Search Results (509)

Particle image velocimetry (PIV) flow measurements are common practice in laboratory settings in a wide variety of fields involving fluid dynamics, including biology, physics, engineering, and medicine. Dynamic fluid pressure is a notoriously difficult property to measure non-intrusively, yet its variation is a driving flow force and critical to model correctly. Techniques have been developed to estimate the pressure from velocity and velocity gradient measurements. Here, we highlight a novel application of boundary conditions when applying such pressure estimation techniques based on two-dimensional PIV data; the novel method is especially relevant to problems with complex boundary conditions. As such, it is demonstrated with PIV measurements of in vivo fish suction-feeding, which represents a challenging flow environment. Suction-feeding is a common method for capturing prey by aquatic organisms. Suction-feeding is a complex fish–fluid interaction governed by various hydrodynamic forces and the dynamic behavior of the fish (motion and forces). This study focuses on estimating the pressure within the flow field surrounding the mouth of a Bluegill sunfish (Lepomis macrochirus) during suction-feeding utilizing two-dimensional PIV measurements. High-speed imaging was used for measurements of the fish kinematics (duration and amplitude). Through the Poisson equation, the pressure field is estimated from the PIV velocity measurements. The boundary conditions for the pressure field are determined from the integral momentum equation, separately for three phases of the suction-feeding cycle. We demonstrate the utility of the technique with this case study on fish suction-feeding by quantifying the pressure field that drives the flow towards the buccal cavity, a feeding mechanism known to be dominated by pressure spatial variations over the feeding cycle. Full article

Curtain deflector coating is a widely employed technique for producing thin, uniform films in numerous industrial applications. The flow dynamics in curtain coating become complex near the corner region due to the interaction of the moving substrate and the falling liquid curtain. In this study, an analytical investigation is conducted for the steady, in-compressible, and creeping flow of a Maxwell fluid, under the Navier slip condition at the substrate. The mathematical model is derived from the conservation of mass and momentum representing the nonlinear system which is solved using the Langlois recursive technique in combination with the inverse method. The inclusion of the Navier slip boundary condition in this research makes it novel and remove the singularity which produce the unstable stresses at a sharp corner due to no slip, but the Navier slip gives a stable solution for the stresses at a sharp corner. The analysis demonstrates that substrate slip significantly reduces tangential stresses and enhances the stability of the coating flow. Residual error analysis is also performed to verify the accuracy and convergence of the analytical solutions. The results provide a deeper understanding of how slip effects can be utilized to improve coating uniformity and optimize the operational performance of curtain deflector coating systems. Full article

Thin-film organic solar cells (TFOSCs) are gaining momentum as next-generation photovoltaic technologies due to their lightweight nature, mechanical flexibility, and low cost-effective fabrication. In this pioneering study, we report for the first time the incorporation of cobalt-doped zinc sulfide $(\mathrm{Z}\mathrm{n}\mathrm{S}/\mathrm{C}\mathrm{o})$ nanoparticles (NPs) into a poly(3-hexylthiophene) (P3HT) and [6,6]-phenyl-C61-butyric acid methyl ester (PC61BM) bulk-heterojunction photoactive layer. $\mathrm{Z}\mathrm{n}\mathrm{S}/\mathrm{C}\mathrm{o}$ NPs were successfully synthesized via a wet chemical method and integrated at varying concentrations (1%wt, 3%wt, and 5%wt) to systematically investigate their influence on device performance. The optimal doping concentration of 3%wt yielded a remarkable power conversion efficiency (PCE) of 4.76%, representing a 102% enhancement over the pristine reference device (2.35%) under ambient laboratory conditions. The observed positive trend is attributed to the localized surface plasmon resonance (LSPR) effect and near-field optical enhancement induced by the presence of ZnS/Co NPs in the active layer, thereby increasing light-harvesting capability and exciton dissociation. Comprehensive morphological and optical characterizations using high-resolution scanning electron microscopy (HRSEM), high-resolution transmission electron microscopy (HRTEM), and spectroscopic techniques confirmed uniform nanoparticle dispersion, nanoscale crystallinity, and effective light absorption. These findings highlight the functional role of $\mathrm{Z}\mathrm{n}\mathrm{S}/\mathrm{C}\mathrm{o}$ NPs as dopants in enhancing TFOSC performance, providing valuable insights into optimizing nanoparticle concentration. This work offers a scalable and impactful strategy for advancing high-efficiency, flexible, and wearable organic photovoltaic devices. Full article

