Computational Point Of View Research Articles

Analysis and Design of Integration Model for API Management and CI/CD at Directorate General of Taxation

The Directorate General of Taxes (DGT) currently utilizes Application Programming Interface (API) to enhance efficiency in tax data exchange with external parties. DGT is facing challenges due to the rising number of published APIs and the increasing connections from external parties to the DGT system, which necessitates a speedy API issuance process. The objective of this research is to assist the Directorate General of Taxation (DGT) in developing an integrated API management system with Continuous Integration/Continuous Deployment (CI/CD). The system design process is conducted using the Standards and Architectures for E-Government Application (SAGA) framework, encompassing Enterprise Viewpoint, Technology Viewpoint, Computational Viewpoint, Information Viewpoint, and Engineering Viewpoint. A qualitative method is employed, including interviews to gain insights into the existing issues. Additionally, information regarding systems and technologies is documented for gap analysis. The results of this analysis are then utilized to design the architecture of the API management system, applications, and technologies. This research yields a model of the API management system integrated with CI/CD at DGT. The model is developed using 3Scale and Jenkins software. Following validation, the API management system at DGT operates effectively with three DGT API systems and three API users.

3D printing slicing algorithm and partition scanning strategy for big data

3D printing technology is a popular new technology that combines information technology with mechanical engineering and material science, and is being widely used in various fields. From the computer point of view, the basic technology of 3D printing is to slice and scan each part of the 3D model. The slicing algorithm used directly affects the speed of slicing and the effect of post-printing, and the result of slicing needs to be optimized. This paper analyzes the slicing algorithm and partition scanning strategy related technologies of 3D printing models and solves the practical problems of models in the 3D printing process. This paper first studies the slicing algorithm in the 3D printing process, then obtains and intercepts the cross-sectional contour, and finally performs mosaic and partition scanning on the sliced polygon, and uses experiments to verify the effectiveness of the research. The experimental results show that compared with other 3D printing methods, the printing method in this paper reduces the slicing time by 23.61%, and the fitting error range and the fluctuation range of the residual height of the fusion volume are smaller.

Analytical results for uncertainty propagation through trained machine learning regression models

Machine learning (ML) models are increasingly being used in metrology applications. However, for ML models to be credible in a metrology context they should be accompanied by principled uncertainty quantification. This paper addresses the challenge of uncertainty propagation through trained/fixed ML regression models. Analytical expressions for the mean and variance of the model output are obtained/presented for certain input data distributions and for a variety of ML models. Our results cover several popular ML models including linear regression, penalised linear regression, kernel ridge regression, Gaussian Processes (GPs), support vector machines (SVMs) and relevance vector machines (RVMs). We present numerical experiments in which we validate our methods and compare them with a Monte Carlo approach from a computational efficiency point of view. We also illustrate our methods in the context of a metrology application, namely modelling the state-of-health of lithium-ion cells based upon Electrical Impedance Spectroscopy (EIS) data.

Structure-preserving reduced order model for cross-diffusion systems

In this work, we construct a structure-preserving Galerkin reduced-order model for the resolution of parametric cross-diffusion systems. Cross-diffusion systems are often used to model the evolution of the concentrations or volumic fractions of mixtures composed of different species, and can also be used in population dynamics (as for instance in the SKT system). These systems often read as nonlinear degenerated parabolic partial differential equations, the numerical resolutions of which are highly ex- pensive from a computational point of view. We are interested here in cross-diffusion systems which exhibit a so-called entropic structure, in the sense that they can be formally written as gradient flows of a certain entropy functional which is actually a Lyapunov functional of the system. In this work, we propose a new reduced-order modelling method, based on a reduced basis paradigm, for the resolution of parameter-dependent cross-diffusion systems. Our method preserves, at the level of the reduced-order model, the main mathematical properties of the continuous solution, namely mass conservation, non- negativeness, preservation of the volume-filling property and entropy-entropy dissipation relationship. The theoretical advantages of our approach are illustrated by several numerical experiments.

