Hatay Mustafa Kemal Üniversitesi Akademik Veri Yönetim Sistemi
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Developing a mathematical model with scattering effects for predicting 2-D muon interaction position in a large single scintillator
No full textThis study examines the feasibility of using a large single scintillator with conventional photomultiplier tubes for muon tomography applications. The performance in reconstructing muon beam interaction positions on a large scintillator using Anger logic with both linear and logarithmic weights, as well as a new mathematical model, is presented. A 50cm×50cm scintillator with a 2×2 array of small PMTs for light readout was configured in the Geant4 simulation program. Various surface finishing models and detector design parameters, such as scintillator thickness, surface roughness, and air gap thickness, were tested to evaluate their impact on position resolution. A new logarithmic weight was introduced to characterize the true interaction position using a Gaussian fit. The log-likelihood method was applied to the reconstructed position distributions using both linear and logarithmic weights. The bias related to the true interaction position at the center of the reconstructed position distributions was corrected at the expense of position resolution. The beam interaction position resolution was mapped relative to the true interaction position using both weighting methods. Finally, significant improvements in position resolution across the entire scintillator surface were achieved through mathematical modeling that accounted for the scattering effects of scintillation photons. With the developed model, a position resolution of approximately 8 mm rms near the center of the scintillator was achieved using only four PMTs with a diameter of 2.5 cm
Driving Circular Economy Transitions through Technological Innovation in the Top Five Waste-Generating Economies
No full textThe rapid increase in global consumption has led to substantial growth in waste generation, posing a significant challenge to achieving the Sustainable Development Goals (SDGs), particularly SDGs 11 and 12, which emphasize waste reduction. Technological progress in waste management and the ability to adapt these innovations to environmental needs play a crucial role in mitigating waste‑related pressures. This study investigates how economic growth, urbanization, environmental policy stringency (EPS), and innovations in waste management, recycling and reuse, and wastewater treatment affect the load capacity factor (LCF) in the top five waste-generating economies (U.S., Japan, Germany, France, and Türkiye) from 2005 to 2020. Using the Augmented Mean Group (AMG) and Half‑Panel Jackknife (HPJ) estimation approaches, the analysis reveals that economic growth and urbanization reduce LCF, indicating increased environmental degradation. In contrast, patents related to waste management, recycling, and wastewater treatment technologies improve LCF, supporting progress toward SDGs 11 and 12. EPS, however, shows no significant effect, highlighting inconsistencies in policy enforcement across these economies. Overall, the findings underscore the need for stronger regulatory frameworks that tighten environmental policies and incentivize technological innovation to advance circular economy transitions
Evaluating the Impact of Toolbox Training on Health Professionals in Radiation Environments: A Randomized Controlled Trial
No full textIn recent years, the use of radiation diagnostics, treatment services, and many areas of health services has become widespread with technological developments. The widespread use of radioactive substances and radiation-producing devices in health services has increased the need for effective radiation protection programs, and the need for training in terms of the applicability of these programs is also increasing. This randomized controlled trial was conducted to evaluate the effect of on-the-job toolbox training defined as a short, informal safety meeting led by a supervisor that focuses on specific workplace hazards or safe work practices on the knowledge, practices, and safety-related behaviors of healthcare professionals working in radiation environments. Sixty-three participants from the radiology units of a university hospital in Hatay, Türkiye, were randomly assigned to an intervention group (n=33) or a control group (n=30). The intervention group received brief, face-to-face training sessions using visual materials in their work environments. Data was collected before and after the training and at two follow-up periods. After the training, radiation protection knowledge increased significantly in the intervention group (p<0.001), while no change was observed in the control group. Observational assessments revealed that there was an improvement in practice scores in the intervention group and that the gains were largely maintained over time. Additionally, adverse event reporting, an important quality indicator, increased significantly in the intervention group, indicating increased safety awareness. These results confirm that toolbox training is an effective method for improving both knowledge and safe practices among healthcare professionals. Its brief, practical, and workplace-based format contributes to increased engagement and retention of learning. The findings support the integration of toolbox training into in-service training programs as a complement or alternative to traditional methods. Future studies should examine its long-term effectiveness and applicability in a variety of healthcare settings
Type 1 Diabetes Stigma Assessment Scale (Dsas-1) For Adolescents: A Study of Validity and Reliability
