Additional Complexity Research Articles

Effects of simplicial centrality on robustness of higher-order multiplex world trade networks

Abstract World trade networks are exhaustively described by pairwise interactions, and overlook higher-order structure from the outcome of collective interactions at the level of groups of nodes like multilateral trade agreements. To address this limitation, \textcolor{red}{we collect multiplex world trade networks, including the bilateral regional trade agreement network, which represents pairwise interactions; the multilateral regional trade agreement network, which naturally represents a higher-order network structure; and the import and export trade network, which represents pairwise interactions and additional complexities.} The analysis of simplicial centrality, including degree, closeness, and subgraph at 0, 1, and 2-simplex \textcolor{red}{levels, reveals that intra-level correlations are high, while inter-levels may exhibit significant disparities.} Nodes with low centrality at higher-order levels could influence network robustness due to the diversity of interactions and higher-order dependencies. Simplicial centrality on robustness of multiplex world trade networks under random and targeted attacks reveals that the complex connectivity of higher-order levels renders them more vulnerable post-attack. An optimization strategy of the rebalancing of network centrality is proposed to enhance the robustness, and the simulation shows risks posed to central nodes are minimized and opportunities for peripheral nodes to partake in global trade are broadened.

Transgender athletes and the sex category: change in competition level

Most contemporary sports have a sex category with two sub-categories (male and female). The participation of transwomen in the female category has been the subject of heated discussion, from the standpoint of fairness and safety. However, no research has been conducted to determine how the competition level has changed for transgender athletes who have changed their sex sub-category following transition, which brings additional complexity to the issue. This study examines changes in competition level of transgender athletes, comparing pre- and post-transition changes in the sex (male/female) category. This study was an investigation of the content of English-language newspapers published between 1 January 2016 and 31 August 2021 that identified individual transgender athletes. Nexis Uni was used to investigate gender (transwoman or transman), sport, and competition levels. Competition levels were classified as international, national, and regional. Change of sex sub-category was compared statistically with the decrease/increase in competition level. The study examined 2220 articles, which yielded usable data from 68 athletes (52 transwomen, 16 transmen) in 23 sports. The study found that transwomen were more likely to participate in the female category than transmen in the male category; and that, when the sex sub-category was changed, 41% of transwomen improved their competition level, while no transmen improved their competition level.

Ecosystem transition due to deer overabundance: Insights from long‐term studies and future considerations

AbstractThe overabundance of certain deer species is emerging as a critical issue in many forested regions across the Northern Hemisphere, including those in Japan. In the field of deer impact studies, the prevailing notion has been that overabundant deer populations could cause drastic changes of ecosystem states. This study comprises a review of the historical discussion surrounding ecosystem changes caused by deer overabundance, from theoretical frameworks to in situ observations and experiments. The synthesis highlights the potential for state transitions, shifting ecosystems from forest to nonforest states, and in some scenarios to a so‐called alternative stable states. However, detecting these transitions poses challenges due to the enduring impacts of past deer activity and the nonequilibrium nature of forest dynamics. Furthermore, this study reveals additional multifield complexities arising from the interactive effects of deer overabundance and global changes on future forest dynamics. To address these challenges, new avenues for research are proposed, emphasizing the importance of sustained efforts in conducting valuable long‐term studies.

Leaf Identification in High-Density LiDAR-RGB Data

Abstract. Leaf detection through automated segmentation of 3D data is becoming a crucial technique in many applications of digital agriculture. Some 3D segmentation techniques that can be mentioned are based on normal differences and median normalised vector growth. However, applying these approaches to high canopy density data remains challenging. In this study, we propose a processing flow for leaf detection in high canopy density LiDAR-RGB point clouds. First, a noise removal technique inspired by Moving Least Squares (MLS) was applied to the LiDAR point cloud, and a RGB colour was assigned to each point by combining computer vision and photogrammetric methods. Moreover, once the data were suitable for leaf detection, the branches were filtered using the Statistical Outlier Removal (SOR) filter based on an analysis of the statistical behaviour of the neighbourhood. Afterwards, an unsupervised DBSCAN (Density-Based Clustering Non-Parametric Algorithm) method was used to segment similar points. Finally, the points within each cluster were identified as leaf or non-leaf using the RGB values implemented by our method; ground points were filtered out using a maximum height threshold. As a result, the leaf, non-leaf, and ground point identifiers were correct in 98.9% of cases, with the branch filtering technique SOR proving effectiveness in removing branches with low information loss and without additional complex point densification steps in reconstruction. This SOR-based solution overcomes major challenges in semantic segmentation (leaves and branches) in high-density data and potentially contributes to precision agriculture.

