Search Results (10,465)

Dendritic cells (DCs) are essential for antiviral immunity but are also susceptible to HIV-1 infection. Although sensing and restriction pathways in DCs are well described, the mechanisms underlying latent infection and its functional consequences remain unclear. In this study, we performed transcriptomic profiling of monocyte-derived DCs harboring transcriptionally active (Active-HIV) or latent HIV-1 (Latent-HIV) proviruses using a dual-reporter virus. Gene set enrichment analysis revealed suppression of metabolic and stress-modulatory programs in Active-HIV compared to unexposed DCs. In contrast, Latent-HIV showed broad downregulation of pathways, including interferon and innate responses and metabolic programs, indicating a hyporesponsive and dampened antiviral state despite the absence of differentially expressed genes (DEGs). DEG analysis of Active-HIV versus Latent-HIV showed that active transcription associates with cellular stress, cytoskeletal remodeling, and RNA processing. Functional analyses further demonstrated the activation of RNA processes, the suppression of antigen-presentation pathways, and altered membrane and cytoskeletal signaling in Active-HIV. These pathways suggest that transcriptionally active HIV-1 is linked to cellular programs supporting replication, coinciding with a metabolically strained yet immunologically engaged state that may impair antigen presentation. Conversely, latently infected DCs display a hyporesponsive state consistent with proviral silencing. This dichotomy reveals distinct mechanisms of DC dysfunction that may facilitate HIV-1 persistence and immune evasion. Full article

Active involvement in creative activities, known as creative health, has been shown to enhance wellbeing, with museums serving as unique spaces for health promotion; however, visitors often require guidance to derive significant benefits from these institutions. This study, part of the larger ASBA (Anxiety, Stress, Brain-friendly museum Approach) project, evaluates the first phase of an intervention specifically focused on a Mindfulness protocol adapted to museum contexts. It has employed a single-group pre–post design with 79 healthy adults recruited from the non-clinical population. Participants were involved in a 15 min standardized mindfulness practice adapted from Mindfulness-Based Stress Reduction (MBSR) in either an art or science museum. State anxiety (SAI) and mood (VAS) were assessed at baseline and post-intervention, alongside personality traits (BFI-10) and interest measures to identify individual moderators of treatment response. The practice appeared to reduce state anxiety significantly in both settings, with large effect sizes. Specific moderators emerged: openness to experience predicted anxiety reduction in the art museum, whereas science interest predicted outcomes in the science setting. These findings suggest that brief, standardized mindfulness protocols implemented through the ASBA framework can provide promising immediate benefits for visitor wellbeing across diverse museum environments. Full article

The purpose of this research was to investigate whether music empirically associated with therapeutic effects contains intrinsic informational structures that differentiate it from other sound sequences. Drawing on ontology, phenomenology, nonlinear dynamics, and complex systems theory, we hypothesize that therapeutic relevance may be linked to persistent structural patterns embedded in musical signals rather than to stylistic or genre-related attributes. This paper introduces the Discriminating Music Sequences (DiMuSes) method, an unsupervised, structure-oriented analytical framework designed to detect such patterns. The method applies 24 scalar evaluators derived from statistics, fractal geometry, nonlinear physics, and complex systems, transforming sound sequences into multidimensional vectors that characterize their global temporal organization. Principal Component Analysis (PCA) reduces this feature space to three dominant components (PC1–PC3), enabling visualization and comparison in a reduced informational space. Unsupervised k-Means clustering is subsequently applied in the PCA space to identify groups of structurally similar sound sequences, with cluster quality evaluated using Silhouette and Davies–Bouldin indices. Beyond clustering, DiMuSe implements ranking procedures based on relative positions in the PCA space, including distance to cluster centroids, inter-item proximity, and stability across clustering configurations, allowing melodies to be ordered according to their structural proximity to the therapeutic cluster. The method was first validated using synthetically generated nonlinear signals with known properties, confirming its capacity to discriminate structured time series. It was then applied to a dataset of 39 music and sound sequences spanning therapeutic, classical, folk, religious, vocal, natural, and noise categories. The results show that therapeutic music consistently forms a compact and well-separated cluster and ranks highly in structural proximity measures, suggesting shared informational characteristics. Notably, pink noise and ocean sounds also cluster near therapeutic music, aligning with independent evidence of their regulatory and relaxation effects. DiMuSe-derived rankings were consistent with two independent studies that identified the same musical pieces as highly therapeutic.The present research remains at a theoretical stage. Our method has not yet been tested in clinical or experimental therapeutic settings and does not account for individual preference, cultural background, or personal music history, all of which strongly influence therapeutic outcomes. Consequently, DiMuSe does not claim to predict individual efficacy but rather to identify structural potential at the signal level. Future work will focus on clinical validation, integration of biometric feedback, and the development of personalized extensions that combine intrinsic informational structure with listener-specific response data. Full article