No full textOut-of-field dose assessment in electron beam radiotherapy: experimental measurements, GEANT4/GATE Monte Carlo simulations, and evaluation of the Eclipse GGPB algorithm
No full textThe aim of this study was to quantitatively evaluate out-of-field (peripheral) doses in electron beam radiotherapy, to compare experimental measurements with treatment planning system (TPS) calculations and Monte Carlo (MC) simulations, and to discuss their clinical relevance. Peripheral dose measurements were performed in a water phantom using a Roos parallel-plate ionization chamber for 6, 9, 12, and 15 MeV electron beams delivered by a Varian linear accelerator with various applicator sizes. Under identical conditions, dose distributions were recalculated with the Eclipse TPS using the General Gaussian Pencil Beam (GGPB) algorithm and with Geant4/GATE MC simulations. The dependence of peripheral dose on energy, applicator size, and distance from the field edge was assessed. Experimental data showed that peripheral doses increased with both beam energy and applicator size, while decreasing with greater depth and distance from the field border. TPS calculations consistently underestimated peripheral doses, with discrepancies up to 30% at higher energies and near-field regions. By contrast, MC simulations demonstrated excellent agreement with measurements across all energies and field sizes, with differences typically within 1%-2%. The results demonstrate that the GGPB algorithm tested in this study substantially underestimates out-of-field doses. Experimental dosimetry and validated MC simulations are essential for comprehensive dose evaluation, particularly for estimating secondary cancer risks, protecting adjacent critical organs, and ensuring the safety of implantable devices. The integration of GPU-accelerated Monte Carlo algorithms and AI-based prediction models into clinical workflows may further improve the accuracy and efficiency of peripheral dose calculations, ultimately enhancing patient safety
First report of blueberry red ringspot virus (Soymovirus maculavaccinii) in highbush blueberries in Türkiye
No full textBlueberry red ringspot virus (BRRV) (Soymovirus maculavaccinii) belongs to theSoymovirus genus in the Caulimoviridae family, causes red ringspot disease inhighbush blueberry (Vaccinium corymbosum L.), and has been reported in blueberrygrowingregions in the United States, Japan, and some European countries. Intensivevirus surveillance conducted since 2015 in Türkiye, where wild and cultivatedblueberries are widely grown, has identified blueberry mosaic-associated virus (BlMaV)as the most prevalent virus, with blueberry leaf mottle virus (BLMoV) recently reported(Çağlayan et al. 2025). In August 2023, red ringspot symptoms (Supplementary Fig. 1)resembling BRRV infection were observed on leaves and stems of the Darrow cultivarin Kocaeli province in Turkiye, although the fruits were symptomless. To confirm thepresence of BRRV, DNA was extracted from 290 symptomatic and asymptomaticblueberry plants using DNeasy Plant Mini Kit (Qiagen, Germany) and subjected to PCRusing primers targeting the putative translational transactivator (BRRSV3F/BRRSV4R:Polashock et. al., 2009) and coat protein (BRRSV 13F/BRRSV 14R: Glasheen et al.2002) genes of BRRV. DNA fragments of the expected sizes were successfullyamplified from only five symptomatic plants using both primer pairs (SupplementaryFig. 2), and were sequenced in both orientations and submitted to GenBank (Acc. No.OR684355-59 for the putative translational transactivator gene and OR886932-36 forthe coat protein gene). Sequence and phylogenetic analysis (Supplementary Fig. 3)revealed that the Turkish BRRV isolates were closely related to an isolate fromSlovenia (Acc. No. JF421559), exhibiting 99.79% (coat protein) to 100% (translationaltransactivator) nucleotide sequence identity. These findings represent the firstdetection of BRRV in highbush blueberries in Türkiye. Considering rapid expansion ofblueberry cultivation in the country, results of this study highlight the importance ofimplementing strict propagation and management strategies. Effective measures willbe essential to mitigate the spread of BRRV and other blueberry viruses, therebyprotecting crop health and ensuring sustainable production.</p
The field evaluation of the diagnostic performance of a commercial pregnancy-associated glycoprotein-based lateral flow assay for early pregnancy detection in ewes
No full textThe present study aimed to determine the diagnostic accuracy of the commercial Alertys OnFarm Pregnancy Test (AOPT) based on pregnancy-associated glycoprotein (PAG) for early pregnancy diagnosis in ewes. A total of 100 multiparous Awassi ewes were synchronised for estrus and mated during the non-breeding season. Whole blood samples were collected from the jugular vein of all ewes on days 21, 28, and 35 after mating. The AOPT was first performed on the collected whole blood samples. Plasma PAG concentrations were then determined by Enzyme-Linked Immunosorbent Assay (ELISA). Pregnancy diagnosis was made on the same days by transrectal ultrasonography (USG), which was accepted as the reference standard. The sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and accuracy of AOPT compared to USG on days 21, 28, and 35 after mating were calculated. In addition, the diagnostic performance of plasma PAG concentrations for predicting pregnancy status was evaluated by determining cut-off values and conducting receiver operating characteristic (ROC) curve analysis. When the results were examined, the sensitivity of AOPT was calculated as 77.55 %, specificity as 