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Based on the continuous growth of the economy, widespread adoption of intermittent renewable energy sources, and extensive use of information and communication technologies, conventional electric power systems are no longer able to meet the enormous demands of the information age. Diverse renewable energy technologies have been quickly developed to address the energy issue and environmental damage. However, since renewable energy sources are unpredictable and erratic, the widespread use of different renewable energy technologies has consequently put significant strain on the security and dependability of conventional power networks. The Smart Grid (SG) is a modernized electrical network that makes use of cutting-edge communication, control, and information technology to facilitate the integration of renewable energy sources, increase energy efficiency, and improve dependability and security. The invention of computational modeling and evaluation methodologies for SG energy transmission and production networks is the main topic of the research. The Internet of Energy (IoE), which will eventually replace the conventional power production and distribution networks, increases the need to be familiar with the proper computing tools in order to conduct any future SG investigation. The software for simulation that is significant to the modeling and analysis of electrical power production, transmission, distribution, and related systems is examined in this research. The study's conclusions are anticipated to aid in the creation of power generation and transmission systems that are more effective, dependable, and sustainable.

Abstract Longitudinal video archives of behaviour are crucial for examining how sociality shifts over the lifespan in wild animals. New approaches adopting computer vision technology hold serious potential to capture interactions and associations between individuals in video at large scale; however, such approaches need a priori validation, as methods of sampling and defining edges for social networks can substantially impact results. Here, we apply a deep learning face recognition model to generate association networks of wild chimpanzees using 17 years of a video archive from Bossou, Guinea. Using 7 million detections from 100 h of video footage, we examined how varying the size of fixed temporal windows (i.e. aggregation rates) for defining edges impact individual‐level gregariousness scores. The highest and lowest aggregation rates produced divergent values, indicating that different rates of aggregation capture different association patterns. To avoid any potential bias from false positives and negatives from automated detection, an intermediate aggregation rate should be used to reduce error across multiple variables. Individual‐level network‐derived traits were highly repeatable, indicating strong inter‐individual variation in association patterns across years and highlighting the reliability of the method to capture consistent individual‐level patterns of sociality over time. We found no reliable effects of age and sex on social behaviour and despite a significant drop in population size over the study period, individual estimates of gregariousness remained stable over time. We believe that our automated framework will be of broad utility to ethology and conservation, enabling the investigation of animal social behaviour from video footage at large scale, low cost and high reproducibility. We explore the implications of our findings for understanding variation in sociality patterns in wild ape populations. Furthermore, we examine the trade‐offs involved in using face recognition technology to generate social networks and sociality measures. Finally, we outline the steps for the broader deployment of this technology for analysis of large‐scale datasets in ecology and evolution.

Identifying proteins that interact with drug compounds has been recognized as an important part in the process of drug discovery. Despite extensive efforts that have been invested in predicting compound-protein interactions (CPIs), existing traditional methods still face several challenges. The computer-aided methods can identify high-quality CPI candidates instantaneously. In this research, a novel model is named GraphCPIs, proposed to improve the CPI prediction accuracy. First, we establish the adjacent matrix of entities connected to both drugs and proteins from the collected dataset. Then, the feature representation of nodes could be obtained by using the graph convolutional network and Grarep embedding model. Finally, an extreme gradient boosting (XGBoost) classifier is exploited to identify potential CPIs based on the stacked two kinds of features. The results demonstrate that GraphCPIs achieves the best performance, whose average predictive accuracy rate reaches 90.09%, average area under the receiver operating characteristic curve is 0.9572, and the average area under the precision and recall curve is 0.9621. Moreover, comparative experiments reveal that our method surpasses the state-of-the-art approaches in the field of accuracy and other indicators with the same experimental environment. We believe that the GraphCPIs model will provide valuable insight to discover novel candidate drug-related proteins.

A novel deep-learning paradigm for synthesis imaging by radio interferometry in astronomy was recently proposed, dubbed “Residual-to-Residual DNN series for high-Dynamic range imaging” (R2D2). In this work, we start by shedding light on R2D2's algorithmic structure, interpreting it as a learned version of CLEAN with minor cycles substituted with a deep neural network (DNN) whose training is iteration-specific. We then proceed with R2D2's first demonstration on real data, for monochromatic intensity imaging of the radio galaxy Cygnus A from S -band observations with the Very Large Array. We show that the modeling power of R2D2's learning approach enables delivering high-precision imaging, superseding the resolution of CLEAN, and matching the precision of modern optimization and plug-and-play algorithms, respectively uSARA and AIRI. Requiring few major-cycle iterations only, R2D2 provides a much faster reconstruction than uSARA and AIRI, known to be highly iterative, and is at least as fast as CLEAN.

Puede que se haya enterado o puede que no, pero en las dos últimas décadas ha surgido un movimiento secreto y peligroso en los departamentos de humanidades de todo el mundo. Las llamadas “humanidades digitales” (o “HD” para los entendidos), que están acabando con toda la financiación convencional de las actividades humanísticas tradicionales, aporta un sombrío espíritu empresarial y una mentalidad tecnocrática sobre lengua, la historia, los clásicos, la arqueología y cualquier otro espacio disciplinar en el que pueda poner sus manos. Aparentemente encargadas de pervertir los fundamentos humanísticos del pensamiento crítico y sustituirlos por mentalidades tecnosolucionistas, las humanidades digitales están creciendo y prosperando ante nuestras narices y muchos parecen ni siquiera haberse dado cuenta del peligro. Abstract You may or may not have heard about it, but over the past two decades a secret and dangerous movement has emerged in humanities departments around the world. The so-called "digital humanities" (or "HD" to those in the know), which is wiping out all conventional funding for traditional humanities activities, brings a shady entrepreneurial spirit and a technocratic mindset to language, history, classics, archaeology, and any other disciplinary space it can get its hands on. Seemingly charged with perverting the humanistic foundations of critical thinking and replacing them with technosolutionary mentalities, the digital humanities are growing and thriving under our noses and many do not even seem to realize the danger. DOI: 10.5281/zenodo.10433401

