Search Results (24,142)

This article proposes a gas-only continuation framework for steady-state loadability analysis in natural gas transmission networks based on a direction-free reformulation of the General Flow Equation (GFE). The proposed formulation introduces signed pipe flows directly as state variables, thereby representing bidirectionality intrinsically. As a result, flow reversals are handled without switching logic, while the branch geometry and criticality mechanism of the underlying gas-network equilibrium map are preserved. On this basis, a Gas Continuation Method (GCM) is developed to trace equilibrium branches directly in native gas-load space under specified gas-load stress. The method distinguishes the last admissible operating point from the mathematical critical point and incorporates a formal diagnosis to determine whether the detected limiting condition is consistent with a Saddle-Node Bifurcation (SNB). The proposed framework is validated on a three-node benchmark, a realistic Belgian gas transmission network, and a 40-node test system. The results show accurate agreement with Newton–Raphson (NR) solutions in the regular operating regime, robust branch tracing near limiting conditions where standalone NR loses convergence, and consistent handling of signed pipe flows under load-induced flow reversal and under algebraic orientations assigned a priori opposite to the solved physical flow. The Belgian and 40-node cases further show that the operational admissibility limit may precede the mathematical critical point, so pressure-based feasibility and branch-level criticality emerge as related but distinct notions. These features make the proposed methodology a rigorous and practical tool for identifying admissibility limits, interpreting critical behavior, and assessing loadability margins in gas transmission networks. Full article

Private Networks or Standalone Non-Public Networks (SNPNs) are essential for Industry 4.0 and enterprise connectivity. However, most existing studies rely on simulations, evaluate only a single radio access technology, or report raw key performance indicators (KPIs) without an interpretable quality assessment framework. In practical deployment, operators require measurement-driven evidence to assess the performance and feasibility of 4G LTE and 5G SNPN solutions. This study presents a controlled experimental comparison of software-defined radio (SDR)-based 4G LTE and 5G SNPNs using the same Universal Software Radio Peripheral (USRP) platform, Open5GS, srsRAN, and commercial off-the-shelf user equipment (COTS-UE). The evaluation was conducted in an indoor environment under line-of-sight (LOS) and non-line-of-sight (NLOS) conditions. Experimental iPerf3 results show that the SDR-based 5G SNPN achieves higher downlink and uplink throughput than the SDR-based 4G LTE SNPN across all tested scenarios. The 5G deployment reaches up to 55 Mbps downlink and 40.5 Mbps uplink under LOS conditions, while maintaining 42 Mbps downlink and 28 Mbps uplink under NLOS conditions. Furthermore, 5G achieves lower latency than 4G LTE, with average values ranging from 21 ms to 31 ms. To provide interpretable network quality assessment, a Mamdani fuzzy logic-based Network Quality Index (NQI) with 81 inference rules is proposed to map signal-to-interference-plus-noise ratio (SINR), throughput, latency, and jitter into linguistic quality levels. The proposed approach enables nonlinear integration of heterogeneous KPIs and provides a technology-agnostic framework for practical SNPN deployment. Full article

As the complexity, scale, and nonlinearity of modern engineering optimization problems continue to increase, traditional optimization algorithms face significant challenges in achieving high solution accuracy, fast convergence, and robust performance. To address these issues, this paper proposes a Rural Tourism Migration-based Improved Equilibrium Optimizer (RTM-IEO), aiming to enhance the global search capability and adaptive balance between exploration and exploitation. Specifically, an adaptive lens imaging opposition-based learning strategy is introduced to effectively expand the search space and maintain population diversity. A dynamic elite-guided elimination mechanism is designed to strengthen exploitation capability and accelerate convergence by reconstructing inferior individuals using high-quality solutions. In addition, a multi-stage rural tourism migration strategy is developed to dynamically regulate the search behavior across different optimization phases, enabling a more flexible and efficient search process. The effectiveness of the proposed algorithm is comprehensively validated on the CEC2021 and CEC2022 benchmark suites, where RTM-IEO demonstrates superior performance in terms of convergence accuracy, convergence speed, and robustness compared with several representative state-of-the-art algorithms. The statistical superiority of the proposed method is further confirmed through Friedman mean ranking and Wilcoxon rank-sum tests. To further evaluate its practical applicability, RTM-IEO is applied to the sustainable economic dispatch problem of a microgrid integrating renewable energy sources, including wind power and photovoltaic generation, along with energy storage systems and controllable units. The optimization objective simultaneously considers economic cost minimization and sustainable operation requirements, such as improving renewable energy utilization and reducing dependence on fossil-fuel-based generation. Experimental results indicate that the proposed method achieves a significant reduction in daily operating cost (exceeding 52% compared with benchmark algorithms), while effectively promoting low-carbon energy utilization and enhancing overall system sustainability. Overall, the proposed RTM-IEO provides an efficient and reliable optimization framework for addressing complex global optimization problems, particularly in scenarios requiring a coordinated balance between economic performance and sustainable development. Full article