92.15 %, PPV as 90.47 %, NPV as 81.03 % and accuracy as 85.00 % at 21 days after mating and showed strong agreement with transrectal USG (Kappa= 0.699; P < 0.001). In contrast, the sensitivity (95.55 %) and accuracy (93.00 %) of AOPT on day 28 improved significantly, showing almost perfect agreement with USG (Kappa= 0.859; P < 0.001). On day 35, AOPT achieved the highest diagnostic performance compared to other pregnancy days with 100 % sensitivity, 96.49 % specificity, and 98.00 % accuracy (Kappa= 0.959; P < 0.001). Plasma PAG levels (OD) were significantly higher in pregnant ewes than in non-pregnant ewes at all time points (P < 0.001). The optimal cut-off values for pregnancy detection were determined as > 0.28 on day 21 (AUC= 0.941), > 0.48 on day 28 (AUC= 0.986), and > 0.43 on day 35 (AUC= 1.000). In conclusion, AOPT can be considered a practical, rapid, and reliable method for early pregnancy detection in ewes, particularly from day 28 post-mating. However, possible false-negative results that may occur very early in pregnancy and false-positive results due to embryonic loss later in pregnancy necessitate careful interpretation and, if necessary, repeat testing or confirmation with USG.The present study aimed to determine the diagnostic accuracy of the commercial Alertys OnFarm Pregnancy Test (AOPT) based on pregnancy-associated glycoprotein (PAG) for early pregnancy diagnosis in ewes. A total of 100 multiparous Awassi ewes were synchronised for estrus and mated during the non-breeding season. Whole blood samples were collected from the jugular vein of all ewes on days 21, 28, and 35 after mating. The AOPT was first performed on the collected whole blood samples. Plasma PAG concentrations were then determined by Enzyme-Linked Immunosorbent Assay (ELISA). Pregnancy diagnosis was made on the same days by transrectal ultrasonography (USG), which was accepted as the reference standard. The sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and accuracy of AOPT compared to USG on days 21, 28, and 35 after mating were calculated. In addition, the diagnostic performance of plasma PAG concentrations for predicting pregnancy status was evaluated by determining cut-off values and conducting receiver operating characteristic (ROC) curve analysis. When the results were examined, the sensitivity of AOPT was calculated as 77.55 %, specificity as 92.15 %, PPV as 90.47 %, NPV as 81.03 % and accuracy as 85.00 % at 21 days after mating and showed strong agreement with transrectal USG (Kappa= 0.699; P < 0.001). In contrast, the sensitivity (95.55 %) and accuracy (93.00 %) of AOPT on day 28 improved significantly, showing almost perfect agreement with USG (Kappa= 0.859; P < 0.001). On day 35, AOPT achieved the highest diagnostic performance compared to other pregnancy days with 100 % sensitivity, 96.49 % specificity, and 98.00 % accuracy (Kappa= 0.959; P < 0.001). Plasma PAG levels (OD) were significantly higher in pregnant ewes than in non-pregnant ewes at all time points (P < 0.001). The optimal cut-off values for pregnancy detection were determined as > 0.28 on day 21 (AUC= 0.941), > 0.48 on day 28 (AUC= 0.986), and > 0.43 on day 35 (AUC= 1.000). In conclusion, AOPT can be considered a practical, rapid, and reliable method for early pregnancy detection in ewes, particularly from day 28 post-mating. However, possible false-negative results that may occur very early in pregnancy and false-positive results due to embryonic loss later in pregnancy necessitate careful interpretation and, if necessary, repeat testing or confirmation with USG.</div
Rootstock-mediated salinity resilience in cucumber (Cucumis sativus L.): integrating physiological traits, genomic stability and machine learning
No full textBackground: Salt stress is a major abiotic constraint in cucumber (Cucumis sativus L.), reducing biomass, photosynthesis, and genomic stability. Grafting onto salt-tolerant Cucurbita rootstocks is a promising strategy to enhance plant resilience. Recently, machine learning (ML) has provided new opportunities to capture complex trait interactions and identify key predictors of stress tolerance. Results: We evaluated two cucumber cultivars (Cagla F1, Minimix F1) grafted onto four interspecific Cucurbita maxima × Cucurbita moschata rootstocks (TZ148, Devrim, Cremna, Kublai) under 0 vs. 100 mM NaCl for 30 days in a soilless fertigation system. Morphological, physiological, and molecular traits were evaluated, including biomass accumulation, chlorophyll content (SPAD) and incident photosynthetically active radiation (PAR), and genomic template stability (GTS) using ISSR markers. Salt stress reduced growth and biomass (leaf FW − 56%, root DW − 74%) and lowered SPAD and relative water content (RWC); grafting—especially with TZ148 (and to a lesser extent Kublai)—mitigated these losses by maintaining chlorophyll content (SPAD) and biomass under salinity. Grafted combinations, especially TZ148/Cagla, maintained higher stability (GTS: 88%, GC: 0.07), confirming the protective role of grafting. ML approaches, including Principal Component Analysis (PCA) and Random Forest (RF), clearly separated control vs. salinity and, while grafting types showed only partial separation, RF consistently ranked root/stem fresh weight, SPAD, leaf area, and fruit weight as top predictors. Conclusion: Grafting significantly improved cucumber tolerance to salinity by sustaining biomass, photosynthetic capacity proxies (SPAD), and genomic integrity. ML-based analyses added predictive power and biological interpretation, confirming grafting with appropriate rootstocks as a sustainable strategy for cucumber production in saline nutrient solution conditions
