Search Results (1,556)

This paper introduces an innovative artificial neural networks-based analytical solver for fractional partial differential equations (fPDEs), combining neural networks (NNs) with symbolic computation. Leveraging the powerful function approximation ability of NNs and the exactness of symbolic methods, our approach achieves notable improvements in both computational speed and solution precision. The efficacy of the proposed method is validated through four numerical examples, with results visualized using three-dimensional surface plots, contour mappings, and density distributions. Numerical experiments demonstrate that the proposed framework successfully derives exact solutions for fPDEs without relying on data samples. This research provides a novel methodological framework for solving fPDEs, with broad applicability across scientific and engineering fields. Full article

Addressing the lack of dynamic monitoring methods for assessing the combined toxicity of mixed pesticides, this study developed a fluorescent probe, CCHL, specifically responsive to leucine aminopeptidase (LAP). The probe utilized Cy7-COOH (CCH) as the fluorophore, with fluorescence recovery triggered by enzymatic hydrolysis. Spectral characterization confirmed a linear response between the probe and LAP activity within a concentration range of 0–0.9 μg/mL (R² = 0.992), along with excellent selectivity in the presence of coexisting biomolecules. Application experiments demonstrated that the combination of chlorfenapyr and beta-cyfluthrin significantly reduced LAP activity, revealing a notable antagonistic effect. The novel sensing strategy developed here provides a real-time, visualized analytical tool for evaluating the combined effects of mixed pollutants, demonstrating significant potential for environmental toxicology monitoring. Full article

This study presents an innovative pipeline for processing, compressing, and remotely visualizing large-scale numerical simulations of fluid dynamics in a virtual wind tunnel (VWT), leveraging virtual and augmented reality (VR/AR) for enhanced analysis and high-end visualization. The workflow addresses the challenges of handling massive databases generated using Direct Numerical Simulation (DNS) while maintaining visual fidelity and ensuring efficient rendering for user interaction. Fully immersive visualization of supersonic (Mach number 2.86) spatially developing turbulent boundary layers (SDTBLs) over strong concave and convex curvatures was achieved. The comprehensive DNS data provides insights on the transport phenomena inside turbulent boundary layers under strong deceleration or an Adverse Pressure Gradient (APG) caused by concave walls as well as strong acceleration or a Favorable Pressure Gradient (FPG) caused by convex walls under different wall thermal conditions (i.e., Cold, Adiabatic, and Hot walls). The process begins with a .vts file input from a DNS, which is visualized using ParaView software. These visualizations, representing different fluid behaviors based on a DNS with a high spatial/temporal resolution and employing millions of “numerical sensors”, are treated as individual time frames and exported in GL Transmission Format (GLTF), which is a widely used open-source file format designed for efficient transmission and loading of 3D scenes. To support the workflow, optimized Extract–Transform–Load (ETL) techniques were implemented for high-throughput data handling. Conversion of exported Graphics Library Transmission Format (GLTF) files into Graphics Library Transmission Format Binary files (typically referred to as GLB) reduced the storage by 25% and improved the load latency by 60%. This research uses Unity’s Profile Analyzer and Memory Profiler to identify performance limitations during contour rendering, focusing on the GPU and CPU efficiency. Further, immersive VR/AR analytics are achieved by connecting the processed outputs to Unity engine software and Microsoft HoloLens Gen 2 via Azure Remote Rendering cloud services, enabling real-time exploration of fluid behavior in mixed-reality environments. This pipeline constitutes a significant advancement in the scientific visualization of fluid dynamics, particularly when applied to datasets comprising hundreds of high-resolution frames. Moreover, the methodologies and insights gleaned from this approach are highly transferable, offering potential applications across various other scientific and engineering disciplines. Full article

This article investigates the combined effects of different common defects on the structural integrity and operational and environmental safety in the operation of an existing Light Cycle Oil (LCO) storage tank. This study correlates all the tank defects (like corrosion and local plate thinning, deformations, and local stress concentrators) against the loads and their combinations that occur during the tank’s lifetime. All the information gathered by various inspection techniques is used together to create a digital twin of the equipment that will be further analyzed by Finite Element Analysis. A tank condition assessment is a complex activity, and it is based on the experience of the engineer performing it. Since there are multiple methods for performing a comprehensive analysis, starting from the basic visual inspection (which is the most important) and some measurements followed by analytical calculations, up to full wall thickness measurements, 3D scan of deformations and FEA analysis of the tank digital twin, it depends on the engineer performing the evaluation to chose the best method for each particular case from technical and economical point of views. The goal of this article is to demonstrate that analytical and FEA methods have the same result and also to establish a well-determined standard calculation model for future applications. Full article