²¹²Pb is an important medical radionuclide for targeted alpha therapy, and its reliable supply depends on the efficient production of parent nuclides such as ²²⁸Ra, ²²⁸Th, and ²²⁴Ra. Natural thorium resources are abundant and represent a potential source of these radionuclides. However, the separation and enrichment of trace radium from thorium-rich high-salinity systems remain challenging due to extremely low radium concentrations and Th/Ra mass ratios on the order of 10⁹. In this work, a radium separation strategy based on BaSO₄ co-precipitation was developed. The precipitation behavior of BaSO₄, precipitation kinetics, radium co-precipitation efficiency, and thorium recovery in concentrated thorium nitrate solutions were systematically investigated. The results show that elevated ionic strength and competitive interactions between Th⁴⁺ and SO₄²⁻ reduce the effective sulfate activity under high-thorium conditions, making excess sulfate necessary to achieve efficient BaSO₄ precipitation. Under optimized conditions, the radium co-precipitation recovery exceeded 80% at a Ba²⁺ concentration of 3 mM. Meanwhile, thorium exhibited negligible incorporation into the BaSO₄ phase and could be almost completely recovered via subsequent hydroxide precipitation. The proposed method features operational simplicity, use of common reagents, low cost, and compatibility with high-salinity matrices. It provides a feasible technical pathway for the subsequent production of high-purity ²²⁸Th or ²²⁴Ra and the preparation of ²²⁸Th/²¹²Pb or ²²⁴Ra/²¹²Pb generator systems. Full article

Decommissioned airfields are increasingly recognized as strategic sites for ecological regeneration, climate adaptation, and the creation of new public spaces. However, research on their transformation has predominantly focused on the environmental performance of Nature-based Solutions (NBS), often overlooking the role of inherited spatial morphology in structuring regeneration processes and outcomes. This paper proposes an AI-assisted, morphology-based proto-ontological framework for analyzing and designing post-airfield architecture. The framework was developed through the inductive and comparative analysis of a corpus of 32 urban post-airfield regeneration projects, from which recurrent inherited morphologies, transformation actions, spatial devices, and NBS were identified and structured into a relational sequence. The framework was then applied to two contrasting case studies: Maurice Rose Airfield Park (Frankfurt) and Xuhui Runway Park (Shanghai); these were selected for their different transformation logics. The results show that similar airfield morphologies can generate markedly different climatic, ecological, social, and memory-related outcomes depending on how they are transformed and linked to NBS. The study demonstrates that inherited airfield morphologies are not passive remnants but operative spatial structures, and that NBS should be understood as spatially embedded and form-generating design components. The proposed proto-ontology offers a transferable analytical model and a basis for future computational and generative design applications. Full article

In this paper, based on the Schauder fixed point theorem, the (generalized) Darbo fixed point theorem, and the (generalized) Banach contraction mapping principle, we study the mixed-type fractional impulse evolution equation with non-local and delay terms, and obtain the existence and uniqueness theorems under whether the operator is compact or not. The order of the derivative in this paper is $0<\alpha <1$, this fractional order introduces a series of problems concerning compactness, continuity, and convergence. We overcome these problems using methods such as H$\ddot{o}$lder inequality and Minkowski inequality. Moreover, under the condition of the non-compact measure, the non-negative constant is extended to an unbounded Lebesgue-integrable function. In addition, when obtaining the uniqueness of the solution through the (generalized) Banach contraction mapping principle, the non-negative constant L in the Lipschitz condition is extended to an unbounded Lebesgue integrable function. Finally, a case study is conducted to demonstrate the validity of the theoretical results. Full article

