Search Results (1,341)

Rule-defined rankings often transform continuous marks, discrete judgments, trimming rules, caps, truncation, and tie-breaking variables into a single official order. When ranking margins are small, a formally valid outcome may nevertheless be sensitive to marginal changes in the recorded decision state. This article presents Score Cloud Analysis as a rule-aware statistical sensitivity-reporting method for such systems. The method represents the official score as a deterministic function of recorded inputs and recomputes scores and ranks under finite perturbations, rather than relying on local linear approximations. It defines deterministic diagnostics, including directional Group A/Group C (A/C) decision exposures and their aggregate contested-point exposure, total sensitivity exposure, fragility-to-margin ratios, Score Cloud overlap, and single-call rank sensitivity, and separates these from scenario-conditional Monte Carlo Rank Cloud frequencies. The method is illustrated using a synthetic Wushu Taolu case study because that setting contains majority decisions, trimmed rater marks, discrete difficulty values, Head Judge adjustments, and tie-break rules. The synthetic experiment is an internal-consistency stress test, not an empirical validation and not an estimate of judging-error rates. A small sensitivity study varies perturbation scale and intra-athlete dependence to show which conclusions are scenario-specific. The method separates procedural validity from local rank robustness and is transferable to other reconstructable, rule-based, rater-mediated ranking systems. Full article

The rapid development of large model-driven agent applications, such as digital assistants and robots, requires 6G radio access networks (RAN) to deliver enhanced flexibility, adaptability, and low-latency capabilities. However, the existing RAN user plane (UP) architecture suffers from coarse decoupling granularity and significant cross-layer functional redundancy. These limitations severely hinder the on-demand orchestration and dynamic reconfiguration required by heterogeneous agent services. To address these challenges, this paper proposes a ComBERT-driven service-based RAN UP decoupling method, specifically targeting the functional coupling and redundancy between the PDCP and RLC sublayers. First, we develop a domain-specific language model, ComBERT, by pre-training a BERT model on a 3GPP protocol corpus and fine-tuning it on text-matching tasks to deeply comprehend protocol semantics. Subsequently, ComBERT is utilized to extract semantic features from UP functional components, employing a sliding window mechanism to overcome truncation in lengthy protocol texts and using cosine similarity to measure functional relevance. Finally, a threshold-based fusion algorithm is designed to identify and merge cross-layer redundant functions, thereby forming independent service units with distinct responsibilities. These fused units serve as the basic building blocks for scenario-specific orchestration. Simulation results demonstrate that the proposed method reduces the number of UP components by 12.5%, 18.7%, and 18.2% in eMBB, URLLC, and mMTC scenarios, respectively. Simultaneously, it decreases average processing delays by 7.9%, 10.2%, and 11.0% across these respective scenarios. Ultimately, this approach effectively improves the lightweight deployment, processing efficiency, and reconfiguration capabilities of the service-based UP, providing a crucial foundation for on-demand service orchestration in 6G networks tailored to agent services. Full article

Multi-view fringe projection profilometry systems that integrate a telecentric projector with multiple oblique-view cameras offer unique advantages for inspecting high dynamic-range surfaces featuring densely packed, intricate microstructures. Nevertheless, such systems encounter fundamental calibration challenges, namely, sign ambiguity in the rotation matrices and truncated extrinsic parameters inherent to telecentric projector models, as well as difficulties in multi-view point cloud registration. This paper introduces a novel calibration framework with three principal contributions. First, we resolve the sign ambiguity by calibrating the telecentric projector under a quasi pinhole model and directly transferring the extrinsic sign conventions, thereby obviating the need for costly precision displacement stages or elaborate virtual targets. Second, we fix the axial-gauge freedom by constraining the origin of the projector coordinate system to lie on the XY-plane of the camera coordinate system. Third, we establish precise relative poses between all cameras and a designated reference camera, enabling unified multi-view point cloud registration directly within the projector coordinate frame, which substantially reduces alignment errors and accelerates data processing. Experimental results demonstrate marked improvements in accuracy: reprojection root-mean-square errors of 0.084 pixels for the cameras and 0.106 pixels for the projector, corresponding to in-plane spatial resolutions of 0.21 µm and 0.26 µm, respectively. The proposed method offers a robust solution for micron-level inspection in semiconductor packaging and precision manufacturing. Full article

