Search Results (320)

Porous intermetallic compounds have the properties of porous materials as well as a combination of covalent and metallic bonds, and they exhibit high porosity, structural stability, and corrosion resistance. In this work, a porous TiNi₃ intermetallic compound was fabricated through reactive synthesis of elemental powders. Next, detailed studies of its phase composition and pore structure characteristics at different sintering temperatures, as well as its corrosion behavior against an alkaline environment, were carried out. The results show that the as-prepared porous TiNi₃ intermetallic compound has abundant pore structures, with an open porosity of 56.5%, which can be attributed to a combination of the bridging effects of initial powder particles and the Kirkendall effect occurring during the sintering process. In 1 M KOH solution, a higher positive corrosion potential (−0.979 V_SCE) and a lower corrosion current density (1.18 × 10⁻⁴ A∙cm⁻²) were exhibited by the porous TiNi₃ intermetallic compound, compared to the porous Ni, reducing the thermodynamic corrosion tendency and the corrosion rate. The corresponding corrosion process is controlled by the charge transfer process, and the increased charge transfer resistance value (713.9 Ω⋅cm²) of TiNi₃ makes it more difficult to charge-transfer than porous Ni (204.5 Ω⋅cm²), thus decreasing the rate of electrode reaction. The formation of a more stable passive film with the incorporation of Ti contributes to this improved corrosion resistance performance. Full article

Accurate modeling of ionospheric variability is critical for space weather forecasting and GNSS applications. While machine learning approaches have shown promise, progress is hindered by the absence of standardized benchmarking practices and narrow test periods. In this paper, we take the first step toward fostering rigorous and reproducible evaluation of AI models for ionospheric forecasting by introducing IonoBench: a benchmarking framework that employs a stratified data split, balancing solar intensity across subsets while preserving 16 high-impact geomagnetic storms (Dst ≤ $-100$ nT) for targeted stress testing. Using this framework, we benchmark a field-specific model (DCNN) against state-of-the-art spatiotemporal architectures (SwinLSTM and SimVPv2) using the climatological IRI 2020 model as a baseline reference. DCNN, though effective under quiet conditions, exhibits significant degradation during elevated solar and storm activity. SimVPv2 consistently provides the best performance, with superior evaluation metrics and stable error distributions. Compared to the C1PG baseline (the CODE 1-day forecast product), SimVPv2 achieves a notable RMSE reduction up to $32.1\%$ across various subsets under diverse solar conditions. The reported results highlight the value of cross-domain architectural transfer and comprehensive evaluation frameworks in ionospheric modeling. With IonoBench, we aim to provide an open-source foundation for reproducible comparisons, supporting more meticulous model evaluation and helping to bridge the gap between ionospheric research and modern spatiotemporal deep learning. Full article

Rural landscapes play a key role in preserving ecological processes, cultural identity, and socio-economic well-being, yet these areas often face challenges such as land degradation, water scarcity, and an inadequate road network. A sustainable approach to rural landscape and tourism planning is essential for enhancing both environmental resilience and socio-economic vitality in areas facing degradation and global change. This study aims to develop and validate an integrated methodological workflow that combines Landscape Character Assessment (LCA), ECOVAST guidelines, SWOT analysis, and open-source GIS techniques, complemented by a bottom-up approach of spontaneous fruition mapped through Wikiloc heatmaps. The framework was applied to a case study in Paternò, Eastern Sicily, Italy—a territory distinguished by its key local values such as Calanchi formations, proximity to Mount Etna, and cultural heritage. Through this application, eight distinct Landscape Units (LUs) were delineated, and key strengths, weaknesses, opportunities, and threats for sustainable development were identified. Using open-access data and a survey-free protocol, this approach facilitates detailed landscape assessment without extensive fieldwork. The methodology is readily transferable to other rural Italian and Mediterranean contexts, providing practical guidance for researchers, planners, and stakeholders engaged in sustainable tourism development and landscape management. Full article