Recent advancements in prosthetic technology have increasingly focused on enhancing dexterity and autonomy through intelligent control systems. Vision-based approaches offer promising results for enabling prosthetic hands to interact more naturally with diverse objects in dynamic environments. Building on this foundation, the paper presents a vision-guided grasping algorithm for a prosthetic hand, integrating perception, planning, and control for dexterous manipulation. A camera mounted on the set up captures the scene, and a Bounding Volume Hierarchy (BVH)-based vision algorithm is employed to segment an object for grasping and define its bounding box. Grasp contact points are then computed by generating candidate trajectories using Rapidly-exploring Random Tree Star (RRT*) algorithm, and selecting fingertip end poses based on the minimum Euclidean distance between these trajectories and the object’s point cloud. Each finger’s grasp pose is determined independently, enabling adaptive, object-specific configurations. Damped Least Square (DLS) based Inverse kinematics solver is used to compute the corresponding joint angles, which are subsequently transmitted to the finger actuators for execution. Our intention in this work was to present a proof-of-concept pipeline demonstrating that fingertip poses derived from a simple, computationally lightweight geometric representation, specifically an AABB-based segmentation can be successfully propagated through per-finger planning and executed in real time on the Linker Hand O7 platform. The proposed method is validated in simulation, and experimental integration on a Linker Hand O7 platform. Full article

Small and medium-sized enterprises (SMEs) play a central role in employment and regional economic development, yet they are highly vulnerable to shocks such as pandemics, energy price spikes, and supply chain disruptions. Scenario modelling, stress testing, and digital twins are used to assess resilience, yet most applications focus on large firms in single-country settings. This article develops a model to stress test the resilience of Slovenian SMEs, calibrated with parameters and mechanisms derived from South African SME resilience studies. A system dynamics model with stocks for cash, inventory, and productive capacity is specified and subjected to demand, supply, financial, and compound shock scenarios, with and without resilience measures such as liquidity buffers, customer and supplier diversification, and basic digital planning capabilities. Results indicate non-linear tipping points where small reductions in liquidity sharply increase the likelihood of distress, and show that combinations of liquidity, diversification, and collaborative supply chain practices reduce the depth and duration of output losses. The study demonstrates how evidence from an African context can inform resilience strategies in a small European economy and provides a transparent, portable modelling architecture that can be adapted to other settings. Implications are discussed for SME managers and for policies supporting sustainable, resilient enterprise ecosystems. Full article

Dorsomorphin, a pyrazolo[1,5-a]pyrimidine derivative, inhibits the bone morphogenetic protein (BMP) pathway by targeting the type I BMP receptors active in receptor-like kinases. However, the investigation of its—and its derivatives’—antiproliferative activity towards endothelial and cancer cell lines still requires reinforcement with additional studies. In the presented work, several dorsomorphin derivatives have been efficiently synthesized, based on a previously reported synthetic protocol with minor modifications. The endeavor was reinforced by a molecular docking study on the interactions of the designed derivatives with various protein targets, while the inhibitory effects of the synthesized novel molecules on the proliferation of murine leukemia cells (L1210), human T-lymphocyte cells (CEM), human cervix carcinoma cells (HeLa), and endothelial cells (human dermal microvascular, HMEC-1, and bovine aortic endothelial cells, BAECs) were investigated. Among the compounds tested, diphenol 22, emerged as the most promising bioactive lead since it demonstrated half-maximal inhibitory concentration (IC₅₀) values below 9 μM in all tested lines except HeLa cells. In the same context, the carbamate derivative 6 was determined as a potent inhibitor of endothelial cell proliferation in BAECs at a low micromolar range. In conclusion, the presented work not only reveals promising antiproliferative dorsomorphin derivatives but also sets the basis for further exploitation of dorsomorphin’s bioactive portfolio, based on bioactivity results and molecular modeling calculations. Full article

