Search Results (396)

Bioconvection phenomena play a pivotal role in diverse applications, including the synthesis of biological polymers and advancements in renewable energy technologies. This study develops a comprehensive mathematical model to examine the effects of key parameters, such as the Lewis number (Lb), Peclet number (Pe), volume fraction (φ), and angle of inclination (α), on the flow and heat transfer characteristics of a nanofluid over an inclined cylinder embedded in a non-Darcy porous medium. The investigated nanofluid comprises nano-encapsulated phase-change materials (NEPCMs) dispersed in water, offering enhanced thermal performance. The governing non-linear partial differential equations are transformed into dimensionless ordinary differential equations using similarity transformations and solved numerically via the Network Simulation Method (NSM) and an implicit Runge–Kutta method implemented through the bvp4c routine in MATLAB R2021a. Validation against the existing literature confirms the accuracy and reliability of the numerical approach, with strong convergence observed. Quantitative analysis reveals that an increase in the Peclet number reduces the shear stress at the cylinder wall by up to 18% while simultaneously enhancing heat transfer by approximately 12%. Similarly, the angle of inclination (α) significantly boosts heat transmission rates. Additionally, higher Peclet and Lewis numbers, along with greater nanoparticle volume fractions, amplify the density gradient of microorganisms, intensifying the bioconvection process by nearly 15%. These findings underscore the critical interplay between bioconvection and transport phenomena, providing a framework for optimizing bioconvection-driven heat and mass transfer systems. The insights from this investigation hold substantial implications for industrial processes and renewable energy technologies, paving the way for improved efficiency in applications such as thermal energy storage and advanced cooling systems. Full article

Remote photo-plethysmography (rPPG) is a useful camera-based health motioning method that can measure the heart rhythm from facial videos. Many well-established deep learning models can provide highly accurate and robust results in measuring heart rate (HR) and heart rate variability (HRV). However, these methods are unable to effectively eliminate illumination variation and motion artifact disturbances, and their substantial computational resource requirements significantly limit their applicability in real-world scenarios. Hence, we propose a lightweight multi-frequency network named MFF-Net to measure heart rhythm via facial videos in a short time. Firstly, we propose a multi-frequency mode signal fusion (MFF) mechanism, which can separate the characteristics of different modes of the original rPPG signals and send them to a processor with independent parameters, helping the network recover blood volume pulse (BVP) signals accurately under a complex noise environment. In addition, in order to help the network extract the characteristics of different modal signals effectively, we designed a temporal multiscale convolution module (TMSC-module) and spectrum self-attention module (SSA-module). The TMSC-module can expand the receptive field of the signal-refining network, obtain more abundant multiscale information, and transmit it to the signal reconstruction network. The SSA-module can help a signal reconstruction network locate the obvious inferior parts in the reconstruction process so as to make better decisions when merging multi-dimensional signals. Finally, in order to solve the over-fitting phenomenon that easily occurs in the network, we propose an over-fitting sampling training scheme to further improve the fitting ability of the network. Comprehensive experiments were conducted on three benchmark datasets, and we estimated HR and HRV based on the BVP signals derived by MFF-Net. Compared with state-of-the-art methods, our approach achieves better performance both on HR and HRV estimation with lower computational burden. We can conclude that the proposed MFF-Net has the opportunity to be applied in many real-world scenarios. Full article

For automobile and aerospace engineers, implementing Hall currents and thermal radiation in cooling systems helps increase the performance and durability of an engine. In the case of solar energy systems, the effectiveness of heat exchangers and solar collectors can be enhanced by the best use of hybrid nanofluids and the implementation of a Hall current, thermophoresis, Brownian motion, a heat source/sink, and thermal radiation in a time-dependent hybrid nanofluid flow over a disk for a Bayesian regularization ANN backpropagation algorithm. In the current physical model of Cobalt ferrite CoFe2O4 and aluminum oxide Al2O3 mixed with water, a new category of the nanofluid is called the hybrid nanofluid. The study uses MATLAB bvp4c to unravel such intricate relations, transforming PDEs into ODEs. This analysis enables the numerical solution of several BVPs that govern the system of the given problem. Hall currents resulting from the interaction between magnetic fields and the electrically conducting nanofluid, and thermal radiation as an energy transfer mechanism operating through absorption and emission, are central factors for controlling these fluids for use in various fields. The graphical interpretation assists in demonstrating the character of new parameters. The heat source/sink parameter is advantageous to thermal layering, but using a high Schmidt number limits the mass transfer. Additionally, a backpropagation technique with Bayesian regularization is intended for solving ordinary differential equations. Training state, performance, error histograms, and regression demonstration are used to analyze the output of the neural network. In addition to this, there is a decrease in the fluid velocity as magnetic parameter values decrease and a rise in the fluid temperature while the disk is spinning. Thermal radiation adds another level to the thermal behavior by altering how the hybrid nanofluid receives, emits, and allows heat to pass through it. Full article

