Multifactor analysis of delayed absorption of subretinal fluid after scleral buckling surgery

Background The purpose of this study is to assess the absorption of subretinal fluid (SRF) after scleral buckling (SB) surgery for the treatment of rhegmatogenous retinal detachment (RRD). We also examined related factors that may affect the delayed absorption of SRF. Methods This retrospective study included patients who underwent successful SB surgery for the treatment of macula-off RRD and in which the retina was reattached after the surgery. The patients were categorized according to gender, duration, age, the number, and location of retinal breaks. Subfoveal choroidal thickness (SFCT), height of subretinal fluid (SRFH), and the choriocapillaris flow density (CCFD) within 3 × 3 mm macular fovea were included. Delayed absorption was determined by the SRF that remained unabsorbed for 3 months after the procedure. The endpoint was determined when the SRF could no longer be observed. Results A total of 62 patients (63 eyes) were enrolled. In 35 eyes (56.45%) SRF was completely absorbed and in 28 (43.55%) eyes delayed absorption of SRF in macular areas was observed at 3 months after surgery. A young age (< 35 years), inferior retinal breaks were associated with good outcomes by applying multivariable analysis on the rate of SRF absorption after SB instead of gender, the number of breaks, and duration (p < 0.05). CCFD was significantly different between the SRF group and the non-SRF group after SB (0.66 ± 0.04% vs 0.63 ± 0.05%, P < 0.05). SRFH showed a moderate positive correlation with SFCT (rs = 0.462, p = 0.000), however, using binary logistic regression analysis it was determined that SFCT was not related to the absorption of the SRF. Conclusions The absorption of SRF after SB may be correlated with choriocapillaris flow density. Age and location of breaks are significant factors affecting the absorption of SRF. The duration of disease is an uncertain factor due to several subjective reasons.

A Psychometric Evaluation of the Short Grit Scale

Grit, the passion and perseverance for long-term goals, has received attention from personality psychologists because it predicts success and academic achievement. Grit has also been criticized as simply another measure of self-control or conscientiousness. A precise psychometric representation of grit is needed to understand how the construct is unique and how it overlaps with other constructs. Previous research suggests that the Short Grit Scale (Grit-S) has several psychometric limitations, such as uncertain factor structure within and across populations, uncertainty about reporting total or subscale scores, and different assessment precision at low and high levels on the construct. We conducted modern psychometric techniques including parallel analysis, measurement invariance, extrinsic convergent validity, and Item Response Theory models on two American samples. Our results suggest that the Grit-S is essentially unidimensional and that there is construct overlap with the self-control construct. Subscale factors were the result of an item doublet, where two items had highly correlated uniquenesses, showed similar item information, and were more likely to exhibit measurement bias. Findings replicated across samples. Finally, we discuss recommendations for the use of the Grit-S based on the theoretical interpretation of the unidimensional factor and our empirical findings.

It’s a macroporous world; we just model in it

&lt;p&gt;Many soil physical models assume a homogeneous domain and equilibrium conditions, even as decades of evidence have suggested that such states are rarely present in the real world. Instead, natural soils tend to be characterized by physical heterogeneity (e.g., macropores) and non-equilibrium movement of water, solutes and gases (e.g., preferential flow and transport), making it critical to develop physically realistic yet parsimonious descriptors of these processes. In this presentation we discuss recent advances using multi-domain descriptions of soils to model preferential flow and subsurface contaminant movement under field conditions. Here we emphasize the use of simplifying assumptions and straightforward parameterizations, and consider whether those factors constrain the ability of such models to realistically represent underlying physical mechanisms. We also discuss results of an innovative field experiment aimed at constraining macropore porosity, which is a key yet highly uncertain factor in such multi-domain models. Finally, we consider the relevant scales of these multi-domain models, and whether such approaches merit consideration in larger (e.g., hillslope- or catchment-scale) simulations. &amp;#160;&amp;#160;&amp;#160;&lt;/p&gt;

OPTIMIZED MANAGEMENT STRATEGY FOR CONSTRUCTION PROJECTS CONSIDERING THE TRADE-OFF OF ESTIMATE SCHEDULE AND COST AT COMPLETION

Nowadays, the minimization of project time and cost is an important issue. However, time and cost problems are difficult to solve. They are affected by the uncertain factor. Then, the construction project always fails to achieve the effectiveness of time and cost performance. It causes delays and cost overrun. In this research, SOS-NN-LSTM is required to establish the estimate schedule to completion (ESTC) and estimate cost to completion (ECTC) prediction model based on time now performance. Then, the prediction model will be integrated with MOSOS to obtain the optimal prediction value. The integration is needed because there is no direct equation to calculate the ESTC and ECTC. The Pareto curve identified based on the prediction values of MOSOS. The Pareto curve is used to determine the optimal trade-off between project duration and project cost. Then, the indifference curve is used to solve the trade-off problem between estimate schedule at completion (ESAC) dan estimate cost at completion (ECAC) which give the decision-maker preference.

