Sulfate- and pH-driven metabolic flexibility in sugarcane vinasse dark fermentation stimulates biohydrogen evolution,sulfidogenesis or homoacetogenesis

Dark fermentation of sugarcane vinasse can be used as a “cleaning” step to remove sulfate prior to methanogenesis because sulfidogenic conditions can be successfully established in parallel with biohydrogen production. Using a 2² central composite rotational design (CCRD) and response surface methodology (RSM), this study assessed the impacts of bicarbonate and sulfate availability on the establishment of sulfidogenesis in the thermophilic (55 °C) fermentation of vinasse in batch reactors, equally assessing the impacts on biohydrogen evolution. CCRD-RSM results indicated the favoring of biohydrogen production at the lowest sulfate and bicarbonate concentrations, whilst the opposite was observed for sulfidogenesis. Glycerol, lactate, and hydrogen were the preferential electron donors utilized by sulfate-reducing bacteria (SRB), whilst ethanol was markedly consumed only at high sulfate concentrations. SRB were inhibited by sodium when dosing excess NaHCO₃ and Na₂SO₄. Complementary tests revealed maximum biohydrogen production (2.40 mmol) out of the CCRD, at pH exceeding 7.5 with no interference of sulfidogenesis. Non-efficient biohydrogen production was observed at low pH (<5.0; ～1.90 mmol) because the uptake of lactate was inhibited. Meanwhile, homoacetogenesis was established under intermediate pH range (5.5–6.5), as revealed by the accumulation of acetate (up to 2.5 g L^?1). 16S rRNA gene amplicon sequencing further revealed the genera Thermoanaerobacterium/Pseudoclostridium, Desulfotomaculum/Desulfohalotomaculum and Sporomusaceae/Moorella as the main biohydrogen-producing, sulfate-removing and biohydrogen-consuming (homoacetogens) microbial groups, respectively. Hence, using a single inoculum source, vinasse may provide a butyrate-rich (along with biohydrogen-rich biogas) or a sulfate-free and acetate-rich fermented effluent, depending mainly on proper pH control.     

Strength and deformation properties of frozen sand under a true triaxial stress condition

In recent years, the mechanical properties of frozen soils under complex stress states have attracted significant attention; however, limited by the test apparatus, true triaxial tests on frozen soils have rarely been conducted. To study the strength and deformation properties of frozen sand under a true triaxial stress state, a novel frozen soil testing system, i.e., a true triaxial apparatus, was developed. The apparatus is mainly composed of a temperature control system, a servo host system, a hydraulic servo loading system, and a digital control system. Several true triaxial tests were conducted at a constant minor principal stress (σ₃) and constant intermediate principal stress ratio (b) to study the effect of intermediate principal stress (σ₂) on the mechanical properties of frozen sand. The test results showed that the stress–strain curve can be mainly divided into three stages, with evidence of strain hardening characteristics. The strength, elastic modulus, and friction angle increased with the increase in b from 0 to 0.6, but decreased when increasing b from 0.6 to 1, whereas the cohesion varied little with the variation in b. The deformation in the direction of σ₂ changed from dilative to compressive and that in the direction of σ₃ remained dilative throughout.     

Can regional policies shape migration flows?

Our empirical analysis focuses on the effect of regional policies on migration attraction factors in Europe. We employ a regression discontinuity design to assess the causal relationship between the reception of large amounts of public funds and migration flows in the EU-15 regions. In highly-subsidised regions, we find a large increase in the share of foreign citizens from less-developed countries when compared to low-subsidised regions with similar pre-treatment characteristics. The analysis shows that such an increase is due to the positive impact of the European regional policy on job market opportunities as well as the improvement of public goods supply.     

No-Reference Video Quality Assessment using novel hybrid features and two-stage hybrid regression for score level fusion

This paper presents a novel No-Reference Video Quality Assessment (NR-VQA) model that utilizes proposed 3D steerable wavelet transform-based Natural Video Statistics (NVS) features as well as human perceptual features. Additionally, we proposed a novel two-stage regression scheme that significantly improves the overall performance of quality estimation. In the first stage, transform-based NVS and human perceptual features are separately passed through the proposed hybrid regression scheme: Support Vector Regression (SVR) followed by Polynomial curve fitting. The two visual quality scores predicted from the first stage are then used as features for the similar second stage. This predicts the final quality scores of distorted videos by achieving score level fusion. Extensive experiments were conducted using five authentic and four synthetic distortion databases. Experimental results demonstrate that the proposed method outperforms other published state-of-the-art benchmark methods on synthetic distortion databases and is among the top performers on authentic distortion databases. The source code is available at https://github.com/anishVNIT/two-stage-vqa.     

