Representation Of System Research Articles

Perspectives of African athletes on the prospects and challenges of being managed by former professional athletes

ABSTRACT This study explores the perspectives of African athletes who are managed by former professional athletes, focusing on their experiences, perceptions, and the impact of having a manager with a background in professional sports. Using a qualitative phenomenological research design, the study conducted in-depth interviews with 30 African athletes in football, and athletics. The findings reveal that participants generally view former professional athletes as empathetic, relatable, and credible managers who understand the unique challenges of being an athlete. However, some participants expressed concerns about the lack of formal managerial training among former athletes and the potential for favouritism. The study concludes that while former professional athletes bring valuable insights to player management, they must complement their experiential knowledge with formal training to address the complexities of modern sports management. These research findings will meaningfully inform ongoing dialogues about player representation systems, global athletic labour flows, and career transition management. By prioritizing African athletes’ firsthand experiences, the study provides crucial insights for developing more equitable certification standards for agents and enhanced protective measures for athletes operating across borders.

CMIP6 Representation of Declining Sea Ice and Arctic Cyclones in the Current Climate

Abstract The Arctic climate system is changing rapidly with important implications in the Arctic and beyond. The interaction between the sea ice and Arctic cyclones makes it an important topic to be understood in the warming climate. We analyzed an ensemble of Coupled Multimodel Intercomparison Project (CMIP6) model simulations from 1985 to 2014 to determine model skill in depicting Arctic cyclones and their relationship with sea ice. A comprehensive climatology of Arctic cyclones and sea ice concentrations (SIC) was produced and compared to the ERA5 reanalysis product. The models reproduced the observed sea ice spatial patterns and trend well. However, the models struggled to capture the concurrent patterns and trends in Arctic cyclone characteristics that were evident in the reanalysis data. The models underestimated local cyclogenesis in the Arctic, which led to an overall underestimation of Arctic cyclone counts. Lead/lag analysis of ERA5 data suggests that reduced sea ice in the warm season can drive increased cyclone counts in the following cold season, which then reduces SIC in the next warm season in a feedback cycle that appears to be missing from the CMIP6 models. The results also revealed deviations between CMIP6 and ERA5 cyclone intensities. The magnitude and sign of the intensity differences varied based on model resolution, surface roughness parameterization, and skill in the representation of cyclogenesis location. This work highlights the need to improve sea ice‐atmosphere interactions and the representation of synoptic systems in the next generation of global models.

Recommendations to Improve Text-Based Representation Systems for Polyolefins.

Despite advances in text-based structure representation systems for polymers, there remain major gaps not addressed by current platforms. These shortcomings will prevent polymer informatics from reaching its full potential in making real impacts, particularly in connection with commercial polymer product development. Recommendations to close these gaps are provided from an industrial perspective focused on polyolefins.

AniSOAP: Machine Learning Representations for Coarse-grained and Non-spherical Systems

AniSOAP: Machine Learning Representations for Coarse-grained and Non-spherical Systems

An Institutional Fail-Safe? How the Gap in Judicial Independence Between High and Low Courts Explains the Reversal of Corruption Convictions of Former Heads of Government

When former heads of government are criminally convicted for corruption by the judiciary of the countries they once ruled, are these decisions definitive, or are they later reversed? This article examines the role of courts in overturning such convictions and theorizes that reversals are more likely when high courts are less independent than low courts, making them more prone to facilitating political accommodation. The study tests this institutional fail-safe hypothesis using a global dataset of all convictions of former heads of government between 1946 and 2022 and their potential reversals. The findings indicate that corruption convictions of former leaders are more vulnerable to overturns when the gap in judicial independence is greater, helping to protect political elites against the enforcement of anticorruption laws. This pattern is most pronounced in democracies, in proportional representation systems and after electoral turnovers—contexts that heighten incentives for elite accommodation. By identifying the institutional and political conditions under which conviction overturns are most likely, the article contributes to broader debates on the limits of criminal accountability and the mechanisms through which anticorruption efforts from within the legal system may be neutralized.

