Miracle-dz/UniFAR-FTUs huggingface dataset:
Assessing the Karst Groundwater Quality and Hydrogeochemical Characteristics of a Prominent Dolomite Aquifer in Guizhou, China

Karst groundwater is one of the primary water resources in most provinces of Southwestern China where karst topography is strongly featured. In Guizhou Province, a prominent geologic sedimentary formation is the dolomite stratum which exists as the restricted platform facies and potentially provides a large reservoir for drinking water. A proper understanding and evaluation of its hydrogeochemical characteristics and water quality is the key to ensuring the drinking water safety. In the present study, groundwater samples were collected from 25 locations of the dolomite aquifer across Guizhou to determine their major chemical compounds, including the cations (K+, Na+, Ca2+, Mg2+) and the anions (HCO3−, F−, Cl−, NO3−, SO42−), as well as the pH, total hardness, and total dissolved solids. HCO3− and Ca2+ were found to be the dominant anion and cation, respectively, which is characteristic of typical karst groundwater and supports the overall observation of a slightly weak acid to weak alkaline environment in the studied groundwater, as the pH measurements ranged from 6.80 to 8.37. Fuzzy comprehensive evaluation method is used to evaluate the groundwater quality based on typical drinking water safety standard. The results show that the groundwater in most of the studied aquifers is of reasonably good quality. However, in some aquifers, concentrations of NO3− and/or SO42− were found to be excessively high. Overall, the studied dolomite aquifer in its natural environment as investigated in the present study can be considered as a potential geological stratum for water resources exploitation in Guizhou.
Effect of Strategic Leadership and Strategic Thinking on Firms’ Competitive Advantage: Evidence from Fast Moving Consumer Goods Sub-Sector

Purpose: This study examines the effect of strategic leadership and strategic thinking on the competitive advantage of fast moving consumer goods (FMCG) in Nigeria. The study employed survey research design, by administering structured questionnaire on members of staff of seven FMCG. Methodology: The sample size of the study was determined using Raosoft sample size determination method, at 95% confidence level and 4% confidence interval. Three hypotheses were formulated and the ordinary least square was employed in estimating the regression models with the aid of STATA version 14. Findings: The findings revealed that strategic leadership and strategic thinking both have positive and significant effect on FMCG competitive advantage. The combined effect of strategic leadership and strategic thinking on FMCG competitive advantage equally revealed a positive and significant combined effect. The study further revealed that strategic leadership and strategic thinking have a combined adjusted coefficient of determination (adjusted R2) of 0.479, which suggest that 47.9% variation in FMCG competitive advantage is accounted for by the strategic leadership and strategic thinking. The empirical findings of this study provide evidence that strategic leadership and strategic thinking play important roles in FMCG competitive advantage in Nigeria. This imply that strategic leadership and strategic thinking can both enhance the competitive advantage of FMCG. Originality/Value: This study is unique as it combines strategic leadership and thinking, which is the only study that examines the performance of FMCGs from the perspective of strategic leadership and thinking in Nigeria.
Factors Affecting Decisions To Choose Application Based Transportation

