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The rapid expansion of edge–cloud computing infrastructures has intensified both cybersecurity demands and the associated energy consumption and carbon footprint of intrusion detection systems (IDS). This paper presents GreenShield, a unified low-carbon cybersecurity framework that integrates energy-efficient deep learning-based intrusion detection with knowledge distillation and dynamic quantization, ASCON lightweight cryptography, hierarchical federated learning with gradient compression, and a carbon-aware scheduling engine across distributed edge–fog–cloud architectures. GreenShield employs a threat-adaptive quantization mechanism that scales model precision (4–32 bit) based on real-time threat levels and a carbon-conscious scheduling controller that dynamically aligns security workload execution with renewable energy availability forecasts. Extensive experiments on the UNSW-NB15 and CIC-IDS2017 datasets demonstrate that GreenShield achieves 98.73% detection accuracy with 67.4% energy reduction compared to conventional deep learning-based IDS, while reducing operational carbon emissions by up to 97.6% (equivalent to approximately 2.8 kg CO 2 -eq per hour savings in a typical edge deployment). The hierarchical federated learning architecture reduces communication overhead by 58.2% through Top-k gradient sparsification, and the dynamic quantization mechanism achieves 71.3% inference energy reduction during low-threat periods. These results establish GreenShield as a viable, scalable solution for sustainable cybersecurity that supports carbon-conscious security workflows in next-generation edge–cloud computing environments.

The fast growth of edge–cloud computing infrastructures has increased the cybersecurity burden even as it has substantially amplified the energy use and carbon footprint of intrusion detection systems (IDSs). In order to overcome this challenge, this paper suggests GreenShield, which is a framework of low-carbon cybersecurity involving lightweight cryptography, deep learning that is energy efficient, and carbon conscious system optimization across distributed edges and in cloud setup. GreenShield employs a hierarchical federated learning architecture with integrated knowledge distillation and a carbon-aware scheduling controller that dynamically adjusts security response execution based on threat intensity and renewable energy availability. As extensive experiments on the UNSW-NB15 and CIC-IDS2017 datasets show, GreenShield attains 98.73% detection accuracy and is 67.4% more energy efficient than traditional deeplearning-based IDSs. Further, the suggested system reduces the operational carbon emissions up to 97.6%, which is equivalent to a reduction of around 2.8 kg CO2-equivalent/per hour in a typical edge-deployment situation, yet it does not undermine the performance of the detection. These findings suggest that GreenShield can be one of the meaningful alternatives in providing viable and scalable sustainable cybersecurity that supports carbon-conscious security workflows in the future edge–cloud computing architecture.

Background Intraoral radiography is a core diagnostic skill in dental practice. However, technical errors, such as cone cuts and positioning inaccuracies, remain common among undergraduate dental students, leading to image retakes and unnecessary radiation exposure. This study aimed to evaluate the impact of centering rings combined with a structured educational package on intraoral radiographic quality, technical errors, and student confidence. Methods A quasi-experimental pre–post study was conducted among 109 dental students and interns at the College of Dentistry, University of Ha’il, Saudi Arabia. Baseline assessments included radiographic performance, technical errors, retakes, image quality, and self-reported confidence. Participants then received a standardized educational intervention on intraoral radiography and centering ring use. Post-training assessments were performed using identical evaluation criteria. Pre- and post-intervention outcomes were compared using appropriate paired statistical tests, and correlation analyses were performed to examine associations between confidence, image quality, and retake frequency. Chi-square, Wilcoxon signed-rank tests, and Spearman’s correlation were used to assess group differences, training effects, and associations, with p  < 0.05 considered significant. Results Among 109 dental students (52.3% female), baseline cone cuts were observed in 15.6% of periapical and 26.6% of bitewing radiographs. Following the intervention, cone cuts decreased significantly (periapical: Z = − 3.300, p  = 0.001; bitewing: Z = − 2.629, p  = 0.009), with a marked improvement in image quality (periapical: Z = − 2.753, p  = 0.006; bitewing: Z = − 5.506, p  < 0.001). Retake frequency decreased and self-reported confidence increased significantly (Z = − 7.446, p  < 0.001). Gains were observed across all academic levels, particularly for technique-sensitive bitewing imaging. Higher post-training image quality was associated with fewer retakes ( r  = − 0.278, p  = 0.003), indicating improved technical proficiency and radiation safety. Conclusion Observed improvements in image quality, reduced cone cuts, fewer retakes, and increased student confidence were noted following the introduction of centering rings and a focused educational intervention. These findings suggest a positive association between the intervention and radiographic performance in undergraduate training.

