Gümüşhane ÜniversitesiGümüşhane ÜniversitesiAçık Erişim Arşivi · RDL Open

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  • MakaleAçık Erişim2027
    A hierarchical metaheuristic framework for CPT-based soil liquefaction prediction with Kolmogorov–Arnold Network variants

    Günen, Mehmet Akif; Öztürk, Kaşif Furkan

    Soil liquefaction is a critical geotechnical hazard that threatens the safety of civil infrastructure under seismic loading, necessitating robust and interpretable prediction models. This study proposes a hierarchical framework for CPT-based soil liquefaction prediction using meta-heuristic-optimized Kolmogorov–Arnold Network (KAN) variants. The framework follows a two-stage optimization strategy. First, six state-of-the-art meta-heuristic algorithms are statistically compared to identify the most effective optimization method. Second, the selected algorithm is employed to optimize six KAN variants, including FastKAN, EfficientKAN, FourierKAN, ChebyKAN, JacobiRKAN, and PadéRKAN. Statistical comparisons based on the Wilcoxon signed-rank test demonstrate that the Colony-Based Search algorithm (CSA) achieves lower objective-function values than the competing meta-heuristics (p<0.01) and is therefore adopted as the core optimization strategy. Performance evaluation reveals that the CSA-optimized ChebyKAN model achieves the highest predictive accuracy, with an F1 Score of 0.8875 ± 0.011 and an AUC of 0.9115 ± 0.013. Pairwise McNemar tests detected no statistically significant difference in classification decisions for any evaluated model pair (p>0.01). SHapley Additive exPlanations analysis identified qc, qc1N, and amax as the variables with the largest model-specific attributions, whereas d, c, and σv received lower attributions. A cost-sensitive error analysis further showed that ChebyKAN produced the lowest false-negative rate and the lowest asymmetric error cost under 5:1 and 10:1 false-negative-to-false-positive cost scenarios, supporting its conservative behaviour for safety-critical liquefaction screening. The results establish the proposed CSA–KAN framework as a reliable, high-accuracy, and interpretable solution for CPT-based soil liquefaction assessment in geotechnical engineering practice.

    • SDG 9 · Sanayi, Yenilikçilik ve Altyapı
    İncele
  • MakaleYalnız künye2027
    Efficacy of blue light-assisted ClO2 for Salmonella decontamination on basil leaves: inactivation kinetics, quality preservation, and microbiota dynamics

    Lian, Yi; Liu, Songlin; Ozturk, Samet; Wei, Xinyao

    Frequent Salmonella outbreaks associated with basil leaves highlight the need for effective postharvest decontamination. Conventional microbial sanitizers often fail to inactivate pathogens while preserving product quality, creating a demand for novel decontamination technologies suitable for basil. In this study, the microbial safety of basil under typical consumption scenarios was evaluated and a blue light-assisted ClO2 (COM) decontamination process for basil leaves was developed. Mathematical models were used to describe the microbial inactivation kinetics, and a systematic comparison was conducted against traditional single-chemical sanitizers (H2O2, HClO, ClO2, and 75% ethanol) in terms of microbial safety and final product quality. Results showed that simulated cooking scenarios, including pizza baking and hot soup immersion, were insufficient to reduce the Salmonella levels below the detection limit (<10 CFU/g) under a high initial load of 9.7 log CFU/g. Compared to the single-chemical treatments, 100 mg/L COM treatment achieved significantly higher decontamination (p < 0.05) by reducing Salmonella and background microbiota by 4.8 and 3.5 log CFU/g, respectively, with treatment time of 15 min. The inactivation kinetics of Salmonella showed a non-linear inactivation kinetics, which was captured by the Weibull model with a low RMSE value (RMSE = 0.16), although the AICc value (11.37) indicated limited model parsimony. Furthermore, the COM treatment significantly reduced bacterial richness (with the Chao1 index decreasing from 316 to 71) and effectively inactivated key spoilage organisms (Pseudomonas) as well as members of the Enterobacteriaceae, Actinomycetota, and Bacillota groups. Compared with the 100 mg/L ClO2 treatment alone (1.40 mg/kg), the 100 mg/L COM treatment resulted in a ClO2 residue concentration below the detection limit, without significantly altering visual appearance, color, chlorophyll content, total phenolics, or polyphenol oxidase activity (p > 0.05). Overall, blue light-assisted ClO2 treatment effectively reduced microbial contamination and reduced ClO2 residues while preserving the physical quality of basil, representing a promising decontamination approach for the fresh produce industry.

