Shrinkage Factor Research Articles

A Modified Debiased Inverse-Variance Weighted Estimator in Two-Sample Summary-Data Mendelian Randomization.

Mendelian randomization uses genetic variants as instrumental variables to estimate the causal effect of exposure on outcome from observational data. A common challenge in Mendelian randomization is that many genetic variants are only modestly or even weakly associated with the exposure of interest, a setting known as many weak instruments. Conventional methods, such as the popular inverse-variance weighted (IVW) estimator, could be heavily biased toward zero when the instrument strength is weak. To address this issue, the debiased IVW (dIVW) estimator and the penalized IVW (pIVW) estimator, which are shown to be robust to many weak instruments, were recently proposed. However, we find that the dIVW estimator tends to produce an exaggerated estimate of the causal effect, especially when the effective sample size is small. Although the pIVW estimator has better statistical properties, it is slightly more complex, and the idea behind this method is also a bit less intuitive. Therefore, we propose a modified debiased IVW (mdIVW) estimator that directly multiplies a shrinkage factor with the original dIVW estimator. After this simple modification, we prove that the mdIVW estimator not only has second-order bias with respect to the effective sample size, but also has smaller variance and mean squared error than the preceding two estimators. We then extend the proposed method to account for the presence of instrumental variable selection and balanced horizontal pleiotropy. We demonstrate the improvement of the mdIVW estimator over the competing ones through extensive simulation studies and real data analysis.

Changes monitoring in Hongjiannao Lake from 1987 to 2023 using Google Earth Engine and analysis of climatic and anthropogenic forces

The research aimed to quantify the lake area dynamics, evaluate the changes in distance and rate of lake shorelines quantitatively and spatially and investigate the key factors influencing the Hongjiannao Lake (HL) area shrinkage. The study used remote sensing (RS) data from Landsat TM/ETM+ and OLI images and Google Earth Engine (GEE), a cloud platform for obtaining the lake surface area and island information from 1987 to 2023. A modified normalized difference water index (MNDWI) was applied to water area extraction. Digital Shoreline Analysis System (DSAS) was employed to assess net shoreline movement (NSM) and depict the lake shoreline length and rate changes. Furthermore, the water level was derived by ASTER GDEM V2 using the waterline method and lake boundaries. Six climatic features (temperature, precipitation, potential and actual evaporation, aridity index, and actual water difference) were investigated to find the driving factors of lake area shrinkage by correlation and factor analysis. The results reveal that during 1987–2023, the HL area has undergone four separate phases: stable (1987–1997), shrinkage (1998–2015), growth (2016–2019), and reduction (2020–2023). The most substantial negative change (−7.45%) in the HL area was observed in 1998. NSM analysis demonstrates that the lake has experienced both expansion and shrinkage at various times and locations. According to Water Balance Method, the water volume of HL exhibited variations, ranging from −0.1895 to −0.009 km³. The average yearly change in lake volume, water level, and area displayed similar characteristics with high inconstancy. Correlation and factor analysis of lake area and climatic factors demonstrate that higher precipitation, low temperatures, less potential evaporation level, lower actual evaporation rates, and more minor differences in water levels are associated with an increase in lake area. In contrast, the opposite conditions lead to a reduction in lake size.

Unleashing innovation: 3D-printed biomaterials in bone tissue engineering for repairing femur and tibial defects in animal models - a systematic review and meta-analysis.

3D-printed scaffolds have emerged as an alternative for addressing the current limitations encountered in bone reconstruction. This study aimed to systematically review the feasibility of using 3D bio-printed scaffolds as a material for bone grafting in animal models, focusing on femoral and tibial defects. The primary objective of this study was to evaluate the efficacy, safety, and overall impact of these scaffolds on bone regeneration. Electronic databases were searched using specific search terms from January 2013 to October 2023, and 37 relevant studies were finally included and reviewed. We documented the type of scaffold generated using the 3D printed techniques, detailing its characterization and rheological properties including porosity, compressive strength, shrinkage, elastic modulus, and other relevant factors. Before incorporating them into the meta-analysis, an additional inclusion criterion was applied where the regenerated bone area (BA), bone volume (BV), bone volume per total volume (BV/TV), trabecular thickness (Tb. Th.), trabecular number (Tb. N.), and trabecular separation (Tb. S.) were collected and analyzed statistically. 3D bio-printed ceramic-based composite scaffolds exhibited the highest capacity for bone tissue regeneration (BTR) regarding BV/TV of femoral and tibial defects of animal models. The ideal structure of the printed scaffolds displayed optimal results with a total porosity >50% with a pore size ranging between 300- and 400µM. Moreover, integrating additional features and engineered macro-channels within these scaffolds notably enhanced BTR capacity, especially observed at extended time points. In conclusion, 3D-printed composite scaffolds have shown promise as an alternative for addressing bone defects.