This paper develops an analytical turbulence model for open-ended squeeze film damper (SFD) application. Prandtl’s mixing length theory is adopted to describe the momentum transfer within the damper for its thin-film turbulent flow. A novel turbulence coefficient function is developed to describe the effective fluid viscosity such that the classical Reynolds equation remains applicable. Model validation is presented by (i) comparing the damping coefficient obtained by several existing empirical formulas and (ii) correlating the rotor dynamic prediction with the experimental measurement of an integrated rotor-SFD test rig. This work provides a reduced form of turbulence coefficient for certain SFD implementations. It quantifies the turbulence effect under different operating conditions, which is valued as a practical tool to assess the significance of turbulence consequences in rotor dynamic applications. Full article

The Yangtze River Economic Belt (YEB), serving as a pivotal transportation corridor connecting eastern and western China and a national strategic development hub, plays a central role in driving high-quality economic development (HQAED) across the country. Based on the new development paradigm with emphasis on green transformation and transportation integration, this study proposes a comprehensive evaluation framework for an HQAED index (HQAED) across five core dimensions. Employing the entropy-weighted CRITIC method to quantify provincial HQAED values, combined with Dagum–Gini coefficient analysis to examine regional inequality patterns and determinants, and complemented by kernel density estimation (KDE) for temporal dynamics analysis, this research reveals four key findings: (1) There are significant disparities in HQEDI levels across the YEB, with a clear east–west gradient: the lower reaches > middle reaches > upper reaches. (2) While the dimensions of green development and shared development have shown steady growth despite initial disadvantages, the openness dimension faces structural challenges that require particular attention. (3) The overall Gini coefficient fluctuates between 0.068 and 0.094, indicating moderate regional disparities with relatively limited inequality. (4) The rightward shift in the HQEDI kernel density curves confirms overall progress, but also highlights widening disparities in the upstream regions and growth stagnation in the midstream areas. Practically, the entropy–CRITIC fusion methodology offers a transferable framework for emerging economies measuring sustainability-transition progress, while the quantified “green transportation empowerment” effects provide actionable levers for policymakers to optimize ecological compensation mechanisms and cross-regional infrastructure investments. Full article

Medium- and large-scale heat sinks are critical for thermal load management in high-performance systems. However, their high heat flux densities and limited space complicate cooling, leading to risks of overheating, performance degradation, or failure. This study employs the Cascaded Lattice Boltzmann Method (CLBM) to enhance their thermal performance. This numerical approach is known for being stable, accurate when dealing with complex boundaries, and efficient when computing in parallel. The numerical code was validated against a benchmark configuration and an experimental setup to ensure its reliability and accuracy. While previous studies have explored mixed convection in cavities or heat sinks, few have addressed configurations involving side air injection and boundary conditions periodicity in the transition-to-turbulent regime. This gap limits the understanding of realistic cooling strategies for compact systems. Focusing on mixed convection in the transition-to-turbulent regime, where buoyancy and forced convection interact, the study investigates the impact of Rayleigh number values ($5\times {10}^7$ to $5\times {10}^8$) and Reynolds number values (${10}^3$ to $3\times {10}^3$) on heat transfer. Simulations were conducted in a rectangular cavity with periodic boundary conditions on the vertical walls. Two heat sources are located on the bottom wall ($T_h$ = 50 °C). Two openings, one on each side of the two hot sources, force a jet of fresh air in from below. An opening at the level of the cavity ceiling’s axis of symmetry evacuates the hot air. Mixed convection drives the flow, exhibiting complex multicellular structures influenced by the control parameters. Calculating the average Nusselt number (Nu) across the surfaces of the heat sink reveals significant dependencies on the Reynolds number. The proposed correlation between Nu and Re, developed specifically for this configuration, fills the current gap and provides valuable insights for optimizing heat transfer efficiency in engineering applications. Full article

