Search Results (12,217)

To address the problem of the degraded positioning accuracy of the long-baseline undifferenced network RTK (URTK) under extreme space weather conditions, herein, we propose a user-side atmospheric delay estimation strategy based on the undifferenced network RTK concept to enhance positioning performance in geomagnetic storm environments. First, an ambiguity-resolution model that jointly estimates atmospheric error parameters is used to fix the carrier-phase integer ambiguities for long-baseline reference stations. The accurately fixed inter-station ambiguities are then linearly transformed to recover station-specific undifferenced integer ambiguities; undifferenced observation errors at each reference station are computed to generate corresponding undifferenced correction terms. Lastly, recognizing that ionospheric delays vary sharply during geomagnetic storms and can severely compromise the availability of regional undifferenced correction models, we estimate the residual atmospheric parameters on the user side after applying regional corrections. Experimental results show that the server side is not significantly impacted during geomagnetic storms and can continue operating normally. On the user side, augmenting the solution with atmospheric parameter estimation effectively improves positioning availability. Under strong geomagnetic storms, the proposed mode improves user-station positioning accuracy by 63.7%, 60.7%, and 64.4% in the east (E), north (N), and up (U) components, respectively, relative to the conventional user-side solution; under moderate storm conditions, the corresponding improvements are 16.7%, 10.0%, and 11.1%. Full article

Bone-anchored implants have transformed prosthetic technology by providing a promising alternative to traditional socket-based prostheses through enhanced stability, comfort, and natural limb functionality. These advancements result from developments in osseointegration techniques, improved surgical methods, and innovative implant materials. To address current limitations, continued research remains essential to enhance safety and effectiveness, thereby promoting wider adoption of these advanced prosthetic solutions. This study focuses on modeling bone-anchored implants for limb prostheses in amputees. The research evaluates structural behavior and performance of osseointegrated implants under various conditions while optimizing implant design. The investigation examines different materials including aluminum, Ti-6Al-4V, and Ti-6Al-4V coated with 10 µm platinum. Additionally, implants of different lengths (207 mm, 217 mm, and 197 mm) were analyzed. The results indicate that Ti-6Al-4V and Ti-6Al-4V coated with ten µm platinum reduce stress by 46% and 65%, respectively. Ti-6Al-4V coated with platinum demonstrates the lowest equivalent stress, highlighting the coating’s effectiveness. Furthermore, the coated implant exhibits the lowest deformation—22.92% less than aluminum and 5.13% less than uncoated Ti-6Al-4V. Shorter implant lengths reduce deformation through increased stiffness, whereas longer implants, such as the 217 mm length display greater deformation due to enhanced flexibility. Full article

MXenes, an emerging class of two-dimensional (2D) transition metal carbides, nitrides, and carbonitrides, have demonstrated exceptional potential in tribology: the study of friction, wear, and lubrication. Their remarkable mechanical strength, thermal stability, and tunable surface chemistry make them ideal candidates for solid lubricants, lubricant additives, and protective coatings in mechanical systems. This review comprehensively examines the tribological performance of MXenes under diverse environmental conditions, including high temperatures, vacuum, humid atmospheres, and liquid lubricants. A particular emphasis is placed on the influence of surface terminations (-OH, -O, -F) on friction reduction and wear resistance. Additionally, we discuss strategies for enhancing MXene performance through hybridization with polymers, nanoparticles, and ionic liquids, enabling superior durability in applications ranging from micro/nano-electromechanical systems (MEMS/NEMS) to aerospace and biomedical devices. We also highlight recent advances in experimental characterization techniques and computational modeling, which provide deeper insights into MXene tribomechanics. Despite their promise, key challenges such as oxidation susceptibility, high synthesis costs, and performance variability hinder large-scale commercialization. Emerging solutions, including eco-friendly synthesis methods and optimized composite designs, are explored as pathways to overcome these limitations. Overall, MXenes represent a transformative avenue for developing next-generation tribological materials that combine high efficiency, sustainability, and multifunctionality. Continued research and innovation in this field could unlock groundbreaking advancements across industrial and engineering applications. Full article

In this work, the advection-dispersion model (ADM) is time-fractionalized by the exploitation of Atangana-Baleanu (AB) differential operator to describe contaminant transport in a geological environment. Dispersion, adsorption, and decay, which are known as the foremost transport mechanisms, are considered. The exact solutions of the suggested Atangana-Baleanu advection-dispersion models (AB-ADMs) are acquired using Fourier sine transform and Laplace transform. The classical ADMs are demonstrated to be the special limiting cases of the suggested models. The high consistency among the suggested models and experimental data denotes that the AB-ADMs characterize contaminant transport more effectively. Additionally, the corresponding numerical and graphical results are explored to demonstrate the necessity, effectiveness, and suitability of the suggested models. Full article

