Miracle-dz/UniFAR-FTUs huggingface dataset:
UZIG USGS Research: Advances through Interdisciplinary Interaction

Because vadose zone research relates to diverse disciplines, applications, and modes of research, collaboration across traditional operational and topical divisions is especially likely to yield major advances in understanding. The Unsaturated Zone Interest Group (UZIG) is an informal organization sponsored by the USGS to encourage and support interdisciplinary collaboration in vadose or unsaturated zone hydrologic research across organizational boundaries. It includes both USGS and non‐USGS scientists. Formed in 1987, the UZIG operates to promote communication, especially through periodic meetings with presentations, discussions, and field trips. The 10th meeting of the UZIG at Los Alamos, NM, in August 2007 was jointly sponsored by the USGS and Los Alamos National Laboratory. Presentations at this meeting served as the initial basis for selecting papers for this special section of Vadose Zone Journal, the purpose of which is to present noteworthy cutting‐edge unsaturated zone research promoted by, facilitated by, or presented in connection with the UZIG.
Application Research of Marine Engine Simulation Teaching Platform

With the development of information technology for equipment investment, loss of large number of teaching support materials consumption experiments, the establishment of education system simulation, simulation laboratory teaching has become a hot issue. This article discusses the design of marine ships built control system and simulation platform. The platform can be used for ship turbines comprehensive experiment. It has integrated a variety of experimental features.
Comparative Study of the Influence of Full and Partial Halo Geomagnetic Storms on High Latitude Ionosphere

We have studied and compared the effects of full and partial halo geomagnetic storms on the high latitude ionosphere. The study used the total electron content (TEC) data obtained from the global positioning system (GPS) to examine the level of response of high latitude ionosphere around Ny Alesund, Norway to full and partial halo geomagnetic storms of June 23rd 2015 and January 1st 2016 respectively. This study was carried out using a dual frequency ground based GNSS observations at high latitude (NYAL: 78.56oN, 11.52oE) ionospheric station in Norway. The vertical TEC (VTEC) was extracted from Receiver Independent Exchange (RINEX) formatted GPS-TEC data using the GOPI Software developed by Seemala Gopi. The GOPI software is a GNSS-TEC analysis program which uses ephemeris data and differential code biases (DCBs) in estimating slant TEC (STEC) prior to its conversion to VTEC. From the results, the responses of the high latitude before the storm days were more positive than on the storm days. Also the overall response of the high latitude to the full halo geomagnetic storm was more positive with more impact than that of the partial halo geomagnetic storm.
A Filtering Balun Utilizing Multi-Ring Loaded Vertical Transition Structure With 161% Fractional Bandwidth and >5.8f₀ Stopband

An ultra-broadband filtering balun with microstrip line-slotline (ML-SL) vertical transition structure and multi-ring slotline termination is proposed in this brief. The proposed multi-ring structure consisting of three closed slotline loops connecting in series with each other is stretched beyond ML-SL transitions as a termination load and helps introduce five transmission poles (TPs). Furthermore, a hybrid structure integrating backside coplanar waveguide (CPW) and topside microstrip-line stepped-impedance resonators (SIRs) are employed to achieve a high harmonic rejection within a wide frequency range. In addition, a low insertion loss can be obtained owing to the reduced radiation loss brought by the fully symmetry of the ring loops. The fabricated balun filter demonstrates a 161% fractional bandwidth (FBW) centering around f0 of 3.3 GHz, a minimum in-band insertion loss of 0.37 dB, and a wide harmonic rejection band up to 20 GHz (5.8f0) with a minimum rejection level of 18.9 dB. To the best of the authors’ knowledge, the achieved fractional bandwidth and stopband are among the best.
On the analysis of power law distribution in software component sizes