Flexible serial capacity allocation with intensive care application

Serial delay structures are of natural and practical interest, as in production, communications and health care. Flexible service allocation might well be beneficial, particularly to handle overflow. The availability of bed capacity for Intensive Care (IC) provision was prominent in hospitals during the corona period. By integrating an Intensive Care Unit (ICU) with a flexible Step Down Unit (SDU) for intensive care overflow, IC availability might be greatly enhanced. This will lead to an unsolvable system that has to be evaluated by numerical computation (or simulation). First, as an approximate modeling and from a computational point of view, it is shown that by an artificial capacity flexibility principle, related to a principle known for parallel structures within telecommunications, an analytic so-called product form expression can be obtained for a flexible serial structure, such as an ICU-SDU complex. The principle will be referred to as serial repositioning (SR). The analytic form appears to be of both theoretical and computational interest. Theoretical as the closed form expression under this principle seems new for a serial structure. For indistinguishable intensive care at the ICU and SDU it is also proven to be insensitive, i.e. to only depend on the IC service by its mean. Computational as it provides easily computable estimates and bounds. For distinguishable overflow services, in contrast, insensitivity is numerically shown not to hold. Nevertheless, from a practical point of view, the insensitivity appears virtually true. Throughout numerical support is provided for small and realistic scale. It shows that the result •provides a reasonable first order of magnitude approximate,•supports an intuitive notion of bounds,•strongly reduces computational times and leads to qualitative results. For example, it shows that full flexibility for overflow service might be most useful but not necessarily required. A reallife academic hospital case is numerically studied. It illustrates that just a small number of flexible beds can well be beneficial. The results can be seen as representative for ICU-SDU dimensioning itself as well as for possible application to other serial structures, such as in flexible manufacturing.

Unsupervised tail modeling via noisy cross‐entropy minimization

AbstractEstimation of dynamic mixture distributions is a difficult task, because the density contains an intractable normalizing constant. To overcome this difficulty, we develop an approach that maximizes, by means of the cross‐entropy method, a Monte Carlo approximation of the log‐likelihood function. The proposed noisy cross‐entropy approach is unsupervised, since it does not require the specification of a threshold between the distributions. Moreover, it bypasses the evaluation of the normalizing constant, combining good statistical properties with a modest computational burden. Both simulation‐based evidence and empirical applications suggest that noisy cross‐entropy estimation is comparable or preferable to existing methods in terms of statistical efficiency, but is less demanding from the computational point of view.

Strong core and Pareto-optimality in the multiple partners matching problem under lexicographic preference domains

We study strong core and Pareto-optimal solutions for multiple partners matching problem under lexicographic preference domains from a computational point of view. The restriction to the two-sided case is called stable many-to-many matching problem and the general one-sided case is called stable fixtures problem. We provide an example to show that the strong core can be empty even for many-to-many problems, and that deciding the non-emptiness of the strong core is NP-hard. On the positive side, we give efficient algorithms for finding a near feasible strong core solution and for finding a fractional matching in the strong core of fractional matchings. In contrast with the NP-hardness result for the stable fixtures problem, we show that finding a maximum size matching that is Pareto-optimal can be done efficiently for many-to-many problems. Finally, we show that for reverse-lexicographic preferences the strong core is always non-empty in the many-to-many case.

Intrinsic universality in automata networks I: Families and simulations

An automata network (AN) is a finite graph where each node holds a state from a finite alphabet and is equipped with a local map defining the evolution of the state of the node depending on its neighbors. They are studied both from the dynamical and the computational complexity point of view. Inspired from well-established notions in the context of cellular automata, we develop a theory of intrinsic simulations and universality for families of automata networks. We establish many consequences of intrinsic universality in terms of complexity of orbits (periods of attractors, transients, etc) as well as hardness of the standard well-studied decision problems for automata networks (short/long term prediction, reachability, etc). In the way, we prove orthogonality results for these problems: the hardness of a single one does not imply hardness of the others, while intrinsic universality implies hardness of all of them. We also compare our notions of universality to intrinsic universality of cellular automata. This paper is the first of a series of three: in the second one, we develop a proof technique to establish intrinsic universality based on an operation of glueing; in the third one we study the effect of update schedules on intrinsic universality for concrete symmetric families of automata networks.

Density-based evolutionary model of the space debris environment in low-Earth orbit

Lethal untrackable debris objects pose the highest risk to the sustainability of the space environment, and thus, shall be included in the assessment of the long-term effect of mitigation and remediation measures to the space debris problem. The introduction of centimetre-sized particles in the debris evolutionary models represents a challenge from a computational cost point of view. To answer this need, this work proposes a novel probabilistic debris environment propagator. The method classifies the objects population into intact objects and fragmentation debris. The evolution of the former population is retrieved through an individual definition of each object’s mission profile. A continuum approach is adopted for the characterisation of the fragments, whose density distribution in orbital elements is propagated in time through the continuity equation. The intrinsic computational efficiency of the density-based fragments cloud models is leveraged to make the method agnostic to the lowest fragments size considered. A second classification of the population of intact objects into species, such as payloads, rocket bodies, mission related objects and constellations, ensures a faithful replication of their orbit evolution. Fragmentation debris caused by intact objects explosion and accidental fragments-intact object collision are included in a probabilistic fashion at the detected fragmentation epoch, to account for their feedback effect onto the environment. The model is applied to estimate the evolution of the space debris population in low-Earth orbit up to 200 years from the reference epoch, with and without the inclusion of a future launch traffic pattern, and considering a different fulfilment of the post-mission disposal phase.

