WoS İndeksli Yayınlar Koleksiyonu

Permanent URI for this collectionhttps://hdl.handle.net/20.500.12469/4465

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Policy Advice and Capacity
(Edward Elgar Publishing Ltd, 2025) Bali, Azad Singh; Coban, M. Kerem
Citation - WoS: 13
Citation - Scopus: 13
Retracted: A Nano-Scale Design of a Multiply-Accumulate Unit for Digital Signal Processing Based on Quantum Computing (Retracted Article)
(Springer, 2024) Ahmadpour, Seyed-Sajad; Navimipour, Nima Jafari; Yalcin, Senay; Bakhshayeshi Avval, Danial; Ul Ain, Noor
Digital signal processing (DSP) is used in computer processing to conduct different signal-processing tasks. The DSPs are used in the series numbers representing a continuous variable in a domain such as time, area, or frequency. The multiply-accumulate (MAC) unit is crucial in various DSP applications, including convolution, discrete cosine transform (DCT), Fourier Transform, etc. Thus, all DSPs contain a critical MAC unit in signal processing. The MAC unit conducts multiplication and accumulation operations for continuous and complicated DSP application processes. On the other hand, in the MAC structure, the stability of the circuit and the occupied area pose some significant challenges. However, high-performance quantum technology can easily overcome all the previous shortcomings. Hence, this paper suggests an efficient MAC for DSP applications using a Vedic multiplier, half adder, and accumulator based on quantum technology. All the proposed structures have used a single-layer layout without rotated cells. The suggested architecture is designed and validated based on the QCADesigner 2.0.3 tool. The findings revealed that all the developed circuits have a simple architecture with fewer quantum cells, optimal area, and low latency.
Macroeconomics of Greening Turkish Agriculture: A General Equilibrium Analysis of Input Rationalization Policies
(Routledge Journals, Taylor & Francis Ltd, 2026) Yeldan, A. Erinc; Karakoc, Ulas
With the aid of an applied general equilibrium model, we study the macroeconomic effects of various policy alternatives to stimulate the implications of the greening of Turkish agriculture. Our results suggest that the reduction in chemicals, including synthetic fertilizers, and fossil oil combustion at alternative rates of 30% and 50% would significantly reduce carbon emissions, but at the expense of adverse effects on agricultural output. In response, the negative effects on agricultural output can be reversed by a targeted investment programme that could facilitate technological change and a commensurate rationalization of the rural economy, resulting in enhanced gains in agricultural productivity. We argue that the warranted funds towards such productivity-enhancing investments can be earmarked by the introduction of a nationwide carbon tax, and that they would boost not only agricultural output and rural incomes but could also mitigate the adverse transition costs on GDP and social welfare.
Fourier's Law Breakdown for the Planar-Rotor Chain with Long-Range Interactions
(Elsevier, 2026) Lima, Henrique Santos; Tsallis, Constantino; Eroglu, Deniz; Tirnakli, Ugur
Fourier's law, which linearly relates heat flux to the negative gradient of temperature, is a fundamental principle in thermal physics and widely applied across materials science and engineering. However, its validity in low-dimensional systems with long-range interactions remains only partially understood. We investigate here the thermal transport along a onedimensional chain of classical planar rotators with algebraically decaying interactions 1/ with distance ( >= 0), known as the inertial a-XY model. Using nonequilibrium simulations with thermal reservoirs at the boundaries, we numerically study the thermal conductance as a function of system sizea, temperature , and . We find that the results obey a universal scaling law characterized by a stretched-exponential function with -dependent parameters. Notably, a threshold at approximate to 2 separates two regimes: for >= , Fourier's law holds with size-independent conductivity = , while for < , anomalous transport is observed, corroborating (with higher precision) the results reported in Phys.Rev.E94,042117(2016). These findings provide a quantitative framework for understanding the breakdown of Fourier's law in systems with long-range interactions. The simulation is carried out by assuming the equations of motion, which include Langevin heat baths applied to the first and last particles, and are integrated using the Velocity Verlet algorithm. The conductance is calculated from the connection between Lagrangian heat flux and heat equation for typical values of (, , ). For large , the results can be collapsed into an universal -stretched exponential form, namely proportional to -() , where = [1 + (1-)]1/(1-). The parameters (, , ,) are -dependent, and is the index of the -stretched exponential. This form is achievable due to the ratio /( - 1) being almost constant with respect to the lattice size. These findings provide significant insights into heat conduction mechanisms in systems with long-range interactions.
