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Мета дослідження – розробити і обгрунтувати комплексну методику розвитку витривалості школярів середніх класів засобами велотуризму. Матеріал і методи. В даному дослідженні приймали участь велотуристи 12-13 років Липкуватіської загально–освітньої школи l-lll ступенів (9 спортсменів склали експериментальну групу, 9 – контрольну). Для визначення функціональних можливостей школярів середніх класів застосовувались наступні проби і тести: Гарвардський степ-тест, проба Штанге, проба Генчі, проба Серкіна. Розроблено методику розвитку витривалості, яка передбачає застосовування комплексу засобів велотуризму. Комплекс складається з 2-х частин: комплексна дистанція «тріал і фігурне водіння велосипеда» і велопохід. Тріал являє собою дистанцію довжиною 100-200 м. з природними та штучними перешкодами (крутий схил; яр; піщані; заболочені та інші важкопрохідні дільниці; струмки; калюжі; стовбури дерев, що лежать, тощо) по вузькому коридору (до 1,5м), утвореному природними і штучними обмеженнями (стрічки між деревами або стійками тощо). Фігурне водіння велосипеда передбачає застосування наступних перешкод: «Щілина», «Змійка між стійками», «Коридор», «Колія», «Коло», «Вісімка», «Ворота», «Кільце», «Перенесення предмету», «Зигзаг», «Гойдалка», «Змійка між фішками», «Стоп-лінія. Фініш». Особливістю походу є те, що окрем розвитку витривалості, він вирішував завдання виховного та освтнього характеру шляхом ознайомлення з цікавими місцями Слобожанщини, зустрічами з цікавими людьми тощо. Результати. Показано, що авторська методика комплексного розвитку витривалості, що поєднує засоби фігурного вождіння та велопоходу, є більш ефективною у порівнянні з традиційною методикою розвитку витривалості школярів середніх класів, яка містить виключно подолання дистанцій; виявлено, що застосування комплексної методики розвитку витривалості школярів середніх класів сприяє підвищенню часу затримки дихання за пробами Штанге, Генчі, Серкіна, а також зменшенню ЧСС спокою, ЧСС після стандартного навантаження та підвищенню якості відновлення працездатності. Висновок. Розроблена методика може застосовуватись в підготовці юних велотуристів.

Foam diversion acidizing can effectively solve the problem of acid distribution with severe heterogeneity between and within layers. Based on the foam diversion principle, the gas trap theory, and volume conservation principle, the foam slug diversion acidizing model was established and solved considering the change of bottomhole temperature and deviation factor of foam. The simulation results show that the change of temperature has a great influence on the diversion effect at the initial stage of injection, but a small influence at the middle and late stage. The effect of temperature on the highly permeable layer is greater than that of temperature on the low permeability layer. The deviation factor of foam is mainly controlled by temperature at the initial stage, and by pressure at the middle and late stage, and the whole process shows a downward trend, which has little influence on the diversion effect. The quasi-skin factor of gas trap is the most important parameter that influences the effect of foam diversion. The water saturation of the low permeability layer rises faster than that of the high permeability layer, and the effect of diversion is obvious. The research results have a strong guiding significance for foam diversion acidizing.

We investigate the dynamics of Cournot’s duopoly model with time delays, where firms compete at the R&D level and settle on their quantity based on shared earnings at the second stage. Stability conditions of the equilibrium points and occurrence of Hopf bifurcations are analyzed. Furthermore, we present numerical simulations to furnish experimented evidence for the system’s complex behaviors.

O objetivo deste trabalho é apresentar uma metodologia para fornecer uma solução para o Problema de Roteamento de Veículos Green Bi-objetivo (Bi-objective Green Vehicle Routing Problem, BGVRP). A metodologia, ilustrada por meio de um estudo de caso (problema de distribuição de jornais) e instâncias da literatura, foi dividida em três etapas: Etapa 1, tratamento dos dados; Etapa 2, abordagens meta-heurísticas (híbridas ou não híbridas), utilizadas comparativamente; Etapa 3, análise dos resultados, com comparação dos algoritmos. Uma otimização de 19,9% foi alcançada para a Função Objetivo 1 (FO1; minimização das emissões de CO2) e, consequentemente, o mesmo percentual para a minimização da distância total, e 87,5% para a Função Objetivo 2 (FO2; minimização da diferença na demanda). Abordagens meta-heurísticas híbridas alcançaram resultados superiores para o estudo de caso e as instâncias. Desta forma, o procedimento aqui apresentado poderá trazer benefícios para a sociedade já que considera questões ambientais, além do balanceamento do trabalho entre os roteiros, garantindo economia e satisfação para possíveis usuários.

This research presents a dynamic modeling approach to predict flashover voltage (FOV) of polymer insulators. Pre-flashover conditions can be used to represent analytical formulations for different stages of dry band arcing activities on the surface of polluted insulator. In this study, experimental results of pollution flashover tests are used to develop a circuit model of pollution arcs based on Obenaus model. Estimation of circuit values is done by use of intelligent algorithm with no dependency on profile of insulators which is also more beneficial than typical analytical calculations. Then, separation and investigation of discharge and pollution voltage is done and proper classification is made. This classification is later used to predict pre-flashover conditions and pollution flashover. Gaussian model is used to make fitness of voltage of pollution layer and voltage of discharge on the surface of insulator. Gaussian representation of each stage in pollution flashover contains critical signals leading to complete discharge. Based on the obtained model, a flowchart is presented to predict dynamic stages of flashover. Comparison to various experimental data shows close correlations with the results of proposed model. An online monitoring system can be developed according to obtained results in order to prevent pollution flashover.

