WoS İndeksli Yayınlar Koleksiyonu

Permanent URI for this collectionhttps://hdl.handle.net/20.500.12573/394

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Wireless Communication System Design for Point Machine Detection and Monitoring in Railways
(Springer, 2026) Talu, Burak; Cetin, Fatih; Kilic, Veli Tayfun; Elden, Burakhan; Sanlier, Saban Duran
This paper reports on a wireless communication system that is used in railways to instantly detect and continuously monitor point machine positions. Also, with the system the position information is transmitted wirelessly to a train driver. The designed system is composed of TX and RX units. It has a compact structure and is fully modular. The TX unit of the system is placed near the railway point machines and the RX unit is located on a train. The designed system was constructed, and measurements were obtained on-site. Results show that the system point machine position data were accurately transmitted at 1350 m range which is much longer than the safe braking distance of a train. In addition, the measured 1 s data sampling time of the system allows the driver to continuously monitor current RPM positions as well as state changes. The maximum delay was found to be 3 s in the limit range of the communication. It is found that the system has a low power consumption and the designed system can work for long hours. The findings indicate that the designed wireless communication system has a high potential to be used in railways to prevent accidents and contribute to the overall safety and efficiency of operations.
Views on Climate Change, Climate Action and Mental Health, in Young People with and without Existing Depression Symptoms: A Qualitative Study
(Elsevier, 2026) Kaya, M. Siyabend; Hawkins, Ed; McCabe, Ciara
Background: Youth mental health is in crisis. Climate change has the potential to tip more young people into depression and anxiety. Knowing how young people with and without depression symptoms view climate change could guide interventions to mitigate against climate induced mental health issues. Materials and Methods: We carried out in-depth, semi-structured interviews with (N = 27) young people aged 18-25 (M-age = 20.3 years). Participants were grouped as healthy controls (C, N = 16, < 16 score on Mood and Feelings Questionnaire, MFQ) or had high depression symptoms (HD, N = 11, >= 27, MFQ). Using thematic analysis, we explored participants views on climate change, climate action, climate messaging, climate agency and mental health. Results: From the interviews, eight key themes emerged: (1) Negative environmental events - Climate change was understood as ranging from weather changes to natural disasters. (2) Mental health impacts - Most participants reported increased anxiety and depression, with the HD group being more pessimistic about climate change prevention. (3) Benefits of action - Focus on individual efforts. (4) Non-disruptive vs. disruptive actions - Preference for non-disruptive solutions. (5) Hope and Fear in climate messaging - balance is needed. (6) Local and global action - Emphasis on combining both approaches. (7) Leadership - Responsibility placed on politicians, institutions, and environmentalists. (8) Shared responsibility - Families, educators, governments, and celebrities all have a role in climate action. Conclusion: These findings offer valuable insights into the perspectives of young people with and without existing symptoms of depression. Notably, identifying differences-such as varying levels of climate pessimism-based on depression status highlights the importance of climate communication strategies that not only effectively address climate change but also safeguard youth mental health. This is important as those with existing depression symptoms may be more vulnerable to the psychological impacts of climate change.
Understanding the Role of Birt-Hogg-Dube Syndrome Associated FLCN-1 In Cilia
(Springer Nature, 2025) Pucak, Damla; Kaplan, Oktay I.; Cevik, Sebiha
The Understanding the Role of Mitochondria in Cilia and Ciliopathy
(Springer Nature, 2025) Cevik, Sebiha; Guzel, Fatma; Yilmaz, Gul Hanim; Oner, Sadik; Sezer, Abdullah; Kaplan, Oktay I.