Green hydrogen production and the use of fuel cells (FCs) in microgrid (MG) systems have become viable and feasible solutions due to their continuous cost reduction and advancements in technology. Furthermore, green hydrogen electrolyzers and FC can mitigate fluctuations in renewable energy generation and various demand-related disturbances. Proper incorporation of electrolyzers and FCs can enhance load frequency control (LFC) in MG systems. However, they are subjected to multiple false data injection attacks (FDIAs), which can deteriorate MG stability and availability. Moreover, most existing LFC control schemes—such as conventional PID-based methods, single-degree-of-freedom fractional-order controllers, and various optimization-based structures—lack robustness against coordinated and multi-point FDIAs, leading to significant degradation in frequency regulation performance. This paper presents a new, modified, multi-degree-of-freedom, cascaded fractional-order controller for green hydrogen-based MG systems with high fluctuating renewable and demand sources. The proposed LFC is a cascaded control structure that combines a 1+TID controller with a filtered fractional-order PID controller (FOPIDF), namely the cascaded 1+TID/FOPIDF LFC control. Furthermore, another tilt-integrator derivative electric vehicle (EV) battery frequency regulation controller is proposed to benefit from EVs installed in MG systems. The proposed cascaded 1+TID/FOPIDF LFC control and EV TID LFC methods are designed using the powerful capability of the exponential distribution optimizer (EDO), which determines the optimal set of design parameters, leading to guaranteed optimal performance. The effectiveness of the newly proposed cascaded 1+TID/FOPIDF LFC control and design approach employing multi-generational-based two-area MG systems is studied by taking into account a variety of projected scenarios of FDIAs and renewable/load fluctuation scenarios. In addition, performance comparisons with some featured controllers are provided in the paper. For example, in the case of fluctuation in RESs, the measured indices are as follows: ISE (1.079, 0.5306, 0.3515, 0.0104); IAE (15.011, 10.691, 9.527, 1.363); ITSE (100.613, 64.412, 53.649, 1.323); and ITAE (2120, 1765, 1683, 241.32) for TID, FOPID, FOTID, and proposed, respectively, which confirm superior frequency deviation mitigation using the proposed optimized cascaded 1+TID/FOPIDF and EV TID LFC control method. Full article

Let $\mathfrak{R}$ be a prime ring, and let F denote a generalized derivation associated with a derivation d of $\mathfrak{R}$. Consider I as a nonzero ideal of $\mathfrak{R}$, and let $m,n,k,l$ be fixed positive integers. In this study, we explore the behavior of the generalized derivation F within the structures of both prime and semiprime rings that satisfy the functional identity ${[F\left(\eta \right),d\left(\tau \right)]}_m={\eta }^n{[\eta ,\tau ]}_l{\eta }^k$, $\forall ,\eta ,\tau \in I.$ Furthermore, we extend this investigation to the framework of Banach algebras, analyzing how generalized derivations operate in such algebras. A comparative discussion is also presented to highlight the distinctions and similarities in the behavior of generalized derivations within Banach algebraic settings under the above structural condition. Full article

Dissolved organic carbon (DOC) represents the most readily available and crucial carbon source for soil microorganisms, influencing their community structure, nutrient cycling, and metabolic functions. However, the interplay between functional genes and the organic components of DOC remains poorly understood. In this study, a 33-year fertilization experiment on black soil was carried out, setting up five fertilization treatments: unfertilized control (CK), nitrogen and potassium (NK), nitrogen, P and potassium (NPK), NPK plus straw (NPKS), and NPK plus manure (NPKM). The variation characteristics of soil DOC composition and carbon-degrading functional gene abundance under different fertilization treatments were systematically analyzed. The study found that applying chemical fertilizers combined with organic materials significantly increased soil organic carbon (SOC) and DOC contents in the thin-layer black soil of Gongzhuling. The soil DOC in this region is primarily derived from external inputs (Fresh plant-derived materials). Parallel factor analysis identified four fluorescent components: C1 as visible fulvic acid-like substances, C2 as humic acid-like substances, C3 as ultraviolet fulvic acid-like substances, and C4 as long-wavelength humic-like substances. Among these, NPK plus straw significantly enhanced the fluorescence intensity of the humic acid-like component (C2) and the total fluorescence intensity. The fluorescence intensity of the humic acid-like component increased by 36.0–208.9%, and the total fluorescence intensity increased by 23.8–270.9% compared to the CK. Moreover, the study found that the phylum composition of carbon-degrading microorganisms remained stable under different fertilization treatments. However, NPK plus straw significantly reduced the total abundance of carbon-degrading genes and influenced the composition and transformation of DOC by regulating the expression of key carbon-degrading genes ICL and abfA. These results offer new insights into the mechanisms by which fertilizer management affects the composition and stability of DOC in black soils via microbial functional gene pathways. Full article

