Previous Projection Research Articles

Projected volume of primary total joint arthroplasty in the USA from 2019 to 2060.

The global incidence of total joint arthroplasty (TJA) has consistently risen over time, and while various forecasts differ in magnitude, future projections suggest a continued increase in these procedures. Differences in future United States projections may arise from the modeling method selected, the nature of the national arthroplasty registry employed, or the representativeness of the specific hospital discharge records utilized. In addition, many models have not accounted for ambulatory surgery as well as all payer types. Therefore, to attempt to make a more accurate model, we utilized a national representative sample that included outpatient arthroplasties and all insurance types to predict the volumes of primary TJA in the USA from 2019 to 2060. A national, all-payer database was queried. All patients who underwent primary total hip arthroplasty (THA) and total knee arthroplasty (TKA) from January 1, 2010, to December 31, 2019, were identified using international classification of disease Ninth Revision (9) and Tenth Revision (10) codes and current procedure terminology codes. Absolute frequencies and incidence rates were calculated per 100,000 for both THA and TKA procedures, with 95% confidence intervals. Mean growth in absolute frequency and incidence rates were calculated for each procedure from 2010 to 2014, and 2010 to 2019, with 95% confidence intervals (CI). The overall increase in THA and TKA procedures are expected to grow + 10 and + 36%, respectively, using linear regressions and + 9 and + 37%, respectively. The most positive mean growth in procedure frequency occurred from 2010 to 2014 for THA (+ 24, 95% Confidence Interval (CI): + 21, + 27) and 2010-2019 for TKA (+ 11%, 95% CI: + 9, + 14). There positive trend patterns in incidence rate growth for both procedures, with similar 2010-2019 incidence rates + 6%) for THA (+ 3%, 95% CI: + 0, + 6%) and TKA (+ 3%, 95% CI: + 1%, + 6%). Utilizing a nationally representative database, we demonstrated that TJA procedures would continue with an increased growth pattern to 2060, though slightly decreased from the surge from 2014 to 2019. While this finding applies to the representativeness of the population at hand, the inclusion of outpatient arthroplasty and all payer types validates an approach that has not been undertaken in previous projection studies.

Forecasting the progression of human civilization on the Kardashev Scale through 2060 with a machine learning approach

Energy has been propelling the development of human civilization for millennia. Humanity presently stands at Type 0.7276 on the Kardashev Scale, which was proposed to quantify the relationship between energy consumption and the development of civilizations. However, current predictions of human civilization remain underdeveloped and energy consumption models are oversimplified. In order to improve the precision of the prediction, we use machine learning models random forest and autoregressive integrated moving average to simulate and predict energy consumption on a global scale and the position of humanity on the Kardashev Scale through 2060. The result suggests that global energy consumption is expected to reach ~ 887 EJ in 2060, and humanity will become a Type 0.7449 civilization. Additionally, the potential energy segmentation changes before 2060 and the influence of the advent of nuclear fusion are discussed. We conclude that if energy strategies and technologies remain in the present course, it may take human civilization millennia to become a Type 1 civilization. The machine learning tool we develop significantly improves the previous projection of the Kardashev Scale, which is critical in the context of civilization development.

The Stereographic Projection in Topological Modules

The stereographic projection is constructed in topological modules. Let A be an additively symmetric closed subset of a topological R-module M such that 0∈int(A). If there exists a continuous functional m*:M→R in the dual module M*, an invertible s∈U(R) and an element a in the topological boundary bd(A) of A in such a way that m*−1({s})∩int(A)=⌀, a∈m*−1({s})∩bd(A), and s+m*bd(A)\{−a}⊆U(R), then the following function b↦−a+2s(m*(b)+s)−1(b+a), from bd(A)\{−a} to (m*)−1({s}), is a well-defined stereographic projection (also continuous if multiplicative inversion is continuous on R). Finally, we provide sufficient conditions for the previous stereographic projection to become a homeomorphism.