    • SDG 2 · Açlığa Son
    • SDG 3 · Sağlık ve Kaliteli Yaşam
    • SDG 12 · Sorumlu Üretim ve Tüketim
    İncele
  • MakaleYalnız künye2026
    Optimizing compton suppression systems: A parametric monte carlo study of NaI scintillator geometry

    Kaya, Ali; Çelik, Necati; Kaya, Selim

    Compton suppression systems are widely used to enhance gamma-ray spectral quality in applications such as nuclear safeguards and environmental radioactivity measurements. Although optimization of high-purity germanium (HPGe) detectors has been extensively reported, the geometric optimization of secondary sodium iodide [NaI(Tl)] scintillation detectors remains less studied. This work investigates the influence of annular and plug NaI(Tl) detector geometry—length, thickness, and radius—on Compton suppression performance using Monte Carlo simulations. Performance is evaluated in terms of the peak Compton suppression factor (CSFpeak) and total Compton suppression factor (CSFtotal). The results show that CSFpeak and CSFtotal increase with plug length (2–10.2 cm) and annular thickness (1–5.85 cm), with saturation observed beyond approximately 8–10 cm and 4–5 cm, respectively, due to reduced incremental photon absorption. The plug detector radius (3.72–5.1 cm) exhibits a nonlinear effect, with optimal performance achieved at 4.55–4.83 cm, beyond which gains are marginal (<5%). Suboptimal geometries (plug length <6 cm, annular thickness <3 cm, plug radius <4.28 cm) result in 20–60% lower suppression performance, increasing Compton continuum contributions in 662 keV spectra. The optimal configuration—plug length of 8–10 cm, plug radius of 4.55–4.83 cm, and annular thickness of 4–5 cm—achieves more than 90% of maximum CSFpeak and CSFtotal while maintaining a balance between performance and detector volume. These results provide quantitative design guidelines for optimizing NaI(Tl)-based Compton suppression systems for improved gamma-ray spectrometry performance.

    İncele
  • MakaleYalnız künye2026
    Optimal design of a novel enhanced inerter-based tuned mass damper in seismic-excited structures considering SSI effects

    Araz, Onur; Shahraki, Mohammad Alibabaei

    This paper proposed a novel enhanced inerter-based tuned mass damper (E-TMDI) for the control of damped single-degree-of-freedom structures due to ground acceleration. The apparatus represents an energy-dissipating passive absorber assembled from a pair of inerter elements, a pair of springs, a single mass, and a single damper. The present work directly integrates soil-structure interaction (SSI) effects within the optimization process, thereby yielding more accurate and dependable outcomes. For a clearer insight into the control mechanism of E-TMDI, the frequency-domain transfer function for responses is derived, while the influences of various model parameters on the displacement amplitude magnification factor of the superstructure both considering and neglecting SSI are examined. Four design parameters (i.e., f 1 first frequency ratio, f 2 secondary frequency ratio, ξ1 damping ratio, and κ inertance ratio of the second inerter to the first) of E-TMDI are obtained using particle swarm optimization (PSO) algorithm to minimize peak displacement amplitude of the structure. Results from the frequency domain analyses in this research indicate that the proposed E-TMDI has better efficiency and frequency band property both traditional TMD and TMDI configurations when equipped with same total mass (μ) and inertance ratio (β) in suppressing structural vibrations. Additionally, the stability of the proposed device is examined through simulations of the E-TMDI-equipped structure under a range earthquake record. The findings reveal that the E-TMDI maintains superior effectiveness even when the host structure experiences diverse earthquake events. This validation, conducted under real earthquake records, confirms the reliability of this design framework, filling a significant gap in determining the seismic control performance of E-TMDI under varying soil conditions.