Prediction models for multiphase-flow-induced corrosion of API X80 steel in CO2/H2S environment

In the offshore oil and gas industry, mainly focusing on the use of rigid or flexible pipes of subsea infrastructure applied to risers or flowlines, one of the greatest difficulties is the interpretation of the combined effects of the various correlated phenomena (hydrodynamic effects of intermittent flow, the effects of corrosivity of the environment in addition to variations in pressure, temperature, and dynamic loading). On the basis of this scenario, defining the degree of severity of each of the correlated system variables becomes of fundamental importance for establishing reliable criteria for selecting materials for subsea application. The established flow pattern directly affects the corrosion rate (or the pipe material mass loss), but the balance of other variables including possible changes in the physical and transported fluid chemical properties may increase the damage up to an order of magnitude, which is a piece of information normally not foreseen in design criteria. Therefore, to improve the understanding of the corrosion study influenced by multiphase flow, a testing loop was designed and assembled at the Corrosion and Protection Laboratory of the Institute for Technological Research, in which API X80 steel coupons were positioned in locations with a 0° and 45° inclinations. Tests were conducted by varying the partial pressure of the gaseous phase containing blends of CO₂ and H₂S with N₂ balance, mixed with the liquid phase containing light oil and heavy oil in water with salinity (NaCl)-simulating oil well conditions with 80% water cut. The main objective of this study is to establish models that can predict the corrosion intensity in conditions close to those obtained experimentally. To achieve results, the multiple regression and Box–Cox transformation methods were applied. These models will make possible damage prediction and optimization of matrix parameters for the multiphase-loop test.     

Cryptographic Lightweight Encryption Algorithm with Dimensionality Reduction in Edge Computing

Edge Computing is one of the radically evolving systems through generations as it is able to effectively meet the data saving standards of consumers, providers and the workers. Requisition for Edge Computing based items have been increasing tremendously. Apart from the advantages it holds, there remain lots of objections and restrictions, which hinders it from accomplishing the need of consumers all around the world. Some of the limitations are constraints on computing and hardware, functions and accessibility, remote administration and connectivity. There is also a backlog in security due to its inability to create a trust between devices involved in encryption and decryption. This is because security of data greatly depends upon faster encryption and decryption in order to transfer it. In addition, its devices are considerably exposed to side channel attacks, including Power Analysis attacks that are capable of overturning the process. Constrained space and the ability of it is one of the most challenging tasks. To prevail over from this issue we are proposing a Cryptographic Lightweight Encryption Algorithm with Dimensionality Reduction in Edge Computing. The t-Distributed Stochastic Neighbor Embedding is one of the efficient dimensionality reduction technique that greatly decreases the size of the non-linear data. The three dimensional image data obtained from the system, which are connected with it, are dimensionally reduced, and then lightweight encryption algorithm is employed. Hence, the security backlog can be solved effectively using this method.     

Generalized linear model based monitoring methods for high-yield processes

Emerge in technology brought well-organized manufacturing systems to produce high-quality items. Therefore, monitoring and control of products have become a challenging task for quality inspectors. From these highly efficient processes, produced items are mostly zero-defect and modeled based on zero-inflated distributions. The zero-inflated Poisson (ZIP) and zero-inflated Negative Binomial (ZINB) distributions are the most common distributions, used to model the high-yield and rare health-related processes. Therefore, data-based control charts under ZIP and ZINB distributions (i.e., Y-ZIP and Y-ZINB) are proposed for the monitoring of high-quality processes. Usually, with the defect counts, few covariates are also measured in the process, and the generalized linear model based on the ZIP and ZINB distributions are used to estimate their parameters. In this study, we have designed monitoring structures (i.e., PR-ZIP and PR-ZINB) based on the ZIP and ZINB regression models which will provide the monitoring of defect counts by accounting the single covariate. Further, proposed model-based charts are compared with the existing data-based charts. The simulation study is designed to access the performance of monitoring methods in terms of run length properties and a case study on the number of flight delays between Atlanta and Orlando during 2012–2014 is also provided to highlight the importance of the stated research.     

基于主成分分析法的熟化菠萝蜜种子品质评价

以菠萝蜜种子为原料,研究水煮、汽蒸、焙烤3种熟化方式对其感官品质、色泽、质构、糊化度、基本营养含量、脂肪酶抑制率、质量损失率和微观结构的影响,并对其品质指标进行主成分分析。结果表明:水煮、汽蒸、焙烤3种熟化方式对不同指标有较大影响,水煮方式糊化度最高且质量损失率最大;焙烤显著加深菠萝蜜种子色泽且脂肪酶抑制率最大;汽蒸熟化后菠萝蜜种子感官评分最高;通过主成分分析可知,焙烤方式获得的品质评价综合得分最高,可作为菠萝蜜种子预熟化的较优选择。