Amplified future risk of compound droughts and hot events from a hydrological perspective

Global warming can result in changes in droughts and hot events (or compound droughts and hot events, CDHEs), which can take a heavy toll on the society and environment. Recent studies have made substantial progress in the projection of these events. However, previous projection studies mostly focus on the concurrences of meteorological droughts and hot events but ignore the difference among various CDHEs. Specifically, the concurrence of hot events and different types of droughts (e.g., agricultural droughts and hydrological droughts) has been seldom explored from a hydrological perspective. Based on phase six of the Coupled Model Intercomparison Project (CMIP6), we evaluate changes in different types of CDHEs, including compound meteorological drought-hot events (CMDHEs), compound agricultural drought-hot events (CADHEs) and compound hydrological drought-hot events (CHDHEs), for different future periods at the global scale. Based on comparisons with data from Global Land Data Assimilation System Version 2 (GLDAS-2.0), CMIP6 can reproduce the overall spatial distribution and temporal variation of different CDHEs at the global scale. In addition, the frequency and spatial extent of the three compound events show a marked increase during different future periods relative to the base period 1995–2014. The projected increase in global average frequency of CMDHEs in the long term period is lower than that of CADHEs (increase by 73.74% and 113.95% for CMDHEs and CADHEs, respectively). The uncertainty in the simulation of CADHEs and CHDHEs is relatively larger than CMDHEs in the future periods over most regions. The results of this study highlight the urgent demand for adaptation measures of CDHEs to cope with compound extremes in the future.

Projection and Rescaling Algorithm for Finding Maximum Support Solutions to Polyhedral Conic Systems

We propose a simple projection and rescaling algorithm that finds maximum support solutions to the pair of feasibility problems: [Formula: see text] where L is a linear subspace in [Formula: see text] and [Formula: see text] is its orthogonal complement. The algorithm complements a basic procedure that involves only projections onto L and [Formula: see text] with a periodic rescaling step. The number of rescaling steps and, thus, overall computational work performed by the algorithm are bounded above in terms of a condition measure of the above pair of problems. Our algorithm is a natural but significant extension of a previous projection and rescaling algorithm that finds a solution to the full support problem: [Formula: see text] when this problem is feasible. As a byproduct of our new developments, we obtain a sharper analysis of the projection and rescaling algorithm in the latter special case.

Multi-country SDGs indicator projections involving impacts of economic shocks: A protocol applied to the COVID-19 pandemic for ASEAN-5 countries

Economies often experience large shocks, necessitating the revision of development indicator forecasts, including Sustainable Development Goals (SDGs) indicators. Many of those, predicted for 2030, require continued monitoring and re-estimation of how great the impact of these shocks will be, e.g., comparing the achievements with and without the shocks (counterfactual). In this paper, we design a protocol to create datasets containing 2030 SDGs indicator projection estimates that can be used to monitor the extent to which current economic shocks will affect the trajectories of those indicators. We combine official United Nations Statistics Division (UNSTAT) SDGs indicator data and economic growth projections data and fit them into the protocol. The protocol includes filtering UNSTAT SDGs indicators for regression analysis connecting them with economic growth. We assume that the difference in economic growth projections before and after a shock is primarily caused by the shock. This implies that our protocol is less suitable for an episode of more subtle shocks or shocks with multiple causes. We use these estimates to create the SDGs indicators projection dataset. We applied this to ASEAN-5 countries and the COVID-19 pandemic. The same protocol can be used for other countries as well as other economic shocks.•The protocol is useful to monitor how previous projection trajectories of SDGs indicators are affected by relevant large economic shocks, such as those due to the COVID-19 pandemic. The resulted dataset can also be used for comparing achievements, with and without shocks (counterfactual).•This protocol can be used by national and international agencies, especially those in charge of planning, monitoring, and evaluating the SDGs agenda. The protocol and the resulting data would also be helpful to researchers working on SDGs issues.•In this paper, the protocol to create the projection dataset of SDGs applies for the ASEAN-5 countries using the COVID-19 shocks. These can also be applied for other countries and other economic shocks.

Impact of COVID-19 Mortality on U.S. Life Expectancy by Socioeconomic Rank of County of Residence

Mortality disparities due to COVID-19 pandemic in the US accentuated the gap in the targeted public health and education response among disadvantaged communities. We use county data from John Hopkins University of Medicine in conjunction with county socioeconomic decile rankings, and weekly national data from the Centers for Disease Control to uncover the impact of county-level socioeconomic deprivation on the spatio-temporal dynamic of COVID-19 mortality. We estimate that over the course of 2020, the pandemic reduced the life expectancy at birth by 1.33 years (95% CI 1.0-1.56), and by 0.84 years (95% CI 0.59-1.0) by age 85 across all county SES decile groups, relative to the previous year's projection. The highest losses occurred in counties at the ends of the deprivation spectrum, and least affecting those in its middle. Decomposition of the impact of the COVID-19 mortality by seasonal time periods of 2020 reveals that coastal urban and high-SES counties endured a heavy death toll in the initial stages of the pandemic, though managed to cut it by more than a half by the end of 2020. The least affluent, inland, and rural counties have experienced a dramatic and lasting increase in deaths toward the second half of the year. We find that preexisting socioeconomic disparities before COVID-19 remained in force during the pandemic, leaving populations at all ages residing in underserved communities at a greater risk. This both calls into question and further instructs the ongoing public health interventions enabling more effective and equitable infectious disease mitigation strategies.

