Comparison of Clinical Results Between a 4-point Scleral Fixation of Intraocular Lenses Using Gore-tex Suture and a 2-point Fixation Using Prolene Suture

Purpose: To compare clinical outcomes between a 4-point scleral fixation of intraocular lenses (IOLs) using Gore-Tex suture or a 2-point scleral fixation using Prolene sutureMethods: In this multicenter, retrospective cohort study, patients were enrolled who had undergone a pars plana vitrectomy and either a 4-point scleral fixation using Gore-Tex suture or a 2-point scleral fixation using Prolene suture. Preoperative biometrics, postoperative refractive outcomes, and postoperative surgical complication rates were evaluated.Results: Thirty-seven eyes underwent scleral fixation with Gore-Tex suture, while 44 eyes underwent scleral fixation with Prolene suture. Postoperative best corrected visual acuity was 0.20 (± 0.34) in the Gore-Tex group and 0.21 (± 0.28) in the Prolene group (logMAR, 20/32 on the Snellen scale) (p = 0.691). No significant difference was found in the average prediction error between the Gore-Tex (-0.13 ± 0.68 D) and Prolene (-0.21 ± 1.27 D) groups (p = 0.077). The postoperative complication rate was lower in the Gore-Tex group (17%) than in the Prolene group (41%) (p = 0.023).Conclusion: A 4-point scleral fixation using Gore-Tex suture may be a good alternative to a conventional scleral fixation using Prolene suture for IOL implantations in eyes without capsular support, with a lower risk of postoperative complications.

Неэмпирический анализ изотопических сдвигов и резонансных эффектов в инфракрасном спектре высокого разрешения фреона-22 (CHF-=SUB=-2-=/SUB=-Cl), обогащенного -=SUP=-13-=/SUP=-C

The IR transmittance spectrum of an isotopic mixture of chlorodifluoromethane (CHF2Cl, Freon-22) with a 33% fraction of 13C and a natural ratio of chlorine isotopes was measured in the frequency range 1400-740 cm–1 with a resolution of 0.001 cm–1 at a temperature of 20C. An ab initio calculation of the structure and sextic potential energy surface and surfaces of the components of the dipole moment has been carried out by the the electronic quantum-mechanical method of Möller-Plesset, MP2/cc-pVTZ. Then the potential was optimized by replacing the harmonic frequencies with the frequencies calculated by the electronic method of coupled clusters, CCSD(T)/aug-cc-pVQZ. The fundamental and combination frequencies were calculated using the operator perturbation theory of Van Vleck (CVPTn) of the second and fourth order (n=2,4). Resonance effects were modeled using an additional variational calculation in the basis up to fourfold VCI excitation (4). The average prediction error for the fundamental frequencies of the 12C isotopologues was ~1.5 cm–1. The achieved accuracy made it possible to reliably predict the isotopic frequency shifts of the 13C isotopologues. It is shown that the strong Fermi resonance ν4/2ν6 dominates in the 12C isotopologues and is practically absent in 13C. The literature assumption [Spectrochim. Acta A, 44: 553] about the splitting of ν1 (CH) due to the resonance ν1/ν2+ν7+ν9 is confirmed. The coefficients of the polyadic quantum number are determined. The analysis made it possible to carry out a preliminary identification of the centers of the vibrational-rotational bands of isotopologues 13CHF235Cl и 13CHF237Cl in the spectrum of the mixture in preparation for individual analyzes of the vibrational-rotational structures of individual vibrational transitions.

Locational Marginal Price Forecasting Using SVR-Based Multi-Output Regression in Electricity Markets

Electricity markets provide valuable data for regulators, operators, and investors. The use of machine learning methods for electricity market data could provide new insights about the market, and this information could be used for decision-making. This paper proposes a tool based on multi-output regression method using support vector machines (SVR) for LMP forecasting. The input corresponds to the active power load of each bus, in this case obtained through Monte Carlo simulations, in order to forecast LMPs. The LMPs provide market signals for investors and regulators. The results showed the high performance of the proposed model, since the average prediction error for fitting and testing datasets of the proposed method on the dataset was less than 1%. This provides insights into the application of machine learning method for electricity markets given the context of uncertainty and volatility for either real-time and ahead markets.

