Computational Perspective Research Articles

Spreadsheets in Secondary School Statistics Education: Using Authentic Data for Computational Thinking

Computational thinking (CT) is gaining attention in education as a part of digital literacy and can be addressed in several disciplines, including mathematics. Through the lens of Brennan and Resnick’s framework, we investigated how computational concepts, practices, and perspectives can be addressed in upper-secondary statistics lessons using spreadsheets through design-based research. Three classes of, in total, 58 16- to 17-year-old 11th-grade students explored several authentic real-life data sets in three 2-h sessions using spreadsheets. We evaluated the intervention by analyzing students’ workbooks, spreadsheet files, interviews, and questionnaires. The findings indicate that (1) students successfully engaged in computational concepts through using formulas, parameters, and conditional statements, (2) fruitfully applied data practices, and (3) demonstrated awareness of the relevance of CT for their everyday and future lives. These results highlight the potential of the use of spreadsheets in secondary school for developing computational thinking skills. Implications for further integration of CT in the mathematics curriculum are discussed.

Adsorption of thiotepa anticancer by the assistance of aluminum nitride nanocage scaffolds: A computational perspective on drug delivery applications

This work was carried out by the importance of providing insights into the nano-based drug delivery of anticancers. To this aim, a representative model of aluminum nitride (AN) nanocage scaffold and two of its boron and gallium doped forms (BAN and GAN) were investigated towards the adsorption of thiotepa (Tep) anticancer. Density functional theory (DFT) calculations were performed to evaluate the stabilized structures and their corresponding electronic features. The results indicated the contribution of N-head and S-head of Tep to interactions with each scaffold resulting six complexes; NTep@AN and STep@AN, NTep@BAN and STep@BAN, NTep@GAN and STep@GAN. In each complex model, the characteristic features were obtained based on the interactions details and frontier molecular orbitals related features. In this regard, the investigated scaffolds were found suitable for adsorbing the Tep substance with different strengths and frontier molecular orbitals levels yielding the possibility of assigning specified recovery time and conductance. As a consequence, the models of investigated scaffolds were found suitable to work as possible carriers of Tep anticancer for approaching the nano-based drug delivery purposes.

A surrogate-assisted evolutionary algorithm based on multi-population clustering and prediction for solving computationally expensive dynamic optimization problems

The computationally expensive dynamic optimization problems (CEDOPs) arise when the objective function of a dynamic optimization problem is prohibitively expensive, or even almost impossible, to obtain. Such problems are extremely challenging from a computational perspective, due to the combination of dynamic environments and computationally expensive objective functions. In this paper, we present a surrogate-assisted evolutionary algorithm based on multi-population clustering and prediction (SAEA/MPCP) to tackle CEDOPs. In the solution process, SAEA/MPCP employs three basic strategies. First, to improve the solution speed and the ability to interact with the environment of the algorithm, the initial population were selected using the strategy of clustering and differential prediction. Second, in order to increase the distribution and diversity of individuals and improve the solution accuracy of the algorithm, the generation of sample points for surrogate model was performed using integrated multi-population strategies. Third, we replace the two kinds of high and low confidence samples in the Co-kriging model with the generated samples from the previous strategy, which further improves the construction and optimization efficiency of the surrogate model. Finally, we set up two types of CEDOPs with different degrees of computational cost and experimentally compare them with a variety of state-of-the-art dynamic optimization algorithms (DOAs), respectively. The experiments fully demonstrate the effectiveness of SAEA/MPCP algorithm to some of the best known DOAs.