Chili (Capsicum sp.) is a high-value crop cultivated by farmers, but its production is vulnerable to weather extremes (such as irregular rainfall, high temperatures, and storms), pest and disease outbreaks, and spatially fragmented cultivation, resulting in unstable yields and income. Remote sensing (RS) and geographic information systems (GIS) offer promising tools for the timely, spatially explicit monitoring of chili crops. Despite growing interest in agricultural applications of these technologies, no systematic review has yet synthesized how RS and GIS have been used in chili production. This systematic review addresses this gap by evaluating existing literature on methodological approaches and thematic trends in the use of RS and GIS in chili crop monitoring and management. Following Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines a comprehensive literature search was conducted using predefined keywords across Scopus, Web of Science, and Google Scholar. Sixty-five peer-reviewed articles published through January 2025 were identified and grouped into different thematic areas: crop mapping, biotic stress, abiotic stress, land suitability, crop health, soil and fertilizer management, and others. The findings indicate RS predominantly serves as the primary analytical method (82% of studies), while GIS primarily supports spatial integration and visualization. Key research gaps identified include limitations in spatial resolution, insufficient integration of intelligent predictive models, and limited scalability for smallholder farming contexts. The review highlights the need for future research incorporating high-resolution RS data, advanced modelling techniques, and spatial decision-support frameworks. These insights aim to guide researchers, agronomists, and policymakers toward enhanced precision monitoring and digital innovation in chili crop production. Full article

Wine reviews can connect words to flavours; they entwine sensory experiences into vivid stories. This research explores the intersection of artificial intelligence and oenology by using state-of-the-art neural networks to decipher the nuances in wine reviews. For more accurate wine classification and to capture the essence of what matters most to aficionados, we use Hierarchical Attention Networks enhanced with pre-trained embeddings. We also propose an approach to create captivating marketing images using advanced text-to-image generation models, mining a large review corpus for the most important descriptive terms and thus linking textual tasting notes to automatically generated imagery. Compared to more conventional models, our results show that hierarchical attention processes fused with rich linguistic embeddings better reflect the complexities of wine language. In addition to improving the accuracy of wine classification, this method provides consumers with immersive experiences by turning sensory descriptors into striking visual stories. Ultimately, our research helps modernise wine marketing and consumer engagement by merging deep learning with sensory analytics, proving how technology-driven solutions can amplify storytelling and shopping experiences in the digital marketplace. Full article

This study presents a comprehensive analysis of the M/Hypo_K/1/m queuing model to evaluate the performance of marshaling yards in freight rail classification systems. The model effectively captures the complex, multi-phase nature of service and repair processes by incorporating hypo-exponential probability distributions. The marshaling yard is modeled as a finite-capacity, single-server queue subject to potential server failures, reflecting real-world disruptions. Two complementary methodological frameworks are employed: a mathematical model based on continuous-time Markov chains (CTMCs) and a simulation model constructed using Colored Petri Nets (CPNs). In the analytical approach, both service time and repair time follow hypo-exponential distributions, which are used to approximate the gamma distribution. The simulation model built in CPN Tools allows for dynamic visualization and performance evaluation. In the CPN model, we applied a gamma distribution, which allowed us to evaluate the accuracy of the approximation implemented in the analytical model. The result indicated that utilization of the marshaling yard in primary shunting was approximately 23.81%, and with secondary shunting, 22.53%. The study output proves that the hypo-exponential distribution is able to approximate the gamma distribution. This dual-framework approach, combining analytics with simulation, provides a deeper understanding of system behavior, supporting data-driven decisions for capacity planning, failure mitigation, and operational optimization in freight rail networks. Full article

