Flexible Framework Research Articles

Interactions of multiple stressors on the Bombay-duck Harpadon nehereus population in a complex estuarine ecosystem

In an era marked by unprecedented anthropogenic change, marine systems are increasingly subjected to interconnected and dynamic external stressors, which profoundly reshape the behavior and resilience of marine ecological components. Nevertheless, despite widespread recognition of the significance of stressor interactions, there persist notable knowledge deficits in quantifying their interactions and the specific biological consequences that result. To bridge this crucial gap, this research detected and examined the causal relationships between five key exogenous stressors in a complex estuarine ecosystem. Furthermore, a Bayesian Hierarchical Spatio-temporal modeling framework was proposed to quantitatively evaluate the distinct, interactive, and globally sensitive effects of multiple stressors on the population dynamics of a crucial fish species: Harpadon nehereus. The results showed that interactions were detected between fisheries pressure (FP), the Pacific Decadal Oscillation index (PDO), runoff volume (RV), and sediment load (SL), with five of these interactions producing significant synergistic effects on H. nehereus biomass. The SL*PDO and RV*PDO interactions had positive synergistic effects, albeit through differing processes. The former interaction amplified the individual effects of each stressor, while the latter reversed the direction of the original impact. Indeed overall, the synergistic effect of multiple stressors was not favorable, with FP in particular posing the greatest threat to H. nehereus population. This threat was more pronounced at high SL or negative PDO phases. Therefore, local management efforts aimed at addressing multiple stressors and protecting resources should consider the findings. Additionally, although the velocity of climate change (VoCC) failed to produce significant interactions, changes in this stressor had the most sensitive impacts on the response of H. nehereus population. This research strives to enhance the dimensionality, generalizability, and flexibility of the quantification framework for marine multi-stressor interactions, aiming to foster broader research collaboration and jointly tackle the intricate pressures facing marine ecosystems.

Improving and re-developing the Clerical Resolution Online Widget (CROW): open-source software for clerical review

ObjectivesClerical review is an integral part of linkage and is fundamental to quality assurance. CROW is a low dependency, adaptable, clerical matching tool that has been deployed across 40 linkage projects. Due to limitations of the interface, with the initial version (including the limited ability to view large clusters); this project sought to re-develop the tool as a flask application. ApproachOur approach was to transform the existing CROW desktop application into a Flask (web based) application. Flask was chosen because it is a flexible web framework that enabled the development of a more dynamic interface, as well as better integration with the infrastructure of secure environments. We also engaged with accessibility experts to ensure the inclusivity of our product. Once the development was completed, testing was carried out by a range of users and stakeholders, to identify further issues and bugs for resolution. ResultsThe successful redevelopment of CROW into a Flask application improved functionality, usability, parametrisation, and accessibility of the tool. Feedback from users indicates that because of using CROW, the clerical matching experience has improved vastly. Users reported that CROW increased accessibility, reducing eye strain and fatigue. CROW also increases efficiency and leads to better decision making. ConclusionThe successful redevelopment of CROW is a significant improvement to the clerical review process for data linkage projects. ImplicationsCROW is open-source and low dependency so could be adapted to work in most environments. Therefore, it could be adapted and used by the wider data linkage community.

A Flexible Multifunctional Cyanoethyl-Modified Bacterial Cellulose Nanofiber Framework for High-Energy and High-Power Density Aqueous Li-Ion Batteries.