In the context of increasingly strong digital transformation and production automation, choosing the right palletizing robot plays a key role in optimizing operational efficiency in industrial chains. However, the wide variety of robot types and specifications complicates decision-making and increases the risk of biased judgments. To overcome this challenge, this study develops an objective multi-criteria decision-making (MCDM) framework that integrates two complementary methods for selecting the optimal industrial pal-letizing robot in the context of modern manufacturing that is increasingly dependent on intelligent automation solutions. Specifically, an improved CRITIC approach is employed to determine objective criteria weights by refining the measurement of contrast intensity and inter-criteria conflict, while normalization ensures comparability of heterogeneous robot parameters. CRADIS is then applied to rank the alternatives based on their relative closeness to the ideal solution. The contributions of this study are twofold: methodological, enhancing the objectivity and robustness of weighting through refined CRITIC and normalization, and practical, offering a reproducible evaluation framework for managers when choosing industrial robots. Application to eight palletizing robots demonstrates that “repeatability” and “power consumption” significantly influence rankings. Sensitivity analysis further confirms the model’s stability and reliability. These findings not only support evidence-based investment decisions but also provide a foundation for extending the method to other industrial technology selection problems. Full article

With access to new energy sources, the problem of reactive power optimization and dispatching has become increasingly important for research. However, the reactive power optimization problem is a mixed integer nonlinear optimization problem. In order to solve the integer variables and nonlinear conditions existing therein, a method for coordinated reactive power optimization and dispatching based on semidefinite programming is proposed. Firstly, a reactive power optimization model considering discrete variables and continuous variables is established with the minimization of total operating cost as the objective function; secondly, the discrete variables are transformed into equality constraints by quadratic equations, and then a solvable semi-definite programming problem is obtained; thirdly, the rank-one constraint is restored by the Iterative Optimization based Gaussian Randomization Method (IOGRM), and the optimal solution equivalent to the original problem is obtained. Finally, the correctness and effectiveness of the proposed model and solution method are verified by analyzing and comparing with the second-order cone programming (SOCP) through the modified IEEE standard example. Full article

The precise analysis of protein interactions in their native cellular context and the sensitive quantification of protein abundance in biological fluids are both fundamental to understanding health and disease. Traditional methods for cellular imaging and biochemical quantification often face limitations in specificity, sensitivity, or the preservation of spatial information. The proximity ligation assay (PLA) is a versatile technological platform developed to overcome these challenges by converting protein recognition events into amplifiable DNA signals, thereby achieving exceptional sensitivity. This foundational principle has given rise to two major formats: in situ PLA (isPLA) and solution-phase PLA. In basic research, isPLA provides high-resolution visualization of protein–protein interactions (PPIs), post-translational modifications (PTMs), and subcellular architecture directly within fixed cells and tissues. In translational and clinical applications, solution-phase PLA enables the highly sensitive quantification of low-abundance biomarkers in liquid samples, which is critical for diagnostics and prognostics in fields such as oncology, neuroscience, and infectious diseases. This review discusses the foundational principles, development, and diverse applications of PLA platforms. We also highlight significant technological advancements, including the development of high-throughput formats, integration with advanced readouts, and the use of alternative affinity reagents. These innovations continue to transform PLA from a targeted validation method into a powerful and multifaceted platform for both fundamental systems biology and clinical diagnostics. Full article

The Bagley–Torvik equation (BTE) is an important model in mathematical physics and mechanics, but obtaining its analytical solution remains challenging. For its numerical treatment, the presence of composite functions in the generalized BTE poses additional difficulties, and efficient approaches for handling nonlinear terms are still lacking in the literature. This study proposes an improved numerical method for the fractional BTE with integral boundary conditions. By employing an integration technique, the original problem is transformed into a weakly singular Fredholm–Hammerstein (F–H) integral equation of the second kind. To address the nonlinear terms, an enhanced piecewise Taylor expansion scheme is developed to construct the discrete form, while the uniqueness of the solution is proven using the contraction mapping theorem in Banach spaces. The convergence and error analyses are rigorously carried out, and numerical experiments confirm the accuracy and efficiency of the proposed approach. Full article