Machine Learning-Based Strength Prediction for Refractory High-Entropy Alloys of the Al-Cr-Nb-Ti-V-Zr System

The aim of this work was to provide a guidance to the prediction and design of high-entropy alloys with good performance. New promising compositions of refractory high-entropy alloys with the desired phase composition and mechanical properties (yield strength) have been predicted using a combination of machine learning, phenomenological rules and CALPHAD modeling. The yield strength prediction in a wide range of temperatures (20–800 °C) was made using a surrogate model based on a support-vector machine algorithm. The yield strength at 20 °C and 600 °C was predicted quite precisely (the average prediction error was 11% and 13.5%, respectively) with a decrease in the precision to slightly higher than 20% at 800 °C. An Al13Cr12Nb20Ti20V35 alloy with an excellent combination of ductility and yield strength at 20 °C (16.6% and 1295 MPa, respectively) and at 800 °C (more 50% and 898 MPa, respectively) was produced based on the prediction.

Predictive model for microclimatic temperature and its use in mosquito population modeling

Mosquitoes transmit several infectious diseases that pose significant threat to human health. Temperature along with other environmental factors at breeding and resting locations play a role in the organismal development and abundance of mosquitoes. Accurate analysis of mosquito population dynamics requires information on microclimatic conditions at breeding and resting locations. In this study, we develop a regression model to characterize microclimatic temperature based on ambient environmental conditions. Data were collected by placing sensor loggers at resting and breeding locations such as storm drains across Houston, TX. Corresponding weather data was obtained from National Oceanic and Atmospheric Administration website. Features extracted from these data sources along with contextual information on location were used to develop a Generalized Linear Model for predicting microclimate temperatures. We also analyzed mosquito population dynamics for Aedes albopictus under ambient and microclimatic conditions using system dynamic (SD) modelling to demonstrate the need for accurate microclimatic temperatures in population models. The microclimate prediction model had an R2 value of ~ 95% and average prediction error of ~ 1.5 °C indicating that microclimate temperatures can be reliably estimated from the ambient environmental conditions. SD model analysis indicates that some microclimates in Texas could result in larger populations of juvenile and adult Aedes albopictus mosquitoes surviving the winter without requiring dormancy.

Climate Data for Field Trials: Onsite Micro Stations Versus ClimateNA

Background Studies in diverse environmental fields require accurate climate data for point locations that are often distant from reliable public weather stations. ‘Onsite’ micro weather stations can be established directly at research locations, but purchase, establishment, and maintenance costs and data gaps can limit their feasibility. Alternatively, climate data for point locations can be predicted from ClimateNA, a publicly available software package, but the prediction accuracy in remote and mountainous locations is uncertain. Results We compared ClimateNA predictions with observations from onsite weather stations located at 11 interior spruce provenance trials in British Columbia, Canada. We found that ClimateNA predictions were highly accurate for temperature variables (average prediction error 0.77°C; most R2 values > 0.99) but moderate for precipitation variables (average prediction error 27mm; 0.21 < R2 values < 0.58) when compared with onsite weather data (with random errors identified). Growth response functions developed with the two data sources showed similar patterns for temperature variables. Conclusions Our results suggest that 1) temperature variables can be accurately predicted at remote and mountainous locations using ClimateNA; 2) precipitation variables are more accurately predicted with ClimateNA than with onsite weather stations, which were considerably affected by random factors; and 3) response functions provide an effective, independent tool to assess alternative sources of climate data. Our results recommend the use of ClimateNA over onsite weather stations, except where highly accurate precipitation data are required, in which case, high-quality onsite weather stations must be established and carefully maintained.

A Time-Dependent Model for Predicting Thermal Environment of Mono-Slope Solar Greenhouses in Cold Regions

