A systematic review of the public–private partnership literature published between 2012 and 2021

After approximately 30 years of development, public-private partnership (PPP) has attracted increased attention as an alternative procurement paradigm. However, fresh research on PPP has emerged in the last decade that needs to be summarized. This study selects publications on PPP that were published in recognized journals between 2012 and 2021 from the Scopus database. In target publications, methodologies employed, contributions made, and fields applied are summarized. Social network analysis is used to summarize five core topics in PPP from a multidisciplinary perspective; they are risk management, contract management, CFFs and CSFs, economic and financial issues, and performance management. Additionally, the research limitations and future development direction of PPP are also examined. This study can shed some light on future research on PPP and can contribute to the practice of PPP

Investigation of Microstructure and Mechanical Performance of IN738LC Superalloy Thin Wall Produced by Pulsed Plasma Arc Additive Manufacturing

The IN738LC Ni-based superalloy strengthened by the coherent γ′-Ni3(Al,Ti) intermetallic compound is one of the most employed blade materials in gas turbine engines and IN738LC thin wall components without macro-cracks were fabricated by pulsed plasma arc additive manufacturing (PPAAM), which is more competitive when considering convenience and cost in comparison with other high-energy beam additive manufacturing technologies. The as-fabricated sample exhibited epitaxial growth columnar dendrites along the building direction with discrepant secondary arm spacing due to heat accumulation. A lot of fine γ′ particles with an average size of 81 nm and MC carbides were observed in the interdendritic region. Elemental segregation and γ–γ′ eutectic reaction were analyzed in detail and some MC carbides were confirmed in the reaction L + MC→γ + γ′. After standard heat treatment, bimodal distribution of γ′ phases, including coarse γ′ particles (385 nm, 42 vol.%) and fine γ′ particles (42 nm, 25 vol.%), was observed. The mechanism of microstructural evolution, phase formation, as well as cracking mechanisms were discussed. Microhardness and tensile tests were carried out to investigate the mechanical performance. The results show that both the as-fabricated and heat-treated samples exhibited a higher tensile strength but a slightly lower ductility compared with cast parts

Improving Covariance-Regularized Discriminant Analysis for EHR-Based Predictive Analytics of Diseases

Linear Discriminant Analysis (LDA) is a well-known technique for feature extraction and dimension reduction. The performance of classical LDA however, significantly degrades on the High Dimension Low Sample Size (HDLSS) data for the ill-posed inverse problem. Existing approaches for HDLSS data classification typically assume the data in question are with Gaussian distribution and deal the HDLSS classification problem with regularization. However, these assumptions are too strict to hold in many emerging real-life applications, such as enabling personalized predictive analysis using Electronic Health Records (EHRs) data collected from an extremely limited number of patients who have been diagnosed with or without the target disease for prediction. In this paper, we revised the problem of predictive analysis of disease using personal EHR data and LDA classifier. To fill the gap, in this paper, we first studied an analytical model that understands the accuracy of LDA for classifying data with arbitrary distribution. The model gives a theoretical upper bound of LDA error rate that is controlled by two factors: (1) the statistical convergence rate of (inverse) covariance matrix estimators and (2) the divergence of the training/testing datasets to fitted distributions. To this end, we could lower the error rate by balancing the two factors for better classification performance. Hereby, we further proposed a novel LDA classifier De-Sparse that leverages De-sparsified Graphical Lasso to improve the estimation of LDA, which outperforms state-of-the-art LDA approaches developed for HDLSS data. Such advances and effectiveness are further demonstrated by both theoretical analysis and extensive experiments on EHR datasets

Comparison of clinical outcomes among total knee arthroplasties using posterior-stabilized, cruciate-retaining, bi-cruciate substituting, bi-cruciate retaining designs: a systematic review and network meta-analysis

Abstract. Background:. Despite the advent of innovative knee prosthesis design, a consistent first-option knee implant design in total knee arthroplasty (TKA) remained unsettled. This study aimed to compare the clinical effects among posterior-stabilized (PS), cruciate-retaining (CR), bi-cruciate substituting (BCS), and bi-cruciate retaining designs for primary TKA. Methods:. Electronic databases were systematically searched to identify eligible randomized controlled trials (RCTs) and cohort studies from inception up to July 30, 2021. The primary outcomes were the range of knee motion (ROM), and the secondary outcomes were the patient-reported outcome measures (PROMs) and complication and revision rates. Confidence in evidence was assessed using Confidence in Network Meta-Analysis. The Bayesian network meta-analysis was performed for synthesis. Results:. A total of 15 RCTs and 18 cohort studies involving 3520 knees were included. The heterogeneity and inconsistency were acceptable. There was a significant difference in ROM at the early follow-up when PS was compared with CR (mean difference [MD] = 3.17, 95% confidence interval [CI] 0.07, 7.18) and BCS was compared with CR (MD = 9.69, 95% CI 2.18, 17.51). But at the long-term follow-up, there was no significant difference in ROM in any one knee implant compared with the others. No significant increase was found in the PROMs and complication and revision rates at the final follow-up time. Conclusions:. At early follow-up after TKA, PS and BCS knee implants significantly outperform the CR knee implant in ROM. But in the long run, the available evidence suggests different knee prostheses could make no difference in clinical outcomes after TKA with extended follow-up

