Computational Perspective Research Articles

Special Functions in Problem Solving Environments: A personal view

This paper discusses some of the philosophical and historical underpinnings of the talk “The Mathieu Functions: Computational and Historical Perspectives” given at the Maple Conference 2022. I also comment on the role Problem Solving Environments (PSEs) play in curating the computational knowledge of the 19th century, which is so necessary for us to think of special functions as answers rather than questions.

Deep Learning-based Sequence Labeling Tools for Nepali

A Part-of-Speech (POS) tagger and Chunker (or shallow parser) are sequence labeling tools, crucial for improving the accuracy of Natural Language Processing (NLP) tasks like parsing, named entity recognition, sentiment analysis, information extraction, and so on. Developing such tools for a low-resource language is an arduous task. Nepali is a relatively resource-poor Indian language and has not been able to evolve from a computational perspective. Therefore, we present effective part-of-speech tagging and chunking tools for the Nepali text using sequential deep learning models—Bidirectional Long Short-Term Memory Network with a Conditional Random Field Layer (BI-LSTM-CRF) and other LSTM-based models exploring both character and word embeddings of the Nepali texts. Word Embedding has been used to capture syntactic as well as semantic information whereas character embedding has been applied to capture the morphological as well as shape information of words and also to handle the out-of-vocabulary problem. The developed chunker is the first statistical chunker for the Nepali language. A baseline model with a Conditional Random Field has also been developed to identify the optimum feature set for the aforementioned tasks. The BI-LSTM-CRF model produced an accuracy of 99.20% and 98.40%, for Nepali POS tagging and chunking, respectively. This is the highest-ever accuracy for Nepali. Thorough error analysis and observations have also been reported with examples. The developed tools can help advance research in Nepali language processing, improve the accuracy of language technology applications, and contribute to the preservation and promotion of the Nepali language.

On computing discretized Ricci curvatures of graphs: Local algorithms and (localized) fine-grained reductions

Characterizing shapes of high-dimensional objects via Ricci curvatures plays a critical role in many research areas in mathematics and physics. However, even though several discretizations of Ricci curvatures for discrete combinatorial objects such as networks have been proposed and studied by mathematicians, the computational complexity aspects of these discretizations have escaped the attention of theoretical computer scientists to a large extent. In this paper, we study one such discretization, namely the Ollivier-Ricci curvature, from the perspective of efficient computation by fine-grained reductions and local query-based algorithms. Our main contributions are the following.▹We relate our curvature computation problem to minimum weight perfect matching problem on complete bipartite graphs via fine-grained reduction.▹We formalize the computational aspects of the curvature computation problems in suitable frameworks so that they can be studied by researchers in local algorithms.▹We provide the first known lower and upper bounds on queries for query-based algorithms for the curvature computation problems in our local algorithms framework. En route, we also illustrate a localized version of our fine-grained reduction. We believe that our results bring forth an intriguing set of research questions, motivated both in theory and practice, regarding designing efficient algorithms for curvatures of geometrical objects.

Virtual screening of novel mTOR inhibitors for the potential treatment of human colorectal cancer

The abnormal activation of the mTOR pathway is closely related to the occurrence and progression of cancer, especially colorectal cancer. In this study, a rational virtual screening strategy has been established and MT-5, a novel mTOR inhibitor with a quinoline scaffold, was obtained from the ChemDiv database. MT-5 showed potent kinase inhibitory activity (IC50: 8.90 μM) and antiproliferative effects against various cancer cell lines, especially HCT-116 cells (IC50: 4.61 μM), and this was 2.2-fold more potent than that of the cisplatin control (IC50: 9.99 μM). Western blot, cell migration, cycle arrest, and apoptosis assays were performed with HCT-116 cells to investigate the potential anticancer mechanism of MT-5. Metabolic stability results in vitro indicated that MT-5 exhibited good stability profiles in artificial gastrointestinal fluids, rat plasma, and liver microsomes. In addition, the key contribution of the residues around the binding pocket of MT-5 in binding to the mTOR protein was also investigated from a computational perspective.

