The quality criteria for fairing used to protect antenna devices (AD) of radar stations (RS) from environmental influences are the requirements for ensuring and maintaining strength and protective properties with minimal insertion loss of the transmi...
A method based on fluid–structure coupling is used in this study to calculate the response of a rocket fairing as it is falling. Some cases of vibration divergence of the fairing were found, and the influence of some specific factors was analyz...
For hypersonic-vehicle fairing separation, the reliable separation of the fairing and rapid suppression of the separation disturbance are required in scenarios where there is high dynamic pressure and there are abrupt changes in the aerodynamic shape...
In this work, the aerodynamic and structural design of the fairing for vehicles was carried out. The Rankine half body theory, which is one of the theories being used for the aircraft gas turbine engine inlets and turboprop nose cones was adopted for...
To address the challenges of excessive local thinning, poor surface quality, and low production efficiency in traditional multi-pass deep-drawn aluminum alloy fairings, this study investigates the effects of process parameters—including liquid...
A long-span double-deck steel truss suspension bridge is easy to produce vortex-induced vibration (VIV) at low air velocity, which affects bridge service life. Additional aerodynamic measures play a role in suppressing VIV by changing the aerodynamic...
This study explores the relationship between the volumetric fraction of jute fiber and the mechanical properties of epoxy matrix composites for motorcycle fairings, highlighting the feasibility of jute fibers due to their strength and sustainability....
Aerodynamical design is one of the critical technologies in race car engineering, and favorable race car aerodynamics is supposed to provide sufficient negative lift force and keep the center of pressure in the vicinity of center of mass. Taking the...
The EU AI Act makes fairness metrics for high-risk AI systems’ compliance evidence, turning their trustworthiness into a safety and accountability concern. Fairness audits assume that test data reflects the properties of real-world conditions,...
The rapid increase in data volume and variety within the field of machine learning necessitates ethical data utilization and adherence to strict privacy protection standards. Fair federated learning (FFL) has emerged as a pivotal solution for ensurin...
Machine learning (ML) models are increasingly being used for high-stake applications that can greatly impact people’s lives. Sometimes, these models can be biased toward certain social groups on the basis of race, gender, or ethnicity. Many pri...
Lightweight composite structures like fiber metal laminates (FMLs) are widely used to manufacture aircraft structures and substitute metallic parts. While the superior mechanical performance of FMLs, including their high specific strength and excelle...
Fairness in recommendation systems has drawn growing attention due to rising societal and regulatory concerns over algorithmic bias. Existing fairness-aware approaches typically mitigate bias by either removing sensitive attributes via representation...
Almost all academic literature about the causes and consequences of fairness of elections and referenda is based on retrospective evaluations. One of the strongest findings of such studies is that nonvoting is higher among citizens who retrospectivel...
With the increasing reliance on automated decision making, the issue of algorithmic fairness has gained increasing importance. In this paper, we propose a Generative Adversarial Network for tabular data generation. The model includes two phases of tr...
AI fairness is an essential topic as regards its topical and social-societal implications. However, there are many challenges posed by automating AI fairness. Based on the challenges around automating fairness in texts, our study aims to create a new...
AI-assisted decision-making that impacts individuals raises critical questions about transparency and fairness in artificial intelligence (AI). Much research has highlighted the reciprocal relationships between the transparency/explanation and fairne...
Research on the fairness of spectral clustering has gradually increased attention. Normally, existing methods of fair spectral clustering add a fairness constraint to the original objective function so that fairness is guaranteed. However, similar to...
Traffic engineering aims to control infrastructure and population behaviour to achieve optimal usage of road networks. Fairness is fundamental to stimulate cooperation in large populations, and plays an important role in traffic engineering, as it in...
In the past decade, policymakers and commentators across the world have called for the introduction of copyright reform based on the fair use model in the United States. Thus far, Israel, Liberia, Malaysia, the Philippines, Singapore, South Korea, Sr...
The success of a trade fair depends on the close collaboration between organizers and exhibitors with potential visitors. While the literature has invested a great deal of interest in the role of the exhibitor and, to a lesser extent, to the visitor,...
We propose definitions of fairness in machine learning and artificial intelligence systems that are informed by the framework of intersectionality, a critical lens from the legal, social science, and humanities literature which analyzes how interlock...
Outlier detection aims to identify rare, minority objects in a dataset that are significantly different from the majority. When a minority group (defined by sensitive attributes, such as gender, race, age, etc.) does not represent the target group fo...
Recent research by Yucel and colleagues suggests that children perceive equality-based fairness violations (resources being distributed unequally) as less serious than prototypical moral harms, but that making the harmful consequences of unfairness s...
Fair clustering aims to partition a dataset while mitigating bias in the original dataset. Developing fair clustering algorithms has gained increasing attention from the machine learning community. In this paper, we propose a fair k-means algorithm,...
