Modular Implementation Research Articles

P-OGC81 The learning curve for robotic assisted abdominal phase in two stage oesophagectomy

Abstract Background Robotic assisted oesophagectomy (RAO) is increasingly being utilised in the management of oesophageal cancer. RAO implementation into practice has an inevitable learning curve. As oesophagectomy usually involves at least 2 stages, a staggered approach to training and introduction of RAO can be done. A major advantage of this is that the surgeon can concentrate on overcoming the learning curve in one phase of the procedure at a time, whilst the remaining phase can be completed by an established technique. This study looks at the learning curve of a robotic assisted abdominal phase for two-stage oesophagectomy compared to an open abdominal phase to achieve parity. Methods This study uses a prospectively maintained database to retrospectively analyse the abdominal phase of the first 17 RAO compared to the previous 20 open abdominal phase procedures. The cases are sequential, done by a single surgeon at a large UK oesophagogastric referral centre. Operating time, nodal count, and R0 rate were reviewed to determine the number of cases on the learning curve to reach parity with the open procedure. Results The open abdominal phase group had a similar age (65.6 vs 65.7), pre-op anaerobic threshold (13.9 vs 14.6 p = 0.3) but a higher BMI (mean 30.6 vs 24.6 p < 0.05) then the RAO group. All cases were T3 adenocarcinoma except for 2 cases in the robotic group (one HGD and one T2 adenocarcinoma). No RAO cases were converted to open. The mean time for the abdominal phase in the open group was 175.4 minutes with an average nodal count of 32.9. After 8 robotic assisted cases the mean operating time decreased from 267 minutes to 197 minutes, which was when a non-significant difference to the open group (p = 0.094) became apparent. The mean nodal count in the first 8 robotic assisted cases was 29.5 and increased to 38.4 in the subsequent cases. All patients had a R0 resection. Conclusions The multi-phase nature of oesophagectomy allows for modular implementation of a robotic programme. We have found that the learning curve for robotic assisted abdominal is around 8 cases. This allows for parity to open abdominal phase to be achieved regarding operative time, nodal count and R0 resection.

Toward Modular and Flexible Open RAN Implementations in 6G Networks: Traffic Steering Use Case and O-RAN xApps.

The development of cellular wireless systems has entered the phase when 5G networks are being deployed and the foundations of 6G solutions are being identified. However, in parallel to this, another technological breakthrough is observed, as the concept of open radio access networks is coming into play. Together with advancing network virtualization and programmability, this may reshape the way the functionalities and services related to radio access are designed, leading to modular and flexible implementations. This paper overviews the idea of open radio access networks and presents ongoing O-RAN Alliance standardization activities in this context. The whole analysis is supported by a study of the traffic steering use case implemented in a modular way, following the open networking approach.

BOOMER — An algorithm for learning gradient boosted multi-label classification rules

Multi-label classification is concerned with the assignment of sets of labels to individual data points. Due to its diverse real-world applications, e.g., the annotation of text documents with topics, it has become a well-established field of machine learning research. Compared to traditional classification, where classes are mutually exclusive, multi-label classification comes with interesting challenges, most prominently the requirement to take dependencies between labels into account. In this work, we present a modular and customizable implementation of BOOMER – an algorithm for learning gradient boosted multi-label classification rules – that can flexibly be adjusted to different use cases and requirements.

Research on modular implementation method of six-degree-of-freedom robotic arm

Single-joint modular design can reduce the work intensity of designers, and also can broaden the combination form of multi-degree-of-freedom robotic arm. In order to adapt to the changes of multiple degrees of freedom and multiple loads, this paper designs a series of standard modules with similar components and the same standard interface, but with different sizes only, and chooses different drive components according to the load when designing the size, and then designs the size of other parts according to the size of the drive components. The final combination of this series of modules into different degrees of freedom robotic arm, such as three degrees of freedom robotic arm, four degrees of freedom robotic arm or even six degrees of freedom robotic arm. In this paper, the most widely used six-degree-of-freedom robotic arm is used as an example, and a detailed design form is proposed.