    • SDG 9 · Sanayi, Yenilikçilik ve Altyapı
    İncele
  • MakaleYalnız künye2026
    Classification and detection of Salmonella, Escherichia coli O157:H7, and Listeria monocytogenes using Fourier-transform near infrared spectroscopy coupled with machine learning

    Ozturk, Samet; Huang, Lihan; Hwang, Cheng-An; Sheen, Shiowshuh

    This study was conducted to investigate the potential use of Fourier-transform near-infrared (FT-NIR) spectroscopy combined with machine learning (ML) algorithms to accurately identify three foodborne pathogens, including Salmonella spp., Listeria monocytogenes, and Escherichia coli O157:H7. Each bacterial strain (two per pathogen) was individually cultured and purified through sequential washing using ethanol–deionized (DI) water solutions. Each purified culture was transferred into each well of a 96-well cell plate covered with custom-cut filter paper and then vacuum dried at 50 °C under 20 kPa for 1 h. The absorbance spectra of dehydrated bacterial cells were then acquired across the range of 1000–2400 nm using a diffuse reflectance probe connected to a FT-NIR process analyzer. To identify the optimal classification pipeline, the acquired spectra were analyzed using ten different pre-processing methods, three feature selection methods, and supervised algorithms including partial least squares discriminant analysis (PLS-DA), support vector machine (SVM), random forest (RF), artificial neural network (ANN), and convolutional neural network (CNN). Furthermore, the accuracy of the developed ML pipelines was further enhanced through under-sampling and boosting steps. The results demonstrated that SVM, RF, ANN and CNN outperformed PLS-DA, where the classification accuracies were above 90%. The findings demonstrate that using Savitzky-Golay first derivative (SG1) filtering to pre-process the full spectra followed by SVM classification yielded the highest accuracy, achieving 95.3% overall accuracy in ML pipeline. This study highlights the powerful capability of FT-NIR spectroscopy coupled with ML algorithms to detect and identify foodborne pathogens on dehydrated surfaces in process environments.

    • SDG 2 · Açlığa Son
    • SDG 3 · Sağlık ve Kaliteli Yaşam
    İncele
  • MakaleYalnız künye2026
    Effects of dual and single spark plug configurations on energy, exergy, and exhaust emissions in a spark ignition engine

    Altın, İsmail; Bayraktar, Hakan; Sezer, İsmet

    Effects of dual spark plug (DSP) operation in a spark ignition (SI) engine on energy, exergy, and exhaust emissions are numerically investigated in this study. A quasi–dimensional SI engine cycle model is used for the analyses. The practical correlations with no assume the fluid dynamics are occupied for the simulation of intake and exhaust periods and the turbulent flame propagation analogy is assigned for the modeling of combustion. The second law of thermodynamics is added to the cycle model to execute the exergy analysis. The thermodynamic parameters such as in–cylinder pressure and temperature, ignition delay, and burn duration with the engine performance parameters are evaluated in the energy analysis. The exergies transferred via heat, work, and exhaust gases, besides destroyed exergy, fuel chemical exergy, thermomechanical exergy and total exergy are compiled for the exergy analysis. The carbon monoxide (CO), nitrogen oxide (NO), and carbon dioxide (CO2) emissions are defined for emission performance analysis. The results show that DSP operation affects moderately energy, exergy, and exhaust emissions. The best results were obtained at dimensionless spark plug location (ratio of spark plug location radius to cylinder radius–Xsp) of 0.2 for DSP compared to centrally located single spark plug (SSP).

    • SDG 7 · Erişilebilir ve Temiz Enerji
    • SDG 13 · İklim Eylemi
    İncele
  • MakaleYalnız künye2026
    Efficient ensemble learning with multi-scale fusion based deep neural network for skin lesion classification

    Reis, Hatice Catal; Turk, Veysel

    Melanoma, a type of skin cancer, is a severe skin cancer that is critical for early diagnosis but difficult to detect. Deep learning is promising in melanoma diagnosis because it can automatically analyze visual features and perform end-to-end learning. However, achieving the optimum balance between performance and computational efficiency for the effective use of these models in clinical applications is an important research topic. In this study, three innovative methods are proposed for skin cancer detection: PADSCNet, P-DEFF, and M–DEFF. The PADSCNet is an effective neural network developed with CNN and multi-head attention techniques, designed with fusion methods, and stands out with its minimum number of parameters despite being quite deep. The P-DEFF and M–DEFF are based on ensemble learning techniques; P-DEFF was developed with a single deep learning model, and M–DEFF was developed with multiple pre-trained deep learning models. The proposed approaches were tested on the Skin Cancer: Malignant vs. Benign dataset, which was pre-processed using image enhancement techniques. According to the experimental results, PADSCNet achieved accuracy rates of 88.12 %, P-DEFF 93.75 %, and M–DEFF 95.00 %. In additional experiments conducted with the PADSCNet model, 93.12 % accuracy rates were achieved in the CNN for Melanoma Detection Dataset, and 95.00 % accuracy rates were achieved in the PH2 dataset. These findings show that the proposed methods are effective in skin cancer detection and can achieve successful results in clinical applications.

