Miracle-dz/UniFAR-FTUs huggingface dataset:
A Single Mutation in the Carbohydrate-Binding Module Enhances Cellulase Activity in Bacillus Amyloliquefaciens Mutant

From our earlier work, we modified the carbohydrate-binding module (CBM) of Bacillus amyloliquefaciens to increase cellulase activity using cold plasma technology. The cellulase gene (BglC-M) from the mutant was expressed in Escherichia coli BL21(DE3) under the T7 promoter. The hydrolysis activity of the cellulase mutant (BglC-M) was approximately 2.5-fold higher than the control (BglC-W) over a wide range of pH and temperature conditions. The amino acid sequence of the mutant BglC-M contained 471 residues that were almost identical to the control BglC-W. Only a single amino acid, lysine, was replaced by glutamic acid at position 370 (K370E) within the carbohydrate-binding module (CBM). Structure prediction and substrate docking of BglC-M indicated that the single mutation (K370E) might involve cellulose binding of the β-sandwich facilitated by hydrogen bonding. The docking study of cellopentaose with the model structure of BglC-M indicated that the replacement of lysine-370 led to the formation of a hydrogen bond with 436Y, which has a shorter distance (2.6 Å) compared with the control (5.4 Å). As a result, the structure becomes more compact and stable, resulting in increased catalytic efficiency. Finally, the biomass hydrolysis ability of cellulase was investigated on lignocellulosic wastes such as pineapple peel, corncob, and durian peel. The BglC-M enzyme showed a more significant amount of reducing sugar released from all lignocellulosic wastes than the control. This was the first evidence that altering the base composition of the cellulose binding module enhanced the catalytic activity. HIGHLIGHTS Increasing cellulase activity of Bacillus amyloliquefaciens using plasma technology Mutation at cellulose-binding module enhance cellulase hydrolysis activity Greater cellulase activity in the hydrolysis on lignocellulosic wastes GRAPHICAL ABSTRACT
Bridge Inspection and Defect Recognition with Using Impact Echo Data, Probability, and Naive Bayes Classifiers

Interpretation of IE data have been carried out by analyzing IE signals in frequency domain to determine the maximum frequency. However, the current peak frequency method can be inaccurate. The purpose of this research is to introduce features in IE signals that can be used for effective classification and interpretation for bridge deck evaluation through statistical analysis and Naive Bayes classifiers. The dataset contained IE data collected from eight slabs created at Advanced Sensing Technology FAST NDE laboratory (FHWA). A set of statistical features in time domain, normalized peak values, and length of preprocessed signals were used to classify the IE data, statistically. Then, Naive Bayes classifiers was employed to recognize defect area. Finally, the result of statistical classification was compared with frequency approach. The result shows that 19 and 21% of the IE signals collected from the defect area have multiple peaks, respectively. However, 85% of the IE signals collected from the sound set had only one peak. A probability classifier was used to find the relationship between the result of the frequency method and statistical analysis. The result shows that 10% of the IE signals were usable for estimating the thickness in the sound group.
Evaluating APSIM-and-DSSAT-CERES-Maize Models under Rainfed Conditions Using Zambian Rainfed Maize Cultivars

Crop model calibration and validation is vital for establishing their credibility and ability in simulating crop growth and yield. A split–split plot design field experiment was carried out with sowing dates (SD1, SD2 and SD3); maize cultivars (ZMS606, PHB30G19 and PHB30B50) and nitrogen fertilizer rates (N1, N2 and N3) as the main plot, subplot and sub-subplot with three replicates, respectively. The experiment was carried out at Mount Makulu Central Research Station, Chilanga, Zambia in the 2016/2017 season. The study objective was to calibrate and validate APSIM-Maize and DSSAT-CERES-Maize models in simulating phenology, mLAI, soil water content, aboveground biomass and grain yield under rainfed and irrigated conditions. Days after planting to anthesis (APSIM-Maize, anthesis (DAP) RMSE = 1.91 days; DSSAT-CERES-Maize, anthesis (DAP) RMSE = 2.89 days) and maturity (APSIM-Maize, maturity (DAP) RMSE = 3.35 days; DSSAT-CERES-Maize, maturity (DAP) RMSE = 3.13 days) were adequately simulated, with RMSEn being <5%. The grain yield RMSE was 1.38 t ha−1 (APSIM-Maize) and 0.84 t ha−1 (DSSAT-CERES-Maize). The APSIM- and-DSSAT-CERES-Maize models accurately simulated the grain yield, grain number m−2, soil water content (soil layers 1–8, RMSEn ≤ 20%), biomass and grain yield, with RMSEn ≤ 30% under rainfed condition. Model validation showed acceptable performances under the irrigated condition. The models can be used in identifying management options provided climate and soil physiochemical properties are available.
Investigating the Causes and Impacts of Convective Aggregation in a High Resolution Atmospheric GCM