Strategic Modeling of Hybrid Smart Micro Energy Communities: A Decision-Oriented Approach
(MDPI, 2026) Perez-Sanchez, Modesto; Coronado-Hernandez, Oscar E.; McNabola, Aonghus; Erdfarb, Alex; Ramos, Helena M.; Demircan, Isil; Koca, Kemal
Hybrid renewable energy systems are increasingly important for enabling sustainable and resilient energy supply in rural smart communities, yet existing tools often lack the ability to integrate environmental variability, multi-technology interactions, and economic-environmental assessment in a unified framework. This study presents Hybrid Smart Micro Energy Community (HySMEC), a novel modeling approach that combines high-resolution meteorological data, technology-specific generation models, detailed demand characterization, and financial analysis to evaluate hybrid configurations of hydropower, solar PV, wind, battery storage, and grid interaction. Hourly simulations capture seasonal dynamics and system behavior under realistic technical efficiencies, investment costs, and emission factors, enabling a transparent assessment of energy flows, self-consumption, and grid dependence. The results show that hybrid systems can achieve competitive economic performance, low Levelized Costs of Energy, and significant CO2 emission reductions across diverse rural community profiles, even when space or demand constraints are present. The analysis confirms the technical feasibility and environmental benefits of integrating multiple renewable sources with storage, highlighting the importance of self-consumption ratios in improving system profitability. Overall, HySMEC provides a robust and scalable tool to support data-driven design and optimization of distributed energy systems, offering valuable insights for researchers, planners, and decision-makers involved in sustainable rural energy development.
PATL1: A Novel Candidate Gene for Neurodevelopmental Disorders with Motor Impairment
(Springer Nature, 2025) Alders, Marielle; Maas, Saskia; Sezer, Abdullah; Percin, Ferda Emriye; Kayhan, Gulsum; Kaplan, Oktay Ismail; Yenisert, Ferhan
Raster Orientation Effects on the Adhesion of iCVD-Deposited PSA Thin Films on FDM-Printed PLA
(MDPI, 2026) Yilmaz, Kurtulus; Gursoy, Mehmet; Gunes, Aydin; Karaman, Mustafa
The adhesion performance of pressure-sensitive adhesive (PSA) thin films on additively manufactured polymers is strongly governed by surface anisotropy induced during printing. In this study, PSA thin films based on 2-ethylhexyl acrylate (EHA) and acrylic acid (AA) were deposited by initiated chemical vapor deposition (iCVD) onto fused deposition modeling (FDM) printed PLA substrates with different raster orientations (0 degrees, 30 degrees, 60 degrees, and 90 degrees). The deposited films exhibited high optical transparency on glass, and thicknesses consistent with the targeted deposition. Adhesion performance was evaluated using tensile and three-point bending tests, revealing a strong dependence on raster orientation. The 0 degrees raster orientation yielded the highest shear adhesion strengths, reaching 12.03 N/cm2 under tensile loading and 4.59 N/cm2 under bending, along with the largest failure displacements. In contrast, specimens printed at 90 degrees exhibited an approximately 47% reduction in tensile shear adhesion strength and limited deformation prior to failure. SEM analysis showed that raster alignment parallel to the loading direction promoted extensive adhesive deformation and PSA fibrillation, whereas higher raster angles resulted in predominantly interfacial debonding. These results demonstrate that raster orientation is a critical design parameter for tuning PSA adhesion on FDM-printed PLA substrates without modifying adhesive chemistry.
Machine Learning for V2X-Enabled Microgrids: A Bibliometric and Thematic Review of Intelligent Energy Management Applications
(Springer Heidelberg, 2026) Dogan, Yasemin; Unlu, Ramazan
Modern power systems are evolving due to convergence of electric mobility, artificial intelligence, and renewable energy integration. Electric vehicles serve as dynamic, mobile energy storage units playing a vital role in ensuring resilient microgrid operations, via vehicle-to-everything (V2X) technology. However, despite the rise of machine learning (ML) in energy management, much of the existing literature remains fragmented lacking a holistic perspective across all facets of V2X-enabled microgrids. This study fills this gap by conducting a systematic bibliometric and thematic analysis of 310 articles obtained from Web of Science (2013-2024). By combining bibliometric mapping with thematic synthesis, the research identifies dominant and emerging ML techniques-ranging from reinforcement learning to federated learning-and evaluates their roles in microgrid management. The study highlights underexplored areas, including decentralized coordination, encouraging prosumer participation, understanding user behavior, safeguarding cybersecurity, improving real-time optimization, and the effective integration and adaptation of V2X technology within microgrid ecosystems. These gaps emphasize the need for interdisciplinary research and policy frameworks to address the social dimensions of future energy systems. Beyond a comprehensive overview, this paper proposes a research roadmap integrating technical, social, and policy dimensions. It offers actionable guidance for researchers, stakeholders aiming to unlock the potential of intelligent, human-centered, and socially inclusive energy ecosystems. Furthermore, the findings align with UN Sustainable Development Goals (SDG 7, 11, and 13), while also creating a positive impact on humanity by supporting the well-being of both society and the planet. Ultimately, this reinforces the indispensable role of ML in advancing the zero-carbon transition.