Aqueous rechargeable lithium-ion batteries (ARLIBs) are extensively researched due to their inherent safety, typical affordability, and potential high energy density. However, fabricating ARLIBs with both high energy density and power performance remains challenging. Herein, based on cyanoethyl-modified bacterial cellulose nanofibers (CBCNs), a multifunctional fast ion transport framework is developed to construct the flexible free-standing ARLIBs with high areal loading and excellent rate performance. Benefiting from the unique merits of CBCNs, such as ultra-high aspect ratio, excellent toughness, superior adhesion, good lithiophilicity and ideal stability, the flexible free-standing and highly robust electrodes are fabricated and exhibit a long-term stable cycling of 1200 cycles with a high specific capacity of 117 mAh∙g-1 at 15 C. Remarkably, the corresponding full cell with the free-standing high mass loading (45.5 mg∙cm-2) electrodes under the condition of ultra-low addition of battery binder demonstrates a cycle lifespan of over 1000 cycles with a specific capacity of 120 mAh∙g-1 and a capacity decay as low as 0.03% per cycle, which is far superior to those of almost all previous reports. This work provides a strategy for constructing ARLIBs with high energy density and power performance by introducing a unique fast ion transport nanofiber framework.

Developing Predictive Models and Early Warning Systems for Invading Pathogens: Wheat Rusts.

Innovations in aerobiological and epidemiological modeling are enabling the development of powerful techniques to infer connectivity networks for transboundary pathogens in ways that were not previously possible. The innovations are supported by improved access to historical and near real-time highly resolved weather data, multi-country disease surveillance data, and enhanced computing power. Using wheat rusts as an exemplar, we introduce a flexible modeling framework to identify characteristic pathways for long-distance spore dispersal within countries and beyond national borders. We show how the models are used for near real-time early warning systems to support smallholder farmers in East Africa and South Asia. Wheat rust pathogens are ideal exemplars because they continue to pose threats to food security, especially in regions of the world where resources for control are limited. The risks are exacerbated by the rapid appearance and spread of new pathogenic strains, prodigious spore production, and long-distance dispersal for transboundary and pandemic spread.

GIS-Based Optimal Siting of Offshore Wind Farms to Support Zero-Emission Ferry Routes

To achieve net zero emissions from ships by 2050 and align with the IMO 2023 GHG strategy, the maritime industry must significantly increase zero-emission vessels by 2030. Transitioning to fully electric ferry lines requires enhanced energy supply through renewable energy sources (RES) for complete GHG mitigation and net-zero emissions. This study presents a GIS-based framework for optimally selecting offshore wind farm locations to meet the energy demands of electric ferry operations along coastal routes. The framework involves two stages: designing feasible zero-emission ferry routes between islands or to the mainland and identifying optimal offshore wind farm sites by evaluating technical, spatial, economic, social, and environmental criteria based on national legislation and the academic literature. The aim is to create a flexible framework to support decision making for establishing sustainable electric ferry operations at a regional level, backed by strategically located offshore wind farms. The study applies this framework to the Greek Coastal Shipping Network, focusing on areas with potential for future electrification. The findings can aid policymakers in utilizing spatial decision support systems (SDSS) to enhance efficient transportation and develop sustainable island communities.

Modulation of triplet quantum coherence by guest-induced structural changes in a flexible metal-organic framework

Quantum sensing has the potential to improve the sensitivity of chemical sensing by exploiting the characteristics of qubits, which are sensitive to the external environment. Modulation of quantum coherence by target analytes can be a useful tool for quantum sensing. Using molecular qubits is expected to provide excellent sensitivity due to the proximity of the sensor to the target analyte. However, many molecular qubits are used at cryogenic temperatures, and how to make molecular qubits respond to specific analytes remains unclear. Here, we propose a material design in which the coherence time changes in response to a variety of analytes at room temperature. We used the photoexcited triplet, which can be initialized at room temperature, as qubits and introduce them to a metal–organic framework that can flexibly change its pore structure in response to guest adsorption. By changing the local molecular density around the triplet qubits by adsorption of a specific analyte, the mobility of the triplet qubit can be changed, and the coherence time can be made responsive.