Computer aided machining fixture design algorithm and software based on case learning for near-net-shaped jet engine blade

In the blade computer numerical control (CNC) machining process, the machining fixture design for each type of blade will be a very difficult task due to the large variety and quantity of blades. This study proposes a computer-aided machining fixture design algorithm and software based on case learning. The three-dimensional model of the case learning blade and the target blade is firstly projected into a cylinder surface based on the projection algorithm, and the cylinder surface is expanded into a two-dimensional plane based on expansion algorithm. Then, the matching machining fixture layout of the target blade relative to the case learning blade is obtained in two-dimensional space by the mesh affine transformation algorithm according to the geometric edge constraints and the matching relationship of the case learning blade and target blade. At last, the inverse transformations of projection and expansion are used to obtain the final fixture layout of the target blade three-dimensional model based on the previous projection and expansion relationship. The special computer-aided fixture design software of near-net shaped blade is developed, and the machining fixture design and CNC machining experiment based on two typical near-net-shaped blades proved that the computer-aided fixture design algorithm based on case learning is feasible and can be used to realize the intelligent design of near-net-shaped blade machining fixture.

Symmetric projection group approach for promoting homogeneity in the analytic hierarchy process

The complexity of the socio-economic environment requires the involvement of a group of decision makers in the decision-making process. They can contribute the diversity of knowledge and experience that allow all the important aspects of the decision-making problem to be taken into account. In the aggregation process, the weights of the decision makers play an important role. In this paper we have focused on the group analytic hierarchy process (AHP). We emphasized the homogeneity of the opinions of the decision makers as the basis for determining the importance weights of the decision makers. We reviewed the projection method and its upgrades for determining decision makers weights and demonstrated with examples that they are not suitable for application in AHP. We proposed a new symmetric projection method that eliminates the imperfections of the previous projection methods. We proposed a Symmetric Projection Group Approach (SPGA) to derive group weights in AHP. To validate the SPGA, we selected four evaluation measures. The evaluation of the examples shows that the SPGA achieves good evaluation results compared to the other methods for deriving decision makers weights and is suitable for real-world applications.

Helmholtz decomposition-based SPH

BackgroundSPH method has been widely used in the simulation of water scenes. As a numerical method of partial differential equations, SPH can easily deal with the distorted and complex boundary. In addition, the implementation of SPH is relatively simple, and the results are stable and not easy to diverge. However, SPH method also has its own limitations. In order to further improve the performance of SPH method and expand its application scope, a series of key and difficult problems restricting the development of SPH need to be improved. MethodsIn this paper, we introduce the idea of Helmholtz decomposition into the framework of smoothed particle hydrodynamics (SPH) and propose a novel velocity projection scheme for three-dimensional water simulation. First, we apply Helmholtz decomposition to a three-dimensional velocity field and decompose it into three orthogonal subspaces. Then, our method combines the idea of spatial derivatives in SPH to obtain a discrete Poisson velocity equation. Finally, the conjugate gradient (CG) is utilized to efficiently solve the Poisson equation. ResultsThe experimental results show that the proposed scheme is suitable for various situations and has higher efficiency than the current SPH projection scheme. ConclusionCompared with the previous projection scheme, our solution does not need to modify the particle velocity indirectly by pressure projection, but directly by velocity field projection. The new scheme can be well integrated into the existing SPH framework, and can be applied to the interaction of water with static and dynamic obstacles, even for viscous fluid.

Solving a Split Feasibility Problem by the Strong Convergence of Two Projection Algorithms in Hilbert Spaces

The goal of this manuscript is to establish strong convergence theorems for inertial shrinking projection and CQ algorithms to solve a split convex feasibility problem in real Hilbert spaces. Finally, numerical examples were obtained to discuss the performance and effectiveness of our algorithms and compare the proposed algorithms with the previous shrinking projection, hybrid projection, and inertial forward-backward methods.