Entropy-based models to randomise real-world hypergraphs

Network theory has often disregarded many-body relationships, solely focusing on pairwise interactions: neglecting them, however, can lead to misleading representations of complex systems. Hypergraphs represent a suitable framework for describing polyadic interactions. Here, we leverage the representation of hypergraphs based on the incidence matrix for extending the entropy-based approach to higher-order structures: in analogy with the Exponential Random Graphs, we introduce the Exponential Random Hypergraphs (ERHs). After exploring the asymptotic behaviour of thresholds generalising the percolation one, we apply ERHs to study real-world data. First, we generalise key network metrics to hypergraphs; then, we compute their expected value and compare it with the empirical one, in order to detect deviations from random behaviours. Our method is analytically tractable, scalable and capable of revealing structural patterns of real-world hypergraphs that differ significantly from those emerging as a consequence of simpler constraints.

Digital twin representation of socio-technical systems through a distributed co-simulation approach for crisis management

The increasing frequency and severity of significant risks to public safety posed by natural disasters or human-induced events have underscored a critical need for evaluating the vulnerability of urban regions with a focus on their essential infrastructures. This paper presents a novel methodology for the virtual representation of infrastructure vulnerabilities and functional impairments during hazard situations. Thereby, focus is on the mapping of interdependencies among critical infrastructure systems and the cascading effects that can arise from failures within these heterogeneous sectors by means of a digital twin representation. An integration of simulation models for urban infrastructure components, particularly in relation to the built environment and emergency response systems is introduced. Leveraging a modular co-simulation architecture, the framework facilitates the analysis of cascading effects across multiple infrastructure systems, such as water, electricity, gas, and telecommunications. As a proof-of-concept example, urban flooding due to heavy rainfall is considered to illustrate the framework’s capabilities in predicting system states and assessing structural impacts on critical infrastructures as well as consequential ramifications for emergency relief units. The findings contribute valuable insights, thereby advertising the utilization of the presented methodology in decision-making and training resources aiding the enhancement of the resilience of urban environments against both natural and intentional threats.

Multiscale Modeling in Systems Biology

Multiscale modeling in systems biology is a methodological approach designed to represent, integrate, and simulate complex biological phenomena occurring across various organizational levels, from the molecular to the tissue scale. In contrast to reductionist perspectives, this holistic framework acknowledges that biological processes emerge from dynamic interactions among components operating simultaneously in multiple spatial and temporal scales. Its development has been facilitated by the growing availability of omics data and the evolution of advanced computational tools, enabling the creation of realistic and predictive simulations.This article reviews theoretical foundations and current applications of multiscale modeling in key fields such as personalized medicine, computational pharmacology, tissue engineering, and clinical simulation. It covers integration strategies such as hierarchical and concurrent coupling, and highlights the use of specialized platforms like GROMACS, NAMD, SimBiology, and PhysiCell. The advantages of this modeling approach include the design of individualized treatments, virtual testing of biomaterials, and the optimization of clinical trials through simulated cohorts.Multiscale models allow not only a more accurate representation of biological systems but also enable the anticipation of pathophysiological dynamics, reduce drug development timelines, and enhance clinical decision-making. Their future effectiveness will depend on data interoperability, algorithmic refinement, and integration with artificial intelligence. Ultimately, multiscale modeling is a foundational tool for advancing toward a more predictive, contextual, and adaptive biology suited to the evolving challenges of contemporary medicine.

Optimal representation in biological systems.