The natural variability and moisture sensitivity of bamboo limit its widespread use in construction applications. To address these challenges, densification and delignification processes have emerged as promising modification techniques. Densification and delignification processes can lead to significant improvements in the physical, mechanical, and chemical properties of solid wood. In this study, a two-step process of delignification and densification was carried out on Dendrocalamus asper bamboo specimens. The objective was to assess whether the optimized parameters of densification for natural bamboo on an open pressing system can be transferred for delignified bamboo. Delignification was achieved using an alkali solution (NaOH and Na₂SO₃) with two different temperature settings (25 °C or 100 °C). The pre-treated samples were dried in one of the two different conditions, either at 100 °C for 24 h or 25 °C for 30 days, resulting in four different groups with an average moisture content ranging from 7 to 10%. The samples were densified to 50% of their original thickness through an open thermo-mechanical press system at 160 °C with a compression rate of 6.7 mm/min and compared to densified bamboo without delignification (reference). The compression stress required to achieve a 50% degree of densification was evaluated, with untreated samples exhibiting an average value close to 17 MPa. Following treatment, the compression stress ranged from 7 to 13.4 MPa, indicating that the exposure to a high pH solution facilitates the densification process. However, a reduction in flexural properties (MOR, LOP, and MOE) was observed on the alkali-treated samples after a three-point bending test. Physical properties (water absorption and thickness swelling) were not altered after delignification. These findings demonstrate that the direct application of a densification process optimized for natural bamboo is not fully effective for chemically modified bamboo, highlighting the need for adjustments. Delignified bamboo showed an increase in free space after chemical treatment, which should be further densified under higher degrees. Full article

This study introduces a novel and versatile application of neural networks (NNs) to enhance two distinct aspects of Wireless Power Transfer (WPT) systems. First, a compact NN architecture is presented for accurate distance estimation and automated impedance matching in a WPT system. Trained on either impedance measurements or scattering parameters acquired from the transmitter side, this NN effectively predicts the inter-coil distance and identifies optimal capacitance values for maximizing power transfer. Validation using both simulated and experimental data demonstrates consistently low prediction error rates. Second, a separate NN is employed to predict the optimal transmission frequency for minimizing the phase angle between voltage and current, thereby maximizing the power factor. This NN, validated on experimental data spanning various load conditions and inter-coil distances, achieves performance comparable to traditional PI control, but with significantly faster prediction speeds. This speed advantage is crucial for real-time applications and directly contributes to improved power efficiency. The results presented in this study, including the high accuracy of distance and capacitance prediction and the rapid determination of optimal frequencies for power factor maximization, showcase the significant potential of NNs for optimizing WPT systems. These findings open the way for more efficient, adaptable, and intelligent wireless energy transfer solutions, with potential applications ranging from dynamic charging of electric vehicles to real-time optimization of implantable medical devices. Full article

In today’s rapidly evolving global landscape, the need to cultivate innovation-ready, globally competent talent has become a strategic imperative. This study critically investigates how sustainable open innovation strategies—particularly within non-profit organizations and university alliances—can serve as a catalyst for global talent development. Responding to the growing demand for interdisciplinary, cross-sectoral collaboration, the research employs a robust mixed-methods approach, integrating the Analytic Hierarchy Process (AHP) and Fuzzy Analytic Hierarchy Process (FAHP) to evaluate and prioritize key strategic factors. The findings reveal that initiatives such as international internship programs, operational funding mechanisms, joint research ventures, and technology transfer are essential drivers in creating environments that nurture and scale global talent. Building on these insights, this study introduces a structured, sustainable innovation model that categorizes strategies into three tiers—collaborative, interactive, and foundational service-oriented actions—providing a practical roadmap for resource optimization and strategic planning. More than a theoretical exercise, this research offers actionable guidance for non-profit leaders, academic administrators, and corporate partners. It highlights the reciprocal value of multi-sector collaboration and contributes to a broader understanding of how mission-driven innovation ecosystems can foster resilient, future-ready workforces. By positioning non-profit–academic partnerships at the center of global talent strategies, the study sets a foundation for rethinking how institutions can co-create value in addressing pressing global challenges. Full article