Synthesis, spectral, antifungal, in silico molecular docking, ADME and DFT studies of some 2-(2-hydrazineyl)thiazole derivatives: Computational and experimental perspective

A series of thiazole Schiff bases (4a-4q) is synthesized and evaluated for the antibiofilm potential against fungal species C. albicans. The compounds 4a, 4b, 4c, 4p and 4q showed significant antibiofilm activity. Compounds 4a, 4b and 4c showed antibiofilm activities at 100 µg/mL concentrations, whereas 4p, and 4q showed activities around 50 µg/mL concentrations. Microscope study also supports the antibiofilm activity of compounds 4a,4b,4c, 4p and 4q. Molecular mechanism of action of bioactive derivatives for antibiofilm activities was studied by analysing the expression levels of some genes involved in the biofilm formations via qPCR. The molecular docking simulations with CYP51 revealed these compounds interact via hydrophobic and Van der Waals binding. The MESP surfaces were plotted using DFT method which also supported the molecular docking interactions.The ADME profile of these compounds revealed favourable pharmacological characteristics.

Recent Progress of Non‐Noble Metallic Heterostructures for the Electrocatalytic Hydrogen Evolution

Developing energy production, storage, and conversion technologies based on sustainable or renewable energy is essential to address the energy and environmental crisis. Electrochemical water splitting is one of the most promising approaches to realize the production of green hydrogen. The design of catalytic materials with low cost, high activity, and long‐term stability and the exploration of specific reaction mechanisms are the key focus for the involved electrochemical hydrogen evolution reaction (HER). Recently, substantial efforts have been devoted to the rational design and synthesis of non‐noble metallic heterostructures with fascinating synergistic effects among different components. These heterostructured materials demonstrate comprehensive properties exceeding the estimations by the rule of mixtures and display high activity and long‐term stability in industrial conditions for HER. Herein, the reaction mechanism and key parameters for improving catalytic performance in the HER process are discussed in detail. The latest advances in heterostructures based on synthetic methods and electrocatalytic characteristics from experimental and computational perspectives are summarized according to the role of various components. Herein, insights are provided in this review into an in‐depth understanding of the heterostructures as HER electrocatalysts, and the opportunities and challenges to scale up future‐oriented developments are highlighted.

Equilibrium Means Equity? An E-CARGO Perspective on the Golden Mean Principle

In the team allocation problem (TAP), eliminating team disparities aims at keeping an equilibrium of the resource or ability among teams for equality. For this concern, existing literature merely utilized the golden mean principle to eliminate team disparities from a static perspective. Few of them reasonably investigate the pros and cons of this principle from a computational perspective. Moreover, maintaining equilibrium is a dynamic process and requires dynamic adjustment, especially after considering team members’ self-efforts and adaptivity. With respect to the environments—classes, agents, roles, groups, and objects (E-CARGO) model and its role-based collaboration (RBC) methodology, this article formalizes and solves the TAP, i.e., revised group role assignment (GRA) problem, from both the individual and team’s perspective. Based on the revised GRA, this article provides novel insight into the effectiveness of dynamically maintaining equilibrium, which may help decision-makers be proactive in building more sustainable teams. Relevant large-scale simulation experiments are conducted in this article to verify the proposed method. This article reveals a social paradox: even though considering all about the team members’ self-efforts and adaptivity, equilibrium still seems inequitable. Conversely, pursuing equilibrium may bring the Matthew effect.

Accurate Average Run Length Analysis for Detecting Changes in a Long-Memory Fractionally Integrated MAX Process Running on EWMA Control Chart

Numerical evaluation of the average run length (ARL) when detecting changes in the mean of an autocorrelated process running on an exponentially weighted moving average (EWMA) control chart has received considerable attention. However, accurate computation of the ARL of changes in the mean of a long-memory model with an exogenous (X) variable, which often occurs in practice, is challenging. Herein, we provide an accurate determination of the ARL for long-memory models such as the fractionally integrated MAX processes (FIMAX) with exponential white noise running on an EWMA control chart by using an analytical formula based on an integral equation. From a computational perspective, the analytical formula approach is accomplished by solving the solution for the integral equation obtained via the Fredholm integral equation of the second kind. Moreover, the existence and uniqueness of the solution for the analytical formula were confirmed via Banach’s fixed-point theorem. Its efficacy was compared with that of the ARL derived by using the well-known numerical integral equation (NIE) technique under the same circumstances in terms of the ARL percentage accuracy and computational processing time. The percentage accuracy was 100%, which indicates excellent agreement between the two methods, and the analytical formula also required much less computational processing time. An example to illustrate the effectiveness of the proposed approach with a process involving real data running on an EWMA control chart is also provided herein. The explicit formula method offers an accurate determination of the ARL and a new approach for validating its computation, especially for long-memory scenarios running on EWMA control charts.