Component‐based software development (CBSD) is an active area of research. Ascertaining the quality of components is important for overall software quality assurance in CBSD. One of the important metrics for measuring defects, analyzability, efforts, and cost in CBSD is component size. The paper presents an analytical model based on maximization of Tsallis entropy to obtain closed form expression for component size distribution (maximum Tsallis entropy component size distribution, MTECSD) in steady state. It is found that the component size distribution follows power law asymptotically. A procedure based on generalized Jensen–Shannon measure is developed to estimate model parameters. A detailed analysis of many popular probability distributions along with MTECSD is carried out on many diverse real data sets of component‐based softwares. The analysis reveals that lognormal and MTECSD distributions fit well to component sizes in many software conforming the presence of power law behavior. The software whose component size distributions are described by MTECSD are in equilibrium implying that new defects in these software systems occur occasionally. Power law behavior in component sizes also imply high variation leading to difficulty in software analyzability. The precise knowledge of component size distribution also provides an alternative method to compute efforts and cost estimates by modified COCOMO model.
Effects of plasma treatments of polypropylene adhesive joints used in the automotive industry

Plasma treatment has been used in recent years to activate the surfaces of adhesive substrates and thus as an adhesion promoter between adhesive and substrates. The use of plasma treatments is widely adopted in the automotive industries especially for polymers that present low surface energy, such as polypropylene. In this work, polypropylene substrates used in the automotive industries have been treated with two different techniques: vacuum and atmospheric plasma. Then, polyurethane and methacrylate adhesives have been used to bond single lap joints (SLJs). Typically, these two adhesives cannot bond polypropylene substrates without surface treatments. An experimental plan has been designed to investigate the process parameters that can increase the functional polar groups (FPGs) maximizing the adhesion strength. Besides the types of plasma, two different gas carriers (air and nitrogen) and different treatment times have been investigated. The substrates, treated and not treated, have been assessed through scanning electron microscopy, energy-dispersive X-ray analysis, and Fourier-transform infrared spectroscopy to quantitatively assess the increment of FPGs after the different treatments. The experimental plan shows that the atmospheric plasma can improve the surface of the substrates by using a smaller time. Mechanical tests on SLJs show that methacrylate and polyurethane cannot bond polypropylene substrates without the plasma treatment. On the other hand, the treated substrates can form a strong bonding with the adhesive since all SLJs exhibit a substrate failure. Mechanical tests have been also carried out after three different aging cycles showing that the adopted plasma treatment is not affected by the aging cycles.
Decolorization of textile wastewater by electrooxidation process using different anode materials: Statistical optimization

The presence of reactive dyes in textile wastewater is a serious environmental concern due to their associated mutagenic and carcinogenic effects. The present study aims to analyze the effect of different anodic materials on the decolorization of a real textile wastewater effluent. For this purpose, four different anodic materials—TiO2‐coated platine, TiO2‐coated ruthenium dioxide (RuO2) (viz., RuO2), titanium dioxide (TiO2), and graphite—were connected, respectively, to titanium dioxide (TiO2) used as a cathode electrode. Color and cost optimization studies were performed using the response surface methodology and the Box–Behnken experimental design (BBD). According to ANOVA results, the R2 values for Pt/TiO2, RuO2/TiO2, TiO2/TiO2, and graphite/TiO2 electrode pairs were found to be 97.4%, 93.8%, 92.44%, and 92.2%, respectively, indicating a good compatibility as it is close to one. The results show that color removal efficiencies at the optimal conditions were 86.3%, 90.8%, 91.5%, and 93.6% for Pt/TiO2, graphite/TiO2, TiO2/TiO2, and RuO2/TiO2, respectively. Furthermore, energy consumption cost at the optimum conditions was also evaluated, and the results were as follows: Pt/TiO2 (0.95 €/m3), graphite/TiO2 (0.74 €/m3), TiO2/TiO2 (0.31 €/m3), and RuO2/TiO2 (0.26 €/m3). Consequently, this research paper shows that all of the tested anodic materials give satisfactory color removal efficiencies higher than 86%. When energy consumption and color removal are considered together, the use of TiO2/TiO2 and RuO2/TiO2 pairs would be preferred.
Predicting the Terminal Ballistics of Kinetic Energy Projectiles Using Artificial Neural Networks