The continuous change in the entrance cross-section of a parallel-plate flow channel generally affects the mass and heat transfer on the walls of the channel. In this paper, an electrochemical parallel-plate flow channel equipped with a selenoid pulse generator has been developed to enhance the convective mass transfer on the walls of a mass transfer flow system such as an electrodeposition cell, absorption column, flow reactor, etc. A number of experimental studies have been conducted to determine the distribution of the mass transfer coefficients on the bottom wall of a parallel-plate channel for the flow conditions with/without a pulse in the research. Here, the distribution of the convective mass transfer coefficients has been determined by the electrochemical limiting diffusion current technique (ELDCT) using nickel local cathodes arranged on the bottom surface of the flow channel. The experimental results show the effects of the parameters used, which are the flow Reynolds number, opened/closed (OP/CL) ratio, and pulse number, on the distribution of mass transfer coefficients. The results have revealed that the pulse generator altered the flow structure and increased the turbulent intensity at Re < 2860 flow conditions. Within the range of Reynolds number 950 < Re < 2860, the mass transfer correlation was given as $Sh=67.02{Re}^{0.897}{\left({\displaystyle \frac{Op}{Cl}}\right)}^{-0.059}S{c}^{1/3}$. According to the research findings, the highest k_M values were obtained at Re = 2860 with an (OP/CL) ratio of 1/2. If a parallel-plate flow reactor with a pulse generator is designed using these flow conditions, it will yield a reactor that is both more efficient and more compact than a reactor without a pulse generator. Full article

Spinach, leafy vegetables with growing demand and high nutritional value, has a heightened focus on nitrate content. An open-field experiment evaluated the potential of vis-NIR-SWIR hyperspectral data for classifying spinach nitrate content. Shallow artificial neural networks (ANN) and ensemble techniques—majority voting (MV) and stacked generalization (stacked)—were applied. The competitive adaptive reweighted sampling (CARS), its stability version (SCARS), Elastic Net, and modified boosted versions of each (CARSplus, SCARSplus, and ENplus) were used as feature selection methods. ANNs were optimized for hidden layer size. The resulting models were further used in ensemble techniques by grouping them into two sets: one with all models and another with models trained using the three boosted feature selection subsets (fifty-three wavelengths). The best-performing ANNs were based on the SCARS, SCARSplus, and full datasets, achieving an accuracy (Acc) of 0.83. While the majority voting approach did not improve performance (Acc 0.82), the stacked ensemble models reached Acc 0.88. Notably, stacked performed well also with models trained on 53 wavelengths, demonstrating strong potential for transferability as the required sensors would be less complex than those used in this study. Furthermore, a simulation of the practical application was conducted using Italian Ministry of Health official data with the scope of showing a potential use case in improving nitrate management and for advancing efficient farming practices in agriculture. The stacked models demonstrated their utility in doubling the monitoring capacity for internal quality assurance in spinach farming within a regulated framework. Full article

Background: Previous studies have suggested that electromagnetic pulse (EMP) can induce openings in the blood–brain barrier (BBB). However, the temporal variation and spatial distribution of BBB permeability after EMP radiation are difficult to assess using conventional histopathological approaches. Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) is a valuable tool for the in vivo evaluation of BBB permeability. The main purpose of this study was to investigate the temporal variation and spatial distribution of BBB permeability after EMP radiation in rats using DCE-MRI. Methods: The dose of EMP was estimated through simulations utilizing a digital rat model comprising 16 distinct brain regions. Then, the changes in BBB permeability of the different rat brain regions at different time points (3 h and 24 h) after EMP radiation were evaluated using quantitative DCE-MRI. Furthermore, the spatial difference in BBB permeability was assessed 3 h after exposure. Finally, the dose–effect relationship between the electric field strength and the BBB permeability was also investigated. Results: The results demonstrated that the changes in the values of volume transfer constant (ΔK^trans) significantly increased in several rat brain regions at 3 h after 400 kV/m EMP radiation. These changes vanished 24 h after exposure. Meanwhile, no significant spatial differences in BBB permeability were observed after EMP radiation. Moreover, Pearson’s correlation analysis showed that there was a significant positive linear relationship between BBB permeability and the electric field strength within an external electric field strength range of 0 to 400 kV/m at 3 h after EMP radiation. Conclusions: EMP radiation can induce a reversible increase in BBB permeability in rats. Moreover, no significant differences in BBB permeability were found across different brain regions. Additionally, the degree of BBB permeability was positively correlated with the regional electric field strength of EMP radiation within an external electric field strength range of 0 to 400 kV/m at 3 h after EMP radiation. These results indicate the promising potential of employing EMP for transient openings in the BBB, which could facilitate clinical pharmacological interventions via drug delivery into the brain. Full article