Investigation on the molecular, electronic, biological and spectroscopic properties of a novel cobalt complex: An intuition from an experimental and computational perspective

The preparation, spectroscopic, biological and electrical characterization, in addition of the X-ray crystal structure investigation of a novel tree-dimensional compound [C6H16N2 Co(NCS)4] is reported. The synthetized compound crystallizes in the P212121 space group. The structure is made up of layers of cobalt (II) bridged by terminally bound thiocyanate-N ligand achieving distorted MN4 tetrahedron surroundings around the metal center. The main source of the distortion of the ideal tetrahedron is due to the geometrical constraints issued from the occurrence of piperazinium ligand, the values of the N–Co–N bond angles covering the range 101.64(11)–116.81(12)°.The vibrational modes of the functional groups were investigated using FT-IR measurements. Hirshfeld surface analysis was performed to investigate the short contacts and hydrogen bonds in the compound. In addition of the study of the photoluminescent behavior which reveals a high optical property. Explorations of impedance spectroscopy on [C6H16N2 Co(NCS)4] reveal fascinating electrical behavior which reveals that the Co complex component can be used as an electronic chip and under certain conditions, this semiconductor have the ability to act either as a conductor which can be a considered as a pure element. The thermal analysis was used to confirm the crystal thermal stability. Finally, the compound’s antioxidant activity and the molecular docking were occurred which suggest that the Cobalt complex acts as a COX-2 inhibitor Agent.

Developing the optimal vessel fleet size and mix model to minimize the transportation cost of offshore wind farms

It is crucial for the present generation of offshore wind farms to develop ways to reduce their operation and maintenance expenses to sell electricity at competitive market pricing. A wind farm located offshore will typically consist of a huge number of turbines, each of which must be maintained regularly. It is vital to make maintenance plans that reduce production losses and downtime. Also, it is essential to effectively use expensive resources such as service vessels and technician hours. This article starts with an overview of the vessel fleet size and mix problem that arises while maintaining offshore wind farms using a fleet of maintenance vessels. The main emphasize of this work is to establish the best assignments of vessels to the routes and technicians on the vessels in terms of cost. In this paper an integer programming model is formulated taking into consideration the relevant characteristic peculiarities and limits encountered in the vessel management of offshore wind farm. An algorithm is developed that has been implemented using a commercial solver. A case study is done to show the computational and dimensional perspectives of the problems in real-life settings. The findings give not only the optimal cost but also the comprehensive arrangements, both of which are directly applicable to the process of planning for maintenance vessel fleet size and mix selection.

Computational perspectives on Chlorpyrifos and its degradants as human glutathione S-transferases inhibitors: DFT calculations, molecular docking study and MD simulations

Chlorpyrifos is the toxicant chemical from the class of organophosphorus insecticides. The insecticide undergoes environmental degradation to chlorpyrifos‐oxon (CPYO), des‐ethyl chlorpyrifos (DEC), 3,5,6‐trichloro‐2‐methoxypyridine (TMP) and 3,5,6‐trichloro‐2‐pyridinol (TCP). Herein, CPF along with its degradants were optimized employing density functional theory (DFT) and B3LYP/6-311G+(d,p) basis set to elucidate their thermal and frontier molecular orbital properties. The DFT outcome revealed that TCP showed the lowest HOMO-LUMO gap (4.38 eV), also highest dipole moment, electrophilicity index and basicity. Docking was done using AutoDock 4.2.6 against human glutathione S-transferases to search binding affinity and interactions of all pollutants with the protein. The docking results expressed that TCP required least binding energy (−5.51 kcal mol−1) which is relatable to the DFT studies and might act as the most powerful inhibitor. GROMACS 5.1.1 was utilized to perform simulation studies for each ligand–protein docked complexes. Results concluded that CPF, DEC, TMP, CPYO and TCP could possibly perform as toxic and inhibit enzymatic activity by interrupting the metabolic pathways in humans.

Theoretical study on bio-convection of micropolar fluid with an exploration of Cattaneo–Christov heat flux theory