Manipulating complex light fields through highly anisotropic scattering medium (HASM) remains a fundamental challenge due to the intricate underlying physics and broad application potential. We introduce a unified theoretical and experimental framework for generating and controlling arbitrarily polarized curved caustic beams using an extended polarization transfer matrix (EPTM) for the first time, enabling intuitive polarization transformation through HASM. The EPTM is experimentally measured via a four-step phase-shifting technique, and its submatrices are independently modulated with tailored caustic phase profiles. This strategy facilitates the creation of diverse high-resolution caustic beams, including Gaussian and vortex types with tunable energy distribution, polarization states, and vorticity. The achievement of polarization transformation through HASM by our approach offers versatile manipulation over optical field properties such as multiple high-resolution caustic beams, angular momentum flux, and polarization, paving the way for enhanced functionality in advanced optical systems. Full article

Delays caused by motor vehicle traffic, accidents, and environmental pollution present considerable challenges to sustainable urban mobility. To address these issues, transportation system users are encouraged to adopt active transportation methods, micromobility options, and public transit. Electric scooters have become a notably popular micromobility choice, especially following the emergence of vehicle-sharing companies in 2018, a trend that gained further momentum during the COVID-19 pandemic. This study explored the demographic characteristics, attitudes, and behaviors of e-scooter users in Istanbul through an online survey conducted from 1 September 2023 to 1 May 2024. A total of 462 e-scooter users participated, providing valuable insights into their preferred modes of transportation across 24 different scenarios specifically designed for this research. The responses were analyzed using various machine learning techniques, including Artificial Neural Networks, Decision Trees, Random Forest, and Gradient Boosting methods. Among the models developed, the Decision Tree model exhibited the highest overall performance, demonstrating strong accuracy and predictive capabilities across all classifications. Notably, all models significantly surpassed the accuracy of discrete choice models reported in existing literature, underscoring the effectiveness of machine learning approaches in modeling transportation mode choices. The models created in this study can serve various purposes for researchers, central and local authorities, as well as e-scooter service providers, supporting their strategic and operational decision-making processes. Future research could explore different machine learning methodologies to create a model that more accurately reflects individual preferences across diverse urban environments. These models can assist in developing sustainable mobility policies and reducing the environmental footprint of urban transportation systems. Full article

In this paper, we investigate the stability and feasibility of an anisotropic stellar model under $f(R,T)$ gravity that embraces the Karmarkar condition. In order to develop the $f(R,T)$ gravity model, the functional form of $f(R,T)$ is taken into consideration as the linear function of the trace of the energy-momentum tensor T and the Ricci scalar R, respectively. This study proposes a well-known form of the radial metric function and finds another metric function by employing the Karmakar condition, which provides the exact solution to the field equation. The expression of the model parameters is derived by matching the obtained interior solutions with the Schwarzschild exterior metric over the bounding surface of a celestial object, along with the requirement that the radial pressure vanish at the boundary. The current estimated data of the star, pulsar 4U1608-52, is used to graphically explore the model. The physical attributes of the celestial object are thoroughly examined within the framework of the present model. Adjusting the model parameter, a detailed analysis of the stability criterion is presented that involves the adiabatic index, the Herrera cracking technique, and the causality condition. Furthermore, the Tolman–Oppenheimer–Volkhoff equation is used to analyze the stellar model’s equilibrium state. In order to maintain the stability condition of the anisotropic stellar structure, a suitable range for the model parameter is determined by the graphical analysis of the present model in this study. In addition, the numerical values of the physical parameters related to the compact stars Her X-1, LMC X-4, Cen X-3 and KS1731-207 are used to examine the model solution within the desired range of the model parameter. Full article