Anisotropic multivariate deconvolution using projection on the Laguerre basis

We investigate adaptive density estimation in the additive model Z=X+Y, where X and Y are independent d-dimensional random vectors with non-negative coordinates. Our goal is to recover the density of X from independent observations of Z, assuming the density of Y is known. In the d=1 case, an estimation procedure using projection on the Laguerre basis has already been studied. We generalize this procedure in the multivariate case: we establish non-asymptotic upper bounds on the mean integrated squared error of the estimator and we derive convergence rates on anisotropic functional spaces. Moreover, we provide data-driven strategies for selecting the right projection space (for d=1, we improve the previous projection procedure). We illustrate these procedures on simulated data, and in dimension d=1 we compare our procedure with the previous adaptive projection procedure.

Modeling the effect of lockdown timing as a COVID-19 control measure in countries with differing social contacts

The application, timing, and duration of lockdown strategies during a pandemic remain poorly quantified with regards to expected public health outcomes. Previous projection models have reached conflicting conclusions about the effect of complete lockdowns on COVID-19 outcomes. We developed a stochastic continuous-time Markov chain (CTMC) model with eight states including the environment (SEAMHQRD-V), and derived a formula for the basic reproduction number, R0, for that model. Applying the {R}_{0} formula as a function in previously-published social contact matrices from 152 countries, we produced the distribution and four categories of possible {R}_{0} for the 152 countries and chose one country from each quarter as a representative for four social contact categories (Canada, China, Mexico, and Niger). The model was then used to predict the effects of lockdown timing in those four categories through the representative countries. The analysis for the effect of a lockdown was performed without the influence of the other control measures, like social distancing and mask wearing, to quantify its absolute effect. Hypothetical lockdown timing was shown to be the critical parameter in ameliorating pandemic peak incidence. More importantly, we found that well-timed lockdowns can split the peak of hospitalizations into two smaller distant peaks while extending the overall pandemic duration. The timing of lockdowns reveals that a “tunneling” effect on incidence can be achieved to bypass the peak and prevent pandemic caseloads from exceeding hospital capacity.

A Higher-Order Singular Value Decomposition-Based Radio Frequency Interference Mitigation Method on High-Frequency Surface Wave Radar

Recently, high-frequency surface wave radar (HFSWR) has been widely applied in ocean surface dynamic parameter measurement. However, the radar echoes backscattered from ocean surface tend to be contaminated by the external radio frequency interference (RFI), and the mitigation of RFI becomes an intractable problem, especially for wide beam HFSWR. The HFSWR measured data are multichannel. The higher-order singular value decomposition (HOSVD) is an effective method to improve the accuracy of subspace estimation by exploiting this multidimensional structure. In this article, we develop an RFI mitigation method based on the HOSVD algorithm and orthogonal subspace projection. Simulations indicate that, compared with the previous orthogonal subspace projection RFI cancellation schemes, the proposed method has significant advantages in keeping the desired signals while suppressing the interference. The proposed method is applied to the experimental data of the HFSWR with severe RFI. The ocean surface currents inverted from the RFI-mitigated data agree reasonably with the tidal features in the radar detection area. After RFI mitigation, the performance of the HFSWR system is significantly improved on the effective current detection range and precision.

An improved quantum projection filter

This work extends the previous quantum projection filtering scheme in Gao et al. (2019), by adding an optimality analysis result. A reformulation of the quantum projection filter is derived by minimizing the truncated Stratonovich stochastic Taylor expansion of the difference between the true quantum trajectory and its approximation on a lower-dimensional submanifold through quantum information geometric techniques. Simulation results for a qubit example demonstrate better approximation performance for the new quantum projection filter.

Acute radiation risk assessment and mitigation strategies in near future exploration spaceflights

As more exploration spaceflights are planned to travel beyond the protective Earth magnetosphere to deep space destinations, acute health risks due to possible high radiation doses during severe Solar Particle Events (SPEs) are of greater concern to mission planners and management teams. It is expected that some degree of Acute Radiation Syndromes (ARS) symptoms may be observed, but the specific list of health risks that are relevant to exploration missions has been ambiguous and debatable in the past. This mini-review gives a brief summary of the features of radiation exposure if astronauts encounter severe SPEs beyond Low Earth Orbit (LEO), the evidence of ARS radiobiological studies at exposure levels close to recommended limits, and the shortcomings of previous dose projection approaches for ARS risk assessment. Some ARS biomathematical models, particularly those pertinent to the dose ranges that severe SPEs beyond LEO could generate, are reviewed and evaluated, focusing on their capability to predict the incidence of performance incapacitation and time-phased health effects with subsequent medical care recommendations. Using onboard active dosimeter input for estimating organ doses and likely clinical outcomes for SPEs in real time, a new strategy for ARS assessment and mitigation is described to cope with the potential threats of severe SPEs for planned deep space missions.