The COVID-19 pandemic is already considered one of the biggest global health crises. In Rio Grande do Norte, a Brazilian state, the RegulaRN platform was the health information system used to regulate beds for patients with COVID-19. This article explored machine learning and deep learning techniques with RegulaRN data in order to identify the best models and parameters to predict the outcome of a hospitalized patient. A total of 25,366 bed regulations for COVID-19 patients were analyzed. The data analyzed comes from the RegulaRN Platform database from April 2020 to August 2022. From these data, the nine most pertinent characteristics were selected from the twenty available, and blank or inconclusive data were excluded. This was followed by the following steps: data pre-processing, database balancing, training, and test. The results showed better performance in terms of accuracy (84.01%), precision (79.57%), and F1-score (81.00%) for the Multilayer Perceptron model with Stochastic Gradient Descent optimizer. The best results for recall (84.67%), specificity (84.67%), and ROC-AUC (91.6%) were achieved by Root Mean Squared Propagation. This study compared different computational methods of machine and deep learning whose objective was to classify bed regulation data for patients with COVID-19 from the RegulaRN Platform. The results have made it possible to identify the best model to help health professionals during the process of regulating beds for patients with COVID-19. The scientific findings of this article demonstrate that the computational methods used applied through a digital health solution, can assist in the decision-making of medical regulators and government institutions in situations of public health crisis.

Using computational methods and chemical intuition, the proposed structure of janthinolide A is shown to be incorrect. It is further shown that the material described as janthinolide A is highly likely to be janthinolide C. [ABSTRACT FROM AUTHOR]

Previous examinations of fully convective M-dwarf stars have highlighted enhanced rates of nanoflare activity on these distant stellar sources. However, the specific role the convective boundary, which is believed to be present for spectral types earlier than M2.5V, plays on the observed nanoflare rates is not yet known. Here, we utilize a combination of statistical and Fourier techniques to examine M-dwarf stellar lightcurves that lie on either side of the convective boundary. We find that fully convective M2.5V (and later subtypes) stars have greatly enhanced nanoflare rates compared with their pre-dynamo mode-transition counterparts. Specifically, we derive a flaring power-law index in the region of 3.00 ± 0.20, alongside a decay timescale of 200 ± 100 s for M2.5V and M3V stars, matching those seen in prior observations of similar stellar subtypes. Interestingly, M4V stars exhibit longer decay timescales of 450 ± 50 s, along with an increased power-law index of 3.10 ± 0.18, suggesting an interplay between the rate of nanoflare occurrence and the intrinsic plasma parameters, e.g., the underlying Lundquist number. In contrast, partially convective (i.e., earlier subtypes from M0V to M2V) M-dwarf stars exhibit very weak nanoflare activity, which is not easily identifiable using statistical or Fourier techniques. This suggests that fully convective stellar atmospheres favor small-scale magnetic reconnection, leading to implications for the flare-energy budgets of these stars. Understanding why small-scale reconnection is enhanced in fully convective atmospheres may help solve questions relating to the dynamo behavior of these stellar sources. [ABSTRACT FROM AUTHOR]

A long-wave asymptotic model is developed for the flow of an axisymmetric viscous film lining the interior of a tube for the case where slip occurs at the tube wall. Both the case of a falling film with a passive air core and that of a film driven up the tube by pressure-driven airflow are considered. The impact of slip on the net liquid volume flux is discussed, and linear stability analysis of the evolution equation is conducted to identify the impact of slip on the phase speed and growth rates of disturbances in each case. The presence of slip enhances the growth rates, though its impact on phase speed depends on the film thickness and the strength of the core airflow. For some parameter combinations, slip can modify the phase speed without altering the base flow. The nonlinear evolution of the free surface is then studied numerically. For falling films, increasing the slip length reduces the critical thickness required for plug formation to occur. Families of travelling wave solutions are found via continuation and are used to derive a simple formula for the dependence of this critical thickness on the slip length; this formula is shown to hold for small slip length. For air-driven films, the topology of streamlines in the film can be altered by slip at the wall; if the slip length is large enough, it can prevent regions of recirculation from forming at the wave crest. [ABSTRACT FROM AUTHOR]

An important feature of turbulent boundary layers are persistent large-scale coherent structures in the flow. Here, we use Dynamic Mode Decomposition (DMD), a data-driven technique designed to detect spatio-temporal coherence, to construct optimal low-dimensional representations of such large-scale dynamics in the asymptotic suction boundary layer (ASBL). In the ASBL, fluid is removed by suction through the bottom wall, resulting in a constant boundary layer thickness in streamwise direction. That is, the streamwise advection of coherent structures by the mean flow ceases to be of dynamical importance and can be interpreted as a continuous shift symmetry in streamwise direction. However, this results in technical difficulties, as DMD is known to perform poorly in presence of continuous symmetries. We address this issue using symmetry-reduced DMD (Marensi et al., 2023), and find the large-scale dynamics of the ASBL to be low-dimensional indeed and potentially self-sustained, featuring ejection and sweeping events at large scale. Interactions with near-wall structures are captured when including only a few more modes. [ABSTRACT FROM AUTHOR]

We introduce a prescriptive machine learning approach to speed up the process of solving mixed-integer convex optimization (MICO) problems. We solve multiple optimization instances and train a machine learning model in advance, which we use to solve new instances. Previous works have shown that the predictions of classification algorithms enable us to solve optimization problems much faster than commercial solvers. What distinguishes this paper from the previous work is that we use a prescriptive algorithm, Optimal Policy Trees (OPT), instead of classification algorithms. Whereas classification algorithms aim to predict the correct label and consider all other labels equally undesirable, a prescriptive approach takes into account all the available decision options and their counterfactuals. We first introduce an algorithm that is purely based on OPT and also its extension. We compare their performance with Optimal Classification Trees (OCT) on various MICO problems. Test problems include transportation optimization, portfolio optimization, facility location, and hybrid vehicle control. We also experiment on real-world instances taken from the Mixed Integer Programming Library. OPT-based methods have a significant edge on finding feasible solutions, whereas OCT-based methods have a slight edge on the degree of suboptimality. The proposed extension of the pure OPT algorithm improves on the suboptimality of the solutions the algorithm produces. History: Accepted by Andrea Lodi, Area Editor for Design & Analysis of Algorithms–Discrete. Funding: The research was funded in part by a grant from OCP to MIT. [ABSTRACT FROM AUTHOR]