A comparative analysis of metaheuristic algorithms for optimizing curved roof structures

This article explores the optimal design of curved steel structures with a focus on minimizing their weight by determining the most suitable cross-sections. Customized optimization algorithms were developed to identify structural designs that meet safety and durability requirements, adhering to design constraints set by the ASD-AISC specification. To ensure reliable results, the study employs a variety of metaheuristic optimization methods: Biogeography-Based Optimization (BBO), Dynamic Harmony Search (DHS), Wolf Colony Algorithm (WCA), and Honey Badger Algorithm (HBA). The Honey Badger Algorithm, a relatively new addition to the field, was used for the first time in the context of structural optimization for curved roof systems. This unique approach aims to evaluate its performance in civil engineering problems and compare it with other established algorithms. The major challenges of this study lie in the inherent complexity of dome structures, which involve a large number of elements and nonlinear constraints. The subdivision of the structure into smaller groups was necessary to manage the computational load, although this introduced additional complexities. Despite these challenges, the metaheuristic methods demonstrated their robustness in addressing such intricate engineering problems. Additionally, data retrieval is facilitated through Open Application Interface (OAPI) functions, enabling seamless data transfer between SAP 2000 and Visual Basic. The study's final design example involves a dome model with 2556 elements, which was both modeled and optimized. The results demonstrate the effectiveness of these optimization algorithms in achieving structurally sound and efficient designs. The insights gained from this study contribute to our understanding of optimized cross-sections in curved steel structures, offering valuable guidance for improving structural performance and minimizing material usage.

An Innovative Honeypot Architecture for Detecting and Mitigating Hardware Trojans in IoT Devices

The exponential growth and widespread adoption of Internet of Things (IoT) devices have introduced many vulnerabilities. Attackers frequently exploit these flaws, necessitating advanced technological approaches to protect against emerging cyber threats. This paper introduces a novel approach utilizing hardware honeypots as an additional defensive layer against hardware vulnerabilities, particularly hardware Trojans (HTs). HTs pose significant risks to the security of modern integrated circuits (ICs), potentially causing operational failures, denial of service, or data leakage through intentional modifications. The proposed system was implemented on a Raspberry Pi and tested on an emulated HT circuit using a Field-Programmable Gate Array (FPGA). This approach leverages hardware honeypots to detect and mitigate HTs in the IoT devices. The results demonstrate that the system effectively detects and mitigates HTs without imposing additional complexity on the IoT devices. The Trojan-agnostic solution offers full customization to meet specific security needs, providing a flexible and robust layer of security. These findings provide valuable insights into enhancing the security of IoT devices against hardware-based cyber threats, thereby contributing to the overall resilience of IoT networks. This innovative approach offers a promising solution to address the growing security challenges in IoT environments.

A New Method of Generating Truncated Bivariate Families of Distributions

The modeling of complex data has attracted several researchers for the quest of generating new probability distributions. The joint modeling of two variables asks for some additional complexities as a bivariate distribution is needed. The field of research in developing bivariate families of distributions is somewhat new. In certain situations, the domain of data is restricted and some truncated distribution is required. Several univariate truncated families of distributions are available for modeling of a single variable but the bivariate truncated families of distributions has not been studied and in this paper, we have proposed a new bivariate truncated families of distributions. A specific sub-family has been proposed by using the bivariate Burr as a base-line distribution, resulting in a bivariate truncated Burr family of distributions. Some important statistical properties of the proposed family has been studied, which include the marginal and conditional distributions, bivariate reliability, and bivariate hazard rate functions. The maximum likelihood estimation for the parameters of the family is also carried out. The proposed bivariate truncated Burr family of distributions is studied for the Burr baseline distributions, giving rise to the bivariate truncated Burr-Burr distribution. The new bivariate truncated Burr-Burr distribution is explored in detail and several statistical properties of the new distribution are studied, which include the marginal and conditional distributions, product, ratio, and conditional moments. The maximum likelihood estimation for the parameters of the proposed distribution is done. The proposed bivariate truncated Burr-Burr distribution is used to model some real data sets. It is found that the proposed distribution performs better than the other distributions considered in this study.

