Search Results (362)

This study evaluates the performance of tubular solar stills coated with gamma aluminium nanocoatings at concentrations of 5%, 10%, and 15%, compared to a conventional tubular solar still. This is the first experimental study to apply gamma aluminium nanocoatings on tubular solar stills (TSS). The stills were tested for three days, from 9:00 a.m. to 5:00 p.m., under consistent conditions with varying water depths of 1 cm, 2 cm, and 3 cm. The results indicated that the 5% nanocoating achieved the highest water yield, producing 2.571 L/m² with a 1 cm water depth. The 10% coating produced 2.514 L/m², while the conventional solar still generated 2.286 L/m². Thermal efficiency was highest on Day 1 for the 5% concentration, reaching 60.9%, followed by 10% concentration at 59.1%, while the 15% concentration showed the lowest efficiency at 33.8%. In terms of cost-effectiveness, the 5% concentration was the most economical, with the lowest cost per litre (CPL) of USD 0.10 and a payback period of 3.03 months. The 10% concentration had a CPL of USD 0.11 and a payback period of 3.33 months, while the 15% concentration had the highest CPL at USD 0.19 and the longest payback period of 5.63 months. Overall, the 5% concentration offered the best balance of water yield, efficiency, and cost-effectiveness. This research highlights γ-Al₂O₃ as an innovative, cost-effective material for solar distillation, paving the way for sustainable freshwater production. Full article

The demand for biomass has been growing in recent years for several reasons, related to environmental, economic, and social trends. In the context of global climate changes and the depletion of natural resources, the recycling of plant biomass waste is a promising strategy for sustainable development that contributes to minimizing waste, improving resource efficiency, and achieving the goal of creating a circular economy. One of the highly demanded products of agricultural waste recycling is glucose. Glucose is an important organic substrate that allows a number of value-added products to be obtained. In this review, we focused on the commercially significant products of glucose oxidation: gluconic and glucaric acids. This review summarized the latest available data on the scope of the application of each product as well as the methods of their production. The capabilities and limitations of currently used methods of synthesis were highlighted. Full article

The current study demonstrates the buckling properties of composite laminates reinforced with MWCNT fillers using a novel higher-order shear and normal deformation theory (HSNDT), which considers the effect of thickness in its mathematical formulation. The hybrid HSNDT combines polynomial and hyperbolic functions that ensure the parabolic shear stress profile and zero shear stress boundary condition at the upper and lower surface of the plate, hence removing the need for a shear correction factor. The plate is made up of carbon fiber bounded together with polymer resin matrix reinforced with MWCNT fibers. The mechanical properties are homogenized by a Halpin–Tsai scheme. The MATLAB R2019a code was developed in-house for a finite element model using C⁰ continuity nine-node Lagrangian isoparametric shape functions. The geometric nonlinear and linear stiffness matrices are derived using the principle of virtual work. The solution of the eigenvalue problem enables estimation of the critical buckling loads. A convergence study was carried out and model efficiency was corroborated with the existing literature. The model contains only seven degrees of freedom, which significantly reduces computation time, facilitating the comprehensive parametric studies for the buckling stability of the plate. Full article

Glaucoma is a leading cause of irreversible blindness, normally associated with dysfunction and degeneration of the trabecular meshwork (TM) as the primary cause. Trabecular meshwork stem cells (TMSCs) have emerged as promising candidates for TM regeneration toward glaucoma therapies, yet their molecular characteristics remain poorly defined. In this study, we performed a comprehensive transcriptomic comparison of human TMSCs and human TM cells (TMCs) using RNA sequencing and microarray analyses, followed by qPCR validation. A total of 465 differentially expressed genes were identified, with 254 upregulated in TMSCs and 211 in TMCs. A functional enrichment analysis revealed that TMSCs are associated with development, immune signaling, and extracellular matrix remodeling pathways, while TMCs are enriched in structural, contractile, and adhesion-related functions. A network topology analysis identified CXCL3, CXCL6, and BMP2 as robust TMSC-specific hub genes, and LMOD1 and BGN as TMC-specific markers, with expression patterns confirmed by qPCR. These findings define distinct molecular signatures of TMSCs and TMCs, providing reliable biomarkers for cell identity and a foundation for future stem cell-based therapies targeting TM dysfunction in glaucoma. Full article

