SDG 9 · Sanayi, Yenilikçilik ve Altyapı · SDG

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MakaleYalnız künye2026/r/gumushane/13718
Improving the hydrothermal reduced graphene oxide supercapacitor with heat-moisture-treated potato starch as binder
Daulay, Amru; Yassaroh, Yassaroh; Samada, Lukmanul Hakim; Amrulloh, M. Hanif; Kumaş, Cavit; Nariswari, Mentari Kirana; Putri, Devi Mariska; Chambioso, Yugo
Alternative binders that can be processed in water are still in the early stages of development for more efficient and sustainable supercapacitors. However, their electrochemical performance is still below average. This study uses heat-moisture-treated starch (starch HMT) as a binder for reduced graphene oxide (rGO) supercapacitors. Starch HMT has been fabricated at 120 °C. The XRD diffractogram exhibits peaks at 2θ angles of 15°, 17°, 22°, and 24°. The 2θ values indicate that potato starch is of type B. FTIR spectra show the formation of OH, C–O–C, C-O, C–O–C, and C-H bonds. SEM images show cracks in the morphology. Electrochemical testing shows a specific capacity of 230 F/g at a current density of 1 A/g. The charge–discharge stability of the electrodes at 5000 cycles is 90%. These results offer a framework for developing electrode materials and binders for supercapacitors.
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MakaleAçık Erişim2026/r/gumushane/13706
Real maritime measurement-driven machine learning for path loss modeling in shore-to-ship wireless links
Sağır, Nuğman; Eyüboğlu, Ekrem; Kuzulugil, Kenan; Tuğcu, Zeynep Hasırcı; Çavdar, İsmail Hakkı
Reliable propagation modeling remains a fundamental requirement for the design and deployment of maritime wireless communication systems, particularly in shore-to-ship scenarios characterized by highly variable environmental conditions. Unlike conventional empirical and deterministic models that rely on theoretical assumptions, the proposed approach is entirely measurement-driven, enabling more accurate and robust propagation modeling. Four regression algorithms—Gaussian Process Regression, Least Squares Boosting, Decision Tree, and Support Vector Regression—are evaluated using a feature set that includes transmission distance, sea state, weather conditions, and antenna configurations. Among the tested models, the Decision Tree achieves the best performance, with an RMSE of 1.18 dB and an R2 of 0.96, corresponding to an error reduction of approximately 45.6% compared to the best-performing empirical model. Feature importance analysis shows that transmission distance and antenna geometry dominate signal attenuation, while environmental variables have a limited influence at this frequency. Unlike most existing studies that emphasize deep learning or black-box models, this work demonstrates that lightweight and interpretable regression models can achieve comparable accuracy while enabling real-time link planning and practical deployment. The focus is on engineering applicability rather than proposing new learning architectures. Overall, the results indicate that measurement-driven machine learning provides accurate and interpretable solutions for shore-to-ship path loss modeling under real maritime conditions.
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Kitap BölümüYalnız künye2026/r/gumushane/13704
Control Tower-Driven Multi-Echelon Inventory Positioning in Pharmaceutical Supply Chains
Şimşek, Ahmet Bahadır; Şimşek, Aybala
Pharmaceutical supply chains face high shortage risk when demand uncertainty interacts with lead-time variability, while shelf-life limits, cold-chain requirements, and quality-release delays raise the cost of both stockouts and excess inventory. This chapter proposes a control tower–driven decision-support framework for multi-echelon inventory positioning that converts visibility into actionable policies. A multi-echelon optimization module generates candidate target-stock and reorder policies across manufacturing and distribution echelons. Monte Carlo–based scenario analysis tests robustness under demand, lead-time, and disruption uncertainty. An MCDA layer then ranks policies using total cost, service level, shortage probability, expediting intensity, days of supply, and recovery time, with sensitivity analysis when weights are uncertain. The framework provides a signal-to-decision map, event-triggered policy updates, and a compact metric dictionary for shortage-risk management without proprietary field data.