A computational perspective towards the identification of promising lead molecules against 6-hydroxy-methyl dihydropterin pyrophosphokinase (HPPK) from Acinetobacter baumannii

Acinetobacter baumannii is an ESKAPE pathogen that causes endocarditis, pneumonia, blood infections, urinary tract infections, and several other illnesses. In addition, it is mainly responsible for nosocomial infection-related mortality. Gram-negative A. baumannii bacterium (AYE Strain) has high MDR and XDR levels. Due to its function in synthesizing purines and amino acids, folic acid is a significant molecule necessary for the growth of bacteria. The metabolic pathway of folate production is therefore a potential therapeutic target to inhibit bacterial growth. In the current study, the three-dimensional model of 6-Hydroxy-methyl dihydropterinpyrophosphokinase (HPPK) was predicted and subsequently processed through a virtual high throughput screening (vHTS) against compounds from Enamine HTSC library, that could bind to its active site. Three lead candidates (Z73322064, Z354558542, and Z906123504) and a control molecule (7,8 dihydro-7,7-dimethyl-6-hydroxymethlypterin; Accession Number: DB02278) were identified using several screening criteria namely estimated binding affinity, estimated inhibition constant, drug-like properties, ADME properties, mode of binding, and interaction patterns of the screened compounds. The physiological behavior of ligand binding on the HPPK enzyme was then studied using molecular dynamics simulations of apo and ligand bound complexes. This study proposed the following three molecules: Z73322064, Z354338542, and Z906123504 as promising lead candidates against the substrate-binding site of the HPPK enzyme from A. baumannii using global, essential dynamics studies along with MM/PBSA based binding free energy analysis. Communicated by Ramaswamy H. Sarma

The ability of zinc oxide nanostructures for removal of Pb2+, Cu2+, Ag2+ and Cd2+ ions from wastewater: computational perspective

In the heavy metal removal industry, adsorption is a famous and commonplace procedure well-recognized for its flexibility and utility. It is also a cost-effective procedure. Moreover, there are several adsorption procedures that are reversible on the one hand, and adsorbents may be reused after passing different desorption techniques on the other. The foundation of this research is to apply the calculations of the density functional theory (DFT) based on ab initio with an aim to explore the heavy metal atoms’ adsorption properties on ZnO–graphene resembling structure sheets and zinc oxide (ZnO) nanotubes. The heavy metals considered in this study include (Cu2+), (Pb2+), (Cd2+), and (Ag2+). In the initial stage, this study optimises both nanostructures. In the next phase, this study computes the steadiest configuration, the energy of adsorption, and the distance of equilibrium for each heavy metal with nanostructures utilising the mentioned DFT method. The findings were analyzed and compared. According to our case-by-case findings, ZnO-nanotubes exhibited better adsorption behaviour than ZnO–graphene sheets due to a smaller distance for equilibrium and higher adsorption energy. we’ve created the diagrams of the electronic density of states (DOS) in order to analyze the molecule adsorption electrical properties.

Sensor-based augmented visual feedback for coordination training in healthy adults: a scoping review.

Recent advances in sensor technology demonstrate the potential to enhance training regimes with sensor-based augmented visual feedback training systems for complex movement tasks in sports. Sensorimotor learning requires feedback that guides the learning process towards an optimal solution for the task to be learned, while considering relevant aspects of the individual control system-a process that can be summarized as learning or improving coordination. Sensorimotor learning can be fostered significantly by coaches or therapists providing additional external feedback, which can be incorporated very effectively into the sensorimotor learning process when chosen carefully and administered well. Sensor technology can complement existing measures and therefore improve the feedback provided by the coach or therapist. Ultimately, this sensor technology constitutes a means for autonomous training by giving augmented feedback based on physiological, kinetic, or kinematic data, both in real-time and after training. This requires that the key aspects of feedback administration that prevent excessive guidance can also be successfully automated and incorporated into such electronic devices. After setting the stage from a computational perspective on motor control and learning, we provided a scoping review of the findings on sensor-based augmented visual feedback in complex sensorimotor tasks occurring in sports-related settings. To increase homogeneity and comparability of the results, we excluded studies focusing on modalities other than visual feedback and employed strict inclusion criteria regarding movement task complexity and health status of participants. We reviewed 26 studies that investigated visual feedback in training regimes involving healthy adults aged 18-65. We extracted relevant data regarding the chosen feedback and intervention designs, measured outcomes, and summarized recommendations from the literature. Based on these findings and the theoretical background on motor learning, we compiled a set of considerations and recommendations for the development and evaluation of future sensor-based augmented feedback systems in the interim. However, high heterogeneity and high risk of bias prevent a meaningful statistical synthesis for an evidence-based feedback design guidance. Stronger study design and reporting guidelines are necessary for future research in the context of complex skill acquisition.