This paper proposes a stochastic dispatch framework for multi-terminal high-voltage direct current (MT-HVDC) systems that explicitly accounts for uncertainty in photovoltaic (PV) generation and electrical demand while preserving computational tractability. The economic–environmental dispatch problem is formulated as a mixed-integer second-order cone programming (MI-SOCP) model, where the SOCP relaxation provides a convex representation of the network constraints, and the mixed-integer component captures the discrete charging/discharging states of battery energy storage systems (BESS). This formulation ensures that, for any fixed set of binary decisions, the remaining problem reduces to a standard convex SOCP, enabling efficient solution via branch-and-bound methods with tight continuous relaxations. Uncertainty is incorporated through a chance-constrained optimization (CCP) approach, where forecast errors are modeled using bounded truncated distributions and reformulated into deterministic convex constraints via quantile-based approximations, yielding a risk-aware dispatch strategy that avoids optimistic bias. Numerical studies on an 11-bus MT-HVDC test system demonstrate that accounting for uncertainty increases total operating costs by up to 31.87% and CO₂ emissions by up to 37.41% when both PV and demand uncertainties are considered simultaneously at a confidence level of 0.9, compared to the deterministic solution. Power demand uncertainty has a substantially greater impact than PV generation uncertainty, leading to cost increases of 28.09% versus 3.85% at the highest confidence level. Validation on a modified IEEE 24-bus MT-HVDC system confirms the scalability and computational efficiency of the proposed approach, achieving global optimality in a pure solver time of 1.34 s with a maximum relative relaxation gap of $5.81\times {10}^{-9}$, demonstrating numerical exactness and suitability for day-ahead scheduling. The results also highlight the critical role of BESSs in providing operational flexibility, with storage strategies differing significantly under uncertainty during early hours while converging to deterministic behavior later. The findings reveal a clear trade-off between economic performance and operational reliability as the confidence level increases, confirming the effectiveness of the proposed approach for integrating renewables and storage in modern HVDC grids, while also identifying important limitations regarding independence assumptions and scalability to larger systems. Full article

Background/Objectives: The emergence of plasmid-mediated mcr genes has enabled horizontal dissemination of resistance to colistin, a last-resort antibiotic against multidrug-resistant Enterobacterales. In Tunisia, genomic data on mcr-positive Escherichia coli are still limited. This study reports the genomic characterization of human clinical mcr-positive E. coli isolates from the Military Hospital of Tunis. Methods: Between August 2023 and March 2025, seven E. coli isolates with low-level colistin-resistance (MIC = 4–8 µg/mL) were collected from six patients. They were characterized by antibiotic susceptibility testing and WGS to determine resistome, MLST, genetic relatedness, and plasmid content. Results: The E. coli isolates belonged to diverse sequence types (STs), except for two isolates collected from the same patient 2.5 months apart, which were highly related. Overall, this pattern is consistent with a polyclonal spread. The mcr-1.1 gene was located on IncI2 (n = 5) or IncX4 (n = 2) plasmids, which exhibited high similarity both among themselves and in comparison with plasmids previously reported in human and livestock isolates. Most isolates were multidrug-resistant, harboring acquired resistance genes to multiple antibiotic classes, and chromosomal mutations conferring fluoroquinolone resistance. Three isolates additionally carried chromosomal insertions of the bla_CTX-M-55 gene. Resistance to cefiderocol was observed in one isolate and was associated with CirA and Fiu truncation. Conclusions: These findings highlight ongoing dissemination of mcr-1.1-positive E. coli isolates in Tunisia, primarily driven by plasmid transfer. Continuous genomic surveillance and One Health-oriented antibiotic stewardship are essential to limit the spread of colistin-resistance and the emergence of resistance to newer agents such as cefiderocol. Full article

