Miracle-dz/UniFAR-FTUs huggingface dataset:
How to Conduct the Agricultural Field Experiments

The experiment is usually associated with a scientific method for testing certain phenomena, which applies to science of agriculture as well. Since the agriculture is broad and therefore the experiments are diverse. Regarding to the agriculture is the work of Paul Richards that experimentation is at the heart of agricultural practice. The reason why agricultural experiments are something different for farmers and agriculturist is not their capacity to experiment as such but the embedding of experiments in a specific ecological, material and institutional environment. Since past the changes are examined in the organization of agricultural experiments and a gradual shift in the role of experiments in the connection between science and practice is very important with respect to the time and need of people. Initially, the link was considered to be established through various forms of experiments, rooted in an integrated social and technical understanding of subject but it turned into a connection primarily established through various forms of communication.
NLRP3 Susceptible Gene Polymorphisms in Patients with Primary Gouty Arthritis and Hyperuricemia

Polymorphisms have been identified to predispose to primary gouty arthritis (GA) and hyperuricemia (HUA). Here, we accessed the five polymorphisms of rs10754558, rs35829419, rs3738448, rs3806268, and rs7525979 in NLRP3 on GA and HUA susceptibility. We collected 1198 samples (314 GA, 377 HUA, and 507 controls) for this case-control study. Our data detected that the rs3806268 (GA vs. AA: OR = 0.65, p = 0.012) was significantly associated with the susceptibility to GA. The rs3738448 (TT vs. GG: OR = 2.05, p = 0.024) and rs7525979 (TT vs. CC: OR = 1.96, p = 0.037) were significantly associated with the susceptibility to HUA. The rs3806268 AG genotype presented decreased risk of GA among the hypertension (OR = 0.54, p = 0.0093), smoking (OR = 0.59, p = 0.018), and no obesity (OR = 0.60, p = 0.0097) subjects compared to the GG genotype group. The rs3738448 TT genotype demonstrated increased risk of HUA among the hypertension (OR = 4.10, p = 0.0056) and no drinking population (OR = 3.56, p = 0.016) compared to the GG genotype group. The rs7525979 TT genotype demonstrated increased risk of HUA among the hypertension (OR = 4.01, p = 0.0064) and no drinking population (OR = 3.24, p = 0.034) compared to the CC genotype group. Furthermore, a significant haplotype effect of rs10754558/C-rs35829419/C-rs3738448/G-rs3806268/A-rs7525979/C was found (OR = 1.60, p = 0.0046) compared with GCGAC haplotype. Bioinformatics analyses indicated that rs3738448, rs3806268, and rs7525979 might influence the gene regulation, while the T-allele of rs3738448 increased the stability of NLRP3-mRNA. Collectively, our case-control study confirms NLRP3 polymorphisms might participate in regulating immune and inflammation responses in GA and HUA.
A Novel Dynamic Load Modeling for Power Systems Restoration: An Experimental Validation on Active Distribution Networks

In this paper, a novel approach to assess the power demand of distribution networks during a restoration process following an outage is presented. The possibility of correctly estimating such power demand represents a very important support in the choice and management of reliable restoration paths which significantly contributes to increase the resilience of the power system. In fact, due to the ever-growing penetration of renewable energy sources in the worldwide networks, electrical systems are often pushed to operate closer to their design limits and so, in particular conditions, black outs can be more frequent compared to the past. In this context, the aim of this work is to develop a general expression which can consider and represent all the key elements which affect the active and reactive power demand of distribution grids during the restoration process. The proposed method considers also the relationship between distributed generation and frequency behaviour in the power demand estimation. This aspect is generally neglected in literature although it has a significative impact. The effectiveness of the present approach is experimentally demonstrated on field. More specifically, the developed tool is applied to estimate the power demand of two real distribution networks and the tool results are compared with on-field recordings. This comparison demonstrates that the proposed approach represents a promising tool which, together with the restoration tests, could be an important ally in the design and management of the restoration plan of electrical networks.
Chemistry, taxonomy and ecology of the potentially chimpanzee-dispersed Vepris teva sp.nov. (Rutaceae) endangered in coastal thicket in the Congo Republic