ZnO/M nanocomposites incorporating transition metal oxides (M = CuO, Fe₂O₃, Mn₃O₄) demonstrate significantly tuned functional properties relevant for advanced device applications. Photoluminescence studies reveal modified optical behavior, with all composites showing reduced emission intensity. Blue shifts for Fe 2 O 3 and Mn 3 O 4 composites contrast with a violet shift observed in the CuO composite, reflected in an I B /I V ratio of 1.023, 1.018, and 0.936, respectively. Electrical characterization shows substantially enhanced performance in nanocomposites. Higher dielectric constants and improved AC conductivity values are recorded, particularly in ZnO/CuO samples. Relaxation dynamics shift toward higher frequencies, with peaks in the electric modulus (M’’) observed at 1860 kHz for ZnO/CuO, compared to 100 kHz for pure ZnO, and Cole-Cole analysis confirming non-Debye type behavior. Unique electrical transport emerges in ZnO/Fe 2 O 3 , where two successive semicircles in impedance plots suggest complex charge conduction pathways with grain boundary resistance reaching 185 MΩ. Magnetic properties show notable enhancement through composite formation. All nanocomposites exhibit strengthened ferromagnetic character compared to pure ZnO, with saturation magnetization increasing progressively from 0.035 emu/g (ZnO) through 0.039 (ZnO/CuO), 0.050 (ZnO/Fe 2 O 3 ), to 0.058 emu/g (ZnO/Mn 3 O 4 ). The materials demonstrate hard magnetic behavior with coercivity values of 70–80 Oe and double exchange interactions dominating, supported by an effective magnetic anisotropy (K eff ) increasing from 148 emu·Oe/g for ZnO to 432 emu·Oe/g for ZnO/Mn 3 O 4 . These simultaneous improvements across optical, electrical, and magnetic domains position ZnO/M nanocomposites as promising candidates for emerging technologies including spintronic devices, high-frequency telecommunications, and advanced energy storage systems.

Graphene derivatives have demonstrated high potential for various applications, including environmental ones. In this work, graphene oxide nano-sheets were obtained by utilizing a simple chemical method and were tested for water treatment applications. The pollutant adsorption ability of the produced GO was adjusted through a proper oxidation treatment of the graphene nano-sheets. The GO treatment time was systematically varied to control the oxidation level of the graphene nano-sheets and was found to considerably affect the GO’s properties and performance in removing methylene blue. The microscopic studies showed well-exfoliated, few-layer GO nano-sheets. EDS and FTIR techniques were used to probe the presence of oxygen functional groups on the GO surface. The XRD investigations revealed various crystallinity levels of the prepared GO nano-sheets depending on the treatment time. The MB degradation efficiency was maximized by optimizing the GO treatment time. The results showed that the oxidation treatment parameters of GO play a major role in adjusting its properties and can be effectively utilized to boost its performance for water purification applications.

