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Impact of Rubber Dam Thickness On Image Quality Parameters in Photostimulable Phosphor Plates
No full textObjective: The aim of this study is to investigate the effects of different rubber dam thicknesses on image quality in two exposure protocols.
Methods: For the study, three rubber dam thicknesses were used: thin (0.14 mm), medium (0.18 mm), and heavy (0.22 mm). To mimic clinical conditions, a rubber dam was used in two layers. Exposure was performed using two different protocols: Protocol 1: 0.080 s exposure time, 65 kV, 7 mA, and Protocol 2: 0.160 s exposure time, 65 kV, 7 mA. For each thickness and protocol, 47 measurements were taken (n = 47). Radiographic images were exported in TIFF and analyzed using ImageJ software. The region of interest was determined, and signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), Michelson, and Weber contrast values were obtained. One-way ANOVA, post hoc Tukey, and intra-class correlation were used for statistical analysis.
Results: No statistical difference was detected for protocol 1 between rubber dam thicknesses in SNR, CNR, Michelson, and Weber contrasts (p > 0.05). For protocol 2 (longer exposure time), heavy and medium thickness had lower SNR and CNR values than the thin one (p < 0.05). Michelson and Weber contrasts were statistically changed in different thicknesses of rubber dam (p < 0.05). ICC values were good and excellent.
Conclusions: The thick rubber dam reduced SNR and CNR values; likewise, Michelson and Weber contrasts were changed, which pointed a reduced image quality and negatively affected object visibility. Short exposure times are recommended to maintain image quality in clinical situations requiring the use of a thick rubber dam
An Intelligent Multi-Stage GA–SVM Hybrid Optimization Framework for Feature Engineering and Intrusion Detection in Internet of Things Networks
Full text linkThe rapid growth of IoT networks necessitates efficient Intrusion Detection Systems (IDS) capable of addressing dynamic security threats under constrained resource environments. This paper proposes a hybrid IDS for IoT networks, integrating Support Vector Machine (SVM) and Genetic Algorithm (GA) for feature selection and parameter optimization. The GA reduces the feature set from 41 to 7, achieving a 30% reduction in overhead while maintaining an attack detection rate of 98.79%. Evaluated on the NSL-KDD dataset, the system demonstrates an accuracy of 97.36%, a recall of 98.42%, and an F1-score of 96.67%, with a low false positive rate of 1.5%. Additionally, it effectively detects critical User-to-Root (U2R) attacks at a rate of 96.2% and Remote-to-Local (R2L) attacks at 95.8%. Performance tests validate the system’s scalability for networks with up to 2000 nodes, with detection latencies of 120 ms at 65% CPU utilization in small-scale deployments and 250 ms at 85% CPU utilization in large-scale scenarios. Parameter sensitivity analysis enhances model robustness, while false positive examination aids in reducing administrative overhead for practical deployment. This IDS offers an effective, scalable, and resource-efficient solution for real-world IoT system security, outperforming traditional approaches
Beliefs About Emotions and Positive Emotion Regulation: Do Fears of Social Evaluation Moderate the Relationship?
No full textIndividuals hold a variety of beliefs about emotions, which can influence how they regulate specific emotions. Additionally, concerns about social evaluations can shape how people's beliefs about emotions are associated with the way they manage their feelings. In this study, we investigate the beliefs about undesirability of positive emotions and controllability of feeling good in relation to positive emotion regulation strategies (i.e., positive rumination vs. dampening). Within the scope of this study, the concepts of fear of happiness and discomfort with positive emotions were examined in relation to beliefs about undesirability. Moreover, we considered the moderating roles of both fears of positive and negative evaluation in the relationships between those beliefs and the regulation strategies. Our findings (N = 411) indicated that both fear of happiness and discomfort with positive emotions were associated with lower positive rumination but were associated with higher dampening. On the contrary, beliefs about the controllability of feeling good were associated with higher positive rumination but with lower dampening. However, neither of the fears of social evaluation moderated the relationship between emotion beliefs and positive emotion regulation strategies. Our findings highlight the role of emotional beliefs in positive emotion regulation and suggest that interventions targeting these beliefs can improve emotion regulation skills
Machine learning-driven classification of acidic and alkaline enzymes in the α/β hydrolase family
No full textAcidophiles and alkaliphiles are microorganisms that thrive in extremely acidic and alkaline environments, respectively. Although extensive research has been conducted on the classification of thermophilic enzymes, far fewer studies have focused on the classification of acidic enzymes, particularly using machine learning. This study aims to classify acidic and alkaline enzymes within the α/β hydrolase family using machine learning methods based on their amino acid sequences and physicochemical properties, independent of secondary structural variations. In this study, 403 bacterial enzymes from the α/β hydrolase family were analyzed to classify acidic and alkaline enzymes. We applied several machine learning models, including Decision Tree, Random Forest, eXtreme Gradient Boosting, and Support Vector Machine, along with analyses of amino acid sequences and their chemical properties. The Random Forest model achieved 76% accuracy and an AUC of 0.90 ± 0.03, revealing that alanine, lysine, and the grand average of hydropathicity (GRAVY) were key features contributing to the classification of acidic and alkaline enzymes. Alanine and glycine showed a positive correlation with acidic characteristics, suggesting their importance in acidic environments and their potential as targets for enzyme engineering. The identified key amino acids and physicochemical properties provide insights into enzyme engineering and industrial applications under extreme conditions
Assessment of mandibular canal proximity to molar root apices in a Turkish subpopulation: A cone-beam computed tomography study
No full textBackground: The close anatomical relationship between root apices and the mandibular canal (MC) may be of clinical importance in preventing inferior alveolar nerve (IAN) injury during the root canal treatment (RCT) of mandibular molars.