Progressive gas adsorption squeezing through the narrow channel of a soft porous crystal of [Co2(4,4′-bipyridine)3(NO3)4]

Reactions of the ternary components of Co2+ ion, 4,4′-bipyridine, and NO3− give several coordination polymers, which are often obtained in mixed phases. Herein, we explore the condition for the selective formation of Co-1D chain and Co-tongue-and-groove coordination polymers and find reversible interconversion pathways between them. The crystal structures of Co-tongue-and-groove in desolvated and two different CO2-adsorbed states show a one-dimensional corrugated channel with small windows through which CO2 is unlikely to pass. Nevertheless, a sufficient amount of CO2 is adsorbed at 195 K. The CO2 molecules are accommodated in the swollen cavity, forcing their way through the seemingly impermeable window of the channel, which we have named squeezing adsorption. The local motion of the ligand of the window frame plays an essential role in the guest permeation, which proves that the tongue-and-groove coordination polymers are essentially locally flexible porous frameworks.

Transmuted cosine Topp-Leone G family of distributions: properties and applications

In contemporary data analysis, the need for flexible and adaptable probabilistic models capable of capturing complex dependencies and tail behaviors in real-life datasets has become increasingly apparent. In light of this demand, A new trigonometric generalized family of distribution, the "Transmuted Cosine Topp-Leone G Family" is proposed in this study. Established on the foundations of, this family combines the adaptability of the Topp-Leone distribution with the periodicity of the cosine function and transmutation theory to produce a flexible framework that may be used to represent a wide range of real-life phenomena. We derive some of the statistical properties of the introduced family such as; survival and hazard functions, and moment and moment-generating functions. Moreover, the model parameters are estimated using the Maximum Likelihood method, and a Monte Carlo simulation was performed to ascertain the behavior and the consistency of the estimates. We investigate the shape of the distribution parameters which results in distributions with left-skew, right-skew, increasing, and decreasing shapes. Lastly, through empirical demonstrations, we showcase the efficacy of the family TrCTLG models in fitting lifetime datasets.

Computational Methods for Fast Bayesian Model Assessment via Calibrated Posterior p-values

Posterior predictive p-values (ppps) have become popular tools for Bayesian model assessment, being general-purpose and easy to use. However, interpretation can be difficult because their distribution is not uniform under the hypothesis that the model did generate the data. Calibrated ppps (cppps) can be obtained via a bootstrap-like procedure, yet remain unavailable in practice due to high computational cost. This article introduces methods to enable efficient approximation of cppps and their uncertainty for fast model assessment. We first investigate the computational tradeoff between the number of calibration replicates and the number of MCMC samples per replicate. Provided that the MCMC chain from the real data has converged, using short MCMC chains per calibration replicate can save significant computation time compared to naive implementations, without significant loss in accuracy. We propose different variance estimators for the cppp approximation, which can be used to confirm quickly the lack of evidence against model misspecification. As variance estimation uses effective sample sizes of many short MCMC chains, we show these can be approximated well from the real-data MCMC chain. The procedure for cppp is implemented in NIMBLE, a flexible framework for hierarchical modeling that supports many models and discrepancy measures. Supplementary materials for this article are available online.

CELEBRATE Feeding: A Responsive Approach to Food and Feeding in Early Learning Settings.

Early learning and child care (ELCC) settings in Canada follow nutrition standards that outline food provisions, with many also encouraging responsive feeding practices that help to create a supportive environment for children. Caregivers who lack confidence in children's ability to regulate their own intake, or those who feel stressed about mealtime, may unknowingly engage in less responsive feeding practices. The CELEBRATE Feeding Approach is a flexible framework, driven by behaviour change theory, that builds on previous definitions and concepts of responsive feeding in ELCC environments. Through this approach, there is an intentional focus on supporting early childhood educators to implement feeding practices that are more responsive. The approach incorporates 13 target educator behaviours related to the three overlapping categories of CELEBRATE language, CELEBRATE Mealtime, and CELEBRATE Play. These practices recognize and support the development of a child's sense of autonomy, confidence, and self-regulation not only at mealtimes but also through play-based exploration and language that is used throughout the day around food and feeding. The goal is that children will be open to a wide variety of food, develop their self-regulation skills, and build the foundation for a positive relationship with food throughout their lifetime.