IoT-enabled computer vision-based parts inspection system for SME 4.0

This paper presents a Computer vision-based Measurement (CViM) system for three-dimensional (3D) mechanical parts. The system integrates with internet of things (IoT) and demonstrates a low cost and modular implementation of SME 4.0 framework. A complete system design including hardware and software is presented. Three cameras are used to capture various images that expose different dimensions of the mechanical part. Back lighting is used to illuminate the part placed on the V-shaped translucent surface, which results in images with improved contrast thereby improving the measurement accuracy compared to front lighting. The software algorithm performs camera calibration to eliminate the lens distortion and pre-processing for image enhancement and noise elimination. Next, image binarization is performed to highlight the edges of the object. The desired dimensions are extracted from the image followed by pixel to physical measurement mapping. The system is tested by measuring physical dimensions of a drill bit and a screwdriver bit and it achieves an accuracy of tenth of a millimeter (mm). Several dimensions of the test parts are measured, such as, total length, back radius, groove length etc., which are the most number of dimensions considered, compared to previous studies. The results demonstrate that the maximum difference between the measured and the actual dimensions is ± 0.05 mm. A conceptual model of IoT-enabled CViM system for parts inspection in small and medium-sized industries is also presented. The system has a fairly low cost and is very suitable for such industries with similar accuracy requirements for their parts inspection.

Mathematical model of excitation-contraction in a uterine smooth muscle cell

The Bursztyn et al. (2007) paper proposes a mathematical model of excitation-contraction in a myometrial smooth muscle cell (SMC). The model incorporates processes of intracellular Ca^2+ concentration control, myosin light chain (MLC) phosphorylation and stress production. We create a modularized CellML implementation of the model, which is able to simulate these processes against the original data.

Mathematical model of excitation-contraction in a uterine smooth muscle cell

The Bursztyn et al. (2007) paper proposes a mathematical model of excitation-contraction in a myometrial smooth muscle cell (SMC). The model incorporates processes of intracellular Ca^2+ concentration control, myosin light chain (MLC) phosphorylation and stress production. We create a modularized CellML implementation of the model, which is able to simulate these processes against the original data.

A Quantitative Model of Human Jejunal Smooth Muscle Cell Electrophysiology

The Poh et al. (2012) paper describes the first biophysically based computational model of human jejunal smooth muscle cell (hJSMC) electrophysiology. The ionic currents are described by either a traditional Hodgkin-Huxley (HH) formalism or a deterministic multi-state Markov (MM) formalism. We create a modularized CellML implementation of the model, which is able to reproduce clamping behaviours of individual currents and whole cell action potential traces. In addition, some inconsistencies have been uncovered and discussed in this paper.

A Quantitative Model of Human Jejunal Smooth Muscle Cell Electrophysiology

The Poh et al. (2012) paper describes the first biophysically based computational model of human jejunal smooth muscle cell (hJSMC) electrophysiology. The ionic currents are described by either a traditional Hodgkin-Huxley (HH) formalism or a deterministic multi-state Markov (MM) formalism. We create a modularized CellML implementation of the model, which is able to reproduce clamping behaviours of individual currents and whole cell action potential traces. In addition, some inconsistencies have been uncovered and discussed in this paper. EDITOR'S NOTE (v2): A typographical correction in the reproducibility report was made. (v3): This Abstract was appended with a note on changes made to versions.

A Quantitative Model of Human Jejunal Smooth Muscle Cell Electrophysiology

The Poh et al. (2012) paper describes the first biophysically based computational model of human jejunal smooth muscle cell (hJSMC) electrophysiology. The ionic currents are described by either a traditional Hodgkin-Huxley (HH) formalism or a deterministic multi-state Markov (MM) formalism. We create a modularized CellML implementation of the model, which is able to reproduce clamping behaviours of individual currents and whole cell action potential traces. In addition, some inconsistencies have been uncovered and discussed in this paper. EDITOR'S NOTE (v2): A typographical correction in the reproducibility report was made. (v3): This Abstract was appended with a note on changes made to versions.