This article presents a method for calibration of dynamic vehicle weighing systems (WIM—Weigh-In-Motion) involving the calibration of all WIM stations operating within a given road network segment as a single process. A key assumption of the method is the presence of at least one scale with significantly higher accuracy than the calibrated systems in this part of road network. This reference scale function may be played by a static scale, slow-pass scale (LS-WIM—Low-Speed WIM) for measurement of vehicle axle load or by a selected WIM system with heightened accuracy. Both the reference scale and all systems undergoing calibration must be equipped with a system for the automatic recognition of vehicle registration number plates. The reference scale makes it possible to determine axle load values considered as benchmark values. Then, for each vehicle weighed on the reference scale and subsequently on any WIM system operating within the analysed area, the relative difference between the reference result and the WIM system measurement is calculated with respect to the reference value. This difference forms the basis for the operation of the algorithm estimating the coefficients of the static characteristic of the calibrated WIM system (so-called calibration coefficients), which are then used to determine corrected weighing results. The estimation of the coefficients is updated after each identified vehicle that has previously been weighed on the reference scale is considered. The article presents both the results of simulations and experimental studies concerning the proposed spatial method of calibration. The results obtained allow for an assessment of the effectiveness of the proposed solution. As can be seen from the analyses conducted, this method leads to a significant reduction in systematic error of vehicle weight measurement. Unfortunately, it does not eliminate random errors. The spatial calibration approach described in this paper has certain limitations. The main ones include the impact of ANPR system errors on calibration effectiveness, cases where a vehicle is unloaded or loaded between WIM stations, and the propagation of systematic errors from the reference systems to the other WIM systems. A significant advantage of the proposed spatial calibration method is that it can operate effectively using weighing data from a single reference WIM system and does not require heavy traffic volumes. Full article

By itself or combining with other cooling technologies, the dew point indirect evaporative cooler (DIEC) will be the preferred solution for cooling buildings. However, there are still some gaps in the research on DIEC performance, one of which is that 3-D (3-dimensional) models and methods are not widely used to comprehensively indicate the cooling mechanism. Most of the available numerical methods adopted 1-D or 2-D models. Existing 3-D models and methods either ignore the water film and plate or are so complicated in the grid system and numerical calculation induced by huge size differences among calculation regions that their attractions are weak. A novel simplified numerical method for DIEC performance is first suggested in this paper, and then, its validity and more efficiency than an existing 3-D numerical method are verified with the help of experimental data and numerical results. Finally, the effects of structure and operating parameters on the performance of a plate DIEC are analyzed by this present method and COMSOL Multiphysics 6.3 software, especially η/η₀ (the reinforcement factor), which was innovatively introduced. Similar results to those of existing literature were obtained, which further indicated the practicability of this simplified method. In the conditions involved in this paper, a channel length of 1.5 m, a width of 4 mm, Re_in (the Reynolds number at the inlet) of 1483, and a (the air ratio) of 0.33 are recommended. In the condition suggested by this paper, η/η₀ is close to 1.2. In the same conditions, this proposed method reduces the number of mesh elements by approximately 58% and the wall-clock computational time by approximately 52% under the reported workstation conditions, and its value would be more obvious for more complicated problems. Full article

Generative artificial intelligence has emerged as a tool that can influence students’ ideation, development, and communication of design solutions. A quasi-experimental mixed-methods study investigated the application of three generative AI tools, specifically Vizcom, Krea.ai 1, and ChatGPT 4, to furniture design in an undergraduate interior design course offered at a private Jordanian university. This study sought to address a gap in region-specific AI tools for design education. Thirty-four third-year students completed both a manual design activity (pre-test) and a redesign activity with AI assistance (post-test) after structured training. Twenty-seven participants completed both activities and were included in the analyses. Experience was measured with the Creativity Support Index (CSI) and NASA Task Load Index (NASA-TLX); qualitative measures included open-ended questions and expert-juror ratings of the 27 resulting designs. Quantitative results were analyzed with descriptive statistics and Wilcoxon signed-rank tests (SPSS v28). AI-assisted workflows significantly enhanced exploration (Z = −3.42, p < 0.001, r = 0.66) and approached significance for workflow efficiency (Z = −1.97, p = 0.049, r = 0.38). Students reported decreases in mental, time, and effort burden, while feelings of expressiveness and ownership remained. Jury experts concluded that 67.6% of students achieved improved outcomes using AI tools. Students experienced moderate frustration with prompt creation, tools operating in English, and AI’s inability to design intricate details. Generative AI can be most beneficial during the conceptual stages, enabling broader exploration and greater efficiency. With careful pedagogy and ethical discussion, students can use AI tools without losing ownership of their designs. Consideration for generative AI software implementation in interior design programs in Jordan and across the MENA region is provided. Full article