Vesicle Picker: A tool for efficient identification of membrane protein complexes in vesicles

Electron cryomicroscopy (cryo-EM) has recently allowed determination of near-atomic resolution structures of membrane proteins and protein complexes embedded in lipid vesicles. However, particle selection from electron micrographs of these vesicles can be challenging due to the strong signal contributed from the lipid bilayer. This challenge often requires iterative and laborious particle selection workflows to generate a dataset of high-quality particle images for subsequent analysis. Here we present Vesicle Picker, an open-source program built on the Segment Anything model. Vesicle Picker enables automatic identification of vesicles in cryo-EM micrographs with high recall and precision. It then exhaustively selects all potential particle locations, either at the perimeter or uniformly over the surface of the projection of the vesicle. The program is designed to interface with cryoSPARC, which performs both upstream micrograph processing and downstream single particle image analysis. We demonstrate Vesicle Picker’s utility by determining a high-resolution map of the vacuolar-type ATPase from micrographs of native synaptic vesicles (SVs) and identifying an additional protein or protein complex in the SV membrane.

Enhancing Erasure Resilience in Reed-Solomon Codes: Theory and Applications in Data Storage Systems

Reed-Solomon (RS) codes are widely utilized in data storage and communication systems due to their robust error detection and correction capabilities. However, traditional RS codes encounter challenges in addressing the increasing data corruption rates demanded by modern storage systems. This paper aims to overcome these limitations by modifying and extending the structure of traditional RS codes, particularly enhancing their recovery capabilities in the face of significant data loss. We begin by reviewing the theoretical foundations of RS codes and existing extension methods and discussing emerging technologies integrated into RS codes. We then present detailed methodologies for RS code encoding, erasure recovery mechanisms, soft decision decoding, and interleaving coding techniques, followed by a series of experiments designed to test the proposed methods. The experimental results indicate that soft-decision decoding outperforms traditional hard-decision decoding under high signal-to-noise ratio conditions, albeit with increased computational complexity as the list of candidate codewords grows. Interleaved Reed-Solomon (IRS) coding offers improved performance under low signal-to-noise ratio conditions but may introduce additional system complexity due to the interleaving and deinterleaving processes. We conclude with a summary of the research findings, a discussion of the study’s limitations, and suggestions for future research directions.

Understanding the Flow Field in a Highly Loaded Tandem Compressor Cascade

Abstract Large flow turning in compressor cascades with single airfoils requires an effective control of the boundary layer growth under the diffusing flow. An alternative approach consists of distributing the loading over two subsequent airfoils, using a so-called tandem blade, to, in some measure, restart the boundary layer before flow separation occurs. It is, however, not always clear whether the benefits of the two-blade setup justify the additional manufacturing complexity. The present work explores the tandem blade concept using a gradient-based optimization method to produce an efficient, highly loaded compressor cascade blade directly. A comparison between two-dimensional single and tandem configurations is first presented to clarify the benefits of one over the other. The geometry is optimized for each concept using a gradient-based optimization technique to improve the pressure loss coefficient at multiple operating points for a given flow-turning constraint. While the optimized single and tandem blade designs have similar performance, the lower solidity of the latter provides a lighter compressor stage for the considered operating range. A three-dimensional tandem compressor cascade based on the two-dimensional study is then optimized to account for secondary flows. The aerodynamic performance and the operating range are assessed and compared, along with a study of the physical phenomena surrounding the tandem configuration. The resulting geometry presents similar non-conventional features observed during the two-dimensional study that exploits the flow mechanism of two-airfoil configurations.