    • SDG 3 · Sağlık ve Kaliteli Yaşam
    İncele
  • MakaleAçık Erişim2026
    CsPbBr3 single-crystal growth by temperature-lowering method as a case study for EGS4 benchmarking against commercial radiation detectors

    Akbulut Özen, Songül; Özen, Murat; Çelik, Necati

    CsPbBr3 single-crystals were synthesized using a hydrobromic acid–based temperature-lowering method, and their structural and optical properties were confirmed by XRD, DSC, and UV–Vis analyses. A solubility curve was established to optimize growth conditions, enabling enlargement of the seeded crystals. The radiation detection potential of CsPbBr3 was evaluated using EGS4 Monte Carlo simulations across photon energies ranging from 10 keV to 1 MeV. Simulated full-energy peak efficiencies and resolution values were compared with conventional detectors (Si(Li), NaI, and HPGe) and with alternative perovskite derivatives (CH3NH3PbBr3, Cs4PbBr6, CsPb2Br5). CsPbBr3 exhibited efficiency scaling with detector volume and resolution behavior consistent with the statistical 1/√E dependence typical of direct-gap semiconductors. While HPGe maintained superior intrinsic resolution, CsPbBr3 offered promising room-temperature performance without cryogenic requirements. These results demonstrate that the temperature-lowering method provides a viable route to scalable CsPbBr3 single-crystals and confirm their potential as cost-effective, high-Z semiconductor detectors for X- and γ-ray applications. The findings establish a foundation for the further optimization of perovskite-based radiation detection technologies.

    İncele
  • MakaleAçık Erişim2026
    Distribution and radiological risk assessment of natural and artificial radionuclides in soils from the Aktutan hydrothermal alteration zone, Türkiye

    Kaya, Ali

    This study investigates the distribution of natural radionuclides (226Ra, 232Th, and 40K) and the artificial radionuclide 137Cs in soil samples collected from the Aktutan hydrothermal alteration zone in Gümüşhane, Türkiye. Hydrothermal systems are known to enhance radionuclide mobility and enrichment due to fluid–rock interactions, making them important environments for radiological assessment. However, no previous comprehensive radiological study has been conducted in this region. A total of 12 soil samples were collected and analyzed using high-purity germanium (HPGe) gamma-ray spectrometry. The measured activity concentrations ranged from 16 to 58 Bq·kg−1 for 232Th, 5 to 282 Bq·kg−1 for 226Ra, and 246 to 647 Bq·kg−1 for 40K, while 137Cs was detected in several samples with a maximum value of 5.7 Bq·kg−1. The elevated 226Ra concentrations observed in specific locations may be associated with localized hydrothermal alteration and possible uranium enrichment. Radiological hazard parameters, including absorbed dose rate, annual effective dose, radium equivalent activity, and hazard indices, were calculated. Most values were within internationally recommended limits; however, two sampling sites exhibited elevated radiological risk indicators, particularly in terms of excess lifetime cancer risk. Although the number of samples is limited, the selected locations represent areas of intense hydrothermal alteration and provide preliminary insight into radionuclide distribution in the region. The findings highlight the importance of localized geological controls on radioactivity and suggest the need for continuous environmental monitoring in areas with elevated radionuclide concentrations.

    İncele
  • MakaleAçık Erişim2026
    A novel SSI-based optimization framework for non-traditional TMDI system under near- and far-fault ground motions