Remanufacturing is a sustainable product reutilization strategy to realize responsible consumption and production. However, it has remained a largely untapped opportunity for enhancing productivity due to some behavioral matters such as fairness conc...
This article examines the role of fairness in software across diverse application contexts, with a particular emphasis on healthcare, and introduces the concept of algorithmic (individual) meta-fairness. We argue that attaining a high degree of fairn...
The rapid progress of large language models (LLMs) has fostered the development of domain-specific variants in law, medicine, and finance. However, existing legal LLMs still struggle to generate contextually grounded and regulation-compliant response...
As regulatory requirements increasingly shape automated lending decisions, fairness remains a critical challenge in high-stakes domains, particularly credit scoring. Although artificial intelligence models can achieve strong predictive performance, t...
The present work introduces virtual fairs, particularly the perceived benefits, challenges, and relevance of such fairs for recruitment and employer branding. These aspects were explored in a semi-structured interview with an organizer of virtual fai...
18 October 2023
The rise of machine-learning applications in domains with critical end-user impact has led to a growing concern about the fairness of learned models, with the goal of avoiding biases that negatively impact specific demographic groups. Most existing b...
Airtime fairness, or time-based fairness, has been well recognized as a method to solve WiFi performance anomalies and provide a balance between fairness and spectrum efficiency in multi-rate wireless networks. However, the definition of airtime is v...
This study aims to investigate the effects of geographical indication (GI) and fair trade knowledge (FTK) on U.S. female consumers’ perceived brand equity and the purchase intention of fair trade apparel handicrafts. Employing a web-based exper...
Wherever there is group life, there has been a social division of labor and resource allocation, since ancient times. Examples include ant colonies, bee colonies, and wolf colonies. Different roles are responsible for different tasks. The same is tru...
Data sharing and reuse are crucial to enhance scientific progress and maximize return of investments in science. Although attitudes are increasingly favorable, data reuse remains difficult due to lack of infrastructures, standards, and policies. The...
This paper studies optimal decisions in an authorized remanufacturing closed-loop supply chain (CLSC) consisting of a manufacturer, a retailer, and an authorized third-party remanufacturer with dual-fairness concerns (distributional fairness concerns...
The importance of behavioral factors in the process of decision making is widely recognized in literature and practice. The aim of this paper is to examine the influence of collectors’ multiple fairness concerns on pricing decisions in a closed...
The excessive compensation packages of CEOs of U.S. corporations in recent years have brought to the foreground the issue of fairness in economics. The conventional wisdom is that the free market for labor, which determines the pay packages, cares on...
Ensemble clustering has become a widely used technique for improving robustness and accuracy by combining multiple clustering results. However, traditional ensemble clustering methods often fail to provide fair treatment between groups defined by sen...
Algorithmic prediction models are increasingly used to support decision-making in high-stakes environments, including emergency departments (ED). However, aggregate performance metrics may obscure systematic differences in classification errors or ca...
We report an experiment designed to study bargaining behavior between one buyer and multiple sellers with complementarity and how it is influenced by fairness concern and information transparency. We base our setup on a structured alternating-offer b...
A dominating set of a graph is a subset of vertices such that every vertex is either contained in the set or adjacent to at least one vertex in it. A dominating set is called k-fair if each vertex not in this set is adjacent to exactly k vertices of...
An interesting and challenging problem in mathematics is how to construct fair dice pairs. In this paper, by means of decomposing polynomials in a residue class ring and applying the Discrete Fourier Transformation, we present all the 2000 fair dice...
Fairness in the agro-food system is an increasingly important issue. Ensuring fair and ethical practices in the agro-food chain is essential for sustainable, effective, and resilient agro-food systems. Identifying and understanding fairness-enabling...
Connectivity is one of the essential needs in today’s standards in many aspects of life, starting with personal relationships, education, and remote work and ending with the security and economy of countries. However, connectivity is susceptibl...
We propose introducing fairness constraints to one of the most famous multi-criteria decision-making methods, the analytic hierarchy process (AHP). We offer a solution that guarantees consistency while respecting legally binding fairness constraints...
The rise of machine learning-driven decision-making has sparked a growing emphasis on algorithmic fairness. Within the realm of clustering, the notion of balance is utilized as a criterion for attaining fairness, which characterizes a clustering mech...
Machine Learning (ML) models are increasingly used in credit risk assessment, creating a growing need for decision mechanisms that balance predictive performance and fairness. Fairness-aware threshold optimization has emerged as an attractive post-pr...
Most research on fairness in Machine Learning assumes the relationship between fairness and accuracy to be a trade-off, with an increase in fairness leading to an unavoidable loss of accuracy. In this study, several approaches for fair Machine Learni...
This paper discusses the concept of ‘fairness’ in transport, specifically regarding women using public transport, future autonomous vehicle taxis or bicycle sharing. Women generally have varying and complex mobility patterns compared to men and suffe...
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