5916 research outputs found
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Electoral pressure, reputational risks, and coalition politics: How established parties respond to the rise of anti-immigrant parties
No full textThe rise of anti-immigrant parties has reshaped global politics, forcing mainstream parties to recalibrate their strategies. This study examines how Turkish political parties responded to the emergence of the Victory Party (Zafer Partisi) in 2021, which placed immigration at the center of political debate. Drawing on 1,089 parliamentary group speeches (2011-2023) and elite interviews with key party figures, we identified three key factors shaping party responses: voter overlap with radical-right parties; reputational risks associated with shifting policy positions; and access to political power. Our findings revealed five strategies: issue avoidance; amplification; cooptation; repositioning; and reinforcement. Unlike conventional models that emphasize voter competition, we highlight the role of political power in shaping party strategies, particularly in competitive authoritarian settings. This study advances the understanding of how mainstream parties navigate niche party pressures, offering a broader perspective beyond Eurocentric and electoralist frameworks
Beneficiary appointment and delivery planning in a conflict setting
No full textIn this study, we explore the challenges faced by humanitarian organizations engaged in relief efforts for internally displaced individuals during armed conflicts. Based on our semistructured interviews with three local nongovernmental organizations (LNGOs) in Syria, we introduce a new appointment scheduling problem to improve decision-making for aid delivery planning in conflict settings. Operating in a highly resource-constrained environment, these LNGOs face complexities that necessitate effective decision support tools to streamline supply delivery at relief facilities, where a large number of registered beneficiaries are served. Our proposed appointment scheduling problem aims to optimize the allocation of delivery times for various supplies, taking into account the urgency of needs and operational limitations. We present a heuristic that addresses the complexities of the proposed scheduling problem in a flexible way. The heuristic can accommodate simple rules derived from LNGOs' operational policies on the ground, such as imposing a single visit per beneficiary, delivering a single supply type per day, and preallocating time slots to conflict groups. We present a case study based on the Latakia district of Syria to assess the performance of our heuristic and the effectiveness of simplified delivery strategies. Our results not only showcase the efficiency of the heuristic, but also provide valuable managerial insights. We find that cross-training of staff is more beneficial when supplies are relatively abundant. Furthermore, the simplified delivery policies are effective in certain conditions contingent upon various factors, including supply scarcity, difficulty of travel, and the level of conflict in the population.Publisher versio
Artificial intelligence paradigms for next-generation metal-organic framework research
No full textAfter the development of the famous "Transformer" network architecture and the meteoric rise of artificial intelligence (AI)-powered chatbots, large language models (LLMs) have become an indispensable part of our daily activities. In this rapidly evolving era, "all we need is attention" as Google's famous transformer paper's title [Vaswani et al., Adv. Neural Inf. Process. Syst. 2017, 30] implies: We need to focus on and give "attention" to what we have at hand, then consider what we can do further. What can LLMs offer for immediate short-term adaptation? Currently, the most common applications in metal-organic framework (MOF) research include automating literature reviews and data extraction to accelerate the material discovery process. In this perspective, we discuss the latest developments in machine-learning and deep-learning research on MOF materials and reflect on how their utilization has evolved within the LLM domain from this standpoint. We finally explore future benefits to accelerate and automate materials development research.European Union (EU) Marie Curie Actions ; Agence Nationale de la Recherche (ANR) Agence nationale pour le developpement de la recherche en sante (ANDRS) ; Ghent University ; European Research Council (ERC) ; European Research Council (ERC) ; Institut Universitaire de Franc
Lean operations and firm resilience-contrasting effects of COVID-19 and economic recession
No full textIn recent times, there has been a call in the Operations Management discipline to study the effect of operations strategies during pandemics. Firms adopt various strategies to sustain their competitiveness and reduce the likelihood of financial distress. Operating lean is one of these strategies to achieve sustainable efficiency and success. However, there is little empirical evidence on whether lean is an effective strategy for reducing future financial distress and remaining resilient and viable. In this study, we examine if a firm's operational leanness along three dimensions - inventory, property/plant/equipment (PPE), and supply chain - impacts the probability of future financial distress and if the effects from these dimensions are substitutable. An equally interesting and related question is whether and how this relationship is affected by challenging macroeconomic times that cause shocks to the supply chain. Specifically, we study the contextual effect of 2020 COVID-19 and also the 2001 and 2008 economic recessions. Our results address the impact of operating lean as well as highlight the differential effects of the pandemic and the economic recession on the relationship between operational leanness and the likelihood of financial distress
Two-stage transformerless BI6-Topology-Based microinverter