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MakaleAçık Erişim2026/r/gumushane/13680
Multi-criteria techno-economic evaluation of electric buses with vehicle-integrated photovoltaic for enhanced sustainability
Başoğlu, Mustafa Engin
This study presents a multi-criteria techno-economic evaluation of electric buses (e-buses) equipped with vehicle-integrated photovoltaics (VIPV) to support the global transition toward zero-emission transportation. Aligned with international climate goals like the Paris Agreement and the European Union's 2050 carbon neutrality target, the research assesses ten 12-meter and 18-meter commercial e-bus models from various manufacturers. A computationally efficient methodology is employed, utilizing PVGIS-derived global horizontal irradiation (GHI) data specifically for Gümüşhane, Türkiye. The evaluation framework simultaneously analyzes several critical indicators: PV-based range extension, CO2 emission reduction, grid-connected charging frequency, battery depth of discharge (DoD), and payback period (PP). Findings from the baseline scenario—assuming 50% usable roof area and 22% module efficiency—reveal that e-buses can achieve a daily PV range of up to 19.16 km and annual CO2 emission savings ranging from 1709 to 2596 kg. Additionally, VIPV integration effectively mitigates battery aging by providing a peak DoD reduction of 6.91% and decreases grid-charging dependency by up to 5% during high-irradiance months. In an optimistic scenario featuring 80% usable roof area and 30.1% efficiency, the daily PV range reaches a maximum of 46 km, while the reduction in grid-charging frequency improves significantly to 21.16%. Economic assessments, including Net Present Value (NPV) analysis, confirm the investment's viability, with PPs as short as 3.76 years. This research provides a comprehensive, scalable framework and practical guidance for municipalities and fleet operators to enhance the sustainability of public transport (PT) through solar energy integration.
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Kitap BölümüYalnız künye2026/r/gumushane/13660
Bio-based and Biodegradable Nanocomposites for Clean Energy Applications
Bozbeyoglu, Pinar; Altuner, Elif Esra
The growing global demand for energy and the environmental concerns associated with fossil fuels have accelerated the search for sustainable alternatives. Bio-based and biodegradable nanocomposites represent a new generation of materials that combine ecological compatibility with superior mechanical, thermal, and electrical properties. This chapter provides a comprehensive overview of bio-based polymer matrices (such as PLA, PHA, PBS, PEF, PVA, and natural polymers like cellulose, chitosan, and lignin) and their integration with nanofillers, including graphene, carbon nanotubes, TiO2, ZnO, and nanoclay. The synergistic effects of these hybrid structures are discussed in the context of clean energy technologies, such as solar cells, fuel cells, batteries, supercapacitors, and piezoelectric nanogenerators. In addition, the chapter highlights green synthesis routes, characterization methods, life cycle assessment (LCA), and industrial perspectives, emphasizing how nanocomposite design bridges performance enhancement with environmental sustainability.
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MakaleYalnız künye2026/r/gumushane/13644
Cash holdings in an emerging market technology sector: evidence from BIST firms within the trade-off and pecking order frameworks
Pala, Fahrettin; Karalı, Muhammet; Karabayır, Mehmet Emin; Henni, Mohamed Djafar; Barut, Abdulkadir
This study investigates the determinants of corporate cash holdings in technology firms listed on Borsa İstanbul (BIST) by integrating the Trade-off Theory and the Pecking Order Theory. Using annual panel data covering the period 2013–2023, the study examines how firm-specific characteristics and macroeconomic factors influence corporate liquidity decisions in an R&D-intensive sector. The empirical analysis is conducted using a random-effects panel model estimated with Driscoll–Kraay standard errors to account for heteroskedasticity, autocorrelation, and cross-sectional dependence. The robustness of the findings is further assessed through firm fixed-effects, firm-and-year fixed-effects, one-year lagged specifications, and an interaction analysis. The results show that profitability is the most important determinant of corporate cash holdings, providing strong support for the Pecking Order Theory, whereas financial leverage exhibits a weak negative effect. In contrast, firm size, liquidity needs, R&D expenditures, dividend payments, GDP growth, and interest rates do not have statistically significant direct effects on cash holdings, while inflation exhibits weak evidence of a negative relationship. Moreover, the interaction analysis reveals that interest rates positively moderate the relationship between R&D expenditures and corporate cash holdings, indicating that innovation-oriented technology firms tend to retain larger cash reserves under tighter monetary conditions. The findings highlight that macroeconomic conditions influence corporate cash holdings not only directly but also through their interaction with firm-specific characteristics. The study contributes to the corporate finance literature by providing new evidence from an emerging market and offers practical implications for corporate managers and policymakers in designing effective corporate liquidity management strategies under changing monetary conditions.