This research explores the heat transfer rate for micropolar fluid in a channel flow. In spite of formal Fourier’s law, the Cattaneo–Christov heat flux design is implemented in energy system. Using appropriate dimensionless parameters, the guiding coupled partial differential equations that represent the fluid flow are modified into ordinary differential equations. By executing Runge–Kutta integration procedure and the shooting method, the numerical results are achieved. The impacts of thermal relaxation time and bio-convection flow of micropolar fluid are examined in this assessment. Graphical analyses are used to assess the effects of physical factors for the momentum, micro-rotation, concentration, density of micro-organisms and temperature gradient. The skin friction values, motile density number, heat and mass transfer rate are the fascinating physical quantities whose numerical data are computed and validated against different parametric values. The variational iteration method (VIM) and Adomian decomposition method are the analytical modules which have been incorporated here for solving the nonlinear systems for showing better approximity. It is found from the study that larger the thermal relaxation time values, the more likely they are to increase heat transfer, hence lowering the fluid temperature. Moreover, both Fourier and Cattaneo–Christov heat conduction module exhibit qualitatively similar influence on embedded parameters also the temperature profile diminishes for larger values of [Formula: see text]. The culminations evidently disclose that the bio-convection Peclet number and the motile microbes parameter enhance the density of motile micro-organisms. From a computational perspective, the VIM is more effective, practical and ease of use. The numerical and analytical results are compared well with the existing articles. The optimum parameter level for maximum heat transfer is considered to be [Formula: see text]. Taguchi approach was successfully used to determine the optimum design parameters for maximum heat transfer is 1.724012 for the parameters A-5:B-5:C-0.5:D-0.5:E-0.7.

Knowledge representation and acquisition for ethical AI: challenges and opportunities

Machine learning (ML) techniques have become pervasive across a range of different applications, and are now widely used in areas as disparate as recidivism prediction, consumer credit-risk analysis, and insurance pricing. Likewise, in the physical world, ML models are critical components in autonomous agents such as robotic surgeons and self-driving cars. Among the many ethical dimensions that arise in the use of ML technology in such applications, analyzing morally permissible actions is both immediate and profound. For example, there is the potential for learned algorithms to become biased against certain groups. More generally, in so much that the decisions of ML models impact society, both virtually (e.g., denying a loan) and physically (e.g., driving into a pedestrian), notions of accountability, blame and responsibility need to be carefully considered. In this article, we advocate for a two-pronged approach ethical decision-making enabled using rich models of autonomous agency: on the one hand, we need to draw on philosophical notions of such as beliefs, causes, effects and intentions, and look to formalise them, as attempted by the knowledge representation community, but on the other, from a computational perspective, such theories need to also address the problems of tractable reasoning and (probabilistic) knowledge acquisition. As a concrete instance of this tradeoff, we report on a few preliminary results that apply (propositional) tractable probabilistic models to problems in fair ML and automated reasoning of moral principles. Such models are compilation targets for certain types of knowledge representation languages, and can effectively reason in service some computational tasks. They can also be learned from data. Concretely, current evidence suggests that they are attractive structures for jointly addressing three fundamental challenges: reasoning about possible worlds + tractable computation + knowledge acquisition. Thus, these seems like a good starting point for modelling reasoning robots as part of the larger ecosystem where accountability and responsibility is understood more broadly.

In silico-Based Structural Prediction, Molecular Docking and ADMET Analysis of Novel Imidazo-Quinoline Derivatives as Pf Purine Nucleoside Phosphorylase Inhibitors

Introduction: The prolonged antimalarial therapy with the marketed drug has developed multi-resistant strains of Plasmodium parasites that emerge as a consequential global problem. Therefore, designing new antimalarial agents is an exclusive solution to overcome the alarming situation. Methods: The integrated computational perspectives, such as pharmacophore mapping, 3D-QSAR and docking studies have been applied to improve the activity of the imidazo-quinoline scaffold. The best hypothesis AARRR_1 (Survival score 5.4609) obtained through pharmacophore mapping revealed that imidazo-quinoline scaffold is found to be vital for antimalarial activity. The significant CoMFA (q2 = 0.728, r2 = 0.909) and CoMSIA (q2 = 0.633, r2 = 0.729) models, developed by using molecular field analysis with the PLS method, showed good predictive ability with r2 pred values of 0.9127 and 0.7726, respectively. Docking studies were performed using Schrodinger and GOLD software with the Plasmodium falciparum purine nucleoside phosphorylase enzyme (PDB ID-5ZNC) and results indicated that the imidazo-quinoline moiety facilitates the interaction with Tyr 160. Results: In addition, some compounds are screened from the ZINC database based on structural requirements to verify the relevance of the research. Finally, designed molecules and ZINC database compounds were screened through the ADMET tool to evaluate pharmacokinetic and druglikeness parameters. Conclusion: Thus, these exhaustive studies suggested that established models have good predictability and would help in the optimization of newly designed molecules that may lead to potent antimalarial activity for getting rid of resistance issues.