No full textThis paper proposes an application for the boost inverter 6 (BI6) topology as a microinverter, leveraging its capability of single-stage boost operation. A front-end DC-DC boost converter is added at the input side of the BI6 to increase the voltage-boosting gain and implement the maximum power point tracking (MPPT) technique. The BI6 is operated with a fixed boosting duty cycle, leaving two control variables in the system: the modulation index of the BI6, controlled by a current controller, and the duty cycle of the front-end boost converter, controlled by the MPPT controller. A MATLAB/Simulink simulation has been carried out to validate the proposed microinverter system
A comparative analysis of large deformation behavior of thin flat and corrugated steel plates under static and blast loading
No full textThis paper investigates the large deformation behavior of thin flat and corrugated (crimp) steel plates used in prefabricated blast-resistant modular structures through finite element simulations. The study evaluates plate responses under static monotonic, cyclic static, and dynamic blast loads using pressure-impulse (P-I) curves. Closed-form models based on yield line theory are developed to predict deflection-pressure curves for both flat and corrugated plates, showing strong agreement with finite element analysis. The results indicate that the pressure-deformation behavior of flat plates changes significantly upon yielding. Under cyclic loading, their stiffness decreases substantially until membrane action mitigates the effects during unloading, which also results in a notable reduction in hysteretic energy dissipation capacity. In contrast, the cyclic analysis of corrugated plates reveals decreased load-carrying capacity due to buckling from their profile, yielding, and plastic deformations. However, these plates exhibit substantial energy dissipation and maintain consistent initial stiffness throughout hysteretic loops, with minimal deviation. The findings highlight the significant influence of aspect ratio on plate behavior. Flat plates show a highly sensitive pressure-displacement relationship based on their aspect ratio, while corrugated plates exhibit minimal sensitivity in both static and dynamic conditions. Corrugated plates display consistent one-way bending behavior, largely independent of their aspect ratio. Dynamic blast analysis reveals that corrugated plates perform better in impulse-sensitive regions across all response levels, while flat plates excel in pressure-sensitive regions, particularly at medium and high levels
Analyzing transaction graphs via motif-based graph representation learning for cryptocurrency price prediction
No full textDecentralized and transparent nature of cryptocurrencies have lately increased investors interest in them. Forecasting cryptocurrency's price accurately is crucial to come up with a good investment strategy, and such a forecast requires one to consider its unique attributes as well as high volatility. Even though many existing studies have focused on analyzing the cryptocurrency transaction graph topology, studies on the analysis of transaction graph's impact on prices are quite limited. In this paper, we explore the forecasting ability of blockchain transaction graph-based attributes on Bitcoin's and Ethereum's future price via deep learning methods. More specifically, we came up with motif convolution module (MCM), a motif-based graph representation learning approach to take local structural knowledge into account more strongly in node and edge-attributed transaction graphs encoding substantial structural knowledge. Our proposed MCM constructs a motif dictionary without supervision, and employs a new motif convolution operation while extracting the vertices local structural context. Afterwards, we learn high-level vertex embeddings by using such structural context via multilayer perceptron and graph neural network. Overall, we extract the attributed transaction graphs temporally-evolving low-dimensional representations, and use such embedding data together with historical prices within self-attention-based LSTM to predict the future prices accurately. Our proposed approach outperforms all considered baselines in terms of both price and price direction prediction, showing the promise of efficient integration of transaction data into cryptocurrency price prediction.TÜBİTAKPublisher versio
A behavioral study of capacity allocation in revenue management
No full textWe investigate how human decision-makers handle the two-class capacity allocation problem-a foundational revenue management problem and a cornerstone for more sophisticated scenarios. We consider the problem with ordered and unordered arrivals, and then introduce a simplified version with single up-front decision. We explore the decisions across two price ratios, symbolizing heterogeneous vs. more homogeneous customer bases. We find that decision-makers ineffectively allocate capacity, resulting in suboptimal revenues and underutilization of RM potential. Theoretically, the decision-making format for the capacity does not matter, whereas we find that decision-makers set higher protection levels when making an up-front decision rather than sequential decisions and earn higher revenues for heterogeneous customers. Decision-makers perform poorly in sequential decision-making, and heuristics can be useful in improving performance. Moreover, we observe several regularities in decision-making. With higher customer heterogeneity, decision-makers tend to accept customers who would have been declined by the optimal policy, indicating they are less demanding. Conversely, with more homogeneous customers, they tend to turn away customers who would have been accepted by the optimal policy, implying they are more demanding. Lastly, we investigate whether the decision-making is affected by feelings of regret and find strong effects for both winner's and loser's experienced regret