Flexible Model Research Articles

The Role of Innovation in the Competition Between Fintechs and Traditional Banks in Brazil

The Brazilian banking market has distinct and peculiar characteristics, resulting from its historical evolution, specific cultural traits and control exercised by the Central Bank. This article aims to analyze, from the perspective of one of the largest Brazilian banks, the role of innovation, products and processes, in the competition between fintechs and traditional banks. The methodology used was a single case study, qualitative and descriptive approach, with data collection through interviews with managers and customers of Alfa Bank and internal and external documentary survey. It has been evident that the emergence of Fintechs, with its flexible business model, with reduced operational costs, provided by product and process innovation, impacted the banking market in Brazil, traditionally dominated by a small number of large banks, which operated in a traditional way. The analyzed bank, known as Alfa, follows technological developments and improves its main products and services, taking into account suggestions from both employees and customers. The results also showed that Brazilian customers recognize the importance of innovation, especially technology-based, but also value the physical presence of traditional banks, which inspires more confidence in them than Fintechs, which operate online.

Inducible and reversible SOD2 knockdown in mouse skeletal muscle drives impaired pyruvate oxidation and reduced metabolic flexibility.

Skeletal muscle mitochondrial dysfunction is a key characteristic of aging muscle and contributes to age related diseases such as sarcopenia, frailty, and type 2 diabetes. Mitochondrial oxidative distress has been implicated as a driving factor in these age-related diseases, however whether it is a cause, or a consequence of mitochondrial dysfunction remains to be determined. The development of more flexible genetic models is an important tool to test the mechanistic role of mitochondrial oxidative stress on skeletal muscle metabolic dysfunction. We characterize a new model of inducible and reversible mitochondrial redox stress using a tetracycline controlled skeletal muscle specific short hairpin RNA targeted to superoxide dismutase 2 (iSOD2). iSOD2 KD and control (CON) animals were administered doxycycline for 3- or 12- weeks and followed for up to 24 weeks and mitochondrial respiration and muscle contraction were measured to define the time course of SOD2 KD and muscle functional changes and recovery. Maximum knockdown of SOD2 protein occurred by 6 weeks and recovered by 24 weeks after DOX treatment. Mitochondrial aconitase activity and maximum mitochondrial respiration declined in KD muscle by 12 weeks and recovered by 24 weeks. There were minimal changes in gene expression between KD and CON muscle. Twelve-week KD showed a small, but significant decrease in muscle fatigue resistance. The primary phenotype was reduced metabolic flexibility characterized by impaired pyruvate driven respiration when other substrates are present. The pyruvate dehydrogenase kinase inhibitor dichloroacetate partially restored pyruvate driven respiration, while the thiol reductant DTT did not. We use a model of inducible and reversible skeletal muscle SOD2 knockdown to demonstrate that elevated matrix superoxide reversibly impairs mitochondrial substrate flexibility characterized by impaired pyruvate oxidation. Despite the bioenergetic effect, the limited change in gene expression suggests that the elevated redox stress in this model is confined to the mitochondrial matrix.

Dual career in the workplace: co-creation of a conceptual framework by employers and employee-sportspersons incorporating corporate social responsibility and brand alignment.

The purpose of this study was to provide an evidence base and conceptual framework to inform new guidelines for achieving a balance between sports and employment commitments (i.e., dual career, DC) of the employee-sportspersons. To shape a DC discourse in the workplace, the distinct and combined views of the employee-sportspersons (i.e., the Employee), the managers (i.e., the Employer) were considered. Following a concept mapping methodology, 257 international participants (25% employers and 75% employee-sportspersons) sorted and rated 50 potential statements associated with DC circumstances and supports in the workplace. Six distinct clusters emerged, with the combined employers-employee co-creation scenario assigning 6 statements to the micro dimension (Cluster 1 = Workplace Benefits), 4 statements to the meso dimension (Cluster 2 = Role of National Sports Governing Bodies), 19 statements to the macro dimension (Cluster 3 = Dual Career Policy Development), 4 and 5 statements to the organizational dimensions (Cluster 4 = Employee-Employer Collaboration and Responsibility; Cluster 5 = Sport Career Integration), and 12 statements to the policy (Cluster 6 = Workplace Strategies for Dual Career Support) dimension. With respect to the employers, the employee-sportspersons showed higher scores (p < 0.05) for importance of clusters 2, 4, and 6, and for feasibility of clusters 2 and 6. These findings suggest priorities for changes within the DC dimensions identified, and envisage flexible models for aligning corporate brand values and corporate social responsibility strategies through meaningful and proactive DC support of the employee-sportspersons in the workplace. The findings provide a rigorously derived evidence base to inform the formulation of new DC workplace guidelines.

