Miracle-dz/UniFAR-FTUs huggingface dataset:
Extraction Method of Baseflow Recession Segments Based on Second-Order Derivative of Streamflow and Comparison with Four Conventional Methods

Baseflow recession analysis is widely used in hydrological research, water resource planning and management, and watershed hydrogeological research. The first step of baseflow recession analysis is to extract the baseflow recession segments from the hydrograph. Different extraction results lead to different analysis results. At present, the four major recession segment extraction methods applied by hydrologists are mostly based on experience, and there is no clear theoretical basis. Therefore, this study derives a second-order derivation (Sec-D) recession segment extraction method based on the power law relationship between storage and discharge. Moreover, by applying the Sec-D method and the four conventional extraction methods to four hydrological stations in the Tao’er River basin in northeastern China, the differences in the recession segment extraction, determination of basin-wide hydrogeological parameters, and groundwater balance estimation are compared. The results demonstrate that, contrary to the four conventional methods, the Sec-D method can effectively eliminate the early recession stage affected by the surface runoff or rainfall and some streamflow data with more than 1% non-sequential error. The hydraulic conductivity of the four basins estimated by the Sec-D method is between 2.3 × 10−5–4.9 × 10−5 m/s, and the aquifer thickness is between 131.2 and 202.5 m. However, the four conventional extraction methods may underestimate (by about 2.5 times) the basin-wide hydraulic conductivity and overestimate (by about 3 times) the aquifer thickness. The groundwater balance elements calculated by the Sec-D method and the four conventional methods present similar intra-annual fluctuation characteristics; the correlation coefficients of daily evapotranspiration calculated by the five methods ranged from 0.7 to 0.95, and those of daily effective groundwater recharge ranged from 0.95 to 0.99. The use of the Sec-D method in baseflow recession analyses is significant for future studies and can be combined with conventional methods.
The architectural framework of a cyber physical logistics system for digital-twin-based supply chain control

Personalised production allows the supply chain (SC) to exist in various dynamic fluctuations within a make-to-order (MTO) environment. An SC for personalised production has redundant inventory and operation capacity; therefore, it requires a system that can achieve recoverability for operation resilience. Thus, a standalone cyber physical system (CPS) has limitation for SC control with MTO. To solve this problem, the CPS must be coordinated, and a systematic approach is required. This study proposes a cyber physical logistics system (CPLS) that is coordinated with the agent cyber physical production systems in a multi-level CPS structure. This multi-level architectural framework is designed to provide technical functionalities for resilient SC control. The service composition procedures of technical functionalities on the distributed digital twin (DT) simulation are divided into type and instance stages. The operation procedures of the DT application and technical functionality of the DT engine are suggested. The proposed CPLS has appropriate service composition and operation for the bullwhip and ripple effects, which are the two main SC-related problems. This study illustrates an early case of CPLS that can minimise differences among assets using distributed DT simulation; furthermore, the study establishes an SC and production plan based on the DT simulation results.
Green imidazolium ionic liquid selectively facilitates Ce(III) ion transport through supported liquid membrane

ABSTRACT Cerium, a rare-earth element, is very important in industry and technology. It has particular applications in catalytic processes, hydrogen storage, lighting and polishing. The extraction of cerium ions can be carried out easily with solvent and membrane extraction processes from an aqueous medium. The process for the removal of cerium ions in a liquid membrane stabilised on the polytetrafluoroethylene (PTFE) was examined with the presence of imidazolium ionic liquid and organophosphorus extractants. The outcomes demonstrated that ionic liquid improves the extraction procedure and increases the permeation coefficient through the supported liquid membrane (SLM) membrane. The impact of process parameters such as the acidity of feed and recovery phase and concentration of carriers were investigated, and the maximum permeation coefficient was observed at 30%, 20% and 10% v/v for D2EHPA, TBP and C6MIM.NTF2 extractant concentrations, respectively, and cerium ions of 200 mg/L, feed pH of 6 and 2 M for the acidity of the stripping phase. Also, the resistances of both phases were modelled and calculated with the consideration of the rapid reaction in the interface with the first-order reactions.
Monitoring evolution of melt ponds on first-year and multiyear sea ice in the Canadian Arctic Archipelago with optical satellite data

Abstract The evolution of melt ponds on Arctic sea ice in summer is one of the main factors that affect sea-ice albedo and hence the polar climate system. Due to the different spectral properties of open water, melt pond and sea ice, the melt pond fraction (MPF) can be retrieved using a fully constrained least-squares algorithm, which shows a high accuracy with root mean square error ~0.06 based on the validation experiment using WorldView-2 image. In this study, the evolution of ponds on first-year and multiyear ice in the Canadian Arctic Archipelago was compared based on Sentinel-2 and Landsat 8 images. The relationships of pond coverage with air temperature and albedo were analysed. The results show that the pond coverage on first-year ice changed dramatically with seasonal maximum of 54%, whereas that on multiyear ice changed relatively flat with only 30% during the entire melting period. During the stage of pond formation, the ponds expanded rapidly when the temperature increased to over 0°C for three consecutive days. Sea-ice albedo shows a significantly negative correlation (R = −1) with the MPF in melt season and increases gradually with the refreezing of ponds and sea ice.
Genotypic diversity weakens competition within, but not between, plant species