Fault prognosis based on single model is generally inaccurate due to the varying working conditions. A multistage fault prognosis methodology combining stage identification with Bayesian networks (BNs) and time series approach with particular emphasis on the autoregressive moving average (ARMA) model is proposed to solve this problem. In the first stage, degradation data are identified, and outliers are marked by the Euclidean distance. Degenerate attributes of outliers are finely identified by BNs and matched to the corresponding model. In the second stage, the ARMA model is used for prognosis according to the results of the fine identification. Subsequently, the double-precision identification and ARMA submodel prognosis are carried out alternately throughout the prognosis process. Three degradation types of PMSM are simulated to verify the applicability of the method. Result shows that it can track the changes in the degradation in time and obtains better results.

This research investigates the Three Dimensional-Single Bin-Size Bin-Packing Problem (3D-SBSPP). The aim is to pack n boxes of different dimensions and weights into a minimum number of equally balanced bins. The boxes can be rotated according to six possible orientations. Balancing the bins, volume and weight wise, is of high importance. To achieve this goal, a three-stage heuristic was developed. In the first stage, the different possible layers are generated. Each layer is formed by boxes of same type packed according to a common given orientation. The second stage generates solution candidates using the layers formed in stage 1. In stage 3, the boxes to be shipped are placed in the bins following the solution candidates of stage 2 that give the best volumetric and/or weight utilization. The proposed heuristic was successfully validated through an exhaustive experimental study using a variety of problem instances and involving comparisons with other existing heuristics.

A landslide, Ashegong, blocked the Garang River, a tributary of the Yellow River on the northeastern Tibetan Plateau. Six optically stimulated luminescence (OSL) samples were collected from ~35-m-thick lacustrine sediments of the landslide-dammed lake deposited on the bank of Garang River in this study. Samples yielded 40.3 ± 2.7 ka at the top of the lacustrine section and 21.8 ± 1.4 ka at the base of lacustrine layer, which indicated that the dammed lake last approximate 18.5 ka. This landslide dammed lake forming at 40.3 ± 2.7 ka, appeared to be associated with the wetter conditions and extensive rainfall affected by paleomonsoon during marine oxygen isotope stage (MIS) 3 on the northeastern Tibetan Plateau. The blockage by Ashegong landslide had a vital role in shaping river network such as causing the river diversion. Understanding the geomorphic process of landslide damming event is benefit for reflecting paleoclimatic change, tectonic uplift, forecast and mitigate hazards in mountainous area.

The effect of electrode orientation in an electrohydrodynamic (EHD) gas pump inside a square channel is experimentally examined for its effective use in thermal management. The two-stage gas pump w...

The article is devoted to the actual problem in design projection of children’s choreographic costumes based on a bionic creative source. The analysis aimed to identify the scope of use of vegetables as a creative source for the production of fashion collections. It has been found that designers use vegetables for making clothes, for sketching the models of clothes, as well as a source of inspiration for form, decoration and colour choices. A figurative and stylistic analysis of bionic creative sources and the transformation of plant images into stage costumes for a children’s dance show was carried out in order to come up with models of stage costumes. During the process of costume design, the following principles were adopted: imitation of the bioform in separate elements of the costume, analogousness of silhouette solutions to natural bioforms, bionic design projection with the identification of functionality, and figurative and associative expressiveness of the costume. As a final step, the scenario of performance, as well as the requirements of the children and the creative source were considered when designing a collection of children’s stage costumes.

Massive quantities of sludge are being generated with the rise in populace and increase of wastewater produced. Ordinary sand and paved drying beds are still designed within the conventional way. This research focused on creating modern procedures to reduce the drying up duration for the combined sludge produced. This included the use of direct thermal solar heated water pipes, wick vertical drain, and horizontal filtration combined with bottom drainage for three built tanks utilizing freshly combined primary and secondary sludge with initial concentrations ranging from 0.42%−3% for the different filling cycles performed over two stages under changing climatic conditions. In-situ measurements of solid content, temperature and sludge height were taken. For stage one, the solids content reached 8.53% for solar tank, 7.42% for wick vertical drain tank and 17.4% for the conventional paved dry tank after 105 days of operation. For stage two, solids content reached 6.9% and 6.6% for solar and wick vertical drain tanks respectively and 7.93% in the conventional PDB combined with horizontal flow after 35 days of operation. Results are promising and could lead to better operational performance of PDB while decreasing the required time required; hence maximizing and upgrading the existing facilities.