Economic management of microgrid using flexible non-linear load models based on price-based demand response strategies

Demand response programmes are used in microgrid research without considering the different price elasticity of distinct load types. To evaluate the impacts of demand response efforts, it is necessary to utilise a mix of nonlinear and linear models for creating load-responsive models in a manner that is realistic. With an aim to address this gap in research, an exhaustive technoeconomic investigation is being conducted to determine the effect that price-dependent demand response programmes have on the optimal scheduling of microgrids when linear and nonlinear load models are present. The flexible elasticity model is used to accurately describe how customers respond to changes in electricity price. Four load models, including exponential, hyperbolic, linear, and logarithmic, were constructed for each demand response programme. Rime is a newly created physics-based algorithm that has been deployed to handle the anticipated energy management problem that the microgrid may be experiencing. To evaluate the efficacy of the proposed strategy, four case studies based on grid price and participation scenarios have been carried out. The findings from our research are notably impactful, revealing an 8 % reduction in energy consumption when applying the critical peak price (CPP) load demand model. This efficiency gain translates into a notable enhancement in the load factor, increasing from 0.83 to 0.86, and a significant drop in overall generation costs from $25,463 to $21,823 under optimal conditions. These results vividly illustrate the practical value of our proposed research work, showcasing their ability to generate substantial energy savings and cost efficiencies in microgrid operations. The improvements in operational efficiency and cost-effectiveness underscore the potential of these models to refine microgrid management, delivering both economic and performance gains that are crucial for advancing sustainable energy solutions.

Development of an inclusive, scalable, and flexible hydrologic modeling system: Establishing integrated flood simulation system at agricultural watersheds

In this study, we developed a comprehensive hydrological modeling system to address the diverse needs of hydrologists and researchers. The system comprised nine modules, each serving a specific purpose. These modules include a multiplicative random cascade model, frequency analysis, inflow simulation, Hydrologic Engineering Center – 5, Hydrologic Engineering Center – River Analysis System, and farmland drainage simulations. The system follows a structured sequence within a large framework, beginning with user inputs for the initial condition information and period specifications for frequency analysis. It then calculates the time-disaggregated precipitation data utilizing the Dask distributed server for efficient computation. Subsequent module computations were conducted on dedicated mask workers. The Integrated Database serves as a comprehensive repository for simulation studies, encompassing historical precipitation data, shared socioeconomic pathways, climate change scenario data, and reservoir and farmland survey data. This system has been used in several studies and has provided cohesive and reliable results for flood simulations.

Solving Two-stage Quadratic Multiobjective Problems via Optimality and Relaxations

This paper focuses on the study of robust two-stage quadratic multiobjective optimization problems. We formulate new necessary and sufficient optimality conditions for a robust two-stage multiobjective optimization problem. The obtained optimality conditions are presented by means of linear matrix inequalities and thus they can be numerically validated by using a semidefinite programming problem. The proposed optimality conditions can be elaborated further as second-order conic expressions for robust two-stage quadratic multiobjective optimization problems with separable functions and ellipsoidal uncertainty sets. We also propose relaxation schemes for finding a (weak) efficient solution of the robust two-stage multiobjective problem by employing associated semidefinite programming or second-order cone programming relaxations. Moreover, numerical examples are given to demonstrate the solution variety of our flexible models and the numerical verifiability of the proposed schemes.

GexMolGen: cross-modal generation of hit-like molecules via large language model encoding of gene expression signatures.