A Wide-Beam Antenna for Automotive Radar Based on Improved Particle Swarm Algorithm

Article A Wide-Beam Antenna for Automotive Radar Based on Improved Particle Swarm Algorithm Bin Wang 1, Zhenhua Li 1 and Shufei Yin 1,2,* 1 Shanghai Baolong Automotive Corporation, Shanghai 201619, China 2 School of Automotive Engineering, Wuhan University of Technology, Wuhan 430070, China * Correspondence: yinshufei@chinabaolong.net Received: 26 August 2024; Revised: 9 September 2024; Accepted: 11 September 2024; Published: 23 September 2024 Abstract: The traditional automotive corner radar antenna has the problem that the horizontal filled of view (FOV) is not large enough, which affects wide detection area capability and driving safety. In order to solve the problem, this paper proposes a wide-beam antenna design method for automotive corner radar based on improved particle swarm optimization (IPSO) algorithm. The mathematical model of comb-line antenna for 77 GHz corner radar is established and combined with the IPSO algorithm to achieve wide beam. The algorithm avoids the search process falling into local optimum by introducing an adaptive shrinkage factor and chaotic disturbance when the non-superior particle position is updated. The computational results show that the convergence of the IPSO algorithm is significantly better than standard particle swarm optimization (SPSO) in wide beam forming under the same number of iterations.

Tensile Deformability of Shotcrete in Tunnel Primary Support: A Case Study

Shotcrete strain in the primary support of a tunnel will produce non-loading strain at an early age due to the influence of its own temperature change, hardening shrinkage, spraying force, and other factors, which means that current strain-monitoring results fail to reflect the real strain, and the strain value after stabilization is high. In addition, tensile strain may be evident in the final result, even exceeding the tensile warning value, but, in actuality, the on-site lining is very stable, with no cracks or damage. Therefore, it is necessary to understand the strain characteristics of shotcrete in the primary support of a tunnel. Based on the long-span tunnel project at Shishan Road Station on the Qingdao Metro Line 6, in situ and indoor pull tests of concrete strain were designed while only considering temperature change, hardening shrinkage, and spraying force. This study shows the following: (1) The strain in shotcrete is greatly affected by temperature changes, hardening shrinkage, and shotcrete force in the first three days, reaching its peak value in the second to third days, while tending to be stable at about the seventh day. (2) The real strain of the shotcrete was tested, and the warning value was adjusted from 90 με to 120 με. (3) The strain value at the third day was taken as the initial value, and the previous monitoring results were revised. The revised results align with the trends shown during real tests performed on-site, providing guidance for tunnel engineering support monitoring.

Assessment of geometrical variability in 3D printed ZrO2: Effects of printing and thermal post-processing

3D printing is developing rapidly and enables the production of parts manufactured using different materials. These includes zirconium dioxide (ZrO2), which can be of particular interest for bone tissue engineering and implantology. However, highly accurate part-dimensions are a must for these applications, which is why this study addresses geometrical deviations which occur during the printing process and thermal post-processing.Six sets of test geometries with 50 individual features were 3D printed with two different ZrO2 slurries (3 mol% yttria-stabilized ZrO2) and scanned with a profilometer. After debinding and sintering, the profilometer scan was repeated and the deviations and shrinkage factors were determined.A notable difference is observed when the same ceramic is processed using two different slurries. For instance, one used ceramic slurry, LithaCon 210, exhibits shrinkage factors of shrXY=21.2±3.4%(n=78) and shrZ=23.6±0.54%(n=24) for protruding structures, while the other ceramic slurry, LithaCon 280, shows shrinkage factors of shrXY=21.7±3.3%(n=78) and shrZ=24.5±0.55%(n=24).Geometric deviations differed for intruding (like holes and slots) and protruding (like pillars) geometries, being more pronounced in case of intruding geometries, especially where printing overhangs occur.Although the shrinkage during sintering needs further investigation, these experimental findings are a good starting point to validate and refine simulation models for shrinkage and improve production processes of 3D printed ceramics.