The existence of service application for ojek and call taxi is a solution for people who have difficulty using public transport due to its accessibility and point to point pick up service. This study aims to determine the factors that influence customer decisions in using application-based “Motorbike Taxi” like Go-Jek. This research was conducted on the Putera Batam University’s students that have used Go-Jek application, with a sample size of 100 students as its respondents. The sampling technique used is incidental sampling method and is estimated to represent the population. Data analysis method used in this research is descriptively quantitative analysis of the factors that influence students in using the services of Go-Jek. The results showed that service quality factors that influence students’ decisions to choose transportation services based on “Motorbike Taxi” service orders consist of 6 factors: service, completeness of driving, security, trust, responsiveness and reliability, where the most dominant factor is service factor. Keywords— Application-based Transportation, Service Quality, Customer Decisions Abstrak Hadirnya aplikasi layanan order ojek dan taksi menjadi solusi bagi masyarakat yang kesulitan untuk menggunakan angkutan umum karena lebih mudah diakses serta bisa menjemput pelanggan sampai ke rumah. Penelitian ini bertujuan untuk mengetahui faktor-faktor yang mempengaruhi mahasiswa menggunakan transportasi berbasis aplikasi yaitu Go-Jek. Penelitian ini dilakukan pada mahasiswa Universitas Putera Batam dengan jumlah responden sebanyak 100 mahasiswa. Populasi pada penelitian ini adalah mahasiswa Universitas Putera Batam yang pernah menggunakan aplikasi Go-Jek. Penentuan besarnya sampel yang dibutuhkan dalam penelitian ini adalah 100 sampel dengan teknik pengambilan sampel menggunakan metode convenience sampling dan diperkirakan dapat mewakili populasi. Metode analisis data yang digunakan dalam penelitian ini adalah analisis kuantitatif secara deskriptif yaitu mendeskriptifkan faktor-faktor yang mempengaruhi mahasiswa dalam menggunakan jasa Go-Jek. Hasil penelitian menunjukan bahwa faktor-faktor kualitas pelayanan yang mempengaruhi keputusan mahasiswa untuk memilih transportasi berbasis aplikasi layanan order ojek terdiri dari 6 faktor yaitu pelayanan, kelengkapan kendaraan, keamanan, kepercayaan, tanggap dan keandalan di mana faktor yang paling dominan adalah faktor pelayanan. Kata Kunci— Transportasi Berbasis Aplikasi, Kualitas Layanan, Keputusan Pelanggan
Leaching loss of dissolved organic nitrogen from cropland ecosystems

Although widely studied in grassland and forest ecosystems, dissolved organic nitrogen (DON), an important form of nitrogen (N) lost through leaching, has received little attention in cropland ecosystem research. A global literature review and partial least squares path modeling (PLS–PM) were adopted to assess the quantity and composition of N leaching loss under different cropping systems, soil types, and management practices, as well as to identify the major factors controlling DON leaching. Annual total dissolved nitrogen (TDN, organic + inorganic N) leaching under different cropping systems ranged from 4.0 to 383.2 kg N ha−1. Vegetable and rice production systems showed the greatest and least TDN leaching, respectively. Across different cropping systems, DON accounted for 4.7%–34.9% of TDN in leachate. The NH4 +–N form of N leaching is negligible in most upland cropping systems (<2.3%), but not so in lowland rice systems. The largest ratio of DON leaching to TDN leaching was found in a rice–wheat rotation (34.9%). Catch crop slightly increased DON leaching; however, it greatly decreased TDN leaching. Cropping systems in which animal manure or plant compost was applied or lowland rice was included showed a relatively high DON leaching ratio. Accordingly, in investigating DON leaching, more attention should be focused on cropping systems with manure application or lowland rice systems. Compared with climatic conditions and soil properties, field management practices (irrigation, chemical N input, and manure amendments) proved to be the factors most strongly influencing DON leaching. This suggests that optimizing water and N management practices is the most effective way of reducing the risk of DON leaching and increasing N use efficiency, particularly for vegetable and rice production systems.
Nanoscale spherical-supported membranes as novel platforms for improving the phage display screening of antibody mimetics against membrane protein targets