Dual-Functional Additive Bipyridine Cobalt Complex for Highly Efficient Kinetics and Lithium Regulation in Lithium–Sulfur Batteries

Dual-Functional Additive Bipyridine Cobalt Complex for Highly Efficient Kinetics and Lithium Regulation in Lithium–Sulfur Batteries

A comparison of eight weakly dispersive Boussinesq-type models for non-breaking long-wave propagation in variable water depth

Weakly dispersive Boussinesq-type models are extensively used to model long-wave propagation in coastal areas and their interaction with coastal infrastructures. Many equations falling in this category have been formulated during the last decades, but few detailed comparisons between them can be found in the literature. In this work, we investigate theoretically and with computational experiments eight variants of the most popular models used by the coastal engineering community. Both weakly nonlinear and fully nonlinear models are considered, hoping to understand better when the additional complexity of the latter class of models is necessary or justified. We provide an overview and discuss the properties of these models, including the linear dispersion relation in uniform water depth, the second-order nonlinear coupling coefficient, the shoaling gradient, and the sensitivity to wave trough instabilities. The models are then numerically discretised using the same general strategy in a single numerical code, using fourth-order methods for time and space discretisation. Their capacity to simulate coastal wave propagation and their transformation when approaching the shore is assessed on three challenging one-dimensional benchmarks. It appears that fully nonlinear models are more consistent than their weakly nonlinear counterparts, which can occasionally perform better but show different behaviours depending on the case.

Urban Air Mobility Passengers’ Profiling: Evidence from Milan Airports, Italy

Urban air mobility (UAM) is expected to offer new travel options for passengers to and from airports in the near future, despite uncertainties associated with regulatory issues, environmental concerns, and societal impacts. This paper analyzes the socioeconomic and behavioral factors that could influence users’ modal choices (including UAM services) for accessing and egressing airports. Using revealed and stated preference data collected at Milan airports (Italy), mixed logit and hybrid choice modeling specifications are estimated and compared with profile potential UAM passengers. Our findings suggest that the level of service, socioeconomic factors, and trip-related variables explain passengers’ choices better than latent traits such as fear of flying, propensity for technological advances, and expectations about the safety of UAM services. In other words, the additional complexity of hybrid choice modeling is not justified by the slight gain in likelihood compared with the estimated mixed logit model. The results also indicate that high-income individuals traveling for business purposes are the most likely demand segment to use UAM services, at least initially. Moreover, highly educated individuals and employees who have their travel expenses reimbursed for work trips are less likely to choose UAM services for airport access or egress, preferring traditional ground taxis.

Evaluating robo-advisors through behavioral finance: a critical review of technology potential, rationality, and investor expectations

The mini review assesses the value propositions of robo-advisors through the lens of behavioral finance. Despite their promise of data-driven, rational investment strategies, robo-advisors may not fully replicate the personalized service of human financial advisors or eliminate human biases in decision-making. A content analysis of 80 peer-reviewed articles and publications was conducted, focusing on the intersection of financial technology and behavioral finance. Literature was retrieved using The Chicago School University Library's OneSearch and the EBSCO host database, with key terms including “robo-advisor,” “investment behavior,” “risk tolerance,” “financial literacy,” and “affective trust.” The review identifies four key limitations of robo-advisors: (1) their inability to replicate the service-relationship of human advisors; (2) the presence of human bias in supposedly rational algorithms; (3) the inability to minimize market risk; and (4) their limited impact on improving users' financial literacy. Instead, robo-advisors temporarily compensate for a lack of financial knowledge through passive investment strategies. The findings suggest that integrating behavioral finance principles could enhance the predictive power of robo-advisors, though this would introduce additional complexities. The review calls for further research and regulatory measures to ensure that these technologies prioritize investor protection and financial literacy as they continue to evolve.

