Sürdürülebilir Kalkınma Amaçları

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• MakaleAçık Erişim2026/r/gumushane/13848
  Variability of Schmidt Rebound Values in Volcanic Rocks (Basalt and Lapilli Tuff): Comparative Effect of Surface Roughness, Alteration, and Testing Methods

  Karaman, Kadir; Külekçi, Gökhan; Çakır, Yaşar; Kolaylı, Hasan

  Enhancing the sustainability and safety of rock engineering requires understanding how micro-structural and alteration conditions influence the geomechanical properties of rocks in geotechnical projects. The determination of surface hardness using the Schmidt Hammer is an interdisciplinary experimental method employed in mining, geology, and civil engineering. This study quantitatively evaluates the effects of surface roughness, weathering degree, and evaluation procedures on Schmidt rebound values obtained from basalt and lapilli tuff. Field measurements on eight surfaces produced rebound values between 10 and 60, with standard deviations ranging from 2.4 to 11, reflecting substantial variability related to roughness and alteration. Laboratory results showed that cut surfaces yielded the highest hardness values (Mean ≈ 57–58) with very low variability (SD ≈ 1.1–1.6), whereas natural surfaces exhibited markedly lower rebound values (Mean ≈ 19–22) and greater scatter (SD ≈ 4–4.5). A strong correlation (R2 > 0.97) was observed between JRC roughness and rebound values in laboratory-prepared samples. The percentage difference among the USBR, ASTM, and Sumner & Nel methods remained below 5% when the standard deviation of measurements was under 2, indicating that method selection becomes critical only for heterogeneous surfaces. Mineralogical heterogeneity further increased variability in lapilli tuff, whereas basalt provided highly consistent responses. Overall, this study introduces quantitative thresholds linking roughness, weathering, and statistical variability, offering a more rigorous and reproducible framework for interpreting Schmidt hardness measurements.

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• MakaleAçık Erişim2026/r/gumushane/12307
  A Personalized Target Placement Optimization Framework for VR-Based Upper Extremity Rehabilitation

  Türe, Hayati; Kalfa, Eren; Aslan, Muhammed Emin; Işık, Buket Özdemir; Topçu, Osman; Özdemir, Erhan; Sarıhan, Köksal

  Featured Application: The proposed framework can be directly integrated into existing VR rehabilitation platforms to provide real-time personalized target placement for post-stroke upper extremity therapy, requiring no additional hardware beyond a standard CPU. Virtual reality (VR)-based rehabilitation is an established modality for upper extremity motor recovery; however, existing systems frequently rely on fixed, random, or therapist-tuned target placement that disregards patient-specific motor capacity and population-level priors. This study proposes a cross-patient collaborative swarm intelligence framework that derives zone-based patient profiles from real VR trajectories and augments them with a similarity-weighted cohort prior distilled from clinically similar patients’ successful trajectory clouds and zone-transition graphs. A hybrid Ant Colony Optimization (ACO)–Particle Swarm Optimization (PSO) algorithm optimizes 12 targets per session across a 27-zone ( (Formula presented.) ) workspace using a five-component fitness function encompassing reachability, zone balance, movement efficiency, heatmap-guided challenge coverage, and swarm-flow consistency. The framework was evaluated retrospectively on a single-center cohort of 36 post-stroke patients and 6373 sessions under a leakage-safe simulation protocol with 70/30 chronological splits; outcomes are model-based proxy success rates derived from each patient’s profile rather than directly observed task success. The hybrid strategy achieved a mean simulated success rate of 85.5% ± 5.5%, a 36.4% relative improvement over random placement (Wilcoxon (Formula presented.), Cohen’s (Formula presented.) ); the leakage-safe split yielded 80.1% on the held-out segment versus 61.1% for random, with no statistically significant train–test gap ( (Formula presented.) ). Ablation confirmed both PSO and ACO are individually necessary ( (Formula presented.) pp, (Formula presented.) ). Total session-start computation is 78 ms on standard CPU hardware. These findings constitute a proof-of-concept that collaborative personalized swarm optimization can substantially outperform heuristic target placement under in silico evaluation; clinical efficacy in terms of standardized motor outcome measures remains to be established in a prospective randomized controlled trial, and the findings should be replicated across centers, task modes, and a larger cohort before generalization.