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MakaleAçık Erişim2026/r/gumushane/13615
Integrated MCDM-based evaluation of urban intersection congestion
Tona, Aziz Ugur; Maraş, Erdem Emin
This study aimed to systematically evaluate the factors affecting traffic congestion at urban intersections and rank them based on congestion levels. Three multi-criteria decision-making (MCDM) techniques—Content validity method (CVM), best-worst method (BWM), and technique for order preference by similarity to ideal solutions (TOPSIS)—were used in an integrated framework. First, 15 potential congestion-related criteria were identified through literature and expert input. Using CVM, 8 key criteria were selected based on field-measured traffic data, official accident records obtained from relevant authorities, and structured on-site observational measurements. Their optimal weights were then calculated via a linear optimization model within the BWM. These weights were fed into TOPSIS for estimation and ranking of junction congestion. The examination also showed the Karayolları intersection to be the most congested (score: 0.711) and Toplu Konut Boulevard Intersection to be the least congested (score: 0.140). The findings provide essential information for decision-makers, aiding initiatives to enhance urban traffic flow by pinpointing crucial crossings and suggesting alternative geometric configurations. In addition, a sensitivity analysis across different weight conditions has confirmed the reliability and practical usefulness of the model. This proposed approach serves also as a tactical tool to prioritize interventions and improve the efficacy of transportation networks in urban environments.
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MakaleYalnız künye2026/r/gumushane/13612
A Dense and Sparse Attention in Vision Transformer for Remote Sensing Image Classification
Celik, Kemal
Satellite imagery plays a crucial role in various applications, such as environmental monitoring, urban planning, and disaster response. Previously, Convolutional neural networks have played a crucial role in improving remote sensing scene classification by effectively capturing spatial features. However, they often struggle with long-range dependencies and complex spectral patterns in satellite images. Recently, transformer-based architectures, initially designed for natural language processing, have been adopted in computer vision to leverage self-attention mechanisms for capturing long-range dependencies. To effectively extract the long-range dependencies and complex patterns, this paper proposes a novel method combining dense and sparse attention mechanisms to enhance remote sensing scene classification in different scales. The dense attention mechanisms capture comprehensive connections within satellite images, followed by sparse attention mechanisms applied at both tile and patch scale levels. This multi-scale attention strategy allows the model to efficiently focus on important spatial and spectral features while managing computational complexity. Extensive experimental evaluations on three benchmark datasets, such as Merced, AID, and Optimal-31, demonstrate significant improvements in classification accuracy, achieving 98.9, 97.5, and 97.8, respectively. Experimental results show that the proposed framework outperforms CNN and Transformer baselines, enhancing the accuracy and robustness of remote sensing scene classification for geospatial analysis.