High-Performance and Low-Power Quantum-Dot Multiply-Accumulate Design for Next-Generation Supercomputing Platforms
(Springer, 2026) Ahmadpour, Seyed-Sajad; Zohaib, Muhammad; Rasmi, Hadi; Jafari Navimipour, Nima
The rapid growth of high-performance computing (HPC) and supercomputing applications necessitates hardware architectures that provide both high computational performance and strong energy efficiency under real-time and massively parallel workloads. However, conventional complementary metal-oxide semiconductor (CMOS) technologies face fundamental challenges, including excessive power consumption, leakage currents, and severe scaling limitations, which restrict their suitability for future exascale systems. To overcome these limitations, emerging nanotechnologies such as Quantum-dot Cellular Automata (QCA) have gained significant attention due to their ultra low-power consumption and high device density. In this work, we present a high-performance and low-power Quantum-Dot Multiply-Accumulate (Q-Dot MAC) unit, where MAC denotes a fundamental arithmetic operation combining multiplication and accumulation, extensively used in scientific computing, and signal processing. The proposed QCA-based architecture is specifically designed to satisfy the high-frequency (HF) operational demands of modern HPC environments, enabling sustained high-throughput computation. The main objective of this design is to realize a compact, energy-efficient, and physically stable MAC unit suitable for large-scale deployment in energy-constrained supercomputing platforms. Exploiting the inherent parallelism and high-density layout characteristics of QCA, the proposed MAC architecture efficiently executes key computational kernels required in HPC workloads, including large-scale matrix multiplication, convolution operations, and scientific simulations. The proposed QCA-based circuits demonstrate significant performance and area efficiency improvements compared with the best existing designs in the literature. Specifically, the half adder (HA) achieves a 20.51% reduction in cell count and a 25% reduction in area, and the complete MAC unit provides a 22.84% decrease in cell count, a 9.03% reduction in occupied area and a 14.25% in delay. These results confirm the efficiency and scalability of the proposed design. The low-area enables the integration of large arrays of MAC units, facilitating scalable systolic and Single Instruction Multiple Data (SIMD) architectures required in supercomputing environments.
Distinct Temporal Dynamics of Speech and Gesture Processing: Insights From Event-Related Potentials Across L1 and L2
(American Psychological Association, 2026) Ozer, Demet; Soyman, Efe; Badakul, Ayse Nur; Arslan, Burcu; Yilmaz, Fatma Sena; Goksun, Tilbe
This study examined the neural and behavioral processing of speech and iconic gestures across L1-Turkish and L2-English when participants attended the speech or gesture channel. We recorded electroencephalogram activity in Experiment 1 and reaction times in Experiment 2 (24 participants in each) during a mismatch task where concurrent speech and gesture expressed either matching or mismatching information in relation to a preceding action. Participants were asked to detect whether the gesture (gesture-focused task) or the speech (speech-focused task) was related to the preceding action. Speech was presented in Turkish or English in separate blocks. In Experiment 1, we focused on N400 and N2 components as indices of late semantic processing and early sequential matching, respectively. In the gesture-focused task, our results demonstrated a gesture mismatch effect, which was evident in more negative N400 amplitudes for mismatching than matching gestures only in the context of simultaneous matching speech. In the speech-focused task, we observed the N2 effect, which was apparent in more negative N2 amplitudes for mismatching than matching speech, regardless of the simultaneous gesture. These dynamics were largely reflected in reaction times in Experiment 2. These results point to potentially distinct neural and temporal dynamics in processing speech versus gestures and suggest that speech processing might be instantiated earlier, whereas gestures recruit later stages of processing. Notably, we observed some differential patterns across L1-Turkish and L2-English, suggesting that speech and gesture processing may operate differently across languages. Our findings highlight a complex interplay between modality, modality focus, language, and neural processing of multimodal information.