Describing the ligandin properties of Plasmodium falciparum and vivax glutathione transferase towards bromosulfophthalein from empirical and computational modelling viewpoints

Research has spotlighted glutathione transferase (GST) as a promising target for antimalarial drug development due to its pivotal role in cellular processes, including metabolizing toxins and managing oxidative stress. This interest arises from GST’s potential to combat multidrug resistance in existing antimalarial drugs. Plasmodium falciparum GST (PfGST) and Plasmodium vivax GST (PvGST) are key targets; inhibiting them not only disrupt detoxification but also reduce their antioxidant capacity, a critical feature for potent antimalarials. Bromosulfophthalein (BSP), a clinical liver function dye, emerged as a potent cytosolic GST inhibitor. This study explored BSP’s inhibitory properties on PfGST and PvGST, showcasing its binding capabilities through empirical and computational analyses. The study revealed BSP’s ability to significantly inhibit GST activity, altering the proteins’ structures and stability. Specifically, BSP binding induced spectral changes and impacted the proteins’ thermal stability, reducing their melting temperatures. Computational simulations highlighted BSP's strong binding to PfGST and PvGST at their dimer interface, stabilized by various interactions, including hydrogen bonds and van der Waals forces. Notably, BSP’s binding altered the proteins’ compactness and conformational dynamics, suggesting a potential non-competitive, allosteric inhibition mechanism. This study provided novel insights into BSP’s candidacy as an antimalarial drug by targeting PfGST and PvGST. Its ability to disrupt crucial functions of these enzymes’ positions BSP as a promising candidate for further drug development in combating malaria Communicated by Ramaswamy H. Sarma

Storytelling systems: Progress towards generation informed by models of the reader

The present paper attempts to identify specific points of contact between efforts to generate narrative in the field of artificial intelligence and the field of narratology. Such points of contact centre around the tasks of modelling processes related to the construction of narrative in computational terms. A number of existing research efforts to develop systems related to storytelling are reviewed. An analysis of the functionalities that they cover suggests that storytelling might be profitably studied in terms of at least four different sub-tasks: Fabula generation, narrative discourse composition, text generation, and narrative interpretation. An analysis from a computational point of view of the context in which narrative is used as a means of communication between author and reader is presented. This shows that parts of the cognitive processes involved are yet to be modelled, which suggests that the set of sub-tasks identified may continue to grow with further research.

Cyanobacterial pigment adsorbed on TiO2 thin films

The rise in toxicity related to cyanobacterial bloom in freshwater is a current problem that perturbs the trophic chain and risks the ecosystems and human health. Currently, the use of biomass as a potential source of value-added bio-products is an important goal to be achieved in the scope of a sustainable bio-economy. Thus, taking advantage of such bacteria is needed. In the present work, we studied the use of cyanobacterial biomass coming from the Malambo swamp in Colombia as a source of Phycocyanobilin (C-PC) and Chlorophyll-a (Chla) which were used as natural pigments for TiO2 thin films. The concentration obtained of C-PC and Chla extracted were 215 μg/mL and 0.417 μg/mL, respectively. We modeled the natural dye adsorption kinetics on TiO2 thin films through three different models. The Langmuir model showed the best fitting, indicating that the pigment extracted from cyanobacterial biomass can sensitize thin TiO2 film through the formation of a monolayer. Furthermore, the TiO2 films present higher adsorption of C-PC (25.8 mg/g) than Chla (23.3 mg/g). Finally, the adsorption modes were assessed using periodic DFT approximations, which is a remarkable method for studying the structure and properties of solid materials. In terms of binding energies, it was found that the dye shows the strongest interaction with TiO2 through the titanium atom. Thus, the main contribution of this work is directed to explore in deep the natural dye adsorption on TiO2 from both experimental and computational point of view.

Relay-empowered beyond 5G radio access networks with edge computing capabilities

Relevant services envisaged for beyond 5G (B5G) systems, such as extended reality and holographic communications, have extremely demanding user experience requirements with significant computational and communication demands. While edge computing aims to address the computation requirements by offloading the computational tasks to edge servers near the user, the communication will take advantage of the technologies developed for 5G New Radio jointly with an never-before-seen degree of network densification. This paper proposes the use of relays with edge computing capabilities. The approach's potential for B5G are identified, and a system model is defined to characterize both computational and communications viewpoints. Based on this, results are provided to highlight the gains and limitations of the proposed approach from a system-level perspective. Finally, the main challenges for enabling relays with computing capabilities in B5G deployments are discussed.