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Kitap BölümüYalnız künye2026/r/gumushane/13568
Rethinking IoT Security in the Era of Quantum Computing: Literature Reviews
Hanna, Yacoub; Tonyali, Samet; Harrilal-Parchment, Ricardo; Akkaya, Kemal
As cryptographically relevant quantum computers get scaled up and stabilized, traditional cryptographic systems are one step closer to being obsolete, necessitating the development of robust and future-proof alternatives. This chapter investigates the landscape of quantum-based algorithms and protocols, with a focus on their applicability to the Internet of Things (IoT) ecosystem, ranging from low-power edge sensors to more capable embedded systems. The theoretical basis and practical applications of Quantum Resistant Cryptography (QRC) and Quantum Key Distribution (QKD) are analyzed, examining their computational requirements, energy consumption, scalability, and integration possibilities in IoT architectures with limited resources. In particular, how various Post-Quantum Cryptography (PQC) schemes, such as lattice-based, code-based, and hash-based cryptography, perform across different classes of IoT hardware is discussed. The chapter further investigates ongoing standardization efforts, implementation obstacles, and innovative solutions specifically designed for heterogeneous IoT environments. Moreover, the feasibility of QKD in resource-constrained scenarios is examined, including low-power QKD implementations for embedded and edge devices in IoT networks. Also, the trade-offs between security and efficiency are discussed, considering real-world deployment scenarios and resource constraints. Furthermore, a comprehensive threat model is presented, and the security of current literature is analyzed based on this model. By combining insights from recent literature, a comprehensive understanding of how quantum-based security methodologies can be integrated across the IoT spectrum is provided to guarantee long-term security resilience in the era of quantum computing. This chapter aims to serve as a foundational reference for researchers and practitioners seeking to build quantum-resistant or quantum-enabled IoT infrastructures.
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MakaleAçık Erişim2026/r/gumushane/13565
Energy Consumption in the EU: The Role of Productivity and Efficiency-Driven Technology; [AB’de Enerji Tüketimi: Üretkenlik ve Verimlilik Odaklı Teknolojinin Rolü]
Çakır, Mehmet Ali; Ökte, Mehmet Kutluğhan Savaş
This study investigates how productivity and productivity-intensive technologies affect energy consumption in selected EU countries from 1996 to 2021. Despite technological progress, fossil fuels still dominate, posing environmental and economic concerns. Using the AMG method, results show that multifactor productivity significantly reduces energy use, while productivity-intensive technologies offer smaller but meaningful reductions. In contrast to prior research linking technology to higher energy demand, this study highlights the importance of efficiency-focused innovation. Findings support the EU’s 2030 energy reduction target and suggest policymakers should prioritise productivity-intensive technologies, on-the-job training, and digital tools to decouple growth from energy consumption.
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MakaleAçık Erişim2026/r/gumushane/13555
Institutional and relational underpinnings of vertical FDI spillovers in international buyer-supplier relationships: Insights from automotive suppliers
Nas, Tulay Ilhan; Sahin, Fatih; Golgeci, Ismail; Okan, Tarhan
This study takes a fresh approach to explore the mechanisms of foreign direct investment (FDI) spillovers in the context of emerging economy firms, specifically focusing on the Turkish automotive industry. Given emerging economy firms' inherent constraints in R&D and resources, FDI serves as a critical avenue for them to acquire technological and managerial knowledge. While previous literature has extensively analyzed spillovers from an economic perspective, our research introduces a neoinstitutional perspective in organizational sociology to understand how these spillovers manifest and influence productivity and innovation. Importantly, we examine the role of international buyer-supplier relationships, a key aspect in shaping spillover outcomes. In so doing, we draw on rich qualitative data from 7 Turkish direct and sub-suppliers of large automotive multinational enterprises (MNEs). Our findings underscore the significant influence of the institutional context and relational mechanisms on the effectiveness of FDI spillovers, revealing why anticipated positive effects may not always materialize. This study contributes to the existing literature by addressing the limitations of prior research and provides practical insights for policymakers in emerging economies, empowering them to leverage FDI for economic growth and organizational success.
