Hepatitis B surface antigen (HBsAg) seroclearance is an ideal treatment end point in chronic hepatitis B (CHB), linked to improved clinical outcomes. Baseline HBsAg level is a key predictor, but pooled evidence on the annual incidence of spontaneous HBsAg clearance in untreated CHB patients, stratified by baseline HBsAg levels, is lacking. We conducted a systematic review and meta-analysis to quantify this incidence. Six databases were searched for cohort studies and randomized trials of untreated adult CHB patients reporting baseline HBsAg levels and annual clearance rates. Data were collected and analyzed using the random-effects model. Ten studies involving 17,439 patients with 16,134.82 person-years of follow-up were included. The overall pooled annual incidence of spontaneous HBsAg clearance was 1.52% (95% CI 1.11-1.98%), with high heterogeneity (I2 = 94.8%). Excluding three outlier studies reduced heterogeneity (I2 = 47.2%) and yielded a revised incidence of 1.22% (95% CI 1.06-1.39%). Stratification by baseline HBsAg revealed a pronounced gradient: 3.83% (95% CI 2.73-5.10%) for < 100 IU/mL, 1.34% (95% CI 1.14-1.56%) for 100-1000 IU/mL, and 0.51% (95% CI 0.32-0.73%) for > 1000 IU/mL. Spontaneous HBsAg clearance remains a relatively uncommon event in untreated CHB patients, yet its incidence is highly dependent on baseline HBsAg levels, with a higher likelihood of clearance at lower levels. These findings provide benchmark data for clinical decision-making regarding antiviral therapy initiation and for the design of future clinical trials. Current evidence is limited, underscoring the need for more high-quality, finely stratified studies in this area.

Background/Objectives: Safe placement of sacral vertebra 1 (S1) screws is essential in lumbosacral instrumentation and iliosacral fixation. Existing anatomical safe zones are largely based on averaged geometry and do not provide quantitative probability estimates for permissible deviations from an ideal entry point. This study aims to develop a probabilistic, computed tomography-based (CT-based) safe zone model for S1 screw placement. Methods: This retrospective imaging-based anatomical modeling study will analyze 1000 anonymized lumbosacral CT scans. A reproducible reference entry point will be defined on the lateral S1 projection, and bilateral offset-based virtual screw trajectories will be evaluated. Two independent raters will classify each trajectory as intraosseous or extraosseous. Probabilistic safety maps will be generated by aggregating binary classifications across offsets and directions. Interobserver reliability will be assessed using Cohen's kappa, and anatomical influences will be analyzed using multivariable regression models. Results: The study is expected to generate continuous probabilistic safety maps illustrating the likelihood of intraosseous S1 screw placement across predefined offset distances and directions from the reference entry point. These maps are anticipated to demonstrate a gradual transition from high to low safety probabilities rather than a binary safe-unsafe boundary, and to identify anatomical factors influencing screw containment. Conclusions: This protocol describes a CT-based probabilistic modeling approach to S1 screw placement that aims to provide a more nuanced and quantitative definition of anatomical safe zones. If successful, the proposed method may improve preoperative planning and intraoperative decision-making by moving beyond averaged geometric constraints toward probability-informed screw placement.

Study of the ideal insertion point and angle for the antegrade posterior column screw with the anterior approach in acetabular fracture.

Topological complexity of ideal limit points

Multi-objective optimization of oxygen transfer and hydrodynamic shear in dual-impeller stirred tanks via integrated computational fluid dynamics and machine learning framework.

ABSTRACT Prior work conjectures that representational gaps may arise due to biases in who contacts politicians. However, direct measures of legislator contact by members of the public are elusive. This study leverages a unique data source to evaluate partisan and ideological bias in public outreach: witness slips in the Illinois General Assembly, online forms individuals can use to support or oppose specific pieces of legislation. Using these expressed positions, we document two key facts. First, witnesses are significantly more supportive of Republican‐sponsored legislation than Democratic‐sponsored legislation. Second, after estimating ideal points for witnesses, we find witnesses are ideologically closer to Illinois Republicans than Democrats. Additional analyses reveal important ideological heterogeneity by policy jurisdiction and interest group affiliation. Together, the results support one theoretical explanation of why legislators systematically mischaracterize public opinion: the views they are exposed to differ significantly from those of the general public.