Designing de novo molecules with specific biological activity is an essential task since it holds the potential to bypass the exploration of target genes, which is an initial step in the modern drug discovery paradigm. However, traditional methods mainly screen molecules by comparing the desired molecular effects within the documented experimental results. The data set limits this process, and it is hard to conduct direct cross-modal comparisons. Therefore, we propose a solution based on cross-modal generation called GexMolGen (Gene Expression-based Molecule Generator), which generates hit-like molecules using gene expression signatures alone. These signatures are calculated by inputting control and desired gene expression states. Our model GexMolGen adopts a "first-align-then-generate" strategy, aligning the gene expression signatures and molecules within a mapping space, ensuring a smooth cross-modal transition. The transformed molecular embeddings are then decoded into molecular graphs. In addition, we employ an advanced single-cell large language model for input flexibility and pre-train a scaffold-based molecular model to ensure that all generated molecules are 100% valid. Empirical results show that our model can produce molecules highly similar to known references, whether feeding in- or out-of-domain transcriptome data. Furthermore, it can also serve as a reliable tool for cross-modal screening.

Modification of a Flexible Reinforcement Model for Root-Soil Complexes Based on the Growth Period

Modification of a Flexible Reinforcement Model for Root-Soil Complexes Based on the Growth Period

A Modeling Framework for Quantifying Spatial Recruitment Dynamics Using Abundance Estimation and Sibship Analysis

Quantifying recruitment at the sibling group offers a powerful methodology for understanding density-dependent and environmental drivers of recruitment. We propose a modeling framework that combines sibship and abundance estimation datasets to estimate mean sibling group size, sibling group size process error, environmental and density-dependent effects on sibling group size, dispersal, and mortality rate. Geographic states in the model consist of discrete habitat patches connected via dispersal. Simulations were used to investigate the influence of sampling processes and sibling group size on parameter estimation within our modeling framework. Mean sibling-group size, environmental effects on recruitment, and dispersal rate among habitat patches were estimated with high accuracy under a wide range of sampling conditions, including imprecise out-of-model estimates of capture probability and subsampling both within and among habitat patches. Density-dependent effects on recruitment and process error tended to be estimated with lower accuracy, though accuracy improved as sibling group size or sampling intensity increased. The main contribution of this research is a flexible quantitative modeling framework for parameterizing mechanistic models of recruitment dynamics with empirical sibship data.

Locally Adaptive Shrinkage Priors for Trends and Breaks in Count Time Series

Non-stationary count time series characterized by features such as abrupt changes and fluctuations about the trend arise in many scientific domains including biophysics, ecology, energy, epidemiology, and social science domains. Current approaches for integer-valued time series lack the flexibility to capture local transient features while more flexible models for continuous data types are inadequate for universal applications to integer-valued responses such as settings with small counts. We present a modeling framework, the negative binomial Bayesian trend filter (NB-BTF), that offers an adaptive model-based solution to capturing multiscale features with valid integer-valued inference for trend filtering. The framework is a hierarchical Bayesian model with a dynamic global-local shrinkage process. The flexibility of the global-local process allows for the necessary local regularization while the temporal dependence induces a locally smooth trend. In simulation, the NB-BTF outperforms a number of alternative trend filtering methods. Then, we demonstrate the method on weekly power outage frequency in Massachusetts townships. Power outage frequency is characterized by a persistent low level trend with occasional spikes. These illustrations show the estimation of a smooth, non-stationary trend with adequate uncertainty quantification.

Design and optimization of volute directing plate and stubble board for enhancing liquidity and anticlogging of tillering straw in spreading operation

In this paper, a novel volute linear directing plate and inverted “Y” structure stubble board are first designed to address the problem of straw agglomeration. Then a flexible and breakable discrete element model of tillering straw is proposed, and a discrete element coupling model of flexible tillering straw-crushing and strip-laying device is also constructed. The results show that the model can predict the operation performance of the crushing and strip-laying more accurately. Finally, the effect of spindle speed, directing plate radius and stubble board angle are investigated in-depth. In addition, the structure optimization of the directing plate and stubble board is conducted by response surface method, and the corresponding field test is also conducted. It is demonstrated that the coefficient of seed zone cleaning increased by 4.6 %, and the coefficient of straw ridge disturbance decreased by 34.6 %, meanwhile, the device performance is greatly improved after optimization.