Generalised Image Processing Method for Quantitative Analysis of Nucleus, Cell and Focal Adhesion Clusters

Abstract Focal adhesion clusters (FAC) are dynamic and complex structures that help cells to sense physicochemical properties of their environment. Research in biomaterials, cell adhesion or cell migration often involves the visualization of FAC by fluorescence staining and microscopy, which necessitates quantitative analysis of FAC and other cell features in microscopy images using image processing. Fluorescence microscopy images of human umbilical vein endothelial cells (HUVEC) obtained at 63x magnification were quantitatively analysed using ImageJ software. A generalised algorithm for selective segmentation and morphological analysis of FAC, nucleus and cell morphology is implemented. Further, a method for discrimination of FAC near the nucleus and around the periphery is implemented using masks. Our algorithm is able to effectively quantify different morphological characteristics of cell components and shows a high sensitivity and specificity while providing a modular software implementation.

Modular implementation framework of partitioned path-following strategies: Formulation, algorithms and application to the finite element software Cast3M

Damage, cracking, and strain localization mechanisms often lead to unstable structural responses characterized by snap-backs (i.e., force and displacement decrease simultaneously). Standard nonlinear Newton-based solution algorithms with displacement/force control cannot capture the equilibrium curve in its entirety. This can be overcome using path-following formulations. A general (i.e., valid for any finite element code) implementation framework can be designed and applied to the Cast3M software by collecting the essential concepts and the formalism of partitioned path-following arc-length algorithms. Thanks to these developments, Cast3M is now capable of processing path-following equations without any major modifications. Three path-following constraints were selected to demonstrate the applicability of this framework: a first one on the combination of the displacement increment at a given set of nodes, a second one on the maximum strain increment over the computational domain, and a third one on the maximum elastic predictor of the damage/plastic criterion function over the computational domain. Two- and three-dimensional strain localization simulations show that the proposed framework behaves in a stable and convergent manner, even when multiple severe snap-back instabilities are present. Users of Cast3M shall find the proposed study helpful in that it allows them to focus on developing new path-following equations for the software. Cast3M is developed by the French Alternative Energies and Atomic Energy Commission (CEA) and freely available for research purposes. The developments discussed in this paper have been made available to the user/developer community along with Cast3M 2021 (release date: June 2021).

A Quantitative Model of Human Jejunal Smooth Muscle Cell Electrophysiology

The Poh et al. (2012) paper describes the first biophysically based computational model of human jejunal smooth muscle cell (hJSMC) electrophysiology. The ionic currents are described by either a traditional Hodgkin-Huxley (HH) formalism or a deterministic multi-state Markov (MM) formalism. We create a modularized CellML implementation of the model, which is able to reproduce clamping behaviours of individual currents and whole cell action potential traces. In addition, some inconsistencies have been uncovered and discussed in this paper.

Gen3sis: A general engine for eco-evolutionary simulations of the processes that shape Earth's biodiversity.

Understanding the origins of biodiversity has been an aspiration since the days of early naturalists. The immense complexity of ecological, evolutionary, and spatial processes, however, has made this goal elusive to this day. Computer models serve progress in many scientific fields, but in the fields of macroecology and macroevolution, eco-evolutionary models are comparatively less developed. We present a general, spatially explicit, eco-evolutionary engine with a modular implementation that enables the modeling of multiple macroecological and macroevolutionary processes and feedbacks across representative spatiotemporally dynamic landscapes. Modeled processes can include species' abiotic tolerances, biotic interactions, dispersal, speciation, and evolution of ecological traits. Commonly observed biodiversity patterns, such as α, β, and γ diversity, species ranges, ecological traits, and phylogenies, emerge as simulations proceed. As an illustration, we examine alternative hypotheses expected to have shaped the latitudinal diversity gradient (LDG) during the Earth's Cenozoic era. Our exploratory simulations simultaneously produce multiple realistic biodiversity patterns, such as the LDG, current species richness, and range size frequencies, as well as phylogenetic metrics. The model engine is open source and available as an R package, enabling future exploration of various landscapes and biological processes, while outputs can be linked with a variety of empirical biodiversity patterns. This work represents a key toward a numeric, interdisciplinary, and mechanistic understanding of the physical and biological processes that shape Earth's biodiversity.