Thermoelectric properties of sintered Ba2AgSi3 crystals and search for impurities to control conductivity type by first-principles calculation

In this study, the basic properties of Ba2AgSi3 were investigated in detail from both experimental and computational viewpoints. Polycrystalline Ba2AgSi3 formed by an arc-melting apparatus under an argon atmosphere was ground into powders, and then powder samples were sintered using the spark plasma sintering method. Both n-type and p-type samples were obtained. This may be due to a slight deviation from the stoichiometric composition. The energy bandgap of Ba2AgSi3 was measured to be around 0.17 eV from the temperature dependence of electrical conductivity and was in agreement with that by first-principles calculations. Sintered samples exhibited a high Seebeck coefficient of −273 μV K−1 and a high power factor of 0.38 mW m−1 K−2 at 307 K for n-type samples. They were 217 μV K−1 and 0.23 mW m−1 K−2, respectively, at 320 K for p-type samples. The electronic structures of impurity-doped Ba2AgSi3 were also discussed using first-principles calculations to investigate the insertion site of impurity atoms. The calculations suggest that the substitution of B (P) at any Si site shifts the Fermi level and transforms it into p-type (n-type) semiconductors. On the other hand, substitution of Ba or Ag sites with B or P is unlikely to occur in terms of formation energy.

Conceptual analogy between the water hammer phenomenon in free-surface and pressurized-pipe flows

ABSTRACT The water hammer phenomenon, commonly acknowledged in pressurized pipe-flows is relevant to various water distribution systems. This phenomenon constitutes a major concern for hydraulic researchers and designer, given the dramatic consequences of this phenomenon on hydraulic structures and human life. This study aimed at expanding research on the water hammer waves in the free-surface flow framework. The main objective was to address a comprehensive simulation of the free-surface wave behavior caused by the abrupt closure of sluice-gate in prismatic open channel. The one-dimensional Saint-Venant equations embedding the Boussinesq add-on was used to predict the free-surface wave behavior; which was discretized using the (2–4)-dissipative scheme. Results evidenced the analogy between the water hammer wave behavior in free-surface and pressurized pipe flows. Furthermore, results illustrated that the water hammer maneuver produced a sudden depth rise, about three times of the normal depth value in the cross section adjacent to the gate. From a computational point of view, the proposed solver provided simplicity and accuracy attributes in simulating shock-waves in free-surface-flow. This solver also consumed low computational time compared with the conventional or multiple grid technique – based Finite Element Algorithm.

Convex hulls of monomial curves, and a sparse positivstellensatz

AbstractConsider the closed convex hull K of a monomial curve given parametrically as $$(t^{m_1},\ldots ,t^{m_n})$$ ( t m 1 , … , t m n ) , with the parameter t varying in an interval I. We show, using constructive arguments, that K admits a lifted semidefinite description by $$\mathcal {O}(d)$$ O ( d ) linear matrix inequalities (LMIs), each of size $$\left\lfloor \frac{n}{2} \right\rfloor +1$$ n 2 + 1 , where $$d= \max \{m_1,\ldots ,m_n\}$$ d = max { m 1 , … , m n } is the degree of the curve. On the dual side, we show that if a univariate polynomial p(t) of degree d with at most $$2k+1$$ 2 k + 1 monomials is non-negative on $${\mathbb {R}}_+$$ R + , then p admits a representation $$p = t^0 \sigma _0 + \cdots + t^{d-k} \sigma _{d-k}$$ p = t 0 σ 0 + ⋯ + t d - k σ d - k , where the polynomials $$\sigma _0,\ldots ,\sigma _{d-k}$$ σ 0 , … , σ d - k are sums of squares and $$\deg (\sigma _i) \le 2k$$ deg ( σ i ) ≤ 2 k . The latter is a univariate positivstellensatz for sparse polynomials, with non-negativity of p being certified by sos polynomials whose degree only depends on the sparsity of p. Our results fit into the general attempt of formulating polynomial optimization problems as semidefinite problems with LMIs of small size. Such small-size descriptions are much more tractable from a computational viewpoint.

Dynamical arrest transition of a bidisperse two-patchy colloidal dispersion: A dynamic Monte Carlo study.