Combating Disinformation on Social Media and Its Challenges: A Computational Perspective

Combating Disinformation on Social Media and Its Challenges: A Computational Perspective

Exploring Tradeoffs in Automated School Redistricting: Computational and Ethical Perspectives

The US public school system is administered by local school districts. Each district comprises a set of schools mapped to attendance zones which are annually assessed to meet enrollment objectives. To support school officials in redrawing attendance boundaries, existing approaches have proven promising but still suffer from several challenges, including: 1) inability to scale to large school districts, 2) high computational cost of obtaining compact school attendance zones, and 3) lack of discussion on quantifying ethical considerations underlying the redrawing of school boundaries. Motivated by these challenges, this paper approaches the school redistricting problem from both computational and ethical standpoints. First, we introduce a practical framework based on sampling methods to solve school redistricting as a graph partitioning problem. Next, the advantages of adopting a modified objective function for optimizing discrete geometry to obtain compact boundaries are examined. Lastly, alternative metrics to address ethical considerations in real-world scenarios are formally defined and thoroughly discussed. Our findings highlight the inclusiveness and efficiency advantages of the designed framework and depict how tradeoffs need to be made to obtain qualitatively different school redistricting plans.

MicroRNAs as powerful tool against COVID-19: Computational perspective.

Severe acute respiratory syndrome coronavirus 2 is the virus that is responsible for the current pandemic, COVID-19 (SARS-CoV-2). MiRNAs, a component of RNAi technology, belong to the family of short, noncoding ssRNAs, and may be crucial in the battle against this global threat since they are involved in regulating complex biochemical pathways and may prevent viral proliferation, translation, and host expression. The complicated metabolic pathways are modulated by the activity of many proteins, mRNAs, and miRNAs working together in miRNA-mediated genetic control. The amount of omics data has increased dramatically in recent years. This massive, linked, yet complex metabolic regulatory network data offers a wealth of opportunity for iterative analysis; hence, extensive, in-depth, but time-efficient screening is necessary to acquire fresh discoveries; this is readily performed with the use of bioinformatics. We have reviewed the literature on microRNAs, bioinformatics, and COVID-19 infection to summarize (1) the function of miRNAs in combating COVID-19, and (2) the use of computational methods in combating COVID-19 in certain noteworthy studies, and (3) computational tools used by these studies against COVID-19 in several purposes. This article is categorized under: Infectious Diseases > Computational Models.

Manufacturing Cost Estimation Using Piecewise Function Approaches

<p><big>This paper describes two novel approaches to cost estimation of manufactured products where a data set of similar products have known manufactured costs. The methods use the notion of piecewise functions and are (1) clustering and (2) splines. Cost drivers are typically a mixture of categorical and numeric data which complicates cost estimation. Both clustering and splines approaches can accommodate this. Through four case studies, we compare our approaches with the often-used regression models. Our results show that clustering especially offers promise in improving the accuracy of cost estimation. While clustering and splines are slightly more complex to develop from both a user and a computational perspective, our approaches are packaged in an open-source software. This paper is the first known to adapt and apply these two well-known mathematical approaches to manufacturing cost estimation.</big></p>

Elucidating the spectroscopic and physicochemical properties for a pH-dependent glyphosate structure from a computational perspective

Glyphosate is the most used broad-spectrum agrochemical due to its non-selective action mechanism. However, there are concerns regarding being a threat to aquatic and terrestrial lifeforms and the environment. In this study, we conducted a systematic theoretical investigation of the effect of pH on the electronic structure, physicochemical, and spectroscopic properties (IR, Raman, UV–vis, ECD, EI-MS, and NMR) of glyphosate and its zwitterionic structures. The lipophilicity of the zwitterions was studied with ADMET and DFT, and a significant discrepancy between the models was observed. Theoretical EI-MS provided insights into the degradation pathways and possible intermediates. The effect of protonation and hydration on the magnetic properties was described based on an implicit-explicit strategy to compute NMR chemical shifts for the zwitterions, in which the solvent has a considerable effect on the chemical shifts. Overall, our findings suggest that spectroscopic properties can be used to describe glyphosate-based zwitterionic structures.