At present, there is no cure for the human genetic disorder ataxia-telangiectasia (A-T), which is managed by supportive care. This disorder arises due to mutations in the ATM (ataxia-telangiectasia mutated) gene and is characterised by a defect in the response to DNA damage, oxidative stress, mitochondrial dysfunction and immune deficiency. The ATM protein is activated by DNA damage, reactive oxygen species (ROS), and a variety of other stimuli, which leads to the phosphorylation or altered cellular localisation of multiple protein substrates that participate in cellular defence pathways. ATM plays a central role in orchestrating cellular defence against stress, which forms a focal point for approaches to treating the symptoms in this disorder. These strategies involve boosting mitochondrial function and dampening the inflammatory response. A more direct approach to treatment is gene therapy, yet the leading method using Adeno-Associated Virus (AAV) is hampered by the large size of the ATM gene itself. The use of antisense oligonucleotides (ASO) as an alternative gene therapeutic approach to treat patients has been increasingly utilised. However, only certain mutations fit the criteria for ASO-based intervention, which encourages rescue through readthrough of premature truncation mutations. For this reason, small-molecule-based therapies addressing the multi-system nature of the disease are urgently required. We address these different approaches to therapy and the outcome of numerous recent clinical trials with A-T patients, as well as ongoing research that has potential to lead to therapy. Full article

(1) Background: The Hepatitis B virus (HBV) is characterized by extensive genetic diversity, including low-frequency variants that contribute to disease progression. We aimed to characterize low-frequency variants and evaluate their potential clinical impact. (2) Methods: We utilized 104 HBV near-full-length sequences generated using next-generation sequencing (NGS) from people living with HIV (PLHIV). We used an in-house bioinformatics suite (HBVgenomeR v5.9.7) to filter for low-frequency variants (5–50%), which were compared to escape and drug resistance mutations (DRMs) and hepatocellular carcinoma (HCC)-associated mutations reported at the consensus level. Unclassified variants were characterized by HBV open reading frames (ORFs) to determine mutation frequency per genomic region. (3) Results: A total of six escape mutations were detected in 8/104 (7.7%) sequences, with _surfaceN131T being the most prevalent (5/8). We also observed six DRMs in 30/104 (28.8%), with _rtV173L being the most prevalent (21/30). Truncation mutations were also observed with _rtA181T/_sW172* and _rtM204I/_sW196L being the most prevalent. A total of 8/104 (7.7%) sequences had four variants associated with HCC. The _xP46S was the highest observed HCC-associated mutation at 5/8. We report 1152 unique uncharacterized variants across all ORFs, and these were found in 94/104 (90.4%) sequences. The RNaseH domain had the highest burden (330/1152, 28.6%). (4) Conclusions: Deep sequencing results identified clinically significant mutations, including those below the 20% detection limit of traditional sequencing, that would go unreported. This highlights the possible underreporting of mutational burden in people living with HBV/HIV, indicating the importance of deep sequencing to aid in HBV/HIV understanding and management. Full article

Addressing the challenges of low environmental modeling accuracy and inadequate obstacle avoidance precision in complex obstacle scenarios in unmanned aerial vehicle (UAV) 3D path planning, this study proposes a UAV 3D path planning method that integrates the truncated signed distance field (TSDF) with an improved particle swarm optimization algorithm (IPSO). A unified planning space integrating a voxel occupancy grid with a truncated signed distance field is constructed offline: the Euclidean distance to obstacle surfaces is truncated and confined within an effective band, whose extent is coordinated with the UAV safety distance threshold determined by physical dimensions and task requirements, thereby preserving the continuous geometric information needed for safety assessment. On this basis, the continuous distance and gradient information provided by the truncated distance field are utilized to formulate a piecewise continuous, distance-based threat cost function, replacing traditional binary collision detection; the distance and gradient are further embedded into the initialization, fitness evaluation, and velocity update procedures of the particle swarm. Moreover, an adaptive inertia weight and a Lévy escape mechanism are introduced to improve search efficiency and global exploration capability. Experimental results demonstrate that under dense discrete safety verification, the proposed method achieves a 100% success rate in complex unstructured environments and that the safety distance threshold can be flexibly adjusted according to task requirements while consistently satisfying the specified safety requirement. The resulting paths achieve a favorable balance among length, smoothness, and controllable safety margin, validating the effectiveness of the proposed method. Full article