Non-coding RNAs (ncRNAs) play a critical role in the occurrence and development of numerous human diseases. Consequently, studying the associations between ncRNAs and diseases has garnered significant attention from researchers in recent years. Various computational methods have been proposed to explore ncRNA–disease relationships, with Graph Neural Network (GNN) emerging as a state-of-the-art approach for ncRNA–disease association prediction. In this survey, we present a comprehensive review of GNN-based models for ncRNA–disease associations. Firstly, we provide a detailed introduction to ncRNAs and GNNs. Next, we delve into the motivations behind adopting GNNs for predicting ncRNA–disease associations, focusing on data structure, high-order connectivity in graphs and sparse supervision signals. Subsequently, we analyze the challenges associated with using GNNs in predicting ncRNA–disease associations, covering graph construction, feature propagation and aggregation, and model optimization. We then present a detailed summary and performance evaluation of existing GNN-based models in the context of ncRNA–disease associations. Lastly, we explore potential future research directions in this rapidly evolving field. This survey serves as a valuable resource for researchers interested in leveraging GNNs to uncover the complex relationships between ncRNAs and diseases. [ABSTRACT FROM AUTHOR]

In the last few decades, theoretical and technical advancements in computer facilities and computational techniques have made molecular modeling a useful tool in liquid-phase enantioseparation science for exploring enantioselective recognition mechanisms underlying enantioseparations and for identifying selector–analyte noncovalent interactions that contribute to binding and recognition. Because of the dynamic nature of the chromatographic process, molecular dynamics (MD) simulations are particularly versatile in the visualization of the three-dimensional structure of analytes and selectors and in the unravelling of mechanisms at molecular levels. In this context, MD was also used to explore enantioseparation processes promoted by amylose and cellulose-based selectors, the most popular chiral selectors for liquid-phase enantioselective chromatography. This review presents a systematic analysis of the literature published in this field, with the aim of providing the reader with a comprehensive picture about the state of the art and what is still missing for modeling cellulose benzoates and the phenylcarbamates of amylose and cellulose and related enantioseparations with MD. Furthermore, advancements and outlooks, as well as drawbacks and pitfalls still affecting the applicability of MD in this field, are also discussed. The importance of integrating theoretical and experimental approaches is highlighted as an essential strategy for profiling mechanisms and noncovalent interaction patterns. [ABSTRACT FROM AUTHOR]

Increasing evidence regarding lipids' beneficial effects on human health has changed the common perception of consumers and dietary officials about the role(s) of food lipids in a healthy diet. However, lipids are a wide group of molecules with specific nutritional and bioactive properties. To understand their true nutritional and functional value, robust methods are needed for accurate identification and quantification. Specific analytical strategies are crucial to target specific classes, especially the ones present in trace amounts. Finding a unique and comprehensive methodology to cover the full lipidome of each foodstuff is still a challenge. This review presents an overview of the lipids nutritionally relevant in foods and new trends in food lipid analysis for each type/class of lipids. Food lipid classes are described following the LipidMaps classification, fatty acids, endocannabinoids, waxes, C8 compounds, glycerophospholipids, glycerolipids (i.e., glycolipids, betaine lipids, and triglycerides), sphingolipids, sterols, sercosterols (vitamin D), isoprenoids (i.e., carotenoids and retinoids (vitamin A)), quinones (i.e., coenzyme Q, vitamin K, and vitamin E), terpenes, oxidized lipids, and oxylipin are highlighted. The uniqueness of each food group: oil‐, protein‐, and starch‐rich, as well as marine foods, fruits, and vegetables (water‐rich) regarding its lipid composition, is included. The effect of cooking, food processing, and storage, in addition to the importance of lipidomics in food quality and authenticity, are also discussed. A critical review of challenges and future trends of the analytical approaches and computational methods in global food lipidomics as the basis to increase consumer awareness of the significant role of lipids in food quality and food security worldwide is presented. [ABSTRACT FROM AUTHOR]

Hydrodynamic approaches that treat granular materials as a continuum via the homogenization of discrete flow properties have become viable options for efficient predictions of bulk flow behaviours. However, simplified boundary conditions in computational fluid dynamics are often adopted, which have difficulty in describing the complex stick-slip phenomenon at the boundaries. This paper extends the lattice Boltzmann method for granular flow simulations by incorporating a novel frictional boundary condition. The wall slip velocity is first calculated based on the shear rate limited by the Coulomb friction, followed by the reconstruction of unknown density distribution functions through a modified bounce-back scheme. Validation is performed against a unique plane Couette flow configuration, and the analytical solutions for the flow velocity profile and the wall slip velocity, as functions of the friction coefficient, are reproduced by the numerical model. The transition between no-slip and partial-slip regimes is captured well, but the convergence rate drops from second order to first order when slip occurs. The rheological parameters and the basal friction coefficient are calibrated further against the discrete element simulation of a square granular column collapsing over a horizontal bottom plane. It is found that the calibrated continuum model can predict other granular column collapses with different initial aspect ratios and slope inclination angles, including the basal slip and the complex internal flow structures, without any further adjustments to the model parameters. This highlights the generalization ability of the numerical model, which has a wide range of application in granular flow predictions and controls. [ABSTRACT FROM AUTHOR]

A multi-agent deep reinforcement learning (DRL)-based model is presented in this study to reconstruct flow fields from noisy data. A combination of reinforcement learning with pixel-wise rewards, physical constraints represented by the momentum equation and the pressure Poisson equation, and the known boundary conditions is used to build a physics-constrained deep reinforcement learning (PCDRL) model that can be trained without the target training data. In the PCDRL model, each agent corresponds to a point in the flow field and learns an optimal strategy for choosing pre-defined actions. The proposed model is efficient considering the visualisation of the action map and the interpretation of the model operation. The performance of the model is tested by using direct numerical simulation-based synthetic noisy data and experimental data obtained by particle image velocimetry. Qualitative and quantitative results show that the model can reconstruct the flow fields and reproduce the statistics and the spectral content with commendable accuracy. Furthermore, the dominant coherent structures of the flow fields can be recovered by the flow fields obtained from the model when they are analysed using proper orthogonal decomposition and dynamic mode decomposition. This study demonstrates that the combination of DRL-based models and the known physics of the flow fields can potentially help solve complex flow reconstruction problems, which can result in a remarkable reduction in the experimental and computational costs. [ABSTRACT FROM AUTHOR]