Smart assessment of the unified power system efficiency in the context of smart grid: an ideal point–based multi-criteria method

Introduction Given growing challenges in agriculture and food systems, such as climate change and economic instability, higher education must train professionals to work across disciplines and address complex, real-world problems. Preparing students for dynamic and uncertain environments requires not only technical knowledge but also collaboration and critical reflection, which can be enhanced by experiential education. For Agroecology, Food Systems, Weed Science, and similar higher education programs, the topic of edible weeds provides an excellent context for interdisciplinary experiential learning. Edible weeds are common, yet undervalued components of the associated agrobiodiversity that simultaneously present crop management challenges for farmers and nutritious food sources. This dual role makes edible weeds an ideal entry point for students to critically reflect on the challenges of our food systems and connect ecological and cultural dimensions of food. To explore this potential, we developed the Edible Weeds Feast, an assignment for Ethnobotany and Weed Ecology undergraduate students at Montana State University. Methods Working in teams, students identify and collect edible weeds, design recipes, present their chosen species’ ecology and nutritional properties, and prepare dishes for a shared meal. We present students’ assessments of this assignment conducted in 2023 and 2024, using post-assignment surveys and focus group interviews. Results Findings revealed strong overall approval of the Edible Weeds Feast. Students reported perceived knowledge gains in several fields, from plant identification to the cultural significance of wild plants, with the strongest perceived gains on the nutritional benefits of edible weeds. The positive overall assessment did not differ between students in Ethnobotany and Weed Ecology. Students emphasized that the experiential, collaborative structure of the assignment enhanced their enjoyment and deepened their learning, while also facilitating exchange between agricultural and food systems perspectives. Critical student feedback centered on organizational aspects, for example, the timing of the plant gathering, which will inform refinements of future iterations. Discussion The Edible Weeds Feast represents an impactful experiential and interdisciplinary assignment that can inspire similar pedagogies in higher education.

Single fluorescent molecules, acting as ideal point dipoles, offer a unique means to probe light-matter interactions at the nanoscale. Here, we exploit this property to map the chiral and vectorial structure of tightly focused optical fields using individual, immobilized terrylene diimide molecules. By scanning the excitation focus under linear and circular polarization, we obtain three-dimensional fluorescence excitation maps that directly visualize the handedness and symmetry breaking inherent to circularly polarized light. The measured patterns show excellent quantitative agreement with a full vectorial diffraction model, enabling the accurate determination of both molecular orientations and the local field structure. This approach establishes single molecules as quantitative nanoprobes of optical chirality, offering new strategies for characterizing complex light fields and polarization effects in nanophotonic, plasmonic, and anisotropic materials.

BackgroundTumor growth rate (TGR) is a dynamic biomarker for evaluating therapeutic response and prognosis in unresectable hepatocellular carcinoma (uHCC) with triple therapy, yet its clinical utility and role in guiding treatment optimization require further validation.MethodsThis study included 68 uHCC patients receiving transarterial chemoembolization (TACE) with systemic combination therapy. Propensity score matching (PSM) was applied to balance baseline confounders. Cubic spline models explored nonlinear associations between TGR and survival risk and found the ideal cut-off points. Kaplan-Meier (KM) analysis compared overall survival (OS) and progression-free survival (PFS) between TGR subgroups, Cox multivariate regression evaluated the independent prognostic value of TGR.ResultsCox analysis confirmed TGR0 as an independent prognostic factor for OS (hazard ratio (HR) 1.035, 95% confidence interval (CI): 1.013–1.057, p = 0.020) and PFS (HR 1.033, 95%CI: 1.014–1.053, p = 0.001). After stratified for TGR0/TGR1 and matched, the low-TGR0 group showed significantly longer median OS (not reached vs. 13.09 months, p = 0.002) and PFS (12.11 vs. 4.38 months, p = 0.006) than the high-TGR0 group. In the subgroup analysis, after propensity score matching(PSM), the low-TGR1 group demonstrated a more favorable prognosis compared with high-TGR1(mOS: not reached vs. 16.97 months, p = 0.024; mOS1: not reached vs. 11.78 months, p = 0.022).ConclusionsDynamic TGR monitoring identifies heterogeneous therapeutic responses, guiding timely personalized treatment adjustments and prognostic stratification in uHCC.Supplementary InformationThe online version contains supplementary material available at 10.1186/s12876-025-04596-2.