This study presents a comparative machine-learning investigation for predicting the optical absorption coefficient of truncated pyramidal GaN/Al_xGa_1−xN quantum dots. The physical dataset is generated by solving the three-dimensional Schrödinger equation using a coordinate-transformation method combined with the finite-difference method (FDM). The coordinate transformation maps the sloping boundaries of the truncated pyramidal geometry onto a regular computational domain, enabling an accurate representation of the quantum-dot shape and facilitating its numerical treatment using the FDM. The absorption coefficient is then calculated as a function of photon energy for different alloy compositions. Using photon energy and alloy composition as input features, Artificial Neural Network (ANN), Random Forest (RFR), Decision Tree (DT), and k-Nearest Neighbor (KNN) models are developed and evaluated. A second-degree polynomial regression model is also considered as a classical baseline. Under the point-wise random 80/20 split, all models show excellent agreement with the numerical results, with $R^2$ values close to unity. KNN generally provides the lowest prediction errors across most alloy compositions, whereas ANN achieves slightly lower MSE and RMSE values at $x=0.5$. Furthermore, leave-one-composition-out validation identifies ANN as the most effective model for predicting unseen compositions, achieving a mean $R^2$ of 0.848 and an NRMSE of 7.19%. These findings demonstrate that KNN is particularly effective for local interpolation within the sampled domain, while ANN provides stronger composition-wise generalization. The proposed framework offers an efficient surrogate for computationally demanding numerical simulations of the optical properties of quantum nanostructures. Full article

Background/Objectives: Health systems involve numerous stakeholders interconnected through nonlinear relationships. While Data Envelopment Analysis (DEA) has been widely used to measure health system efficiency, conventional estimates may exhibit finite-sample bias. An important question, therefore, concerns how health system performance can be measured more reliably, and what factors explain cross-country differences in efficiency. This study introduces an integrated framework that combines Two-Stage Bootstrap DEA with machine learning to assess the performance of the health systems of 26 OECD countries using 2022 data. Methods: In the first step, technical efficiency scores are computed using an output-oriented constant returns to scale (CRS) DEA model. Subsequently, bias-corrected efficiency estimates are derived using the Bootstrap procedure proposed by Simar and Wilson. In the second step, truncated regression analysis and machine learning-based partial dependence analysis, the latter validated through leave-one-out cross-validation, are employed to investigate the determinants of efficiency. Results: The Bootstrap procedure reveals statistically significant differences from conventional DEA results, and bias-corrected results indicate that South Korea, Canada, and the United States achieve the highest efficiency levels. The findings show that tobacco use prevalence has a significantly negative association with health system efficiency and alcohol consumption exhibits a negative, threshold-type pattern, while GDP per capita and out-of-pocket health expenditure display more complex, nonlinear effects. Furthermore, the scenario analysis indicates that a 10% reduction in tobacco use yields the largest predicted single-intervention improvement, while combined interventions produce additional but sub-additive gains. Conclusions: The proposed framework presents a transparent and validated approach for assessing and explaining health system performance, generating findings relevant to the development of evidence-based health policy. Full article

The pressuremeter test (PMT) measures in situ soil properties under the original stress state with minimal disturbance. However, interpreting PMT data for constitutive parameters remains reliant on empirical correlations, and a key challenge is the poorly understood size effect arising from the equipment dimensions. This study aims to systematically quantify such size effects to provide a scientific basis for optimizing the design of laboratory PMTs. A series of 36 PMT simulations were performed using the finite element method (FEM), incorporating the Duncan–Chang E-B hyperbolic model. Six cylindrical soil models of diameters ranging from 0.6 m to 2.4 m were established for both sand and clay, under three overburden pressures (200 kPa, 1000 kPa and 3000 kPa). The radial stress, strain distributions and borehole wall displacement were systematically analyzed. The analysis reveals that the size effect originates from the truncation of the radial strain integration path. In all cases, borehole wall displacement increases with model diameter, characterized by a steep rise for diameters below 1.2 m and a plateau for those above 1.2 m. Although clay produces larger displacements than sand, and higher stress produces larger displacements than lower stress, the identified pattern remains robust. Considering both the displacement–diameter relationship and practical cost constraints, an optimal equipment diameter of 1.2 m is recommended. Full article