A Solvable Time-Inconsistent Principal-Agent Problem

We consider the dynamic contract model with time inconsistency preference of principal-agent problem to study the influence of the time inconsistency preference on the optimal effort and the optimal reward mechanism. We show that when both the principal and the agent are time-consistent, the optimal effort and the optimal reward are the decreasing functions of the uncertain factor. And when the agent is time-inconsistent, the impatience of the agent has a negative impact on the optimal contract. The higher the discount rate of the agent is, the lower the efforts provided; agents tend to the timely enjoyment. In addition, when both the principal and the agent are time-inconsistent, in a special case, their impatience can offset the impact of uncertainty factor on the optimal contract, but, in turn, their impatience will affect the contract.

An Improved Fair Allocation Based on Contribution Rate and Its Application

Oil production task allocation (OPTA) is affected by various factors, and each one has a different impact on oil production. Therefore, the fair distribution of production task to each production branch is really a hard work for an oil company, so a fair allocation based on contribution rate (ABCR) has been proposed to solve this problem in this paper. The algorithm of ABCR, unlike other existing algorithms, takes into account the differences of members’ contribution (DMC), which can be expressed by member contribution rate (MCR) based on the certainty and uncertainty factors. Two steps are implemented to gain the differences of members’ contribution. First, we use Principal Component Analysis (PCA) to reduce factors for certain factor and construct a new factor with Analytic Hierarchy Process (AHP) for uncertain factor. Then, the MCR is evaluated by AHP. Based on member contribution rate, member goal, and alliance target, a fair allocation can be obtained by ABCR. Finally, we propose an evaluation criterion for allocation. Case study shows that the resource allocation results of ABCR not only are more reasonable than those of the other methods but also can prevent unfair allocation and enhance the production environment, thereby improving the enthusiasm for production.

Approaches to examination of Liquidity and Volatility Risk Pricing in Stock Markets–Implications for Indian Case

Liquidity and Volatility Risks is twin asset pricing issues that altogether affect the operational functioning and pricing in stock markets all over the globe. In developed countries as well in emerging markets, the researchers have put in large efforts to find the liquidity and volatility risk structures in individual stocks and well as market as a whole. There is also an on-going research to explore whether there is a common uncertain factor across these risk classes. In this paper we provide a conceptual framework of the issue of liquidity and volatility and also the approaches used by researchers to measure the liquidity and volatility risk.

Neural-network-based real-time trajectory replanning for Mars entry guidance

The real-time trajectory replanning method which is used for the guidance of Mars entry is investigated in this paper. Comparing with the traditional Mars entry guidance methods, such as the reference-trajectory tracking guidance and predictor–corrector guidance, the real-time trajectory replanning method can increase the reliability of the mission remarkably. When faults occur during the Mars entry phase, a replacement trajectory will be planned quickly. Due to the limited onboard computing capacity, replanning the trajectory onboard is a challenging task. Corresponding to this problem, the neural network is trained to approximate the dynamics of the atmospheric entry. The uncertain factor of the atmospheric density is also included in the neural network. Then, by using the characters of the neural network, the analytical expressions of the Jacobian which are needed in trajectory optimization are derived. Finally, an estimation-replanning guidance procedure is introduced. The numerical simulation shows that the proposed guidance strategy can decrease the error of final states effectively, and the neural network approximation improves the computational speed of the nonlinear programming solver remarkably, which makes the method more suitable for use onboard.

RESH: A Secure Authentication Algorithm Based on Regeneration Encoding Self-Healing Technology in WSN

In the real application environment of wireless sensor networks (WSNs), the uncertain factor of data storage makes the authentication information be easily forged and destroyed by illegal attackers. As a result, it is hard for secure managers to conduct forensics on transmitted information in WSN. This work considers the regeneration encoding self-healing and secret sharing techniques and proposes an effective scheme to authenticate data in WSN. The data is encoded by regeneration codes and then distributed to other redundant nodes in the form of fragments. When the network is attacked, the scheme has the ability against tampering attack or collusion attack. Furthermore, the damaged fragments can be restored as well. Parts of fragments, encoded by regeneration code, are required for secure authentication of the original distributed data. Experimental results show that the proposed scheme reduces hardware communication overhead by five percent in comparison. Additionally, the performance of local recovery achieves ninety percent.