Early detection and diagnosis of plant diseases is critical for ensuring global food security and sustainable agricultural practices. This review comprehensively examines latest advancements in crop disease risk prediction, onset detection through imaging techniques, machine learning (ML), deep learning (DL), and edge computing technologies. Traditional disease detection methods, which rely on visual inspections, are time-consuming, and often inaccurate. While chemical analyses are accurate, they can be time consuming and leave less flexibility to promptly implement remedial actions. In contrast, modern techniques such as hyperspectral and multispectral imaging, thermal imaging, and fluorescence imaging, among others can provide non-invasive and highly accurate solutions for identifying plant diseases at early stages. The integration of ML and DL models, including convolutional neural networks (CNNs) and transfer learning, has significantly improved disease classification and severity assessment. Furthermore, edge computing and the Internet of Things (IoT) facilitate real-time disease monitoring by processing and communicating data directly in/from the field, reducing latency and reliance on in-house as well as centralized cloud computing. Despite these advancements, challenges remain in terms of multimodal dataset standardization, integration of individual technologies of sensing, data processing, communication, and decision-making to provide a complete end-to-end solution for practical implementations. In addition, robustness of such technologies in varying field conditions, and affordability has also not been reviewed. To this end, this review paper focuses on broad areas of sensing, computing, and communication systems to outline the transformative potential of end-to-end solutions for effective implementations towards crop disease management in modern agricultural systems. Foundation of this review also highlights critical potential for integrating AI-driven disease detection and predictive models capable of analyzing multimodal data of environmental factors such as temperature and humidity, as well as visible-range and thermal imagery information for early disease diagnosis and timely management. Future research should focus on developing autonomous end-to-end disease monitoring systems that incorporate these technologies, fostering comprehensive precision agriculture and sustainable crop production. Full article

The calculation of Total Transfer Capability (TTC) for transmission corridors serves as the foundation for security region determination and electricity market transactions. However, existing TTC methods often neglect corridor correlations, leading to overly optimistic results. TTC computation involves complex stability verification and requires enumerating numerous renewable energy operation scenarios to establish security boundaries, exhibiting high non-convexity and nonlinearity that challenge gradient-based iterative algorithms in approaching global optima. Furthermore, practical power systems feature coupled corridor effects, transforming multi-corridor TTC into a complex Pareto frontier search problem. This paper proposes a MOEA/D-FRRMAB (Fitness–Rate–Reward Multi-Armed Bandit)-based method featuring: (1) a TTC model incorporating transient angle stability constraints, steady-state operational limits, and inter-corridor power interactions and (2) a decomposition strategy converting the multi-objective problem into subproblems, enhanced by MOEA/D-FRRMAB for improved Pareto front convergence and diversity. IEEE 39-bus tests demonstrate superior solution accuracy and diversity, providing dispatch centers with more reliable multi-corridor TTC strategies. Full article

Plastic pollution, particularly from polyethylene terephthalate (PET), poses significant environmental concerns due to ecosystem persistence and extensive packaging use. Conventional recycling methods face inefficiencies, high costs, and limited scalability, necessitating sustainable alternatives. Biodegradation via PET hydrolases offers promising eco-friendly solutions, although most natural PET-degrading enzymes are thermophilic and require energy-intensive high temperatures. In contrast, psychrophilic enzymes function efficiently at extremely low temperatures but often lack stability under moderate conditions. Therefore, this study aimed to enhance the ability of the Mors1 enzyme from Moraxella TA144 to operate effectively under mesophilic conditions, which is closer to the optimal conditions for environmental application. Three strategic hydrophobic substitutions (K93I, E221I, and R235F) were introduced in loop regions, generating the mutant variant Mors1^MUT. Comparative characterization revealed that Mors1^MUT retained 98% of its activity at pH 9 and displayed greater resilience across both acidic and alkaline conditions than did the wild-type enzyme. Thermal stability assays revealed that Mors1^MUT preserved 61% of its activity at 40 °C and 14% at 50 °C, whereas the wild-type enzyme was fully inactivated at these temperatures. The enzymatic hydrolysis of PET films significantly improved with Mors1^MUT. Gravimetric analysis revealed weight losses of 0.83% for Mors1^WT and 3.46% for Mors1^MUT after a 12-day incubation period. This corresponds to a 4.16-fold increase in hydrolysis efficiency, confirming the enhanced catalytic performance of the mutant variant. The improvement was further validated by scanning electron microscopy (SEM), atomic force microscopy (AFM), and attenuated total reflectance–Fourier transform infrared (ATR-FTIR) analysis. Optimization of the reaction parameters through response surface methodology (enzyme load, time, pH, temperature, and agitation) confirmed increased PET hydrolysis under mild mesophilic conditions. These findings establish Mors1^MUT as a robust mesophilic PETase with enhanced catalytic efficiency and thermal stability, representing a promising candidate for sustainable PET degradation under environmentally relevant conditions. Full article