Gender Equity, Internationalization, and the Quintuple Helix: Comparative NLP Analysis of University Strategies in Japan and Turkiye
(Univ Louisiana Monroe, 2026) Rogler, Andreas; Morozumi, Akiko; Coymak, Ahmet; Bengu, Elif
As higher education institutions (HEIs) seek to align with Sustainable Development Goals (SDGs), integrating diversity, equity, and inclusion (DEI) into internationalization strategies has become increasingly central. In this study, we analyze 209 university strategic plans, 86 from Japan (2022-2027) and 123 from Turkiye (2019-2023), to examine how institutional discourse frames gender equity, with a particular focus on SDG 5, gender equality. We identify clear and distinct national patterns using natural language processing (NLP) techniques (e.g., keyword frequency analysis, named entity recognition, and syntactic parsing) and are guided by the quintuple helix model (QHM). Japanese universities tend to emphasize societal engagement and forward-looking commitments through abstract language. In contrast, Turkish universities adopt a more bureaucratic and retrospective tone, often referring explicitly to named target groups. We find that both countries show limited engagement with intersectional identities and marginalized populations such as female faculty, migrants, and refugees, and both underutilize the civil society and environmental dimensions of the QHM. Although inclusive values frequently appear, strategic plans rarely include clear details on how to reach these goals. Based on our analysis, we propose a scalable, reproducible framework for evaluating inclusive internationalization. Our findings underscore the importance of moving beyond symbolic discourse and calling for more accountable, stakeholder-driven planning processes that embed DEI into the structural, curricular, and governance systems of HEIs.
Frequency-Based Deep Occlusion Awareness Instance Segmentation
(MDPI, 2026) Guzel, Yasin; Aydin, Zafer; Talu, Muhammed Fatih
One major challenge faced by deep learning-based methods that detect target objects in the form of bounding boxes is object occlusion. High degrees of occlusion significantly diminish the accuracy of instance segmentation. Nonetheless, complex-valued Fourier descriptors can robustly represent object boundaries using minimal information. In this study, the impact of integrating Fourier descriptors-renowned for their strong representational capacity-with deep network models (UNet) that exhibit high generalization performance on instance segmentation accuracy was investigated. Within the scope of the research, nine network models were designed based on different strategies for utilizing frequency components. These variants fall into four strategy families: (i) UNet-style spectrum regression on fixed low-frequency windows (FUNet), (ii) magnitude-guided frequency selection/ROI construction (FUNet-Thr, FUNet-BBox), (iii) sequence models over tokenized FFT coefficients (BiLSTM Patch/Sorted), and (iv) encoder-only spectrum predictors with different depth/capacity (EncoderFFT1/2). To fairly evaluate the models' performance in segmenting objects subjected to disruptive factors (e.g., occlusion, blurring, noise), a specialized synthetic dataset was prepared. The task is formulated as single-target (single-instance), single-class segmentation. This dataset, automatically generated according to initial parameter values, contains images of objects moving at various speeds within a single frame. Among these models, the one termed FUNet, which relies on partial matching of central frequency components, achieved the highest segmentation accuracy despite the disruptive effects. Under the challenging Dataset 8 setting, the proposed FUNet achieved the highest overlap-based performance (Dice = 0.9329, IoU = 0.8842) among Attention U-Net, U-Net, and FourierNet, with statistically significant gains confirmed by paired per-image tests.
Effects of Gelatinization Process on Some Physicochemical Parameters, Pasting Characteristics and Some Nutritional Properties of Pulsed Based Flour Blends
(Springer, 2026) Kahraman, Kevser; Yuksel, Ferhat; Karaman, Safa
In this study, a flour mixture was composed by three different flours (wheat flour (WF), cranberry bean flour (CBF) and lentil flour (LF)) depending on a constructed mixture design and some physicochemical parameters, pasting characteristics and some nutritional properties were investigated before and after gelatinization process. The highest total dietary fiber content was determined for the sole cranberry bean flour. After gelatinization of the samples, total dietary fiber levels of the samples increased significantly, and it ranged between 4.70 and 25.16% for uncooked samples and 8.46-29.09% for cooked samples. Resistant starch (RS) content of the samples was also affected by the gelatinization process. Wheat flour showed an increase in the RS content after gelatinization process and similar increment in the RS content was observed for the sole lentil flour. Peak viscosity was the highest for the wheat flour (2318 cP) and lowest for the lentil flour (716.5 cP). Glycemic index of the cooked samples changed significantly, and it ranged between 94.4 and 123.5. This study showed that making flour composite and gelatinization process had a significant effect on the pasting properties and nutritional characteristics of the pulse-based flour mixture.