A comprehensive DFT study on organosilicon-derived fungicide flusilazole and its germanium analogue: A computational approach to Si/Ge bioisosterism

Bioisosterism, a specific molecular alteration processing, is an effective approach used in agrochemical development studies as well as in pharmaceutical sciences. Analyzing some parameters such as size, volume, charge, aqueous/lipid solubility, polarizability, degree of hybridization, intra- and intermolecular interactions, which may be caused by bioisosteric changes, by using quantum chemical methods, provides speed and different perspectives to the studies. Towards this end, this study approached the Si/Ge bioisosterism from a computational perspective by performing DFT-based calculations on flusilazole, a widely used fungicide, and its germanium analogue. The reflections of the replacement of silicon with germanium in the structure of flusilazole on the structural parameters, frontier molecular orbital energies, quantum chemical descriptors and electrostatic surface properties were examined. Additionally, FT-IR spectra of the aforementioned molecules were visualized and investigated in detail. Natural bond orbital analyzes were also performed to determine the intermolecular interactions and their corresponding stabilization energies. Besides, 1-octanol/water partition coefficients (logPow), the quantitative identifier of lipophilicity, were calculated. It was determined that both molecules could exhibit similar lipophilic character with a difference of 0.09 logarithmic units in logPow value.

Swarm Control for Distributed Construction: A Computational Complexity Perspective

Over the last 20 years, human interaction with robot swarms has been investigated as a means to mitigate problems associated with the control and coordination of such swarms by either human teleoperation or completely autonomous swarms. Ongoing research seeks to characterize those situations in which such interaction is both viable and preferable. In this article, we contribute to this effort by giving the first computational complexity analyses of problems associated with algorithm, environmental influence, and leader selection methods for the control of swarms performing distributed construction tasks. These analyses are done relative to a simple model in which swarms of deterministic finite-state robots operate in a synchronous error-free manner in 2D grid-based environments. We show that all three of our problems are polynomial-time intractable in general and remain intractable under a number of plausible restrictions (both individually and in many combinations) on robot controllers, environments, target structures, and sequences of swarm control commands. We also give the first restrictions relative to which these problems are tractable, as well as discussions of the implications of our results for both the design and deployment of swarm control assistance software tools and the human control of swarms.

An Historical Review of the Simplified Physical Fire Spread Model PhyFire: Model and Numerical Methods

A historical review is conducted of PhyFire, a simplified physical forest fire spread model developed by the research group on Numerical Simulation and Scientific Computation (SINUMCC) at the University of Salamanca. The review ranges from the first version of the model to the current one now integrated into GIS, considering all the mathematical problems and numerical methods involved throughout its development: finite differences, mixed, classical and adaptive finite elements, data assimilation, sensitivity analysis, parameter adjustment, and parallel computation, among others. The simulation of processes as complex as forest fires involves a multidisciplinary effort that is constantly being enhanced, while posing interesting challenges from a mathematical, numerical, and computational perspective, without losing sight of the overriding aim of developing an efficient, effective, and useful simulation tool.

Gaussian-accelerated molecular dynamics to probe mutations in Parkinson's LRRK2 gene.

Parkinson's disease is the second most common neurodegenerative disease followed by Alzheimer's disease. Leucine-rich repeat kinase 2 (LRRK2) is one of many mutated genes associated with Parkinson's disease. LRRK2 has been shown to function in intracellular trafficking and wrap around microtubules. While an open LRRK2 conformation is considered to be inactive, a closed conformation favors oligomerization around microtubules, thus affecting the motility of microtubule-based motor proteins. Despite recent advances exhibiting the effects of single-point mutations on LRRK2, structural dynamics from a computational perspective are still limited. Mutations in LRRK2 can affect protein dynamics and conformation, but can also induce allosteric effects. Gaussian-accelerated molecular dynamics (GaMD) is therefore employed to understand the effect of mutants on the dynamics of the LRRK2 protein. Unlike traditional molecular dynamics, GaMD is an enhanced sampling technique where the boost potentials of the system are raised in order to lower energy barriers. This allows for the investigation of complex biological processes while sampling the potential energy landscape of the entire system. Analyzing the dynamics of particular mutations in LRRK2 could explain why specific mutations result in kinase activation and oligomerization.