This paper presents a survey of image processing techniques proposed in the literature for extracting key cereal crop growth metrics from high spatial resolution, typically proximal images. The descriptive crop growth metrics considered are: crop canopy cover, above ground biomass, leaf area index (including green area index), chlorophyll content, and growth stage. The paper includes an overview of relevant fundamental image processing techniques including camera types, colour spaces, colour indexes, and image segmentation. The descriptive crop growth metrics are defined. Reference methods for ground-truth measurement are described. Image processing methods for metric estimation are described in detail. The performance of the methods is reviewed and compared. The survey reveals limitations in image processing techniques for cereal crop monitoring such as lack of robustness to lighting conditions, camera position, and self-obstruction. Directions for future research to improve performance are identified.

In the periods following sales on online shopping carnivals, demand for express transportation is expected to precipitously surge within a short time, potentially causing express system overload and delivery delays. In this study, we address a service network design problem of a hub-and-spoke structured express transportation network during periods of surging demands. We consider a new strategy of upgrading existing spokes into temporary hubs to expand the network capacity with low costs. We propose a two-stage bi-objective robust model based on a time-space network to integrate the spoke-upgrading selection, service-scheduling, and vehicle-routing for designing an express service network that is both efficient and economic. To solve the model, we develop an enhanced column-and-constraint generation algorithm. Finally, a case study of a real express logistics company in China is demonstrated to validate the effectiveness of the proposed strategy and to offer decision support in real problems.

The cyclone separatoris an important separation device. This paper presents a new type of embedded two-stage cyclone, which includes a 2nd-stage cyclone (internal traditional cyclone) with multiple inlets and a 1st-stage cyclone (outer cylinder) that unifies the 2nd-stage cyclone inlets into one inlet. The Taguchi experimental method was used to studythe two-stage cyclone separator's inlet area on its performance. Studies have shown that the increase of the 1st-stage cyclone inlet area and the increase in the number of 2nd-stage cyclone inlets (N) positively affect reducing the pressure drop and a negative effect on efficiency. It is recommended to use 2S (the original 1st-stage cyclone inlet area) of the 1st-stage cyclone inlet area and 2N of the 2nd-stage cyclone inlets when separating fine particles. Compared with a traditional cyclone, the pressure drop is reduced by 1303 Pa, the mass separation efficiency (Eq) is increased by 0.56%, and the number separation efficiency (En) is increased by 2.05%. When separating larger particles, it is recommended to use 2S of the 1st-stage cyclone inlet area and 4N of the 2nd-stage cyclone inlets. Compared with a traditional cyclone, although En decreases slightly, the pressure drop is reduced by 3055 Pa, and the Eq is increased by 0.56%. The research results provide new insight into the design of the cyclone.

Combined loading–creep–wetting tests were performed to investigate the wetting deformation behaviours of rock-fill material by using a modified triaxial apparatus. Results show that the wetting deformation of rock-fill material can be classified into two components, namely, instantaneous wetting deformation and time-dependent wet-state creep deformation. The former occurs simultaneously with rock-fill wetting and possesses the characteristics of parallelism and non-hardening; the latter occurs over time after the rock-fill is wetted and exhibits features similar to those of general creep deformations in rock-fill material. Wetting deformation is induced by the physical-state weakening of the wetted rock-fill material; the wetting can be intuitively considered as a generalised load. Then, a new constitutive model of two-stage wetting deformation for rock-fill material is proposed. In the model, the instantaneous shear wetting strain is in a hyperbolic relationship with the stress level; the instantaneous volumetric wetting strain is described by the dilatancy equation of conventional triaxial loading; and the time-dependent wet-state creep strain is calculated using a general rock-fill creep model. The new model was also used to simulate triaxial wetting tests of rock-fill material, and its validity and practicability were further evaluated.

Renewable energy has been employed to reduce greenhouse emissions and global warming. VPP is an example of renewable energy integration technology that enables users to tap into unlimited renewable energy supplies while reducing their reliance on traditional power sources. This research looks at a synchronized control grid integrated VPP with parabolic trough solar collector thermal system (PTSCTS), a wind generator (WG), and an electric vehicle (EV). An active power control strategy on an interconnected microgrid in revised form is attempted taking into consideration the communication delay. The proportional integrator derivative (PID) and two-stage (PI)-(1+PD) controllers are used in the proposed control strategy. Firefly algorithm (FA), butterfly optimization algorithm (BOA), particle swarm optimization (PSO), and recent Grasshopper optimization algorithm (GOA) have been used for optimizing the control parameters of the controllers. The analysis shows that the new GOA algorithm performs better in terms of control strategy than other optimization tools such as FA, BOA, and PSO. The sensitivity analysis of the proposed controller under different conditions has been done, and it has been explored that the proposed Two-stage PI-(1 + PD) controller parameters are very robust in dealing with the parametric variations of the integrated VPP model.