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MakaleAçık Erişim2026/r/gumushane/13526
Towards Greening the BRICS: Uncovering the Impact of Green Energy, Green Technology and Forest Cover on Environmental Quality
Henni, Mohamed Djafar; Ayaydın, Hasan; Akbulut Yıldız, Gizem; Orhan, Abdullah; Pilatin, Abdulmuttalip; Abdelkader, Salim Bourchid
The BRICS economies, facing the threat of climate change, face a policy challenge in transitioning from fossil-fuel-based energy systems and improving environmental quality. This necessitates urgent policy changes in the outdated energy infrastructure of BRICS countries. However, there still remains a policy gap regarding how countries in the BRICS, a group of rapidly developing economies, can grow their economies in line with the Sustainable Development Goals. The aim of our study is to investigate the impact of green energy, green technology, and forest cover on environmental quality in BRICS countries. The BRICS group of countries offers an ideal field of study for both examining the impacts of green energy, green technology, and forest areas on environmental quality in developing economies, as well as for evaluating national and global energy policies. Although numerous studies have empirically examined the relationship between environmental variables and green initiatives, the impact of green initiatives on the load capacity factor has been overlooked. These studies have generally used various econometric methods and have not included machine learning in the process. This study addresses this research gap by focusing on green energy and green technology, which are preferred for their various advantages and make significant contributions to the load capacity factor. To analyze this relationship in BRICS economies between 2000 and 2022, the Augmented Mean Group (AMG) estimator and Machine Learning algorithms were used. According to the results, strong evidence emerges of a positive relationship between green energy, the digital economy, forested area, and the load capacity factor, while a negative relationship exists between green technology, growth, and the load capacity factor. Based on robust empirical findings, renewable energy sources are a key driver of clean energy adoption and can ultimately increase the load capacity factor in BRICS economies. The results also imply that, since developments in green technological innovation in BRICS countries are still in their infancy, investments in green technologies for a sustainable environment need to be qualitatively increased.
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MakaleYalnız künye2026/r/gumushane/13524
Mechanical properties of polypropylene fiber-reinforced shotcrete based chromite flotation waste using a nondestructive testing method
Aksüt, Yavuz Selim; Seferoğlu, Mehmet Tevfik
This study examines the mechanical behavior and non-destructive assessment of shotcrete reinforced with polypropylene fibers (PPFs) and incorporating chromite flotation waste (CFW) as a partial substitute for fine aggregate. Shotcrete mixtures were prepared with four CFW replacement levels (0–30%) and four PPFs volume fractions (0–1%) and their compressive strength, splitting tensile strength and static modulus of elasticity were evaluated. Poisson's ratio and ultrasonic pulse velocity (UPV) were experimentally determined. The effect of fiber reinforcement and waste incorporation on the mechanical behavior was evaluated through statistical correlation, simple regression and multivariable predictive modeling approaches. The results showed that moderate PPFs contents (0.25–0.5%) generally enhanced compressive and tensile performance while excessive fiber dosage (1%) led to strength and stiffness reductions due to increased heterogeneity, fiber agglomeration and void formation at high CFW replacement levels. Increasing CFW content resulted in systematic reductions in UPV values with an average decrease of approximately 3–4% per 10% CFW replacement indicating changes in matrix continuity and internal structure. However, these UPV reductions were not directly proportional to losses in mechanical strength or elastic stiffness. Correlation analyses revealed weak to moderate relationships between UPV and mechanical properties confirming that UPV alone is insufficient to represent the complex behavior of fiber-reinforced, waste-based shotcrete systems. Simple UPV-based regression models exhibited limited predictive capability for both compressive strength (R2 ~ 0.16) and static modulus of elasticity (R2 ~ 0.002). In contrast, multivariable regression models incorporating normalized UPV, CFW replacement ratio and PPFs volume fraction together with their interaction terms, significantly improved prediction accuracy. The proposed models achieved high coefficients of determination (R2 ~ 0.86 for compressive strength and R2 ~ 0.97 for static elastic modulus) with substantially reduced root mean square error (RMSE) values. Finally, the findings demonstrate that UPV can be effectively used as a non-destructive indicator for estimating the strength and stiffness of PPFs reinforced shotcrete incorporating CFW only when material specific parameters are explicitly considered.