Winter road snow melting is an important issue for traffic safety in cold regions. Traditional snow-melting materials, due to their high chloride content, cause severe corrosion to the environment and infrastructure. To solve this issue, a new type of Low Chloride Salt Snow-Melting Filler (LCSMF) was developed in this study, and its optimal preparation process and dosing amount were determined through system optimization and performance evaluation. First, the best carrier and the best hydrophobic modifier were determined by material comparison, and the modification conditions were optimized: modification temperature, modification time, and modifier dosage. Secondly, the active ingredient of snow melting was optimized by orthogonal experiment, and the optimal ratio of the active ingredient was determined by Simplex Centroid Design (SCD) test using Minitab software. The optimized salt-storing filler was incorporated into the asphalt mixture by isovolumetrically replacing the mineral powder, and its comprehensive performance was evaluated by snow melting performance (ice-road bond test) and pavement performance (high temperature stability, low temperature crack resistance, water stability test). The results showed that the salt-storing filler significantly deteriorated the low-temperature crack resistance and water stability, which could be defined as performance shortcomings. Based on this finding, snow-melting performance, low-temperature crack resistance, and water stability were selected as key indicators to construct a multi-objective optimization model. The NSGA-II algorithm was used to generate the Pareto front, and the ideal point method (TOPSIS) was employed to select the solution closest to the ideal point from the Pareto front. The optimal dosage was finally determined: in the AC gradation, the salt-storing filler could replace 99.9% of the mineral powder, and in the SMA gradation, it could replace 86.7% of the mineral powder. This study provides an efficient and environmentally friendly new material for winter road snow melting and offers a scientific basis for its practical application.

In multi-objective particle swarm optimization (MOPSO), challenges persist, including low diversity in external archives, ambiguous individual optimal choice mechanisms, high sensitivity to parameter settings, and the arduous task of balancing global exploration and local exploitation capabilities. To address these issues, this paper introduces a novel multi-objective particle swarm optimization algorithm named HCRMOPSO. The proposed algorithm innovatively leverages hierarchical clustering based on Ward’s linkage to generate the center of mass as reference points, which are then combined with the ideal point and crowding distance. This effectively maintains the external archive, thereby resolving the diversity deficiency commonly found in traditional MOPSO archives. Additionally, HCRMOPSO fuses multiple particles to update the personal best positions. It also adaptively tunes the flight parameters according to the diversity information within each particle’s neighborhood, enhancing the algorithm’s adaptability. Notably, a new strategy is designed for two specific types of particles, further optimizing the search process. The performance of HCRMOPSO is rigorously evaluated against ten existing algorithms on 22 standard test problems. Experimental results demonstrate that HCRMOPSO outperforms its counterparts on multiple benchmarks, showcasing superior effectiveness in handling multi-objective optimization tasks.

Abstract In low-luminosity active galactic nuclei like M87 * and Sgr A * , the accretion flow in the vicinity of the black hole is in the collisionless regime, meaning that the collisional mean free path of charged particles is much larger than the dynamical length scales. To properly model the particle energization and emission from the collisionless accretion flow, a promising approach is to employ the global general-relativistic particle-in-cell simulations—a newly developed, fully kinetic, first-principles method. However, it has been challenging to set up an initial condition that involves collisionless gas with finite angular momentum. We present, for the first time, a class of analytic kinetic equilibria of collisionless tori around a Kerr black hole. We have successfully implemented the collisionless tori in our GPU-based GRPIC code framework Aperture, and found them to be stable for hundreds to thousands of dynamical times in 2D axisymmetric simulations when there is no initial seed magnetic field. These kinetic equilibria serve as ideal starting points for future studies of the physics of collisionless accretion and jet launching.

This article introduces a novel empirical method for estimating the ideological orientations of U.S. regulatory agencies across different presidential administrations. Employing a measurement model based on item response theory and analyzing data on planned regulations from the Unified Agenda and the president’s discretionary review of those regulations, as implemented by the Office of Information and Regulatory Affairs, the study provides dynamic estimates of agency ideal points from the Clinton through the Trump administrations. The model uses NOMINATE ideal points of presidents to link the estimated agency ideal points to legislative ideal points. The resulting estimates correlate positively with existing measures of agency ideology, highlight controversial regulators, and demonstrate that agency ideologies shift over time due to emerging issues that divide the parties. The study also finds that agencies located ideologically closer to the president are more productive, as evidenced by their regulatory output.