A ∼50 km resolution atmospheric general circulation model (GCM) is used to investigate the impact of radiative interactions on spatial organization of convection, the model's mean state, and extreme precipitation events in the presence of realistic boundary conditions. Mechanism‐denial experiments are performed in which synoptic‐scale feedbacks between radiation and dynamics are suppressed by overwriting the model‐generated atmospheric radiative cooling rates with its monthly varying climatological values. When synoptic‐scale radiative interactions are disabled, the annual mean circulation and precipitation remain almost unchanged, however tropical convection becomes less aggregated, with an increase in cloud fraction and relative humidity in the free troposphere but a decrease in both variables in the boundary layer. Changes in cloud fraction and relative humidity in the boundary layer exhibit more sensitivity to the presence of radiative interactions than variations in the degree of aggregation. The less aggregated state is associated with a decrease in the frequency of extreme precipitation events, coincident with a decrease in the dynamical contribution to the magnitude of extreme precipitation. At regional scales, the spatial contrast in radiative cooling between dry and moist regions diminishes when radiative interactions are suppressed, reducing the upgradient transport of energy, degree of aggregation, and frequency of extreme precipitation events. However, the mean width of the tropical rain belt remains almost unaffected when radiative interactions are disabled. These results offer insights into how radiation‐circulation coupling affects the spatial organization of convection, distributions of clouds and humidity, and weather extremes.
Thin-slab maximum intensity projection of CT angiography for collateral score and clot burden score evaluation: comparison with conventional CT angiography.

Background This study aimed to exam the effects of thin-slab maximum intensity projection (TS-MIP) of computed tomography angiography (CTA) for collateral score (CS) and clot burden score (CBS) evaluation in patients with large-vessel-occlusion (LVO) stroke in the anterior circulation. Methods Of 241 consecutive patients with LVO stroke admitted to our center between August 2015 and June 2020, 187 patients were enrolled. CS and CBS were evaluated on conventional CTA and TS-MIP separately. Outcome at 90 days was classified as good if modified Rankin scale (mRS) was ≤2 and as poor if mRS was >2. The correlations between CS and CBS and clinical outcomes were assessed. Receiver operating characteristic (ROC) curve analysis was used to determine the diagnostic values of CS and CBS. Multivariate logistic regression analysis was performed to identify the independent predictors of 90-day good clinical outcomes. Results The correlation coefficient for clinical outcomes was significantly better for CS based on TS-MIP than that based on conventional CTA (-0.444 vs. -0.285, P=0.039); no significant difference was found in the CBS evaluation (TS-MIP: -0.356 vs. conventional CTA: -0.320, P=0.348). For predicting good clinical outcomes, TS-MIP-based CS was associated with larger area under the curve (AUC) (0.709 vs. 0.609, P=0.004) and higher sensitivity (69.1% vs. 42.0%, P=0.001) than CS based on CTA. In multivariable logistic regression analysis, the factors independently associated with good outcomes were National Institutes of Health Stroke Scale (NIHSS) score at admission (OR =1.147; P<0.001), TS-MIP-based CS (OR =0.326; P<0.001), final modified treatment in cerebral infarction (mTICI) score of 2b/3 (OR =0.098; P<0.001), and hemorrhagic transformation (OR =3.662; P<0.001). Conclusions TS-MIP-CTA is superior to conventional CTA for evaluation CS and CBS, and TS-MIP-based CS may be a useful predictor of clinical outcome.
Functional, Flavor and Visual Traits of Hydroponically Produced Tomato Fruit in Relation to Substrate, Plant Training System and Harvesting Time

Currently, a great portion of tomatoes is produced by soilless cultivation systems and the substrate selection among the various materials is one of the most important factors affecting yield and quality traits. On the other hand, grafting has been successfully used in soilless systems to ensure long-term cultivation. However, due to the high cost of grafted seedlings, plant training systems are sought. Given the fact that most literature refers to studies intended to mainly reveal production differences among treatments and the quality aspect was secondary, the present study was focused on the evaluation of tomato fruit functionality, flavor and visual traits. Tomato plants cv ‘Beef Bang F1’ were cultivated in a glasshouse hydroponic culture in four substrates: rockwool slabs, perlite in sacks, pumice in sacks and pumice in 9 L pots. The type of cultivated plants used were self-rooted or grafted onto ‘Defensor’ trained in single and double stems. Tomato fruit were harvested three times during the season (6 June, 31 July, 6 November). The fruit quality was measured based on visual (average fruit mass, and Minolta color values), flavor (dry mass, soluble solids content, titratable acidity, pH, flesh firmness) as well as functional traits (total phenolic content, ascorbic acid, lycopene, β-carotene, total carotenoid content and antioxidant capacity). Harvest time was the most important factor followed in many of these cases by the substrate (flavor and functional traits), as well as in certain cases by the plant grafting/training (flavor traits and antioxidants) or by both in some flavor traits and antioxidants. Correlation of color values with lycopene, though significant, was weak. Each individual harvest time revealed the rise in different parameters. Pumice, whether used in pot or in sack, enhanced the visual and flavor attributes the most, self-rooted plants and mid-summer harvest resulted in the highest tomato fruit quality.
Machine Learning Algorithm for Estimating Surface PM2.5 in Thailand