Background/Objectives: The key components of the blood–brain barrier (BBB) are endothelial cells, pericytes, astrocytes, and the capillary basement membrane. The BBB serves as the main barrier for drug delivery to the brain and is the most restrictive endothelial barrier in the body. Nearly all large therapeutic molecules and over 90% of small-molecule drugs cannot cross the BBB. To overcome this challenge, nanotechnology, particularly drug delivery systems such as nanoparticles (NPs), have gained significant attention. Methods: Poly(lactide-co-glycolide) (PLGA) and albumin-based NPs (bovine/human), with or without transferrin (Tf) ligands (BSA, HSA, BSA-Tf, HSA-Tf), and nanolipid carriers (NLC) were synthesized. The interactions of these NPs with human brain microvascular endothelial cells (hBMECs), human brain vascular pericytes (hBVPs), and human astrocytes (hASTROs) were analyzed. Results: At doses of 15.62 µg/mL, 31.25 µg/mL, and 62.5 µg/mL, none of the NPs caused toxic effects on hBMECs, hBVPs, or hASTROs after 3 h of incubation. All NPs were internalized by the cells, but BSA-Tf and HSA-Tf showed significantly higher uptake in hBMECs in a dose-dependent manner. Ultrastructural analysis revealed notable differences between NP formulation and cell type. Conclusions: Our findings underscore the potential of ligand-targeted NPs to selectively interact with BBB endothelial cells. Ultrastructural analysis reveals distinct cellular processing pathways for various NP formulations across BBB-associated cell types, with autophagy emerging as a crucial mechanism for NP handling in pericytes and astrocytes. Changes in NP chemical properties upon biological exposure present significant challenges for nanomedicine design, emphasizing the need for further investigation into NP interactions at the cellular and subcellular levels. Full article

Increasingly, humans are exposed to different activities at work, at home, and in general in their daily lives that generate episodes of stress. In many cases, these episodes could produce disorders in their health and reduce their quality of life. For this reason, it is crucial to implement mechanisms that can detect stress in individuals and develop applications that provide feedback through various activities to help reduce stress levels. Physiological parameters, such as galvanic skin response (GSR) and heart rate (HR) are indicative of stress-related changes. There exist methodologies that use wearable sensors to measure these stress levels. In this study, a sensor of blood volume pulse (BVP) and an electrocardiography (ECG) sensor were utilized to obtain metrics like heart rate variability (HRV) and pulse arrival time (PAT). Their features were extracted, processed, and analyzed for anxiety detection. The classification performance was evaluated using decision trees, a support vector machine (SVM), and meta-classifiers to accurately distinguish between “stressed” and “non-stressed” states. We obtained the best results with the SVM classifier using all the features. Additionally, we found that the ECG AD8232 sensor provided more reliable data compared to the photoplethysmography (PPG) signal obtained from the MAX30100 sensor. Therefore, the ECG is a more accurate tool for assessing emotional states related to stress and anxiety. Full article

Background: Bilateral vestibulopathy (BVP) is a disabling condition characterized by a deficit in vestibular function on both sides. Current diagnostic criteria consider instrumental data only from horizontal canals, excluding vertical canals and otolithic function, with the possibility of not including some variants of BVP. This study aims to evaluate vestibular functions in people with chronic vestibular syndrome through a comprehensive battery of tests. Methods: This diagnostic accuracy study included patients who met criteria for probable BVP. The index test included a thorough evaluation of the vestibular system, using the video Head Impulse Test (vHIT) to measure the gain of the angular vestibulo-ocular reflex (aVOR) in all six semicircular canals and the cervical and ocular vestibular-evoked myogenic potentials (VEMPs) to assess otolith function. The diagnostic criteria established by the Barany Society were considered the standard reference, including only the horizontal vHIT as an instrumental assessment. Results: 78 patients (41 male, age 61.40 ± 12.99) were enrolled. The Barany criteria showed a low ability to rule out BPV (sensitivity = 46%). The median Dizziness Handicap Inventory (DHI) varied from 66 to 69 among the models studied, and a significant difference in DHI scores between positive and negative tests was observed for the Barany criteria and the six-canals vHIT model. Conclusions: Our findings highlight the potential to transform BPV diagnostic criteria. The identification of new bilateral vestibular dysfunction variants through improved diagnostic tools calls for revising current criteria, with promising implications for patient care and understanding of etiological and prognostic aspects. Full article