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MakaleAçık Erişim2026/r/gumushane/13522
In-depth Analysis of the Control Unit for Multiple Voltage Rails to Drive a Variety of ICs
Yanarateş, Cağfer; Altan, Aytaç
Generating a negative output voltage rail from a positive input voltage is possible by reconfiguring an ordinary buck regulator into an inverting buck-boost (IBB) topology. Rather than operating considerations such as voltage and current stresses, the main challenge associated with this transformation arises from differences in the small-signal closed-loop characteristics. The most critical distinction is the presence of a right-half-plane (RHP) zero in the control-to-output transfer function of the IBB, which introduces non-minimum-phase behaviour, reduces the achievable phase margin, and may lead to instability and poor load transient response. For conventional PI controllers, the buck and IBB converters resulted in settling times of 5.0 ms and 6.8 ms, with overshoots of 13.65% and 8.46%, respectively, thus demonstrating the performance limitations of fixed-gain designs. As a solution, an auto-tuned PI controller with adaptive features has been proposed, which operates effectively in both the buck and IBB topologies. The proposed scheme achieves a significantly faster settling time of 2.27 ms with overshoot and undershoot below 2%, thereby ensuring superior robustness and dynamic performance compared to conventional approaches.
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MakaleAçık Erişim2026/r/gumushane/13510
Semi-experimental approach for hydrogen storage of as-produced zeolitic imidazolate framework-8 without post-processing
Öztürk, Banu; Asan, Abdurrahman; Köse, Dursun Ali; Kalkişim, Ahmet Tahir; Öztürk, Zeynel
Low-cost hydrogen storage systems are essential for the widespread use of hydrogen, particularly in mobile platforms such as unmanned aerial vehicles, to increase their range. Hydrogen storage in metal–organic frameworks (MOFs), which offer high capacity through adsorption, is crucial to achieving this goal. This study investigates the hydrogen storage capacity of zeolitic imidazolate framework-8 (ZIF-8), produced via reactive extrusion, which allows for high production capacity and low-cost manufacturing of MOFs. Furthermore, particular attention was given to the storage capacity of ZIF-8 in its as-produced form. An atomistic model of the as-produced ZIF-8, including impurities selected from a series of simulated models containing different amounts of impurity, was developed. The validity of the selected structure was then verified through experimental powder X-ray diffraction and Brunauer–Emmett–Teller (BET) surface area measurements. Subsequently, hydrogen adsorption capacities for varying pressures and temperatures were calculated. The hydrogen adsorption simulations indicated that the atomistic model of the as-produced ZIF-8 exhibited a hydrogen storage capacity of 1.76 wt.% at 77 K and 1 bar, corresponding to a BET surface area of 516 m2/g. In comparison, the experimentally synthesized as-produced ZIF-8 showed a capacity of 1.145 wt.% under the same conditions, with a BET surface area of 520 m2/g. The study demonstrates that the gas storage performance of experimentally produced structures, even without further purification, can be accurately determined through simulation models in this semi-experimental study.
