Search Results (1,982)

To improve the accuracy of gravimetric liquid hydrogen flow standard devices, the self-weight of the weighing tank must be minimized, because the total mass of the liquid hydrogen contained in the tank is far smaller than the structural mass of the tank itself, which severely compromises the sensitivity of gravimetric measurement. In this study, a three-dimensional finite element model of a vacuum-insulated liquid-hydrogen weighing tank was developed in ABAQUS. The inner and outer shells were modeled with 06Cr19Ni10 (304) and 06Cr17Ni12Mo2 (316) austenitic stainless steels, and Polyamide 6 (PA6) was used for the internal support. Three operating stages were considered: evacuation of the annulus (interlayer pressure reduced from 0.1 MPa to 0 MPa), pre-cooling to −253 °C, and pressurization of the inner tank (internal pressure increased from 0.1 MPa to 1 MPa). The equivalent stress and deformation were compared for different materials and wall thicknesses to evaluate structural safety and weight-reduction potential. The proposed configuration (inner shell 1.6 mm and outer shell 1.0 mm) achieves a mass reduction of more than 50% relative to the 3 mm minimum wall thickness commonly adopted for cryogenic vessels, while keeping stresses below the allowable limits. This reduction enables the use of higher-resolution load cells and thereby lowering the measurement uncertainty of the liquid hydrogen flow standard device and providing technical support for lightweight and cost-effective design, with potential applicability to other cryogenic tank systems. Full article

Conventional acoustic time-of-arrival (TOA) estimation in complex indoor environments is highly susceptible to multipath reflections and occlusions, resulting in unstable measurements and limited physical interpretability. This paper presents a smartphone-based indoor localization method built on vision-assisted acoustic channel modeling, and develops a fusion anchor integrating a pan–tilt–zoom (PTZ) camera and a near-ultrasonic signal transmitter to explicitly perceive indoor geometry, surface materials, and occlusion patterns. First, vision-derived priors are constructed on the anchor side based on line-of-sight reachability, orientation consistency, and directional risk, and are converted into soft anchor weights to suppress the impact of occlusion and pointing mismatch. Second, planar geometry and material cues reconstructed from camera images are used to generate probabilistic room impulse response (RIR) priors that cover the direct path and first-order reflections, where environmental uncertainty is mapped into path-dependent arrival-time variances and prior probabilities. Finally, under the RIR prior constraints, a path-wise posterior distribution is built from matched-filter outputs, and an adaptive fusion strategy is applied to switch between maximum a posteriori (MAP) and minimum mean square error (MMSE) estimators, yielding debiased TOA measurements with calibratable variances for downstream localization filters. Experiments in representative complex indoor scenarios demonstrate mean localization errors of 0.096 m and 0.115 m in static and dynamic tests, respectively, indicating improved accuracy and robustness over conventional TOA estimation. Full article

This paper proposes a resource optimized cascaded quantum surface repetition code architecture integrated with a Union Find (UF) enhanced hybrid decoder, which suppresses biased noise and improves the scalability of quantum error correction through synergistic inner outer quantum code collaboration. The hybrid architecture employs inner quantum repetition codes for local error suppression and outer rotated quantum surface codes for topological robustness, reducing auxiliary quantum qubits by 12.5% via shared stabilizers and compact lattice embedding. An optimized UF decoder employing path compression and adaptive cluster merging achieves near-linear time complexity $\mathcal{O}\left(n\alpha \right(n\left)\right)$, outperforming minimum-weight perfect matching (MWPM) decoders $\mathcal{O}\left(n^{2.5}\right)$. Under Z-biased noise $\eta =10$, simulations demonstrate a 28.2% error threshold, 2.6% higher than standard quantum surface codes, and 15% lower logical error rates via dynamic boundary expansion. At code distance $d=7$, resource savings reach 9.3% with maximum relative error below 8.5%, fulfilling fault-tolerance criteria. The UF decoder exhibits 38% threshold advantage over MWPM at low bias $\eta \approx {10}^{-3}$ and 15% less degradation at high noise $p=0.5$, enabling scalable real-time decoding. This framework bridges theoretical thresholds with practical resource constraints, offering a noise-adaptive QEC solution for near-term quantum devices including photonic quantum systems referenced in the paper’s background on repetition cat qubits. Full article