Modular Design and Implementation of an Automatic Power Factor Regulator for Energy Savings on Fishing Vessels

This study describes the experiences of modular design and implementation of an automatic power factor regulator for improving energy efficiency on Taiwanese longline fishing vessels. The load capacities of such vessels are typically divided into three categories, depending on the gross tonnage or fish carrying capacity: less than 100 kW, from 100 kW to less than 250 kW, and larger than 250 kW. For attaining proper power factor improvement, a compensation unit with a total of 30 kVar, 60 kVar and 90 kVar was sized and assembled with capacitors of 10, 15 and 20 kVar to enable control within 12 steps. Slots for additional capacitors of 10 kVar were reserved and connected in parallel. Based on the experiences, fuel consumption can be reduced and the payback period of the implemented equipment occurs to be within a little more than a year.

Self-contained and modular structured illumination microscope.

We present a modular implementation of structured illumination microscopy (SIM) that is fast, largely self-contained and that can be added onto existing fluorescence microscopes. Our strategy, which we call HIT-SIM, can theoretically deliver well over 50 super-resolved images per second and is readily compatible with existing acquisition software packages. We provide a full technical package consisting of schematics, a list of components and an alignment scheme that provides detailed specifications and assembly instructions. We illustrate the performance of the instrument by imaging optically large samples containing sequence-specifically stained DNA fragments.

General Purpose Optimization Library (GPOL): A Flexible and Efficient Multi-Purpose Optimization Library in Python

Several interesting libraries for optimization have been proposed. Some focus on individual optimization algorithms, or limited sets of them, and others focus on limited sets of problems. Frequently, the implementation of one of them does not precisely follow the formal definition, and they are difficult to personalize and compare. This makes it difficult to perform comparative studies and propose novel approaches. In this paper, we propose to solve these issues with the General Purpose Optimization Library (GPOL): a flexible and efficient multipurpose optimization library that covers a wide range of stochastic iterative search algorithms, through which flexible and modular implementation can allow for solving many different problem types from the fields of continuous and combinatorial optimization and supervised machine learning problem solving. Moreover, the library supports full-batch and mini-batch learning and allows carrying out computations on a CPU or GPU. The package is distributed under an MIT license. Source code, installation instructions, demos and tutorials are publicly available in our code hosting platform (the reference is provided in the Introduction).

A design for a generic and modular bio-economic farm model

ContextPast reviews of policy impact assessment studies using bio-economic farm models (BEFM) called for the development of a generic and modular implementation that can be maintained by a network of modellers. A main reason for these calls is the project-oriented way in which model developers receive funding. It favours the development of new models with case-study specific features over the maintenance and extension of well-tested, more generic ones which allow comparing results in a consistent way across many case-studies. The demand for more generic tools also reflects the dynamic landscape of policy measures within larger policy frameworks like the Common Agricultural Policy (CAP). These policy frameworks move increasingly away from a ‘one-size-fits-all’ approach of policy design towards more flexible systems, giving greater freedom to shape, implement, and target policy measures to specific regions, farm management systems and farm types. This creates new challenges for model-based impact assessment as applied models have to reflect the variety of policy measures and characteristics of targeted farmers and rural communities. ObjectiveThe aim of this paper is to first address key questions regarding the functionality and implementation of such a modular BEFM that can be maintained and expanded by a user group, and second to develop concrete proposals of necessary model features, model design and shared development. MethodsThis paper builds on literature research, including a detailed review of four models that are used extensively for impact assessment within the EU and were developed by multiple teams over a longer period of time. From there, necessary and desirable features of a generic and modular BEFM are identified and requirements for model design regarding modularity, software engineering, and shared development are discussed. Results and conclusionsThis feeds into the development of concrete proposals of how modularity and flexibility can be addressed in the development, application and maintenance of a BEFM. At the end, a list of design decisions and implementation steps is proposed to build a modular BEFM that can be maintained by a network of researchers. SignificanceThe concept for a network-based generic and modular bio-economic farm model responds to the demand for analytical tools in agricultural policy impact analysis. The paper develops a research agenda to overcome observed limitations in the current landscape of such models.