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MakaleAçık Erişim2026/r/gumushane/13491
Thermochemical Activation of Lightweight Slag–Perlite Alkali-Activated Slag (AAS): Overcoming Aggregate Brittleness and Sulfate Degradation
Eker, Hasan; Demir Şahin, Demet
The successful realization of a circular economy in the cement industry, coupled with a substantial reduction in carbon emissions, relies on the development of sustainable alternative binder systems. This study investigated the physicomechanical performance and sulfate resistance of composites produced by alkali activation of natural perlite and blast furnace slag. The aim of the research was to improve mechanical properties under low- and medium-alkalinity conditions (5–10 M NaOH). The samples were cured at an ambient temperature of 20 °C and then treated with heat at 60 °C. These samples were then mechanically processed and subjected to five soak–dry cycles in 5% and 10% Na2SO4 solutions. The results showed that heat treatment resulted in the formation of a dense C-A-S-H gel, increasing compressive strength approximately eightfold, from 11.64 MPa to 92 MPa. However, perlite’s porous and brittle structure limits its flexural strength to 0.27 MPa; this value is insufficient for structural applications. Under severe sulfate attack (10% Na2SO4), samples cured at ambient temperature showed a 12% mass increase in the first cycle due to solution infiltration into capillary voids. As a consequence of extensive ettringite and gypsum formation, the specimens experienced severe deterioration, resulting in a complete loss of mechanical integrity and a residual compressive strength of 0 MPa. In contrast, heat-treated samples showed limited ion diffusion due to a denser matrix and an improved aggregate interface transition zone, resulting in a 2.6% mass increase and a residual compressive strength of 5.17 MPa. Consequently, the obtained findings indicate that thermally treated alkali-activated slag–perlite composites exhibit high resistance against sodium sulfate attack and may have potential for use in specific industrial environments with high sulfate concentrations. However, the performance of these materials under more complex aggressive conditions, such as mining environments involving magnesium sulfate exposure and acidic drainage waters, should be further validated through future studies.
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MakaleYalnız künye2026/r/gumushane/13482
Impact of soil-structure interaction on earthquake response of high-rise steel frames equipped with TMD and FTMD optimized through cloud drift optimization
Alibabaei Shahraki, Mohammad; Araz, Onur; Ahmadi, Hossein
Vibration control of tall buildings under various seismic and foundation conditions remains a critical issue in earthquake engineering. This paper investigates the performance of Tuned Mass Damper (TMD) and Friction Tuned Mass Damper (FTMD) systems in reducing the seismic response of fixed-base and Soil-Structure Interaction (SSI) analyses of 20- and 40-storey steel moment-resisting frames. The current study uses the OpenSees software and the Cloud Drift Optimization (CDO) technique, aiming at minimizing the normalized H 2 -norm of the roof displacement. The performance of the seismic control is investigated by performing nonlinear dynamics analyses with the help of a set of 14 records, of which 7 records are far-field and the remaining ones are near-field records. Results show that both TMD and FTMD are effective in decreasing roof displacement on 20- and 40-story buildings subjected to fixed-base and SSI excitations. More precisely, results for the average reduction in roof displacement are 33.4% and 27% when FTMD is subjected to SSI loading, while those values were 10.7% and 16.5% when TMD was subjected to SSI loading in the case of 20- and 40-story buildings, respectively. Results are based on multiple variables including base shear, kinetic energy, and damping energy. The results demonstrate that the FTMD system provides superior seismic response reduction compared to the conventional TMD.
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MakaleAçık Erişim2026/r/gumushane/13458
Critical Success Factors for Building Resilience in Circular Supply Chains of Electric Vehicle Batteries: Evidence from an Emerging Country
Erol, Ismail; Oztel, Ahmet; Peker, Iskender; Ar, Ilker Murat; Searcy, Cory
Given the global expansion of electric vehicles (EVs), decision makers in developing and emerging countries must address important challenges across EV battery supply chains (EVBSCs) toward circularity. For example, batteries usually make up about 40% of an EVs’ value, and the race to achieve net zero emissions will further underscore the critical need for vital minerals and metals, such as lithium, cobalt, and graphite, necessary to make batteries. Stakeholders routinely question the resilience of circular (C) EVBSCs worldwide, from mining valuable materials to manufacturing the batteries necessary to support the widespread deployment of EVs. Identifying and investigating critical success factors (CSFs) of any system is a necessary step in achieving its targets. Little research, however, has been performed to investigate the CSFs for building resilience in EVBSCs, particularly those focused on building a circular supply chain. The goal of this research is, therefore, to systematically scrutinize the CSFs of resilient C-EVBSCs in Türkiye. To this end, a decision framework applying inter-valued neutrosophic ISM-MICMAC is proposed. Based on expert opinions, an application of the decision framework finds that effective government policies, directives, and incentives and well-established dynamic capabilities, are key driving CSFs to building resilience in a C-EVBSC.
