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Enhanced corrosion inhibition of mild steel in acidic environments using essential oils of broad-leaved cattail: A comprehensive of electrochemical and theoretical studies
No full textIn this study, high-performance environmental deterrent with natural essential oil (HE) in mild steel corrosion inhibition examined in 1 M HCl solution. Both potentiodynamic polarization (PP) curves and electrochemical impedance spectroscopy (EIS) were performed to study the corrosion behavior of HE. Results indicated that the HE reduced the corrosion current density from 983 to 42 µA cm-2, and the inhibition efficiency reach a maximum of 95.7 % at 0.5 g/L. In addition, the ∆Gads value of -41.023 kJ/mol indicates the electrostatic interactions between studied inhibitor and mild steel by chemisorption. The protective influence of HE was verified by energy-dispersive X-ray analysis (EDS) in conjunction with SEM, which revealed the formation of a stable protective coating on the steel surface. MC, MD and DFT calculations confirmed the experimental data obtained. These results offer crucial new insights into the method by which HE prevents corrosion, contributing to the development of new strategies for protecting metal surfaces in acidic environments
A novel metaheuristic-enhanced quantum-classical neural network for attack detection in agriculture IoT systems
No full textThis paper addresses the growing cybersecurity challenges in Agricultural Internet of Things (AG-IoT) environments, where large-scale sensor networks generate high-dimensional and complex traffic data that traditional machine learning (ML) models struggle to analyze effectively. To improve intrusion detection performance under these conditions, we propose a hybrid quantum–classical neural network model designed to capture complex patterns in AG-IoT traffic. High-dimensionality is first reduced using a binary Starfish Optimization Algorithm (SFOA) for feature selection, inspired by starfish foraging and regeneration behaviors. SFOA was chosen due to its superior convergence performance benchmarked against 100 modern metaheuristic algorithms, making it highly suitable for large AG-IoT datasets. A secondary mutual-information-based reduction further selects the most informative features for quantum processing. These features are then encoded into quantum circuits and evaluated using three architectures: a fully quantum model, a hybrid model combining quantum feature transformation with a classical classifier, and an enhanced hybrid model incorporating additional classical layers. All quantum experiments were executed on noise-free quantum simulators rather than physical hardware. Experiments conducted on the publicly available Farm-Flow dataset, which includes 1.3 million AG-IoT traffic flows across eight attack categories, demonstrate that the proposed hybrid approach achieves 90.10% accuracy in binary classification and 84.60% accuracy in multiclass intrusion detection. These findings suggest that the model provides performance comparable to conventional ML methods reported in the literature. Ultimately, this study highlights that quantum machine learning (QML), when paired with optimized feature selection, offers a promising and effective direction for securing AG-IoT systems against evolving cyber threats
The mediating role of expectational trust in the relationship between emotional intelligence and awareness about child sexual abuse perpetrators
No full textRelationship between crustal magnetic anomalies and earthquake activity in Malatya and surrounding region after the 2023 Kahramanmaraş earthquakes, southeastern Türkiye
No full textMülkiyetin Korunması Perspektifinden Bir Fesih Sebebi Olarak İşçinin İşyerindeki Malları Zarara Uğratması
No full textMixed Convection Heat Transfer Investigation in a Vertical Duct Having Heated Trapezoidal Obstacles Employing Nanofluid of Cuo-Water
No full textAssessment of the flotation separation of four typical LIB cathodes: Emphasizing the unique flotation behavior propelled by the combined impact of ultrafine particle size and LiFePO4 surface properties
No full textIn recent years, lithium‑iron phosphate batteries have significantly increased their market share in the lithium-ion batteries (LIBs) industry due to their exceptional stability and low cost. Since flotation, which is a promising technique for separating electrode materials in LIBs, exploits differences in surface properties to separate materials like graphite from cathode materials, studies on the differences in flotation separation between LFP cathodes and graphite anodes remain scarce, and systematic comparisons of flotation behavior between LFP and traditional Ni, Mn, and Co-containing cathode materials are lacking. To address this gap, this study explores systematically the flotation separation efficiency and mechanisms of graphite anodes and four typical LIB cathodes, namely lithium‑iron phosphate (LFP), lithium‑manganese oxide (LMO), lithium‑cobalt oxide (LCO), and nickel‑cobalt‑manganese ternary oxide (NCM) via micro-flotation experiments and surface characterization. Raw materials from unassembled batteries were employed to preserve intrinsic properties such as wettability and particle size. The results show that in the single flotation systems of four cathode materials and anode graphite (emulsified kerosene 350 g·t−1, sec-octanol 100 g·t−1), the recovery rate of LFP is 84.98 %, which is higher than that of LMO (2.93 %), LCO (7.73 %) and NCM (7.04 %), but still lower than that of graphite (98.98 %). In the mixed flotation system of cathode and anode materials (emulsified kerosene 0 g·t−1, sec-octanol 100 g·t−1), only a small amount of LMO, LCO and NCM enter the froth product, while the recovery rate of LFP reaches 82.23 %, significantly higher than that of graphite (40.11 %). A series of characterization tests revealed that the abnormal preferential flotation behavior of hydrophilic LFP likely originates from the synergistic effects of its ultrafine particle size, high specific surface area, and surface chemical properties. This study provides critical insights for the development of green LIB recycling technologies and theoretical innovations in the separation of complex multiphase systems
Cognitive disengagement syndrome symptoms in pediatric atopic dermatitis: behavioral and emotional correlates
No full textThis study aimed to investigate the prevalence of Cognitive Disengagement Syndrome (CDS) symptoms in children diagnosed with atopic dermatitis (AD) and to examine their associations with attention-deficit/hyperactivity symptoms and emotional, behavioral, and social functioning. A cross-sectional design was used with 182 children aged 7–13 years: 88 with AD and 94 healthy controls matched for age and sociodemographic factors. Parent-reported measures included the Barkley Child Attention Scale (BCAS), the Turgay DSM-IV-Based Disruptive Behavior Disorders Rating Scale (T-DSM-IV-S), and the Strengths and Difficulties Questionnaire (SDQ). Children with AD showed significantly higher BCAS daydreaming and sluggishness scores than controls (p < 0.001), suggesting greater CDS symptom tendencies in this group. In attention scores were significantly higher in the AD group (p < 0.001), while hyperactivity/impulsivity scores did not differ (p = 0.777). SDQ inattention-hyperactivity (p = 0.022), emotional problems (p < 0.001), and total difficulties (p < 0.001) were also higher in the AD group. The proportion of children with CDS symptoms but without ADHD symptoms was greater in the AD group (p = 0.002). Children with AD exhibited higher levels of CDS symptoms accompanied by attentional, emotional, and behavioral difficulties. Recognizing these neuropsychological characteristics alongside dermatologic management may contribute to more comprehensive care. However, the cross-sectional design and reliance on parent-reported data should be considered when interpreting the findings