Intraspecific diversity can affect the ecological function of communities. Because community function is the outcome of species interactions, understanding how intraspecific diversity affects species interactions may shed light on the mechanism by which intraspecific diversity affects communities. Competition among and within plant species may be particularly important, as the relative strengths of these interactions can influence species coexistence and community stability. We established a field experiment in a New England salt marsh to determine how plant intraspecific diversity affects both intra‐ and interspecific competition. Focal plants of two species (Spartina alterniflora and Spartina patens) were planted with S. alterniflora neighbours that were either genotypically identical to one another (monoculture) or genotypically distinct (polyculture). After two growing seasons, S. alterniflora performance was enhanced in the presence of polyculture conspecific neighbours compared to monoculture neighbours, whereas S. patens was not strongly affected by diversity of its S. alterniflora neighbours. This suggests that intraspecific competition was weakened by intraspecific diversity, potentially even shifting to facilitation, but we did not find strong evidence of intraspecific diversity impacting interspecific competition. By weakening competition within species, intraspecific diversity may promote coexistence of genotypes within that species. Synthesis. The community‐level effects of intraspecific diversity may be mediated in part by its effect on intraspecific competition/facilitation. Furthermore, stronger intraspecific competition and decreased coexistence resulting from reduced diversity may exacerbate ongoing declines in biodiversity in response to global change.
Komitmen Organisasi Memoderasi Pengaruh Partisipasi Anggaran, Asimetri Informasi, dan Self Esteem pada Budgetary Slack

This study purposed to figure out the effect of budgeting participation, information asymmetry, and self esteem on budgetary slack with organizational commitment as moderated variable in the Regional Organization of Badung Regency. The population in this study consisted by Head of Regional Organization, Head of Planning and Reporting and Head of Financial from the Regional Organization Unit of Badung Regency. Total number of respondents were 102 respondents by using saturated sampling method. Data analysis technique processed using Moderation Regression Analysis (MRA). The results showed that budgeting participation has a positive effect on budgetary slack, while information asymmetry and self esteem have a negative effect on budgetary slack. Organizational commitment proved to weaken the relationship between of budgeting participation, information asymmetry and self esteem on budgetary slack.
Deep learning-based object detection and geographic coordinate estimation system for GeoTiff imagery

A deep learning-based system has been created to autonomously analyze GeoTiff aerial imagery in order to retrieve information about objects type and their geographic coordinates. This research focuses on applying a Convolutional Neural Network (CNN) to detect objects and estimate the geographic coordinate of airplanes, ships and cars in those images. The system prototype was tested to measure the accuracy and precision for object detection. Furthermore, a Mean Absolute Error (MAE) analysis is done to the system to measure object coordinate estimation performance. The accuracy and precision for object detection of the system prototype are 81,05% and 93,29%, respectively. The system has MAE values which vary from 0,000012° to 0,000034° for object coordinate estimation.
Numerical Simulations of the Effect of Cold Fuel Temperature on In-Nozzle Flow and Cavitation Using a Model Injector Geometry

In the present study, Large Eddy Simulations (LES) have been performed with a 3D model of a step nozzle injector, using n -pentane as the injected ﬂuid, a representative of the high-volatility components in gasoline. The inﬂuence of fuel temperature and injection pressure were investigated in conditions that shed light on engine cold-start, a phenomenon prevalent in a number of combustion applications, albeit not extensively studied. The test cases provide an impression of the in-nozzle phase change and the near-nozzle spray structure across different cavitation regimes. Results for the 20 o C fuel temperature case (supercavitating regime) depict the formation of a continuous cavitation region that extends to the nozzle outlet. Collapse-induced pressure wave dynamics near the outlet cause a transient entrainment of air from the discharge chamber towards the nozzle. The extent of air entrainment appears noticeably reduced on the simulations with colder fuel ( − 10 o C , incipient cavitation) due to the shorter length of the continuous cavitation region, which impedes the backwards air motion. A comparison of the examined cases suggests that in addition to the in-nozzle ﬂow, the injection temperature also has a noticeable effect on the macroscopic structure of the emanating fuel spray. On the colder injection, a symmetric jet is formed in the near-nozzle region, in contrast to the supercavitating cases that demonstrate a concave surface on the side of the step nozzle edge (a byproduct of intense air entrainment) and an increased spray opening angle. The above observations attest that the size and intensity of the cavitation features tend to become suppressed as the temperature of the injected fuel decreases. This has a binary effect: the injection of cold fuel may adversely inﬂuence the near-nozzle jet formation, while at the same
Difficulty Curve-Based Procedural Generation of Scrolling Shooter Enemy Formations