The U.S. Army requires the evaluation of new weapon and vehicle systems through the use of experimental testing and vulnerability/lethality modeling & simulation. The current modeling and simulation methods being utilized often require significant amounts of time and subject matter expertise. This means that quick results cannot be provided to address new threats encountered in theatre. Recently, there has been an increased focus on rapid results for modeling and simulation efforts that can also provide accurate results. Accurately modeling the penetration and residual properties of a ballistic threat as it progresses through a target is an extremely important part of determining the effectiveness of the threat against that target. This paper proposes the application of artificial neural networks to the prediction of the terminal ballistics of kinetic energy projectiles. By shifting the computational complexity of the problem to the fitting (regression) phase of the algorithm, the speed of the algorithm during an analysis is improved when compared to other terminal ballistic models for kinetic energy projectiles. An improvement in overall analysis time can also be realized by removing the need for input preparation by a subject matter expert prior to using the algorithm for an analysis.
Designing a Computer Pencil and Tablet for Handwriting

We designed a computer pencil and tablet for Freestyle,TM a multimedia communications system based on handwritten annotations. Although graphics pens and tablets are already in use for handprinted character recognition and CAD applications, designing a pen and tablet specifically for rapid, continuous handwriting required substantial changes. Design criteria came from a laboratory study in which evaluators performed various writing and drawing tasks with a working pen and tablet. Evaluators rated the working system, plus model pens and tablets that were variants on a proposed design. Field observations of working prototypes also contributed to the final product.
Analysis of factors affecting motor vehicle tax revenue (South Sumatra Province 2006-2020)

This study aims to determine the effect of changes in investment, consumption, DGFB per capita, inflation on motor vehicle tax revenues in South Sumatra Province. Motor vehicle tax revenues were sampled with the research period 2006-2020. The research used the multiple linear regression method. The results showed that changes in investment, consumption, and inflation did not significantly affect motor vehicle tax revenues in South Sumatra in 2006-2020. Meanwhile, DGFB per capita has a significant positive effect on motor vehicle tax revenues in South Sumatra in 2006-2020.
An integrated HFMEA simulation-based multi-objective optimisation model to improve the performances of hospitals: A case study

ABSTRACT There are many problems with hospital departments that decrease patients’ satisfaction and hospitals’ performance. Although there are many qualitative and quantitative methods to improve related performances, some of them consider a single department and the rest don't consider related priorities. Therefore, this paper proposes an integrated model based on Six Sigma to consider the performances of all departments with priorities based on experts’ knowledge. This model utilizes qualitative methods including fishbone diagrams and Healthcare Failure Mode and Effect Analysis to find problems and their priorities based on experts’ knowledge and data. Then, quantitative methods including a mathematical model, Discrete Event Simulation, and Non-dominated Sorting Genetic Algorithm II are utilized to find solutions for preferred problems as a simulation-based multi-objective optimization model. Based on a case study in a well-known general hospital, the proposed model finds the hospital’s most important problems and represents the impacts of different improvement plans before implementation.
Building an effective model of intelligent entrepreneurship development in digital economy

This paper explores peculiarities in the formation and development of intelligent entrepreneurship as a new social phenomenon of national economies and the imperatives of its manifestation in the economic environment. The relationship between intelligent, informational, and innovative activities that shape the environment of modern entrepreneurship has been established. A new understanding of the essence of "intelligent entrepreneurship" and "talent management" categories has been proposed. The value of human capital, the realization of intelligent capabilities of the individual is recognized as the basic value of intelligent entrepreneurship. It has been proven that in modern conditions the most promising for national economies is the transition to open innovation models. It is noted that the development and effective use of intelligent potential are due to the need to increase the efficiency of innovation, science, and education systems, which ensures the economic growth of the national economy. The structure of intelligent potential of entrepreneurship under the conditions of digital transformation of the economy has been built. Original views on the essence and significance of the talent management system are presented. It is noted that intelligent entrepreneurship requires a holistic approach. The classification of communities of intelligent practitioners has been devised. The system of management of advanced reproduction of the latest knowledge of business entities has been formed. The directions of digital transformation of intelligent entrepreneurship have been outlined. A system-integrated methodological approach to determining the Index of digital transformation of business entities has been devised. The study results build on the theoretical and practical aspects of intelligent entrepreneurship, contribute to the effective use and development of the intelligent potential of entrepreneurial organizations and their network associations.
High Order Godunov Type Multimesh Method for 3d Impact Problems of Elastoplastic Media