Continuing a survey of the chemistry of species of the largely continental African genus Vepris, we investigate a species previously referred to as Vepris sp. 1 of Congo. From the leaves of Vepris sp. 1 we report six compounds. The compounds were three furoquinoline alkaloids, kokusaginine (1), maculine (2), and flindersiamine (3), two acridone alkaloids, arborinine (4)and 1-hydroxy-3-methoxy-10-methylacridone (5), and the triterpenoid, ß-amyrin (6). Compounds 1-4 are commonly isolated from other Vepris species, compound 5 has been reported before once, from Malagasy Vepris pilosa, while this is the first report of ß-amyrin from Vepris. This combination of compounds has never before been reported from any species of Vepris. We test the hypothesis that Vepris sp.1 is new to science and formally describe it as Vepris teva, unique in the genus in that the trifoliolate leaves are subsessile, with the median petiolule far exceeding the petiole in length. Similar fleshy-leathery 4-locular syncarpous fruits are otherwise only known in the genus in Vepris glaberrima (formerly the monotypic genus Oriciopsis Engl.), a potential sister species, but requiring further investigation to confirm this phylogenetic position. We briefly characterise the unusual and poorly documented Atlantic coast equatorial ecosystem, where Vepris teva is restricted to evergreen thicket on white sand, unusual in a genus usually confine to evergreen forest. This endemic-rich ecosystem with a unique amphibian as well as plants, extends along the coastline from the mouth of the Congo River to southern Rio Muni, a distance of about 1000 km, traversing five countries. We map and illustrate Vepris teva and assess its extinction risk as Endangered (EN B1ab(iii)+B2ab(iii)) using the IUCN standard. Only three locations are known, and threats include port and oil refinery construction and associated activities, with only one protected location, the Jane Goodall Institute’s Tchimpounga Reserve. Initial evidence indicates that the seeds of Vepris teva are dispersed by chimpanzees, previously unreported in the genus
The Role of Capital Structure, Human Resources, and Technology in Increasing Salt Farmers’ Income

The natural resources of hot salty springs can be used by farmers to produce salt. The salt farmer profession has not provided an economic improvement for the farmers themselves. The purpose of this study was to determine the role of capital structure, human resources and technology in increasing the income of salt farmers in Jono Village. The research method used in this study was qualitative descriptive approach. The data of this study were obtained from interviews, observations and documents. Data analysis usedin this study was an interactive model from Miles and Huberman, 2014. The study was carried out in Jono Village, Tawangharjo District, Grobogan Regency. The results of this study are the capital structure of salt farmers obtained from collectors and human resources from family labor which are still low. The technology used by salt farmers is still very traditional. The results of this study provide recommendations for improving the capital structure, quality of capital resources and technology to improve the economy of the salt farmers.
Thermo-electro-mechanical coupling dynamic analysis of piezoelectric structures via stabilized node-based smoothed radial point interpolation method

Abstract The thermo-electro-mechanical coupling (TEMC) problem of the piezoelectric structure was analyzed. And the stabilized node-based smoothed radial point interpolation method (SNS-RPIM) was utilized during the computation. And the fundamental formulas were derived under thermo-mechanical load and the model of the problem was analyzed with triangular elements. The existence of thermal stress was taken into consideration. The dynamic behaviors for the TEMC problem were investigated by SNS-RPIM and finite element method (FEM) when different kinds of thermo-mechanical loadings were applied. The superiority of the proposed scheme for the TEMC problems was verified. The empirical constant was set to negative. Besides, the temperature rise can make the model softer and thermal load increase, which leads to a decrease of structural stiffness and natural frequency, thus, the displacement increases.
Force and deformation behaviour of roof truss system with cold-formed steel channel section

Cold-formed steel is widely used in Malaysia’s construction for roof truss structures. This study aims to investigate the force and deformation behaviour of a roof truss system and evaluate the results from analytical and experimental investigation. One roof truss was constructed by using LC7510 cold-formed steel channel which is 5.4m long and 0.651m height with angle about 20 degree and eight concentrated loads were acting on the top chord. The deflection of the roof truss was analyzed by LVDT’s. STAAD Pro was used to carry out the analytical investigation. Three models with three load cases been modeled and compared with actual roof truss. The critical load case is Load Case 2 which leaded to higher deflections. The percentage difference on deflection between Model 1 with Load Case 2 is 15.4% and the roof truss failed on connection direct to the support due to buckling.
Research on random forest algorithm based on unbalanced data