Comprehensive microRNA-Seq Transcriptomic Analysis of Tay-Sachs Disease Mouse Neuroglia Revealed Distinct miRNA Profiles
(Springer Nature, 2025) Sacar, Muserref Duygu; Orhan, Mehmet Emin; Kaya, Beyza; Yanbul, Selman; Seyrantepe, Volkan; Demir, Secil Akyildiz
BrAIn: A Comprehensive Artificial Intelligence-Based Morphology Analysis System for Brain Organoids and Neuroscience
(Wiley, 2026) Polatli, Elifsu; Guner, Huseyin; Bastanlar, Yalin; Karakulah, Gokhan; Evranos, Ali Eren; Kahveci, Burak; Guven, Sinan
Human-induced pluripotent stem cells (iPSCs) offer transformative potential for biomedical research, with iPSC-derived organoids providing more physiologically relevant models than traditional 2D cell cultures. Among these, brain organoids (BO) are particularly valuable for drug screening, disease modeling, and investigations into molecular pathways. Accurate representation of brain morphology is critical, as more complex organoid structures better mimic the human brain. Deep learning (DL) and machine learning (ML) approaches have become integral to analyzing organoid morphology, yet tools for comprehensive, time-resolved assessments are scarce. Here, we introduce BrAIn, a DL-based application for analyzing the developmental progression of BOs. BrAIn tracks their evolution from embryoid bodies (EBs) and quantifies parameters including area, Feret diameter, perimeter, roundness, and circularity. It also classifies budding and abnormal morphologies of 3D organoids and detects monolayer neural rosette structures, key features of neuronal differentiation. Designed with accessibility in mind, BrAIn provides a no-code interface, enabling researchers of all technical backgrounds to conduct advanced morphological analyses with ease. Our study demonstrates the application of BrAIn to evaluate the effects of different growth conditions-static, orbital shaker, and microfluidic chip-based-on BO development. Orbital shaker cultures resulted in the largest organoids, while chip-based systems achieved more homogeneous growth. Both conditions produced organoids with greater morphological complexity compared to static culture. BrAIn emerges as a robust, user-friendly tool to quantify BO development and explore how versatile growth conditions influence their morphology and maturation.
An Adaptation Mechanism of Model Reference Adaptive System Based on Variable Structure Control for Online Parameter Estimation of IPMSM
(Wiley, 2026) Tekgun, Burak; Barut, Murat; Ates, Ertugrul
This study introduces stator currents-based model reference adaptive system (MRAS) estimators that employ variable structured control (VSC) in the adaptation mechanism to enable the online estimation of stator resistance and permanent magnet (PM) flux in interior permanent magnet synchronous motors (IPMSMs). These MRAS estimators estimate stator resistance and PM flux by analysing the error between the stator currents measured as the reference model and the stator currents generated by the adaptive model. The performance of the proposed estimators is assessed through simulation studies. Furthermore, the proposed approach is compared to a conventional MRAS employing a fixed-gain proportional-integral (PI) controller. Simulation results and error analyses indicate that the VSC-based MRAS algorithms outperform traditional PI-based MRAS in terms of accuracy and reliability. Additionally, the proposed method eliminates the reliance on a fixed-gain PI controller, a common component in conventional MRAS systems.
Non-Contact Acoustic Screening for Sleep Apnea: A Subject-Aware Deep Learning Approach
(Springer Science and Business Media Deutschland GmbH, 2026) Aygün Çakıroğlu, M.; Kizilkaya Aydoǧan, E.; Bolatturk, Ö.F.; Aydoğan, S.; Ismailoǧullari, S.; Delice, Y.