Generative Adversarial Networks (GANs) have emerged as powerful tools in artificial intelligence (AI) with growing relevance in medical imaging. In dermatology, GANs are revolutionizing image analysis, enabling synthetic image generation, data augmentation, color standardization, and improved diagnostic model training. This narrative review explores the landscape of GAN applications in dermatology, systematically analyzing 27 key studies and identifying 11 main clinical use cases. These range from the synthesis of under-represented skin phenotypes to segmentation, denoising, and super-resolution imaging. The review also examines the commercial implementations of GAN-based solutions relevant to practicing dermatologists. We present a comparative summary of GAN architectures, including DCGAN, cGAN, StyleGAN, CycleGAN, and advanced hybrids. We analyze technical metrics used to evaluate performance—such as Fréchet Inception Distance (FID), SSIM, Inception Score, and Dice Coefficient—and discuss challenges like data imbalance, overfitting, and the lack of clinical validation. Additionally, we review ethical concerns and regulatory limitations. Our findings highlight the transformative potential of GANs in dermatology while emphasizing the need for standardized protocols and rigorous validation. While early results are promising, few models have yet reached real-world clinical integration. The democratization of AI tools and open-access datasets are pivotal to ensure equitable dermatologic care across diverse populations. This review serves as a comprehensive resource for dermatologists, researchers, and developers interested in applying GANs in dermatological practice and research. Future directions include multimodal integration, clinical trials, and explainable GANs to facilitate adoption in daily clinical workflows. Full article

The phase transformation of fayalite from copper slag during oxidation roasting was systematically studied in this work with an analysis using X-ray diffraction, X-ray photoelectron spectroscopy, vibrating sample magnetometer, scanning electronic microscope, and energy dispersive spectrometer. The results show that the oxidation of fayalite occurs at ≥300 °C. Fayalite is first oxidized into amorphous Fe₃O₄ and SiO₂ during oxidation roasting. The former then converts into Fe₂O₃ while the latter converts into cristobalite solid solution with increasing temperature. Meanwhile, the specific saturation magnetization of roasted products increases from 9.43 emu/g at 300 °C to 20.66 emu/g at 700 °C, and then decreases to 7.31 emu/g at 1100 °C. The migration of iron in fayalite is prior to that of silicon during oxidation roasting. Therefore, the thickness of the iron oxide layer on the particle surface steadily increases with roasting temperature, from about 1.0 μm at 800 °C to about 5.0 μm at 1100 °C. This study has guiding significance for the iron grain growth in copper slag during the oxidation-reduction roasting process. Full article

The primary objective of this study is to provide a more precise and beneficial mathematical model for assessing corruption dynamics by utilizing non-local derivatives. This research aims to provide solutions that accurately capture the complexities and practical behaviors of corruption. To illustrate how corruption levels within a community change over time, a non-linear deterministic mathematical model has been developed. The authors present a non-integer order model that divides the population into five subgroups: susceptible, exposed, corrupted, recovered, and honest individuals. To study these corruption dynamics, we employ a new method for solving a time-fractional corruption model, which we term the q-homotopy analysis transform approach. This approach produces an effective approximation solution for the investigated equations, and data is shown as 3D plots and graphs, which give a clear physical representation. The stability and existence of the equilibrium points in the considered model are mathematically proven, and we examine the stability of the model and the equilibrium points, clarifying the conditions required for a stable solution. The resulting solutions, given in series form, show rapid convergence and accurately describe the model’s behaviour with minimal error. Furthermore, the solution’s uniqueness and convergence have been demonstrated using fixed-point theory. The proposed technique is better than a numerical approach, as it does not require much computational work, with minimal time consumed, and it removes the requirement for linearization, perturbations, and discretization. In comparison to previous approaches, the proposed technique is a competent tool for examining an analytical outcomes from the projected model, and the methodology used herein for the considered model is proved to be both efficient and reliable, indicating substantial progress in the field. Full article

Melanin-based biosorbents (MiCS), derived from chestnut shells, were encapsulated in sodium alginate to obtain MiCS@Alg, useful in a column adsorption study. MiCS contains various acidic surface groups able to participate in the removal of cationic pollutants from aqueous solutions. The MiCS and MiCS@Alg were characterized by Fourier-transform Infrared Spectroscopy (FTIR), Scanning Electron Microscopy (SEM), and Dynamic Light Scattering (DLS), while zeta potential and particle size analyses were performed to gain deeper insight into surface charge behavior. Batch adsorption experiments were carried out at three different temperatures, demonstrating that the adsorption kinetics followed a pseudo-second-order (PSO) model and that the Freundlich model best described the equilibrium data. The process was found to be endothermic and spontaneous, with maximum adsorption capacities of 300.2 mg g⁻¹ (BR2), 201.5 mg g⁻¹ (BY28) and 73.08 mg g⁻¹ (NH₃) on MiCS, and 189.3 mg g⁻¹ (BR2), 117.1 mg g⁻¹ (BY28) and 50.06 mg g⁻¹ (NH₃) on MiCS@Alg at 45 °C and compared with the unmodified chestnut shell. The MiCS and MiCS@Alg exhibited good adsorption performance, improved environmental compatibility, and greater reusability. Overall, these results highlight MiCS@Alg as a cost-effective, sustainable, and highly promising novel biosorbent for the removal of cationic pollutants (BR2, BY28, and NH₃) from water. Full article