Predicting heat flow in the Iranian plateau and surrounding areas based on machine learning approach

While Surface Heat Flow (HF) is an important constraint unveiling the Earth interior's thermal structure, estimates over the Iranian plateau are sparse. In the presence of sparse estimates, machine learning provides a statistical-based prediction of HF based on a supervised predictor trained in the far-field regions. Here, we imply the machine learning technique of Gradient Boosting Regression Tree (GBRT) which has been proved to be efficient for predicting HF projecting complexities and nonlinearities of input features into predicted HF. Our results provide a robust map of HF with resolution of one degree and uncertainty of up to ±6 mW/m2 over Iran and surrounding regions. The predicted HF has an average value and minimum standard deviation of 59 and 10 mW/m2, respectively. The quality of the algorithm performance is 16%, indicated by normalized Root-Mean-Square Error (RMSE), and linear correlation of predicted HF with validation set is 97%. Total number of trees considerably prevents overfitting which is believed to be solely controllable by shrinkage factor, maximum tree depth and cross-validation scheme. The three most important features, having the highest influence on the output HF, are thermal Lithosphere-Asthenosphere Boundary (LAB), distance to volcanoes and distance to trenches. The extreme importance of LAB in HF prediction of Iran indicates that the lithospheric thermal structure is significantly controlled by lithospheric thickness in the Iranian plateau. Selection of petrologically and seismologically consistent LAB guarantees the precision of the predicted HF. Our results imply that high HF in central Iran is in agreement with extensive magmatism since the Paleozoic. Additionally, the high HF in Zagros keel (originally Proterozoic as the Zagros keel appears to be the Arabian plate front) indicates the tectonically active system of the Arabia-Eurasia collision zone, high likely, in the form of lithospheric mantle deformation.

Research on spatiotemporal patterns and influencing factors of county-level urban shrinkage in urbanizing China

County-level cities, as small-scale urban units in China's urban system, have shrunk due to uneven urban development in urbanizing China. This research constructed a framework for understanding China's county-level urban shrinkage. It defined county-level cities and measured shrinkage using the city shrinkage model, explored the spatiotemporal patterns combined with shrinking degrees and trajectories, and analysed the shrinking types based on city and city periphery perspectives. Then, the influencing factors were identified via the OLS regression model. Taking 2846 county-level cities as research units, the results showed that 180 and 1050 county-level cities shrunk in 2000–2010 and 2010–2020, respectively. Slight and moderate shrinkage dominated, but the proportions of high and severe shrinkage noticeably increased. In terms of shrinking trajectory, recent shrinkage was the main shrinking type. The spatial extent of shrinking county-level cities increased dynamically; the northeast, north, central and northwest regions were core agglomerations, and the east region emerged as an added agglomeration during 2010–2020. Absolute shrinkage became the main shrinking type significantly driven by "city–city" and "off-site urbanization" migration. Urbanization level, economic development, ageing degree, etc., were factors causing shrinkage. This study contributes to advancing understanding among scholars and Chinese policymakers about county-level urban shrinkage in urbanizing China.