Socio-ecological systems (SESs) exhibit nonlinear feedback across environmental, social, and economic processes, requiring integrative analytical tools capable of representing such coupled dynamics. This study presents a quantitative framework that integrates a compartmental model of a global human–ecosystem with two complementary optimization approaches (Fisher Information (FI) and Multi-Objective Optimization (MOO)) to evaluate policy strategies for sustainability. The model represents biophysical and socio-economic interactions across 15 compartments, incorporating feedback loops between greenhouse gas (GHG) accumulation, temperature anomalies, and trophic–economic dynamics. Six policy-relevant decision variables were selected (wild plant mortality, sectoral prices (agriculture, livestock, and industry), base wages, and resource productivity) and optimized under temporal (25-year) and magnitude (±10%) constraints to ensure policy realism. FI-based optimization enhances system stability, whereas the MOO framework balances environmental, social, and economic objectives using the Ideal Point Method. Both approaches prevent the systemic collapse observed in the baseline scenario. The FI and MOO strategies reduce terminal global temperature by 11.4% and 15.0%, respectively, relative to the baseline (35 °C → 31.0 °C under FI; 35 °C → 29.7 °C under MOO). Resource-use efficiency, measured through the resource requirement coefficient (λ), improves by 8–10% under MOO (0.6767 → 0.6090) and by 6–7% under FI (0.6668 → 0.6262). These outcomes offer actionable guidance for long-term climate policy at national and international scales. The MOO framework provided the most balanced outcomes, enhancing environmental and social performance while maintaining economic viability. Overall, the integration of optimization and information-theoretic approaches within SES models can support evidence-based public policy design, offering actionable pathways toward resilient, efficient, and equitable sustainability transitions.

Justification of the choice of the optimal material of the liquid phase of composites with a two- phase reinforcement scheme was carried out on the basis of solving the multi-criteria problem of optimization by methods of ideal point and uniform optimization when used as the main optimization criteria the results of the experimental studies of these materials on the evaluation of tensile and impact strength, as well as losses of these values under conditions of influence of low negative temperatures (–30 and –50 °C).

Abstract Lead halide perovskites are promising next‐generation optoelectronic materials due to their solution processability, tunable bandgap and excellent photoelectric properties. However, achieving deep‐blue emission in all‐inorganic CsPbX 3 nanocrystals remains challenging due to phase separation, halide volatilization and insufficient stability, limiting industrial application. Herein, a collaborative strategy of “chlorine source regulation—defect passivation—fiber integration” is proposed. By incorporating β‐cyclodextrin chloride (βCD‐Cl) into CsPbBr 3 , we synthesized large‐scale deep‐blue CsPbBr 3‐x Cl x @βCD‐Cl microcrystals via a mechanosynthesis route. Flexible blue‐light fiber films are fabricated via electrospinning, showing a photoluminescence quantum yield (PLQY) of 55.79% and excellent environmental stability, with only a 16 nm red shift observed after 171 days in water. Additionally, the fiber films enable near‐infrared (750 nm)‐to‐blue photon upconversion (450‐490 nm), achieving unprecedented bilirubin degradation efficiency (40% within 20 min). They can serve as core components for next‐generation phototherapeutic blankets, combining spectral selectivity (blocking harmful radiation &lt; 420 nm) with therapeutic light transmission, eliminating neonatal retinal phototoxicity risks without requiring protective eyewear. The full solution‐processed white‐light fiber film is prepared, with CIE coordinates (x = 0.32, y = 0.34) near the ideal white light point. Overall, this study clarifies the molecular structure—performance relationships, overcomes stability bottlenecks, and supports photodynamic therapy and bio‐photonic devices.