In any game where the player must face large formations of enemies, the way the formations are formed significantly influences the difficulty level of the game. As proper difficulty levels are essential to player’s enjoyment, the enemy formations need to be arranged properly. In this paper we experimented with how to procedurally generate enemy formations in a vertical scrolling shooter game while keeping the difficulty levels proper. Generations of the formations were done with genetic algorithm where the fitness function calculated two variables: difficulty curve, which reflects a formation’s difficulty level, and enemy variety. The difficulty curve took into account difficulty points, which measured the danger levels of on-screen enemies throughout the game’s duration. The fitness of difficulty curve of an enemy formation was acquired through point-by-point comparison between the curve and a human-designed one, which represented the ideal difficulty curve as intended by the game’s developer, and calculation of the root-mean-square error. The genotype of an individual was represented by a 5x40 grid containing enemy genes and empty space genes. We ran 300 generations in total and each was done in 100 iterations and had 40 individual genotypes. The results show that the amount of enemy genes in initial genotypes influences the population’s fitness progression. We then discuss the implications of our findings and possible directions of future researches.
Research on equipment fault prediction expert system based on big data dimension reduction

Due to the complex structure and massive volume of large equipment in petrochemical enterprises, it was very difficult to carry out inspection and maintenance work. The traditional expert system used a single knowledge base and reasoning machine, so the processing efficiency and prediction accuracy were low. In this paper, the dimensionality reduction which is used to process a large amount of physical information collected by the sensors, remove redundant components and extract main characteristic parameters. Meanwhile, combined with the powerful pattern recognition and judgment ability of fuzzy neural network, the forward reasoning mechanism and error back propagation keeping continuous training correction until meeting the accuracy requirements, the expert system gave the equipment fault prediction conclusion. Finally, taking main fan oil station circulating oil pump and motor of heavy oil catalytic cracking unit and nine stages of bearing damage as monitoring objects, the fault predictions was implemented by the following steps: data collection, feature extraction, dimension reduction of big data and expert system with the fusion of fuzzy neural network, and it is proved that the expert system with fuzzy neural network based on dimension reduction can greatly improve the convergence speed.
Experimental Study of Heat Transfer to Small Cylinders in a Subsonic, High-temperature Gas Stream

A Nusselt-Reynolds number relation for cylindrical thermocouple wires in crossflow was obtained from the experimental determination of time constants. Tests were conducted in exhaust gas over a temperature range of 2000 to 3400 R, a Mach number range of 0.3 to 0.8, and a static-pressure range from 2/3 to 1-1/3 atmospheres, yielding a Reynolds number range of 450 to 3000. The correlation obtained is Nu=(0.428 plus or minus 0.003) times the square root of Re* with average deviations of a single observation of 8.5 percent. This relation is the same as one previously reported for room-temperature conditions.
Adsorption of sarin and chlorosarin onto the Al12N12 and Al12P12 nanoclusters: DFT and TDDFT calculations

This study provides details of the electronic and optical structures and binding energies of sarin (SF) and chlorosarin (SC) with Al–N and Al–P surfaces of Al12N12 and Al12P12 nanoclusters in the gas phase. The adsorption mechanism of SF and SC on these nanoclusters containing the Al3+ central cation was studied. Optimized geometries and thermodynamic parameters of SF and SC adsorption complexes were calculated. SF and SC are chemisorbed on these nanoclusters because of the formation of PO···Al bonds. The chemical bond is formed between an oxygen atom of SF and SC and an aluminum atom of fullerene‐likes (chemisorption). However, the binding energies of the complexes with the Al12N12 nanocluster are larger than these values for the Al12P12 nanocluster. The interaction enthalpy and Gibbs free energy of all studied systems were found to be negative. We can conclude that SF and SC will be adsorbed preferably on Al12N12 nanocluster.
Birds of a feather flock together? The survival of underrepresented groups within parliamentary parties, 1991–2015

This study focuses on the exit of MPs of underrepresented groups – i.e., women, visible minorities and the lower educated– from parliamentary parties. Research has mostly focused on what brings these politicians into parliament, but not on what determines their survival within parliamentary parties. Based on the economic and sociological literature on organisations, we develop the Homophily Hypothesis, positing that MPs from underrepresented groups will be more likely to exit their parliamentary party if their share within the parliamentary party is low. Utilising the PATHWAYS dataset, based on 5889 unique MPs that served during 36 legislative periods across eight Western European countries between 1990 and 2015, this hypothesis is confirmed. These findings have important implications for the literature on descriptive representation and legislative turnover.
Relative Reduction of the Biological and phylogenetic diversity of oral microbiome in diabetic and pre-diabetic subjects

