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Markov Decision Processes: Monotonicity of Optimal Policy in Exponential and Quasi-Hyperbolic Discounting Parameters
No full textIntertemporal preferences of decision makers, i.e., the way they discount delayed utilities, impact their decisions. Empirical evidence suggests that individuals commonly have hyperbolic discounting preferences. This can result in time-inconsistent behavior, e.g., procrastination, which may be a barrier to adopting preventive behavior such as machine maintenance and patient adherence to treatment. In this paper, we theoretically compare the actions of individuals based on their discounting characteristics. We consider the Hyperbolic Discounting (HD) model, which is more representative of individual behavior than Exponential Discounting (ED). We formulate a discrete-time finite-horizon Markov decision process with Quasi-Hyperbolic Discounting (QHD), an analytically tractable function representing HD and present sufficient conditions that ensure the monotonicity of the optimal policy in the discounting parameters. We consider submodular maximization or supermodular maximization problems. Our paper is the first to investigate the monotonicity of the optimal policy in QHD parameters for these problems. Moreover, we compare the optimal actions under ED and QHD. We apply our results to the settings of machine maintenance, individual health behavior and inventory control. We provide numerical examples that show there might not be monotonicity if our sufficient conditions are not met. Also, we explore the discrepancy between the expected total exponentially-discounted rewards of the actions obtained from QHD and of the actions that are optimal under ED, and observe that this discrepancy is affected mainly by the present bias.AXA Award Grant from the AXA Research Fund; Scientific and Technological Research Council of Turkiye (TUBITAK) [221M581]This research was funded by the AXA Award Grant from the AXA Research Fund and the Scientific and Technological Research Council of Turkiye (TUBITAK) grant 221M581
Hands-On Docking With Molegro Virtual Docker
No full textMolegro Virtual Docker (MVD) integrates state-of-the-art search algorithms and scoring functions dedicated to protein-ligand docking simulations. It implements differential evolution as a search engine and MolDock and Plants scores to calculate binding affinity. In this work, we describe a workflow focused on how to build regression models to predict the inhibition of cyclin-dependent kinase 2 (CDK2). We employ available structural and binding data to construct machine learning models to calculate CDK2 inhibition based on the atomic coordinates obtained through docking simulations performed with MVD. We present a hands-on approach to show how to integrate docking results and machine learning methods available at Scikit-Learn to build targeted scoring functions. Our regression models show superior predictive performance compared with classical scoring functions. All CDK2 datasets and Jupyter Notebooks discussed in this work are available at GitHub: https://github.com/azevedolab/docking#readme. We made the source code of the program SAnDReS 2.0 available at https://github.com/azevedolab/sandres. © 2025 Elsevier B.V., All rights reserved.Coordenação de Aperfeiçoamento de Pessoal de Nível Superior, CAPES; Conselho Nacional de Desenvolvimento Científico e Tecnológico, CNPq, (306298/2022-8); Conselho Nacional de Desenvolvimento Científico e Tecnológico, CNP
The State Gives Me the Right Not to Do It: Reproductive Governance, Structural Violence and Barriers to Abortion Care in Türkiye
No full textAbortion in T & uuml;rkiye is legal through ten weeks gestation without restriction as to reason, but it is often difficult to access. In recent years Turkey has undertaken an aggressive pronatalist politics of reproduction resulting in extensive reproductive governance that regulates reproductive health care in general and abortion access in specific. This research uses nationwide data collected in 2024 from public and private hospitals to ascertain the availability of abortion in T & uuml;rkiye. Currently, less than one third of hospitals report providing abortion care but this drops to just 5.1 % for public hospitals while 50.4 % of private hospitals offer abortion services. However, the lack of availability does not rely on simply denial but encompasses a multilayered system of barriers that restrict access. While some institutions simply stated that they do not perform abortions, many others cited varying obstacles that hindered their ability to provide such care. These included declarations that abortion is illegal, doctors' refusal to perform abortions, and use of gestational limits less than the ten weeks provided in law. Adding to these barriers is the lack of available information about abortion availability, the substantial distances required to travel to access care, and the price of abortion at private hospitals. By limiting abortion access, the state undermines individual autonomy and endangers the health and safety of pregnant people. In practice, legality offers little protection: the combination of institutional refusal, misinformation, geographic and financial hurdles, and political hostility makes abortion effectively inaccessible for many. T & uuml;rkiye's case illustrates how legal rights can be hollowed out by restrictive governance, leaving reproductive freedom precarious despite formal legality
Evaluation of Railway Intelligent Transportation Systems to Construct Safer Railway Transport Systems with a Novel Decision-Making Model