Fundamental to our understanding of planetary bulk compositions is the relationship between their masses and radii, two properties that are often not simultaneously known for most exoplanets. However, while many previous studies have modeled the two-dimensional relationship between planetary mass and radii, this approach largely ignores the dependencies on other properties that may have influenced the formation and evolution of the planets. In this work, we extend the existing nonparametric and probabilistic framework of MRExo to jointly model distributions beyond two dimensions. Our updated framework can now simultaneously model up to four observables, while also incorporating asymmetric measurement uncertainties and upper limits in the data. We showcase the potential of this multidimensional approach to three science cases: (i) a four-dimensional joint fit to planetary mass, radius, insolation, and stellar mass, hinting of changes in planetary bulk density across insolation and stellar mass; (ii) a three-dimensional fit to the California Kepler Survey sample showing how the planet radius valley evolves across different stellar masses; and (iii) a two-dimensional fit to a sample of Class-II protoplanetary disks in Lupus while incorporating the upper limits in dust mass measurements. In addition, we employ bootstrap and Monte Carlo sampling to quantify the impact of the finite sample size as well as measurement uncertainties on the predicted quantities. We update our existing open-source user-friendly MRExo Python package with these changes, which allows users to apply this highly flexible framework to a variety of data sets beyond what we have shown here. [ABSTRACT FROM AUTHOR]

Background: Today, artificial intelligence-based computational approach is facilitating multitasking and interdisciplinary analytical research. For example, the data gathered during an analytical research project such as spectral and chromatographic data can be used in predictive experimental research. The spectral and chromatographic information plays crucial role in pharmaceutical research, especially use of instrumental analytical approaches and it consume time, man power, and money. Hence, predictive analysis would be beneficial especially in resource-limited settings. Main body: Computational approaches verify data at an early phase of study in research process. Several in silico techniques for predicting analyte's spectral and chromatographic characteristics have recently been developed. Understanding of these tools may help researchers to accelerate their research with boosted confidence and prevent researchers from being misled by incorrect analytical data. In this communication, the properties of chemical compounds and its relation to chromatographic retention will be discussed, as well as the prediction technique for UV/IR/Raman/NMR spectrograms. This review looked at the reference data of chemical compounds to compare the predictive ability in silico tools along with the percentage error, limitations, and advantages. Conclusion: The computational prediction of analytical characteristics offers a wide range of applications in academic research, bioanalytical method development, computational chemistry, analytical method development, data analysis approaches, material characterization, and validation process. [ABSTRACT FROM AUTHOR]

Digital trace data and computational methods are increasingly being used by researchers to study mental health phenomena (i.e. psychopathology and well-being) in social media. Computer-assisted mental health research is not simply a continuation of previous studies, but rather raises ethical, conceptual and methodological issues that are critical to behavioural science but have not yet been systematically explored. Based on a systematic review of n = 147 studies, we reveal a multidisciplinary field of research that has grown immensely since 2010, spanning the humanities, social sciences, and engineering. We find that a substantial majority of studies in our sample lack a standardized form of ethical consideration, focus on specific constructs and have a rather narrow focus on specific social media platforms. Based on our findings, we discuss how computational elements have influenced mental health research, highlight academic gaps and suggest promising directions for future studies. [ABSTRACT FROM AUTHOR]

Anecdotal evidence suggests a link between online message boards and the rise of far-right movements, which have achieved growing electoral success globally. Press accounts and scholarship have suggested these message boards help to radicalize like-minded users through exposure to shared media insulated from cross-cutting viewpoints (e.g., Hine et al. 2017 ; Palmer 2019). To better understand what role online message boards might play for supporters of right-wing populist movements, we focus on the Spanish political party Vox and its supporters' use of the message board ForoCoches, a fan site for car enthusiasts, which became an important platform for the party. Using more than 120,000 messages collected from threads mentioning the party between 2013–2019, we examine the URLs shared to show how mainstream news media events shape the conversation online and how users not only were exposed but deeply engaged with cross-cutting news sources. We argue that the use of sites such as ForoCoches should be viewed in the context of a broader increasingly hybrid political and media landscape where activity online and offline cannot be understood separate from one another. Moreover, our findings suggest that the online political discussions that take place in Vox-related threads on ForoCoches resemble normatively positive deliberative spaces—albeit in this case in support of illiberal political positions. In other words, our findings complicate conventional notions about the benefits of political talk, especially online, as a democratically desirable end in and of itself. [ABSTRACT FROM AUTHOR]