Research on Flexible Data Model of MES System based on Time-series Database

In the process of advancing towards digitization and intelligence in manufacturing, Manufacturing Execution Systems (MES) have gradually become the core of production processes. The design of the data model is crucial as it affects the scalability, maintainability, and stability of the system. This study focuses on addressing issues in MES systems such as high investment costs, imperfect data models, lack of flexibility, and inefficient data flow. It proposes a novel flexible data model based on time-series databases.This paper introduces the definition and characteristics of time-series databases, analyzes the shortcomings of existing MES system data models, and proposes a novel flexible MES data model based on time-series databases. Finally, the practicality and efficiency of the new data model are validated through experiments.

An Evaluation of an Algorithm for the Selection of Flexible Survival Models for Cancer Immunotherapies: Pass or Fail?

An Evaluation of an Algorithm for the Selection of Flexible Survival Models for Cancer Immunotherapies: Pass or Fail?

The Development of Short Plays in China

Short plays have rapidly risen in popularity due to their novel format and flexible business models, yet they face challenges such as content homogeneity, vulgar material, and profitability issues. This paper analyzes the current development, challenges, and solutions for short plays in China. It proposes measures including content innovation, copyright protection, technology enhancement, policy support, and business model innovation to promote sustainable development in the short play industry. In the future, with the widespread adoption of mobile internet and technological advancements, short plays are expected to expand their market share, improve production quality, and enhance viewing experiences, becoming a vital component of the entertainment industry.

N = 3−3 lines in the extreme ultraviolet spectrum of europium (Z = 63)

Using high-resolution flat-field spectrographs at the LLNL SuperEBIT electron beam ion trap, we have observed extreme ultraviolet spectra (λ 32–43 Å) of highly charged ions of Eu ( Z = 63) under a range of production conditions suitable for Na- through Zn-like ions. Almost 30 lines were identified with the aid of Flexible Atomic Code computations and spectral modeling. While some of the wavelength measurements are among the most accurate in this wavelength range, some of the identifications are still tentative. We discuss how experimental conditions and spectral complexity interact in the analysis process.

A Rewriting-logic-with-SMT-based Formal Analysis and Parameter Synthesis Framework for Parametric Time Petri Nets

This paper presents a concrete and a symbolic rewriting logic semantics for parametric time Petri nets with inhibitor arcs (PITPNs), a flexible model of timed systems where parameters are allowed in firing bounds. We prove that our semantics is bisimilar to the “standard” semantics of PITPNs. This allows us to use the rewriting logic tool Maude, combined with SMT solving, to provide sound and complete formal analyses for PITPNs. We develop and implement a new general folding approach for symbolic reachability, so that Maude-with-SMT reachability analysis terminates whenever the parametric state-class graph of the PITPN is finite. Our work opens up the possibility of using the many formal analysis capabilities of Maude—including full LTL model checking, analysis with user-defined execution strategies, and even statistical model checking—for such nets. We illustrate this by explaining how almost all formal analysis and parameter synthesis methods supported by the state-of-the-art PITPN tool Roméo can be performed using Maude with SMT. In addition, we also support analysis and parameter synthesis from parametric initial markings, as well as full LTL model checking and analysis with user-defined execution strategies. Experiments show that our methods outperform Roméo in many cases.