Membrane proteins represent the majority of therapeutic targets for the antibody-based drugs available today. These are routinely identified via phage display screening, but traditional antigen presentation methods require membrane protein targets to be detergent-solubilised in order to preserve their native conformations post-purification. Unfortunately, detergent solubilisation can not only lead to gradual target denaturation over time, but the detergent micelles can also occlude important epitopes on the extramembranous loops and thus prevent the discovery of antibody binders. The current thesis aimed to demonstrate that, by reconstituting purified membrane proteins into spherical-supported bilayer lipid membranes (SSBLMs) deposited on nanosized substrates, a versatile platform can be constructed for performing phage display screening against membrane protein targets, while not only presenting these within a native-like lipid environment, but also eliminating detergents from the screening phase altogether. For providing proof-of-concept, 100- and 200 nm silica nanoparticles were covered with POPC SSBLMs embedding the bacterial nucleoside transporter NupC. Full substrate coverage and the correct formation of the lipid bilayer components were established via spectrofluorometry, using fluorescent labelling and small-angle X-ray scattering (SAXS) respectively, while Western blotting and high-affinity antibody binding confirmed the presence of SSBLM-embedded NupC. The platform was then used to screen designed ankyrin repeat proteins (DARPins) against a His6-tagged construct of NupC across different screening formats so as to offer a comparison to the classic 96-well plate antigen presentation method. Following that, the DARPin binders showing the highest potential affinity for NupC were purified and subjected to further binding validation assays against two other constructs – detergent-solubilised double Strep-tagged NupC and SSBLM-embedded untagged/wild-type NupC – in order to identify any binders targeting extramembranous epitopes that would be accessible in vivo as well. Ultimately, the results presented throughout indicated that SSBLMs constitute a promising means of screening antibody binders against membrane protein targets embedded in a close-to-native format.
Clinical Evaluation of Artificial Intelligence based Preoperative Plans for Total Knee Arthroplasty

A preoperative plan is a virtual plan that defines the implant position and orientation allowing a surgeon to prepare for surgery. However, the default preoperative plan for total knee arthroplasty proposed by the manufacturer requires changes to be made by the surgeon in more than 90% of the cases. Previous studies have shown that artificial intelligence can be used to create better preoperative plans compared to manufacturer’s default plans. However, the quality of artificial intelligence based preoperative plans has not yet been compared to surgeon approved preoperative plans. The purpose of this study is to compare default, artificial intelligence and surgeon approved preoperative plans, by having them scored on a range from 1 (totally unacceptable plan) to 5 (no corrections needed) by an experienced surgeon, while being blinded to the plan type. Through a Wilcoxon signed rank test with α=0.05, AI based preoperative plans were found to be a significant improvement upon the default plans (p-val=0.000136), while the differences in score between AI and surgeon approved preoperative plans were insignificant (p-val= 0.083). Consequently these results indicate that AI generated preoperative plans for TKA are an improvement upon current default plans, which could increase the surgeon’s planning efficiency when applied in clinical practice.
USABILITY TESTING ON THE ASAHAN COVID-19 WEB PORTAL USING SYSTEM USABILITY SCALE (SUS)

COVID-19 ( Coronavirus Disease 2019 is a pandemic that is sweeping the world today. From the WHO data, the number of confirmed cases increases every day, including in Indonesia. Misinformation is commonplace as people are active in seeking information about COVID-19. Valid information and real are needed to be a trusted source of knowledge. In this case, Indonesia ranging from the central government to local governments provide a web-based information portal to make a trusted information center to the community, including in Asahan Regency. Usability is one of the factors in the success of a government website. minimum to find out how easy it is to use and the purpose of its use achieved according to end-users. Usability testing in this study uses the System Usability Scale (SUS) method because SUS is a valid and reliable usability testing tool even with a small sample. The SUS Score results made on the web portal COVID-19 Asahan Regency amounted to 70.19 which shows that the website is acceptable but still not usable as a whole because of grade-scale=C and adjective-rating=ok category. From these results, it can be seen that this portal website still needs to be developed back to a better stage. Keywords: COVID-19; System Usability Scale (SUS); Usability; Website
Data Augmentation using Adversarial Networks for Tea Diseases Detection

Deep learning technology has a better result when trained using an abundant amount of data. However, collecting such data is expensive and time consuming. On the other hand, limited data often be the inevitable condition. To increase the number of data, data augmentation is usually implemented. By using it, the original data are transformed, by rotating, shifting, or both, to generate new data artificially. In this paper, generative adversarial networks (GAN) and deep convolutional GAN (DCGAN) are used for data augmentation. Both approaches are applied for diseases detection. The performance of the tea diseases detection on the augmented data is evaluated using various deep convolutional neural network (DCNN) including AlexNet, DenseNet, ResNet, and Xception. The experimental results indicate that the highest GAN accuracy is obtained by DenseNet architecture, which is 88.84%, baselines accuracy on the same architecture is 86.30%. The results of DCGAN accuracy on the use of the same architecture show a similar trend, which is 88.86%.
Phytate dephosphorylation by Lactobacillus pentosus CFR3