Complex systems, as interwoven miscellaneous interacting entities that emerge and evolve through self-organization in a myriad of spiraling contexts, exhibit subtleties on global scale besides steering the way to understand complexity which has been under evolutionary processes with unfolding cumulative nature wherein order is viewed as the unifying framework. Indicating the striking feature of non-separability in components, a complex system cannot be understood in terms of the individual isolated constituents' properties per se, it can rather be comprehended as a way to multilevel approach systems behavior with systems whose emergent behavior and pattern transcend the characteristics of ubiquitous units composing the system itself. This observation specifies a change of scientific paradigm, presenting that a reductionist perspective does not by any means imply a constructionist view; and in that vein, complex systems science, associated with multiscale problems, is regarded as ascendancy of emergence over reductionism and level of mechanistic insight evolving into complex system. While evolvability being related to the species and humans owing their existence to their ancestors' capability with regards to adapting, emerging and evolving besides the relation between complexity of models, designs, visualization and optimality, a horizon that can take into account the subtleties making their own means of solutions applicable is to be entailed by complexity. Such views attach their germane importance to the future science of complexity which may probably be best regarded as a minimal history congruent with observable variations, namely the most parallelizable or symmetric process which can turn random inputs into regular outputs. Interestingly enough, chaos and nonlinear systems come into this picture as cousins of complexity which with tons of its components are involved in a hectic interaction with one another in a nonlinear fashion amongst the other related systems and fields. Relation, in mathematics, is a way of connecting two or more things, which is to say numbers, sets or other mathematical objects, and it is a relation that describes the way the things are interrelated to facilitate making sense of complex mathematical systems. Accordingly, mathematical modeling and scientific computing are proven principal tools toward the solution of problems arising in complex systems' exploration with sound, stimulating and innovative aspects attributed to data science as a tailored-made discipline to enable making sense out of voluminous (-big) data. Regarding the computation of the complexity of any mathematical model, conducting the analyses over the run time is related to the sort of data determined and employed along with the methods. This enables the possibility of examining the data applied in the study, which is dependent on the capacity of the computer at work. Besides these, varying capacities of the computers have impact on the results; nevertheless, the application of the method on the code step by step must be taken into consideration. In this sense, the definition of complexity evaluated over different data lends a broader applicability range with more realism and convenience since the process is dependent on concrete mathematical foundations. All of these indicate that the methods need to be investigated based on their mathematical foundation together with the methods. In that way, it can become foreseeable what level of complexity will emerge for any data desired to be employed. With relation to fractals, fractal theory and analysis are geared toward assessing the fractal characteristics of data, several methods being at stake to assign fractal dimensions to the datasets, and within that perspective, fractal analysis provides expansion of knowledge regarding the functions and structures of complex systems while acting as a potential means to evaluate the novel areas of research and to capture the roughness of objects, their nonlinearity, randomness, and so on. The idea of fractional-order integration and differentiation as well as the inverse relationship between them lends fractional calculus applications in various fields spanning across science, medicine and engineering, amongst the others. The approach of fractional calculus, within mathematics-informed frameworks employed to enable reliable comprehension into complex processes which encompass an array of temporal and spatial scales notably provides the novel applicable models through fractional-order calculus to optimization methods. Computational science and modeling, notwithstanding, are oriented toward the simulation and investigation of complex systems through the use of computers by making use of domains ranging from mathematics to physics as well as computer science. A computational model consisting of numerous variables that characterize the system under consideration allows the performing of many simulated experiments via computerized means. Furthermore, Artificial Intelligence (AI) techniques whether combined or not with fractal, fractional analysis as well as mathematical models have enabled various applications including the prediction of mechanisms ranging extensively from living organisms to other interactions across incredible spectra besides providing solutions to real-world complex problems both on local and global scale. While enabling model accuracy maximization, AI can also ensure the minimization of functions such as computational burden. Relatedly, level of complexity, often employed in computer science for decision-making and problem-solving processes, aims to evaluate the difficulty of algorithms, and by so doing, it helps to determine the number of required resources and time for task completion. Computational (-algorithmic) complexity, referring to the measure of the amount of computing resources (memory and storage) which a specific algorithm consumes when it is run, essentially signifies the complexity of an algorithm, yielding an approximate sense of the volume of computing resources and seeking to prove the input data with different values and sizes. Computational complexity, with search algorithms and solution landscapes, eventually points toward reductions vis à vis universality to explore varying degrees of problems with different ranges of predictability. Taken together, this line of sophisticated and computer-assisted proof approach can fulfill the requirements of accuracy, interpretability, predictability and reliance on mathematical sciences with the assistance of AI and machine learning being at the plinth of and at the intersection with different domains among many other related points in line with the concurrent technical analyses, computing processes, computational foundations and mathematical modeling. Consequently, as distinctive from the other ones, our special issue series provides a novel direction for stimulating, refreshing and innovative interdisciplinary, multidisciplinary and transdisciplinary understanding and research in model-based, data-driven modes to be able to obtain feasible accurate solutions, designed simulations, optimization processes, among many more. Hence, we address the theoretical reflections on how all these processes are modeled, merging all together the advanced methods, mathematical analyses, computational technologies, quantum means elaborating and exhibiting the implications of applicable approaches in real-world systems and other related domains. [ABSTRACT FROM AUTHOR]

What do berries, avocado, quinoa, and ginger have in common? These food items are often regarded as superfoods, a marketing term that overstates the importance of single food items for one's health and wellbeing. In the present paper, we set out to investigate how purported superfoods are represented in the discourse of online news. We use computational language models to extract the unique topics and terms used to discuss superfoods. Our results show that news coverage is dominated by many specific claims about the healing properties of superfoods. The structural topic model further demonstrates that articles mentioning superfoods are more likely to include topics about a) nutrients, physical appearance, and health in the same context, b) retail strategies, and c) scientific research about the health benefits of superfoods. These results illustrate complex representations of superfoods in news media. [ABSTRACT FROM AUTHOR]

This paper discusses how to design, solve and estimate dynamic programming models using the open source package niqlow. Reasons are given for why such a package has not appeared earlier and why the object-oriented approach followed by niqlow seems essential. An example is followed that starts with basic coding then expands the model and applies different solution methods to finally estimate parameters from data. The niqlow approach is used to organize the empirical DP literature differently from traditional surveys which may make it more accessible to new researchers. Features for efficiency and customization are also discussed. [ABSTRACT FROM AUTHOR]

Current research applies an energy-based design model to improve performance in existing modern buildings, in Rio de Janeiro, from the 1940's, improving these buildings' shading systems. This article proposes a methodology tested through a case study, the Nova Cintra building. The methodology starts by analysing the original shading system performance, regarding insolation, illuminance and air temperature. Using these results, proposes two computacional methods to improve performance: (1) a combinatorial modelling process, recombining the existing shading systems positions in the building's north façade; and (2) a transformation process, using parametric and algorithmic–parametric modelling, to improve the existing shading systems performance. Both processes use optimization algorithms. The results of these modelling and optimization methods are compared with the results of the original system and suggests an improvement between 111.1% and 590.4% for insolation; between 360.9% and 84.4% for illuminance; and between 2.9% and 3.0% for air temperature, considering winter and summer solstices. This improvement aims at reducing the buildings' energy consumption and foresees the production of renewable energy from solar harvesting, to mitigate climate change. [ABSTRACT FROM AUTHOR]