We here sought to assess thrombotic and hemorrhagic complications and associated risk factors during pregnancy, delivery, and postpartum in women with prosthetic heart valves (PHV). Methods: The retrospective cohort study covered January 2011 to December 2022. The objective of the study was to assess the risk factors and frequency of thrombotic and hemorrhagic complications during pregnancy, delivery, and the postpartum period in women with PHV based on the experience of one perinatal center. We included 88 pregnancies with 77 prosthetic heart valves (PHV), which were divided into two groups, mechanical valve prostheses (MVP) (n = 64) and biological valve prosthesis (BVP) (n = 24). In the study we analyzed pregnancy outcomes, as well as thrombotic and hemorrhagic complication frequencies. Results: Of 88 pregnancies, 79 resulted in live births. In the MVP group, there were six miscarriages (9.4%) and two medical abortions (3.1%), including one due to Warfarin’s teratogenic effects. No miscarriages were reported in the BVP group, but one fetal mortality case (4.2%) occurred. During pregnancy, 11 MVP cases (17.2%) experienced thrombotic complications. In the BVP group, one patient (4.2%) had transient ischemic attack (TIA). Two MVP cases required surgical valve repair during pregnancy, and one in the post-delivery stage was caused by thrombotic complications. Postpartum, two MVP cases had strokes, and in one MVP patient, pulmonary embolism was registered, while no thrombotic complications occurred in the BVP group. Hemorrhagic complications affected 15 MVP cases (17.9%) in the postpartum period. There were no registered cases of maternal mortality. Conclusions: The effective control of anti-factor Xa activity reduced thrombotic events. However, the persistently high incidence of postpartum hemorrhagic complications suggests a need to reassess anticoagulant therapy regimens, lower target levels of anti-Xa, and reduce INR levels for discontinuing heparin bridge therapy. Despite the heightened mortality risk in MVP patients, our study cohort did not have any mortality cases, which contrasts with findings from other registries. Full article

This research presents an efficient and highly accurate cubic B-spline method (CBSM) for solving second-order linear boundary value problems (BVPs). The method achieves sixth-order convergence, supported by rigorous error analysis, ensuring rapid error reduction with mesh refinement. The effectiveness of the CBSM is validated through four numerical examples, showcasing its accuracy, reliability, and computational efficiency, making it well-suited for large-scale problems. A comparative analysis with existing methods confirms the superior performance of the CBSM, positioning it as a practical and powerful tool for solving second-order BVPs. Full article

This study assessed knowledge, attitudes, and practices related to food hygiene among mothers from Malawi’s rural communities against the WHO Five Keys to Safer Food (WHO-FKSF) and good pre-and post-harvest practices (GPPHPs) as reference points. Five hundred twenty-two mothers from six rural communities across two districts were selected for the survey. The results indicated limited knowledge among participating mothers regarding managing food hazards, including mycotoxins, bacteria, viruses, and parasites (BVPs). A significant proportion (89.5%) of women reported inconsistent or no handwashing with soap after using the toilet. In addition, 48.7% failed to plant with the first good rains, 38.7% neglected to check for moldy cobs during harvesting, 57.4% dried maize on bare soil, and 99.2% bought maize with noticeable mold. Higher education, knowledge, and positive attitudes were associated with enhanced BVP control practices, while larger households and positive attitudes were linked to improved mold/mycotoxin management (p < 0.05). Mothers showed lower (p < 0.05) knowledge and attitude levels regarding molds than BVPs but demonstrated relatively better practices for mold control. A comprehensive education program based on the WHO Five Keys to Safer Foods, tailored to local socio-cultural norms and incorporating mold and mycotoxin management guidelines, is recommended. Full article

This paper concerns control BVPs, driven by ODEs x′t=ut, using controls u0· &u1· in L1a,b,R2. We ask these two controls to satisfy a very simple restriction: at points where their first coordinates coincide, also their second coordinates must coincide; which allows one to write (u1−u0)·=v·1,f· for some f·. Given a relaxed non bang-bang solution x¯·∈W1,1a,b,R2, a question relevant to applications was first posed three decades ago by A. Cellina: does there exist a bang-bang solution x^· having lower first-coordinate x^1·≤x¯1·? Being the answer always yes in dimension d=1, hence without f·, as proved by Amar and Cellina, for d=2 the problem is to find out which functions f· “are good”, namely “allow such 1-lower bang-bang solution x^· to exist”. The aim of this paper is to characterize “goodness of f·” geometrically, under “good data”. We do it so well that a simple computational app in a smartphone allows one to easily determine whether an explicitly given f· is good. For example: non-monotonic functions tend to be good; while, on the contrary, strictly monotonic functions are never good. Full article