ABSTRACT We have used NASA's Modern-Era Retrospective analysis for Research and Applications, Version 2 (MERRA2) reanalysis data of aerosols and meteorology into a machine learning algorithm (MLA) to estimate surface PM2.5 concentration in Thailand. One year of hourly data from 51 ground monitoring stations in Thailand was spatiotemporally collocated with MERRA2 fields. The integrated data then used to train and validate a supervised MLA' random forest' to estimate hourly and daily PM2.5 concentrations. The MLA is cross-validated using a 10-fold random sampling approach. The trained MLA can estimate PM2.5 with close to zero mean bias across the country. The correlation coefficient of 0.95 with slope and intercept values of 0.95 and 0.88 are achieved between observed and estimated PM2.5. The MLA also shows underestimation at hourly scale under very clean conditions (PM2.5 80 µg m-3). The hourly data also demonstrate high skill in following the diurnal cycle during different seasons of the year. The daily mean PM2.5 (24-hour) values follow day-to-day variability very well, showing high value during winter months (November to February) and lower during other seasons. The trained MLA has the potential to reprocess the MERRA2 timeseries for the region, and the bias corrected data can be used in other applications such as long-term trend analysis and health exposure studies. The MLA can also be applied to GEOS forecasted fields to generate bias corrected air quality forecasts for the region.
New Urbanization, Energy-Intensive Industries Agglomeration and Analysis of Nitrogen Oxides Emissions Reduction Mechanisms

With the deepening of urbanization and industrialization, as well as the exacerbation of energy consumption, China is facing a severe situation in which nitrogen oxide (NOx) emissions reduction is imperative. In this study, it is aimed to put forward countermeasures and suggestions to reduce NOx emissions by analyzing the impact and mechanism of new urbanization, the agglomeration of energy-intensive industries and mutual interactions on China’s NOx emissions. By analyzing the data of 30 provinces in China from 2006 to 2017, this paper adopted the system generalized method of moments (SYS-GMM) and intermediary effect model to introduce four variables, such as: energy efficiency, human capital, industrial structure and energy structure, which were for empirical analysis. From the results, it was shown that: (1) NOx emissions in China have an accumulated effect; (2) new urbanization inhibits NOx emissions, whilst the agglomeration of energy-intensive industries intensifies NOx emissions. New urbanization weakens the negative impact of the agglomeration of energy-intensive industries on NOx emissions reduction and, (3) among the impacts of new urbanization on NOx emissions, the energy efficiency and human capital reflect the intermediary effect mechanism. At the same time, in the impact of the agglomeration of energy-intensive industries on NOx emissions, the industrial structure and energy structure show the mechanisms of the intermediary effect and masking effect, respectively.
Formulation and Evaluation of Ficus Benghalensis Emulgel for its Anti- Rheumatoid Arthritis Effect

The current study focuses on the development and characterization of ficus benghalensis powdered aerial roots emulgel to avoid the first-pass effect and strengthen bioavailability while reducing dosage intervals and dose-related deleterious reactions. three formulations with the same concentration and different polymers were formulated. Ethanolic and petroleum ether extract of dried aerial roots of ficus benghalensis were prepared by using different gelling agents like Carbopol 934, Carbopol 940, and Xanthane gum was formulated. The prepared formulations were evaluated for their qualitative as well as quantitative tests, physical appearance, pH, viscosity, spreadability, consistency, homogeneity, moisture loss, and finally in vitro anti-arthritic activity. Depending on the outcomes, it was observed that to all the formulation, F1 formulation containing Carbopol 940 with 4.6% moisture loss, 3780.3±5.0 viscosity and 6.1±0.1 PH and 43.7±1.53 spreadability shows better activity then all the other. Herbal emulgel of ethanolic extract of dried aerial roots of ficus benghalensis line, when compared with diclofenac emulgel, confirms the anti-arthritic activity through invitro release method.
Artificial Intelligence in the Context of Global Resource Mobility. What Can Be Expected from It?