This paper introduces a fractional generalization of the classical Legendre differential equation based on the Atangana–Baleanu–Caputo (ABC) derivative. A novel fractional Legendre-type operator is rigorously defined within a functional framework of continuously differentiable functions with absolutely continuous derivatives. The associated initial value problem is reformulated as an equivalent Volterra integral equation, and existence and uniqueness of classical solutions are established via the Banach fixed-point theorem, supported by a proved Lipschitz estimate for the ABC derivative. A constructive solution representation is obtained through a Volterra–Neumann series, explicitly revealing the role of Mittag–Leffler functions. We prove that the fractional solutions converge uniformly to the classical Legendre polynomials as the fractional order approaches unity, with a quantitative convergence rate of order $O(1-\alpha )$ under mild regularity assumptions on the Volterra kernel. A fully reproducible quadrature-based numerical scheme is developed, with explicit kernel formulas and implementation algorithms provided in appendices. Numerical experiments for the quadratic Legendre mode confirm the theoretical convergence and illustrate the smooth interpolation between fractional and classical regimes. An application to time-fractional diffusion in spherical coordinates demonstrates that the operator arises naturally in physical models, providing a mathematically consistent tool for extending classical angular analysis to fractional settings with memory. Full article

Requirements for novel effective antiviral agents against SARS-CoV-2 emphasizes the importance of robust in vitro screening platforms. We developed a test system based on spike-pseudotyped lentiviruses, carrying either luc+ or EGFP reporter genes as a payload, and a human non-small cell lung carcinoma (NSCLC) cell line, overexpressing ACE2 (H1299-hACE2). The cell origin makes our system resemble lung epithelium infection. Transmission electron microscopy confirmed that the spike glycoproteins on the pseudotyped lentiviral particles resemble native SARS-CoV-2 spike glycoproteins, thus validating their use in inhibitor screening. H1299-hACE2 cells showed significantly higher infection rate (p < 0.005) with spike-pseudotyped lentiviruses compared to parental H1299 cells, as determined by luciferase and fluorescence assays. The susceptibility of the stable H1299-hACE2 cell line to a broad panel of SARS-CoV-2 variants (Wuhan, Beta, Delta, Omicron) was assessed here for the first time in a unified experimental setting. Infection of H1299-hACE2 cells with SARS-CoV-2 induced cell fusion and syncytium formation with subsequent cell death. The developed pseudovirus-based assay was further used for assessment of the antiviral properties of derivatives of 1,7,7-trimethyl-[2.2.1]-bicycloheptane-potential spike protein inhibitors, which possess moderate activity against lentiviral particles. The H1299-hACE2/spike-pseudotyped lentivirus assay is, therefore, a reliable, high-efficiency platform for screening spike-mediated entry inhibitors. The cell line obtained during the development of the platform can be used to isolate and study new variants of SARS-CoV-2. Full article

This paper examines an optimal investment–consumption problem in a setting where the financial environment is influenced by both stochastic factors and delayed effects. The investor, endowed with Constant Relative Risk Aversion (CRRA) preferences, allocates wealth between a risk-free asset and a single risky asset. The short rate follows a Vas$\check{\mathrm{i}}$ček-type term structure model, while the risky asset price dynamics are driven by a delayed Heston specification whose variance process evolves according to a Cox–Ingersoll–Ross (CIR) diffusion. Delayed dependence in the wealth dynamics is incorporated through two auxiliary variables that summarize past wealth trajectories, enabling us to recast the naturally infinite-dimensional delay problem into a finite-dimensional Markovian framework. Using Bellman’s dynamic programming principle, we derive the associated Hamilton–Jacobi–Bellman (HJB) partial differential equation and demonstrate that it generalizes the classical Merton formulation to simultaneously accommodate delay, stochastic interest rates, stochastic volatility, and consumption. Under CRRA utility, we obtain closed-form expressions for the value function and the optimal feedback controls. Numerical illustrations highlight how delay and market parameters impact optimal portfolio allocation and consumption policies. Full article