The widespread adoption of Electric Vehicles (EVs) is critically dependent on the deployment of efficient charging infrastructure. However, existing facility location models typically treat charging duration as an exogenous parameter, thereby neglecting the traveler’s autonomy to make trade-offs between service time and energy needs based on their Value of Time (VoT). This study addresses this theoretical gap by developing a heterogeneous network design model that endogenizes both charging mode selection and continuous charging duration decisions. A bi-objective optimization framework is formulated to minimize the weighted sum of infrastructure capital expenditure and users’ generalized travel costs. To ensure computational tractability for large-scale networks, an exact linearization technique is applied to reformulate the resulting Mixed-Integer Non-Linear Program (MINLP) into a Mixed-Integer Linear Program (MILP). Application of the model to the Hubei Province highway network reveals a convex Pareto frontier between investment and service quality, providing quantifiable guidance for budget allocation. Empirical results demonstrate that the marginal return on infrastructure investment diminishes rapidly. Specifically, a marginal budget increase from the minimum baseline yields disproportionately large reductions in system-wide dwell time, whereas capital allocation beyond a saturation point yields diminishing returns, offering negligible service gains. Furthermore, sensitivity analysis indicates an asymmetry in technological impact: while extended EV battery ranges significantly reduce user dwell times, they do not proportionally lower the capital required for the foundational infrastructure backbone. These findings suggest that robust infrastructure planning must be decoupled from anticipations of future battery breakthroughs and instead focus on optimizing facility heterogeneity to match evolving traffic flow densities. Full article

Symbiotic radio (SR) has recently emerged as a promising paradigm for enabling spectrum- and energy-efficient massive connectivity in low-power Internet-of-Things (IoT) networks. By allowing passive backscatter devices (BDs) to coexist with active primary link transmissions, SR significantly improves spectrum utilization without requiring dedicated spectrum resources. However, most existing studies on multi-tag multiple-input multiple-output (MIMO) SR systems assume homogeneous traffic demands among BDs and primarily focus on rate-based performance metrics, while neglecting system-level task completion time (TCT) optimization under heterogeneous data requirements. In this paper, we investigate a joint performance optimization framework for a multi-tag MIMO symbiotic radio network. We first formulate a weighted sum-rate (WSR) maximization problem for the secondary backscatter links. The original non-convex WSR maximization problem is transformed into an equivalent weighted minimum mean square error (WMMSE) problem, and then solved by a block coordinate descent (BCD) approach, where the transmit precoding matrix, decoding filters, backscatter reflection coefficients are alternatively optimized. Second, to address the transmission delay imbalance caused by heterogeneous data sizes among BDs, we further propose a rate weight adaptive task TCT minimization scheme, which dynamically updates the rate weight of each BD to minimize the overall TCT. Simulation results demonstrate that the proposed framework significantly improves the WSR of the secondary system without degrading the primary link performance, and achieves substantial TCT reduction in multi-tag heterogeneous traffic scenarios, validating its effectiveness and robustness for MIMO symbiotic radio networks. Full article

This paper presents an Artificial Intelligence (AI)-driven framework for high-precision indoor localization using single-link Wi-Fi channel state information (CSI), targeting real-time deployment in complex multipath environments. To overcome challenges such as signal distortion and environmental dynamics, the proposed system integrates adaptive and unsupervised intelligence modules into the localization pipeline. A refined two-stage time-of-flight (TOF) estimation method is introduced, combining a minimum-norm algorithm with a probability-weighted refinement mechanism that improves ranging accuracy under non-line-of-sight (NLOS) conditions. Simultaneously, an adaptive parameter-tuned DBSCAN algorithm is applied to angle-of-arrival (AOA) sequences, enabling unsupervised spatio-temporal clustering for stable direction estimation without requiring prior labels or environmental calibration. These AI-enabled components allow the system to dynamically suppress multipath interference, eliminate positioning ambiguity, and maintain robustness across diverse indoor layouts. Comprehensive experiments conducted on the Widar2.0 dataset demonstrate that the proposed method achieves decimeter-level accuracy with an average localization error of 0.63 m, outperforming existing methods such as “Widar2.0” and “Dynamic-MUSIC” in both accuracy and efficiency. This intelligent and lightweight architecture is fully compatible with commodity Wi-Fi hardware and offers significant potential for real-time human tracking, smart building navigation, and other location-aware AI applications. Full article