A Novel Modular Approach for Kinematic Modeling and Analysis of Planar Hybrid Manipulators

Abstract For customized design of a hybrid manipulator for a specific application, selection of an appropriate configuration is always a challenge. To assist in this foremost decision in data-driven synthesis, a novel approach is proposed for modular formation of quick configurations. Majorly, a unified methodology is presented for the development of respective kinematic models and differential relations for their performance analyses. This unified modular approach utilizes modular primitives to define planar hybrid configurations. Three types of primitives are introduced as modular components, and the pattern study is detailed. Modeling results from the proposed approach are also compared with normally used partial differentiation with respect to the computational efforts, streamlined modular implementations, and applicability in optimal design approaches. The comparison highlights how the column-wise approach is appropriate for modular methodology. The strategy will help a designer as a tool for analyzing several configurations. Two realistic case studies are demonstrated in this article for application of the methodology in the medical robotics field.

Pythonic Black-box Electronic Structure Tool (PyBEST). An open-source Python platform for electronic structure calculations at the interface between chemistry and physics

Pythonic Black-box Electronic Structure Tool (PyBEST) represents a fully-fledged modern electronic structure software package developed at Nicolaus Copernicus University in Toruń. The package provides an efficient and reliable platform for electronic structure calculations at the interface between chemistry and physics using unique electronic structure methods, analysis tools, and visualization. Examples are the (orbital-optimized) pCCD-based models for ground- and excited-states electronic structure calculations as well as the quantum entanglement analysis framework based on the single-orbital entropy and orbital-pair mutual information. PyBEST is written primarily in the Python programming language with additional parts written in C＋＋, which are interfaced using Pybind11, a lightweight header-only library. By construction, PyBEST is easy to use, to code, and to interface with other software packages. Moreover, its modularity allows us to conveniently host additional Python packages and software libraries in future releases to enhance its performance. The electronic structure methods available in PyBEST are tested for the half-filled 1-D model Hamiltonian. The capability of PyBEST to perform large-scale electronic structure calculations is demonstrated for the model vitamin B12 compound. The investigated molecule is composed of 190 electrons and 777 orbitals for which an orbital optimization within pCCD and an orbital entanglement and correlation analysis are performed for the first time. Program summaryProgram title: PyBESTCPC Library link to program files:https://doi.org/10.17632/xf9kb7yfwr.1Developer’s repository link:https://zenodo.org/record/3925278#.X5KAZS8Rq6sLicensing provisions: GNU General Public License 3Programming language:Python, C＋＋Nature of problem: Efficient and reliable modeling of electronic structures featuring both weakly- and strongly-correlated electrons. Small- and large-scale quantum-mechanical problems at the interface between chemistry and physics comprising both quantum chemical and model Hamiltonians. Specifically, modeling potential energy surfaces of complex electronic structures including bond breaking/formation, elucidating complex electronic structures through the picture of interacting orbitals, describing noncovalent interactions, ultra-cold trapped quantum gases, and a variety of applications in interdisciplinary quantum mechanical-based problems.Solution method: Modular implementation of a series of unconventional (and conventional) electronic structure models based on the pCCD ansatz to solve the electronic Schrödinger equation. These include the description of both ground- and excited-states, the determination of interaction energies, and the analysis and interpretation of electronic wavefunctions. All modules are implemented in the modern Python programming language, where bottleneck operations are handled by C＋＋ code interfaced by the Pybind11 header-only library. The implemented (wavefunction) modules and modular code structure make PyBEST a very efficient alternative to existing electronic structure packages.Additional comments including restrictions and unusual features:PyBEST features unconventional electronic structure methods (pCCD and post-pCCD methods) that are not available in any other quantum chemistry/physics software package. It also includes a general orbital entanglement and correlation module that supports both pCCD and selected post-pCCD methods. PyBEST is designed to be easy to use and code in. Due to its modularity (for instance of the tensor contraction engine), new Python modules and features can be straightforwardly imported and exploited without changing any wavefunction modules directly.