Multimodal Feedback in Automated Training Systems for Cardiopulmonary Resuscitation: A Review
(IEEE-Inst Electrical Electronics Engineers Inc, 2026) Sarac, Mine; Cetin, Sevval; Aslan, Baran; Tas, Ali; Stroppa, Fabio
Incorporating multimodal feedback in automated cardiopulmonary resuscitation (CPR) training systems has emerged as a crucial advancement in medical education, aiming to improve the quality and efficacy of CPR performance. This work presents an extensive and technical literature search on automated training CPR systems, specifically focusing on the feedback modality they provide during training sessions. We completed the search in the IEEE, ACM, Springer, SAGE, Elsevier, MDPI, Scholar, and Scopus databases between 2015 and 2025 (August) using the keywords "CPR," "Virtual Reality (VR)," "Augmented Reality (AR)," "feedback," and "training." We categorized our findings based on the type of feedback provided (i.e., visual, audio, haptic, or a combination of these), the display type used to render the feedback, the technology used to monitor the trainee's performance, and the type of manikin used. We conclude with recommendations for future research.
Energy-Efficient Code Conversion Using Quantum-Dot Nano-Architectures for Internet of Things (IoT) Applications
(Springer, 2026) Chugh, Hemanshi; Patidar, Mukesh; Ahmadpour, Seyed-Sajad; Zohaib, Muhammad
Internet of Things (IoT) is a developing technological trend in which common real-world objects are connected with technologies of sensors, actuators, and communication units, allowing data collection, sharing, and processing via the Internet. One of the important circuits in IoT systems is code converter circuits, which are critical components in data formatting, arithmetic processing, and error-resistant processing in these systems, and hence, have a direct influence on performance and energy resources. However, complementary metal-oxide-semiconductor-based realizations of these converters suffer important drawbacks, including high occupied area, increased power dissipation, propagation delays, and longer latency, and are not suitable in ultra-compact and energy-constrained IoT devices. To overcome these challenges, emerging technologies like the quantum-dot cellular automata (QCA) are offering new alternatives, featuring ultra-low power consumption, low area, and high processing speed, which would make QCA technologies suitable for next-generation IoT applications. This paper proposes high-density and power-efficient bidirectional code converter circuits (BCD to Excess-3-B2X3C and Excess-3 to BCD-X3B2C converters) utilizing QCA technology. In addition, significant improvements are demonstrated by the comparative evaluations, which include an improvement of 7.89% in cells, a reduction of 25% in area-delay cost (A x D2), and a 43.75% in figure of merit (FoM), respectively. Additionally, QCADesigner and QCAPro simulation and power analysis indicate that the switching energy is generally low throughout a wide variety of tunneling energies. The presented QCA-based single layer is more scalable than current designs, which makes it suitable for future IoT integration.
Virtual Reality Headsets for Employee Training in Enterprises: Fuzzy SRP Data-Driven Framework for a Comprehensive Evaluation
(Springer London Ltd, 2026) Aycin, Ejder; Asan, Hakan; Ecer, Fatih; Gorcun, Omer Faruk; Ulutas, Alptekin; Pamucar, Dragan
Virtual reality (VR) is progressively transforming employee training in companies by offering immersive and engaging learning experiences. Nevertheless, the selection of an appropriate VR headset is vital for optimizing training effectiveness. This paper addresses this issue by proposing a novel hybrid fuzzy multi-criteria decision-making model that integrates the improved fuzzy stepwise weight assessment ratio analysis (IF-SWARA) with the fuzzy simple ranking process (F-SRP). The IF-SWARA methodology is employed to compute the relative weights of the selection criteria for VR headsets utilized in employee training, whereas the newly developed F-SRP is implemented to rank the various VR headsets. By employing the IF-SWARA method, the model offers a more nuanced understanding of criteria weights, thereby reflecting the differing significance of various headset features. The research's novelties and contributions are as follows: (1) This study is the first to select VR headsets by applying multi-criteria methods. (2) The F-SRP model is developed for the first time in the literature. (3) The introduced F-SRP methodology allows for a comprehensive ranking of the available VR headsets, facilitating informed decision-making. The paramount indicators for selecting VR headset options for training in enterprises consist of technical specifications, comfort and ergonomics, and screen specifications. The results obtained from the fuzzy SRP indicate that the Apple Vision Pro surpasses the other alternatives. Finally, the robustness and applicability of the proposed model are evaluated through an exhaustive sensitivity analysis. This research possesses broader implications for VR training in enterprises by providing a robust and reliable framework, ultimately contributing to the development of more effective and impactful VR training programs.