In this work, we investigate the spectral, information-theoretic, and thermodynamic properties of a fractal Schrödinger system with position-dependent mass subject to an effective semiconductor-like confinement. We employ a fractal momentum operator and a Von Roos Hamiltonian with BenDaniel–Duke ordering to obtain exact analytical solutions for the energy spectrum and wave functions. The interplay between the fractal parameter $\alpha$, the effective lattice scale $l_0$, and the harmonic confinement strength $\omega$ is explored. We perform a comprehensive analysis of the Shannon entropy, Fisher information, and Fisher–Shannon complexity in both coordinate and momentum spaces. Our results demonstrate that these parameters directly control the localization–delocalization transition and the informational architecture of the quantum states, while satisfying the entropic and Fisher uncertainty relations. Furthermore, we derive the exact partition function and the corresponding thermodynamic properties (free energy, internal energy, entropy, and specific heat) of the system. The analytical framework presented offers valuable insights into the spectral, information-theoretic, and thermodynamic behavior of quantum systems in fractal semiconductor-like environments. Full article

Cross-border e-commerce (CBEC) has emerged as a critical mode of international trade; however, product uncertainty and transaction risk remain persistent barriers to purchase decisions. While digital platforms have developed various solutions, the role of offline experiential knowledge in shaping online purchase behavior remains underexplored. This study examines how tourism-derived information influences purchase intention in CBEC. Drawing on transaction cost theory and uncertainty reduction theory, we propose that tourism-derived information enhances product familiarity and perceived diagnosticity, which subsequently reduce product uncertainty and increase cross-border purchase intention, and further examine the moderating role of transaction uncertainty. A four-week survey in March 2026 collected data from 325 Chinese consumers who had visited Korea and encountered Korean cosmetics and beauty products; data were analyzed using PLS-SEM. Results show that tourism-derived information significantly enhances product familiarity and perceived diagnosticity while directly reducing product uncertainty; reduced product uncertainty, in turn, positively influences purchase intention. Transaction uncertainty strengthens the negative effect of product uncertainty on purchase intention. By reconceptualizing tourism experience as an experience-based informational resource in CBEC and providing a multidimensional perspective on consumer uncertainty, this study contributes to consumer behavior research in digital commerce and offers practical insights for CBEC platform operators and cross-border retailers. Full article

One of the bottlenecks in realizing all-optical computing is the lack of on-chip all-optical logic devices that combine compactness, low loss, and high robustness. Valley photonic crystals (VPCs) have become an important solution for realizing such devices, relying on the excellent transmission characteristics of topological valley states. However, existing structures still face issues such as limited design flexibility. In this paper, a high-performance topological all-optical logic device based on VPCs consisting of circular ring dielectric columns is designed and demonstrated. By introducing the inner radius as an independent design parameter, we construct a new type of VPC and systematically investigate its influence on the photonic band gap. Based on this, we design a beam splitter with high operational bandwidth and low insertion loss (<0.5 dB) and then realize fundamental OR and XOR logic gates, achieving extinction ratios of 18.9 dB for the OR gate and up to 44 dB for the XOR gate at an operating frequency of 193.5 THz. The platform also supports the NOT gate and, through cascading, can implement more logic functions such as the AND gate. Full article

To protect human health and the environment, it is necessary to reduce the number of solid particles and harmful gases in the air or to minimize such pollution. Filtration and separation devices are intended for various industrial operations to capture pollutants from various technological processes. In the introduction, this article points out the use of cyclone filters in individual operations, names the most frequently occurring elements of pollution, and suggests the most suitable method of separation. In paint shops, grinding shops, welding workplaces, machining lines, and when handling powder materials, particles with very different properties are created. An important advantage of using cyclone filters is not only their simple construction but also their usability at high temperatures and pressures. Furthermore, this article highlights that cyclones are easy to maintain, typically contain no moving parts, are simple to manufacture, and are cost-effective, particularly as pre-filtration devices. Their efficiency generally ranges from 50% to 99% and is strongly influenced by design and operating parameters, especially cyclone geometry, which affects pressure drop, flow structure, cut diameter, and fractional collection efficiency. The article also summarizes that various modifications of the inlet, vortex finder, outlet pipe, and cyclone body have been proposed to enhance separation performance, particularly for smaller particles. Nevertheless, due to the centrifugal and inertial nature of cyclone separation, fine and submicrometric particulate matter remains difficult to remove using cyclones alone. Fabric filters are also analyzed as a possible solution, but high loading by coarse particles may cause clogging, increased pressure drop, and higher maintenance costs. In the end, the combination of a cyclone with an electrostatic precipitator is presented as a staged separation approach, enabling efficient removal of both coarse particles and fine particulate matter from the gas stream. Full article