Fair Energy Trading in Blockchain-Inspired Smart Grid: Technological Barriers and Future Trends in the Age of Electric Vehicles

The global electricity demand from electric vehicles (EVs) increased by 3631% over the last decade, from 2600 gigawatt hours (GWh) in 2013 to 97,000 GWh in 2023. The global electricity demand from EVs will rise to 710,000 GWh by 2030. These EVs will depend on smart grids (SGs) for their charging requirements. Like EVs, SGs are a booming market. In 2021, SG technologies were valued at USD 43.1 billion and are projected to reach USD 103.4 billion by 2026. As EVs become more prevalent, they introduce additional complexity to the SG landscape, with EVs not only consuming energy, but also potentially supplying it back to the grid through vehicle-to-grid (V2G) technologies. The entry of numerous independent sellers and buyers, including EV owners, into the market will lead to intense competition, resulting in rapid fluctuations in electricity prices and constant energy transactions to maximize profit for both buyers and sellers. Blockchain technology will play a crucial role in securing data publishing and transactions in this evolving scenario, ensuring transparent and efficient interactions between EVs and the grid. This survey paper explores key research challenges from an engineering design perspective of SG operation, such as the potential for voltage instability due to the integration of numerous EVs and distributed microgrids with fluctuating generation capacities and load demands. This paper also delves into the need for a synergistic balance to optimize the energy supply and demand equation. Additionally, it discusses policies and incentives that may be enforced by national electricity carriers to maintain grid reliability and manage the influx of EVs. Furthermore, this paper addresses emerging issues of SG technology providing primary charging infrastructure for EVs, such as incentivizing green energy, the technical difficulties in integrating diverse hetero-microgrids based on HVAC and HVDC technologies, challenges related to the speed of energy transaction processing during fluctuating prices, and vulnerabilities concerning cyber-attacks on blockchain-based SG architectures. Finally, future trends are discussed, including the impact of increased EV penetration on SGs, advancements in V2G technologies, load-shaping techniques, dynamic pricing mechanisms, and AI-based stability enhancement measures in the context of widespread SG adoption.

An evidence code for eritrea: a mass to fill the eritrean judicio-legislative vacuum or an additional complexity in trials?

An evidence code for eritrea: a mass to fill the eritrean judicio-legislative vacuum or an additional complexity in trials?

Distinct Regions within SAP25 Recruit O-Linked Glycosylation, DNA Demethylation, and Ubiquitin Ligase and Hydrolase Activities to the Sin3/HDAC Complex.

Sin3 is an evolutionarily conserved repressor protein complex mainly associated with histone deacetylase (HDAC) activity. Many proteins are part of Sin3/HDAC complexes, and the function of most of these members remains poorly understood. SAP25, a previously identified Sin3A associated protein of 25 kDa, has been proposed to participate in regulating gene expression programs involved in the immune response but the exact mechanism of this regulation is unclear. SAP25 is not expressed in HEK293 cells, which hence serve as a natural knockout system to decipher the molecular functions uniquely carried out by this Sin3/HDAC subunit. Using molecular, proteomic, protein engineering, and interaction network approaches, we show that SAP25 interacts with distinct enzymatic and regulatory protein complexes in addition to Sin3/HDAC. Additional proteins uniquely recovered from the Halo-SAP25 pull-downs included the SCF E3 ubiquitin ligase complex SKP1/FBXO3/CUL1 and the ubiquitin carboxyl-terminal hydrolase 11 (USP11). Furthermore, mutational analysis demonstrates that distinct regions of SAP25 participate in its interaction with USP11, OGT/TETs, and SCF(FBXO3). These results suggest that SAP25 may function as an adaptor protein to coordinate the assembly of different enzymatic complexes to control Sin3/HDAC-mediated gene expression. The data were deposited with the MASSIVE repository with the identifiers MSV000093576 and MSV000093553.

Differential quadrature lattice Boltzmann method for simulating fluid flows

In this study, a novel method for simulating incompressible flows using the lattice Boltzmann method is presented, aiming to improve robustness. This method is enhanced by the Qadyan numerical method. In the Qadyan method, Partial differential equations are solved using semi-discrete schemes combined with the differential quadrature method. To discretize the spatial derivatives of the lattice Boltzmann equation, the upwind differential quadrature method is employed, while the first-order forward difference scheme and/or fourth-order Runge-Kutta method handle the temporal term. The decision to exclude more complex methods stems from their requirement for refined computational grids. This work focuses on achievable results with similar types of uniform grids. The proposed scheme introduces and evaluates a novel method for solving both steady and unsteady problems. The present numerical method is validated by solving five benchmark problems, including heat diffusion on a slab, Stokes’ first problem, Taylor-Green vortex flow, flow around a flat plate, and flow in a lid-driven cavity. Reasonable agreements are obtained between the solutions obtained using this method and those from analytical/other numerical approaches. The Qadyan formulation delivers more accurate results compared to the finite-difference lattice Boltzmann method, (FDLBM), without requiring an increase in the number of grids. Notably, the novel Qadyan method achieves this increased accuracy without introducing additional complexity to the standard lattice Boltzmann method or resorting to non-uniform grids. This is possible due to the nature of the differential quadrature method, which utilizes more combinations of candidate stencils. The Qadyan method has a higher computational cost in comparison with the FDLBM.