The role of hydropower in decarbonisation scenarios

An increased penetration of renewable energy sources is essential for the energy transition. A major role will be played by wind and solar, as they are widely available. Hydropower is another crucial resource, currently covering large shares of power generation (e.g., Norway, Italy, Brazil). Despite little expected growth, in a context of increasing electrification, improved integration of hydropower can play a critical role thanks to programmable operation. This work addresses the modelling of hydropower flexibility in energy system models and analyses the impact of hydropower operation on CO2 emission-constrained scenarios. To implement the study, a detailed dataset of the Italian programmable hydroelectric plants is created, using open-source information, covering location, rated power, and storage capacity. Inflow timeseries are derived from historical operational data. These new sets of data are employed in OMNI-ES (a multi-node, multi-sector, and multi-vector energy system model) to study optimal configurations and operation of the Italian energy system in decarbonisation scenarios, such as net-zero-CO2 and Fit-for-55 targets. Considering different operational strategies and multiple historical reference years (impacting the inflow), results demonstrate significant changes in hydropower behaviour and highlight its relevance as zero-carbon resource in terms of both power and energy output, influencing the installation of other technologies.

THE NEW WORK REVOLUTION: TECH, TELECOMMUTING, AND FLEXIBILITY REWRITE THE RULES

The rapid transformation of workplace dynamics, driven by technological advancements and evolving workforce expectations, has been significantly accelerated by the global COVID-19 pandemic. Telecommuting and workplace flexibility have emerged as critical trends, reshaped traditional organizational structures and influenced employee experiences. This paper explores the intersection of these trends with technological innovations, assessing their implications for organizational success and sustainability. Through a comprehensive analysis of 261 academic publications sourced from the Web of Science Core Collection, this study employs both trend analysis and sentiment analysis to examine the evolving discourse on telecommuting and flexibility. The trend analysis reveals the increasing adoption and prominence of these models in scholarly research, highlighting key moments of growth and decline in publication activity. Sentiment analysis further elucidates the prevailing attitudes towards these trends, uncovering both optimistic perspectives—emphasizing benefits such as enhanced productivity, employee well-being, and work-life balance—and critical views that focus on challenges, including professional isolation, technological dependence, and the erosion of organizational cohesion. The findings demonstrate the dual role of technology as both an enabler and barrier to the successful implementation of flexible work models. The study concludes by discussing the long-term implications of these trends for leadership, organizational culture, and future research, emphasizing the need for holistic strategies that address both the opportunities and challenges posed by telecommuting and workplace flexibility.

KAN-ODEs: Kolmogorov–Arnold network ordinary differential equations for learning dynamical systems and hidden physics

Kolmogorov–Arnold networks (KANs) as an alternative to multi-layer perceptrons (MLPs) are a recent development demonstrating strong potential for data-driven modeling. This work applies KANs as the backbone of a neural ordinary differential equation (ODE) framework, generalizing their use to the time-dependent and temporal grid-sensitive cases often seen in dynamical systems and scientific machine learning applications. The proposed KAN-ODEs retain the flexible dynamical system modeling framework of Neural ODEs while leveraging the many benefits of KANs compared to MLPs, including higher accuracy and faster neural scaling, stronger interpretability and generalizability, and lower parameter counts. First, we quantitatively demonstrated these improvements in a comprehensive study of the classical Lotka–Volterra predator–prey model. We then showcased the KAN-ODE framework’s ability to learn symbolic source terms and complete solution profiles in higher-complexity and data-lean scenarios including wave propagation and shock formation, the complex Schrödinger equation, and the Allen–Cahn phase separation equation. The successful training of KAN-ODEs, and their improved performance compared to traditional Neural ODEs, implies significant potential in leveraging this novel network architecture in myriad scientific machine learning applications for discovering hidden physics and predicting dynamic evolution.

Robust causal inference for point exposures with missing confounders

AbstractLarge observational databases are often subject to missing data. As such, methods for causal inference must simultaneously handle confounding and missingness; surprisingly little work has been done at this intersection. Motivated by this, we propose an efficient and robust estimator of the causal average treatment effect from cohort studies when confounders are missing at random. The approach is based on a novel factorization of the likelihood that, unlike alternative methods, facilitates flexible modelling of nuisance functions (e.g., with state‐of‐the‐art machine learning methods) while maintaining nominal convergence rates of the final estimators. Simulated data, derived from an electronic health record‐based study of the long‐term effects of bariatric surgery on weight outcomes, verify the robustness properties of the proposed estimators in finite samples. Our approach may serve as a theoretical benchmark against which ad hoc methods may be assessed.