVDC model and the Hardware in Loop test using practical project parameters. The results demonstrate that the proposed method can accurately and fast identify CF.