    Araz, Onur; Forcellini, Davide

    Previous studies on the optimal parameter selection of non-traditional tuned mass damper inerter (N-TMDI) system have predominantly adopted a fixed-base assumption. However, neglecting soil–structure interaction (SSI) in practical applications may lead to significant design inaccuracies and reduced control performance of the N-TMDI system. To overcome this limitation, a new design framework is proposed for the tuning of N-TMDI systems installed in primary structures subjected to ground motion by explicitly considering SSI effects. The novelty of the proposed approach consists of performing a hybrid GA-PSO optimization algorithm to study the efficiency of N-TMDI system under near- and far-field ground motions. This study investigates the influence of varying mass and inerter ratios on the system's dynamic response through frequency-domain analyses. The performance of the N-TMDI system is subsequently validated using real earthquake records, with a specific focus on the comparative effects of near-fault and far-fault ground motions under diverse soil conditions. The results demonstrate that the inerter mass ratio is the most dominant factor in enhancing seismic protection, significantly reducing structural displacement and acceleration compared to conventional TMD. Furthermore, the findings reveal that SSI and the distinct characteristics of ground motions significantly modify the system's control efficiency. This research confirms the robustness of the optimized N-TMDI design, providing a reliable and efficient strategy for seismic structural control under varied seismic scenarios.

    İncele
  • Derleme MakaleAçık Erişim2026
    Quaternary palaeoenvironmental archives of the Indian subcontinent and their imprint on herpetofaunal biogeography: A multi-proxy synthesis

    Kurnaz, Muammer

    This review presents the first integrated proxy, mechanism and biotic-response framework for the Indian Quaternary, linking the Subcontinent's dense network of palaeoenvironmental archives to the biogeographic trajectory of its endemic herpetofauna. Speleothem δ18O records, Arabian Sea and Bay of Bengal sediment cores, Karewa and peninsular lake sequences, Siwalik and Narmada alluvium, and Kurnool cave deposits together document Indian Summer Monsoon (ISM) variability on orbital, millennial, centennial and historical timescales (the historical band here denoting the past ∼2000 years, resolved by dated speleothem and documentary records). Against this proxy backbone, the fossil and molecular phylogeographic record of Indian amphibians and reptiles is evaluated as a biogenic assemblage here, the body of biological evidence (fossils, subfossils and the phylogeographic signatures of living taxa) that, like pollen or foraminifera, records past environmental conditions that complements geological and climatological archives. Three patterns emerge. At orbital scale, ISM variability aligns with Plio–Pleistocene diversification slowdowns in Western Ghats ranoids and with the Late Pliocene lineage divergence of the Himalayan toad Duttaphrynus himalayanus across the Thakkhola basin. Abrupt Holocene excursions, including the 8.2 ka and 4.2 ka events, are resolved at high precision in speleothem and marine archives but remain untested against coalescent demographic inference in Indian herpetofauna. Late Pleistocene aridity, most severe at the Last Glacial Maximum, coincides with community-level impoverishment in peninsular cave faunas at Kurnool, although the available evidence establishes co-occurrence rather than a demonstrated causal role for aridity relative to co-varying thermal and vegetation change. Three proxy-calibrated research priorities follow: microvertebrate taphonomy in Narmada and Karewa sequences, population genomics tied to speleothem chronology, and species-distribution hindcasting to the LGM, mid-Holocene and 4.2 ka intervals. The synthesis contributes to a biogenic assessment of Quaternary-to-Recent climate impacts on life on the Indian Subcontinent, aligned with UN Sustainable Development Goals 13 (Climate Action) and 15 (Life on Land), and supports future projections of amphibian and reptile response to ongoing change.

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  • MakaleYalnız künye2026
    Quality deterioration kinetics of roasted peanut matrices under fixed-time and estimated-lethality at various water activities

    Islam, Shahriar; Li, Zhiwei; Wang, Xin; Tasci, Caner; Ozturk, Samet; Erdoǧdu, Ferruh; Zhang, Qing; Liu, Shuxiang

    Water activity and particle size are critical determinants of quality stability and safety in low-moisture, lipid-rich foods. However, their combined effects during thermal processing of roasted peanut matrices remain insufficiently quantified. This study evaluated quality deterioration in roasted peanut matrices with different structural forms: crushed, flour, powder, and butter. Samples were equilibrated to water activity values of 0.11–0.92 at 25 °C and heated at 80 °C for 120 min. Colour, acid value, sensory score, and changes in relative fatty acid composition were measured. Kinetic experiments were also conducted at aw,25°C 0.32 using D-value-based thermal schedules at 85, 90, and 95 °C to relate microbial lethality to quality loss. Water activity strongly affected quality stability, with the predicted quality-favourable values varying slightly among responses. Therefore, aw,25°C (0.32) was selected as a practical condition for subsequent kinetic evaluation. Matrix performance differed between the fixed-time and D-value-based treatments. These findings describe quality deterioration under fixed-time and D-value-based thermal schedules.