Statistical Reappraisal of the Wax and Mercury Methods for Shrinkage Limit Determinations of Fine-Grained Soils

Because of the hazards associated with handling mercury, most standards organizations have withdrawn the conventional mercury (displacement) method (MM) for shrinkage limit (SL) determination of fine-grained soils. Despite attempts to substantiate the wax (coating) method (WM), which is presently the only standardized MM-testing alternative, the geotechnical community remains somewhat hesitant of its adoption in routine practice. To encourage more widespread use of WM-testing, this study re-examines the level of agreement between the MM- and WM-deduced SL parameters (i.e., SLMM and SLWM, respectively). This was achieved by performing comprehensive statistical analyses on the largest and most diverse database of its kind, to date, entailing SLMM:SLWM measurements for 168 different fine-grained soils having wide ranges of plasticity characteristics (i.e., liquid limit = 31.6–362.0%, plasticity index = 8.2–318.0% and SLMM = 7.1–42.0%). Furthermore, an attempt was made to evaluate the SLWM (in lieu of the SLMM) parameter for performing preliminary soil expansivity assessments using existing SLMM-based classification approaches. It was demonstrated that the MM and WM methods do not produce identical SL values for a given fine-grained soil under similar testing conditions, with their discrepancy being systematic and hence likely arising from the differences between the materials (mercury versus wax) and methodologies involved in performing these tests. New SLWM → SLMM conversion relationships were established, allowing SLMM to be deduced as a function of SLWM with high accuracy. Hence, when inputting SLWM in SLMM-based empirical correlations to predict other geoengineering design parameters, the newly proposed conversion relationships can be employed to minimize systematic prediction errors. It was also demonstrated that plasticity-based correlations, at best, can only provide a rough approximation of SLMM. Hence, when the SL is desired, WM-testing or any other alternative method that directly and reliably measures the soil shrinkage factors should be retained. Finally, the same soil-expansivity rankings, as obtained for existing classification systems employing SLMM results, are achieved using SLWM measurements (i.e., without the need of applying SLWM → SLMM conversion equations).

Impact of thermal lensing on the TMI threshold powers in rod-type Yb-doped fiber amplifiers

The impact of thermal lensing (TL) on the transverse mode instability (TMI) threshold power in rod-type fiber amplifiers is investigated. Simulations are conducted with a 3D coupled mode analysis on a set of five scaled large pitch fiber (LPF) amplifiers. The LPF fibers are represented by surrogate step-index fibers (SIFs) with scaled cladding diameters, core diameters, and numerical apertures for a fixed normalized frequency V equal to 3.0 and scaled modal field overlap integrals with the core and cladding. It is found that thermal lensing decreases the TMI threshold powers due to increases in the TMI nonlinear gain. This gain increase is attributed to an increase in the nonlinear gain overlap integrals that occurs with the reduction in the fundamental mode effective area. Estimates for the TMI threshold power are in good agreement with measurements; however, the simulations overestimate the mode shrinkage factor. This discrepancy is tentatively attributed to the representation of the LPF by an effective SIF. These results may offer opportunities for fiber designs that increase the TMI threshold powers.

Long-Term Effects in Structures: Background and Recent Developments

This article addresses the often overlooked but critical long-term factors of creep, shrinkage, ageing and corrosion in civil engineering structures. The paper emphasizes their substantial impact on structural mechanical resistance and safety, drawing attention to key examples like the Civic Tower in Pavia, the Koror-Babeldaob Bridge and dams in the USA. By exploring the challenges faced in modern engineering, the article sheds light on the need to consider these effects. Various models for predicting creep and shrinkage in concrete structures are introduced as potential solutions. In conclusion, the paper highlights the necessity for engineers to navigate the intricacies of material behaviour for successful construction amidst evolving challenges.

Surface‐methacrylated microcrystalline cellulose bioresins with soybean oil for additive manufacturing via vat photopolymerization

AbstractThe additive manufacturing (AM) industry increasingly looks to differentiate itself by utilizing materials and processes that are green, clean, and sustainable. Biopolymers, bio‐sourced raw materials and light weighting of parts 3D printed with photopolymer resins each represent critical directions for the future of AM. Here, we report a series of bio‐based composite resins with soybean oil derivatives, up to 20% by weight of surface‐methacrylated micro‐crystalline cellulose (MCC) and 60% total bio‐based content for vat photopolymerization based additive manufacturing. The ultimate tensile strengths of the materials were found to increase up to 3X, the Young's moduli increased up to 10X, and the glass transition temperature increased by 11.3°C when compared to the neat resin without surface‐methacrylated MCC as a filler. Working curves and shrinkage factors were used to demonstrate how the surface‐methacrylated MCC causes changes in the dimensions of printed parts, to facilitate development of optimized print parameters based on the UV intensity of the 3D printer being used. These results will allow further development of commercial 3D printable resins with a high concentration of bio‐based fillers that print well and perform on par with conventional resins.