As one of the most popular classification algorithms, random forest has been widely used in various fields of life. It not only has a relatively perfect theoretical background, but also has a good performance in data classification. However, the classification and prediction of unbalanced data sets is still difficult. In order to solve this problem, a classification and prediction model of unbalanced data sets based on adasyn-rf is proposed. Firstly, adasyn algorithm is used to deal with unbalanced data sets, and then RF model prediction is constructed. In order to illustrate the superiority of the model, comparative experiments are added. That is, the original data set and the data set processed by smote, boundary smote and adasyn are combined with two prediction models: random forest model and xgboost model. The experimental results show that the classification and prediction effect of unbalanced data sets based on adasyn-rf model is the best.
PPCNN: An efficient privacy‐preserving CNN training and inference framework

Convolutional neural network (CNN) is one of the representative models of deep learning, commonly used to analyze visual images. CNN model is more accurate when trained on large amounts of data from multiple sources, and the huge training cost makes the model much more valuable. However, data from various sources is often privacy‐sensitive. Therefore, the privacy of these data should be protected during CNN model training and inference. In this paper, we propose an efficient and secure two‐party computation (2PC) framework PPCNN for privacy‐preserving CNN training and inference. Specifically, we use a new secret sharing technique introduced in ABY2.0 to securely compute various computational tasks involved in the CNN training and inference processes. This secret sharing technique can significantly reduce the communication overhead. Meanwhile, we assign these computationally intensive tasks to cloud servers to reduce the computational burden on local devices. We demonstrate the security of these protocols in the semihonest model. In addition, we use the MP‐SPDZ library to simulate our PPCNN framework, and the experiments prove its high efficiency and accuracy.
A Study of Channel Estimation and Postprocessing in Quantum Key Distribution Protocols

This thesis investigate the channel estimation and the postprocessing of the quantum key distribution protocols. In particular, we propose a new channel estimation procedure, in which we utilize the bit sequences transmitted and received by different bases, and a new postprocessing with two-way classical communication. Then, we show that the key generation rates of the QKD protocols with our channel estimation and/or our postprocessing are higher than those with conventional ones.
Separation of Different Blogs from Skin Disease Data using Artificial Intelligence

A combination of environmental conditions may cause skin illness everywhere on the earth, and it is one of the most dangerous diseases that can develop as a result. A major goal in the selection of characteristics is to produce predictions about skin disease instances in connection with influencing variables, which is one of the most important tasks. As a consequence of the widespread usage of sensors, the amount of data collected in the health industry is disproportionately large when compared to data collected in other sectors. In the past, researchers have used a variety of machine learning algorithms to determine the relationship between illnesses and other disorders. Forecasting is a procedure that involves many steps, the most important of which are the preprocessing of any scenario and the selection of forecasting features. A major disadvantage of doing business in the health industry is a lack of data availability, which is particularly problematic when data is provided in an unstructured format. Filling in missing numbers and converting between various types of data take somewhat more than 70% of the total time. When dealing with missing data in machine learning applications, the mean, average, and median, as well as the stand mechanism, may all be employed to solve the problem. Previous research has shown that the characteristics chosen for a model's overall performance may have an influence on the overall performance of the model's overall performance. One of the primary goals of this study is to develop an intelligent algorithm for identifying relevant traits in models while simultaneously eliminating nonsignificant attributes that have an impact on model performance. To present a full view of the data, artificial intelligence techniques such as SVM, decision tree, and logistic regression models were used in conjunction with three separate feature combination methodologies, each of which was developed independently. As a consequence of this, their accuracy, F-measure, and precision are all raised by a factor of ten, respectively. We then have a list of the most important features, together with the weights that have been allocated to each of them.
Deciding between Domains: How Borrowers Weigh Market and Interpersonal Options

Individuals routinely satisfy borrowing needs by transacting in the market or by relying on social relations. In the market domain, price logic leads borrowers to choose the cheaper option; in the interpersonal domain, role-matching logic leads borrowers to choose the relation best matched to the act. But how do individuals choose when faced with options from each domain? Drawing on theories in economic sociology that assert the economic and the social are mutually constitutive, we posit that when market and interpersonal options appear in the same choice set, the characteristics of one option inflect how people assess the other. Through two survey experiments, we show that price sensitivity toward the market option is less when the interpersonal option is role mismatched and that concerns about interpersonal borrowing changing or damaging the relationship attenuate when the market option is expensive. We discuss the implications for studies of stratification and financial decision-making.
Teaching Reform Integration of Scientific Research, Teaching and Psychology