A high thrust liquid oxygen-kerosene fuelled engine is being developed for future launch vehicle applications. Comprehensive hot testing of engine at sea level conditions is required for qualification and acceptance of flight engine and launch vehicle stage. The engine and stage will be tested in a specialized rocket engine and stage hot test facility which deflects the flame safely to avoid any hazard to engine and adjoining mechanical structures. The facility is equipped with water jets to sufficiently cool the rocket plume before impinging on deflector pit walls. The high temperature and high-pressure rocket exhaust jet is cooled with sufficient quantity of water to maintain the impingement wall temperature within safe working limits. In this study, a multi-phase computational fluid dynamics (CFD) methodology is developed for design of water-cooling jet configuration of deflector facility. Multi-phase model validation study using full phase treatment and droplet assumption for water injection is performed prior to water jet optimization analysis of high thrust LOX-kerosene test facility. Validation studies based on simulation and analysis of existing test stand is developed. A discrete particle (DPM) based two phase methodology is chosen to model water injection in RANS framework. The optimum cooling configuration is determined iteratively through numerous multi-phase simulations. The multi-phase analysis showed ineffectiveness of initial water injection scheme which results in facility wall temperature above safe limit. A multi-plane water injection scheme is devised there on with provision of water injection at two axial locations from the nozzle exit. A detailed parametric multi-phase analysis is carried out for design optimization of multi-plane water injection scheme and to determine thermal condition of the test facility. Analysis showed effective rocket plume cooling and lower facility wall temperature with two plane water injection schemes in comparison to initial single plane design configuration. The study highlights the role of CFD based methodology to formulate an engineering solution for design optimization of LOX-kerosene engine hot test facility.

In the last decade, there has been an increasing interest in understanding the effects of changing injected water salinity on the performance of oil reservoirs. This paper aims to investigate the effects of injected water salinity on oil recovery of an Egyptian oil reservoir (Bahariya formation). An experimental work program has been performed using 25 core plugs and 5 different water salinities to study the effects of changing water salinity during both secondary and tertiary stages of waterflooding. The objectives of the experimental work were to (1) investigate the effects of the low water salinity on oil recovery and (2) identify the optimum water salinity and the main reservoir parameters for application of low salinity waterflooding project (LSWF) in Bahariya formation. The results revealed that there is an optimum salinity for waterflooding in the secondary flooding stage. However, for the tertiary flooding stage, the results showed that the controlling factor is not decreasing the salinity, but rather changing the salinity (e.g. either increasing or decreasing). It was also clear that applying the optimum salinity in the secondary recovery stage is more effective than applying it in the tertiary recovery stage. Furthermore, the results showed that the positive impact of LSWF may be expected in reservoirs with high amount of kaolinite, high values of CEC, and low amounts of plagioclase feldspar. This study is an original contribution to develop guidelines for designing optimum salinity waterflooding projects in sandstone reservoirs.

In agricultural production, weed removal is an important part of crop cultivation, but inevitably, other plants compete with crops for nutrients. Only by identifying and removing weeds can the quality of the harvest be guaranteed. Therefore, the distinction between weeds and crops is particularly important. Recently, deep learning technology has also been applied to the field of botany, and achieved good results. Convolutional neural networks are widely used in deep learning because of their excellent classification effects. The purpose of this article is to find a new method of plant seedling classification. This method includes two stages: image segmentation and image classification. The first stage is to use the improved U-Net to segment the dataset, and the second stage is to use six classification networks to classify the seedlings of the segmented dataset. The dataset used for the experiment contained 12 different types of plants, namely, 3 crops and 9 weeds. The model was evaluated by the multi-class statistical analysis of accuracy, recall, precision, and F1-score. The results show that the two-stage classification method combining the improved U-Net segmentation network and the classification network was more conducive to the classification of plant seedlings, and the classification accuracy reaches 97.7%.

In order to predict the monthly usage frequency of members of a car-sharing scheme by analysing the gradual change of behaviour over time, a new model is proposed based on the Markov Chains model with latent stages. The model accounts for changing patterns of frequency from soon after signing up to later stages by including five latent user ‘life stages’. In applying the model to panel data from Montreal’s free-floating carsharing service the authors calculate each user’s ’lifetime’ applied to ‘system operation time’, the time period since the start of the scheme. Three-fold validation reveals effective performance of the model for both lifetime and system operation time dimensions. The model is further applied to illustrate how previous carsharing experience and the extension of the scheme to a larger area can affect usage frequency changes. We conclude that this approach is effective for usage prediction for novel transport schemes.

Yield loss (YLoss) in the ratoon crop due to crushing damage to left stubble from mechanical harvesting of the main crop is a constraint for wide adoption of mechanized rice ratooning technology. Soil drying before the harvest of the main crop has been proposed to overcome this problem. The objective of this study was to determine the effect of soil drying during the mid-to-late grain filling stage of the main crop on grain yield of the ratoon crop in a mechanized rice ratooning system. Field experiments were conducted to compare YLoss between light (LD) and heavy (HD) soil drying treatments in Hubei province, central China in 2017 and 2018. YLoss was calculated as the percentage of yield reduction in the ratoon crop with the main crop harvested mechanically, relative to the grain yield of the ratoon crop with the main crop harvested manually. In comparison with LD, soil hardness was increased by 42.8%–84.7% in HD at the 5–20 cm soil depth at maturity of the main crop. Soil hardness at 5 and 10 cm depths reached respectively 4.05 and 7.07 kg cm−2 in HD. Soil drying treatment did not significantly affect the grain yield of the main crop. Under mechanical harvesting of the main crop, HD increased the grain yield of the ratoon crop by 9.4% relative to LD. Consequently, YLoss was only 3.4% in HD, in contrast to 16.3% in LD. The differences in grain yield and YLoss between the two soil drying treatments were explained mainly by panicles m−2, which was increased significantly by HD in the track zone of the ratoon crop compared with LD. These results suggest that heavy soil drying practice during the mid-to-late grain filling stage of the main crop is effective for reducing YLoss of the ratoon crop in a mechanized rice ratooning system.