During the last few years, patchy colloidal dispersions have emerged as ideal candidates of glass-formers of systems composed of particles that interact with non-isotropic potentials. However, from the computational point of view, the characterization of their dynamical properties close to the glass transition via any kind of molecular dynamics simulation technique can be very difficult due to the slowing down of both the rotational and translational dynamics. Although a plethora of dynamical techniques have been developed to account for the dynamics of patchy colloids, new and complementary simulation techniques are required to explore, much faster and more efficiently, the dynamical arrest transition of patchy colloidal dispersions when computer simulation consists of a large number of particles and, due to the slow particle dynamics at the glass transition, an extended time window is explicitly required. Then, in this contribution, by means of the so-called dynamic-Monte Carlo method, we report on the dynamical arrest transition, both rotational and translational, of a bidisperse patchy colloidal dispersion, following three different paths along the density-temperature plane, including high densities and low temperatures. Although this method has not been extensively tested at extreme thermodynamic conditions, we show that even at the dynamical arrest transition, it allows us to extract good dynamical data from a complex system. Therefore, it turns out to be a promising technique to explore the onset of vitrification of anisotropic colloidal particles.

Brain-inspired chaotic spiking backpropagation.

Spiking neural networks (SNNs) have superior energy efficiency due to their spiking signal transmission, which mimics biological nervous systems, but they are difficult to train effectively. Although surrogate gradient-based methods offer a workable solution, trained SNNs frequently fall into local minima because they are still primarily based on gradient dynamics. Inspired by the chaotic dynamics in animal brain learning, we propose a chaotic spiking backpropagation (CSBP) method that introduces a loss function to generate brain-like chaotic dynamics and further takes advantage of the ergodic and pseudo-random nature to make SNN learning effective and robust. From a computational viewpoint, we found that CSBP significantly outperforms current state-of-the-art methods on both neuromorphic data sets (e.g. DVS-CIFAR10 and DVS-Gesture) and large-scale static data sets (e.g. CIFAR100 and ImageNet) in terms of accuracy and robustness. From a theoretical viewpoint, we show that the learning process of CSBP is initially chaotic, then subject to various bifurcations and eventually converges to gradient dynamics, consistently with the observation of animal brain activity. Our work provides a superior core tool for direct SNN training and offers new insights into understanding the learning process of a biological brain.

MRCI Study of the Electronic Structure and Transition Properties of a Tin Dimer.

The ground and excited states of Sn2 are calculated using the multireference configuration interaction method combined with Davidson correction (MRCI+Q). The influence of the spin-orbit coupling (SOC) effect on the electronic structure is also considered by the state interaction method of Breit-Pauli Hamiltonian. In the calculations, the potential energy curves and spectroscopic constants of 23 Λ-S states and 31 Ω states of Sn2 are obtained. The prominent spectral features in the visible region, new constants, and potential energy curves are discussed. The intensity of weak magnetic and quadrupole transitions in the near IR spectra is also calculated. From a computational point of view, we predict that the weak v'(0-2)-v″(0-5) bands of the magnetic b1Σg,0++-X3Σg,1(Ms=±1)- transition may be detected experimentally; the sub-bands (0, 0), (1, 0), and (2, 0) of the a1Δg,2-X3Σg,1(Ms=±1)- transition also may be observed in experiments since they are not overlapped by the strong electric dipole transition in the same IR region. According to the SOC matrix elements and contributions of the 15Πu0+, 15Πu1 (|Σ| = 0), and 15Πu1 (|Σ| = 2) states to the predissociation line width of the 13Σu- -X3Σg1- transition, the broading and other predissociation features of the 13Σu- state are analyzed. From our calculations, it follows that the strong coupling between the bound 13Σu- state and the repulsive 15Πu state causes the predissociation of the 13Σu- state at the vibrational levels v' ≥ 8. In addition, our results suggest that the previously observed bands of Sn2 in the visible range of 19000-20000 cm-1 should be reassigned into the mixing transitions among the X3Σg,1--23Σu,0-+ and X3Σg,0+--23Σu,1+ manifold. The results are expected to provide new comprehensive information for better understanding the spectra and dynamics of the electronic excited states of the Sn2 molecule.

The intersection curve of an hyperbolic cylinder with a torus sharing the same center

This work aims to classify the families of curves obtained by the intersection of an arbitrary hyperbolic cylinder with an arbitrary torus sharing the same center, based on the number of their connected components and the number of their self-intersections points. The graphic geometric representation of these curves, in GeoGebra, and the respective algebraic descriptions, supported from a theoretical and computational point of view, are of fundamental importance for the development of this work. In this paper, we describe the procedure and the necessary implementation to achieve the outlined objective.