Purpose: To explore the feasibility of using camera-derived, non-contact audio synchronized with PSG for clinically relevant sleep-apnea classification, and to benchmark compact deep models under a subject-aware design using a previously unstudied, real-world dataset. Methods: Thirty-two adults underwent simultaneous polysomnography (PSG) and camera-based non-contact audio recording. The synchronized audio segments were used to train and compare three compact deep-learning architectures (convolutional, attention-augmented, and transformer-based) under a subject-aware evaluation design that prevented identity leakage. Model performance and calibration were assessed at both segment and subject levels using standard statistical tests. Results: Subject-level evaluation was based on a very small, imbalanced test set of six subjects (one positive). Within this limited yet previously unstudied local dataset, the CNN_trans model achieved an apparent perfect ranking performance (AUC = 1.00; 95% CI 0.00–1.00), though this likely reflects the small, imbalanced test cohort, with recall = 1.00 and precision = 0.55. The wide confidence interval reflects substantial statistical uncertainty, and DeLong comparisons showed no significant AUC difference between CNN_trans and CNN_att (ΔAUC = − 0.042; p = 0.43). Conclusion: PSG-synchronized, non-contact audio supports accurate and well-calibrated sleep-apnea classification with compact deep models. This subject-aware evaluation suggests that contactless acoustic monitoring may have potential clinical relevance, motivating larger, multi-site validation. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2026.
Process Optimization of Buckwheat Starch Myristic Acid Complex Film
(John Wiley and Sons Inc, 2026) Koca, E.; Oskaybaş-Emlek, B.; Kahraman, K.; Özbey, A.; Aydemir, L.Y.; Oskaybas Emlek, Betul
In this study, it was aimed to develop an edible film from an amylose-lipid complex with better mechanical properties and water vapor barrier. For this purpose, the buckwheat starch (BS) is modified with myristic acid (MA) and the edible film production process was optimized by using central composite design with 4 center points where film forming solution's glycerol concentration, pH, and the temperature of as dependent variable and tensile strength (TS), elongation at break (EAB) value and Young's modulus (YM) as response. The models were significant for TS and YM, and the glycerol concentration and temperature had a significant effect on the TS of the films. The edible film produced in validated optimized conditions had better EAB (149%) and TS (1.064 MPa), and lower water solubility (44.7%) and water vapor permeability (0.39 g × mm/m2 × h × kPa) than control film (p < 0.05). There was no significant change in color values, but an increase in opacity (2.14). With the formation of the BS-MA complex, increased surface roughness and more hydrophilic (contact angle = 92.4°) films were obtained. These findings demonstrate that the BS-MA complex film has significant potential for practical applications as an edible film. © 2026 Wiley-VCH GmbH.
GenShare: A Blockchain-Based Genomic Data Sharing Platform
(Association for Computing Machinery, 2026) Dedeturk, B.A.; Soran, A.; Bakir-Güngör, B.
Every day, hundreds of gigabytes of data are produced due to the exponential growth of next-generation sequencing and omics technologies. By combining omics data with other data types, such as electronic health record data, panomics research is actively attempting to uncover novel and potentially useful biomarkers. For the effective analysis of high-throughput-derived omics data, it is imperative to establish robust and reliable platforms that prioritize ethical considerations while effectively managing privacy, ownership concerns, and the responsible sharing of data. The GenShare model was proposed to provide an efficient platform that fits these needs. GenShare is a hybrid platform that utilizes blockchain technology. Paillier’s homomorphic encryption scheme in tandem with Intel Software Guard Extension (SGX) serves to enable the sharing of genomic data, execution of count queries, and statistical analysis of genomic data while preserving privacy and avoiding compromise of sensitive information. The objective of this paradigm is to confront security and privacy concerns through the integration of homomorphic encryption and SGX, addressing additional challenges associated with Hyperledger Fabric and Ethereum. In pursuit of this objective, the implementation of the system involved establishing the Hyperledger Fabric network, with various workloads employed to assess the network’s efficiency. Consequently, it was hypothesized that the new GenShare model would enhance the data collection and dissemination cycle and serve as a proficient platform catering to the needs of its users. © 2026 Copyright held by the owner/author(s).
Two-Local Modifications of Sachdev-Ye Model With Quantum Chaos
(American Physical Society, 2026) Hanada, M.; Van Leuven, S.; Oktay, O.; Tezuka, M.