The purpose of this study is to determine the optimal location for siting an onshore wind farm on the island of Skyros, thereby maximizing performance and minimizing the project’s environmental impacts. Seven evaluation criteria are defined across various sectors, including environmental and economic sectors, and six criteria weighting methods are applied in combination with four multicriteria decision-making (MCDM) ranking methods for suitable areas, resulting in twenty-four ranking models. The alternatives considered in the analysis were defined through the application of constraints imposed by the Specific Framework for Spatial Planning and Sustainable Development for Renewable Energy Sources (SFSPSD RES), complemented by exclusion criteria documented in the international literature, as well as a minimum area requirement ensuring the feasibility of installing at least four wind turbines within the study area. The correlations between their results are then assessed using the Spearman coefficient. Geographic information systems (GISs) are utilized as a mapping tool. Through the application of the methodology, it emerges that area A9, located in the central to northern part of Skyros, is consistently assessed as the most suitable site for the installation of a wind farm based on nine models combining criteria weighting and MCDM methods, which should be prioritized as an option for early-stage wind farm siting planning. The results demonstrate an absolute correlation among the subjective weighting methods, whereas the objective methods do not appear to be significantly correlated with each other or with the subjective methods. The ranking methods with the highest correlation are PROMETHEE II and ELECTRE III, while those with the lowest are TOPSIS and VIKOR. Additionally, the hierarchy shows consistency across results using weights from AHP, BWM, ROC, and SIMOS. After applying multiple methods to investigate correlations and mitigate their disadvantages, it is concluded that when experts in the field are involved, it is preferable to incorporate subjective multicriteria analysis methods into decision-making problems. Finally, it is recommended to use more than one MCDM method in order to reach sound decisions. Full article

Objectives: This study aimed to perform matched comparisons of the surgical outcomes of combined anterior cruciate ligament (ACL) and anterolateral ligament (ALL) reconstruction with those of isolated ACL reconstruction, in which allografts were used for the ACL. Methods: Patients who underwent anatomical ACL reconstruction with or without additional ALL reconstruction between 2017 and 2023 and had a minimum follow-up of 2 years were included and grouped according to whether an additional ALL reconstruction was performed. The cohorts were statistically adjusted using an inverse probability of treatment weighting (IPTW) to control for potential confounders related to surgical indication, including age, activity level, sex, rotational knee laxity, and preoperative osteoarthritic grade. Between-group comparisons were conducted for baseline characteristics, clinical outcomes, knee laxity, and radiologic parameters. Results: Fifty-nine patients were included (Group 1: 39 isolated ACL reconstructions; Group 2: 20 combined ACL and ALL reconstructions). Before IPTW adjustment, a significant difference was observed in the preoperative pivot-shift test (p = 0.008), which was no longer significant after weighting. Postoperative functional outcomes and knee stability were comparable between groups; however, the incidence of surgical failure was significantly lower in Group 2 both before and after IPTW adjustment (p = 0.044 and p = 0.049, respectively). Regarding radiologic parameters, the signal-to-noise quotient of the ACL graft was also significantly lower in Group 2, both before and after IPTW adjustment (p = 0.046 and p = 0.038, respectively). Conclusions: In ACL reconstruction using allografts, the addition of ALL reconstruction resulted in more favorable clinical and radiologic outcomes—particularly a lower incidence of surgical failure and greater postoperative graft maturity—compared with isolated ACL reconstruction. Full article