Optimal operation of a compressor station is important since it accounts for 25 % to 50 % of a company’s total operating budget. In short-term management of a compressor station, handling demand uncertainty is important yet challenging. Previous studies either require precise information about the distribution of uncertain parameters or greatly simplify the compressor model. We build a two-stage robust optimization framework of power cost minimization in a natural gas compressor station with non-identical compressors. In the first stage, decision variables are the ON/OFF state of each compressor and discharge pressure. The worst-case cost of the second stage is incorporated in the first stage. First-stage decision variables feasibility is discussed and proper feasibility cuts are also proposed for the first stage. We employ a piece-wise approximation and propose accelerate methods. Our numerical results highlight two advantages of robust approach when managing uncertainty in practical settings: (1) the feasibility of first-stage decision can be increased by up to 45 % , and (2) the worst-case cost can be reduced by up to 25 % compared with stochastic programming models. Furthermore, our numerical experiments show that the designed accelerate algorithm has time improvements of 1518.9% on average (3785.9% at maximum).

Ozone (O3) was introduced into the intake air to control the ignition in a gasoline compression ignition (GCI) engine. An early fuel injection at −68 °CA ATDC was adopted to mix the fuel with the reactive O-radicals decomposed from the O3, before the reduction of the O-radicals due to their recombination would take place. The second injection was implemented near top dead center to optimize the profile of the heat release rate. The engine experiments were performed around the indicated mean effective pressure (IMEP) of 0.67 MPa with a primary reference fuel, octane number 90 (PRF90), maintaining the 15% intake oxygen concentration with the EGR. The quantity of the first injection, the second injection timing as well as the ozone concentration were changed as experimental parameters. The results showed that the GCI operation with the ozone addition makes it possible to reduce the maximum pressure rise rate while attaining high thermal efficiency, compared to that without the ozone. Appropriate combinations of the ozone concentration and the first injection quantity achieve low smoke and NOx emissions. Further, the ozone-assisted GCI operation was compared with conventional diesel operation. The results showed that the indicated thermal efficiency of the ozone-assisted GCI combustion is slightly lower than that of the conventional diesel combustion, but that GCI assisted with ozone is highly advantageous to the smoke and NOx emissions.

An automated truck wash study was conducted at a large layer hen facility to determine the effectiveness of a modified decontamination system for sanitizing semi-trucks and other farm vehicles. The commercial automated power washing system was modified with a fixed gantry that applied a chlorine dioxide (ClO2) disinfectant rinse as the truck exited the biosecurity facility. The truck decontamination study included the primary study plus one smaller Bacillus atrophaeus spore study, as well as air and water sampling. The goal of the field study was to determine the effectiveness of a two-stage automated decontamination system for sanitizing a large, semi-tractor trailer. The primary study objective was to evaluate two power washing techniques (power wash only with a surfactant or power wash with surfactant and a ClO2 rinse). The second objective was to evaluate the decontamination methods on four coupon materials (glass, painted metal, plastic, rubber) to determine the effectiveness of the two-stage wash system on inoculated coupons. The third objective was to determine the effectiveness of the decontamination methods on coupon locations on the truck (front windshield, middle side of trailer, undercarriage). The fourth objective was to determine the effectiveness of the decontamination methods on coupon surface type (coupons coated with or without synthetic grime). The primary study evaluated 48 decontamination treatments to assess their ability to inactivate the MS2 bacteriophage, which is the viral surrogate selected for the study. The results show that the two-stage decontamination treatments increased log10 reduction of the MS2 phage. Log10 reduction increased an average of 247% and 118% for the non-grimed and grimed coupons, respectively, when comparing the automated wash with and without ClO2 rinse across all locations and material types. The average log10 reduction increased from 0.94 to 1.89 for the automated wash and the automated wash + ClO2 rinse, respectively, for the grimed coupons, across all coupon locations and materials. The average log10 reduction increased from 1.23 to 2.17 for the automated wash without ClO2 and the automated wash + ClO2 rinse, respectively, for the non-grimed coupons, across all coupon locations and materials. These results show that combining the ClO2 disinfectant rinse with the automated power wash increased viral efficacy by an average of one log (grimed coupons). Evaluation of the two-stage tuck decontamination system confirms that combining a power wash with a disinfectant rinse increases the ability of the system to sanitize transport trucks and increase farm biosecurity.