This paper proposes a spectral-grid method based on Chebyshev polynomials of the first kind for the numerical solution of second-order singularly perturbed boundary value problems containing two boundary layers. The proposed method possesses several important advantages, including high numerical accuracy, computational efficiency in terms of the number of arithmetic operations, reduced memory requirements, accurate localization and resolution of boundary layers, and applicability to singularly perturbed boundary value problems containing one, two, or multiple boundary layers. In the proposed approach, the computational domain is partitioned into several grid elements, and the solution on each element is approximated by a truncated series of Chebyshev polynomials. Continuity conditions for the solution and its derivatives are imposed at the interfaces between adjacent elements, resulting in a system of algebraic equations for the unknown expansion coefficients. The principal advantage of the method lies in its ability to accurately localize boundary layers by appropriately selecting the lengths of the grid elements and the degrees of the approximation polynomials. Numerical experiments for a wide range of perturbation parameters are presented in the form of tables and graphical illustrations and are compared with existing results available in the literature. The obtained results demonstrate that the proposed spectral-grid method provides highly accurate numerical solutions even for very small values of the perturbation parameter while significantly reducing the maximum absolute error. The convergence of the proposed method has been theoretically established, and its convergence rate has been analyzed. The numerical results confirm the accuracy, computational efficiency, robustness, and reliability of the proposed method for solving singularly perturbed boundary value problems. Full article

This paper introduces efficient randomized fixed-precision and single-pass algorithms for low-tubal-rank approximation of third-order tensors. The proposed fixed-precision algorithms are faster and more efficient than the existing algorithms for approximating the truncated tensor SVD (T-SVD). Furthermore, unlike existing single-pass methods, which directly extend early, unstable matrix algorithms, the proposed approach adapts enhanced and stabilized matrix techniques to the tensor setting. Through extensive numerical experiments, we identify a critical flaw in current single-pass algorithms: using sketching parameters of equal size often produces ill-conditioned tensor least-squares problems, leading to inaccurate approximations. The proposed algorithms are demonstrably robust to this issue, achieving superior performance under identical conditions. We also evaluate the robustness of existing single-pass methods on real-world data tensors, including images and videos, a topic that has not been thoroughly examined before. Numerical results confirm the effectiveness of the proposed methods. Three applications are presented: image compression, video super-resolution, and deep learning. Full article

Baker’s yeast with high freeze tolerance is essential for frozen-dough technology. The CYR1 gene, which encodes adenylate cyclase, is a central element of the cyclic adenosine monophosphate (cAMP) signaling pathway that regulates cellular stress tolerance. In this study, we aimed to enhance the freeze tolerance by modulating the expression level of CYR1. A series of diploid strains (BY14-30, BY14-60, BY14-90, and BY14-120) were constructed via a two-step integration method, in which the CYR1 promoter was truncated by 30, 60, 90, and 120 base pairs, respectively. Compared with the parent strain, strains BY14-30 and BY14-60 exhibited 4.3- and 4.2-fold higher survival rates after freezing, 60.0% and 40.0% increases in post-thaw dough-leavening ability, 88.9% and 64.6% increases in trehalose content, and 60.0% and 82.5% increases in proline levels, respectively. Collectively, our results demonstrate a novel strategy for regulating freeze tolerance in baker’s yeast, leading to improved cell viability and fermentation activity after freezing. Full article

Direction-of-arrival (DOA) estimation with coprime arrays can synthesize an enlarged virtual aperture from a limited number of physical sensors. However, coherent incident sources lead to rank deficiency of the source covariance matrix, while unknown nonuniform sensor noise mainly contaminates the zero-lag component of the difference-coarray covariance. These two effects jointly degrade conventional Coarray Root-MUSIC, Coarray ESPRIT, and interpolation-based virtual-array methods. To address this problem, this paper proposes a Toeplitz–Hankel structured covariance reconstruction method for coherent-source DOA estimation with coprime arrays under unknown nonuniform noise. The method first performs redundancy-aware difference-coarray lag averaging. The zero-lag component is then suppressed during missing-lag interpolation to reduce the bias caused by sensor-dependent noise powers. A Toeplitz positive semidefinite projection is used to enforce covariance validity, and a relaxed Hankel truncated-singular-value-decomposition refinement is introduced to enhance the low-rank spectral structure of the reconstructed virtual covariance sequence. Finally, multi-scale forward–backward spatial smoothing MUSIC is applied for coherent-source DOA estimation. Simulation results with a coprime array of $M=4$ and $N=5$ show that the proposed method provides more accurate and stable DOA estimates than Coarray Root-MUSIC, Coarray ESPRIT, and RV-TSI. Compared with CVX-based THSCR, the proposed method avoids semidefinite programming and nuclear-norm optimization and reduces the average runtime from approximately $11.2$ s per trial to approximately $0.11$ s per trial under the tested setting. Full article