Poly(lactic acid) (PLA)-based nanocomposites containing a mixture of graphene nanoplatelets (GNP) and carbon nanotube (CNT) as hybrid fillers were prepared using a solution-assisted sonication process followed by melt processing. The effects of the filler dispersion on dielectric properties and microwave absorbing (MWA) performance were systematically investigated. Two ionic liquids (ILs), trihexyl-(tetra-decyl)phosphonium bis (trifluoromethanesulfonyl)imide (IL1) and 11-carboxyundecyl-triphenylphosphonium bromide (IL2), were employed as dispersing agents for the carbonaceous fillers. Incorporation of IL-treated fillers resulted in enhanced dielectric permittivity and improved MWA performance of the PLA composites. The MWA properties were evaluated in X- band and Ku-band. A minimum reflection loss (RL) of −34 dB and an effective absorption bandwidth (EAB) of 2.1 GHz were achieved for the composite containing GNP/CNT/IL2 (HB3) at a weight ratio of 2.5:0.5:0.5 wt% with one 3 mm thick layer. The superior performance of IL2 is attributed to π-π and π-cation interactions between its phenyl-containing cation and the carbonaceous fillers, as well as improved compatibility with the PLA matrix due to carboxyl groups. Additionally, three-layered composite structures, combining PLA/GNP as the outer layer with IL-assisted hybrid fillers in the core and PLA/CNT at the bottom layer, achieved an extended EAB of 4.5 GHz for GNP/HB2/CNT arrangement and 4.35 GHz for the GNP/HB3/CNT arrangement, driven by enhanced scattering and internal reflection of microwaves. These results demonstrate the potential of IL-assisted hybrid filler dispersion in PLA for developing biodegradable materials with multifunctional applications as charge storage capacitors and microwave absorbing materials for sustainable electronics. Full article

Microwave technology offers a sustainable alternative to fossil fuel-based drying of particulate materials. Its combination with fluidized bed systems enhances process efficiency and product quality. A pilot-scale dryer with two magnetrons was used to study soybean and pumpkin seed drying. Samples were dried at 50 °C with air velocities twice the minimum fluidization value. Microwave power levels of 0, 350, and 750 W were applied. Weight loss after 30 min reached 32.2–42.5% for soybeans and 42.0–48.2% for pumpkin seeds. Moderate microwave power improved drying efficiency, highlighting the potential of microwave-assisted fluidized bed drying for food processing sustainability. Full article

This paper proposes an efficiency-optimized multi-parameter collaborative non-dimensional selection method for industrial fans. Based on fan similarity theory, selection parameters are transformed into non-dimensional forms. The fan’s best working area (BWA) is defined according to stall margin, flow range, total pressure rise deviation, and minimum efficiency. The initial model selection uses the boundary equations of the defined BWA as screening criteria. Decision parameters comprise Euclidean distance, design point distance, pressure deviation, and current efficiency. These collectively form a multi-objective evaluation function. The NSGA-II algorithm determines the optimal weight distribution of decision parameters, generating a Pareto-optimal solution set. The initially selected models are subsequently subjected to secondary optimization through a comprehensive evaluation function. Selection case studies demonstrate that this method preliminarily screens 7 models that meet the target parameters from 400 candidate models. Secondary screening determines the model with the optimal efficiency and best comprehensive evaluation performance. The method effectively resolves the mismatch between fan model design points and target operational parameters in selection processes. This method integrates directly into selection software platforms and validation with 100 sets of fan selection parameters demonstrates that selected models achieve 99% accuracy. Achieving the secondary optimization function for fan model selection. Full article

Determining protein requirements (PRs) for dogs remains a longstanding challenge. During growth, the rapid rate of protein deposition increases the demand for amino acids. In adult dogs, differences in overall diet digestibility and lower energy requirements of domestic dogs have led to discrepancies between the minimum crude protein (CP) value proposed by the National Research Council (NRC; 80 g of CP/kg of diet) and the 180 g of CP/kg of diet proposed by the European Pet Food Industry Federation (FEDIAF) and the Association of American Feed Control Officials (AAFCO), although most commercially available adult dog feeds offer protein levels that exceed both recommendations. In elderly dogs, physiological changes such as sarcopenia and reduced energy intake indicate a potential increase in PR, although evidence remains scarce. A similar gap exists for pregnant and lactating bitches, since most recommendations rely on extrapolations from growth studies. Classical PR recommendations were based on body weight gain and nitrogen balance (NB), methods that present important limitations. Due to this, stable isotope methods—including ¹³C-leucine, ¹⁵N-glycine, and ¹³C-phenylalanine—have emerged as precise methodological tools, enabling a detailed and dynamic assessment of whole-body protein metabolism, protein quality, and more accurate determination of PR and recommended allowance across different life stages. Full article

