Search Results (10,023)

Background/Objectives: In several contexts, Dried Sample Spot Devices (DSSDs) offer a convenient and safe alternative for sampling, storage, and shipment, allowing the transport and storage of biological samples at room temperature, reducing shipment costs and improving access to diagnostics in faraway sites. This can be pivotal for the use of the therapeutic drug monitoring of anti-HIV treatment: therefore, this study aimed to develop and validate a UHPLC–MS/MS method for the simultaneous quantification of 12 antiretroviral drugs, including the recently introduced long-acting agents, in Dry Plasma Spots (DPSs). Methods: First, 100 µL of plasma sample and 100 µL of internal standard solution were spotted on each DSSD. After complete drying, DPSs were added with an acidifying solution (ammonium acetate buffer pH 4), and then, each sample underwent extraction with hexane-dichloromethane 50:50 (v/v). After tumbling, the organic phase was evaporated and reconstituted for injection. An Acquity UPLC HSS T3 1.8 µm, 2.1 × 150 mm column at 50 °C enabled separation, performed using H₂O + F.A. 0.05% (phase A) and ACN + F.A. 0.05% (phase B) as the mobile phase in gradient elution mode, for a total run time of 15 min. Results: The method was validated over the clinically relevant concentration ranges. For all quality control levels, accuracies ranged from 98.2% to 114.1%, and intra-day and inter-day RSD values ranged from 2.7% to 9.7% and 5.2% to 13.9%, respectively. All analytes demonstrated satisfactory short- and long-term stability in DPSs, confirming the suitability of shipment and storage at room temperature. Conclusions: The method demonstrated robustness and reproducibility in accordance with FDA and EMA guidelines. It ensures satisfactory accuracy and rapid analysis, supporting its application in clinical practice, including for monitoring the newest long-acting drugs. Full article

This paper presents an adaptive optimal consensus tracking control scheme for canonical nonlinear multi-agent systems (MASs) with unknown dynamics, employing an actor–critic reinforcement learning (RL) framework. The scheme integrates a sliding mode mechanism to suppress tracking errors and ensure consensus tracking between the followers and the leader. Additionally, optimal control is designed to find a Nash equilibrium in a graphical game. To address the intractability of obtaining an analytical solution for the coupled Hamilton–Jacobi–Bellman (HJB) equation, a policy iteration algorithm is utilized. Within this algorithm, a critic neural network (NN) approximates the gradient of the optimal value function, while an actor NN approximates the optimal control policy. Together, these networks form a compact actor–critic (AC) architecture that achieves optimal consensus tracking. Furthermore, the proposed method guarantees the boundedness of all closed-loop signals while ensuring consensus tracking. Finally, two simulations are conducted to verify the effectiveness and advantages of the proposed method. Full article