    • SDG 2 · Açlığa Son
    İncele
  • MakaleYalnız künye2026
    Determining the effect of blockchain technology on the medical waste management process in the circular economy framework using integrated FUCOM-ARAS methods

    Batar, Bilge; Peker, İskender

    Background: The circular economy concept aims to reduce environmental impact by promoting resource reuse, minimizing waste through input reduction, and lowering costs. Rising costs are leading healthcare facilities and other organizations to prioritize medical waste management. Blockchain technology, introduced in 2008, functions as a database of auditable and traceable transactions built on a network of trust-based blocks. Key features such as transparency, reliability, accessibility, flexibility, and automation can significantly enhance the efficiency of medical waste management processes, thereby contributing to the circular economy. Objective: This study aims to evaluate the effectiveness of medical waste management—critical for healthcare institutions and sustainable development—within the framework of the circular economy. Multi-criteria decision-making (MCDM) techniques are used to assess the contributions of blockchain technology to this process. Methods: A literature review was conducted to identify relevant criteria and alternatives. The Full Consistency Method (FUCOM), an MCDM technique, was used to calculate the weight of each criterion. The Additive Ratio Assessment (ARAS) method was applied to rank the alternatives. Conclusion: According to the analysis's findings, transparency, traceability, and dependability are the three most important criteria, respectively. The stages of medical waste storage, disposal, and transportation have the highest priority ratings among the available options. According to these results, incorporating blockchain technology into medical waste management can significantly improve system traceability and reliability, enhancing its role in the circular economy. Notably, the storage phase is the most appropriate for implementing blockchain. However, the other phases’ comparatively high appropriateness scores attest to the technology's capacity to be used efficiently across the waste management cycle. This integration promotes traceable and transparent procedures, guaranteeing dependable waste management procedures in healthcare facilities and supporting the objectives of the circular economy and sustainability. Additionally, the sensitivity analysis shows that when the weighting of criterion is altered, the ranking of alternatives changes as well. This dynamic interaction emphasizes how blockchain technology may be successfully integrated into all phases rather than being restricted to just one because waste management is cyclical and holistic.

    • SDG 3 · Sağlık ve Kaliteli Yaşam
    • SDG 9 · Sanayi, Yenilikçilik ve Altyapı
    • SDG 12 · Sorumlu Üretim ve Tüketim
    İncele
  • MakaleYalnız künye2026
    Influence of holmium substitution on structural characteristics and radiation shielding parameters of YBCO (358) superconducting ceramics

    Düzgün, İbrahim; Kaya, Selim; Gül, Osman

    This study reports on the structural characterization, radiation shielding assessment, and magnetic-field response of holmium-substituted Y3-xHoxBa5Cu8O18+δ superconducting ceramics synthesized by the conventional solid-state reaction route. X-ray diffraction measurements on five compositions (x = 0, 0.75, 1.5, 2.25, 3) supported the formation of a dominant orthorhombic Y358-type matrix across the substitution range. Scanning electron microscopy (SEM) combined with energy-dispersive X-ray spectroscopy (EDS) revealed a progressive microstructural evolution with increasing Ho content, including SEM–BEC/EDS-detectable Ho-rich inclusions consistent with Ho2BaCuO5-type secondary particles at higher substitution levels. XCOM-based mass attenuation coefficients, half-value layer, mean free path, effective atomic number, radiation protection efficiency, gamma-ray kerma coefficients, and fast-neutron removal cross-sections were calculated for all five compositions over 50–2000 keV. EGS4 Monte Carlo simulations independently verified the XCOM results for three representative compositions (x = 0, 1.5, and 3). Ho-containing specimens exhibited a significant enhancement in photon absorption near 60 keV, associated with the increased photoelectric contribution of holmium in the vicinity of its K-absorption edge. Among the full five-composition series, the x = 2.25 specimen (ρ = 5.531 g cm−3) exhibited the best calculated shielding performance across the full energy range, owing to its highest bulk density combined with substantial Ho incorporation. The x = 1.5 specimen provided the most balanced calculated radiation-attenuation and hysteretic magnetic response among the three representative compositions examined by EGS4 and M–H measurements at 20 K, retaining approximately 93% of the reference M–H hysteresis area while full substitution reduces it by roughly 67%. These findings establish a multi-parametric reference dataset for Ho-substituted Y358 ceramics and demonstrate the promise of rare-earth-engineered superconducting ceramics as multifunctional materials in radiation-intensive environments.