Personal and Collective Memories and Future Thoughts: A Laboratory Study of Episodic and Non-Episodic Detail
(Sage Publications Inc, 2026) Cheriet, Nawel; Oner, Sezin; Watson, Lynn; Cole, Scott
Self-based mental time travel - the ability to remember past events and imagine future events on a personal timeline - is well-characterized in cognitive science. A similar, but less-understood, ability is that of collective memory and collective future thinking, termed collective mental time travel (CMTT). To our knowledge, this is the first study to investigate the episodic richness of collective memory and future thoughts using an in-person laboratory paradigm. In two studies (UK and Turkey), we examined the effect of Event Type (collective, personal; between-groups) and Temporal Orientation (past, future; within-groups) on quantities of episodic and non-episodic details. Results show that personal events contained more episodic detail compared to collective events, and past events were associated with more episodic detail than future events. The distinction between personal and collective events was more pronounced in the UK than in Turkish sample, hinting at an influence of cross-cultural context on the episodicity of collective memories and future thoughts. Additionally, we observed a relationship between the episodicity of the past and the future exclusively in the UK population and for personal events, partially supporting the constructive episodic simulation hypothesis. These findings initiate a deeper understanding of the underlying cognitive processes that enable humans to engage in collective mental time travel.
Integrating Stable Diffusion via Remote Server APIs for Enhanced Parametric Design Workflows
(Sage Publications Ltd, 2026) Gokmen, Sabri; Alsan, Huseyin Fuat; Arsan, Taner; Ozen, Figen; Keskin, Ebru Ece
The current advancements of deep learning models offer potential applications for computational design through sets of generated images controlled by parametric inputs, yet they remain disconnected from geometry-driven parametric tools. For this reason, we study the implications of text and image-based generation methods to be used in traditional parametric design procedures. We implement this study by integrating Stable Diffusion and ControlNet to Rhino Grasshopper through a Python-based remote-API plug-in. This API allows a direct connection to the diffusion-based image generation methods without any middleware. Our main contribution is to enable architects and designers to interactively generate and investigate new design ideas in their native parametric design environment. We evaluate potential impact on parametric design education with 15 architecture students using a single GPU server running Stable Diffusion v1.5 across three exercises: Text-to-Image, Image-to-Image using Rhinoceros view captures, and Parametric-Model-to-Image with ControlNet. Quantitative results showed that the API-enabled image generation averaged 4-15 seconds per image, allowing seamless integration with parametric workflows for all 15 students in a classroom setting. Performance evaluations show that our approach offers significantly improved efficiency and responsiveness compared to existing diffusion-based tools, highlighting its suitability for seamless integration within parametric design environments. Qualitative feedback indicated improved design ideation, greater fluency in prompt engineering, and enhanced understanding of parametric logic through iterative visual experimentation. These findings demonstrate the potential of real-time AI integration to augment both conceptual design and parametric design education.