Over the last two decades a plethora of new thermoelectric materials, their alloys, and their nanostructures were synfthesized. The ZT figure of merit, which quantifies the thermoelectric efficiency of these materials increased from values of unity to values consistently beyond two across material families. At the same time, the ability to identify and optimize such materials, has stressed the need for advanced numerical tools for computing electronic transport in materials with arbitrary bandstructure complexity, multiple scattering mechanisms, and a large degree of nanostructuring. Many computational methods have been developed, the majority of which utilize the Boltzmann transport equation (BTE) formalism, spanning from fully ab initio to empirical treatment, with varying degree of computational expense and accuracy. In this paper we describe a suitable computational process that we have recently developed specifically for thermoelectric materials. The method consists of three independent software packages that we have developed and: (1) begins from ab initio calculation of the electron–phonon scattering rates, (2) to then be used within a Boltzmann transport simulator, and (3) calculated quantities from the BTE are then passed on to a Monte Carlo simulator to examine electronic transport in highly nanostructured material configurations. The method we describe is computationally significantly advantageous compared to current fully ab initio and existing Monte Carlo methods, but with a similar degree of accuracy, thus making it truly enabling in understanding and assessing thermoelectric transport in complex band, nanostructured materials. [ABSTRACT FROM AUTHOR]

This paper presents a quantitative approach to hedging financial risks associated with changes in international oil prices for companies that import crude oil. The authors utilize the Geometric Brownian Motion model to capture the dynamic behavior of prices over time. To determine the optimal use of Call-options, the authors formulate a linear problem that minimizes the Conditional Value-at-Risk of the distribution of losses relative to the expected budget. The solution to this problem is obtained through a combination of Linear Programming optimization and Monte Carlo simulation. It enables the identification of the best Call-option offer that minimizes the risk of financial losses while staying within budget constraints. The validity of the proposed methodology is demonstrated through detailed examples that showcase its capabilities. [ABSTRACT FROM AUTHOR]

Bathymetry is an important factor affecting wave propagation in coastal environments but is often challenging to measure in practice. We propose a method for inferring coastal bathymetry from spatial variations in surface waves by combining a high-order spectral method for wave simulation and an adjoint-based variational data assimilation method. Recursion-formed adjoint equations are derived to obtain the sensitivity of the wave surface elevation to the underlying bottom topography to any desired order of nonlinear perturbation. We also develop a multiscale optimisation method to eliminate spurious high-wavenumber fluctuations in the reconstructed bathymetry data caused by sensitivity variations over the different length scales of surface waves. The proposed bottom detection method is validated with a realistic coastal wave environment involving complex two-dimensional bathymetry features, non-periodic incident waves and nonlinear broadband multidirectional waves. In numerical experiments at both laboratory and field scales, the bathymetry reconstructed from our method agrees well with the ground truth. We also show that our method is robust against imperfect surface wave data in the presence of limited sampling frequency and noise. [ABSTRACT FROM AUTHOR]

Artificial neural network emulators have been demonstrated to be a very computationally efficient method to rapidly generate galaxy spectral energy distributions, for parameter inference or otherwise. Using a highly flexible and fast mathematical structure, they can learn the nontrivial relationship between input galaxy parameters and output observables. However, they do so imperfectly, and small errors in flux prediction can yield large differences in recovered parameters. In this work, we investigate the relationship between an emulator's execution time, uncertainties, correlated errors, and ability to recover accurate posteriors. We show that emulators can recover consistent results to traditional fits, with a precision of 25%–40% in posterior medians for stellar mass, stellar metallicity, star formation rate, and stellar age. We find that emulation uncertainties scale with an emulator's width N as ∝ N −1, while execution time scales as ∝ N 2, resulting in an inherent tradeoff between execution time and emulation uncertainties. We also find that emulators with uncertainties smaller than observational uncertainties are able to recover accurate posteriors for most parameters without a significant increase in catastrophic outliers. Furthermore, we demonstrate that small architectures can produce flux residuals that have significant correlations, which can create dangerous systematic errors in colors. Finally, we show that the distributions chosen for generating training sets can have a large effect on an emulator's ability to accurately fit rare objects. Selecting the optimal architecture and training set for an emulator will minimize the computational requirements for fitting near-future large-scale galaxy surveys. We release our emulators on GitHub (http://github.com/elijahmathews/MathewsEtAl2023). [ABSTRACT FROM AUTHOR]

The investigation involves utilizing a set of three ordinary differential equations to mathematically model the degradation process of a mixture containing phenol and p-cresol within a continuously agitated bioreactor. The primary focus lies in the stability analysis of equilibrium points within this model. Additionally, the research delves into exploring the influence of fractal dimension and fractional order on the model, incorporating fractal-fractional derivatives and employing three distinct types of kernels. To quantify the concentrations of phenol, p-cresol, and biomass, highly effective computational algorithms have been formulated, enhancing the precision and efficiency of data analysis. In conclusion, the proposed methodology's soundness and accuracy are thoroughly scrutinized and affirmed through extensive computational simulations. [ABSTRACT FROM AUTHOR]

In this study, we examine gender differences in political news coverage to determine whether the media employ stereotypical traits in portrayals of 1,095 U.S. politicians. Using a sample of over 5 million U.S. news stories published from 2010 to 2020, we study the media's attribution of gender-linked (feminine, masculine) and political (leadership, competence, integrity, empathy) traits to U.S. politicians and present new longitudinal evidence for political gender stereotyping in the news. Our findings show that certain gender differences are present in news coverage (e.g., physical traits), some of which have remained unchanged over the past decade (e.g., integrity traits). [ABSTRACT FROM AUTHOR]