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  • MakaleYalnız künye2026
    Local differentially private multi-criteria recommender system

    Demir Gül, Nurgül Nisa; Batmaz, Zeynep; Yargıç, Alper

    While multi-criteria collaborative filtering (MCCF) enhances recommender systems with more accurate and personalized content, it also introduces significant privacy concerns due to the use of fine-grained personal data. Differential privacy (DP), particularly local DP (LDP), is widely used in single-criterion systems to protect privacy. However, LDP-based mechanisms remain unexplored in MCCF systems.This study introduces the first LDP-enabled MCCF framework that applies a criterion-wise perturbation procedure to preserve the structure of multi-dimensional ratings while providing formal privacy guarantees. Three Laplace-based mechanisms as Standard (SLM), Bounded (BLM), and Truncated (TLM) are integrated and evaluated on two Yahoo!Movies subsets under privacy budgets ε ∈ [0.1, 10]. The results show that BLM and TLM consistently outperform SLM, particularly under stricter privacy conditions. On YM20, the root mean squared error (RMSE) loss compared to the unmasked variant ranges from 3.92–140.62% with SLM, 3.92-69.58% with BLM, and 3.68–88.47% with TLM. For YM10, RMSE loss ranges from 1.72–130.12% with SLM, 2.09–63.25% with BLM, and 1.55–75.64% with TLM. Moreover, minimizing noise via min-max normalization generally enhances accuracy, while rating distributions and sparsity of the datasets, clipping strategies and privacy levels further affect outcomes. Overall, the proposed LDP-based MCCF framework achieves competitive prediction quality while ensuring strong privacy guarantees, advancing privacy-preserving approaches in multi-criteria recommendation.

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  • MakaleYalnız künye2026
    A dynamic predictive model for Bacillus cereus growth from spores under temperature abuse conditions

    Juneja, Vijay K.; Osoria, Marangeli; Gao, Juan; Kapoor, Harsimran Kaur; Ozturk, Samet; Purohit, Anuj; Mishra, Abhinav

    Bacillus cereus, a common foodborne pathogen, can cause emetic and diarrheal food poisoning. The associated outbreaks are commonly caused by temperature abuse during food cooling, storage, and transportation. This study developed a model to predict B. cereus growth from spores based on isothermal growth data collected at 10–54.4 °C in tryptic soy broth. A four-strain spore cocktail was heated at 80 °C for 10 min and inoculated at an initial level of ∼2 log CFU/mL. Growth curves at temperatures showing growth (15–50 °C) were fitted using the Baranyi model. The average h0 value was 3.04. The temperature dependence of the maximum specific growth rate was characterized using the modified Ratkowsky model. The estimated theoretical minimum and maximum growth temperatures were 8.94 °C and 54.11 °C, respectively. The lag phase duration was related to temperature using a hyperbolic function. All models showed good agreement with the observed data, with satisfactory fit statistics (R2 = 0.953–0.998 and RMSE = 0.073–0.904). The Baranyi differential equations were solved numerically using a fourth-order Runge–Kutta method to build the dynamic model. Model validation was conducted under two sinusoidal non-isothermal temperature profiles (10–30 °C for 30 h and 25–45 °C for 23 h). The accuracy factor (Af) and bias factor (Bf) values were 1.2117 and 1.2117 for the 10–30 °C profile, and 1.1359 and 1.1288 for the 25–45 °C profile, respectively. These results demonstrated that the dynamic model could precisely predict B. cereus growth and be used as a baseline reference for food system research and risk assessment.

    • SDG 2 · Açlığa Son
    İncele
  • MakaleYalnız künye2026
    Children's voices in disaster recovery: Lessons from the 2023 Turkey-Syria earthquake