Summary Multistage plug and perforation (plug-n-perf) fracturing is commonly used for horizontal well completion in unconventional reservoirs. Uniform distribution of proppant across all clusters in each stage has proved to be challenging with low viscosity slickwater owing to its limited transport capability. Computational fluid dynamics (CFD) has been used to model proppant transport in wellbore to improve perforation and fracturing design for achieving uniform proppant placement. However, traditional CFD modeling of a full-scale stage is computationally expensive, which limits its applicability in the completion design optimization. A new approach was developed in this paper to efficiently predict proppant placement along a multicluster stage based on a machine learning (ML) model trained with extensive CFD modeling results. Its high computational efficiency permits quick sensitivity analyses to optimize perforation and fracturing designs. The new approach was validated against full-stage CFD modeling results as well as post-treatment field diagnostics. Sensitivity analyses show that proppant inertia effect is a key factor affecting proppant placement in heel clusters with higher slurry flow rates, allowing more proppant carried to the toe owing to its higher density in comparison with fluid. Proppant settling allows bottom perforations to accept more proppant than top perforations. This gravitational effect is not negligible near the heel at high flow rates and becomes more dominant near toe clusters where the flow rate is reduced. Near-uniform proppant placement is achievable via perforation design optimization by taking advantage of these two key mechanisms controlling proppant transport in horizontal wellbores. It is demonstrated that in-line perforating designs with all perforations having the same orientation in each cluster or the entire stage, especially with perforations at the bottom or on the side of the wellbore, improve the proppant placement uniformity. However, it is recommended that the optimum perforation design should be identified case by case depending on specific input parameters. The ML-based model developed in this study has overcome some of the limitations from existing models in the literature and is able to provide quick and yet reliable solutions to proppant placement prediction and design optimization.

У статті запропоновано практичні рекомендації, які дозволяють як мінімум удвічі скоротити витрати робочого часу лікаря-стоматолога на етапі заповнення медичної документації при проведенні підрахунку умовних одиниць праці,що відповідають діагнозу й обсягу виконаних маніпуляцій при лікуванні ускладненого карієсу (пульпіту, періодонтиту),генералізованого пародонтиту та захворювань слизової оболонки порожнини рота.

Досягнення сучасних високих стандартів стоматологічної допомоги безпосередньо пов`язане з тривалістю стоматологічного прийому та значним збільшенням витрат часу лікаря для ретельного виконання більшості стоматологічних маніпуляцій. Лікарі змушені в пошуках дорогоцінного часу «спрощувати» діяльність в інших складових своєї роботи. Одним із ефективних шляхів вирішення поданої проблеми може бути зменшення витрат часу на етапі заповнення медичної документації.З метою оптимізації витрат часу лікарями-стоматологами, на етапі заповнення первинної облікової медичної документації (форми № 037/о) пропонуємо до використання таблиці з попередньо підрахованими УОП відповідно до певних діагнозів в залежності від об`єму наданої стоматологічної допомоги. Таблиці створені на основі «Класифікатора процедур у терапевтичній стоматології».

The northern part of Lamongan Regency is an area that is geologically and geographically vulnerable to freshwater. The condition of the existing water sources is not suitable for use as a source of clean water, because the water is brackish and green in color. The eutrophication process occurs due to excessive use of fertilizers for pond business, so that the nitrogen and phosphate content is high. The community in Karanggeneng village uses water from the Bengawan Solo River for daily use. The water condition of Bengawan Solo in the rainy season is dark brown in color because it contains a lot of soil, while in the dry season it is green because it contains a lot of pesticide or fertilizer residues discarded by farmers. From the problems above, it is necessary to socialize the water purification of Bengawan Solo by using the filtration method. Socialization activities are carried out directly to the Karanggeneng village community. This activity is carried out in two stages, the first is the preparation of tools and materials, the second stage is socialization to the community. In the first stage, chemical and physical water quality parameters were tested. In the second stage, the community was very enthusiastic about the implementation of this activity. The use of filtration methods is easy and inexpensive to be applied on a household scale. Suggestions, it is necessary to disseminate information to the wider community so that all people can access clean water for their daily needs. the second stage is socialization to the community. In the first stage, chemical and physical water quality parameters were tested. In the second stage, the community was very enthusiastic about the implementation of this activity. The use of filtration methods is easy and inexpensive to be applied on a household scale. Suggestions, it is necessary to disseminate information to the wider community so that all people can access clean water for their daily needs. the second stage is socialization to the community. In the first stage, chemical and physical water quality parameters were tested. In the second stage, the community was very enthusiastic about the implementation of this activity. The use of filtration methods is easy and inexpensive to be applied on a household scale. Suggestions, it is necessary to disseminate information to the wider community so that all people can access clean water for their daily needs.

Reconfigurable Manufacturing Systems (RMSs) are well recognized by their capability to achieve high responsiveness and flexibility and are receiving increasing interest in today’s versatile manufacturing environment. This paper aims to develop methods to study the performance of RMS architecture consisting of machines and in-stage gantries whose time to failure and time to repair follow geometric distributions. A discrete-time Markov chain model is built to perform an exact analysis for two-stage-one-buffer systems. A decomposition method is developed to analyze multi-stage systems. Moreover, both time-dependent failure and operation-dependent failure modes are considered. Numerical studies that compare the calculated production rate and work-in-process to the simulation results demonstrate that the proposed methods can provide accurate results, especially for balanced systems and for systems with a small number of stages. The impact of different parameters (e.g., buffer capacity, machine/gantry reliability) on the production rate of the two models is also studied. Finally, examples on the applications of the models in the manufacturing systems design and operations are presented.