The goals of this study were to develop an artificial intelligence (AI)-driven automated preoperative planning system for anterior cruciate ligament (ACL) reconstruction by integrating deep learning with computed tomography (CT)-magnetic resonance imaging (MRI) image fusion and segmentation, and to evaluate its accuracy. Structures on CT and MRI scans of 200 knee joints from patients with an intact ACL (aged 18 to 50 years, 81.0% male, all ethnic Chinese) were manually annotated. Fusion of the CT and MRI images was performed using a Dual-UNet registration architecture incorporating multiscale information fusion, enabling dynamic 3D reconstruction of the fused images for ACL insertion site identification and isometry assessment. A deep-learning framework was trained to analyze the fused image to precisely optimize ACL tunnel positioning, including identifying the entrances and exits of the femoral and tibial tunnels. Criteria in the automated planning included proximity to the ideal point, coverage of the anatomical footprint area, and isometric length variation of <2 mm. The accuracy of the AI system was then validated in 36 ACL reconstructions performed in bone models by comparing the drilled femoral and tibial tunnel lengths and graft length between the tunnels with the planned values. Finally, clinical feasibility was tested in 36 patients undergoing ACL reconstruction surgery using 3D-printed patient-specific guides derived from the AI planning, with 36 conventional surgeries as controls. Deviation of tunnel positions from the planned positions was compared between the 2 groups. CT-MRI image fusion was able to generate an individualized 3D model with high segmentation accuracy (Dice coefficient = 0.864). The AI planning required 192 ± 90.2 seconds per case. In the bone model validation, the mean deviation between the planned and executed values was <1 mm for the femoral and tibial tunnel lengths and graft length between the tunnels (all p > 0.05). In the clinical testing, the AI-guided group demonstrated significantly smaller deviations from the ideal point compared with the conventional group in the deep-to-shallow (D-S), high-to-low (H-L), medial-to-lateral (M-L), and anterior-to-posterior (A-P) directions (all p < 0.05). The AI-driven segmentation of CT-MRI fusion images and automatic preoperative ACL reconstruction planning demonstrated the capability to automatically, precisely, and reproducibly generate plans for nearly ideal tunnel entry and exit points with isometric, anatomical, and individualization characteristics. This technology is expected to hold clinical potential for ACL reconstruction, including reduced complication and revision rates and enhanced postoperative function.

Abstract This article presents the first systematic review of unsupervised and semi-supervised computational text-based ideal point estimation (CT-IPE) algorithms, methods designed to infer latent political positions from textual data. These algorithms are widely used in political science, communication, computational social science, and computer science to estimate ideological preferences from parliamentary speeches, party manifestos, and social media. Over the past two decades, their development has closely followed broader NLP trends—beginning with word-frequency models and most recently turning to large language models (LLMs). While this trajectory has greatly expanded the methodological toolkit, it has also produced a fragmented field that lacks systematic comparison and clear guidance for applied use. To address this gap, we identified 25 CT-IPE algorithms through a systematic literature review and conducted a manual content analysis of their modeling assumptions and development contexts. To compare them meaningfully, we introduce a conceptual framework that distinguishes how algorithms generate, capture, and aggregate textual variance. On this basis, we identify four methodological families—word-frequency, topic modeling, word embedding, and LLM-based approaches—and critically assess their assumptions, interpretability, scalability, and limitations. Our review offers three contributions. First, it provides a structured synthesis of two decades of algorithm development, clarifying how diverse methods relate to one another. Second, it translates these insights into practical guidance for applied researchers, highlighting trade-offs in transparency, technical requirements, and validation strategies that shape algorithm choice. Third, it emphasizes that differences in estimation outcomes across algorithms are themselves informative, underscoring the need for systematic benchmarking.

The Global Initiative for Chronic Obstructive Lung Disease (GOLD) recommends a ratio of forced expiratory volume in one second (FEV1) to forced vital capacity (FVC) of less than 70% (FEV1/FVC < 0.7) after bronchodilators as the criteria for obstruction. However, because the FEV1/FVC ratio decreases with age, using a fixed ratio may lead to overdiagnosis of obstruction in the geriatric population. Using the lower limit of normal (LLN) as threshold for obstruction has been suggested. To determine the rate of overdiagnosis using the GOLD criteria compared to LLN in patients aged 60 and older. To find a better threshold with a minimal rate of over- and underdiagnosis. The study population included adults aged 60 years and older who performed pulmonary function test (PFT) at Shaare Zedek Medical Center between 2014 and 2019 with results of FEV1/FVC < 0.7. We included 430 patients aged 60 years and older, 273 males (63.5%) and 157 females (36.5%). Mean age was 72 ± 8 years. Overdiagnosis was found in 35.6% of patients (95% confidence interval 31.1-40.3%) by using the GOLD criteria compared to the LLN. Overdiagnosis was reduced to 6.4% with the 0.65 threshold. The ideal point of the FEV1/FVC ratio where overdiagnosis and underdiagnosis were at their lowest rates was 0.638. Use of the GOLD criteria for airflow obstruction may be associated with an overdiagnosis of more than 35% in patients older than 60 years. Lowering the FEV1/FVC ratio to < 0.65 might be more accurate in this population.