Logistic regression for crystal growth process modeling through hierarchical nonnegative garrote-based variable selection

<p>Single-crystal silicon ingots are produced from a complex crystal growth process. Such a process is sensitive to subtle process condition changes, which may easily become failed and lead to the growth of a polycrystalline ingot instead of the desired monocrystalline ingot. Therefore, it is important to model this polycrystalline defect in the crystal growth process and identify key process variables and their features. However, to model the crystal growth process poses great challenges due to complicated engineering mechanisms and a large amount of functional process variables. In this article, we focus on modeling the relationship between a binary quality indicator for polycrystalline defect and functional process variables. We propose a logistic regression model with hierarchical nonnegative garrote-based variable selection method that can accurately estimate the model, identify key process variables, and capture important features. Simulations and a case study are conducted to illustrate the merits of the proposed method in prediction and variable selection.</p

Flexible Energy Load Identification in Intelligent Manufacturing for Demand Response using a Neural Network Integrated Particle Swarm Optimization

Various Demand Response Programs Have Been Widely Established by Many Utility Companies as a Critical Load Management Tool to Balance the Demand and Supply for the Enhancement of Power System Stability in Smart Grid. While Participating in These Demand Response Programs, Manufacturers Need to Develop their Optimal Demand Response Strategies So that their Energy Loads Can Be Shifted Successfully According to the Request of the Grid to Achieve the Lowest Energy Cost Without Any Loss of Production. in This Paper, the Flexibility of the Electricity Load from Manufacturing Systems is Introduced. a Binary Integer Mathematical Model is Developed to Identify the Flexible Loads, their Degree of Flexibility, and Corresponding Optimal Production Schedule as Well as the Power Consumption Profiles to Ensure the Optimal Participation of the Manufacturers in the Demand Response Programs. a Neural Network Integrated Particle Swarm Optimization Algorithm, in Which the Learning Rates of the Particle Swarm Optimization Algorithm Are Predicted by a Trained Neural Network based on the Improvement of the Fitness Values between Two Successive Iterations, is Proposed to Find the Near Optimal Solution of the Formulated Model. a Numerical Case Study on a Typical Manufacturing System is Conducted to Illustrate the Effectiveness of the Proposed Model as Well as the Solution Approach

Acoustics Based Monitoring and Diagnostics for the Progressive Deterioration of Helical Gearboxes

Abstract Gearbox condition monitoring (CM) plays a significant role in ensuring the operational reliability and efficiency of a wide range of critical industrial systems such as wind turbines and helicopters. Accurate and timely diagnosis of gear faults will improve the maintenance of gearboxes operating under sub-optimal conditions, avoid excessive energy consumption and prevent avoidable damages to systems. This study focuses on developing CM for a multi-stage helical gearbox using airborne sound. Based on signal phase alignments, Modulation Signal Bispectrum (MSB) analysis allows random noise and interrupting events in sound signals to be suppressed greatly and obtains nonlinear modulation features in association with gear dynamics. MSB coherence is evaluated for selecting the reliable bi-spectral peaks for indication of gear deterioration. A run-to-failure test of two industrial gearboxes was tested under various loading conditions. Two omnidirectional microphones were fixed near the gearboxes to sense acoustic information during operation. It has been shown that compared against vibration based CM, acoustics can perceive the responses of vibration in a larger areas and contains more comprehensive and stable information related to gear dynamics variation due to wear. Also, the MSB magnitude peaks at the first three harmonic components of gear mesh and rotation components are demonstrated to be sufficient in characterizing the gradual deterioration of gear transmission. Consequently, the combining of MSB peaks with baseline normalization yields more accurate monitoring trends and diagnostics, allowing the gradual deterioration process and gear wear location to be represented more consistently

Flexible Energy Load Identification in Intelligent Manufacturing for Demand Response using a Neural Network Integrated Particle Swarm Optimization

Various demand response programs have been widely established by many utility companies as a critical load management tool to balance the demand and supply for the enhancement of power system stability in smart grid. While participating in these demand response programs, manufacturers need to develop their optimal demand response strategies so that their energy loads can be shifted successfully according to the request of the grid to achieve the lowest energy cost without any loss of production. In this paper, the flexibility of the electricity load from manufacturing systems is introduced. A binary integer mathematical model is developed to identify the flexible loads, their degree of flexibility, and corresponding optimal production schedule as well as the power consumption profiles to ensure the optimal participation of the manufacturers in the demand response programs. A neural network integrated particle swarm optimization algorithm, in which the learning rates of the particle swarm optimization algorithm are predicted by a trained neural network based on the improvement of the fitness values between two successive iterations, is proposed to find the near optimal solution of the formulated model. A numerical case study on a typical manufacturing system is conducted to illustrate the effectiveness of the proposed model as well as the solution approach