Distributed Learning Meets 6G: A Communication and Computing Perspective

With the ever improving computing capabilities and storage capacities of mobile devices in line with evolving telecommunication network paradigms, there has been an explosion of research interest toward exploring distributed learning (DL) frameworks to realize stringent key performance indicators (KPIs) that are expected in next-generation/6G cellular networks. In conjunction with edge computing, federated learning (FL) has emerged as the DL architecture of choice in prominent wireless applications. This article provides an outline of how DL in general and FL-based strategies specifically can contribute toward realizing part of the 6G vision and strike a balance between communication and computing constraints. As a practical use case, we apply multi-agent reinforcement learning within the FL framework to the dynamic spectrum access (DSA) problem and present preliminary evaluation results. Top contemporary challenges in applying DL approaches to 6G networks are also highlighted.

Development of machine learning models for prediction of antibody non-specificity.

For the development of therapeutic antibodies, it is acknowledged that optimal target binding affinity should be accompanied by high specificity. In recent years, there has been an increased focus on lowering non-specific binding during early-stage drug development. In particular, non-specificity has been recognised as a root-cause for failure in numerous antibody-based drug programs due to unexpected pharmacokinetics and elevated toxicity. Hence, there is a need for predictive tools for flagging and deselecting non-specific antibodies at an early stage to mitigate failure at clinical late-stage development. From a computational design perspective, prediction has remained a challenge as non-specificity is currently a poorly understood biophysical phenomena, typically involving weak interactions to various, unrelated targets and surfaces. In this work, we describe the development of sequence-based machine learning (ML) models for prediction of antibody non-specificity. A dataset of >1000 antibody sequences paired with their respective non-specificity flag, defined by ELISA using a panel of common antigens, were retrieved from public sources (Jain et al., 2017; Boughter et al., 2020). The sequences were embedded using state-of-the-art protein language models for representation of their physiochemical properties, whereafter the vectorized embeddings were served as features for training of binary classifiers for non-specificity flags. A total of 12 different antibody domain-specific models were generated. Highest predictability of non-specificity was obtained for ML algorithms trained on the heavy-chain variable domain (VH), and the complementarity determining regions (CDRs), and resulted in a mean accuracy of 73% across 3-, 5-, and 10-fold cross-validations. The classification probability of non-specificity followed an uptrend with the experimental data (non-specificity flags), thereby mimicking regression behaviour. Altogether, our work demonstrates feasibility for prediction of non-specific binding from the antibody sequence, and that the non-specificity primarily stems from the VH domain, with main contributions from the heavy-chain CDRs.

Grouped variable selection with discrete optimization: Computational and statistical perspectives

We present a new algorithmic framework for grouped variable selection that is based on discrete mathematical optimization. While there exist several appealing approaches based on convex relaxations and nonconvex heuristics, we focus on optimal solutions for the ℓ0-regularized formulation, a problem that is relatively unexplored due to computational challenges. Our methodology covers both high-dimensional linear regression and nonparametric sparse additive modeling with smooth components. Our algorithmic framework consists of approximate and exact algorithms. The approximate algorithms are based on coordinate descent and local search, with runtimes comparable to popular sparse learning algorithms. Our exact algorithm is based on a standalone branch-and-bound (BnB) framework, which can solve the associated mixed integer programming (MIP) problem to certified optimality. By exploiting the problem structure, our custom BnB algorithm can solve to optimality problem instances with 5×106 features and 103 observations in minutes to hours—over 1000 times larger than what is currently possible using state-of-the-art commercial MIP solvers. We also explore statistical properties of the ℓ0-based estimators. We demonstrate, theoretically and empirically, that our proposed estimators have an edge over popular group-sparse estimators in terms of statistical performance in various regimes. We provide an open source implementation of our proposed framework.

