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Advanced Competent Bayesian Regularization Neural Network for Mathematical Modeling of the Immune Diabetes Regulation System (Vol 113, 109036, 2026)
No full textScience Citation Index Expande
MXenes-Based Wearable Contact Lenses: Integrating Smart Technology into Vision Care
No full textMXene-based smart contact lenses seamlessly combine real-time biosensing, therapeutic functions, and enhanced user comfort, revolutionizing ocular health monitoring and treatment. The use of transparent MXene films enables features like photothermal therapy, antimicrobial protection, and dehydration resistance, significantly improving eye protection and disease management. While stability, scalability, and wireless integration pose hurdles, ongoing advancements suggest these lenses hold tremendous potential for transforming digital healthcare and ophthalmic care. © 2025 Elsevier B.V., All rights reserved.Science Citation Index Expande
Sustainable Nanosystems for Targeted Drug Delivery and Their Impacts on Combination Therapies and Personalized Medicine: Advancements and Perspectives
No full textRecent advances in sustainable nanosystems for targeted drug delivery have emphasized biodegradable and biocompatible nanocarriers synthesized through eco-friendly methods to enable precise, controlled, and stimuli-responsive drug release with minimal environmental impact. This review focuses on green-synthesized and sustainable nanocarriers, highlighting emerging strategies that prioritize environmentally responsible design. Eco-friendly fabrication techniques, including microfluidics, solvent-free processes, supercritical fluid methods, and ionic gelation, are discussed for their potential to reduce resource use and environmental burden. Moreover, biomimetic approaches such as cell membrane-coated nanoparticles, virus-like particles, peptide-based nanostructures, and DNA/RNA carriers are examined for their enhanced targeting efficiency and biological compatibility. Besides, the use of waste-derived materials as renewable resources is also explored, reflecting the growing influence of circular economy principles in pharmaceutical nanotechnology. Furthermore, key applications in cancer, infectious diseases, and neurological disorders are summarized, with emphasis on stimuli-responsive platforms that support precision therapy. The novelty of this review lies in its integrated perspective that unifies green synthesis, circular-economy materials, and biomimetic engineering to present a comprehensive roadmap for next-generation sustainable drug delivery. We have also discussed challenges such as material variability, reproducibility, safety concerns, and scale-up issues, while also providing insights into future directions focused on renewable materials, sustainable manufacturing strategies, and smart multifunctional nanosystems. © © 2026. Published by Elsevier B.V
Diagnostic Value of Serum SST2 and MicroRNA-29a in Ovarian Cancer: A Dual-Biomarker Pilot Study
No full textOvarian cancer is frequently diagnosed at an advanced stage due to non-specific symptoms, contributing to high mortality. The limited diagnostic performance of current serum assays in early disease underscores the need for complementary circulating biomarkers. Circulating microRNAs and inflammation-related markers are promising candidates. Although miRNAs are implicated in cancer diagnostics, the role of miRNA-29a in ovarian cancer remains underexplored. Given that sST2 is elevated in several malignancies and is a direct target of miRNA-29a, concurrent evaluation may be informative. This pilot study compared serum miRNA-29a and sST2 levels in 23 ovarian cancer patients and 22 healthy female controls. miRNA-29a expression was quantified by real-time PCR (2(-Delta Delta Ct)), and sST2 was measured by ELISA; diagnostic performance was assessed using ROC analysis. miRNA-29a levels were significantly reduced (p < 0.05), whereas sST2 concentrations were significantly increased (p < 0.001) in patients versus controls. ROC analysis showed modest discrimination for miRNA-29a (AUC 0.678) and higher performance for sST2 (AUC 0.825). No significant correlation was observed between the two markers. These findings suggest that circulating miRNA-29a and sST2 may have biomarker potential in ovarian cancer; larger, well-designed studies are required to confirm clinical utility.Istanbul Okan University Scientific Research Projects Unit [OBAP2024010001]This study was supported by the Istanbul Okan University Scientific Research Projects Unit (Project No: OBAP2024010001)
The Impact of Organizational Blindness on Nurses' Commitment in Healthcare Settings