Probing the interaction of uranyl(VI) complex with bovine serum albumin via in-depth experimental and computational perspectives

Interaction aspects of uranyl(VI) complexes as well as the coordinated ONNO-donor ligand with bovine serum albumin (BSA) were investigated by the fluorescence spectroscopy and computational insights. Under optimal physiological condition, it was observed that there was significant decrease in fluorescence intensity of BSA upon interaction with uranyl(VI) complexes as well as the ligand. The mechanism of interaction between the uranyl(VI) complex and BSA protein was examined by fluorescence measurement. The Stern-Volmer constant, binding affinity, binding constant, standard free energy, and fluorescence lifetime decay profile of BSA in the absence as well as in the presence of uranyl(VI) complex were determined. Furthermore, the conformational binding of uranyl(VI) complexes with BSA protein was explored via molecular docking studies, and confirmed that there is a strong affinity between the Trp-213 residue in the binding pocket of sub-domain IIA and uranyl(VI) complex.

In search of a composite biomarker for chronic pain by way of EEG and machine learning: where do we currently stand?

Machine learning is becoming an increasingly common component of routine data analyses in clinical research. The past decade in pain research has witnessed great advances in human neuroimaging and machine learning. With each finding, the pain research community takes one step closer to uncovering fundamental mechanisms underlying chronic pain and at the same time proposing neurophysiological biomarkers. However, it remains challenging to fully understand chronic pain due to its multidimensional representations within the brain. By utilizing cost-effective and non-invasive imaging techniques such as electroencephalography (EEG) and analyzing the resulting data with advanced analytic methods, we have the opportunity to better understand and identify specific neural mechanisms associated with the processing and perception of chronic pain. This narrative literature review summarizes studies from the last decade describing the utility of EEG as a potential biomarker for chronic pain by synergizing clinical and computational perspectives.

Hierarchical Model of Weak Polyelectrolytes with Ionization and Conformation Consistency

Conventional models of weak polyelectrolytes are formulated in terms of either an average degree of ionization or a rigid polymer conformation. While the decoupling of segment-level ionization and polymer structure simplifies the theoretical procedure and is beneficial from a computational perspective, it misses intrachain correlations (i.e., interactions beyond adjacent monomers) that are important for systems at a low polymer concentration with strong electrostatic interactions. In this work, we propose a hierarchical method that incorporates the liquid-state theories with the site-binding model to account for the effects of the local ionic environment on both inter- and intrachain correlations. A wormlike-chain model is introduced to describe the coupling of polymer conformation and monomeric ionization with the long-range electrostatic interactions accounted for by using the Gibbs–Bogoliubov variational principle. Through an extensive comparison of the theoretical predictions with experimental titration curves for poly(acrylic acid) in different alkali chloride solutions, we demonstrate that the hierarchical model is able to quantify the charging behavior of weak polyelectrolytes over a broad range of solution conditions.

Detecting political biases of named entities and hashtags on Twitter

Ideological divisions in the United States have become increasingly prominent in daily communication. Accordingly, there has been much research on political polarization, including many recent efforts that take a computational perspective. By detecting political biases in a text document, one can attempt to discern and describe its polarity. Intuitively, the named entities (i.e., the nouns and the phrases that act as nouns) and hashtags in text often carry information about political views. For example, people who use the term “pro-choice” are likely to be liberal and people who use the term “pro-life” are likely to be conservative. In this paper, we seek to reveal political polarities in social-media text data and to quantify these polarities by explicitly assigning a polarity score to entities and hashtags. Although this idea is straightforward, it is difficult to perform such inference in a trustworthy quantitative way. Key challenges include the small number of known labels, the continuous spectrum of political views, and the preservation of both a polarity score and a polarity-neutral semantic meaning in an embedding vector of words. To attempt to overcome these challenges, we propose the Polarity-aware Embedding Multi-task learning (PEM) model. This model consists of (1) a self-supervised context-preservation task, (2) an attention-based tweet-level polarity-inference task, and (3) an adversarial learning task that promotes independence between an embedding’s polarity component and its semantic component. Our experimental results demonstrate that our PEM model can successfully learn polarity-aware embeddings that perform well at tweet-level and account-level classification tasks. We examine a variety of applications—including a study of spatial and temporal distributions of polarities and a comparison between tweets from Twitter and posts from Parler—and we thereby demonstrate the effectiveness of our PEM model. We also discuss important limitations of our work and encourage caution when applying the PEM model to real-world scenarios.