P19-11 Establishing scientific confidence in a NAM toolbox and workflow using a flexible framework

P19-11 Establishing scientific confidence in a NAM toolbox and workflow using a flexible framework

TM-OKC: An Unsupervised Topic Model for Text in Online Knowledge Communities

Online knowledge communities (OKCs), such as question-and-answer sites, have become increasingly popular venues for knowledge sharing. Accordingly, it is necessary for researchers and practitioners to develop effective and efficient text analysis tools to understand the massive amount of user-generated content (UGC) on OKCs. Unsupervised topic modeling has been widely adopted to extract human-interpretable latent topics embedded in texts. These identified topics can be further used in subsequent analysis and managerial practices. However, existing generic topic models that assume documents are independent are inappropriate for analyzing OKCs where structural relationships exist between questions and answers. Thus, a new method is needed to fill this research gap. In this study, we propose a new topic model specifically designed for the text in OKCs. We make three primary contributions to the research on topic modeling in this context. First, we build a general and flexible Bayesian framework to explicitly model structural and temporal dependencies among texts. Second, we statistically demonstrate the approximate model inference using mean-field and coordinate ascent algorithms. Third, we showcase the practical value and relative merit of our method via a specific downstream task (i.e., user profiling). The proposed model is illustrated using two real-world datasets from well-known OKCs (i.e., Stack Exchange and Quora), and extensive experiments demonstrate its superiority over several cutting-edge benchmarks.

Numerical and Experimental Study on the Nonlinear Liquid Sloshing in Cassini Tank

Liquid sloshing within propellant tanks of space vehicles has been a major concern in aerospace engineering. The aim of the work in this paper is to develop a flexible computational framework with high precision to simulate three-dimensional large-amplitude liquid sloshing in Cassini tanks. The finite element method is adopted to solve the fluid equations of motion in an arbitrary Lagrangian–Eulerian (ALE) framework, where the characteristic-based split method is combined with a fractional step method for solving the control equations in which the ALE kinematic description is incorporated to track the free liquid surface flexibly and effectively for large-amplitude liquid sloshing in Cassini tanks. In addition, an experimental platform is set up to verify the reliability and effectiveness of the presented method. The numerical results obtained from computer programming by using the ALE finite element method proposed in this paper are compared with the experimental results, proving the model to be successful. The nonlinear phenomena of large-amplitude liquid sloshing, especially rotary sloshing (steady-state swirling and non-steady-state swirling), in Cassini tanks under horizontal harmonic excitation are investigated by both the ground physical experiments and numerical simulations.

Climate Change Prediction Model using MCDM Technique based on Neutrosophic Soft Functions with Aggregate Operators

The increasing impact of climate change necessitates innovative approaches in modeling and prediction to mitigate its adverse effects. This paper introduces a novel methodology integrating Neutrosophic Soft Functions (NSFs) into climate change prediction frameworks. NSFs, a hybrid of Neutrosophic Set Theory and Soft Set Theory, provide a flexible framework for handling uncertain and imprecise information inherent in climate data. This study explores the application of NSFs in capturing the complex interplay of various climatic variables, including temperature, precipitation, humidity, and atmospheric pressure, thereby enhancing the accuracy and reliability of climate change predictions. By incorporating NSFs into existing predictive models, such as neural networks and fuzzy systems, this research demonstrates significant improvements in forecast precision, particularly in scenarios with limited or noisy data. Additionally, the paper discusses the integration of NSFs with advanced machine learning algorithms for climate pattern recognition and anomaly detection, enabling timely identification of climate change indicators and facilitating proactive measures for adaptation and mitigation. Through empirical validation using real-world climate datasets, this study underscores the efficacy of NSFs in enhancing the predictive capabilities of climate change models, thereby contributing to more informed decision-making in climate-related policies and strategies.