Summary Studies on phytate-degrading enzymes from lactobacilli are scarce, despite its potential in improving the nutritional quality of plant-based foods. Therefore, the current investigation deals with the phytate-degrading enzyme produced by a native Lactobacillus pentosus strain. Phytase activity was highest towards the end of the exponential phase. Activity increased in the presence of maltose (381.1%) compared with glucose. The presence of phytate in the media stimulated the enzyme production. The enzyme of interest was a 70 kDa protein with a pH and temperature optima of 5.0 and 55–60 °C, respectively. It retained 46% of activity after exposure to 70 °C for 20 min and also showed broad substrate specificity. It was completely inhibited by Hg2+, Fe2+ and PMSF while being activated by Co2+. This report is the first to show dephytinisation of autoclaved finger millet flour either by fermentation with L. pentosus or by treatment with the corresponding cell-free extract.
Boundary‐aware vehicle tracking upon UAV

Discriminative correlation filters (DCFs) have recently achieved competitive performance in visual tracking benchmarks. However, such trackers perform poorly when the target undergoes occlusion, viewpoint variation or other challenging attributes. To tackle these issues, in this Letter, the authors combine the fast DCF trackers with the precise deep learning methods to eliminate the accumulating drift for the vehicle tracking based on unmanned aerial vehicle platform. Specifically, the authors employ the tracking result of the DCF tracker as the input of the boundary regressing network. After judging the existence of the target in the input patch, the proposed network would estimate the boundary of the target vehicle. Furthermore, the output would be updated to the tracking template, aiming at eliminating the accumulation errors and achieving a long-term tracking. The effectiveness of the proposed algorithm is validated through experimental comparison on widely used tracking benchmark data sets.
Type I collagen C-telopeptide degradation products as bone resorption markers

(27/12/2018) Type I collagen C-telopeptide degradation products as bone resorption markers Degradation products of the C-telopeptides from type I collagen (CTC) can be measured with commercially available immunoassays (e.g., CrossLaps(TM) assays). It is well established that the urinary excretion of CTC fragments is closely correlated with the rate of bone degradation (resorption). Data obtained with a recently developed assay for CTC fragments in serum also suggest that serum CTC is a sensitive and specific index of bone resorption. Several structures of the CTC fragments have been elucidated. It has become clear that the peptide sequence measured in the CrossLaps assays can spontaneously beta-isomerize to produce unusual isoaspartyl peptides. This sign of protein aging is believed to ensure that the urinary CrossLaps ELISA and the Serum CrossLaps One Step ELISA, both specific for beta-isomerized fragments, measure degradation of relatively old bone. Conversely, the alpha-CrossLaps RIA, specific for non-isomerized CTC fragments, measures degradation of relatively young bone. Currently, the assays are being evaluated to clarify their potential clinical applications. They have also been shown to be efficient tools for monitoring antiresorptive therapy. Assessment of future risk of bone loss and fracture is being investigated with promising results. It is expected that the routine use of the CrossLaps assays will become established within the next few years.
Improving the start-up parameters of a gas turbine engine by adaptation the air turbine starter

The paper describes the process of optimizing the blades of a starter air turbine for a gas turbine engine launch system. This task was initiated by the necessity to use an existing turbine when starting a new engine. During the study, it was found that the turbine, although it ensures the conditions for the joint operation with the auxiliary power unit turbine and meet the strength constraints, it does not allow to start the engine within the required time. As a result of studies using mathematical optimization methods involving commercial programs, a variant was found to modernize the baseline turbine, which provides an acceptable value of torque on the shaft with an adequate start-up time at all operating modes with minimal design changes.
Successful Antiviral Therapy Reduces Risk of Schizophrenia Among Chronic Hepatitis C Patients: A Nationwide Real-World Taiwanese Cohort (T-COACH)