This paper presents a unified analytical and computational framework for the study of typhoid fever transmission dynamics governed by a Caputo fractional-order compartmental model of order $\kappa \in (0,1]$. The population is stratified into five epidemiological classes, namely susceptible $\left(\mathcal{S}\right)$, asymptomatic $\left(\mathcal{A}\right)$, symptomatic $\left(\mathcal{I}\right)$, hospitalised $\left(\mathcal{H}\right)$, and recovered $\left(\mathcal{R}\right)$, and the governing system explicitly incorporates asymptomatic transmission, treatment dynamics, and temporary immunity with waning. The use of the Caputo fractional derivative is motivated by the well-documented existence of chronic asymptomatic Salmonella Typhi carriers, whose heavy-tailed sojourn times in the carrier state are naturally encoded by the Mittag–Leffler waiting-time distribution arising from the fractional operator. A complete qualitative analysis of the fractional system is carried out: the basic reproduction number ${\mathcal{R}}_0$ is derived via the next-generation matrix method; local and global asymptotic stability of both the disease-free equilibrium ${\mathcal{E}}_0$ (when ${\mathcal{R}}_0\le 1$) and the endemic equilibrium ${\mathcal{E}}^{*}$ (when ${\mathcal{R}}_0>1$) are established using fractional Lyapunov theory and the LaSalle invariance principle; and the normalised sensitivity indices of ${\mathcal{R}}_0$ are computed to identify transmission-amplifying and transmission-suppressing parameters. Existence, uniqueness, and Ulam–Hyers stability of solutions are established via Banach and Leray–Schauder fixed-point arguments. To complement the analytical results, a fractional physics-informed neural network ($\mathcal{PINN}$) framework is developed to simultaneously reconstruct compartmental trajectories and identify unknown biological parameters from sparse synthetic observations. $\mathcal{PINN}$ embeds the L1-Caputo discretisation directly into the training residuals and employs a four-stage Adam–L-BFGS optimisation strategy to recover five trainable parameters $\Theta$ = $\{\varphi ,\mu ,\sigma ,\psi ,\beta \}$ across three fractional orders $\kappa \in \{1.0,0.95,0.9\}$. The estimated parameters show strong agreement with the true values at the classical limit $\kappa =1.0$ ($\mathrm{MAPE}=2.27\%$), with the natural mortality rate $\mu$ recovered with $\mathrm{APE}\le 0.51\%$ and the transmission rate $\beta$ with $\mathrm{APE}\le 3.63\%$ across all fractional orders, confirming the structural identifiability of the model. Pairwise correlation analysis of the learned parameters establishes the absence of equifinality, validating that $\beta$ can be reliably included in the trainable set. Noise robustness experiments under Gaussian perturbations of $1\%$, $3\%$, and $5\%$ demonstrate graceful degradation ($\mathrm{MAPE}$: $0.82\%\to 3.10\%\to 7.31\%$), confirming the reliability of the proposed framework under realistic observational conditions. Full article

Cross-chain analytics increasingly demand continuous joins across ledgers with asynchronous state evolution. Existing solutions, however, typically assume static snapshots or neglect temporal alignment, yielding semantically inconsistent results when epochs drift. This paper introduces VeriFed, a system for temporally consistent continuous cross-chain joins. We formalize the problem of snapshot-aligned continuous joins, design a Unified Adapter Layer (UAL) to align finalized snapshots across heterogeneous protocols, and develop incremental verification that composes per-chain proofs into a global summary via the Epoch Attestation Mesh (EAM) and the Delta-Linked Proof Forest (DLPF). To sustain high-throughput execution, VeriFed further adopts an incremental multi-objective optimizer that balances latency and monetary cost. Experiments on Ethereum transaction data with a simulated wide-area network (WAN) demonstrate that VeriFed achieves sub-second per-epoch latency (approx. 38 ms) and reduces verification overhead by orders of magnitude compared to state-of-the-art baselines, while effectively detecting tampering with zero false positives. These results confirm consistent efficiency and verifiability under continuous updates. Full article

Sustainable Development Goal 7 (SDG 7) aims to ensure access to affordable, reliable, sustainable, and modern energy for all. Evaluating the effectiveness of national energy policies in achieving this goal requires comprehensive and quantitative assessment frameworks. Countries’ performance toward SDG 7 is influenced by various structural factors, including energy demand growth, dependency on energy imports, renewable energy potential, and policy priorities. Therefore, systematic performance evaluation is essential for understanding policy effectiveness and identifying areas requiring improvement. This study evaluates Türkiye’s SDG 7 energy policy performance on a yearly basis over the period 2014–2023. A multi-criteria decision-making (MCDM) framework combining the Stepwise Weight Assessment Ratio Analysis (SWARA) and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) methods is employed to quantify and compare performance across years. The proposed approach allows for the determination of indicator weights and the ranking of yearly performance levels based on multiple energy sustainability criteria. The results reveal an overall upward trend in Türkiye’s SDG 7 performance during the study period, although notable fluctuations are observed. A significant decline occurs in 2017, followed by a rapid recovery in subsequent years. Another temporary downturn is identified in 2021, while a remarkable improvement emerges in 2023. A sensitivity analysis based on multiple weighting scenarios was also conducted to examine the robustness of the obtained rankings, and the results confirm the stability of the overall ranking structure, with 2023 consistently maintaining the top position across most scenarios. These findings provide insights into how policy measures, market dynamics, and energy system developments influence the country’s progress toward sustainable energy goals. By incorporating a time-based evaluation framework, this study contributes to the SDG 7 literature by offering a quantitative and policy-oriented assessment of national energy performance. The proposed framework also provides a practical analytical tool for policymakers and energy regulators to monitor progress, identify vulnerable areas, and support evidence-based decision-making in the transition toward sustainable and clean energy systems. Full article