There is often considerable anxiety and conflicting advice concerning the benefits of presenting/being evaluated first. We thus investigate how expert evaluators vary in their evaluations of entrepreneurial proposals based upon the order in which they are evaluated. Our research setting is a premiere innovation fund competition in Beijing, China, where the prize money at stake is economically meaningful, and evaluators are quasi-randomly assigned to evaluate written grant proposals without the possibility of peer influence. This enables us to credibly recover a causal position effect. We also theorize and test how heterogeneity in evaluators’ prior (context-specific) judging experience moderates position effects. Overall, we find that a proposal evaluated first requires total assets in the top 10th percentile to merely equal the evaluation of a proposal in the bottom 10th percentile that is not evaluated first. Firm and evaluator fixed-effects models yield consistent findings. We consider evaluation design elements that may mollify these position effects in the discussion section. This paper was accepted by Sridhar Tayur, entrepreneurship and innovation.

This paper considers a class of two-stage stochastic mixed-integer optimization problems where, for a given first-stage solution, we can determine the optimal values of recourse variables sequentially. This class of problems arises in a wide variety of applications. In the case of multivariate discrete distributions for uncertain parameters, a standard stochastic programming formulation of these problems involves an exponential number of scenarios, therefore an exponential number of variables and constraints. We propose a new mixed-integer programming modeling approach where the number of variables and constraints is independent of the number of scenarios and scales at most pseudopolynomially with the problem size. The proposed modeling approach relies on state variables that track the system’s state as the uncertainty realizes sequentially. We demonstrate the advantages of the proposed approach in two applications arising in project scheduling and operating room allocation. Summary of Contribution: This paper proposes a new modeling approach for a class of two-stage stochastic optimization problems that is computationally more efficient than the traditional scenario-based stochastic integer programming models. The proposed modeling approach relies on state variables that track the system's state as the uncertainty realizes sequentially. We demonstrated the efficiency of the proposed approach by computational results on two applications in project scheduling and operating room allocation.

This paper proposes a novel image classification architecture named ensemble cross‐stage partial attention network based on the backbone network DarkNet53 of Yolov3 to improve the feature extraction capability and the interpretability of image classification. This network has multiple advantages, including light model parameters, fast classification speed, and high classification accuracy for small objects and complex images. Local network architectures of different cross‐phases are added in the proposed network structure to reduce the calculation. Furthermore, channel and hybrid domain attention modules, which, respectively, fuse the branch feature with the extracted channel and spatial attention features, are designed for feature extraction of images. Experimental results confirm the improved performance of the proposed approach on the CIFAR‐100, ImageNet, and UCMerced datasets. In addition, experiments on the MSCOCO dataset suggest the application of the proposed method to object detection with satisfactory accuracy.

Restoring the correct masticatory function of broken teeth is the basis of dental crown prosthesis rehabilitation. However, it is a challenging task primarily due to the complex and personalized morphology of the occlusal surface. In this article, we address this problem by designing a new two-stage generative adversarial network (GAN) to reconstruct a dental crown surface in the data-driven perspective. Specifically, in the first stage, a conditional GAN (CGAN) is designed to learn the inherent relationship between the defective tooth and the target crown, which can solve the problem of the occlusal relationship restoration. In the second stage, an improved CGAN is further devised by considering an occlusal groove parsing network (GroNet) and an occlusal fingerprint constraint to enforce the generator to enrich the functional characteristics of the occlusal surface. Experimental results demonstrate that the proposed framework significantly outperforms the state-of-the-art deep learning methods in functional occlusal surface reconstruction using a real-world patient database. Moreover, the standard deviation (SD) and root mean square (RMS) between the generated occlusal surface and the target crown calculated by our method are both less than 0.161 mm. Importantly, the designed dental crown have enough anatomical morphology and higher clinical applicability.

Applications of aerial imaging, especially based on unmanned aerial vehicles (UAVs) platform, rapidly explode in recent years. Meanwhile, vision-based sensing, e.g., detection and recognition, for UAVs becomes increasingly important. Objects in aerial images are usually of tiny size, hence occupying a limited area. Terminology speaking, the images are very sparse in spatial. However, existing work in aerial object detection commonly ignores this point. Conversely, we explore the availability of such a property in improving the detection performance of aerial images. Specifically, we propose a general method, train in dense and test in sparse (TDTS), to exploit sparsity in aerial object detection: 1) in the training stage, the possible positions of object are learned by training a fully convolutional network (called prophet head) and 2) in the testing stage, prophet head identifies the possible object locations to reduce redundant computation in classification and box prediction head by sparse convolution. By extensive experiments on the VisDrone2019-Det data set, we find that the sparsity can not only help to speed up inference but also to improve accuracy. Thus, we argue that the sparsity deserves more attention.