The widespread deployment of electric vehicles (EVs) has introduced substantial challenges to electricity pricing, grid stability, and renewable energy integration. This paper proposes a real-time pricing optimization framework for large-scale EV charging networks incorporating renewable intermittency, demand elasticity, and infrastructure constraints within a high-dimensional optimization model. The core objective is to dynamically determine spatiotemporal electricity prices that simultaneously reduce system peak load, improve renewable energy utilization, and minimize user charging costs. A rigorous mathematical formulation is developed integrating over 40 system-level constraints, including power balance, transmission capacity, renewable curtailment, carbon targets, voltage regulation, demand-side flexibility, social participation, and cyber resilience. Real-time electricity prices are treated as dynamic decision variables influenced by charging station utilization, elasticity response curves, and the marginal cost of renewable and grid-supplied electricity. The problem is solved over 96 time intervals using a hybrid solution approach, with benchmark comparisons against mixed-integer programming (MILP) and deep reinforcement learning (DRL)-based baselines. A comprehensive case study is conducted on a 500-station EV charging network serving 10,000 vehicles integrated with a modified IEEE 118-bus grid model and 800 MW of variable renewable energy. Historical charging data with ±12% stochastic demand variation and real-world solar and wind profiles are used to simulate realistic operational conditions. Results demonstrate that the proposed framework achieves a 23.4% average peak load reduction per station, a 17.9% improvement in renewable energy utilization, and user cost savings of up to 30% compared to baseline flat-rate pricing. Utilization imbalances across the network are reduced, with congestion mitigation observed at over 90% of high-traffic stations. The real-time pricing model successfully aligns low-price windows with high-renewable periods and off-peak hours, achieving time-synchronized load shifting and system-wide flexibility. Visual analytics including high-resolution 3D surface plots and disaggregated bar charts reveal structured patterns in demand–price interactions, confirming the model’s ability to generate smooth, non-disruptive pricing trajectories. The results underscore the viability of advanced optimization-based pricing strategies for scalable, clean, and responsive EV charging infrastructure management in renewable-rich grid environments. Full article

This study develops a comprehensive evaluation framework for urban street alfresco spaces by integrating the Analytic Hierarchy Process (AHP) and Entropy Weight Method. Daxue Road in Shanghai is selected as a representative case to analyze key factors influencing urban street alfresco spaces, which refer to commercially utilized outdoor extensions of building facades along streets, typically in the form of semi-open, publicly accessible areas used for dining, vending, seating, or temporary retail activities. These spaces are typically operated by adjacent businesses or regulated by local policies, and they integrate pedestrian circulation, commercial vibrancy, and spatial adaptability. They serve as critical urban interfaces that foster street-level vibrancy, social interaction, and public life. The evaluation system covers five dimensions: Cognizability, Accessibility, Participation, Emotional Design, and Spatial Diversity. The methodological innovation lies in integrating subjective weights derived from AHP with objective weights obtained through entropy calculations, which enhances the scientific rigor and neutrality of the evaluation. The results show that traffic safety (weight = 0.0644) and locational attributes of streets (weight = 0.0574) are the most influential factors affecting user perception. Compared to previous studies that often prioritize visual aesthetics or commercial density, this study underscores the significance of traffic-related factors, indicating a shift in user preferences in high-density urban environments. The findings provide practical guidance for urban design and policy to improve the quality, safety, and vitality of street-level public spaces in high-density cities. This research contributes to the theoretical foundation for sustainable and human-oriented street regeneration. Full article

The aim of this study is to explore the role of virtual reality in engineering education. Specifically, it analyzed 342 studies that were published during 2010–2025 following a systematic approach. It examined how virtual reality is used in engineering education, explored the document main characteristics, and identified emerging topics. The study also revealed existing limitations and suggested future research directions. According to the outcomes, the following six topics emerged: (i) Immersive technologies in engineering education, (ii) Virtual laboratories, (iii) Immersive and realistic simulations, (iv) Hands-on activities and practical skills development, (v) Engineering drawing, design, and visualization, and (vi) Social and collaborative learning. Virtual reality was proven to be an effective educational tool which supports engineering education and complements existing learning practices. Using virtual reality, students can apply their theoretical knowledge and practice their skills within low-risk, safe, and secure learning environments characterized by high immersion and interactivity. Virtual reality through the creation of virtual laboratories can also effectively support social, collaborative, and experiential learning and improve students’ academic performance, engagement, interaction, and motivation. Learning using virtual reality can also enhance students’ knowledge acquisition, retention, and understanding. Improvements on students’ design, planning, and implementation skills and decision making, problem-solving skills, and visual analytic skills were also observed. Finally, when compared to physical laboratories, virtual reality learning environments offered lower costs, reduced infrastructure requirements, less maintenance, and greater flexibility and scalability. Full article

This study employs the improved modified extended tanh method (IMETM) to derive exact analytical solutions of a higher-order nonlinear Schrödinger (HNLS) model, incorporating $\beta$-fractional derivatives in both time and space. Unlike classical methods such as the inverse scattering transform or Hirota’s bilinear technique, which are typically limited to integrable systems and integer-order operators, the IMETM offers enhanced flexibility for handling fractional models and higher-order nonlinearities. It enables the systematic construction of diverse solution types—including Weierstrass elliptic, exponential, Jacobi elliptic, and bright solitons—within a unified algebraic framework. The inclusion of fractional derivatives introduces richer dynamical behavior, capturing nonlocal dispersion and temporal memory effects. Visual simulations illustrate how fractional parameters $\alpha$ (space) and $\beta$ (time) affect wave structures, revealing their impact on solution shape and stability. The proposed framework provides new insights into fractional NLS dynamics with potential applications in optical fiber communications, nonlinear optics, and related physical systems. Full article