The rotational evolution of pulsars is governed by torque mechanisms whose mathematical structure encodes fundamental symmetries of the underlying physics. We demonstrate that the standard spin-down equation $\dot{f}=-sf-r{f}^3-g{f}^5$ derives from a discrete antisymmetry requirement, namely invariance of the torque under reversal of rotation sense, which restricts the frequency dependence to odd integer powers. We show that physically motivated plasma processes systematically break this symmetry, introducing fractional frequency exponents: viscous Ekman pumping at the crust–superfluid boundary layer ($f^{3/2}$), magnetohydrodynamic turbulent dissipation via Kolmogorov and Sweet–Parker cascades ($f^{10/3}$, $f^{11/3}$), non-linear superfluid vortex dynamics ($f^{5/2}$), and saturated r-mode oscillations ($f^{7-2\beta }$). The central result is an exact analytical resolution of the long-standing Crab pulsar braking index puzzle: the observed $n=2.51\pm 0.01$, which has defied explanation for nearly four decades, emerges naturally from the superposition of magnetic dipole radiation ($\dot{f}\propto f^3$) and boundary layer Ekman pumping ($\dot{f}\propto f^{3/2}$), with analytically derived coefficients yielding a dipole-component surface field $B_p=6.2\times {10}^{12}$ G—higher than the standard $\sqrt{P\dot{P}}$ estimate of $3.8\times {10}^{12}$ G, because that formula conflates dipole and non-dipole torques, but lower than applying the Larmor formula to the full spin-down rate ($7.6\times {10}^{12}$ G), since $32.7\%$ of the total torque is non-radiative boundary-layer dissipation. We develop the Riemann–Liouville fractional calculus formalism for these equations, showing that fractional derivatives break time-translation symmetry through intrinsic memory effects, with solutions expressed in terms of Mittag-Leffler and Fox H-functions that interpolate continuously between exponential (fully symmetric) and power-law (scale-free symmetric) relaxation. Lambert–Tsallis $W_q$ functions with non-extensive parameter q encoding broken statistical symmetry enable equation-of-state-independent inference of neutron star compactness and tidal deformability. Our framework establishes a unified symmetry-based classification of pulsar spin-down mechanisms and predicts frequency-dependent braking indices evolving at rate $\mathrm{d}n/\mathrm{d}t\sim 2\times {10}^{-4}$ yr⁻¹, yielding $\mathrm{\Delta }n\approx 0.01$ over 50 years—testable with current pulsar timing programmes. The formalism provides a coherent theoretical foundation connecting plasma microphysics at the neutron star interior to macroscopic observables in electromagnetic and gravitational wave channels. Full article

In ethics-sensitive product development, Generative AI can improve the efficiency of concept generation, but it also raises challenges related to accountability, value alignment, and decision transparency. To address limitations in current human-AI co-design processes, including unclear allocation of decision-making authority, insufficiently structured translation from design requirements to design constraints, and limited explainability in scheme evaluation, this study proposes an explainable Human–Computer Interaction (HCI)-based decision support framework for human-AI co-design, termed GAGT. The framework integrates Generative AI with multi-criteria decision-making methods. Specifically, the Analytic Hierarchy Process (AHP) is used to structure design requirements and determine their priorities, Grey Relational Analysis (GRA) is used to compare candidate schemes, and the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) is used to support transparent final ranking. Within the framework, human designers are mainly responsible for requirement confirmation, priority judgment, review at key checkpoints, and final scheme selection, while AI mainly supports information organization, candidate scheme generation, and quantitative comparison. The framework was applied to the design of a community medical vehicle through a small-sample, case-based, quasi-experimental study. Compared with the human-only condition, the GAGT-supported condition reduced design time by 56.1%. Compared with the AI-autonomous condition, it showed no observed HIPAA violations and a Value Drift Index of 16.1%, indicating better consistency with human-defined priorities. The results suggest that the proposed framework may improve design efficiency while supporting clearer human oversight and decision explainability in Generative AI-assisted design, and may provide a structured approach to organizing human and AI roles in ethics-sensitive design tasks. Full article