The Experiences and Views of Employees on Hybrid Ways of Working

The contemporary post-COVID-19 corporate environment of instant response and hybrid work settings motivates employees to learn to adjust their expectations. This new corporate working model incorporates flex locations and flex schedules by working at home 1–2 days per week and staying connected for non-urgent requests, even outside business hours. This work setting empowers employees to prioritize work accordingly and to accommodate the fluid schedules of their coworkers. As a result, this new hybrid workplace requires leaders and their teams to face new challenges in terms of communication, coordination, and team connection to remain effective. This research examines the experiences of employees in an SME that applied a hybrid work policy following the post-pandemic crisis, bringing additional complexity to their modern work system. This study investigates employees’ views on the changing work environment as important evidence for HR management to incorporate into future organizational practices. To understand the various principles at play and provide more granular results, this paper includes a business case study (N = 25) where semi-structured interviews were used to identify the views and concerns of employees regarding hybrid work settings. The scope of this case study was to collect empirical data regarding this new agile way of working while understanding participant thinking. The findings suggest that while there are clear benefits in terms of efficiency and flexibility in hybrid work settings, there are also challenges related to social interactions and non-verbal clues. This study enhances conceptual and empirical understanding and supports contemporary research on the future of work.

The diagnostic challenge of a Papillary lesion of the breast in a patient with Peutz-Jeghers syndrome

Abstract Introduction/Objective Peutz-Jeghers syndrome (PJS) is an autosomal dominant disorder with elevated risk of various neoplasms, including breast cancer. The incidence of cancer among PJS patients is estimated to be 15-fold higher than the general population. The spectrum of breast lesions in PJS varies from benign papillomas to invasive carcinomas, presenting significant diagnostic complexities. The case presented here revolves around a unique papillary lesion in a PJS patient. Methods/Case Report A 40-year-old African American female, with PJS presented with a palpable abnormality in the right subareolar region with sanguineous nipple discharge. Multiple prior biopsies revealed intraductal papillomas with no atypia. Imaging showed a 7.0 cm complex solid and cystic subareolar mass, which had increased in size by 42% within a short period. Given the rapid increase in size, malignant transformation was clinically suspected. The patient underwent bilateral mastectomy. Macroscopic examination showed a 9.0 cm tan firm subareolar nodule with solid and cystic components. Histopathology revealed areas consistent with intraductal papilloma, however, additional complexities, including epithelial and myoepithelial proliferations with mild cytologic atypia, squamous, apocrine, and sebaceous metaplasia made the diagnosis challenging. No necrosis or increased mitotic activity was identified. Immunostaining for CK5/6 and p63 highlighted the myoepithelial layer, while estrogen receptor demonstrated heterogeneous positive staining. This picture raised suspicion of adenomyoepithelioma, but there was a significant overlap with intraductal papilloma. A final diagnosis of intraductal papillary neoplasm with epithelial and focal myoepithelial atypia was rendered. Results (if a Case Study enter NA) NA Conclusion This case presented a complex diagnostic dilemma. The rapid growth of the lesion, coupled with cytological atypia, raised significant concern necessitating precise differentiation between adenomyoepithelioma and intraductal papilloma. Further research is imperative to explore the pathophysiology of breast lesions in PJS, their impact on surveillance, management, potential for malignancy, and prognosis. Investigating additional diagnostic modalities and molecular markers to aid in accurate differentiation between benign and malignant papillary neoplasms in PJS patients warrants consideration.