An optimal representation constitutes an efficient set. It is known that for an aggregating system if the cost of representation increases linearly with the number of bases, ternary coding is superior to binary, and coding in e is optimal. This paper investigates the relative efficiency of bases for the cases when the cost complexity is affine (slope-intercept linear), exponential, and logistic and presents new results. It is shown that for representation of structure in logistic maps, which applies often to biological systems and is true for input-output maps of neurons, the optimal base value is near 1.7632, which is consistent with the unary and space coding of information in songbirds. It is shown that the mathematical basis of this result is the solution to the equation

Proactive Approach to Production Control Utilizing Heterogeneous Shop-Level Production Data

This paper presents an approach for integrating data between a production system and its digital twin, focusing on achieving proactivity in production control. Recognizing the unique nature of each production system, this research highlights that a universal, plug-and-play solution is only partially feasible, primarily through general guidelines. The study successfully applied and automated proposed data acquisition methods, resulting in a functional, simulation-based digital twin that adheres to the latest ISO standards. The developed solution incorporates multiple data acquisition strategies, including files containing comma-separated values, a permanent connection to the production control system database, open platform communications unified architecture, and external command files for scenario alteration. The main motivation behind the presented implementation is its application on the shop-floors of small and medium enterprises, where it could provide useful tools for keeping up with the ever-rising competition in the manufacturing sector. This integrated approach allows for affordable and accurate system representation within the proactive simulation concept. The methodology was empirically validated across two distinct production systems: a lab-scale food and beverage line focusing on product tracking, and a sub-assembly line with automated guided vehicle optimization. Despite system variability, the core data acquisition methods demonstrated remarkable adaptability.

The Imaginal Space: A Theory of Scaffolded Minds in a Cultural Niche

We humans have scaffolded minds into an ecological niche. Considering this, the main thesis I will defend in this article is that we humans live surrounded by images, and we spatially organise these images into a particular cultural niche which I call imaginal space, a material or virtual space that we interactively navigate. We use such images in the imaginal space as fundamental props in the imaginative dynamics of our scaffolded minds. The images we find in our imaginal space are public depictive representations. They are twofold objects: on the one hand, they are material objects in that they can be manipulated into the ecological niche; on the other hand, they also are normative objects in that their representational function can be redesigned through a practice of use. As a first consequence of this main thesis, I will contend that public representations emergent into the imaginal space of a certain community may play a crucial regulative role for imagination: emergent systems of public representations can get a canonical status in their community, so that they can exert a normative power on the members of that community regulating their imagistic mental representations. As a second consequence, I will defend that the canonical images emergent in the imaginal space constitute the collective imagery of a human community: they support and influence the development of public narratives in the considered community.

One multimodal plugin enhancing all: CLIP-based pre-training framework enhancing multimodal item representations in recommendation systems

One multimodal plugin enhancing all: CLIP-based pre-training framework enhancing multimodal item representations in recommendation systems

Network modeling of the different SARS-CoV-2 spike protein infection points within the human hematopoietic network.

Network modeling of the different SARS-CoV-2 spike protein infection points within the human hematopoietic network.

Enhancing intrusion detection in containerized services: Assessing machine learning models and an advanced representation for system call data

Enhancing intrusion detection in containerized services: Assessing machine learning models and an advanced representation for system call data

AmpHGT: expanding prediction of antimicrobial activity in peptides containing non-canonical amino acids using multi-view constrained heterogeneous graph transformer

BackgroundAntimicrobial peptide (AMP) prediction has been extensively studied in recent years. However, many existing models do not fully leverage the intrinsic chemical structures of AMPs, such as atomic composition and sidechain group characteristics. Instead, these models often focus on letter composition, positional encodings, and pre-defined chemical-physical descriptors. The incorporation of non-canonical amino acids, which enhance peptide stability and reduce toxicity, is getting more attention in peptide design. Despite this, they are overlooked in predictive studies, as traditional deciphering methods and single-letter representation systems are inadequate for this task. Even though some efforts have been made to expand current alphabets, these approaches remain insufficient, impeding the development of novel AMPs.ResultsA novel deep learning model, termed AmpHGT, was developed based on heterogeneous graphs’ representation of peptides. AmpHGT demonstrates competitive performance against current methods on canonical amino acid benchmarks. Notably, AmpHGT is capable of efficiently classifying antimicrobial peptides with non-canonical amino acids, addressing the limitations of traditional feature extraction methods. In addition, this model is adaptable to handling different conformations, sidechains, and backbones (e.g., α, β, γ), demonstrating its potential to enhance the screening and discovery of AMPs containing non-canonical amino acids.ConclusionsOur study suggests that AmpHGT is reliable for antimicrobial peptide classification task. It may serve as an efficient primary filter for evaluating thousands of mined peptides and provides a good foundation for future studies aimed at producing peptide antibiotics containing non-canonical amino acids.