No full textBaykara Mat, Seda Tuğba/0000-0002-3253-0597; Bakkaloğlu Berkay, Seda/0009-0008-4678-8944Purpose-This study investigates the relationship between organizational blindness and organizational commitment among nurses, exploring how demographic and professional factors shape affective, normative and continuance commitment. By emphasizing workforce well-being, organizational transparency and sustainable healthcare management, the study supports the United Nations Sustainable Development Goals of Decent Work and Economic Growth. Design/methodology/approach-A cross-sectional design was used with 269 nurses employed in a private hospital in T & uuml;rkiye. Data were collected using a Demographic Information Form, the Organizational Commitment Scale and the Organizational Blindness Scale. Descriptive statistics, t-tests, ANOVA, Pearson correlation, linear regression and logistic regression were conducted. Assumptions of normality, homoscedasticity, autocorrelation, and outlier independence were confirmed. Findings-Nurses reported moderate levels of organizational blindness and commitment. Blindness was significantly and negatively but low correlated with commitment (r = -0.266, p < 0.001), explaining 7.1% of the variance (R-2 = 0.071). Being married (OR = 2.05, p = 0.031) and having longer professional experience (p = 0.045) predicted higher commitment, whereas male gender and rotating shifts were linked to greater blindness. Research limitations/implications-The single-site, cross-sectional design limits causal inference and generalizability. Future multi-center and longitudinal studies are recommended. Practical implications-Healthcare leaders should promote open communication, fair scheduling, mentorship and professional development to enhance commitment and reduce blindness. Social implications-Addressing organizational blindness and strengthening commitment can improve nurse retention, organizational culture and patient care quality. Originality/value-A focused literature search (PubMed, Scopus and Web of Science; 2000-2025) revealed no prior Turkish empirical study on this link
A High-Performance Neural Network Algorithm Using a Legendre Ensemble-Based Extreme Learning Machine for Solving Fractional Partial Differential Equations
No full textThe recent advancement in the use of machine learning techniques across various fields has paved the way for innovative approaches to solving fractional partial differential equations (FPDEs), particularly those utilizing neural networks (NNs). These methods enable efficient representation of complete solutions, leveraging the universal approximation capabilities of neural networks. This study presents a neural network-based method that utilizes the ensemble extreme learning machine (EN-ELM) to efficiently solve FPDEs considered in the sense of the Caputo fractional derivative. The proposed approach incorporates Legendre polynomials to expand input features and employs the radial basis function as the activation function for hidden layer neurons. The EN-ELM framework, enhanced with cross-validation, ensures improved accuracy, stability, and reduced computational complexity. Numerical experiments are conducted to validate the approach, demonstrating its superior accuracy, execution time, and error minimization compared to some known methods. The results confirm the robustness and effectiveness of the proposed method for solving FPDEs.Science Citation Index Expande
Ventilation Strategies in Net-Zero Energy Buildings: Balancing Indoor Air Quality and Energy Efficiency
No full textThis review paper examines ventilation strategies in Net-Zero Energy Buildings (NZEBs), with particular focus on balancing indoor air quality (IAQ) and building energy usage. The central question addressed is how ventilation systems can be optimized to meet sustainability goals while maintaining acceptable IAQ with minimal energy use. Reported findings show that heat recovery ventilators reduce HVAC energy by 13.5-19.7% in cold climates, while earth-to-air heat exchangers significantly lower summer demand in Mediterranean regions. Natural ventilation combined with passive design strategies achieve energy savings of up to 62% in educational buildings, and adaptive electrochromic systems yield annual savings of up to 26.6%. Conversely, mechanical ventilation has been shown to increase energy use by about 20% in some cases, underscoring the need for climate- and context-specific solutions. This review paper synthesizes mechanical, natural, hybrid, and smart ventilation performance in a climate-sensitive way, explicitly addressing trade-offs between energy efficiency and IAQ, the role of occupant behavior, and the long-term viability of different approaches when evaluated in an NZEB setting. The findings suggest that hybrid ventilation systems, powered by renewable energy and managed by intelligent controls, are among the most promising pathways toward NZEB targets. However, challenges related to climate variability and occupant behavior remain critical. The insights presented serve as a guideline for developing effective and sustainable ventilation solutions in NZEBs.Science Citation Index Expande
Thermal Conductivity and Viscosity Optimization of CuO/Cyclohexane - Diethyl Amine Non-Polar Hybrid Nanofluid Using Artificial Neural Network and Multi-Objective Particle Swarm Optimization