The impact of artificial intelligence (AI) on business, government, and society is getting more attention. The leading AI sectors have higher productivity but a lower share of GDP than those lagging in digitization and AI. There is a technological gap, with still unknown consequences concerning the social contract, the expected new digital welfare profile, as well as the business strategy about globalization. The hypothesis is that while digitization was already in motion (2000–2005), capital outflow from the US to MHGEs (market high-growth economies) in Asia negatively affected its productivity outcome. Additionally, it is expected that AI will give more market power to multinationals, reshaping the social contract. Thus, the current western social contract will no longer be able to cope with the consequences of the weakness of the nation-state, its policymakers, or the powerful profit-driven multinationals to deal with the overall effect of AI. We aim to look at the impact of this new state of technology on the social contract, focusing on the proper actions of government and business to deal with it. We used a descriptive approach based on desk research concerning productivity data, European government policies, trade model analysis, and business approach to AI. We expect to demonstrate the dynamic interaction of the K/L ratio within the prevailing status of global resources mobility, and the dangers unregulated AI represents to labor. Policy actions are needed concerning the legal status of AI and how to avert the collapse of the social contract and the rise of oligarchic cyber‑autocracies. Our general conclusion is as follows: While capital investments, which would have contributed to improved total factor productivity (TFP) in the USA, went to MHGEs, increasing their GDP growth in less than a decade, the broad use of Artificial Intelligence (AI) will reverse massive offshoring, and new types of manufacturing processes will emerge in developed countries.
Wideband, Low-Profile, Dual-Polarized Machined-Metal Array on a Triangular Lattice

This article presents a dual-polarized frequency-scaled ultra-wide spectrum element (FUSE) array on a triangular lattice, precision-machined from an Aluminum block as a self-supporting structure, similar to all-metal Vivaldi arrays reported by the authors in past work. Whereas dual-polarized FUSE arrays normally consist of offset radiator pairs conforming to the ubiquitous square-lattice egg-crate configuration and capacitively coupled at the ends to achieve ultra-wideband operational frequency ranges, this work is novel in that it uses conventional manufacturing to directly machine the dual-polarized FUSE array on a triangular lattice instead. Triangular lattices for dual-polarized wideband arrays are uncommon in the literature and have the benefit of 15.5% reduction in element counts over a square lattice aperture of the same footprint and upper frequency of operation, making this a useful development for applications requiring minimum element counts, i.e., reduced cost/weight. In this article, embedded element performance predictions for the dual-polarized triangular-lattice FUSE array are presented and validated with measurements of a hardware prototype. Results show similar overall grating-lobe-free performance to a square lattice FUSE array of the same footprint and operational frequencies reported in earlier work. The asymmetries of the triangular lattice result in relative performance degradation versus square lattice FUSE arrays as a trade-off to the benefit of reduced element counts for apertures of similar size.
22 Years of Near-zero Cloud Cover Variability across Varying Geographical Landscapes of Southern Africa: A Surprising Anomaly

In physical geography, clouds are known to dictate global energy budgets and to have crucial ripple effects on other climatic variables such as diurnal range of temperature (DTR), a key indicator of climate change. Here, a 115-year state-of-the-art station based gridded dataset from the Climatic Research Unit (CRU) is interrogated to understand the evolution of cloud cover across southern Africa for the period 1901 - 2016. Results show near-constant variability from 1901 – 1922. It was therefore hypothesised that the observed near-constant variability would result in a similar pattern for some climatic variables such as DTR as the opposite would bring into question our current knowledge of geographical mechanisms underlying DTR control across the region. Further analyses showed little to no association between cloud cover and other climatological variables (including DTR) for the period 1901 – 1922 but strong and significant association from 1923 – 2016. This is the first observational evidence of near-constant cloud cover variability; it is surprising, and counterintuitive. This constant variation can be attributed to limited ground-based observations that went into the construction of the CRU gridded dataset during the 1901 – 1922 period and therefore, caution needs to be exercised by studies that need to use the data for the said period. This is a crucial area of scientific enquiry, and a lack of caution can lead to misleading conclusions on cloud cover evolution and how that relates to climate change.
Spatial aspects of wage curve estimation in Russia

The wage curve is traditionally defined as the negative relationship between wages and unemployment rates (taking into account various control variables). It was empirically shown that the wage curve exists in some countries, including Russia. However, usually in such studies with data for the Russian regions, the mutual influence of Russian regions is not taken into account. This could create the problem of omitted variable bias. In this paper, we took into account the corresponding impact using spatial-econometric models and gave a detailed interpretation of the results. The estimate of the parameter reflecting the effect of unemployment on wages in the model without spatial effects is almost twice the corresponding estimate in the models where these effects are taken into account. According to panel data for 2005–2018 for 81 regions we estimated the partial marginal effects of changes in unemployment in one region on wages in the other regions. Similar calculations were made for the other variables. Using partial marginal effects, we found for each region: 1) the regions most affected by this one, 2) the regions, changes in which will most likely affect the selected one. This is important, for example, for assessing consequences of government programs etc