Artificial intelligence (AI) integration in diagnostic medicine has advanced accuracy and efficiency, particularly in pathology. This study assessed the diagnostic performance of three large language models (LLMs)—ChatGPT (GPT-4-turbo), Grok (xAI), and MANUS—in interpreting histopathology slides of oral lesions. A comparative diagnostic study was conducted using 100 high-resolution slides representing diverse oral pathologies. Images were sourced from a validated textbook and reviewed by two board-certified oral pathologists who provided consensus diagnoses. Each slide was analysed twice by the three AI models using standardized prompts. Diagnostic accuracy, intra-model consistency, inter-model concordance, and agreement with human experts were evaluated using descriptive statistics, Cohen’s kappa, McNemar’s test, and chi-square analysis. All AI models demonstrated high diagnostic accuracy. In the second round, Grok achieved the highest accuracy (97%), followed by MANUS (96%) and ChatGPT (94%). ChatGPT showed the highest intra-model consistency (κ = 0.918), while MANUS and Grok displayed substantial agreement (κ = 0.790 and 0.740). Expert pathologists achieved 98% accuracy. Comparisons between AI models and human diagnoses showed moderate to substantial agreement, with MANUS most aligned with experts. Most misclassifications occurred in histologically ambiguous cases, with no significant differences between AI models. Multimodal LLMs demonstrated strong diagnostic capabilities, consistency, and alignment with expert reasoning in oral histopathology interpretation. Grok was the most accurate, ChatGPT the most consistent, and MANUS the most expert-aligned. These findings support AI integration into digital pathology for diagnostic support, education, and quality assurance, with further validation in clinical datasets recommended.

Chronic kidney disease (CKD) and its treatment through hemodialysis can lead to significant oral complications that impair nutrition and quality of life. However, data on the oral health status of Saudi patients undergoing hemodialysis remain limited. This study aimed to assess the prevalence and predictors of xerostomia, taste alterations, dental caries, tooth wear, gingival inflammation, and tooth loss among hemodialysis patients in the Ha’il region of Saudi Arabia. A cross-sectional survey was conducted among 314 adult CKD patients receiving regular hemodialysis. Data were collected through a validated, structured questionnaire encompassing demographic, medical, and oral health information. Statistical analysis using Chi-square tests and binary logistic regression identified independent predictors of oral health outcomes, with significance set at p  < 0.05. A total of 314 patients undergoing hemodialysis were included. Xerostomia was reported by 46.2% of participants, while 16.2% experienced taste alterations. Dental caries, tooth wear, gingival inflammation, and tooth loss were highly prevalent. Male gender, smoking, xerostomia, taste disturbances, and medication use were significant predictors of dental caries. Tooth wear was associated with male gender and xerostomia, gingival inflammation with male gender and smoking, and tooth loss primarily with older age and xerostomia. Hemodialysis patients experience a high burden of oral disease, influenced by multiple behavioral and systemic factors. Routine oral screening, preventive care, and interprofessional collaboration between nephrology and dental teams are vital to improving oral health and overall well-being in this population.

We reported here the structural, optical, and magnetic properties of Zn 1−x Co x O nanorods (NRs) with x = 0.00, 0.025, 0.05, and 0.30 wt%. The Zn 1−x Co x O NRs samples were fabricated by electrochemical deposition and given the symbols S0, S1, S2, and S3 for x = 0.00, 0.025, 0.05, and 0.30 wt%, respectively. It is found that all NR samples were grown along the (002) plane and have a hexagonal structure. As the Co level increases up to 0.30 wt%, the crystallite size and the texture coefficient are respectively decreased from 57 nm to 0.98 to 25 nm and 0.70. While the diameter of NRs increased from 347 to 1730 nm. Interestingly, the weight% (wt %) of O was increased with increasing Co level. The optical band gap (E g ) was found to be 3.32 eV for the undoped ZnO NRs (S0) and reduced to 2.24 eV with more increase of Co up to 0.30 wt%. At 300 K, the So and S1 exhibit diamagnetic behavior over the field range. For S2, such behavior became weakly ferromagnetic at H  ≤  2000 Oe and diamagnetic at H > 2000 Oe. In contrast, the S3 exhibits strong ferromagnetic behavior of magnetization (M) = 0.14 emu/g at 20 kOe. However, with decreasing temperature to 10 K, the paramagnetic behavior is dominant for all NRs. However, all NRs samples revealed a hysteresis loop After subtracting the paramagnetic and diamagnetic contributions from the M-H curves. The S2 showed the highest value for coercive field of 256 and 263 Oe, as compared to the other NRs (15–65 Oe). Although S3 shows the softest magnetic properties among all samples (with coercive fields of 15–27 Oe), it exhibits the strongest ferromagnetic behavior. The Zfc/Fc measurements show that all the samples are paramagnetic by nature with no sign for blocking temperature of magnetic nanoparticles. Furthermore, the residual magnetization values measured at 300 K (from both FC and ZFC curves) show a general increasing trend with cobalt doping concentration, with measured values of 6.45 × 10⁻⁹, 2.13 × 10⁻⁴, 8.71 × 10⁻⁵, and 6.45 × 10⁻² emu/g for samples S0 through S3, respectively. This work provides new insights into the correlation between electrochemical growth conditions, defect chemistry, and room-temperature ferromagnetism in Co-doped ZnO systems, advancing beyond previous reports through its demonstration of bandgap tuning and robust ferromagnetism in electrochemically grown NRs and temperature-dependent magnetic phase transitions directly correlated with structural parameters.