The electron-impact ionization and partial ionization cross sections are reported for few silicon-chlorine molecules using semi-empirical methods. The partial ionization cross sections are determined using a modified version of the binary-encounter-Bethe model. In this approach, the binary-encounter-Bethe model is modified through a two-step process, namely, transforming the binding energies of the occupied orbitals and introducing a scaling factor. The scaling can be done using either the mass spectrometry data or experimental values of cross sections. It correctly adjusts the scaling term of the BEB model so that the order of magnitude of resulting partial ionization cross sections is the same as that of experimental values. Further, the use of the experimental value of ionization and appearance energy values ensures that the cross sections have a correct threshold. This further mitigates the dependence of cross sections on energy at low values. The role of the scaling factor and the behavior of branching ratios is also examined at different energies. The species whose partial ionization cross sections are reported are highly relevant in plasma processing. However, the proposed model can be extended to any multi-centerd molecular structures comprising a large number of atoms or electrons, except in cases where resonance effects or additional ionization channels become significant. The mass spectrometry data is of utmost importance in computing partial ionization cross sections in order to obtain reliable results. Full article

Augmented consumerism has propelled electronic innovation, leading to unprecedented growth in e-waste. Mishandling of e-waste poses environmental and human health hazards that necessitate a review of existing technologies and regulatory frameworks for effective e-waste management. Over the years, advancements in e-waste treatment technologies have addressed challenges uncovered in conventional e-waste treatment methods. This review comprehensively discusses valorization, regulations, and the environmental and health hazards imposed by e-waste mismanagement. The review adopted the novel VIRE framework to justify the research question and followed PRISMA analysis to filter the research basket. This study highlights that progressive policy frameworks are less efficient until inhibiting factors for successful implementation are addressed, especially in developing countries. The informal sector dominates in impeding the successful implementation of e-waste regulations, requiring integration with the formal sector as an initiative to reduce unlawful e-waste handling. Moreover, e-waste holds significant potential for economic value through precious metal recovery. An integrated approach of thermal techniques followed by bioleaching could be a cost-effective alternative for enhanced metal recovery from e-waste. There exists ample opportunity for further advancement in treatment technologies through the integration of discrete techniques, reframing regulatory frameworks to minimize unauthorized processing, and cooperative international agreements for collective action on sustainable e-waste management. Full article

This work is devoted to the study of the effect of small Bi additives on the functional properties of Pdx:Bi/Al₂O₃ catalysts in the selective oxidation of glucose to gluconic acid. The catalysts obtained by the joint impregnation method were characterized (TEM) by high dispersion of bimetallic nanoparticles with a median diameter of 4–5 nm. The structure of the Pd-Bi solid solution was confirmed via XPS and showed a change in the valence state of Pd and Bi depending on the Bi content, as well as the fraction of the oxidized state of Bi. TPR-H₂ revealed various forms of Pd, including PdO and mixed Pd-O-Bi structures. The Pd10:Bi1/Al₂O₃ catalyst demonstrated the highest efficiency (77.2% glucose conversion, 96% sodium gluconate selectivity), which is due to the optimal ratio between Pd and Bi, ensuring the stabilization of metallic Pd and preventing its oxidation. Full article

The increasing integration of artificial intelligence (AI) in digital ecosystems has reshaped privacy dynamics, particularly for young digital citizens navigating data-driven environments. This study explores evolving privacy concerns across three key stakeholder groups—young digital citizens, parents/educators, and AI professionals—and assesses differences in data ownership, trust, transparency, parental mediation, education, and risk–benefit perceptions. Employing a grounded theory methodology, this research synthesizes insights from key participants through structured surveys, qualitative interviews, and focus groups to identify distinct privacy expectations. Young digital citizens emphasized autonomy and digital agency, while parents and educators prioritized oversight and AI literacy. AI professionals focused on balancing ethical design with system performance. The analysis revealed significant gaps in transparency and digital literacy, underscoring the need for inclusive, stakeholder-driven privacy frameworks. Drawing on comparative thematic analysis, this study introduces the Privacy–Ethics Alignment in AI (PEA-AI) model, which conceptualizes privacy decision-making as a dynamic negotiation among stakeholders. By aligning empirical findings with governance implications, this research provides a scalable foundation for adaptive, youth-centered AI privacy governance. Full article