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MakaleYayıncıda açık erişim2026/r/gumushane/13454
Measurement-aware zero-phase iterative learning control for robust regulation of nonideal boost converters in electric vehicle fast charging
Altan, Aytaç; Alzaidi, Mohammed S.; Yanarateş, Cağfer
Integrating battery energy storage with DC-DC boost converters for electric vehicle fast charging exposes the regulator to ageing-induced parameter drift, periodic load pulses, and, critically, the nonidealities in the output-voltage sensing chain. This paper proposes a measurement-aware, zero-phase iterative learning control scheme for robust output-voltage regulation of a nonideal boost converter whose parameters are matched to those of a commercial Texas Instruments TPS6102x battery regulator. The controller combines an inner proportional-integral stabilizing loop with an outer zero-phase learning law that updates a feedforward correction based on the sensor-captured output trajectory; a forward-backward robustness filter suppresses the amplification of measurement noise and quantization across successive trials without introducing phase lag. The averaged small-signal model is validated against a 960 kHz switched simulation, with agreement within 0.2% at the operating point and a transient-waveform correlation of 0.91, while the bandwidth limitations imposed by the lightly damped resonance and the nonminimum-phase dynamics on feedback are quantified from the loop frequency response. Because feedback alone is bandwidth-limited, the learning feedforward recovers the intrinsic converter speed for the repetitive task and reduces the periodic tracking error by more than an order of magnitude relative to a fairly tuned baseline. A bounded-error convergence theorem shows that, under real sensing, the tracking error converges to a neighbourhood whose size is proportional to the measurement-disturbance level; this is confirmed numerically, with the asymptotic error scaling approximately linearly with the noise standard deviation once the noise dominates the filter bias. Systematic sweeps of converter resolution, noise, and sensor delay show that the zero-phase measurement-aware law remains bounded where a naive learning law diverges, and a 500-sample Monte Carlo study confirms robustness to component tolerances, load variation, and parasitic aging. The primary physical task is the rejection of a repeating high-current charging pulse: over successive charging cycles the scheme reduces the periodic output-voltage sag from 35% to below 0.01%, whereas the best feedback comparator leaves a 17% residual, and the sinusoidal reference used elsewhere serves as a spectral diagnostic that isolates the feedback-bandwidth ceiling at a single known frequency. The learning law is further verified in closed loop on the cycle-by-cycle switched large-signal converter, where it converges and stays bounded. A per-unit scale-invariance analysis then establishes the conditions under which the design obtained on the low-power benchmark transfers to fast-charging power levels, as confirmed on a 400 V, 6.1 kW stage that reproduces the benchmark convergence exactly.
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MakaleYalnız künye2026/r/gumushane/12284
Variable-speed retrofit of a legacy slurry pumping duty: Measured energy reduction with proxy-based operability assessment
Külekçi, Gökhan; Aygün, Muhammet; Göller, Zafer; Yildiz, Onur
Legacy ore beneficiation plants often operate slurry pumps at fixed speed despite variable sump inflow and slurry conditions, which can increase electricity demand and complicate level control. This study evaluates a variable-speed retrofit in which a variable frequency drive was integrated with level feedback and proportional-integral control for a legacy slurry pumping duty. Measured average operating power decreased from 25.6 kW before retrofit to 11.6 kW after implementation, documenting a substantial reduction in electrical demand under the observed operating regime. Because hydraulic-duty data were unavailable for both periods, this result is not presented as a duty-normalised specific-energy reduction. Process interpretation was supported by a bounded proxy framework based on a flowsheet-consistent solids band over 177 days. The proxy analysis linked solids variability to derived slurry density behaviour and operability implications. Overall, the study documents measured electrical-demand reduction and provides a framework for discussing retrofit performance where plant instrumentation is limited.
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