The Arabian Shield of Saudi Arabia represents part of the Arabian–Nubian Shield and forms an exposure of juvenile continental crust on the eastern side of the Red Sea rift. Gabbroic intrusions in Saudi Arabia constitute a significant part of the mafic magmatism in the Neoproterozoic Arabian Shield. This study records the first detailed geological, mineralogical and geochemical data for gabbroic intrusions located in the Gabal Samra and Gabal Abd areas of the Hail region in the Arabian Shield of Saudi Arabia. Geological field relations and investigations, supported by mineralogical and geochemical data, indicate that the gabbroic intrusions are generally unmetamorphosed and undeformed, and argue for their post-collisional emplacement. Their mineralogical and geochemical features reveal crystallization from hydrous, mainly tholeiitic, mafic magmas with arc-like signatures, which were probably inherited from the previous subduction event in the Arabian–Nubian Shield. The gabbroic rocks exhibit sub-chondritic Nb/U, Nb/Ta and Zr/Hf ratios, revealing depletion of their mantle source. Moreover, the high ratios of (Gd/Yb)N and (Dy/Yb)N indicate that their parental mafic melts were derived from a garnet-peridotite source with a garnet signature in the mantle residue. This implication suggests that the melting region was at a depth exceeding ∼70–80 km at the garnet stability field. They have geochemical characteristics similar to other post-collisional gabbros of the Arabian–Nubian Shield. Their origin could be explained by adiabatic decompression melting of depleted asthenosphere that interacted during ascent with metasomatized lithospheric mantle in an extensional regime, likely related to the activity of the Najd Fault System, at the end of the Pan-African Orogeny.

This study reports, for the first time, the green synthesis of zinc selenide (ZnSe) nanoparticles (NPs) capped with Artemisia herba-alba and Calligonum extracts, benchmarked against conventional L-cysteine-capped ZnSe. Using plant extracts as natural capping agents provides an eco-friendly strategy to tailor the surface chemistry and photocatalytic behavior of ZnSe NPs. The NPs were synthesized hydrothermally and characterized by transmission electron microscopy (TEM), X-ray diffraction (XRD), UV–Vis spectroscopy, and photoluminescence (PL), and then evaluated for methylene blue (MB) degradation under UV irradiation. XRD revealed mixed cubic and wurtzite phases with crystallite sizes ranging from 5.6 to 7.7 nm, while the PL analysis suggested more effective charge separation in plant-extract-capped ZnSe. Photocatalytic tests demonstrated that Calligonum-capped ZnSe achieved ~ 40% MB degradation after 180 min, outperforming Artemisia-capped ZnSe (28%) and showing comparable performance to L-cysteine-capped ZnSe (38%). Kinetic analysis further revealed that Artemisia-capped ZnSe exhibited the highest rate constant, indicating superior intrinsic photocatalytic activity. The enhanced performance of plant-capped ZnSe was attributed to phytochemical-induced defect states that promote charge separation and reactive oxygen species generation. These findings establish Artemisia and Calligonum extracts as sustainable capping agents, offering a low-cost, environmentally friendly route for designing ZnSe nanocatalysts with significant potential in wastewater treatment and environmental remediation.