Background/Objectives: Cyclosporine A (CsA) is a key immunosuppressant in post-transplantation therapy protocol characterized by large interindividual and intraindividual pharmacokinetic (PK) variability and a narrow therapeutic range necessitating therapeutic drug monitoring (TDM) to prevent graft rejection and minimize side effects. TDM data can be used for developing PK models with the objective of identification and quantification of variability factors that contribute to the differences in CsA concentrations. Methods: Retrospectively collected data from medical records of 58 patients (children and young adults) regarding CsA blood concentrations, concomitant medications, and laboratory findings of significance were used for the population PK model development in NONMEM^® (version 7.5) with first-order conditional estimation method with interaction (FOCE-I). Simulation of the concentrations and area under the curve (AUC) was performed in the web application e-campsis^®. RStudio (version 4.5.0) was used for the purpose of descriptive statistics analysis and graphs plotting. Results: A one-compartment model with first-order absorption and elimination best described the data. Value of clearance (CL/F) was estimated to be 15 L/h, and volume of distribution (V/F) was 71.1 L for a typical patient weighing 40 kg. Interindividual variability (IIV) on CL/F and V/F was 34.91% and 43.05%, respectively. Interoccasional variability (IOV) was 12.25%. Body weight (WT) was introduced allometrically on CL/F and V/F, with the estimated exponent of 0.89 for CL/F and 1 (fixed) for V/F. According to the final model, CL/F decreases with increasing haemoglobin (HGB) value. A difference of almost 22.5% in CL/F was observed among patients’ HGB values reported in the study. Conclusions: Our findings indicate that HGB levels significantly influence CsA PK, particularly minimum concentration (C_min), highlighting the importance of regular HGB levels monitoring together with CsA levels. Full article

Background/Objectives: To evaluate the clinical and radiological outcomes of surgical interventions stratified by dynamic K-line status and to identify predictors of neurological recovery in multilevel cervical ossification of the posterior longitudinal ligament (OPLL). Methods: This study analyzed 535 patients with multilevel cervical OPLL who underwent anterior cervical discectomy and fusion (ACDF), laminoplasty (LP), or laminectomy with fusion (LF), with a minimum 24 months of follow-up. Patients were classified based on dynamic K-line status—neutral (NK-line) and flexion (FK-line)—into three groups: Group 1 (NK-line [+]/FK-line [+]), Group 2 (NK-line [+]/FK-line [−]), and Group 3 (NK-line [−]/FK-line [−]). Radiographic parameters, JOA scores, and VAS were compared, and multivariate regression identified predictors of recovery. A multinomial inverse probability of treatment weighting (IPTW) analysis was conducted to reduce treatment selection bias. Results: Progressive dynamic K-line negativity was associated with greater cervical kyphosis, a higher canal-occupying ratio, reduced FK-line distance, and poorer neurological recovery. After IPTW analysis, ACDF showed higher adjusted recovery across subgroups. In Group 1, younger age and fewer operative levels predicted better recovery. In Groups 2 and 3, LF demonstrated significantly greater neurological recovery than LP. A larger preoperative FK-line distance and a greater postoperative FK-line distance increase were independent predictors of favorable outcomes. Conclusions: Dynamic K-line stratification has good prognostic value in multilevel cervical OPLL. ACDF remains the most effective procedure across dynamic K-line status groups, and LF is preferred over LP in patients with latent or fixed FK-line (−). Incorporating dynamic K-line metrics into surgical planning could improve procedure selection and enhance neurological recovery. Full article

Multi-criteria decision-making (MCDM) models are moving toward being data-oriented. Meanwhile, MCDM models’ totalitarian reliance on experts’ preferences may reduce the accuracy of results in real-world challenges. Therefore, there is a huge gap in refining MCDM models to be data-structured rather than relying on experts’ and decision-makers’ ideas. In this research article, the primary indifference threshold values of the Indifference Threshold-based Attribute Ratio Analysis (ITARA) model, which is one of the popular objective weighting MCDM techniques, have been investigated and improved to achieve the goal of a full-objective MCDM model. ITARA utilizes decision-makers’ and experts’ opinions to set the indifference threshold values, which are integral to obtaining criteria weights, and since this step is not data-based, unlike the whole technique, it is prone to deficiencies. Three critical frameworks based on the minimum value, standard deviation, and max–min distance are designed to assess the sensitivity of the indifference threshold values and optimize the initialization values to start the model. Two case studies based on actual data are considered in this research to observe the frameworks’ outcomes and the rank reversal phenomenon. The results demonstrated that the assigning weights procedure is deeply sensitive to a max–min framework, while the standard deviation framework illustrated more stable results and a slight change in criteria rankings. The min framework moderately fluctuated between the max–min and standard deviation frameworks. Full article