The Sachdev-Ye-Kitaev (SYK) model may provide us with a good starting point for the experimental study of quantum chaos and holography in the laboratory. Still, the four-local interaction of fermions makes quantum simulation challenging, and it would be good to search for simpler models that keep the essence. In this paper, we argue that the four-local interaction may not be important by introducing a few models that have two-local interactions. The first model is a generalization of the spin-SYK model, which is obtained by replacing the spin variables with SU(d) generators. Simulations of this class of models might be straightforward on qudit-based quantum devices. We study the case of d=3,4,5,6 numerically and observe quantum chaos already for two-local interactions in a wide energy range. We also introduce modifications of spin-SYK and SYK models that have similar structures as the SU(d) model (e.g., H=∑p,qJpqχpχp+1χqχq+1 instead of the original SYK Hamiltonian H=∑p,q,r,sJpqrsχpχqχrχs), which shows strongly chaotic features although the interaction is essentially two-local. These models may be a good starting point for the quantum simulation of the original SYK model. ©2026 American Physical Society.
Noninvasive Condition Monitoring for Eccentricity Fault Detection in Large Hydro Generators
(TÜBİTAK Scientific & Technological Research Council Turkey, 2026) Lemeski, Atena Tazikeh; Tekgun, Didem; Keysan, Ozan; Leblebicioglu, Kemal; Gol, Murat
Eccentricity faults in electric machines remain a critical concern, as they generate uneven magnetic forces that increase vibration and noise, ultimately raising the risk of premature motor failure. This study proposes a method for the early detection of dynamic eccentricity (DE) faults in hydropower plants through an advanced optimization-based parameter identification technique integrated with finite element analysis (FEA). Finite element modeling (FEM) is first used to analyze an existing salient-pole synchronous generator (SPSG) from a hydroelectric power plant in T & uuml;rkiye. The effects of DE faults on the SPSG's magnetic equivalent circuit parameters are then examined under various fault severities. A comprehensive hydropower plant model-including the synchronous generator, governor, and excitation system-is developed in MATLAB/Simulink, with all input parameters obtained from real plant data and equivalent circuit variations extracted from FEA. After completing the modeling stage, including fault scenarios, MATLAB and Simulink are employed together to estimate key magnetic equivalent circuit parameters using a modified particle swarm optimization (MPSO) algorithm, achieving highly accurate parameter estimation. Since the hydropower system allows measurement of the three-phase output currents, parameter estimation is performed based on current variations under different fault conditions. The simulation results verify the method's ability to detect faults with high accuracy; thus, this integrated and noninvasive approach provides a robust framework for ensuring the operational reliability and longevity of large hydro generators.
Citation - WoS: 1
Citation - Scopus: 1
A Novel Biomass-Derived Reductant for Nitric Acid Dissolution of Manganiferous Iron Ore: Comparative Assessment of Organic Reductants
(MDPI, 2025) Top, Soner; Altiner, Mahmut; Vapur, Huseyin; Kursunoglu, Sait; Stopic, Srecko
This study investigates the selective dissolution of manganese from a manganiferous iron ore using nitric acid (HNO3) in the presence of various organic reductants. A series of leaching experiments was performed to evaluate the effects of temperature, reductant type, and leaching time on Mn recovery, with particular emphasis on biomass (horse dung) and tartaric acid as novel reducing agents. The dissolution behaviour of Fe, Mn, Mg, Ca, and Al was systematically examined, revealing that Mn extraction was strongly enhanced in the presence of reductants, while Fe dissolution remained below 10% under all conditions. The maximum Mn dissolution exceeded 90% at 90 degrees C using biomass and reached nearly 85%-90% with tartaric acid at elevated temperatures. Kinetic studies were conducted by applying reaction order models and the shrinking core model. The results indicated that Mn dissolution in HNO3 medium is predominantly controlled by surface chemical reaction, with Arrhenius analysis yielding activation energies of 27.74 kJ/mol for biomass and 21.26 kJ/mol for tartaric acid. These relatively low values confirm the efficiency of organic reductants in facilitating Mn reduction and dissolution. To sum up, comparison of reductant efficiency revealed that, at the lowest concentrations, the dissolution of Mn followed the sequence glucose > sucrose > oxalic acid > tartaric acid > maleic acid > biomass > citric acid > acetic acid. At the highest concentrations, the trend shifted, with citric acid emerging as the most effective, followed by tartaric acid > oxalic acid > glucose > sucrose > maleic acid > biomass > acetic acid.

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