A hydroelastic theoretical model is formulated, and an analytical solution is obtained to investigate the interaction between wave-opposing current loading with compression and a moored floating flexible platform within the framework of Timoshenko–Mindlin beam theory based on the linearized wave and small structural response. By employing the matching technique and orthogonal mode-coupling relation, the closed-form analytical solutions for structural displacement, as well as shear force and bending moment, are obtained. The wave blocking and buckling limit in the presence of compressive force against an opposing current is determined via group and phase velocities from the dispersion relation in the context of the Timoshenko–Mindlin beam theory. Further, the combined influence of opposing current, compressive loading, and key structural design parameters on the hydroelastic response are examined. The results demonstrate that opposing currents and compressive forces can significantly alter the hydroelastic response, highlighting their critical role in structural engineering analysis. The current analysis provides a comprehensive analytical framework that can support the design and optimization of floating flexible platforms in the presence of opposing currents and compressive loads in complex marine environments. Full article

This study proposes a reproducible hybrid computational model for the explainable classification of territorial vulnerability using heterogeneous tabular data. The approach integrates fuzzy logic and extreme gradient boosting in a two-stage architecture that balances interpretability and predictive performance. First, a fuzzy transformation is applied to construct interpretable risk and resilience indicators based on multi-source administrative indicators. The analytical dataset was formed by integrating 11 heterogeneous administrative sources into a single matrix of 166 territorial units and 76 features. The model was evaluated on a stratified 75/25 split of the training and test sets using the F1 score, ROC-AUC, precision, recall, and integrated quality criterion. Experimental results show that the proposed Fuzzy-XGBoost framework achieved an F1 score of 0.7333 on the test dataset, an ROC-AUC of 0.8291, and an Integrated Score of 0.768, outperforming the strongest baseline and improving recall in highly vulnerable areas. Furthermore, probabilistic threshold optimization identified an operating point at τ = 0.35, reducing the number of missed high-risk cases while maintaining acceptable specificity. The results demonstrate that fuzzy feature expansion combined with gradient boosting provides an efficient and interpretable solution for tabular risk classification and decision support problems under heterogeneity and uncertainty. Full article

The peripheries of rare-metal metallogenic systems frequently host skarn-type or hydrothermal vein-type Pb-Zn deposits, though their genetic connections with parental systems remain debated. The newly identified Gariatong W-Mo polymetallic metallogenic system in the Lhasa Terrane displays well-defined Nb-Ta-Rb, Mo-W, W-Mo, W-Bi, and Pb-Zn-Ag metallogenic zoning, establishing it as an exemplary site for investigating genetic relationships between Pb-Zn and rare-metal mineralization. This investigation targets skarn-type Pb-Zn deposits spatially associated with rare-metal orebodies at Gariatong, utilizing integrated analytical approaches, including in situ LA-ICP-MS trace-element analysis of sulfides, sulfur isotope geochemistry, and LA-ICP-MS elemental mapping of sphalerite, to constrain metal sources, characterize fluid evolution, and establish genetic correlations with the rare-metal system. Key findings include the following: (1) sphalerite shows enrichment in Fe, Mn, Co, and Cd, while pyrite contains elevated As, Pb, Co, Cu, and Mn. Fe, Cd, and Mn primarily occur as solid solutions or nanoparticles, whereas As and Pb exist as micro-inclusions. (2) Sphalerite Zn/Cd ratios (73.6–184) and Co-Ni-As ternary diagrams confirm a magmatic–hydrothermal skarn origin. (3) Mineralization occurred under moderate-temperature, mildly oxidized conditions, as constrained by sphalerite Fe contents and mineral assemblages. Sulfur isotope compositions (δ³⁴S = −1.0‰ to 3.2‰; mean: 1.9‰) indicate a magmatic sulfur source. This study reveals that the Nb-Ta-Rb mineralization, quartz-vein- and greisen-type W-Mo deposits, and skarn-type Pb-Zn orebodies—all genetically associated with highly fractionated granites—constitute an integrated magmatic–hydrothermal system with vertical (depth-related) zoning relative to the granitic intrusion. These results provide critical constraints for understanding rare-metal–Pb-Zn genetic associations and suggest that Pb-Zn mineralization may serve as a key exploration indicator for rare metals in the Lhasa Terrane. Full article