Omics studies such as metabolomics, lipidomics, and proteomics have become important for understanding the mechanisms in living organisms. However, the compounds detected are structurally different and contain isomers, with each structure or isomer leading to a different result in terms of the role they play in the cell or tissue in the organism. Therefore, it is important to detect, characterize, and elucidate the structures of these compounds. Liquid chromatography and mass spectrometry have been utilized for decades in the structure elucidation of key compounds. While prediction models of parameters (such as retention time and fragmentation pattern) have also been developed for these separation techniques, they have some limitations. Moreover, ion mobility has become one of the most promising techniques to give a fingerprint to these compounds by determining their collision cross section (CCS) values, which reflect their shape and size. Obtaining accurate CCS enables its use as a filter for potential analyte structures. These CCS values can be measured experimentally using calibrant‐independent and calibrant‐dependent approaches. Identification of compounds based on experimental CCS values in untargeted analysis typically requires CCS references from standards, which are currently limited and, if available, would require a large amount of time for experimental measurements. Therefore, researchers use theoretical tools to predict CCS values for untargeted and targeted analysis. In this review, an overview of the different methods for the experimental and theoretical estimation of CCS values is given where theoretical prediction tools include computational and machine modeling type approaches. Moreover, the limitations of the current experimental and theoretical approaches and their potential mitigation methods were discussed. [ABSTRACT FROM AUTHOR]

This paper presents the Twitter Corpus of Philippine Englishes (TCOPE): a dataset of 27 million tweets amounting to 135 million words collected from 29 cities across the Philippines. It provides an overview of the dataset, and then shows how it can be employed to examine Philippine English (PhilE) and its relationship with extralinguistic factors (e.g. ethno-geographic region, time, sex). The focus is on the patterns of variation involving four PhilE features: (1) irregular past tense morpheme -t, (2) double comparatives, (3) subjunctive were, and (4) phrasal verb base from. My analyses corroborate previous work and further demonstrate structured heterogeneity within PhilE, indicating that it is a multifaceted and dynamic variety. TCOPE has shown itself to be useful for exploring both the "general" features of contemporary PhilE and the different forms of variation within it. It contributes to a deeper understanding of Philippine English(es) over time and in different social contexts. [ABSTRACT FROM AUTHOR]

Uniaxial compressive strength (UCS) is a critical geomechanical property of rocks that is frequently required during the preliminary stage of civil engineering design. To obtain the UCS value needs a time consuming and costly process of samples collection and preparation. There are alternate methods for determining UCS that can be conducted in situ. In this study, an attempt has been made to predict the UCS of limestone from some simple and inexpensive rock index tests such as block punch index (BPI), ultrasonic wave velocity test (Vp), Schmidt's hammer rebound number (SHR), and point load index tests (I_s50). According to extensive experimental results, a database was established for estimation of the UCS via three computational methods such as support vector machine (SVM), adaptive neuro-fuzzy inference system (ANFIS), and multi layer perceptron (MLP). After developing the models and considering several performance indices including the coefficient of determination R^2, variance account for (VAF), root mean squared error (RMSE), and using simple ranking method, the predictive models were applied to obtain the best model. Consequently, SVM approach predicted the UCS of limestone with higher accuracy in comparison to other studied computational methods. [ABSTRACT FROM AUTHOR]

Machine parts are exposed to stress accumulation due to geometric differences. Determining the stress accumulation locations is crucial to the design procedures. Studies on stress concentrations have been conducted in the past using a variety of theoretical and experimental methodologies, and distinct interpretations have been offered depending on the geometry of the machine part to be produced. The ability to complete activities with the least amount of effort and in the shortest amount of time has emerged as a result of the new computer technologies and software that have impacted many aspects of our everyday lives. One of these methods is the artificial neural networks (ANN) model, which is a branch of artificial intelligence. It is argued as a thesis in this study that fast and low-cost solutions can be found to problems in the field of solid mechanics by using the ANN model. For this purpose, a model has been developed to determine the SCF value with the ANN model of a plate with symmetrical V-shaped notch. The graphs obtained from previous experimental studies were converted to digital format and the Kt values obtained for the V-shaped notch problem with different parameters were converted into a data file. In this file, the SCF values to be obtained according to the strength upper limit safety factor value of the machine part, depending on the dimensional dimensions and material type required for the design, are calculated numerically in the form of an Excel file. An ANN-based code was created in MATLAB software and a new solution method was presented for parts containing a V-shaped notch. [ABSTRACT FROM AUTHOR]

The Zwicky Transient Facility is a time-domain optical survey that has substantially increased our ability to observe and construct massive catalogs of astronomical objects by use of its 47 square degree camera that can observe in multiple filters. However the telescope’s i-band filter suffers from significant atmospheric fringes that reduce photometric precision, especially for faint sources and in multi-epoch co-additions. Here we present a method for constructing models of these atmospheric fringes using Principal Component Analysis that can be used to identify and remove these artifacts from contaminated images. In addition, we present the Uniform Background Indicator as a quantitative measurement of the reduced correlated background noise and photometric error present after removing fringes. We conclude by evaluating the effect of our method on measuring faint sources through the injection and recovery of artificial stars in both single-image epochs and co-additions. Our method for constructing atmospheric fringe models and applying those models to produce cleaned images is available for public download in the open source Python package fringez (https://github.com/MichaelMedford/fringez).

We present a numerical study of spatially quasi-periodic travelling waves on the surface of an ideal fluid of infinite depth. This is a generalization of the classic Wilton ripple problem to the case when the ratio of wavenumbers satisfying the dispersion relation is irrational. We propose a conformal mapping formulation of the water wave equations that employs a quasi-periodic variant of the Hilbert transform to compute the normal velocity of the fluid from its velocity potential on the free surface. We develop a Fourier pseudo-spectral discretization of the travelling water wave equations in which one-dimensional quasi-periodic functions are represented by two-dimensional periodic functions on the torus. This leads to an overdetermined nonlinear least-squares problem that we solve using a variant of the Levenberg-Marquardt method. We investigate various properties of quasi-periodic travelling waves, including Fourier resonances, time evolution in conformal space on the torus, asymmetric wave crests, capillary wave patterns that change from one gravity wave trough to the next without repeating and the dependence of wave speed and surface tension on the amplitude parameters that describe a two-parameter family of waves.