An innovative patient clustering method using data envelopment Analysis–Discriminant analysis and artificial neural networks: A case study in healthcare systems

A major lesson healthcare managers learned from the COVID-19 outbreak is the need for more effective patient classification and medical resource allocation for future pandemics. In their view, hospitalization mortality could be greatly reduced if more effective systems for patient classification were in place before the outbreak to evaluate and assign treatment facilities. This study presents a scalable patient clustering approach using a Self-Organizing Map (SOM) of the Artificial Neural Network (ANN) to cluster patients for appropriate treatment allocation. The patients’ membership is forecasted using Data Envelopment Analysis–Discriminant Analysis (DEA-DA). The objectives of this research are to develop a flexible framework that healthcare systems can adopt to cluster patients based on specific testing criteria from medical records and to assign them to suitable medical centers with appropriate treatment resources. This method aims to enhance healthcare system efficiency by ensuring patients with severe illnesses receive care at well-equipped centers, while those with milder symptoms are directed to other suitable facilities. The approach is scalable and adaptable to any type of widespread illness and aims to increase recovery rates and decrease mortality rates, as confirmed by the case study results.

Binary-Cooperative Ultrathin Porous Membrane for Gas Separation.

The construction of ultrathin porous membranes with stable structures is critical for achieving efficient gas separation. Inspired by the binary-cooperative structural features of bones and teeth-composed of rigid hydroxyapatite and flexible collagen, which confer excellent mechanical strength-a binary-cooperative porous membrane constructed with gel-state zeolitic imidazolate frameworks (g-ZIFs), synthesized using a metal-gel-induced strategy, is proposed. The enlarged cavity size and flexible frameworks of the g-ZIF nanoparticles significantly improve gas adsorption and diffusion, respectively. After thermal treatment, the coordination structures forming rigid segments in the g-ZIF membranes appear at the stacked g-ZIF boundaries, exhibiting a higher Young's modulus than the g-ZIF nanoparticles, denoted as the flexible segments. The g-ZIF membranes demonstrate excellent tensile and compression resistances, attributed to the effective translation of binary-cooperative effects of rigidity and flexibility into the membranes. The resulting dual-aperture structure, composed of g-ZIF nanoparticles surrounded by nanoscale apertures at the boundaries, yields a membrane with a stable CO2 permeance of 4834 GPU and CO2/CH4 selectivity of 90 within 3.0MPa.

Reforming Corporate Compliance Systems in China Under the New Company Law: Lessons from The United States and Japan

This article investigates the construction of an ideal compliance system under China’s new Company Law through a comparative legal analysis. It begins by identifying the existing challenges in China’s compliance framework, particularly the historical dominance of shareholder primacy and the uneven distribution of responsibilities under the previous Company Law. These issues have hindered the development of an effective compliance system, necessitating a reevaluation of corporate governance principles in China. The comparative analysis examines the compliance systems in the United States and Japan, focusing on the frameworks and key elements such as fiduciary duties and the application of the business judgment rule. The study highlights significant differences in how these jurisdictions handle compliance, providing insights into potential improvements for China’s system. This section underscores the importance of adapting international best practices to local contexts to enhance the effectiveness of corporate compliance in China. Finally, the article proposes a dual-level compliance concept tailored to China’s unique legal and corporate environment, integrating both private and public interests. It outlines specific compliance duties for directors, supervisors, and other compliance personnel, emphasizing the need for robust information transmission, whistleblower protections, and temporary management systems. The conclusion calls for a comprehensive and flexible compliance framework that not only ensures corporate profitability but also promotes broader societal welfare, aligning with the evolving goals of corporate governance in China. Keywords: Compliance System, Corporate Governance, Fiduciary Duties, Business Judgment Rule, New Company Law