A numerical method for calculating the three-dimensional processes of impact interaction of elastoplastic bodies under large displacements and deformations based on the multi mesh sharp interface method and modified Godunov scheme is presented. To integrate the equations of dynamics of an elastoplastic medium, the principle of splitting in space and in physical processes is used. The solutions of the Riemann problem for first and second order accuracy for compact stencil for an elastic medium in the case of an arbitrary stress state are obtained and presented, which are used at the “predictor” step of the Godunov scheme. A modification of the scheme is described that allows one to obtain solutions in smoothness domains with a second order of accuracy on a compact stencil for moving Eulerian-Lagrangian grids. Modification is performed by converging the areas of influence of the differential and difference problems for the Riemann’s solver. The “corrector” step remains unchanged for both the first and second order accuracy schemes. Three types of difference grids are used. The first – a moving surface grid – consists of a continuous set of triangles that limit and accompany the movement of bodies; the size and number of triangles in the process of deformation and movement of the body can change. The second – a regular fixed Eulerian grid – is limited to a surface grid; separately built for each body; integration of equations takes place on this grid; the number of cells in this grid can change as the body moves. The third grid is a set of local Eulerian-Lagrangian grids attached to each moving triangle of the surface from the side of the bodies and allowing obtain the parameters on the boundary and contact surfaces. The values of the underdetermined parameters in cell’s centers near the contact boundaries on all types of grids are interpolated. Comparison of the obtained solutions with the known solutions by the Eulerian-Lagrangian and Lagrangian methods, as well as with experimental data, shows the efficiency and sufficient accuracy of the presented three-dimensional methodology.
11C-metomidate PET CT versus Adrenal Vein Sampling for diagnosing surgically curable primary aldosteronism: prospective test validation, and impact of somatic genotype and ethnicity on outcomes

Primary aldosteronism (PA) due to a unilateral aldosterone-producing adenoma (APA) is a common, curable cause of hypertension, but invasive methods of diagnosis and treatment contribute to <1% of patients being offered the chance of cure. The primary objective of our prospective within-patient study in 143 patients with PA was to compare accuracy of 11C-metomidate (MTO) PET-CT scanning with adrenal vein sampling (AVS) in predicting biochemical cure from PA and resolution of hypertension. Secondary outcomes addressed heterogeneity of underlying pathogenesis and prediction of patients most likely to achieve complete cure of hypertension. 128 patients reached 6-9 month follow-up. 77/78 surgical patients achieved one or more of the four hierarchically analysed Primary Aldosteronism Surgical Outcome (PASO) criteria for biochemical and clinical success. MTO was not superior to AVS but all four differences in accuracy favored MTO, with 95% CIs >-17%, the pre-specified margin of non-inferiority. The best univariate predictors of complete clinical cure were home systolic blood pressure (SBP) <135 mmHg after one month of spironolactone 100 mg daily (odds ratio 13.0 (3.72, 45.24) p<0.001) and KCNJ5 genotype of the APA (odds ratio 10.37 (2.50, 42.99) p=0.001). The latter remained significant in logistic regression on age, gender, ethnicity, and was itself predicted by elevated urine 18-hydroxycortisol:cortisol ratio. Our findings validate 11C-metomidate PET-CT for accurate, non-invasive detection of patients with unilateral PA, and identify patients most likely to benefit from adrenalectomy.
Improving Detection of False Data Injection Attacks Using Machine Learning with Feature Selection and Oversampling