    Yildiz, Ayse; Simsek, Nurcan; Boluk, Aysegul

    Disasters have a devastating impact on populations, with children being one of the most at-risk groups. Although their vulnerability in disasters is widely recognised, children's own voices are still largely missing from disaster recovery research and planning. This study aims to address that gap by listening directly to the experiences and perspectives of children aged 11 to 14 in Nurdağı, Gaziantep, one of the hardest-hit areas in Türkiye, on disaster relief and recovery. Using a qualitative, child-centred approach, grounded in a socio-ecological, rights-based framework, we conducted interviews with 58 children, alongside 47 additional interviews with parents, teachers, and first responders, to understand how young people coped with life after the earthquake, what challenges they faced, and what they believe would support recovery. The findings show that children struggled with difficult living conditions, trauma, disrupted education, and changing social relationships, but they also demonstrated remarkable insight, emotional intelligence, and a desire to be involved in rebuilding their communities. Many children questioned the fairness of aid distribution and called for more attention to mental health and safer schools. Rather than portraying children as passive victims, the study highlights their capacity to reflect, adapt, and contribute. It calls for more inclusive, responsive recovery strategies that treat children as active participants in disaster planning and post-disaster rebuilding. The study offers actionable recommendations for policymakers, educators, and humanitarian practitioners to better integrate children's perspectives into long-term recovery planning and resilience-building initiatives.

    • SDG 3 · Sağlık ve Kaliteli Yaşam
    • SDG 10 · Eşitsizliklerin Azaltılması
    • SDG 11 · Sürdürülebilir Şehirler ve Topluluklar
    • SDG 16 · Barış, Adalet ve Güçlü Kurumlar
    İncele
  • MakaleYalnız künye2026
    Multi-scale deep fusion architecture for brain tumor detection via local-contextual feature integration

    Reis, Hatice Catal; Turk, Veysel

    Early detection of brain tumors is crucial for enhancing treatment strategies and improving clinical prognosis. However, current conventional methods limit diagnostic accuracy and slow down diagnostic processes due to subjective interpretations and dependence on experts. Deep learning-based approaches have the potential to enhance diagnostic accuracy and facilitate early diagnosis by mitigating current limitations in automatic learning techniques for feature extraction and modeling processes. In this study, an advanced deep learning model, Multi-Scale Local-Context Fusion Network (MSLCFNet), is proposed for automatic diagnosis of brain tumors within the scope of oncological diseases. The proposed model evaluates local spatial features, global patterns, and long-range contextual relationships holistically through multi-layered representations, enabling effective identification of complex structures of tumor morphology. This architecture has the potential to increase diagnostic consistency and minimize the risk of misdiagnosis by reducing inter-observer variability, especially in cases where limited contrast and unclear boundaries lead to diagnostic uncertainty. The proposed model achieved accuracy rates of 96.51% and 98.17% on two different MRI brain tumor datasets, respectively. Additionally, a hybrid model based on MSLCFNet+ResNet50 was proposed in the study to obtain high-level feature representations for brain tumor diagnosis. The model offers a significant improvement in the diagnosis process by reducing misdiagnosis rates to 98.86% and 99.50% accuracy. The proposed methods make a significant contribution to enhancing diagnostic accuracy in clinical decision support systems, as they enable the efficient processing of complex morphological structures.

    • SDG 3 · Sağlık ve Kaliteli Yaşam
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  • MakaleYalnız künye2026
    A reliability generalization meta-analysis of job insecurity scales: Evidence from hospitality and tourism research

    Polat, Eray

    The study examines the pooled reliability estimates of job insecurity (JI) scales employed in hospitality and tourism (H&T) research. By synthesizing reported reliability coefficients and identifying moderators influencing score consistency, this meta-analytic study consolidates the fragmented psychometric evidence in the field. Reliability coefficients were derived from 140 independent samples comprising 56,265 respondents. Pooled reliability estimates were calculated separately for each scale, and moderator analyses were performed to explain the variability. Findings indicate that De Witte's (2000) scale is the most frequently utilized scale. Pienaar et al.’s (2013) scale yielded the highest estimate (α=.94, 95% CI [.89.,97]), whereas Hellgren and Sverke's (2003) scale demonstrated the lowest (α=.82, 95% CI [.78.,85]). Moderator analyses revealed that scale version, publication year, number of items, sample size, scale mean, and gender distribution significantly influenced reliability estimates. The study provides robust, context-dependent evidence to inform the selection of JI scales.

    • SDG 8 · İnsana Yakışır İş ve Ekonomik Büyüme
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  • MakaleAçık Erişim2026
    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.

    • SDG 7 · Erişilebilir ve Temiz Enerji
    • SDG 9 · Sanayi, Yenilikçilik ve Altyapı
    • SDG 11 · Sürdürülebilir Şehirler ve Topluluklar
    • SDG 13 · İklim Eylemi
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