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• MakaleAçık Erişim2026/r/gumushane/12201
  Topic Modeling of Occupational Accident Narratives in the Manufacturing Sector Using BERTopic: Sector-Specific Accident Pattern Analysis

  Aktaş, Esra; Atmaca, Hatice Ediz

  Despite technological developments and increasing awareness in the field of occupational health and safety, occupational accidents remain one of the fundamental problems that continue to have significant economic and social consequences. Particularly in the manufacturing sector, the diversity of production processes, differences in the machinery and equipment used, and the complexity of working environments cause occupational accidents to exhibit risk patterns that are both shared across sectors and specific to individual sectors. In this context, this study conducted a topic modeling analysis using the BERTopic algorithm on 1,600,000 occupational accident narratives to identify sector-specific risk patterns and error types associated with 24 different economic activities within the manufacturing sector. To improve topic modeling performance, a multilingual SentenceBERT-based embedding model and a Turkish-specific tr-MTEB embedding model were compared, and the analysis was carried out using the model that provided higher representational capacity and better topic separation performance for the occupational accident narratives. The findings show that, although the sectors differ in terms of production structure, types of machinery and equipment used, material characteristics, and working environments, occupational accidents largely cluster around similar risk mechanisms. As a result, it was determined that the BERTopic method makes the sectoral risk patterns embedded in occupational accident narratives more visible and provides an analytical basis for developing sector-specific prevention strategies in occupational safety practices.

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• MakaleAçık Erişim2025/r/gumushane/14251
  A Video-Based Mobile Palmprint Dataset and an Illumination-Robust Deep Learning Architecture for Unconstrained Environments

  Koşmaz Sünnetci, Betül; Bingöl, Özkan; Gedikli, Eyüp; Ekinci, Murat; Doğan, Ramazan Özgür; Türk, Salih; Güngör, Nihan

  Featured Application: The proposed framework facilitates secure and user-friendly biometric authentication on mobile platforms, particularly in mobile payment and access control scenarios, under unconstrained conditions. The widespread adoption of mobile devices has made secure and user-friendly biometric authentication critical. However, widely used modalities such as fingerprint and facial recognition show limited robustness under uncontrolled illumination and on heterogeneous devices. In contrast, palmprint recognition offers strong potential because of its rich textural patterns and high discriminative power. This study addresses the limitations of laboratory-based datasets that fail to capture real-world challenges. We introduce MPW-180, a novel dataset comprising videos of 180 participants recorded on their own smartphones in everyday environments. By systematically incorporating diverse illumination conditions (with and without flash) and natural free-hand movements, MPW-180 is the first dataset to adopt a bring-your-own-device paradigm, providing a realistic benchmark for evaluating generalization in mobile biometric models. In addition, we propose PalmWildNet, an SE-block-enhanced deep learning architecture trained with Triplet Loss and a cross-illumination sampling strategy. The experimental results show that conventional methods suffer over 50% performance degradation under cross-illumination conditions. In contrast, our method reduces the Equal Error Rate to 1–2% while maintaining an accuracy above 97%. These findings demonstrate that the proposed framework not only tolerates illumination variability but also learns robust illumination-invariant representations, making it well-suited for mobile biometric authentication.

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• MakaleAçık Erişim2025/r/gumushane/14237
  Investigation of Tensile and Fatigue Behavior of Cr/CrN/TiCrN/TiCrCN Multilayer Films Coated on AA6063 and AZ91 Alloys by Closed-Field Unbalanced Magnetron Sputtering Process

  Yeşildal, Ruhi; Sezer, Sadberk; Karabudak, Filiz

  Despite the widespread use of Mg and Al alloys among light metals in the automobile and aviation industries, they have low tensile and fatigue strength. Therefore, in the present work, AZ91 Mg and AA6063 Al alloys were coated with a multilayer transition metal nitride film (Cr/CrN/TiCrN/TiCrCN) to increase fatigue and tensile strength. Films with Cr/CrN/TiCrN/TiCrCN microstructure architecture were synthesized on the surfaces of AZ91 Mg and AA6063 Al alloys using the CFUBMS (closed-field unbalanced magnetron sputtering) system, one of the PVD (physical vapor deposition) techniques. Films’ structural properties were analyzed by XRD, SEM, and EDAX, whereas mechanical properties were investigated using tensile and rotary bending fatigue testing machines. According to the SEM examination, the Cr, CrN, TiCrN, and TiCrCN multilayer nitride films on the two alloys have a columnar and dense microstructure. The XRD analysis detected Cr (211), CrN (111) and (200), TiN (111), (200) and (222), and TiCN (200) and (311) diffraction peaks. The Cr/CrN/TiCrN/TiCrCN multilayer coating increased the fatigue limit value of AZ91 by 11.22% from 70.26 MPa to 78.15 MPa. The fatigue limit value of AA6063 decreased by 9.79% from 79.71 MPa to 71.9 MPa. After coating, the tensile strength value of AZ91 increased from 137.89 MPa to 139.65 MPa, while the tensile strength of AA6063 decreased from 129.35 MPa to 118.16 MPa.