Spontaneous shrinkage of sporadic vestibular schwannomas: a clinical and radiological analysis.

The natural history of sporadic vestibular schwannoma (VS) is unpredictable, as tumors may or may not grow and can even spontaneously regress. A spontaneous VS shrinkage MRI-based pattern has been proposed with either a scalloped tumor aspect in the cerebellopontine angle or the appearance of a CSF-filled space surrounding the intracanalicular (IC) tumor within an enlarged canal. The authors of this retrospective study aimed to describe the evolution of sporadic VSs with radiological signs of VS regression and to identify prognostic factors for tumor shrinkage. All MRI scans obtained during patient follow-up were reviewed for extracanalicular (EC) and IC size and tumor characteristics. Volumetric measurements were performed on the first and last MRI scans. Shrinkage was considered to have occurred if the tumor size had decreased by ≥ 2 mm in its largest diameter and/or if the volume had decreased by ≥ 20%. Audiometric data were also collected. Among 512 patients under observation for sporadic VSs, 66 (13%) had at least one radiological sign of VS regression and 31 of these (6% overall) had confirmed tumor shrinkage. The mean follow-up was 4 ± 2.5 years. One radiological sign was present on initial MRI in 58% of patients and appeared during the follow-up period in the remaining 42%. Two groups were identified: 31 patients (47%) demonstrated progressive tumor regression during follow-up, and tumors in 35 patients (53%) remained stable once signs of regression were identified (assuming a stabilized regression). The prognostic factors for VS regression were as follows: EC VS extension (p = 0.02), cystic lesion (p = 0.002), and central necrosis (p = 0.02). The mean pure-tone average (PTA) was 43 ± 26.2 dB at the time of diagnosis and 53 ± 28.3 dB at the last visit (p < 0.0001). Among patients with an observed tumor shrinkage, ∆PTA was lower if the inner ear signal on the high-resolution T2-weighted image had improved (-3 ± 8.9 dB, n = 11) than if the inner ear signal had not improved (-10 ± 6.9 dB, n = 20) (p = 0.02) between the initial and last MRI scans. Spontaneous shrinkage of sporadic VSs could be suspected based on two radiological aspects that are indicative of VSs in progressive or stabilized regression and is an additional argument for the conservative management of these tumors. During follow-up, recovery from a reduced to a normal cochlear fluid MRI signal is a good indicator for hearing preservation.

Quantifying the nonlinear factors of urban shrinkage: A case study in Heilongjiang Province, China

With urbanization, urban shrinkage has become a more prevalent and severe global problem. Although many studies have explored it, most of them lack precision, and the complex nonlinear causal mechanisms of urban shrinkage are still poorly understood. Therefore, based on the background of the transformation of traditional industrial cities in China, this research selects Heilongjiang Province as a typical case study and quantify the spatial pattern of shrinking cities. Furthermore, from a nonlinear relational perspective, the complex mechanism of urban shrinkage is revealed by using LightGBM. The results show that the area experienced a large-scale comprehensive shrinkage and severe shrinkage in some areas, while also generating scattered population clusters. In addition, the economic and industrial structure are still the most influential factors for population change, and the industrial structure transformation is still the main obstacle for its development. The research results are conducive to elevating the awareness of urban shrinkage in industrial cities and providing some theoretical guidance for sustainable development.