Due to the limitations of hardware technology, satellite sensors cannot obtain images with high temporal, spatial, and spectral resolutions at the same time. Current spatiotemporal fusion methods try to solve the contradiction between temporal resolution and spatial resolution, which cannot achieve good reconstruction accuracy partly because the data sources are from heterogeneous platforms with long chains difficult to be modeled. Different from the crossing-platform fusion, this work proposes to improve the spatial and temporal resolutions on a single platform. For the 2-m panchromatic images, 8-m multispectral images, and 16-m wide-field-view images captured by the Gaofen-1 satellite, our goal is to produce 2-m multispectral images with high temporal resolutions. Two convolutional neural networks are built to solve this spatiotemporal-spectral fusion issue with pansharpening and spatiotemporal fusion in serial. In the validation stage, the 2-m multispectral images are built and evaluated with the panchromatic images and 8-m multispectral images. The digital and visual evaluations show that our method can produce visually acceptable fusion quality, which may enhance the feasibility of the Gaofen-1 data.

Heat collection through flat plate collector is a common technology in solar thermal utilization. In order to improve the heat collection efficiency of flat plate collector and determine the best inclination angle and glass plate thickness, this paper establishes a collector indoor experimental system, and completes the research of solar radiation intensity, collector inclination angle, mass flow rate and glass thickness on the heat collection performance of corrugated flat plate collector. The result shows that when radiation intensity is 510W/m2, the temperature difference between the inlet and outlet in the radiation decreasing stage is 0.25 °C higher than the radiation increasing stage, and the heat collection in the radiation decreasing stage is 157.5W higher than the radiation increasing stage. The heat collection efficiency under different inclination angles decreases with the increase of radiation intensity, the best inclination angle of the collector is 45° and the maximum heat collection efficiency is 0.89. When the radiation intensity is more than 1000W/m2, heat collection of flat plate collector with glass thickness of 8 mm is 6% higher than that with the thickness of 6 mm, insulating effect of the glass is more obvious. The optimum thickness of the glass plate is 4 mm, the maximum temperature difference is 1.5 °C and the maximum heat collection efficiency is 0.89. The heat collection efficiency at different mass flow rates increases first and then decreases with the increase of radiation intensity. The maximum value of the heat collection efficiency at low mass flow rate is delayed. When the mass flow rate is 0.15 kg/s, 0.13 kg/s and 0.1 kg/s, the maximum heat collection efficiency is 0.89, 0.86 and 0.73, respectively; and the corresponding radiation intensity are 670W/m2, 850W/m2 and 1000W/m2, respectively.

This study provides insight into the effect of eccentric loading on soil arching. A numerical trapdoor model was developed using the discrete element method to investigate the evolution mechanism by considering the magnitude of the surcharge and the deviation distance. The parameters were validated using laboratory results. The numerical results showed that the Ground reaction curve (GRC) on the trapdoor could be divided into two categories. GRC type I included stages of initial soil arching, maximum soil arching, load recovery, and an ultimate stage. For GRC type II, the ultimate stage was replaced by a declining stage. The minimum soil arching ratio increased with the increasing surcharge, but decreased with the increasing deviation distance within 0.75B (B is the trapdoor width). The vertical displacement field inclined as the load deviated from the centered position, transforming from a trapezoid to a quadrilateral. The soil arching effect was degraded by the surcharge.

Synthetic aperture radar (SAR) can monitor the sea surface imprints of tropical cyclones (TCs) with high spatial resolution, day and night. Automatically locating TC center positions in SAR images is a challenging task. This article developed a two-stage, fully automatic TC-center estimation algorithm. First, the sea surface wind directions (SSWDs) at SSWD points are retrieved by the improved local gradient (ILG) method. We incrementally deflected the SSWD outward at a 0.5° angle from -50° to 10° (the negative angles represent clockwise deflection). The heat maps are generated for each of the 121 angles, and the values at each heat map are the cumulative numbers of the lines perpendicular to the compensated SSWDs. The site corresponding to the maximum cumulative number in all 121 heat maps is the coarsely estimated center position. This center search is the culmination if it falls outside the SAR image. Otherwise, the second stage is triggered, and the sub-SAR image (150 km x 150 km) centered at the coarsely estimated center position is extracted. Then, the first-stage procedure is repeated with the sub-SAR image to precisely estimate the center position. Optionally, the precisely estimated center position can be further adjusted by considering that normalized radar cross section (NRCS) is normally minimal at the TC center. We applied the algorithm to 87 SAR images. Five of these images do not contain TC centers. The results are in good agreement with the visually located TC center positions and those in the best track (BT) datasets.

Introduction of renewable energy sources (RESs) and independently operated multi-microgrid (MMG) systems have led to new issues in the management of power systems. In this context, uncertainty associated with RESs as well as intense ramps inflicted on the network called system flexibility constraints have raised new challenges in power systems. The new condition necessitates the implementation of novel frameworks that enable local system operators to efficiently manage the available resources to cope with the flexibility-ramp constraints. Moreover, the new framework should facilitate energy management in a system with an MMG structure considering uncertainty of RESs. Consequently, in this article, we aim to provide a novel framework that composes of a two-level stochastic optimization procedure to optimize the energy management in an MMG, considering uncertainty of RESs as well as grid flexibility constraints. In the proposed scheme, resource scheduling in microgrids (MGs) is conducted in the first level by their control units, while the second-level procedure focuses on the coordination of MGs, considering flexibility constraints. Furthermore, interaction with gas grid as a potential flexible resource is optimized in the second-level procedure. Finally, the provided flexibility-oriented management scheme is implemented to schedule the local resources in a three-MG test system, considering flexibility constraints.