No full textWhile end users typically perceive rail transport as safer than other forms of transportation, it still confronts substantial threats and risks that demand meticulous management. One of the most crucial challenges in rail transport is the management of dense railway traffic on limited infrastructure. The effectiveness of this management is critical to ensuring safety and reliability. To address these challenges, integrating and adapting Railway Intelligent Transportation Systems (RITS) into railway transport systems has become essential for creating a safer and more reliable railway system. A railway system that is poorly structured and does not use advanced technology appropriately struggles to manage these risks effectively. Therefore, the integration of RITS is crucial. Decision-makers must carefully evaluate and select the most suitable RITS to ensure safety and reliability. However, since many conflicting criteria and decision factors affect the evaluation process, selecting the most appropriate RITS is a complex decision problem. This study proposes a new decision-making model by considering these requirements. In this context, the TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) method, enhanced with Intuitionistic Fuzzy Sets and reinforced by integrating Schweizer-Sklar Hamy Mean Operators, was developed as a practical solution to address the decision-making problem. According to the research results, reliability and the use of the most advanced technology are the effective criteria that influence the selection of appropriate RITSs. In addition, A3 Aselsan, one of the key players in the intelligent transport system manufacturing industry, has been determined to be the most suitable alternative for railway transportation systems. Ultimately, extensive reality tests involving sensitivity and comparative analysis were conducted to check the robustness of the model. The analysis proves the model's soundness and practicality
Assessing the Renewable Energy Sources for Sustainable Energy Generation Systems: Interval-Valued Q-Rung Orthopair Fuzzy SWARA-TOPSIS
No full textRenewable Energy Sources (RESs) help decarbonize power systems, but selecting among them is a challenging decision problem due to multiple, often conflicting, technical, economic, environmental, and health-related criteria. Consequently, numerous studies in the literature have attempted to address this decision-making issue using objective, subjective, and fuzzy decision-making procedures. However, there are still unaddressed research gaps in the literature, particularly regarding the explicit modeling of expert hesitation and ambiguity in real-world RES selection cases. The current study develops a decision-making model based on Step-wise Weight Assessment Ratio Analysis (SWARA) and Technique of Order Preference Similarity to the Ideal Solution (TOPSIS) methods integrated with Interval-Valued q-Rung Orthopair Fuzzy Sets (IV-q-ROFSs) to fill these gaps. Unlike previous studies that have predominantly applied conventional fuzzy MCDM techniques, our model introduces the first integration of IV-q-ROFS into RES selection. This novelty enables a more accurate representation of expert hesitation and uncertainty. The study is applied to a real industrial case in Turkey, where six RES alternatives are evaluated across 43 criteria by five senior experts under the supervision of a three-member professionals’ board. Furthermore, the structured robustness check and systematic literature mapping ensure that the proposed approach is methodologically robust and practically relevant for policymakers and energy planners. The application results of the developed model demonstrate that the estimated energy production potential of the RES and the effects of carcinogens generated from utilizing these energy sources are the critical factors influencing the selection of the most appropriate RESs. Solar energy ranked first among the alternatives. The applicability and validity of the developed model are examined by a comprehensive robustness check consisting of tests of sensitivity, comparison, and resilience to the rank reversal problem. Overall, the study provides (i) a novel methodological framework integrating IV-q-ROFS with SWARA and TOPSIS, (ii) empirical evidence from a comprehensive real-world RES selection case, and (iii) policy-relevant insights into the drivers of renewable energy adoption. © 2025 Elsevier Ltd. All rights are reserved, including those for text and data mining, AI training, and similar technologies
Fuel Cell Electric Long-Haul Truck Evaluation for Sustainable Transport Via a Novel Pythagorean Fuzzy Sets-Driven Tool
No full textFossil fuel-powered trucks and vehicles used in road freight transportation play a notable role in the emission of greenhouse gases. Although the road vehicle industry's use of renewable energy is promising in terms of sustainability, the vehicle manufacturing industry's initiatives are still in their infancy. Moreover, existing studies on using electric and renewable energies in transportation have primarily focused on electric automobiles. Considering these research and practice gaps, this work investigates the selection of the most proper fuel cell electric long-haul trucks (FCETs) to restructure the Turkish fleet of long-haul trucks operating nationwide concerning sustainability. However, assessing these vehicles is challenging, as they are produced based on new and advanced technology, with severe and highly complicated uncertainties. Thus, this paper suggests a Pythagorean fuzzy distance measure-based weighted integrated sum product (WISP) with the integration of the symmetry point of criteria (SPC) and relative closeness coefficient (RCC)-based weighting methods. Surprisingly, and unlike the findings of earlier works, the acquired conclusions indicate that refueling time (0.1161) is the most influential factor for FCET selection, followed by range (0.0837) and torque (0.0785) among the 14 criteria. Besides, the first alternative (R1) outperforms the other options, followed by R5 and R7. Finally, robustness and validity checks ensured the consistency, stability, and practicality of the conclusions. The research can guide manufacturers who produce FCETs and aim to enhance the quality and desirability of their products. Furthermore, practitioners and researchers can utilize the proposed model to solve challenging decision-making problems