Light Is Not Always Right: Peri-Iridial Lightness Reduces Attractiveness via Perceived Sex-Typicality Across Human Populations
(Springer, 2026) Fiala, Vojtech; Perea-Garcia, Juan Olvido; Turecek, Petr; Wacewicz, Slawomir; Leongomez, Juan David; Pokorny, Simon; Kleisner, Karel
Evolutionary psychology views the human eye as special. In particular, it claims that the light peri-iridial tissues surrounding a relatively darker iris form a combination that sets us apart from other primates. From this perspective, much less attention has been paid to how eye colouration varies between humans, although evidence indicates that variations in peri-iridial and iridial colouration influence both perceived facial attractiveness and sex-typicality. To determine what aspects of eye colouration influence the perception of faces, we have measured the colour of peri-iridial eye tissues ('the white of the eye') and the iris in nine samples from seven distant cultures (N = 1033) across three continents. The faces were rated on facial attractiveness and sex-typicality by raters from the corresponding populations. Accounting for the effects of skin lightness, age, and facial shape, we ran a Bayesian multilevel model to estimate global and sample-specific effects of colouration of the iris and peri-iridial tissues on perceived sex-typicality and facial attractiveness. This exploratory, cross-sectional study revealed an overall preference for slightly darker peri-iridial tissues in women, whereby this association was mediated by perceived sex-typicality. Our findings challenge the notion that the light-eyed phenotype is universally preferred by human raters. Instead, they suggest a preference for a moderate phenotype, perhaps because very light peri-iridial tissues are typical of faces which are generally perceived as less feminine. Women with bluer irises were generally perceived as more attractive but findings related to other colour channels and iris features were inconsistent and varied across samples.Significance statementThe morphological variation of human eyes is an understudied phenomenon. While attention has been paid to the alleged uniqueness of human eyes (compared to other primates), little is known about how variations in eye colouration influence human perception of faces. Our study included over 1000 individuals from seven culturally distinct regions, mapped human eye variation, and tested how eye colouration influences perceived attractiveness and sex-typicality. In humans, variation in eye colouration is relatively large and differs across populations. Our findings suggest that it affects the perception of faces. Paradoxically, darker peri-iridial regions (scleras) slightly enhance the perception of femininity and female attractiveness, which challenges the idea that lighter eyes are universally preferred. Moreover, blue/light irises were in some contexts linked to a higher attractiveness. These results further refine our understanding of the role of eye colouration in perceived attractiveness.
Adaptive Segmentation of IIoT Time Series Data via Change Point Detection for Machinery Fault Classification
(IEEE-Inst Electrical Electronics Engineers Inc, 2026) Balli, Tugce; Kacar, Saygin; Yetkin, E. Fatih
Predictive maintenance (PdM) is a critical concept in Industry 4.0 that aims to improve manufacturing processes by predicting the remaining useful time of machinery. The development of PdM models relies on access to sufficient data, including condition monitoring and maintenance data from industrial applications. One of the critical aspects of modern PdM approaches is the classification of potential fault signals using time series data collected from IoT devices. However, in most cases, the non-stationary nature of these time series data often causes difficulties with the validity of traditional segmentation techniques when applied to such dynamically changing data patterns. In this work, we propose an adaptive segmentation approach through change point detection to address the inherent non-stationarity of time series, thereby improving the classification performance of traditional classifiers for fault detection problems. By using an adaptive segmentation scheme, we aim to extract more relevant features that will lead to improved classification performance. Taking the time-sensitive nature of the problem into account, we employed three well-known change point detection algorithms (Pruned Exact Linear Time -PELT- algorithm, Binary Segmentation, and Bottom-up segmentation). The effectiveness of the proposed methods is demonstrated by experiments using two different datasets widely used in the PdM literature.