The process industry (PI) plays a vital role in the global economy and faces mounting pressure to enhance sustainability, operational agility, and resource efficiency amid tightening regulatory and market demands. Although artificial intelligence (AI) has been explored in this domain for decades, its adoption in industrial practice remains limited. Recently, machine learning (ML) has gained momentum, particularly when integrated with core PI systems such as process control, instrumentation, quality management, and enterprise platforms. Among ML techniques, reinforcement learning (RL) has emerged as a promising approach to tackle complex operational challenges. In contrast to conventional data-driven methods that focus on prediction or classification, RL directly addresses sequential decision making under uncertainty, a defining characteristic of dynamic process operations. Given RL’s growing relevance, this study conducts a systematic literature review to evaluate its current applications in the PI, assess methodological developments, and identify barriers to broader industrial adoption. The review follows the PRISMA methodology, a structured framework for identifying, screening, and selecting relevant publications. This approach ensures alignment with a clearly defined research question and minimizes bias, focusing on studies that demonstrate meaningful industrial applications of RL. The findings reveal that RL is transitioning from a theoretical construct to a practical tool, particularly in the chemical sector and for tasks such as process control and scheduling. Methodological maturity is improving, with algorithm selection increasingly tailored to problem-specific requirements and a trend toward hybrid models that integrate RL with established control strategies. However, most implementations remain confined to simulated environments, underscoring the need for real-world deployment, safety assurances, and improved interpretability. Overall, RL exhibits the potential to serve as a foundational component of next-generation smart manufacturing systems. Full article

Propylene oxide is the first and only chiral molecule to have been observed in the interstellar medium. Given the mechanisms for forming chiral species, which are important for astrobiology in understanding the origins of life, we report here an experimental and theoretical investigation into the electronic structure of propylene oxide and its evolution from the methylation and epoxidation of ethene. Here, electron momentum spectroscopy is used as an orbital-imaging technique to probe experimental orbital momentum distributions. These are directly compared with theoretical orbital momentum distributions calculated at the equilibrium geometry, and those calculated by considering the vibrational motion of the propylene oxide target. This allows us to identify which molecular orbitals are sensitive to specific vibrational normal modes, thereby facilitating understanding and controlling chemical reactivity. By extending our investigation to include intermediate species along the evolution of ethene through methylation and epoxidation, we can develop an understanding of how the orbital electronic structure evolves through this series of important chemicals. Full article

Rainfall simulators are crucial devices in erosion research, enabling the controlled reproduction of precipitation characteristics for both laboratory and field investigations. This study presents a comprehensive characterization of a rainfall simulator originally designed to assess the erosive effects of precipitation on heritage surfaces. The simulator, installed at the University of León, was evaluated using volumetric methods and disdrometric techniques, employing a Parsivel² optical disdrometer. Simulations were conducted with a falling height of 10 m and high-intensity rainfalls. Spatial uniformity was assessed through thematic mapping and the Christiansen Uniformity (CU) coefficient, revealing limited uniformity across the full wetted area, but an improved performance within the central zone (CU up to 80%). Disdrometric data provided detailed insights into drop size and velocity distributions, enabling the estimation of rainfall intensity, kinetic energy, and momentum, as well as the spatial uniformity of the energetic parameters. Empirical models to estimate the raindrop’s fall velocity were tested against disdrometric measurements, confirming the simulator’s ability to generate rainfall with velocity characteristics comparable to those of natural precipitation. Moreover, the findings underscore the importance of integrating multiple measurement approaches to enhance the reliability and accuracy of rainfall simulator characterization. Full article