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• MakaleAçık Erişim2025/r/gumushane/14096
  Concurrent Validity of the Optojump Infrared Photocell System in Lower Limb Peak Power Assessment: Comparative Analysis with the Wingate Anaerobic Test and Sprint Performance

  Khemiri, Aymen; Negra, Yassine; Ceylan, Halil İbrahim; Hajri, Manel; Njah, Abdelmonom; Hachana, Younes; Yıldız, Mevlüt; Bayrakdaroğlu, Serdar; Muntean, Raul Ioan; Attia, Ahmed

  Aim: This study analyzed the concurrent validity of the Optojump infrared photocell system for estimating lower limb peak power by comparing it with the 15 s Wingate anaerobic test (WAnT) and examining relationships with sprint performance indicators. Methods: Twelve physically active university students (ten males, two females; age: 23.39 ± 1.47 years; body mass: 73.08 ± 9.19 kg; height: 173.67 ± 6.97 cm; BMI: 24.17 ± 1.48 kg·m−2) completed a cross-sectional validation protocol. Participants performed WAnT on a calibrated Monark ergometer (7.5% body weight for males, 5.5% for females), 30 s continuous jump tests using the Optojump system (Microgate, Italy), and 30 m sprint assessments with 10 m and 20 m split times. Peak power was expressed in absolute (W), relative (W·kg−1), and allometric (W·kg−0.67) terms. Results: Thirty-second continuous jump testing produced systematically higher peak power values across all metrics (p < 0.001). Mean differences indicated large effect sizes: relative power (Cohen’s d = 0.99; 18.263 ± 4.243 vs. 10.99 ± 1.58 W·kg−1), absolute power (d = 0.86; 1381.71 ± 393.44 vs. 807.28 ± 175.45 W), and allometric power (d = 0.79). Strong correlations emerged between protocols, with absolute power showing the strongest association (r = 0.842, p < 0.001). Linear regression analysis revealed that 30 s continuous jump-derived measurements explained 71% of the variance in Wingate outcomes (R2 = 0.710, p < 0.001). Sprint performance showed equivalent predictive capacity for both tests (Wingate: R2 = 0.66; 30 s continuous jump: R2 = 0.67). Conclusions: The Optojump infrared photocell system provides a valid and practical alternative to laboratory-based ergometry for assessing lower limb anaerobic power. While it systematically overestimates absolute values compared with the Wingate anaerobic test, its strong concurrent validity (r > 0.80), large effect sizes, and equivalent predictive ability for sprint performance (R2 = 0.66–0.71) confirm its reliability as a field-based assessment tool. These findings underscore the importance of sport-specific, weight-bearing assessment technologies in modern sports biomechanics, providing coaches, practitioners, and clinicians with a feasible method for monitoring performance, talent identification, and training optimization. The results further suggest that Optojump-based protocols can bridge the gap between laboratory precision and ecological validity, supporting both athletic performance enhancement and injury prevention strategies.

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• MakaleAçık Erişim2025/r/gumushane/12400
  Feature Fusion Using Deep Learning Algorithms in Image Classification for Security Purposes by Random Weight Network

  Kiran, Mustafa Servet; Seyfi, Gokhan; Yilmaz, Merve; Esme, Engin; Wang, Xizhao

  Automated threat detection in X-ray security imagery is a critical yet challenging task, where conventional deep learning models often struggle with low accuracy and overfitting. This study addresses these limitations by introducing a novel framework based on feature fusion. The proposed method extracts features from multiple and diverse deep learning architectures and classifies them using a Random Weight Network (RWN), whose hyperparameters are optimized for maximum performance. The results show substantial improvements at each stage: while the best standalone deep learning model achieved a test accuracy of 83.55%, applying the RWN to a single feature set increased accuracy to 94.82%. Notably, the proposed feature fusion framework achieved a state-of-the-art test accuracy of 97.44%. These findings demonstrate that a modular approach combining multi-model feature fusion with an efficient classifier is a highly effective strategy for improving the accuracy and generalization capability of automated threat detection systems.