This study presents an advanced hybrid energy management system (EMS) designed for isolated microgrids, aiming to optimize the integration of renewable energy sources with backup systems to enhance energy efficiency and ensure a stable power supply. The proposed EMS incorporates solar photovoltaic (PV) and wind turbine (WT) generation systems, coupled with a battery energy storage system (BESS) for energy storage and management and a microturbine (MT) as a backup solution during low generation or peak demand periods. Maximum power point tracking (MPPT) is implemented for the PV and WT systems, with additional control mechanisms such as pitch angle, tip speed ratio (TSR) for wind power, and a proportional-integral (PI) controller for battery and microturbine management. To optimize EMS operations, a novel hybrid optimization algorithm, the JSO-GJO (Jellyfish Search and Golden Jackal hybrid Optimization), is applied and benchmarked against Particle Swarm Optimization (PSO), Bacterial Foraging Optimization (BFO), Artificial Bee Colony (ABC), Grey Wolf Optimization (GWO), and Whale Optimization Algorithm (WOA). Comparative analysis indicates that the JSO-GJO algorithm achieves the highest energy efficiency of 99.20%, minimizes power losses to 0.116 kW, maximizes annual energy production at 421,847.82 kWh, and reduces total annual costs to USD 50,617,477.51. These findings demonstrate the superiority of the JSO-GJO algorithm, establishing it as a highly effective solution for optimizing hybrid isolated EMS in renewable energy applications. Full article

Background: According to the World Health Organization (WHO), approximately 5% of children and 2.5% of adults suffer from attention deficit hyperactivity disorder (ADHD). This disorder can have significant negative consequences on people’s lives, particularly children. In recent years, methods based on artificial intelligence and neuroimaging techniques, such as MRI, have made significant progress, paving the way for development of more reliable diagnostic tools. In this proof of concept study, our aim was to investigate the potential utility of neuroimaging data and clinical information in combination with a deep learning-based analytical approach, more precisely, a novel feature extraction technique for the diagnosis of ADHD with high accuracy. Methods: Leveraging the ADHD200 dataset, which encompasses demographic information and anatomical MRI scans collected from a diverse ADHD population, our study focused on developing modern deep learning-based diagnostic models. The data preprocessing employed a pre-trained Visual Geometry Group16 (VGG16) network to extract two-dimensional (2D) feature maps from three-dimensional (3D) anatomical MRI data to reduce computational complexity and enhance diagnostic power. The inclusion of personal attributes, such as age, gender, intelligence quotient, and handedness, strengthens the diagnostic models. Four deep-learning architectures—convolutional neural network 2D (CNN2D), CNN1D, long short-term memory (LSTM), and gated recurrent units (GRU)—were employed for analysis of the MRI data, with and without the inclusion of clinical characteristics. Results: A 10-fold cross-validation test revealed that the LSTM model, which incorporated both MRI data and personal attributes, had the best diagnostic performance among all tested models in the diagnosis of ADHD with an accuracy of 0.86 and area under the receiver operating characteristic (ROC) curve (AUC) score of 0.90. Conclusions: Our findings demonstrate that the proposed approach of extracting 2D features from 3D MRI images and integrating these features with clinical characteristics may be useful in the diagnosis of ADHD with high accuracy. Full article

This article explores the transformative advances in soft machines, where biology, materials science, and engineering have converged. We discuss the remarkable adaptability and versatility of soft machines, whose designs draw inspiration from nature’s elegant solutions. From the intricate movements of octopus tentacles to the resilience of an elephant’s trunk, nature provides a wealth of inspiration for designing robots capable of navigating complex environments with grace and efficiency. Central to this advancement is the ongoing research into bioinspired materials, which serve as the building blocks for creating soft machines with lifelike behaviors and adaptive capabilities. By fostering collaboration and innovation, we can unlock new possibilities in soft machines, shaping a future where robots seamlessly integrate into and interact with the natural world, offering solutions to humanity’s most pressing challenges. Full article