The development of low-emission and reliable propulsion systems is essential for extending the operational capability of unmanned aerial vehicles (UAVs). Although aviation decarbonization is widely recognized as an important objective, it must be considered within the broader context of limited renewable-energy availability. Recent system-level analyses of transportation decarbonization have shown that the allocation of renewable electricity and sustainable fuels should prioritize sectors where direct electrification is most efficient, while hard-to-electrify sectors require alternative pathways. Aviation is one of the most difficult transport sectors to electrify because of strict energy-density requirements, especially for long-endurance airborne platforms. Therefore, sustainable liquid fuels and hybrid propulsion systems should not be considered universal replacements for electrification, but rather complementary solutions for applications where batteries alone cannot provide the required endurance, payload capacity or operational flexibility. In this context, the present study focuses on alcohol–kerosene blends for hybrid UAV power systems, where liquid-fuel energy density and partial emission reduction remain relevant engineering requirements. This work provides one of the first systematic experimental evaluations of ethanol–, butanol– and octanol–kerosene blends in a micro-turboprop engine operating as part of a hybrid UAV power-generation architecture. Unlike previous studies focused mainly on micro-turbojet thrust response, the present work evaluates the coupled influence of alcohol chain length and blending ratio on exhaust gas temperature, gaseous emissions, electrical output and operational stability under multi-load conditions representative of UAV operation. Jet-A and nine alcohol–kerosene blends containing 10%, 20% and 30% ethanol, butanol or octanol by volume were tested over four operating regimes, from idle to 2500 W electrical load. The results show that ethanol blends provided the strongest CO reduction, with E30 reducing CO by 24.9% relative to Jet-A under R3, while E10 offered the most balanced behavior across the full operating range. Higher ethanol fractions improved CO suppression but introduced NOx and low-load stability penalties. Octanol blends, particularly O20, exhibited the most kerosene-like and stable response, supporting reliable power delivery with reduced operational variability. Butanol blends showed intermediate behavior without providing a dominant advantage. A multi-criteria evaluation combining emissions, EGT behavior, relative performance, operational stability and cost identified E10 as the best overall compromise for hybrid UAV use. The study demonstrates that alcohol chain length produces nonlinear system-level effects in hybrid micro-turboprop architectures and provides an experimental basis for fuel selection in low-emission UAV power systems. Full article

The Constrained Application Protocol (CoAP) is widely adopted to ensure end-to-end reliability in resource-constrained Artificial Intelligence of Things (AIoT) and Wireless Sensor Networks (WSNs). However, CoAP’s default retransmission timeout (RTO) mechanism lacks algorithmic responsiveness under volatile channel conditions, and state-of-the-art benchmarks like CoCoA+ and FASOR often suffer from over-conservative backoff states or destabilizing retransmission storms. To overcome these operational bottlenecks, this paper proposes a novel dual-adaptive Dynamic RTO algorithm specifically engineered for heterogeneous IoT deployment scales. The proposed framework dynamically adjusts its parameter inspection cycle (N) based on instantaneous round-trip time (RTT) variance while simultaneously scaling its tuning coefficient (α) in response to real-time packet loss indicators. To rigorously validate the algorithmic resilience, performance evaluations were conducted within a highly volatile network environment governed by the Gilbert–Elliott dynamic loss model across multi-hop linear (1 × 6) and grid (3 × 6, 5 × 6) topologies. Experimental results demonstrate that the proposed Dynamic RTO consistently optimizes the throughput–latency trade-off, achieving a total communication time of 25.92 s in complex grids—outperforming CoCoA+ and FASOR by 14.28% and 8.89%, respectively. Furthermore, the proposed mechanism significantly curtails transmission overhead, restricting the cumulative retransmission footprint to just 59 counts under severe localized impairments, thereby establishing a scalable, resource-efficient, and empirically robust transport-layer solution for next-generation edge-computing infrastructures. Full article