Retraction: A Nano-Scale Design of a Multiply-Accumulate Unit for Digital Signal Processing Based on Quantum Computing
(Springer, 2024) Ahmadpour, Seyed-Sajad; Navimipour, Nima Jafari; Yalcin, Senay; Avval, Danial Bakhshayeshi; Ul Ain, Noor
Notum1a Inhibition Promotes Neurogenesis in the Adult Zebrafish Brain
(Nature Portfolio, 2025) Kocagoz, Yigit; Erdogan, Nuray Sogunmez; Ozdinc, Sevval; Ipekgil, Dogac; Katkat, Esra; Ozhan, Gunes
Notum is a carboxylesterase enzyme that modulates extracellular signaling by hydrolyzing palmitoleoyl residues from proteins, thereby influencing key pathways involved in cell differentiation, survival, and proliferation. While notum1 expression has been identified in the brain, its role in adult neurogenesis remains poorly understood. Using the adult zebrafish brain as a model system, we demonstrate that the notum1a homolog is broadly expressed across various brain cell types but is absent in undifferentiated radial glial cells. Pharmacological inhibition of Notum activity with the small molecule inhibitor ABC99 stimulates activation of radial glial cells, leading to increased neurogenesis. A BrdU pulse-chase assay confirms that ABC99-induced proliferation enhances the production of mature neurons. Despite Notum's established role in Wnt signaling, transcriptional analysis following ABC99 treatment reveals no sustained impact on Wnt pathway targets, suggesting that Notum may regulate neurogenesis through alternative mechanisms. Our findings highlight notum1a as a potential modulator of neural progenitor cell dynamics in the adult brain and suggest that targeting Notum could represent a novel therapeutic strategy for neurodegenerative conditions characterized by impaired neurogenesis.
Revising Humanitarianism and Solidarity Migration Management and Peripheral Europeanism in the UK, Poland, and Hungary
(Routledge, 2025) Foley, James; Gyollai, Daniel; Szalanska, Justyna
This chapter addresses three cases where governments have adopted explicitly Euro-critical or anti-EU stances linked to migration. The primary aim was to understand how nations that reject the established European narrative of international protection have framed their obligations to alleviate the suffering of war and conflicts. This has been broken into three conceptual areas for comparative purposes: humanitarianism, solidarity, and sovereignty. While observing areas of distinction between these states and the EU, the analysis suggests the difficulties involved in hardened contrasts between a cosmopolitan-humanitarian EU and national-sovereigntist states. Instead, the chapter presents a more nuanced picture of how states have developed distinct accounts of humanitarianism and international order. Moreover, there is considerable evidence that narratives of Europeanness developing on the liminal periphery have been reshaping core notions of " European" identity embodied in the official pronouncements of the Commission.
Connecting Neighborhoods with Worksites: Coercion, Labor Migration, and Shipbuilding Workers in Late Ottoman İstanbul
(Cambridge University Press, 2025) Sefer, Akin
This article highlights the state's labor coercion practices and their perpetual characteristic in defining the history of migration and migrants' experiences in the city. It underlines the internal relationship between production processes and relations on the one hand and the mobility of workers and their families on the other. For this purpose, it focuses on the migration dynamics of shipbuilding workers in mid-nineteenth-century & Idot;stanbul, most of whom worked in the Imperial Arsenal (Tersane-i Amire) and dwelled in the neighborhoods of the surrounding quarter of Kas & imath;mpa & scedil;a. I will utilize the population records of one of these neighborhoods, the Seyyid Ali & Ccedil;elebi, where the relationship between the worksite and the residential community was evident, and the wage records of the Imperial Arsenal to understand the relationality of migration and work processes. Based on an analysis of these sources, I will point to the connections between the configuration of migration networks built in or through the Arsenal and the settlement patterns in the neighborhood. I will particularly argue that relations at the workplace and the coercive dynamics that underlined these relations significantly impacted the migration and settlement patterns in the nineteenth century.
Reputation and Energy-Aware Dynamic Hybrid Consensus (Read-HC) Model for IIoT
(British Blockchain Association, 2025) Mitra, Sristi; Sable, Vaishnavi Santosh; Syed, Danium Shahnowaz
Industry 4.0, the fourth industrial revolution, advances (Industrial IoT) IIoT with autonomous, adaptive systems capable of self-learning and self-healing. Blockchain technology offers a decentralised, safe, and auditable framework to exchange and authenticate data through transactions without relying on third parties. Private blockchains, with flexible rules and strong privacy, could therefore be deployed inside IIoT systems to address security concerns and process large volumes of data. Traditional consensus mechanisms in blockchain, such as PoW and PoS, are computationally demanding and energy-intensive and may not be suitable for resource-constrained IIoT scenarios. Centralised architectures are also vulnerable in terms of single-point failures. Therefore, this article introduces a novel blockchain-based approach, Reputation and Energy-Aware Dynamic Hybrid Consensus (READ-HC), that integrates Practical Byzantine Fault Tolerance, Proof of Reputation for emphasising reliable nodes, and an energy-efficient mechanism that preserves resources by dynamically regulating node involvement. This model shows high scalability, low latency, enhanced security, and high energy efficiency, which we found through conducted simulations. READ-HC outperforms traditional consensus mechanisms regarding communication complexity, consensus throughput, and its adaptability to network condition variations. This makes it a viable solution for secure and efficient IIoT networks.