Dielectric and magnetic spherical hollow shells are employed in many applications as standard building units. These structures are commonly subjected to size reduction to obtain a high surface area/volume ratio, a property that is in favor of specific applications. However, the size reduction enhances the importance of physical mechanisms that originate from surfaces, such as the depolarization effect. Here we tackle the problem of dielectric and magnetic spherical hollow shells, consisting of a linear, homogeneous and isotropic parent material, subjected to an external potential, $U_{ext}\left(\mathit{r}\right)$, of any spatial form (either dc (static) or ac of low-frequency (quasistatic limit)). By applying the method-of-linear-recursive-solution (MLRS) to the Laplace equation, we calculate analytically the internal, $U_{int}\left(\mathit{r}\right)$, and total, $U_{tot}\left(\mathit{r}\right)$, potentials in respect to the external one, $U_{ext}\left(\mathit{r}\right)$. From $U_{int}\left(\mathit{r}\right)$ and $U_{tot}\left(\mathit{r}\right)$ we calculate all relevant scalar and vector physical entities of interest. The MLRS unveils straightforwardly the existence of two distinct depolarization factors, $N_l=l/(2l+1)$ and $N_{l+1}=(l+1)/(2l+1)$, both depending on the degree, $l$, however not on the order, $\mathrm{m}$, of the mode of the external potential, $U_{ext}^{(l,m)}\left(\mathit{r}\right)$. These depolarization factors, $N_l$ and $N_{l+1}$, originate from the outer, $r=\mathrm{b}$, and inner, $r=\mathrm{a}$, surfaces and are accompanied by two extrinsic susceptibilities, ${\chi }_{e,l}^{ext}={\chi }_e^{}/(1+N_l{\chi }_e^{})$ and ${\chi }_{e,l+1}^{ext}={\chi }_e^{}/(1+N_{l+1}{\chi }_e^{})$, respectively. Importantly, $N_l+N_{l+1}=1$, irrespective of the degree, $l$, as it should. The properties of spherical hollow shells are investigated through analytical modeling and detailed simulations, with emphasis on application-relevant scenarios including resonance phenomena in scattering, quantitative materials characterization, and shielding/distortion. The generic MLRS strategy provides a flexible and reliable route for analyzing depolarization processes in other dielectric and magnetic building-unit geometries encountered in practice. Full article

The physical characteristics of a heated non-Newtonian Eyring–Powell fluid in a conduit with sinusoidally moving ciliated walls are highlighted in this analytical study. The impact of mass transmission is considered in this model. The dimensional form of the governing equations is simplified using the long-wavelength estimation and suitable transformations to produce a set of dimensionless partial differential equations with pertinent boundary conditions. To solve it, the perturbation technique is utilized applying polynomial solutions. The solutions of temperature, concentrations, and velocity profiles are obtained, and then are further analyzed through graphical results. An accurate mathematical solution for the pressure gradient is achieved by integrating the velocity profile over the elliptic cross-section. The non-Newtonian Eyring–Powell fluid flows quicker through this vertical ciliated elliptic duct than the Newtonian fluid. Moreover, the cilia elliptic movement eccentricity and the wave number for metachronal wave have a dual effect on the velocity profile. Increasing the dimensionless flow rate and occlusion leads to an increase in closed contour size, as seen in the streamline description. Full article