Prognostic model for overall survival of head and neck cancer patients in the palliative phase

BackgroundPatients with head and neck squamous cell carcinoma (HNSCC) enter the palliative phase when cure is no longer possible or when they refuse curative treatment. The mean survival is five months, with a range of days until years. Realistic prognostic counseling enables patients to make well-considered end-of-life choices. However, physicians tend to overestimate survival. The aim of this study was to develop a prognostic model that calculates the overall survival (OS) probability of palliative HNSCC patients.MethodsPatients diagnosed with incurable HNSCC or patients who refused curative treatment for HNSCC between January 1st 2006 and June 3rd 2019 were included (n = 659). Three patients were lost to follow-up. Patients were considered to have incurable HNSCC due to tumor factors (e.g. inoperability with no other curative treatment options, distant metastasis) or patient factors (e.g. the presence of severe comorbidity and/or poor performance status).Tumor and patients factors accounted for 574 patients. An additional 82 patients refused curative treatment and were also considered palliative. The effect of 17 candidate predictors was estimated in the univariable cox proportional hazard regression model. Using backwards selection with a cut-off P-value < 0.10 resulted in a final multivariable prediction model. The C-statistic was calculated to determine the discriminative performance of the model. The final model was internally validated using bootstrapping techniques.ResultsA total of 647 patients (98.6%) died during follow-up. Median OS time was 15.0 weeks (95% CI: 13.5;16.6). Of the 17 candidate predictors, seven were included in the final model: the reason for entering the palliative phase, the number of previous HNSCC, cT, cN, cM, weight loss in the 6 months before diagnosis, and the WHO performance status. The internally validated C-statistic was 0.66 indicating moderate discriminative ability. The model showed some optimism, with a shrinkage factor of 0.89.ConclusionThis study enabled the development and internal validation of a prognostic model that predicts the OS probability in HNSCC patients in the palliative phase. This model facilitates personalized prognostic counseling in the palliative phase. External validation and qualitative research are necessary before widespread use in patient counseling and end-of-life care.

Characterization of Shrink Film Properties for Rapid Microfluidics Lab-on-Chip Fabrication.

Shrink film is a thin sheet of polystyrene plastic that shrinks to 25-40% of its original size when heated. This study investigated the shrinkage factor of the film at different temperatures and baking times to determine the optimal fabrication recipe for shrink film microfluidic device production. Additionally, this study characterized the properties of shrink film, including minimum possible feature size and cross-section geometries, using manual engraving and the CAMEO 4 automated cutting machine. The optimal shrinkage factor ranged from 1.7 to 2.9 at 150 °C and a baking time of 4 min, producing the ideal size for microfluidic device fabrication. The X- and Y-axes shrank ~2.5 times, while Z-axis thickened by a factor of ~5.8 times. This study achieved a minimum feature size of 200 microns, limited by the collapsing of channel sidewalls when shrunk, leading to blockages in the microchannel. These findings demonstrate the feasibility and versatility of using shrink film as a cost-effective and efficient material for the rapid fabrication of microfluidic devices. The potential applications of this material in various fields such as the medical and biomedical industries, bacteria and algae culture and enumeration are noteworthy.

Coupling Relationship between Urbanization and Green Total Factor Productivity in the Context of Population Shrinkage: Evidence from the Rust Belt Region of China

Northeast China, regarded as China’s Rust Belt, has been dealing with numerous issues related to societal transformation and ecological concerns. Data indicate that Northeast China has already become the most severely depopulated region. It is crucial for the region’s sustainable growth to figure out how to balance ecological and urban development in the face of population shrinkage. First, we measured the population shrinkage, urbanization, and green total factor productivity (GTFP) of each city. Second, we calculated the degree of coordination between urbanization and GTFP and spatially visualized it. Finally, we analyzed the influencing factors through further empirical evidence. The findings showed that Northeast China’s cities were generally becoming smaller in terms of population. Urbanization and GTFP both exhibited a climbing tendency, and overall coordination between the two grew gradually. The level of coordination declined as population shrinkage increased. Governmental, technological, and economic factors influenced the level of coordination. The role played by factors influencing coordinated development varied at different levels of shrinkage. The findings not only provide a new research perspective for understanding the coordinated development of socioeconomic and ecological environment in Northeast China, but also provide insights for further improving the relevant policies and promoting the sustainable development and transformation of the region.