Ant Colony Optimization Algorithm for the Futoshiki Puzzle
(Gazi University, 2025) Sen, Banu Baklan; Yasar, Oznur
Futoshiki is a computationally hard problem belonging to the Latin Square Completion-type Puzzles. It is played on a partially filled n x n grid that may include inequality constraints between cells. The objective is to complete the grid such that each row and column contains the integers from 1 to n exactly once, while also satisfying all inequality constraints. In this work, we propose FutoshikiACO, an Ant Colony Optimization-based algorithm to solve Futoshiki instances of fixed size. We evaluate the performance of this stochastic method through computational experiments. Compared to existing deterministic approaches, FutoshikiACO explores a significantly reduced search space. Our results not only demonstrate the inherent complexity of the Futoshiki problem but also highlight the types of instances where ant colony-based metaheuristics are particularly effective in solving such constraint satisfaction problems.
Selection of Underground Hydrogen Storage Systems Using a Novel Fuzzy Model
(Pergamon-Elsevier Science Ltd, 2026) Gorcun, Omer Faruk; Demir, Gulay; Pamucar, Dragan; Simic, Vladimir
Storing hydrogen resources underground can accelerate the transition to renewable energy, facilitate energy supply security, and the adoption and expansion of hydrogen energy, a clean energy source. The selection of sustainable underground hydrogen storage systems is a critical research topic for addressing environmental issues caused using fossil fuels. However, decision-makers still lack a consensus-based and sustainability-oriented framework that can comparatively evaluate alternative underground hydrogen storage geological formations under economic, environmental, social, and technical uncertainties, which constitutes a critical barrier to largescale hydrogen deployment. This issue has become more prominent as fossil-based fuel reserves are gradually decreasing worldwide. In contrast, researchers and practitioners lack a consensus on which underground storage method is most suitable for economical, safe, and efficient hydrogen storage. If this problem is not addressed correctly and reasonable solutions are not obtained, continued dependence on fossil fuels may persist. Alternatively, other renewable energy sources with relatively lower efficiency and performance may be adopted. In both cases, significant delays in achieving the global sustainability goal are likely to occur. We propose an integrated fuzzy decision-making framework (F-WENSLO & Dombi-Bonferroni & F-ARTASI) to address this selection problem under uncertainty. The proposed framework integrates fuzzy WENSLO (Weights by ENvelope and SLOpe) for robust sustainability-based criteria weighting, the Dombi-Bonferroni aggregation operator to model interdependencies among criteria explicitly, and the fuzzy ARTASI (Alternative Ranking Technique based on Adaptive Standardized Intervals) method to provide flexible and stable ranking of geological alternatives beyond rigid distance-based approaches. Key advantages of the proposed model include producing reliable and consistent solutions that accurately reflect real-world conditions for selecting sustainable underground hydrogen storage systems. The results revealed that C14 (job creation and employment opportunities) (0.0603) is the most influential criterion in selecting the most suitable storage system. In addition, salt caverns with an Omega i of 10,5167 have achieved the highest score, placing them in the first position, and it is the most suitable and advantageous underground hydrogen storage option. The suggested decision-making tool can yield reliable and robust solutions in real-world conditions, enabling the planning of infrastructure design for hydrogen energy systems that incorporate sustainability dimensions. In that regard, the developed model possesses the characteristics of an efficient and practical roadmap that can guide policymakers and decision-makers in transitioning from fossil-based energy sources to renewable energy sources. It has been implemented to evaluate underground geological formations that could facilitate the storage of hydrogen energy underground, serving as a case study. The reliability and robustness of this tool have been verified through extensive validation tests.

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