The Homology of Atmospheric Pollutants and Carbon Emissions in Industrial Parks: A Case Study in North China

Industrial parks are well-known as a critical intervention point for global carbon emission reductions due to the high carbon emissions emitted. Conducting carbon accounting research in these parks can provide more precise foundational data for carbon reduction initiatives, promoting low-carbon industrial park development. However, industrial parks, positioned as non-independent accounting units between provincial and industry levels, face severe challenges due to ambiguous boundaries, complex accounting entities, and data selection difficulties that significantly impact the carbon accounting accuracy. This study employed the IPCC emission factor method for industrial parks, taking its management structure as the accounting boundary. Additionally, we constructed a carbon accounting method and representation system by considering the carbon emission flow path and integrating the correlation between pollutant and carbon emissions. By categorizing carbon emissions into five groups, this study obtained emissions from fuel combustion (E1), industrial processes (E2), purchased/sold electricity (E3), purchased/sold heat (E4), and carbon-sequestering products (E5). Between 2016 and 2021, the industrial park’s carbon emissions fell from 15.0783 to 6.7152 million tons, while the intensity dropped from 4.86 to 1.91 tons of carbon dioxide (CO2) per CNY 10,000. The park achieved dual control targets for the total carbon emissions and intensity, with E2 being the main reduction source (70% of total). Meanwhile, total atmospheric pollutants decreased from 9466.19 to 1736.70 tons, with C25 and C26 industries contributing over 99%. In particular, C26 achieved significant reductions in nitrogen oxides (NOx) and sulfur dioxide (SO2), aiding pollution mitigation. A strong positive correlation was found between pollutants and carbon emissions, especially in C26, SO2 (0.77), and NOx (0.89), suggesting NOx as a more suitable carbon emission indicator during chemical production. These findings offer a theoretical framework for using pollutant monitoring to characterize carbon emissions and support decision-making for sustainable industrial development.

Advances in Ecological Modeling: Tools, Approaches, and Future Perspectives

An ecological model serves as a simplified representation of a real-world system, aiming to capture our current understanding of its functioning through the use of mathematical relationships, computer code, and rules. Ecological modeling gained remarkable popularity as a tool in environmental management during the 1970s. Over time, various tools and approaches for ecological modeling have been invented and developed. Ecological models play a crucial role in supporting environmental decision-making by predicting ecological consequences and helping achieve societal objectives. This paper aims to review recent model types, approaches, and tools used by ecologists by consolidating peer-reviewed research articles published from 1984 to 2023. The results revealed that researchers employ unique model types to address specific ecosystem situations. These model types include dynamic, population dynamic, static, structurally dynamic, artificial neural networks, fuzzy, individual-based, and cellular automata, ecotoxicological, spatial, stochastic, and hybrid/integrated models. Each model has limitations in its application and is suitable for specific situations. However, integrated/hybrid models are recommended as they combine multiple model types, enhancing their effectiveness. Different model approaches such as Ecopath, Ecosim, Ecospace, Ecotroph, and Ecopath with Ecosim are utilized for modeling ecosystems and predicting outcomes amidst disturbances caused by anthropogenic factors, fishing impacts, and climate change. These model approaches greatly contribute to our understanding of ecosystems. However, despite the variety of methods available, authors still encounter challenges when using these methods, leading to the evolution and refinement of additional approaches and tools that will continue to emerge in the future.