No full textNanoparticles (NPs) can improve the thermo-physical properties of fluids and increase the effectiveness of heat transfer systems. In this way, achieving optimal properties of nanofluids (NFs) is an important subject. The present work aims to model and optimize the thermo-physical properties of dynamic viscosity (DV) and thermal conductivity (TC) of CuO/Cyclohexane + Diethylamine (DEA) as a non-polar nanofluid with binary base fluids. The input parameters include the temperature and the solid volume fraction (SVF) of NF. Based on available experimental data, the molar weight ratio of the NPs ranges from 0.01 % to 0.06 % with temperatures varying from 298 K to 318 K. The NF is modeled by two trained two-layer feedforward artificial neural networks (ANNs) for the prediction of DV and TC at a specified range of temperature and SVF. The average and maximum relative errors for test datasets are 0.4872 and 0.9106 for DV and 0.4279 and 0.7338 for TC prediction networks, respectively. Through the ANNs' sensitivity analysis, the importance of the SVF rather than the temperature on DV and TC was revealed. Based on the proposed model, a multi-objective optimization problem was formulated to maximize TC and minimize DV simultaneously, and solved using the multi-objective particle swarm optimization (MOPSO) method. Finally, the optimal values of the objective functions and the corresponding input parameters were plotted along with the Pareto optimal points.Chongqing Natural Science Foundation [2024zx039]; Youth project of Chongqing Municipal Education Commission [2023zx047]; Scientific research project of Chongqing Institute of Engineering Technology [kja202305]This work was supported by Supported by Chongqing Natural Science Foundation (No.: 2024zx039) , Youth project of Chongqing Municipal Education Commission (No.: 2023zx047) , and Scientific research project of Chongqing Institute of Engineering Technology (No.: kja202305) .Science Citation Index Expande
Recent Advancement in Hybrid Solar Chimney Power Plants: A Comprehensive Review of Integration Strategies and Performance Enhancements
No full textThe traditional solar chimney power plant (SCPP) has serious constraints in its intermittency, only daylight and sunshine, hence poor capacity factors and inappropriate land utilization, thus making its commercialization difficult. This is a broad review on recent developments in hybrid SCPP systems that incorporate additional technologies to address these limitations and improve overall performance. Key results prove that the combination of SCPPs and external sources of heat (flue gases, geothermal energy, e.g. 75 degrees C hot springs, or nuclear power plant waste heat) allows the SCPPs to be continuously operated, which can be improved significantly to increase the energy production; e.g., integrations with nuclear heat tripled the monthly electricity yield of 25.73 MWh to 63.81 MWh and doubled freshwater production. Photovoltaic (PV) panel integration not only creates more electricity, but also enjoys cooling effects in the collector, enhancing PV efficiency by up to 7 % and total system power production by 5.98 %. Moreover, hybrid systems with desalination and water generation capabilities demonstrated impressive performance, with one new twin-chimney system yielding 209,165 tons of distilled water and energy production 2.9 times more than a conventional SCPP. Ventilation and cooling through phase change materials (PCMs) or earth-air heat exchangers increased ventilation rates by up to 7.2 % and decreased building cooling demand by 40 %. Finally, hybridization offers SCPPs a game-changer and improves their functioning in numerous aspects, allows them to perform the functions of multi-utility system that generates power, desalinates, and purifies the air and makes them far more economically and environmentally sustainable
Suspension of Nanoparticles Impacts on MHD Unsteady Flow Over an Infinite Vertical Porous Surface with Dufour and TGDHS: an RSM Analysis
No full textThis study examines the effects of electromagnetic fields, the Dufour effect, temperature-gradient-dependent heat sources (TGDHS), and radiation absorption on hybrid nanofluid flow over an infinite vertical permeable surface. It specifically investigates the thermal transport characteristics of single-walled carbon nanotubes (SWCNTs) and multi-walled carbon nanotubes (MWCNTs) within the TGDHS framework. The governing equations are solved using a perturbation method, with MATLAB simulations yielding graphical and tabulated results for key physical parameters. The results demonstrate substantial variations in skin friction, heat transfer rates, Nusselt number, and Sherwood number. The hybrid dispersion of SWCNTs and MWCNTs produces a significant heat transfer reduction ranging from 3.20 % to 12.06 %. Response surface methodology (RSM) exhibits excellent predictive performance, achieving an R²value of 99.99 %. Strategic adjustment of magnetic field strength, surface permeability, and heat source intensity effectively enhances or controls heat and mass transfer rates. These findings provide critical guidance for designing advanced thermal management systems. © 2026 The Authors