Large-area metasurfaces were fabricated via a tunable pyramidal interference lithography (PIL) technique, which uses custom-built 2-faced, 3-faced, and 4-faced pyramidal prisms to create metasurfaces with customizable nano- and micro-scale surface feature periodicities. The 2-faced prism produced linear surface relief diffraction gratings, while the 3-faced prism produced metasurfaces with triangular lattices and the 4-faced prism produced metasurfaces with square lattices, all on azobenzene thin films. A double inline prism set-up enabled control over the metasurface feature periodicity, allowing systematic increase in the pattern size. Additional tunability was achieved by placing a prism inline with a lens, allowing precise control over the metasurface feature periodicity. A theoretical model was derived and successfully matched to the experimental results. The resulting metasurfaces were coated with gold and exhibited distinct surface plasmon resonance (SPR) and surface plasmon resonance imaging (SPRi) responses, confirming their functionality. Overall, this work establishes PIL as a cost-effective and highly adaptable metasurface fabrication method for producing customizable periodic metasurfaces for photonic, plasmonic, and sensing applications. Full article

Automotive dashboard cameras are widely used to record driving events and often serve as critical evidence in accident investigations and insurance claims. However, the availability of free and low-cost editing tools has increased the risk of video tampering, underscoring the need for reliable methods to verify video authenticity. Temporal tampering typically involves manipulating frame order through insertion, deletion, or duplication. This paper proposes a computationally efficient framework that transforms high-dimensional video into compact one-dimensional temporal signals and learns tampering patterns using a shallow one-dimensional convolutional neural network (1D-CNN). Five complementary features are extracted between consecutive frames: frame-difference magnitude, structural similarity drift (SSIM drift), optical-flow mean, forward–backward optical-flow consistency error, and compression-aware temporal prediction error. Per-video robust normalization is applied to emphasize intra-video anomalies. Experiments on a custom dataset derived from D²-City demonstrate strong detection performance in single-attack settings: 95.0% accuracy for frame deletion, 100.0% for frame insertion, and 95.0% for frame duplication. In a four-class setting (non-tampered, insertion, deletion, duplication), the model achieves 96.3% accuracy, with AUCs of 0.994, 1.000, 0.997, and 0.988, respectively. Efficiency analysis confirms near real-time CPU inference (≈12.7–12.9 FPS) with minimal memory overhead. Cross-dataset tests on BDDA and VIRAT reveal domain-shift sensitivity, particularly for deletion and duplication, highlighting the need for domain adaptation and augmentation. Overall, the proposed multi-feature 1D-CNN provides a practical, interpretable, and resource-aware solution for temporal tampering detection in dashcam videos, supporting trustworthy video forensics in IoT-enabled transportation systems. Full article

GNSS buoys are essential for real-time elevation monitoring in coastal waterways, yet the vertical coordinate time series are frequently contaminated by complex non-stationary noise, and existing denoising methods often rely on empirical parameter settings that compromise reliability. This paper proposes GVMD-NLM, a hybrid denoising framework optimized by an improved Grey Wolf Optimizer (GWO). The method introduces an adaptive convergence factor decay function derived from the Sigmoid function to automatically determine the optimal parameters (K and $\alpha$) for Variational Mode Decomposition (VMD). Sample Entropy (SE) is then employed to identify low-frequency effective signals, while the remaining high-frequency noise components are processed via Non-Local Means (NLM) filtering to recover residual information while suppressing stochastic disturbances. Experimental results from two datasets at the Dongguan Waterway Wharf demonstrate that GVMD-NLM consistently outperforms SSA, CEEMDAN, VMD, and GWO-VMD. In Dataset One, GVMD-NLM reduced the RMSE by 26.04% (vs. SSA), 17.87% (vs. CEEMDAN), 24.28% (vs. VMD), and 13.47% (vs. GWO-VMD), with corresponding SNR improvements of 11.13%, 7.00%, 10.18%, and 5.05%. In Dataset Two, the method achieved RMSE reductions of 28.87% (vs. SSA), 17.12% (vs. CEEMDAN), 18.45% (vs. VMD), and 10.26% (vs. GWO-VMD), with SNR improvements of 10.48%, 5.52%, 6.02%, and 3.11%, respectively. The denoised signal maintains high fidelity, with correlation coefficients (R) reaching 0.9798. This approach provides an objective and automated solution for GNSS data denoising, offering a more accurate data foundation for waterway hydrodynamics research and water level monitoring. Full article