Abstract Background Chronic hepatitis C (CHC) has been associated with major psychoses, and interferon (IFN)-based therapy may cause psychiatric sequelae. We aimed to evaluate the effects of sustained virological response (SVR) on the incidence of major psychoses in a nationwide Taiwanese CHC cohort. Methods Fifteen thousand eight hundred thirty-six CHC Taiwanese who received IFN-based therapy were enrolled between 2003 and 2015. Of those, 12 723 patients were linked to the National Health Insurance Research Databases for the incidence of major psychoses. Death before major psychoses was considered a competing risk. Results Twenty-four patients developed new-onset major psychoses during 67 554 person-years (3.6 per 10 000 person-years), including 16 affective psychoses, 7 schizophrenia, and 1 organic psychotic condition. The incidence of major psychoses and affective psychoses did not differ between the SVR and non-SVR groups. The 10-year cumulative incidence of schizophrenia were significantly higher in the non-SVR than in SVR patients (0.14% vs 0.04%, P = .036). Cox subdistribution hazards showed that SVR and older age were associated with a significantly lower risk of schizophrenia (hazard ratio = 0.18 and 0.17). Sustained virological response was associated with decreased incidence of schizophrenia and majorly observed among patients with age <45 (P = .02). Conclusions Successful IFN-based therapy might reduce the incidence of schizophrenia among CHC patients, especially among younger patients.
Do vegetation, disturbances, and water influence large mammal distribution?

ABSTRACT Species distribution can be influenced by different factors that may either be biotic or abiotic. In the Serengeti Ecosystem, most of the studies on species distribution have focused on single species. In contrast, few which considered multispecies have been done in larger protected areas such as Serengeti National Park and left the small areas like Kijereshi Game Reserve (KGR) unexplored. Here, we assessed the impact of vegetation, proximity to humans, and the distance from the roads and waterways on large mammal distribution in the KGR. We used line transects to record locations and numbers of large mammals in the KGR while the distances from boundaries, roads, and waterways were calculated by using QGIS. To assess how large mammals varied in response to different vegetation types, proximity to human settlements, and distances to nearest roads and the waterways, Generalized Additive Model (GAM) was used. Our findings revealed that vegetation and proximity to humans do not significantly affect large mammal density. However, we observed a significant non-linear relationship between large mammal density and distances from the roads and waterways. Therefore, the study findings provide a foundation for large mammal studies in the KGR, which are essential in planning and implementation of wildlife tourism activities.
Assessing strengths and weaknesses of DNA metabarcoding‐based macroinvertebrate identification for routine stream monitoring

DNA metabarcoding holds great promise for the assessment of macroinvertebrates in stream ecosystems. However, few large‐scale studies have compared the performance of DNA metabarcoding with that of routine morphological identification. We performed metabarcoding using four primer sets on macroinvertebrate samples from 18 stream sites across Finland. The samples were collected in 2013 and identified based on morphology as part of a Finnish stream monitoring program. Specimens were morphologically classified, following standardised protocols, to the lowest taxonomic level for which identification was feasible in the routine national monitoring. DNA metabarcoding identified more than twice the number of taxa than the morphology‐based protocol, and also yielded a higher taxonomic resolution. For each sample, we detected more taxa by metabarcoding than by the morphological method, and all four primer sets exhibited comparably good performance. Sequence read abundance and the number of specimens per taxon (a proxy for biomass) were significantly correlated in each sample, although the adjusted R2 values were low. With a few exceptions, the ecological status assessment metrics calculated from morphological and DNA metabarcoding datasets were similar. Given the recent reduction in sequencing costs, metabarcoding is currently approximately as expensive as morphology‐based identification. Using samples obtained in the field, we demonstrated that DNA metabarcoding can achieve comparable assessment results to current protocols relying on morphological identification. Thus, metabarcoding represents a feasible and reliable method to identify macroinvertebrates in stream bioassessment, and offers powerful advantage over morphological identification in providing identification for taxonomic groups that are unfeasible to identify in routine protocols. To unlock the full potential of DNA metabarcoding for ecosystem assessment, however, it will be necessary to address key problems with current laboratory protocols and reference databases.