Supervised Learning-Driven Dead Band Control of Occupant Thermostats for Energy-Efficient Residential HVAC
No full textHeating, ventilation, and air conditioning (HVAC) systems play a crucial role in demand-side management (DSM) by shaping residential electricity consumption and enabling flexible, grid-responsive operation. Thermostats in HVAC systems regulate indoor temperature as part of a closed-loop control framework, typically incorporating a fixed temperature dead band–a range around the setpoint where no action is taken–to reduce energy use and prevent frequent cycling of the HVAC system. Although essential for efficiency and equipment longevity, fixed dead bands limit adaptability, as dynamically adjusting them under varying environmental conditions remains challenging for occupants. To address this limitation, we propose a machine learning (ML)-based dead band tuning framework that optimally adjusts thermostat settings in real time. The method integrates conventional optimization with data-driven modeling: a mixed-integer linear programming (MILP) model is first used to generate optimal dead band values under measured outdoor temperature records (diverse seasonal weather scenarios) which are then employed to train the ML-based predictor to learn a real-time discrete dead band decision policy that approximates the MILP-optimal hysteresis-aware decisions. Among the evaluated models, Random Forest demonstrates superior predictive performance, achieving a mean squared error (MSE) of 0.0399 and a coefficient of determination (R2) of 95.75 %. © 2025 Elsevier Ltd
The Unexpected Actor? Civil-Military Relations and Regulatory Agency Control in Brazil
No full textDemocratic backsliding around the world has sparked debate about its impact on public administration and governance. This article explores a growing yet less visible phenomenon threatening democracy. It examines the influence exerted by authoritarian populists over autonomous regulatory agencies through militarized patronage, that is, the discretionary appointment of military officers to civil positions. Scholars have not fully untangled how and why contemporary populists employ militarized patronage, and much less is known about militarization of autonomous regulatory agencies. To fill this gap, we highlight enabling factors underpinning militarized patronage and draw on a unique empirical dataset that integrates military with civil service records to account for the militarization of autonomous regulatory agencies in Brazil during the far-right presidency of Jair Bolsonaro (2019–2022). The article deepens our understanding of the role of civil-military relations in restructuring regulatory governance during populist rule, and the effects of democratic backsliding on regulatory governance. © 2025 The Author(s). Governance published by Wiley Periodicals LLC
Better Reflective Functioning in Mothers Linked to Longer Joint Attention with Infants
No full textJoint attention is a foundational precursor to later developmental outcomes such as vocabulary, intelligence, and theory of mind. Previous research has shown that maternal sensitivity, depressive symptoms, and parent-child attachment security are associated with attention-sharing behaviors between mothers and their infants. The present study examined the relationship between mothers' reflective functioning (the ability to recognize and interpret one's own and one's child's mental states, as well as the behaviors motivated by those mental states) and joint attention. Data were collected from 72 infants aged 10-16 months and their mothers. Results indicated that mothers who reported greater difficulty in understanding and distinguishing between their own and their child's mental states (i.e., higher prementalization) tended to engage in joint attention episodes that were shorter and more frequent, and they were also more likely to terminate these interactions. In contrast, mothers expressing greater interest and curiosity about their infants' mental states spent longer periods in joint attention, initiated these episodes less often, and were less inclined to terminate them. Additionally, mothers who felt more certain about their infants' mental states were less likely to end joint attention episodes. After controlling for infant age and socioeconomic status, higher levels of interest and certainty continued to predict lower maternal termination, while prementalization was still linked to a higher number of joint attention episodes. These findings suggest that mothers' perceptions of their infants' mental states shape how they engage in shared attention during everyday play interactions.Scientific and Technological Research Council of Turkiye [119K854]We sincerely appreciate the contributions of Gizem Akel-Guclue, Suheda Nur Erdogan, Ahmet Mete Durmus, Arda Kaan Sonmez, Asena Sayin, Gunce Ugur, Yasemin Seran Ozyurt, Irmak Kalkan, Irem Gungordue, Ozce Sivis,, and Dilara Ozalp for their invaluable assistance with data collection and coding. We are also deeply grateful to the families who participated in this study. This research was funded by the Scientific and Technological Research Council of Turkiye through a grant awarded to Berna A. Uzundag (Grant ID: 119K854)