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• Derleme MakaleAçık Erişim2025/r/gumushane/11695
  Microplastics in Dairy Products: Occurrence, Characterization, Contamination Sources, Detection Methods, and Future Challenges

  Gürmeriç, Hüseyin Ender; Basaran, Burhan

  In this study, data from 17 studies reporting the presence of microplastics in milk and dairy products in the literature were examined with a product-based systematic approach. In addition, geographical comparisons were made between different countries. In milk and dairy products, the concentration of microplastics has been reported to exhibit a broad range, extending from non-detectable levels to as high as 10,040 MPs per kilogram, contingent upon the specific product types. Milk powder (especially baby milk powder) stands out as the riskiest product group in terms of microplastic content. Although the sizes and colors of the detected microplastics vary significantly, the fiber form is generally predominant. While polyethylene, polypropylene, polyamide and polyester are among the polymers frequently detected, high-temperature-resistant industrial polymers such as polytetrafluoroethylene, polysulfone, polyurethane were also encountered. In addition, the presence of some polymers (such polyvinyl chloride, polyurethane) that are toxicologically risky for human health was reported in the studies. In addition, the study evaluated the chemical, enzymatic and physical methods used for the separation and identification of MPs; the advantages and limitations of FT-IR, Raman and other analysis techniques were revealed. This study reveals that MP contamination in milk and dairy products is a multidimensional problem. The findings show that milk and dairy products are highly susceptible to plastic contamination at every stage of production.

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• MakaleAçık Erişim2024/r/gumushane/14087
  Performance Comparison of Various Tunnel Lighting Scenarios on Driver Lane-Changing Behaviours in a Driving Simulator

  Ozturk, Omer Faruk; Mazlum, Yusuf; Aydin, Metin Mutlu; Coruh, Emine; Bayata, Halim Ferit

  Recent advances in tunnel infrastructure have emphasized safety, operational efficiency and low operating costs. Modern tunnels are equipped with systems to improve both safety and operational performance. This study investigates the effect of tunnel lighting and vehicle breakdown scenarios on driver lane changing behaviour (LCB) using a driving simulator modelled on the third longest twin-tube tunnel. Data were collected from 125 drivers considering various driver characteristics with different lighting conditions and the presence of a stopped vehicle in a lane. The results show that drivers tend to slow down and change lanes more safely in response to red and flashing lights. In contrast, blue sky lights, which are designed to reduce stress and compare with other dangerous scenarios, had no significant effect on LCB. In addition, demographic factors such as gender and previous simulator experience played a role in influencing LCB tendencies. Female drivers and those familiar with simulators showed more cautious behaviour. The findings showed valuable insights into how tunnel lighting systems can improve safety. Results highlighted the potential for dynamic lighting and targeted driver training programs to improve tunnel safety. All these findings may contribute to ongoing efforts to improve traffic management and reduce accidents in tunnel environments.

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• MakaleAçık Erişim2024/r/gumushane/12399
  Modified REL-Based Piecewise Path Loss Modeling Approach for Shore-to-Ship Communication at 5.6 GHz

  Eyuboglu, Ekrem; Hasirci Tugcu, Zeynep; Kuzulugil, Kenan; Cavdar, Ismail Hakki

  The need for reliable and uninterrupted communication systems in the marine environment has become critically important with increasing maritime activities, environmental monitoring, and the spread of autonomous systems. However, the complex structure of electromagnetic wave propagation in a sea environment limits the accuracy of the existing propagation models. Thus, the Modified Round Earth Loss (REL) model was first developed in this study to estimate the path loss more accurately in shore-to-ship communication. Subsequently, a piecewise modeling approach based on the principle of two-segment data modeling was proposed. In the Modified REL model, unlike the traditional REL model, the paths and gains of the direct and reflected waves were not considered equal in the calculations. Moreover, unlike in the classical approach, the receiver height was not taken as a fixed value; the estimated best receiver height value for each measurement was included in the calculations as a representation of the effect of roughness in the sea environment. Thus, the model is better adapted to various environmental conditions. In addition, the proposed piecewise model divides the propagation medium into two regions using a break point calculated by Fresnel zone theory. The Modified REL model was used for the first region and the log-distance model was used for the second region. This method allows for more accurate modeling of signal behaviors, especially at different distances. Experimental measurements and performance evaluations conducted using four different shore-to-ship communication scenarios show that the Modified REL model shows an average improvement of up to 3% in Root Mean Square Error (RMSE) values compared to the classical REL model. Additionally, the proposed piecewise model improves the fitting error of the Modified REL model, which models the data as a single whole, by an average of 22.25%. These findings emphasize the necessity of propagation models that are sensitive and adaptable to environmental changes for maritime communication.

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