Spin angular momentum is a fundamental dynamical property of elementary particles and fields, playing a critical role in light–matter interactions. In optical studies, the optical spin angular momentum is closely linked to circular polarization. Research on the interaction between optical spin and matter or structures has led to numerous novel optical phenomena and applications, giving rise to the emerging field of spin optics. Historically, researchers primarily focused on longitudinal optical spin aligned parallel to the mean wavevector. In recent years, investigations into the spin–orbit coupling properties of confined fields—such as focused beams, guided waves, and evanescent waves—have revealed a new class of optical spin oriented perpendicular to the mean wavevector, referred to as optical transverse spin. In the optical near-field, such transverse spins arise from spatial variations in the momentum density of confined electromagnetic waves, where strong coupling between spin and orbital angular momenta leads to various topological spin structures and properties. Several reviews on optical transverse spin have been published in recent years, systematically introducing its fundamental concepts and the configurations that generate it. In this review, we detail recent advances in spin optics from three perspectives: theory, experimental techniques, and applications, with a particular emphasis on the fundamental physics of transverse spin and the resulting topological structures and characteristics. The conceptual and theoretical framework of spin optics is expected to significantly support further exploration of optical spin-based applications in fields such as optics imaging, topological photonics, metrology, and quantum technologies. Furthermore, these principles can be extended to general classical wave systems, including fluidic, acoustic, and gravitational waves. Full article

The growing demand for renewable energy in space-constrained environments highlights the need for compact, high-efficiency wind energy systems. Conventional bare wind turbine (BWT) arrays suffer from severe wake interactions and performance degradation when operated in tandem or closely spaced configurations. To address these limitations, this study investigates the aerodynamic performance and near-wake dynamics of a novel multi-ducted wind turbine (MDWT) system that integrates passive flow-control technique (PFCT) into an innovative fixed-duct design. The objective is to evaluate how tandem ducted arrangements with this integrated mechanism influence wake recovery, vortex dynamics, and power generation compared with multi-bare wind turbine (MBWT) system. A numerical approach is employed using the Unsteady Reynolds-Averaged Navier–Stokes (URANS) formulation with the k–ω SST turbulence model, validated against experimental data. The analysis focuses on two identical, fixed-orientation ducts arranged in tandem without lateral offset, tested under three spacing configurations. The results reveal that the ducted system accelerates the near-wake flow and displaces velocity-deficit regions downward due to the passive flow-control sheets, producing stronger inflow fluctuations and enhanced turbulence mixing. These effects improve wake recovery and mitigate energy losses behind the first turbine. Quantitatively, the MDWT array achieves total power outputs 1.99, 1.90, and 1.81 times greater than those of the MBWT array for Configurations No. 1, No. 2, and No. 3, respectively. In particular, the second duct in Configuration No. 1 demonstrates a 3.46-fold increase in power coefficient compared with its bare counterpart. These substantial gains arise because the upstream duct–PFCT assembly generates a favorable pressure gradient that entrains ambient air into the wake, while coherent tip vortices and redirected shear flows enhance mixing and channel high-momentum fluid toward the downstream rotor plane. The consistent performance across spacings further confirms that duct-induced flow acceleration and organized vortex structures dominate over natural wake recovery effects, maintaining efficient energy transfer between turbines. The study concludes that closely spaced MDWT systems provide a compact and modular solution for maximizing energy extraction in constrained environments. Full article

Explainable Artificial Intelligence (XAI) has become essential as machine learning systems are deployed in high-stakes domains such as security, finance, and healthcare. Traditional models often act as “black boxes”, limiting trust and accountability. Traditional models often act as “black boxes”, limiting trust and accountability. However, most existing reviews treat explainability either as a technical problem or a philosophical issue, without connecting interpretability techniques to their real-world implications for security, privacy, and governance. This review fills that gap by integrating theoretical foundations with practical applications and societal perspectives. define transparency and interpretability as core concepts and introduce new economics-inspired notions of marginal transparency and marginal interpretability to highlight diminishing returns in disclosure and explanation. Methodologically, we examine model-agnostic approaches such as LIME and SHAP, alongside model-specific methods including decision trees and interpretable neural networks. We also address ante-hoc vs. post hoc strategies, local vs. global explanations, and emerging privacy-preserving techniques. To contextualize XAI’s growth, we integrate capital investment and publication trends, showing that research momentum has remained resilient despite market fluctuations. Finally, we propose a roadmap for 2025–2030, emphasizing evaluation standards, adaptive explanations, integration with Zero Trust architectures, and the development of self-explaining agents supported by global standards. By combining technical insights with societal implications, this article provides both a scholarly contribution and a practical reference for advancing trustworthy AI. Full article