The study introduces a novel standalone hybrid Energy Management System that combines solar PV, wind energy conversion systems, battery storage, and microturbines in order to provide reliable and efficient power under various operating conditions. The developed Quantum-Inspired Multiverse Optimization (QI-MVO) algorithm has thus far allowed for a remarkable efficiency of 99.9% and a 40% reduction in power losses when compared to conventional approaches. A rather speedy convergence to best solutions is exhibited by the methods, which take about 0.07 s for calculation, hence ensuring accurate optimization in complex energy systems. The QI-MVO-based EMS brings in improved reliability and optimal utilization of the system through balanced energy distribution and by maintaining system operational stability. In conclusion, the present work showcases QI-MVO as a sustainable and scalable energy management solution, which sets the stage for optimization strategies wherein hybrid energy management assumes a very important role. Full article

Solar desalination systems turn saltwater water into freshwater, which helps to overcome water scarcity. In this study, the effects of direct (contact) and indirect (non-contact) interactions with water in solar desalination were evaluated. The emphasis was on changing water depths to better understand the performance variances. Contact systems have a direct interface between thermal absorption materials and water, whereas non-contact systems avoid material–water contact to increase longevity. The experiments at two elevated water depths (3 cm and 4 cm) were conducted in a single-slope solar desalination system. The productivity of both touch and non-contact systems was investigated in June, August, and October 2024 to gather sufficient data for the training and testing of various machine learning models used to predict the distillate. Surprisingly, the non-contact structure system produced 15% and 8% more distillate than the contact system at 3 and 4 cm water depths, respectively. This insightful result will aid in building efficient and sustainable solar desalination technologies. The comparative study gives information on the trade-off between contact and non-contact techniques, with implications for future advances in solar-powered desalination technology. Among all the machine learning techniques, random forest regression achieved the highest coefficient determination (R² train of 0.89 and R² test of 0.95 for the non-contact structure system and R² of train 0.85 and R² test of 0.98 for the contact structure system). Machine learning techniques improve solar desalination by allowing for predictive insights and efficient maintenance, ultimately leading to sustainable water production. Full article

Various electron impact scattering cross sections of Sevoflurane are reported up to 5 keV. The elastic cross sections (differential and integral) are computed using the single-centre-expansion formalism within a molecular framework. The ground state target wavefunction is determined at the Hartree–Fock (HF) level. Post-HF corrections are incorporated to make a scattering realistic model. The total interacting potential is defined as the sum of static, correlation–polarization and exchange potentials. These potentials are numerically computed using their local forms. The long-range effects affecting the scattering due to the polar nature of the molecule are incorporated using the Born Top-up approach. The ionization cross sections are obtained from the semi-empirical binary-encounter-Bethe model. The total cross sections are estimated from the incoherent sum of Born-corrected elastic integral and ionization cross sections. The computed results show fairly good agreement with the experimental reported cross sections. Full article

Over 2.2 billion people across the globe face significant barriers to accessing essential ophthalmic care, with elderly, rural, and refugee populations being disproportionately affected, deepening existing disparities in eye care. Three-dimensional printing is a novel technology that has the potential to transform the field and improve access by alleviating many patient-specific barriers. This article delves into the evolution of 3D printing within ophthalmology, highlighting its current applications and future potential. It explores various 3D printing techniques and numerous biomaterials discussing their effectiveness in creating advanced solutions such as bioengineered corneas, ocular prosthetics, and innovative treatments for dry eye syndrome, from punctal plugs to lacrimal gland models. Additionally, 3D printing has revolutionized drug delivery systems for conditions like glaucoma, retinal diseases, and ocular brachytherapy. Whether through 3D printed contact lens-based drug delivery systems or polycaprolactone implants that biodegrade and provide sustained drug release without adverse effects, these systems hold immense potential in the field. Despite its promise, the integration of 3D printing into clinical practice presents challenges, which the article addresses alongside strategies for overcoming them. By mapping out the technological advancements and challenges, this review offers a roadmap for enhancing global eye care accessibility and improving patient outcomes on a global scale. Full article