Critical infrastructures have recently been integrated with digital controls to support intelligent decision making. Although this integration provides various benefits and improvements, it also exposes the system to new cyberattacks. In particular, the injection of false data and commands into communication is one of the most common and fatal cyberattacks in critical infrastructures. Hence, in this paper, we investigate the effectiveness of machine-learning algorithms in detecting False Data Injection Attacks (FDIAs). In particular, we focus on two of the most widely used critical infrastructures, namely power systems and water treatment plants. This study focuses on tackling two key technical issues: (1) finding the set of best features under a different combination of techniques and (2) resolving the class imbalance problem using oversampling methods. We evaluate the performance of each algorithm in terms of time complexity and detection accuracy to meet the time-critical requirements of critical infrastructures. Moreover, we address the inherent skewed distribution problem and the data imbalance problem commonly found in many critical infrastructure datasets. Our results show that the considered minority oversampling techniques can improve the Area Under Curve (AUC) of GradientBoosting, AdaBoost, and kNN by 10–12%.
The Impact of Agricultural Socialized Services to Promote the Farmland Scale Management Behavior of Smallholder Farmers: Empirical Evidence from the Rice-Growing Region of Southern China

Farmland scale management (FSM) is an essential strategy to establish an appropriate management scale for agricultural production, enhance smallholder farmer production efficiency, and improve the utilization rate of farmland. The Chinese government promotes farmland transfer as a tool to establish modern and moderate-scale agriculture. However, farmers remain unable to afford agricultural services and inputs required for appropriate FSM after farmland is transferred-in. This paper aims to examine the impact of agricultural socialized services (ASSs) on the FSM behavior of smallholder farmers through farmland transfer. A theoretical framework for the farmer household production aspect of this relationship is developed. A weighted least squares (WLS) model is applied to empirically examine smallholder farmers’ decisions to expand the scale of farmland induced by the promotion of ASSs based on data collected from 741 households in 2020 in the rice-growing region of southern China. The findings reveal that ASSs have a positive and significant impact on small farmers’ FSM. Small farmers’ behavior regarding farmland transfer is affected positively by the promotion of ASSs. The increase of ASSs encourages small farmers to transfer-in more farmland. However, the impact of ASSs on various steps of agricultural practice varied according to the FSM of smallholder farmers. Our findings imply that the government should take the development of ASSs as one of the main methods for promoting the establishment of moderate and large-scale agriculture and rural revitalization. Strengthening policies and financial support for both private and public ASS providers through financial innovation subsidies and preferential tax policies will help smallholder farmers reduce input costs and increase the scale of production and profits. The findings of this paper will provide a scientific basis and reference for the development of moderate-scale agriculture and rural revitalization.
CUSRA2021: A Radially Anisotropic Model of the Contiguous US and Surrounding Regions by Full‐Waveform Inversion

The lithospheric structure of the contiguous US and surrounding regions offers clues into the tectonic history, including interactions between subducting slabs and cratons. In this paper, we present a new radially anisotropic shear wave speed model of the upper mantle (70–410 km) of the contiguous US and surrounding regions, constrained by seismic full‐waveform inversion. The new model (named CUSRA2021) utilizes frequency‐dependent travel time measurements, from 160 earthquake events recorded by 5,280 stations. The data coverage in eastern US is improved by incorporating more intraplate earthquakes. The final model exhibits clear and detailed shear wave speed anomalies correlating well with tectonic units such as North America Craton (high‐Vs), Cascadia subduction zones (high‐Vs), Columbia Plateau (low‐Vs), Basin and Range (low‐Vs), etc. In particular, the detailed structure of the North America Craton beneath Illinois basin is revealed. The depth of high‐Vs anomaly beneath the North America Craton correlates well with S‐to‐P receiver function and SH reflection results. Besides, the radial anisotropy in the Craton lithosphere shows a layering structure, which may relate to the process of lithospheric accretion and the origin of mid‐lithosphere discontinuities.