The automatic reconstruction of existing buildings has gained momentum through the integration of Building Information Modeling (BIM) into architecture, engineering, and construction (AEC) workflows. This study presents a hybrid methodology that combines deep learning with surface-based techniques to automate the generation of 3D models and 2D floor plans from unstructured indoor point clouds. The approach begins with point cloud preprocessing using voxel-based downsampling and robust statistical outlier removal. Room partitions are extracted via DBSCAN applied in the 2D space, followed by structural segmentation using the RandLA-Net deep learning model to classify key building components such as walls, floors, ceilings, columns, doors, and windows. To enhance segmentation fidelity, a density-based filtering technique is employed, and RANSAC is utilized to detect and fit planar primitives representing major surfaces. Wall-surface openings such as doors and windows are identified through local histogram analysis and interpolation in wall-aligned coordinate systems. The method supports complex indoor environments including Manhattan and non-Manhattan layouts, variable ceiling heights, and cluttered scenes with occlusions. The approach was validated using six datasets with varying architectural characteristics, and evaluated using completeness, correctness, and accuracy metrics. Results show a minimum completeness of 86.6%, correctness of 84.8%, and a maximum geometric error of 9.6 cm, demonstrating the robustness and generalizability of the proposed pipeline for automated as-built BIM reconstruction. Full article

We investigate $f(R,T)$ gravity, where R is the Ricci scalar and T the trace of the energy–momentum tensor, focusing on the subclass defined by $f(R,T)=R+2f\left(T\right)$. Instead of assuming a parametric form, we adopt a non-parametric reconstruction based on genetic algorithms (GA), a machine learning technique that does not rely on predefined models. Using Hubble parameter measurements from cosmic chronometers, baryon acoustic oscillations, and the Dark Energy Spectroscopic Instrument (DESI) data, we reconstruct $H\left(z\right)$ in a model-independent way. This reconstruction allows us to derive both numerical and analytical forms of $f\left(T\right)$ through the modified Friedmann equations. The analytic expression derived via GA provides an excellent fit to the numerical reconstruction. Furthermore, we compare the evolution of the Hubble parameter predicted by our model with that of the standard ΛCDM scenario (Planck), finding a good agreement for $z\lesssim 2$. These results highlight the robustness of GA-based reconstructions and suggest that the functional form of $f(R,T)$ obtained here may serve as a promising tool for further applications in cosmology and astrophysics. Full article

Exosomes, naturally derived extracellular vesicles, have emerged as powerful bio-nanocarriers in precision medicine. Their endogenous origin, biocompatibility, and ability to encapsulate and deliver diverse therapeutic payloads position them as transformative tools in drug delivery, gene therapy, and regenerative medicine. This review presents a comprehensive analysis of exosome-based therapeutics across multiple biomedical domains, including cancer, neurological and infectious diseases, immune modulation, and tissue repair. Exosomes derived from stem cells, immune cells, or engineered lines can be loaded with small molecules, RNA, or CRISPR-Cas systems, offering highly specific and low-immunogenic alternatives to viral vectors or synthetic nanoparticles. We explore endogenous and exogenous loading strategies, surface functionalization techniques for targeted delivery, and innovations that allow exosomes to traverse physiological barriers such as the blood–brain barrier. Furthermore, exosomes demonstrate immunomodulatory and regenerative properties in autoimmune and degenerative conditions, with promising roles in skin rejuvenation and cosmeceuticals. Despite their potential, challenges remain in large-scale production, cargo loading efficiency, and regulatory translation. Recent clinical trials and industry efforts underscore the accelerating momentum in this field. Exosomes represent a promising platform in precision medicine, though further standardization and validation are required before widespread clinical use. This review offers critical insights into current technologies, therapeutic mechanisms, and future directions to unlock the full translational potential of exosomes in clinical practice. Full article