Most greenhouses in the Canadian Prairies shut down during the coldest months (November to February) because of the hefty heating cost. Chinese mono-slope solar greenhouses do not primarily rely on supplemental heating; instead, they mostly rely on solar energy to maintain the required indoor temperature in winter. This study focuses on improving an existing thermal model, entitled RGWSRHJ, for Chinese-style solar greenhouses (CSGs) to increase the robustness of the model for simulating the thermal environment of the CSGs located outside of China. The modified model, entitled SOGREEN, was validated using the field data collected from a CSG in Manitoba, Canada. The results indicate that the average prediction error for indoor and relative humidity is 1.9 °C and 7.0%, and the rRMSE value is 3.3% and 11.5%, respectively. The average error for predicting the north wall and ground surface temperature is 4.2 °C and 2.3 °C, respectively. The study also conducted a case study to analyze the thermal performance of a conceptual CSG in Saskatoon, Canada. The energy analysis indicates the heating requirement of the greenhouse highly depends on the availability of solar radiation. Besides winter, the heating requirement is relatively low in March to maintain 18 °C indoor temperature when the average outdoor temperature was below –4 °C, and negligible during May–August. The results indicate that vegetable production in CSGs could save about 55% on annual heating than traditional greenhouses. Hence, CSGs could be an energy-efficient solution for ensuring food security for northern communities in Canada and other cold regions.

The prediction and analysis of COVID-19 epidemic trend by combining LSTM and Markov method

Corona Virus Disease 2019 (COVID-19) has spread rapidly to countries all around the world from the end of 2019, which caused a great impact on global health and has had a huge impact on many countries. Since there is still no effective treatment, it is essential to making effective predictions for relevant departments to make responses and arrangements in advance. Under the limited data, the prediction error of LSTM model will increase over time, and its prone to big bias for medium- and long-term prediction. To overcome this problem, our study proposed a LSTM-Markov model, which uses Markov model to reduce the prediction error of LSTM model. Based on confirmed case data in the US, Britain, Brazil and Russia, we calculated the training errors of LSTM and constructed the probability transfer matrix of the Markov model by the errors. And finally, the prediction results were obtained by combining the output data of LSTM model with the prediction errors of Markov Model. The results show that: compared with the prediction results of the classical LSTM model, the average prediction error of LSTM-Markov is reduced by more than 75%, and the RMSE is reduced by more than 60%, the mean $${R}^{2}$$ R 2 of LSTM-Markov is over 0.96. All those indicators demonstrate that the prediction accuracy of proposed LSTM-Markov model is higher than that of the LSTM model to reach more accurate prediction of COVID-19.

Modeling and Prediction of Daily Traffic Patterns—WASK and SIX Case Study

The paper studies efficient modeling and prediction of daily traffic patterns in transport telecommunication networks. The investigation is carried out using two historical datasets, namely WASK and SIX, which collect flows from edge nodes of two networks of different size. WASK is a novel dataset introduced and analyzed for the first time in this paper, while SIX is a well-known source of network flows. For the considered datasets, the paper proposes traffic modeling and prediction methods. For traffic modeling, the Fourier Transform is applied. For traffic prediction, two approaches are proposed—modeling-based (the forecasting model is generated based on historical traffic models) and machine learning-based (network traffic is handled as a data stream where chunk-based regression methods are applied for forecasting). Then, extensive simulations are performed to verify efficiency of the approaches and their comparison. The proposed modeling method revealed high efficiency especially for the SIX dataset, where the average error was lower than 0.1%. The efficiency of two forecasting approaches differs with datasets–modeling-based methods achieved lower errors for SIX while machine learning-based for WASK. The average prediction error for SIX reached 3.36% while forecasting for WASK turned out extremely challenging.

Predicted Torque Model in Low-Frequency-Assisted Boring (LFAB) Operations

A low-frequency-assisted boring operation is a key cutting process in the aircraft manufacturing sector when drilling deep holes to avoid chip clogging based on chip breakage and, consequently, to reduce the temperature level in the cutting process. This paper proposes a predicted force model based on a commercial control-supported chip breaking function without external vibration devices in the boring operations. The model was fitted by conventional boring measurements and was validated by vibration boring experiments with different ranges of amplitude and frequency. The average prediction error is around 10%. The use of a commercial function makes the model more attractive for the industry because there is no need for intrusive vibration sensors. The low-frequency-assisted boring (LFAB) operations foster the chip breakage. Finally, the model is generic and can be used for different cutting materials and conditions. Roughness is improved by 33% when vibration conditions are optimal, considered as a vibration amplitude of half the feed per tooth. This paper presents, as a novelty, the analysis of low-frequency vibration parameters in boring processes and their effect on chip formation and internal hole roughness. This has a practical significance for the definition of a methodology based on the torque model for the selection of conditions on other hole-making processes, cutting parameters and/or materials.