Background There is a suggested reciprocal relationship between oral health and systemic disease such as type 2 diabetes. In this relationship, a systemic disease predisposing to oral infection, and when that infection is present, the oral infection aggravates the progression of the systemic disease. Several studies suggested that some oral microbiome constituents are linked to both diabetes, metabolic syndrome and obesity. This study aims to compare the microbial diversity and population structure of oral microbiome among normoglycemic, impaired glucose tolerance (IGT) and diabetic subjects. Methodology This study followed a case-control design (15 T2D patients, 10 IGTs and, 19 controls). Patient records were screened as per the inclusion and exclusion criteria. Assessment of periodontitis and oral health was performed to all subjects. DNA Isolation purification and quantification from collected Saliva samples were performed. 16SrRNA hypervariable regions were amplified and sequenced. Generated sequences were subjected to bioinformatics analysis. Statistical analysis and diversity indices were computed with the statistical software R, the vegan R-package, and Past318 software. Results A total observed number of 551 OTUs. A clear reduction of the number of species (OTUs) was observed in both IGT (412) and diabetic group (372) compared with the normoglycemic group (502). This was associated with a similar pattern of biological diversity among the three groups. Phylogenetic diversity (PD-SBL) value in the normoglycemic group was higher than the diabetic group. The diabetic group had the highest evenness value and the highest microbiome bacterial pathogenic content. Conclusion We observed a clear reduction in the biological and phylogenetic diversity in the diabetic and pre-diabetic oral microbiome in comparison with the normoglycemic oral microbiome. However, this reduction was associated with an increase in the pathogenic content of the hyperglycemic microbiomes.
Aerosol Formation from Isoprene

Laboratory of Atmospheric Chemistry, Paul Scherrer Institut, 5232 Villigen PSI, Switzerland Keywords: aerosol formation, growth, isoprene, nucleation rate, SOA. Due to its high source strength, there has been considerable interest in the potential of isoprene to contribute to the formation of Secondary Organic Aerosol (SOA). Measurements of aerosol formation and growth following isoprene oxidation have been performed in the PSI smog chamber (27 m
FSK-Based Energy and Signal Composite Modulation Strategy for Switched Reluctance Drive System

A DC-distributed power system consists of a large quantity of subsystems with power converters. The interaction among these subsystems will affect the operation performance of the whole system. Therefore, data communication plays a very important role in system control and equipment monitoring. The present study proposes a dual modulation method of power/signal based on Switched Reluctance Drive (SRD), which can transmit signals simultaneously with power conversion and can promise data communication of the system with lower cost. This proposed method uses the ripple inherent as the carrier of the signal in the power circuit and adopts Pulse width modulation (PWM)/frequency shift keying (FSK) composite modulation to realize the reverse transmission of signals from the load to the power supply. Results of simulations and experiments indicate that this new method is effective in data communication.
Identifying attributes associated with brown bear (Ursus arctos) road-crossing and roadkill sites

Habitat fragmentation caused by transportation infrastructure is an issue of growing concern worldwide. We show how secondary roads may affect landscape permeability for brown bears (Ursus arctos Linnaeus, 1758). We focused on identifying environmental variables that govern the selection of road-crossing zones by bears (crossing model). We also investigated whether variables that characterize road-crossing zones differ from those that are typical for bear–vehicle collision sites (collision model). The study area was located in north-central Slovakia. To identify road-crossing sites, we used the GPS fixes of 27 bears and identified 35 bear–vehicle collision sites from a different data set. We used mixed-effects logistic regression to model resource selection at road-crossing sites and to compare bear-crossing sites with bear-kill sites. The crossing model showed that the traffic volume with distance to forest and grassland were the most influential factors in bear selection of road-crossing sites. Results of the collision model indicated that successful road crossings by bears were located at different road sections from vehicle collisions, which differed by a traffic volume of 5000 vehicles/24 h. The outcomes of this study can facilitate improved mitigation measures on secondary roads.
Predictor Selection and Attack Classification using Random Forest for Intrusion Detection

Decision making for intrusion detection is critical in a distributed environment such as cloud or grid computing due to its’ dynamic nature. Wrong or delayed decisions lead to astonishing problems. So that decision making phase is enhanced by means of selecting relevant features for prediction and trained to classify attacks. Initially the common valued features for both normal and attack behavior are removed. The random forest algorithm is used for analyzing the predictors’ importance for intrusion detection. Then random forest algorithm works with the reduced and selected predictors to classify the normal user and attack behavior. Finally the classifications are used to detect intruders. Experiments are conducted and proved that classifier performance can be improved in terms of accuracy, efficiency and detection rate using random forest