Human-Centered Generative Design Framework: An Early Design Framework to Support Concept Creation and Evaluation

Generative design uses artificial intelligence-driven algorithms to create and optimize concept variants that meet or exceed performance requirements beyond what is currently possible using the traditional design process. However, current generative design tools lack the integration of human factors, which diminishes the efforts to understand and inject a broad set of human capabilities, limitations, and potential emotional responses for future human-centered product and service innovation. This paper demonstrates collaborative research in formulating a human-centered generative design framework that injects human factors early in the design for quick-and-dirty concept creation and evaluation. Three case studies overviewing our ongoing multidisciplinary research efforts in synthesizing human and mechanical attributes are presented. The results show that the framework has the potential to enhance human factors representation within generative design workflow. Strategies from a computational design perspective, such as data-driven generative design, digital human modeling, and mixed-reality validation, are discussed as alternative approaches that could be implemented to augment designers.

Carbon Dioxide Conversion on Supported Metal Nanoparticles: A Brief Review

The increasing concentration of anthropogenic CO2 in the air is one of the main causes of global warming. The Paris Agreement at COP 21 aims to reach the global peak of greenhouse gas emissions in the second half of this century, with CO2 conversion towards valuable added compounds being one of the main strategies, especially in the field of heterogeneous catalysis. In the current search for new catalysts, the deposition of metallic nanoparticles (NPs) supported on metal oxides and metal carbide surfaces paves the way to new catalytic solutions. This review provides a comprehensive description and analysis of the relevant literature on the utilization of metal-supported NPs as catalysts for CO2 conversion to useful chemicals and propose that the next catalysts generation can be led by single-metal-atom deposition, since in general, small metal particles enhance the catalytic activity. Among the range of potential indicators of catalytic activity and selectivity, the relevance of NPs’ size, the strong metal–support interactions, and the formation of vacancies on the support are exhaustively discussed from experimental and computational perspective.

Informational content vs. discourse orientation: experimental and computational perspectives

The aim of this study is to investigate how human speakers and computational language models process (i) the informational content and (ii) the discourse orientation of natural language sentences. These two dimensions of meaning have received little attention outside theoretical literature, especially in the computational linguistics domain. To help fill this void, we present the results of four experiments that exploit the specific semantics of two French adverbs, namely presque (≃ ’almost’) and à peine (≃ ’barely’), which put these two dimensions of meaning at odds. Each experiment focuses on one kind of population (humans or language models), and one kind of meaning (informational content or discourse orientation). Our results show that humans are indeed sensitive to informational content and discourse direction, as assumed in the theoretical literature. Language models exhibit a less transparent behavior. Their performances in dealing with the semantics of presque appear in line with predictions based on the way these models are trained, but this does not extend to à peine.

SimuBP: A Simulator of Population Dynamics and Mutations Based on Branching Processes

Originating from the Luria–Delbrück experiment in 1943, fluctuation analysis (FA) has been well developed to demonstrate random mutagenesis in microbial cell populations and infer mutation rates. Despite the remarkable progress in its theory and applications, FA often faces difficulties in the computation perspective, due to the lack of appropriate simulators. Existing simulation algorithms are usually designed specifically for particular scenarios, thus their applications may be largely restricted. There is a pressing need for more flexible simulators that rely on minimum model assumptions and are highly adaptable to produce data for a wide range of scenarios. In this study, we propose SimuBP, a simulator of population dynamics and mutations based on branching processes. SimuBP generates data based on a general two-type branching process, which is able to mimic the real cell proliferation and mutation process. Through simulations under traditional FA assumptions, we demonstrate that the data generated by SimuBP follow expected distributions, and exhibit high consistency with those generated by two alternative simulators. The most impressive feature of SimuBP lies in its flexibility, which enables the simulation of data analogous to real fluctuation experiments. We demonstrate the application of SimuBP through examples of estimating mutation rates.