Objectives: The aim of the present study was to evaluate the impact of the mean distance between MC and the root apices of mandibular first and second molars on the risk of IAN injury during RCT in a Turkish subpopulation.
Material and methods: Cone-beam computed tomography (CBCT) images of 934 patients were evaluated. Mandibular molars were examined, and parasagittal sections were used to measure the shortest linear distance between the root apex and the superior cortical border of MC.
Results: The mesial and distal roots were closer to MC in the 18-25-year age group and in females (p < 0.05). Overall, 284 (10.5%) mesial roots were in intimate contact with or invading MC, and 80 (3.0%) were located very close to MC (<1.00 mm). Among distal roots, 328 (12.2%) were in intimate contact with or invading MC, 76 (2.8%) were very close to MC, and 2,288 (85.0%) were distant from MC.
Conclusions: The risk of IAN injury during RCT was higher for the distal roots of second molar teeth, especially in the age group of 18-25 years and in female patients
Insights into vaccination: a cross-sectional study of knowledge, attitudes, and barriers among community pharmacists in Turkiye
Full text linkBackground: Community pharmacists play a vital role in public health by promoting and providing vaccination services. Their knowledge, attitudes, and perceived barriers are critical determinants of their effectiveness in this role. The primary outcomes of this study were pharmacists’ knowledge, attitudes, and logistical challenges related to vaccination, with the hypothesis that these factors differ according to sex, years of experience, job title, and pharmacy location. Materials and methods: This study is an online cross-sectional survey of all community pharmacists. A standardized 50-item questionnaire was used to obtain demographic information, vaccination knowledge, attitudes toward vaccines, and barriers to vaccination. Descriptive statistics such as frequencies, percentages, means, medians, standard deviations and chi-square tests were applied via SPSS 29.0 to analyze the dataset. Results: An online survey of 489 pharmacists revealed critical findings. Significant knowledge gaps exist, particularly concerning tetanus vaccination: only 59.3% knew the booster dose, and 54.4% recognized the primary 3-dose series. Attitudinally, 18% were ambivalent or did not advocate the influenza vaccine. Demographic analyses revealed complex influences: female pharmacists were less likely to agree that vaccines are safe (OR = 0.57, p = 0.0107) and less likely to feel professional pressure (OR = 0.54, p = 0.0043) than males were. Critically, the perception that tetanus is a serious threat was negatively correlated with age (r = −0.2390, p < 0.001). Major systemic barriers include the lack of authority to administer vaccines, insufficient reimbursements (68.5% reporting inadequacy), and the widespread absence of a method to identify unvaccinated adults (91% reporting no method). Conclusion: Before advocating for an expanded scope to include vaccine administration, substantial efforts to improve pharmacists’ knowledge and attitudes and address logistical barriers must be prioritized
Sexuality Experiences of People with Multiple Sclerosis in Reproductive Age: A Qualitative Study
Full text linkThis study explored how reproductive-age adults living with multiple sclerosis (MS) experience and interpret sexuality, focusing on physical, emotional, and relational dimensions of sexual life. A qualitative design using conventional content analysis was applied. Between February and March 2025, online video interviews were conducted with 25 purposively selected participants, and transcripts were analysed following Graneheim and Lundman’s five-step approach with MAXQDA 10. Participants reported that MS affected their daily routines through chronic fatigue and decreased social participation, and described primary and secondary sexual dysfunctions such as reduced desire, orgasm difficulties, vaginal dryness, and erectile problems. Many participants reported limited partner understanding, difficulties communicating about sexual issues, and little or no professional counselling. Our analysis suggested that the convergence of neurogenic symptoms, fatigue, partner misunderstanding, and insufficient clinical attention contributes to persistent, unmet sexual-health needs. These findings indicate that sexual concerns remain under-addressed in MS management. Integrating culturally sensitive, multidisciplinary, person-centred sexual counselling into neurological services to better integrate sexual health into routine MS care may improve quality of life by addressing physiological, psychological, and relational aspects simultaneously
Multimodal fusions for defect detection of photovoltaic panels by mask R-CNN and hawkfish optimization algorithm