Principal Components Analysis as enhancement Operator and Compression factor

Principal components analysis (PCA) is effective at compressing information in multivariate data sets by computing orthogonal projections that maximize the amount of data variance. Unfortunately, information content in hyper spectral images does not always coincide with such projections. We propose an application of projection pursuit (pp), which seeks to find a set of projections that are "interesting" in the sense that they deviate from the Gaussian distribution assumption.
 Once these projections are obtained, they can be used for image compression, segmentation, or enhancement for visual analysis. To find these projections, a two –step iterative process is followed where we first search for a projection that maximizes a projection index based on the information divergence of the projections estimated probability distribution from the Gaussian distribution and then reduce the rank by projections the data on to the subspace orthogonal to the previous projection . To calculate each projections, we use a simplified approach to maximizing the projection index, which does not require optimization algorithm. It searches for a solution by obtaining a set of candidate projections from the data and choosing the one with the highest projection index. The effectiveness of the method is demonstrated through simulated examples as well as data from the hyper spectral digital imagery collection experiment and the spatially enhanced broadband and array spectrograph system.

Dynamic control of laser source current in projection display

Display technology is the main source for humans to obtain information and entertainment. Increased display screen size is a trend, whether on mobile phone or television, and projection display has become increasingly popular in achieving large screen displays. Previous projection display systems have used a traditional light bulb which cannot be modulated, so the power of the display device is constant and the contrast of the display device can only be improved by controlling the size of the aperture in the light path. With the appearance of solid-state light sources, i.e., lasers, the luminance of the light source can be modulated according to the image content so that the power of the light source can be reduced and the dynamic contrast can be improved simultaneously. A method to modulate the power of the laser light source on a frame-to-frame basis in response to the input image data is shown. An ultrahigh contrast is achieved with the laser display. A dynamic contrast of greater than 100,000:1 can be achieved, and the average power consumption of the laser TV can be reduced by about 20% with typical video source material.

A split random time-stepping method for stiff and nonstiff detonation capturing

In this paper, a new operator splitting method is proposed for capturing stiff and nonstiff detonation waves. In stiff cases, an incorrect propagation of discontinuities might be observed for general shock-capturing methods due to under-resolution in space and time. Previous random projection methods have been applied successfully for stiff detonation capturing at under-resolved conditions. Not relying on random projection of the intermediate state onto two presumed equilibrium states (completely burnt or unburnt) as with the random projection method, the present approach randomly advances or interrupts the reaction process. Each one-way reaction is decoupled from the multi-reaction kinetics by operator splitting. The local temperature is compared with a random temperature within a temperature interval to control the random reaction. Random activation or deactivation in the reaction step serves to reduce the accumulated error of discontinuity propagation. Extensive numerical experiments demonstrate the effectiveness and robustness of the method. For nonstiff problems, the proposed random method recovers the accuracy of general operator splitting methods by adding a drift term.

Downscaled energy demand projection at the local level using the Iterative Proportional Fitting procedure

In this paper we forecast energy demand at the local level using a new two-dimensional downscaling methodology that respects regional and subnational variation and, as well as consistency at the aggregate level. This methodology combines the one-dimensional statistical downscaling and the Iterative Proportional Fitting (IPF) procedure. Previous projection studies based on downscaling methods have focused on allocating one-dimensional quantity indicators such as GDP, population, and emissions, into geographically or administratively lower levels. In case of energy demand projections, however, downscaling involves not only regional allocation of energy demand quantities but also energy mixes of the demands in the regions. Our novel two-dimensional downscaling methodology allows us to downscale national energy projections in both demand quantities and their energy mix to regional levels by combining the statistical downscaling and the IPF procedure. We illustrate the methodology by deriving energy demand projections for provinces and metropolitan cities in Korea from national scenarios. In addition, we provide two more simple applications of the IPF to show utilization potentials of the IPF in energy demand forecasting and statistics.