In the ElastoHydrodynamic Lubrication (EHL) meshing contact model for rough interfaces with convex–concave textured micro-asperities, the geometric morphology of the meshing interface exhibits pronounced multiscale characteristics: the macroscale manifests as the correlation between Interface-Enriched Lubrication (IEL) performance and meshing Anti-Scuffing Load-Bearing Capacity (ASLBC), while the microscale corresponds to the textured morphology of rough interfaces. In numerical simulations of EHL meshing contact, such cross-scale disparities necessitate solving large-scale systems of analytical solution equations. Assuming periodicity or quasi-periodicity at the microscale, various established methods enable decoupling the macroscopic and microscopic scales, such formalized approaches constitute homogenization theory. However, classical asymptotic assumptions may introduce considerable approximation errors. This study proposes a micro-texture-informed homogenized contact model based on multiscale characterization that incorporates the coupled effects of gear interface meshing forces and thermo-elastic deformations, effectively extending the applicability of classical asymptotic homogenization methods. Full article

This paper presents an iterative graph-analytical procedure for determining the roll center height, one of the most critical design parameters influencing vehicle dynamic behavior during cornering. The conventional approaches generally determine roll center locations from suspension kinematics and then evaluate vehicle behavior using multibody or numerical vehicle dynamics models. By contrast, the proposed method is intended for the preliminary design stage and provides a direct correlation between front and rear target roll center heights using tire test data, load transfer and axle-level equilibrium conditions. The main advantage of the method is that it helps define a feasible design space before detailed geometry optimization or MBD validation is performed. The objective is to achieve stable and neutral handling (avoiding intrinsic understeer or oversteer tendencies) during steady-state cornering at a predefined target lateral acceleration. The methodology integrates (i) lateral force equilibrium at the axle level, (ii) a dynamic load transfer model based on axle roll stiffness and roll center heights, and (iii) experimental tire grip characteristics (lateral force–slip angle curves under varying vertical loads), processed through numerical interpolation. The procedure is demonstrated using a vehicle model with specific geometric and mass parameters. The results indicate that the methodology does not yield a single unique solution, but rather a set of correlated roll center heights, allowing the designer to select the most feasible geometric configuration while maintaining neutral handling. As an example, the paper presents a convergent solution for the front and rear roll center heights that satisfy neutrality conditions at a slip angle of approximately 4°. This study provides a fundamental framework for the geometric design of suspension systems and serves as a basis for subsequent numerical and experimental validation. Full article

Wearable neurotechnology depends critically on continuous movement monitoring to characterize motor impairment and recovery in real-world settings. While joint torque serves as a clinically essential kinetic marker, estimating it directly on-device from inertial signals remains challenging due to stringent computational, memory, and energy constraints. Lightweight pipelines typically omit computationally expensive time–frequency processing; however, this omission degrades the observability of dynamics encoded in 1D IMU signals and diminishes the effectiveness of standard knowledge distillation strategies. To enable reliable on-device torque inference, we propose a Physically Guided Dual-Consistency Knowledge Distillation (PDC-KD) framework that explicitly integrates biomechanical priors into the learning process through two collaborative pathways: parameter-manifold alignment and physics-guided compensation. The student network receives guidance through Fisher-information-weighted parameter transfer, ensuring robust knowledge distillation despite significant model capacity mismatch. Furthermore, the framework incorporates a physics-guided regularization term that enforces dynamically consistent torque trajectories via a numerically stable Cholesky-parameterized constraint. Experiments demonstrate that the student model preserves teacher-level predictive accuracy while operating within the stringent resource constraints of edge devices (achieving a 98% parameter reduction, ∼2× faster inference, and ∼1 ms latency). Moreover, the proposed method yields torque estimates with enhanced dynamical consistency, providing an efficient biosignal-processing solution for wearable neurotechnology platforms demanding real-time movement analytics. Full article