We investigate new, straightforward methods for interpolating and integrating discontinuous single and two-variable real valued functions. The method is based on some modified matrix–vector barycentric Lagrange interpolation formulas. We developed seven rules for the optimum distribution of nodes inside the domain of integration: five for single-valued discontinuous functions, and two rules for the two independent variables of discontinuous functions. We designed these rules to depend on the length of the integration domain and the degree of the interpolant polynomials. Thus, we obtained uniform interpolation and minimum roundoff errors. Based on these rules with the application of the modified matrix–vector barycentric formulas, we easily isolated the singularities of the interpolant integrands and evaluated the corresponding interpolant integral values with super accuracy. The obtained numerical solutions of the five given examples show the high accuracy and efficiency of the presented method compared with the exact solutions and with the cited method.

Possibilities of applying automated content analysis in journalism studies include, for example, machine learning to identify topics in journalistic coverage or measuring news diffusion via automated approaches. But how have computational methods been applied thus far? And what are consequences of the “computational turn” in communication science, especially concerning interdisciplinarity? Based on a systematic literature review, this article summarizes the use of automated content analysis in journalism studies. Results illustrate an increasing use of the method by communication scientists, as yet another indicator of methodological interdisciplinarity in communication science. However, there is little evidence of an increase in theoretical interdisciplinarity: Studies relying on computational methods do not increasingly refer to theories from other disciplines. With respect to practical interdisciplinarity, for instance collaborations, our discipline is by no means becoming more interdisciplinary. Instead, we find a shift in favor of technical disciplines. At least up to now, the “computational turn” in communication science should thus not be equated with an “interdisciplinary turn.”

Point-spread function (PSF) estimation in spatially undersampled images is challenging because large pixels average fine-scale spatial information. This is problematic when fine-resolution details are necessary, as in optimal photometry where knowledge of the illumination pattern beyond the native spatial resolution of the image may be required. Here, we introduce a method of PSF reconstruction where point sources are artificially sampled beyond the native resolution of an image and combined together via stacking to return a finely sampled estimate of the PSF. This estimate is then deconvolved from the pixel-gridding function to return a superresolution kernel that can be used for optimally weighted photometry. We benchmark against the

We present a numerical study of spatially quasi-periodic travelling waves on the surface of an ideal fluid of infinite depth. This is a generalization of the classic Wilton ripple problem to the case when the ratio of wavenumbers satisfying the dispersion relation is irrational. We propose a conformal mapping formulation of the water wave equations that employs a quasi-periodic variant of the Hilbert transform to compute the normal velocity of the fluid from its velocity potential on the free surface. We develop a Fourier pseudo-spectral discretization of the travelling water wave equations in which one-dimensional quasi-periodic functions are represented by two-dimensional periodic functions on the torus. This leads to an overdetermined nonlinear least-squares problem that we solve using a variant of the Levenberg-Marquardt method. We investigate various properties of quasi-periodic travelling waves, including Fourier resonances, time evolution in conformal space on the torus, asymmetric wave crests, capillary wave patterns that change from one gravity wave trough to the next without repeating and the dependence of wave speed and surface tension on the amplitude parameters that describe a two-parameter family of waves.

A succinct and systematic form of multiplication for any arbitrary pairs of octonions is devised. A typical expression of multiplication for any pair of octonions involves 64 terms, which, from the computational and theoretical aspect, is too cumbersome. In addition, its internal relation could not be directly visualized via the expression per se. In this article, we study the internal structures of the indexes between imaginary unit octonions. It is then revealed by various copies of isomorphic structures for the multiplication. We isolate one copy and define a multiplicative structure on this. By doing so, we could keep track of all relations between indexes and the signs for cyclic permutations. The final form of our device is expressed in the form of a series of determinants, which shall offer some direct intuition about octonion multiplication and facilitate the further computational aspect of applications.

Metals are beneficial to life, but the presence of these elements in excessive amounts can harm both organisms and the environment; therefore, detecting the presence of metals is essential. Currently, metal detection methods employ powerful instrumental techniques that require a lot of time and money. Hence, the development of efficient and effective metal indicators is essential. Several synthetic metal detectors have been made, but due to their risk of harm, the use of natural pigments is considered a potential alternative. Experiments are needed for their development, but they are expensive and time-consuming. This review explores various computational methods and approaches that can be used to investigate metal–pigment interactions because choosing the right methods and approaches will affect the reliability of the results. The results show that quantum mechanical methods (ab initio, density functional theory, and semiempirical approaches) and molecular dynamics simulations have been used. Among the available methods, the density functional theory approach with the B3LYP functional and the LANL2DZ ECP and basis set is the most promising combination due to its good accuracy and cost-effectiveness. Various experimental studies were also in good agreement with the results of computational methods. However, deeper analysis still needs to be carried out to find the best combination of functions and basis sets.

The Streptomyces strain G222, isolated from a Vietnamese marine sediment, was confidently identified by 16S rRNA gene sequencing. Its AcOEt crude extract was successfully analyzed using non-targeted LC-MS/MS analysis, and molecular networking, leading to a putative annotation of its chemical diversity thanks to spectral libraries from GNPS and in silico metabolite structure prediction obtained from SIRIUS combined with the bioinformatics tool conCISE (Consensus Annotation Propagation of in silico Elucidations). This dereplication strategy allowed the identification of an interesting cluster of a series of putative cyclic and linear lipopeptides of the lichenysin and surfactin families. Lichenysins (3–7) were isolated from the sub-fraction, which showed significant anti-biofilm activity against Pseudomonas aeruginosa MUC-N1. Their structures were confirmed by detailed 1D and 2D NMR spectroscopy (COSY, HSQC, HMBC, TOCSY, ROESY) recorded in CD3OH, and their absolute configurations were determined using the modified Marfey’s method. The isolated lichenysins showed anti-biofilm activity at a minimum concentration of 100 µM. When evaluated for antibacterial activity against a panel of Gram-positive and Gram-negative strains, two isolated lichenysins exhibited selective activity against the MRSA strain without affecting its growth curve and without membranotropic activity. This study highlights the power of the MS/MS spectral similarity strategy using computational methods to obtain a cross-validation of the annotated molecules from the complex metabolic profile of a marine sediment-derived Streptomyces extract. This work provides the first report from a Streptomyces strain of combined cyclic and linear lichenysins and surfactins, known to be characteristic compounds of the genus Bacillus.