The Role of Muhammadiyah Politics in Advocating for Reform of The Legislative Electoral System (DPR/DPRD) in Post-New Order Indonesia (1999–2024)

This study examines the political role of Muhammadiyah as a civil society organization (CSO) in advocating for reform of the legislative electoral system (DPR/DPRD) in post-New Order Indonesia (1999–2024). Using the theory of the function of civil society from Wolfgang Merkel (1999) and the theory of public policy advocacy from Start and Hovland (2004), this study explores the reasons why Muhammadiyah persistently encourages changes in the open proportional system (OLPR), the internal actors involved, and the strategies used. The method in this study uses a qualitative approach with a case study design that relies on primary data sources of interview results supported by related secondary data. The findings show that Muhammadiyah carries out the function of supervision of the state and articulation of social interests, criticizing the OLPR for triggering money politics, polarization, and lack of meritocracy. Muhammadiyah proposed a closed proportional system (CLPR) or Moderate List Proportional Representation (MLPR) through a scientific study by the Institute of Wisdom and Public Policy (LHKP). Advocacy strategies include collaborative advising, lobbying, and public promotion, although hampered by political party resistance and internal dynamics. This research contributes to the study of civil society and elections in transitional democracies, highlighting the importance of reforming the electoral system that is fair and democratic..

A robust optimization approach to address correlation uncertainty in stock keeping unit assignment in warehouses

In this study, we address the problem of assigning correlated Stock Keeping Units (SKUs) to storage locations under uncertain SKUs correlation conditions. The objective is to allocate SKUs within the forward picking area of a warehouse to minimize the total picking distance. To quantify the correlation between SKUs, we employ the joint distribution concept, enabling a more systematic representation of their correlations. The problem is formulated as a Quadratic Assignment Problem (QAP), which becomes computationally intractable at large scales due to its complexity. To mitigate this challenge, the QAP model is linearized, and a robust counterpart is developed to effectively handle uncertainty. The robust model was evaluated through various small-scale scenarios. While it yielded optimal results within an efficient time frame for small-scale problems, the solution time increased significantly as the problem size expanded.

Improved Representations of Land‐Atmosphere Interactions Over the Continental U.S. Through Dynamic Root Modeling

Abstract Previous studies have identified the oversimplified root system representation as a key factor leading to inaccuracies in vegetation‐atmosphere feedbacks. In this study, a dynamic root water uptake scheme in the Noah‐MP land surface model has been coupled to the Weather Research and Forecasting (WRF) model to investigate its impact on the surface hydroclimate variables and land‐atmosphere interactions. To evaluate the impact of the dynamic root, two coupled simulations were conducted, one with the dynamic root water uptake scheme (DynRt) and one with the static root water uptake scheme (StcRt), which is based on the default root representation in Noah‐MP, with slight modifications, primarily in vegetation‐related parameters. Both DynRt and StcRt simulations were conducted with a small ensemble of three members to account for variations in physical parameterizations, initial and boundary forcing and model setup. When compared with reference data sets, the DynRt simulations show improved results than the StcRt simulations, reducing biases in the simulated leaf area index, surface energy fluxes, soil moisture and precipitation. Two different mechanisms through which roots affect land‐atmosphere coupling have been identified. Over the transitional climate zone between the dry and wet climate, the dynamic root scheme affects surface climate and land‐atmosphere coupling mainly through changes in soil moisture through hydraulic redistribution by plant root system. Over the energy‐limited mesic zone, the dynamic root affects regional land‐atmosphere coupling mainly through changes in carbon allocation. This work highlights the importance of dynamic root representation in improving vegetation‐atmosphere simulations by enhancing predictions of water, energy, and carbon fluxes.