Full text linkAccurate detection of photovoltaic (PV) module defects remains challenging due to environmental variability and the limited fault visibility of single-modality imaging. While RGB and electroluminescence (EL) images provide structural and subsurface information, they fail to capture thermal fault characteristics associated with hotspots, cell mismatch, and localized heating. Integrating infrared (IR) imagery offers complementary thermal cues that are critical for comprehensive PV inspection. This paper proposes a multimodal PV defect segmentation framework based on a modified Mask R-CNN architecture that fuses RGB, IR, and EL modalities at the feature level. A dedicated alignment pipeline combining homography transformation and enhanced correlation coefficient refinement ensures geometric consistency across modalities. A Fusion Attention Block adaptively weights modality-specific features, enabling effective cross-modal representation learning. Model hyperparameters and fusion weights are automatically optimized using the HawkFish Optimization Algorithm to improve convergence stability and segmentation robustness. Experiments conducted on statistically paired RGB–EL–IR datasets demonstrate that incorporating IR imagery significantly improves the detection of thermally driven defects and reduces false negatives in low-contrast and ambiguous regions. The proposed framework consistently outperforms unimodal and bimodal baselines, achieving state-of-the-art segmentation accuracy and enhanced defect localization, particularly for heat-related fault patterns. The results confirm that thermal information provides critical diagnostic value that cannot be recovered from RGB or EL data alone. The adaptive fusion strategy and optimization-driven tuning further enhance generalization under real-world conditions. These findings highlight the importance of IR-integrated multimodal learning for reliable and scalable PV module inspection systems
ATR-FTIR spectroscopy combined with chemometrics reveals molecular alterations and anticancer effects of Nigella sativa extract in human colon cancer cells
Full text linkColorectal cancer remains the second leading cause of cancer-related mortality worldwide, with current therapeutic approaches often demonstrating limited efficacy. Nigella sativa L. (NS) seeds, historically valued for their medicinal properties, exhibit promising anticancer potential. This study investigates the molecular effects of NS methanolic extract on CaCo-2 human colon cancer cells, focusing on cellular composition, dynamics, and segregation patterns. Unsupervised chemometric analyses, including principal component and hierarchical cluster analyses, demonstrated a complete separation between control and NS-treated cells, indicating significant molecular divergence, further validated by supervised classification methods. Spectral analysis revealed reductions in unsaturated lipids, proteins, glucose, and DNA levels, along with a shortening of fatty acid acyl chain length. In contrast, saturated lipid and triglyceride content increased, accompanied by enhanced membrane fluidity and lipid disorder, indicating substantial alterations in cellular lipid dynamics and acyl chain flexibility. Furthermore, oxidative stress markers were elevated, as evidenced by increased protein carbonylation, while protein phosphorylation levels declined. NS treatment also induced protein conformational changes, notably an increase in aggregated β-sheet structures, suggesting protein denaturation. These biochemical modifications were strongly associated with NS-enhanced reactive oxygen species (ROS) levels. Overall, this study elucidates the molecular mechanisms underlying the anticancer effects of NS, supporting its potential as an adjunctive therapeutic strategy for colorectal cancer.Funding agency : Altınbaş University Research Fund.
Grant number : PB2018-GZ-TIP-
Enhanced AI-simulink hybrid framework for low-latency interference mitigation and performance optimization in 5 G RF receivers
No full text'The rapid expansion of 5 G wireless communication systems has accelerated the need for intelligent receiver architectures capable of adaptively mitigating interference while preserving signal integrity. This study presents an enhanced hybrid AI–Simulink framework that integrates deep learning-based estimation with a parametric Simulink RF signal chain to detect, predict, and suppress jamming and noise-induced distortion in 5 G RF receivers. A processed dataset of approximately 250,000 labeled signal samples was generated from Simulink simulations and used to train and validate the proposed model. The framework demonstrates substantial improvements in signal-to-noise ratio (SNR) and root mean square error (RMSE) when compared with traditional filtering and baseline machine learning approaches. Experimental results show an SNR enhancement of over +14 dB and consistently low error metrics across multiple interference power levels and frequency configurations. The proposed architecture maintains a compact computational footprint (≈8.4 MB) and supports low-latency inference suitable for integration into hardware-accelerated or embedded execution environments. These outcomes confirm the potential of the proposed hybrid approach as a precise and efficient solution for AI-assisted interference mitigation in 5 G receivers, while also outlining future directions toward over-the-air validation and FPGA-based deployment