This research focuses on finding multiple solutions (MSs) to nonlinear fractional boundary value problems (BVPs) through a new development, namely the predictor Laplace fractional power series method. This method predicts the missing initial values by applying boundary or force conditions. This research provides a set of theorems necessary for deriving the recurrence relations to find the series terms. Several examples demonstrate the efficacy, convergence, and accuracy of the algorithm. Under Caputo’s definition of the fractional derivative with symmetric order, the obtained results are visualized numerically and graphically. The behavior of the generated solutions indicates that altering the fractional derivative parameters within their domain symmetrically changes these solutions, ultimately aligning them with the standard derivative. The results are compared with the homotopy analysis method and are presented in various figures and tables. Full article

As claimed in many papers, the equivalence between the Caputo-type fractional differential problem and the corresponding integral forms may fail outside the spaces of absolutely continuous functions, even in Hölder spaces. To avoid such an equivalence problem, we define a “new” appropriate fractional integral operator, which is the right inverse of the Caputo derivative on some Hölder spaces of critical orders less than 1. A series of illustrative examples and counter-examples substantiate the necessity of our research. As an application, we use our method to discuss the BVP for the Langevin fractional differential equation dψβ,μdtβdψα,μdtα+λx(t)=f(t,x(t)),t∈[a,b],λ∈R, for f∈C[a,b]×R and some critical orders β,α∈(0,1), combined with appropriate initial or boundary conditions, and with general classes of ψ-tempered Hilfer problems with ψ-tempered fractional derivatives. The BVP for fractional differential problems of the Bagley–Torvik type was also studied. Full article

This analysis emphasizes the significance of radiation and chemical reaction effects on the boundary layer flow (BLF) of Casson liquid over a linearly elongating surface, as well as the properties of momentum, entropy production, species, and thermal dispersion. The mass diffusion coefficient and temperature-dependent models of thermal conductivity and species are used to provide thermal transportation. Nonlinear partial differential equations (NPDEs) that go against the conservation laws of mass, momentum, heat, and species transportation are the form arising problems take on. A set of coupled dimensionless partial differential equations (PDEs) are obtained from a set of convective differential equations by applying the proper non-similar transformations. Local non-similarity approaches provide an analytical approximation of the dimensionless non-similar system up to two degrees of truncations. The built-in Matlab (Version: 7.10.0.499 (R2010a)) solver bvp4c is used to perform numerical simulations of the local non-similar (LNS) truncations. Full article

In this paper, we focus on a fractional differential equation 0CDαu(t)+q(t)u(t)=0 with boundary value conditions u(0)=δu(1),u′(0)=γu′(1). The paper begins by pointing out the inadequacies of the study conducted by Ma and Yangin establishing Lyapunov-type inequalities. It then discusses the properties of its Green’s function and investigates extremum problems related to several linear functions. Finally, thorough classification and analysis of various cases for parameters δ and γ are conducted. As a result, a comprehensive solution corresponding to the Lyapunov-type inequality is obtained. Full article

To comprehend the thermal regulation within the conical gap between a disk and a cone (TRHNF-DC) for hybrid nanofluid flow, this research introduces a novel application of computationally intelligent heuristics utilizing backpropagated Levenberg–Marquardt neural networks (LM-NNs). A unique hybrid nanoliquid comprising aluminum oxide, Al2O3, nanoparticles and copper, Cu, nanoparticles is specifically addressed. Through the application of similarity transformations, the mathematical model formulated in terms of partial differential equations (PDEs) is converted into ordinary differential equations (ODEs). The BVP4C method is employed to generate a dataset encompassing various TRHNF-DC scenarios by varying magnetic parameters and nanoparticles. Subsequently, the intelligent LM-NN solver is trained, tested, and validated to ascertain the TRHNF-DC solution under diverse conditions. The accuracy of the LM-NN approach in solving the TRHNF-DC model is verified through different analyses, demonstrating a high level of accuracy, with discrepancies ranging from 10−10 to 10−8 when compared with standard solutions. The efficacy of the framework is further underscored by the close agreement of recommended outcomes with reference solutions, thereby validating its integrity. Full article