Today, open-source Cloud Computing platforms are valuable for geospatial image analysis while the combination of the Google Earth Engine (GEE) platform and new satellite launches greatly facilitate the monitoring of national-scale lake Water Quality (WQ). The main aim of this research is to assess the transferability and performance of published general, natural-only and artificial-only lake WQ models (Chl-a, Secchi Disk Depth-SDD- and Total Phosphorus-TP) across Greece’s WFD (Water Framework Directive) lake sampling network. We utilized Landsat (7 ETM +/8 OLI) and Sentinel 2 surface reflectance (SR) data embedded in GEE, while subjected to different atmospheric correction (AC) methods. Subsequently, Carlson’s Trophic State Index (TSI) was calculated based on both in situ and modelled WQ values. Initially, WQ models employed both DOS1-corrected (Dark Object Subtraction 1; manually applied) and GEE-retrieved respective SR data from the year 2018. Double WQ values per lake station were inserted in a linear regression analysis to harmonize the AC differences, separately for Landsat and Sentinel 2 data. Yielded linear equations were accompanied by strong associations (R² ranging from 0.68 to 0.98) while modelled and GEE-modelled TSI values were further validated based on reference in situ WQ datasets from the years 2019 and 2020. The values of the basic statistical error metrics indicated firstly the increased assessment’s accuracy of GEE-modelled over modelled TSIs and then the superiority of Landsat over Sentinel 2 data. In this way, the hereby adopted methodology was evolved into an efficient lake management tool by providing managers the means for integrated sustainable water resources management while contributing to saving valuable image pre-processing time. Full article

The peculiar electronic properties of graphene, including the universal dc conductivity and the pseudodiffusive shot noise, are usually found in a small vicinity close to the charge neutrality point, away from which the electron’s effective mass raises, and nanostructures in graphene start to behave similarly to familiar Sharvin contacts in semiconducting heterostructures. Recently, it was pointed out that as long as abrupt potential steps separate the sample area from the leads, some graphene-specific features can be identified relatively far from the charge neutrality point. These features include greater conductance reduction and shot noise enhancement compared to the standard Sharvin values. The purpose of this paper is twofold: First, we extend the previous analysis based on the effective Dirac equation, and derive the formulas that allow the calculation of the arbitrary charge transfer cumulant for doped graphene. Second, the results of the analytic considerations are compared with numerical simulations of quantum transport on the honeycomb lattice for selected nanosystems for which considerations starting from the Dirac equation cannot be directly adapted. For a wedge-shaped constriction with zigzag edges, the transport characteristics can be tuned from graphene-specific (sub-Sharvin) values to standard Sharvin values by varying the electrostatic potential profile in the narrowest section. A similar scenario is followed by the half-Corbino disk. In contrast, a circular quantum dot with two narrow openings showing a mixed behavior appears: the conductance is close to the Sharvin value, while the Fano factor approaches the value characterizing the symmetric chaotic cavity. Carving a hole in the quantum dot to eliminate direct trajectories between the openings reduces the conductance to sub-Sharvin value, but the Fano factor is unaffected. Our results suggest that experimental attempts to verify the predictions for the sub-Sharvin transport regime should focus on systems with relatively wide openings, where the scattering at the sample edges is insignificant next to the scattering at the sample–lead interfaces. Full article

High-purity distillation columns typically give rise to multi-variable, strongly coupled nonlinear systems with substantial time delay and significant inertia. The control performance of high-purity distillation columns crucially influences the purity of the final product. Taking into account the process of a high-purity distillation column, this article puts forward a dual-loop modified active disturbance rejection control (MADRC) scheme to improve the control of product purity. During the stable operation of the distillation process, the structures of two control loops are, respectively, approximated by two linear transfer function models via open-loop experiments. Subsequently, the compensation part of the MADRC scheme is designed, respectively, for each approximate model. Furthermore, this paper employs singular perturbation theory to prove the stability of MADRC. The performance of the dual-loop MADRC scheme (MADRC) is compared with that of a proportional–integral–derivative (PID) control scheme, a cascade PID control scheme (CPID), and a regular ADRC scheme (ADRC). The simulations demonstrate that the dual-loop MADRC scheme is capable of efficiently tracking the reference value and exhibits optimal disturbance rejection capabilities. Additionally, the superiority of the dual-loop MADRC scheme is validated through Monte Carlo trials. Full article