Carboxymethyl cellulose (CMC) films, derived from sugarcane bagasse agricultural waste (SCB) incorporated with Betalains-nitrogen-doped carbon dots (Betalains-N–CQDs), derived from beet root waste (BR), offer a sustainable, smart and naked-eye sensor for strawberry packaging due to their excellent fluorescent and shape memory properties. These CMC-Betalains-N–CQDs aim to enhance strawberry preservation and safety by enabling visual detection of common food contaminants such as bacteria, fungi and Pb(II). Crucially, the CMC-Betalains-N–CQD film also exhibits excellent shape memory properties, capable of fixing various shapes under alkaline conditions and recovering its original form in acidic environments, thereby offering enhanced physical protection for delicate produce like strawberries. Optical studies reveal the Betalains-N–CQDs’ pH-responsive fluorescence, with distinct emission patterns observed across various pH levels, highlighting their potential for sensing applications. Scanning Electron Microscopy (SEM) confirms the successful incorporation of Betalains-N–CQDs into the CMC matrix, revealing larger pores in the composite film that facilitate better interaction with analytes such as bacteria. Crucially, the CMC-Betalains-N–CQD film demonstrates significant antibacterial activity against common foodborne pathogens like Escherichia coli, Staphylococcus aureus, and Candida albicans, as evidenced by inhibition zones and supported by molecular docking simulations showing strong binding interactions with bacterial proteins. Furthermore, the film functions as a fluorescent sensor, exhibiting distinct color changes upon contact with different microorganisms and Pb(II) heavy metals, enabling rapid, naked-eye detection. The film also acts as a pH sensor, displaying color shifts (brown in alkaline, yellow in acidic) due to the betalains, useful for monitoring food spoilage. This research presents a promising, sustainable, and multifunctional intelligent packaging solution for enhanced food safety and extended shelf life. Full article

Photoelectrochemical (PEC) sensors have garnered increasing attention due to their high sensitivity, low background signal, and rapid response. The incorporation of hydrogels into PEC platforms has significantly expanded their analytical capabilities by introducing features such as biocompatibility, tunable porosity, antifouling behavior, and mechanical flexibility. This review systematically categorizes hydrogel materials into four main types—nucleic acid-based, synthetic polymer, natural polymer, and carbon-based—and summarizes their functional roles in PEC sensors, including structural support, responsive amplification, antifouling interface construction, flexible electrolyte integration, and visual signal output. Representative applications are highlighted, ranging from the detection of ions, small biomolecules, and biomacromolecules to environmental pollutants, photodetectors, and flexible bioelectronic devices. Finally, key challenges—such as improving fabrication scalability, enhancing operational stability, integrating emerging photoactive materials, and advancing bio-inspired system design—are discussed to guide the future development of hydrogel-enhanced PEC sensing technologies. Full article

This paper introduces a new family of q-special polynomials, termed q-general Bell polynomials, and systematically explores their structural and analytical properties. We establish their generating functions, derive explicit series representations, and develop recurrence relations to characterize their combinatorial behavior. Additionally, we characterize their quasi-monomial properties and construct associated differential equations governing these polynomials. To demonstrate the versatility and applicability of this family, we investigate certain examples, including the q-Gould–Hopper–Bell and q-truncated exponential-Bell polynomials, deriving analogous results for each. Further, we employ computational tools in Mathematica to examine zero distributions and produce visualizations, offering numerical and graphical insights into polynomial behavior. Full article

Building façades traditionally focus on enhancing indoor environmental quality and improving energy performance, but undermine their influence on Outdoor Environmental Comfort (OEC), including thermal, acoustic, and visual conditions. With technological advancements in envelope design, research on new materials and green systems has been introduced in the last few decades. This review examines the role of two key elements—façade materials and green façades—in shaping OEC. A total of 41 peer-reviewed studies (24 on urban scale and 17 on building scale) were categorized into three focus areas: (1) outdoor thermal comfort; (2) outdoor acoustic comfort; and (3) outdoor visual comfort. The analysis was structured across three levels: (a) Performance Determinants; (b) Metrics/Models; and (c) Material or Façade Types. We proposed this analytical structure to highlight the interactions between building façades and OEC domains (thermal, acoustic, and visual comfort). Our results showed façade treatment can impact all three comfort factors related to OEC, but trade-offs must be evaluated. Moreover, the findings highlighted that additional research is required to cover variations in both climate and context conditions, due to their close association with the OEC. Finally, the conceptual framework is presented to synthesize the three comfort domains for sustainable outdoor environments. Full article