The details matter: personalized prediction of live birth after in vitro fertilization in women with polycystic ovary syndrome

ObjectiveTo derive and internally validate a clinical prediction model for live birth in women with polycystic ovary syndrome (PCOS) undergoing in-vitro fertilization (IVF) DesignRetrospective cohort study SettingFour academic reproductive endocrinology clinics Patients207 women with PCOS confirmed by Rotterdam criteria undergoing their first fresh IVF cycle InterventionsNone Main Outcome MeasureThe primary outcome was cumulative live birth (LB) per IVF cycle start. This included any LB that resulted from either fresh embryo transfer or any subsequent frozen embryo transfer from embryos obtained at the index oocyte retrieval. A prediction model was derived using multivariable logistic regression. Covariates considered for inclusion in the prediction model included demographic characteristics, medical history, and prior fertility treatment. Predicted probabilities for live birth were calculated using the prediction model which included the 90% shrinkage factor for each adjusted OR. ResultsThe final model, based on maximization of the area under the receiver operating characteristic curve (AUROC), included age <35 years, White race, presence of polycystic ovaries on ultrasound (PCOM), normal BMI (<25 kg/m2), being metabolically healthy (no metabolic risk factors), and being a non-responder to ovulation induction (OI) agents including letrozole and clomiphene citrate. The AUROC for the model was 0.68 (95% CI: 0.60, 0.77). Predicted probabilities of live birth ranged from 8.1% (95% CI 2.8, 21.5) for a woman who had no favorable predictors to 74.2% (95% CI: 59.5, 84.9) for a woman who had all favorable predictors. ConclusionOur study demonstrates that in addition to anovulation, the underlying pathophysiology and associated co-morbidities alter the likelihood of a successful pregnancy in women with PCOS undergoing IVF. Further validation of this model is needed before it, can serve as a tool to personalize prediction estimates for probability of live birth in women with PCOS.

Communication-efficient and privacy-preserving large-scale federated learning counteracting heterogeneity

Federated learning is a commonly distributed framework for large-scale learning, where a model is learned over massively distributed remote devices without sharing information on devices. It has at least three key challenges: heterogeneity in federated networks, privacy and communication costs. In this paper, we propose three federated learning algorithms to handle these issues gradually. First, we introduce a FedSfDane algorithm (DANE with Shrinkage factor for Federated learning), which improves the inexact approximation of the full gradient, captures statistical heterogeneity and restrains systems heterogeneity across the devices. For avoiding possible privacy leakage in federated learning, a Privacy-preserving FedSfDane algorithm (PFedSfDane) is proposed, which is resistant to adversary attacks. Further, we give a novel Communication-efficient PFedSfDane (CPFedSfDane) algorithm for large-scale federated networks, which effectively handles the above three challenges. We give convergence guarantees for the three algorithms to convex and non-convex learning problems. Numerical experiments illustrate our algorithms outperform FedDANE, FedAvg and FedProx algorithms, especially for highly heterogeneous federated networks. CPFedSfDane improves the prediction accuracy of the state-of-the-art FedDANE algorithm by about 15.0% on sent140 dataset, and has high privacy protection and communication efficiency.

Optimization of Fixture Number in Large Thin-Walled Parts Assembly Based on IPSO

There are lots of researches on fixture layout optimization for large thin-walled parts. Current researches focus on the positioning problem, i.e., optimizing the positions of a constant number of fixtures. However, how to determine the number of fixtures is ignored. In most cases, the number of fixtures located on large thin-walled parts is determined based on engineering experience, which leads to huge fixture number and extra waste. Therefore, this paper constructs an optimization model to minimize the number of fixtures. The constraints are set in the optimization model to ensure that the part deformation is within the surface profile tolerance. In addition, the assembly gap between two parts is also controlled. To conduct the optimization, this paper develops an improved particle swarm optimization (IPSO) algorithm by integrating the shrinkage factor and adaptive inertia weight. In the algorithm, particles are encoded according to the fixture position. Each dimension of the particle is assigned to a sub-region by constraining the optional position range of each fixture to improve the optimization efficiency. Finally, a case study on ship curved panel assembly is provided to prove that our method can optimize the number of fixtures while meeting the assembly quality requirements. This research proposes a method to optimize the number of fixtures, which can reduce the number of fixtures and achieve deformation control at the same time.