Architectural Approaches to Migrating Key Features in Android Apps

The rapid evolution of Android platforms and user expectations necessitates continual enhancement and migration of key features within Android applications. This paper explores various architectural approaches to effectively migrate essential functionalities in Android apps, ensuring scalability, maintainability, and optimal user experience. Initially, the study delineates the challenges inherent in feature migration, including compatibility issues, performance constraints, and the preservation of existing user interfaces. It then examines prominent architectural paradigms such as Model-View-View Model (MVVM), Clean Architecture, and modular architectures, evaluating their suitability for facilitating seamless feature transitions. The research further investigates the role of microservices and component-based designs in enabling incremental migration, allowing developers to update or replace specific modules without overhauling the entire application. Additionally, the paper discusses best practices for managing dependencies, ensuring backward compatibility, and employing automated testing to mitigate risks during the migration process. Case studies of successful Android app migrations are presented to illustrate the practical application of these architectural strategies. The findings highlight that adopting a modular and flexible architectural framework significantly enhances the ability to migrate key features efficiently while minimizing disruptions. Moreover, the integration of robust architectural patterns fosters better code maintainability and accelerates the development cycle for future updates. In conclusion, this study provides a comprehensive guide for Android developers aiming to navigate the complexities of feature migration, advocating for architectural approaches that balance innovation with stability. The insights garnered offer valuable implications for the design and evolution of resilient Android applications in a dynamic technological landscape.

Fluorinated MIL-53(Al) with breathing effect incorporated mixed matrix membranes for enhanced butanol/water pervaporation performance

Fillers of mixed matrix membranes (MMMs) play a crucial role in the pervaporation (PV) separation process. MIL-53(Al) is widely used in the preparation of MMMs due to its high stability, variable pore size, and ease of modification. In this study, fluorinated MIL-53(Al) (4F-MIL-53) was successfully synthesized using a solvent-free method. The introduction of fluorinated group made 4F-MIL-53 have better affinity for butanol and stronger hydrophobicity than MIL-53. Meanwhile, by utilizing its breathing effect, 4F-MIL-53 with narrow pore (4F-MIL-53(np)) could be transformed to 4F-MIL-53 with large pore (4F-MIL-53(lp)) through heat treatment. Also, the flexible framework of 4F-MIL-53 had good affinity with polymer chains. Then, 4F-MIL-53(lp) was introduced into PEBA to prepare MMMs for separating n-butanol/water. Compared with pristine PEBA membrane, the permeation flux and separation factor of the 4F-MIL-53(lp)-20/PEBA MMMs increased by 100.5 % and 90.4 %, respectively. Theoretical calculations indicated that there were multiple intermolecular forces between 4F-MIL-53(lp) and n-butanol, which enhanced the mixed matrix membrane affinity for n-butanol. The calculated self-diffusion coefficient of water in 4F-MIL-53 (lp) was lower than that in MIL-53, indicating that the introduction of fluorinated groups enhanced the hydrophobicity of MMMs. The proposed selection and modification strategies of MOF fillers provide excellent guidance for getting MMMs with high performance.

Sparse Bayesian learning using TMB (Template Model Builder)

Sparse Bayesian Learning, and more specifically the Relevance Vector Machine (RVM), can be used in supervised learning for both classification and regression problems. Such methods are particularly useful when applied to big data in order to find a sparse (in weight space) representation of the model. This paper demonstrates that the Template Model Builder (TMB) is an accurate and flexible computational framework for implementation of sparse Bayesian learning methods.The user of TMB is only required to specify the joint likelihood of the weights and the data, while the Laplace approximation of the marginal likelihood is automatically evaluated to numerical precision. This approximation is in turn used to estimate hyperparameters by maximum marginal likelihood. In order to reduce the computational cost of the Laplace approximation we introduce the notion of an “active set” of weights, and we devise an algorithm for dynamically updating this set until convergence, similar to what is done in other RVM type methods. We implement two different methods using TMB; the RVM and the Probabilistic Feature Selection and Classification Vector Machine method, where the latter also performs feature selection. Experiments based on benchmark data show that our TMB implementation performs comparable to that of the original implementation, but at a lower implementation cost. TMB can also calculate model and prediction uncertainty, by including estimation uncertainty from both latent variables and the hyperparameters. In conclusion, we find that TMB is a flexible tool that facilitates implementation and prototyping of sparse Bayesian methods.