: The construction of new engineering requires the cultivation of innovative and practical engineering talents. Aiming at the common problems existing in the teaching of engineering courses, this paper proposes a teaching reform method based on the organic integration of scientific research, teaching and psychology. Specific measures: Integrate the scientific research projects of master and doctoral supervisors. According to the basic theoretical knowledge of professional courses, scientific research projects are classified. A basic theoretical knowledge system oriented to practical projects is constructed, which effectively improves the ability of new engineering students to master theoretical knowledge, innovate and solve practical engineering problems.
Automated detection and quantification of brain metastases on clinical MRI data using artificial neural networks

Abstract Background Reliable detection and precise volumetric quantification of brain metastases (BM) on MRI are essential for guiding treatment decisions. Here we evaluate the potential of artificial neural networks (ANN) for automated detection and quantification of BM. Methods A consecutive series of 308 patients with BM was used for developing an ANN (with a 4:1 split for training/testing) for automated volumetric assessment of contrast-enhancing tumors (CE) and non-enhancing FLAIR signal abnormality including edema (NEE). An independent consecutive series of 30 patients was used for external testing. Performance was assessed case-wise for CE and NEE and lesion-wise for CE using the case-wise/lesion-wise DICE-coefficient (C/L-DICE), positive predictive value (L-PPV) and sensitivity (C/L-Sensitivity). Results The performance of detecting CE lesions on the validation dataset was not significantly affected when evaluating different volumetric thresholds (0.001–0.2 cm3; P = .2028). The median L-DICE and median C-DICE for CE lesions were 0.78 (IQR = 0.6–0.91) and 0.90 (IQR = 0.85–0.94) in the institutional as well as 0.79 (IQR = 0.67–0.82) and 0.84 (IQR = 0.76–0.89) in the external test dataset. The corresponding median L-Sensitivity and median L-PPV were 0.81 (IQR = 0.63–0.92) and 0.79 (IQR = 0.63–0.93) in the institutional test dataset, as compared to 0.85 (IQR = 0.76–0.94) and 0.76 (IQR = 0.68–0.88) in the external test dataset. The median C-DICE for NEE was 0.96 (IQR = 0.92–0.97) in the institutional test dataset as compared to 0.85 (IQR = 0.72–0.91) in the external test dataset. Conclusion The developed ANN-based algorithm (publicly available at www.github.com/NeuroAI-HD/HD-BM) allows reliable detection and precise volumetric quantification of CE and NEE compartments in patients with BM.
TAIM: ternary activation in-memory computing hardware with 6T SRAM array

Recently, various in-memory computing accelerators for low precision neural networks have been proposed. While in-memory Binary Neural Network (BNN) accelerators achieved significant energy efficiency, BNNs show severe accuracy degradation compared to their full precision counterpart models. To mitigate the problem, we propose TAIM, an in-memory computing hardware that can support ternary activation with negligible hardware overhead. In TAIM, a 6T SRAM cell can compute the multiplication between ternary activation and binary weight. Since the 6T SRAM cell consumes no energy when the input activation is 0, the proposed TAIM hardware can achieve even higher energy efficiency compared to BNN case by exploiting input 0's. We fabricated the proposed TAIM hardware in 28nm CMOS process and evaluated the energy efficiency on various image classification benchmarks. The experimental results show that the proposed TAIM hardware can achieve ~ 3.61× higher energy efficiency on average compared to previous designs which support ternary activation.
WFC3 Design Reference Mission Part III: Proposal Inputs and Outputs

In this ISR we describe the inputs to the individual proposals of the Design Reference Mission (DRM) used for the development of the Wide Field Camera 3 (WFC3). We also include the basic outputs from running each proposal through the WFC3 Exposure Time Calculator. This ISR is the third in a series of ISRs to describe the process that led to the production of the proposals used in the DRM for WFC3, and the subsequent incorporation of the proposals into a representative body of science research areas that will be addressed by WFC3.
Differentiating Gender and Sex in Dental Research: A Narrative Review

While in humans the term “sex” refers to the biological attributes that distinguish subjects as male, female, and intersex, the term “gender” refers to psychological, social, and cultural factors that strongly influence attitudes, behaviors, and relationships of individuals. Recently, it has been emphasized how the integration of these two terms in the design of the research can improve the methodology of the research itself. However, in dental research, the influence of gender has not gained enough consideration and it is often used indiscriminately as a synonym for sex. This narrative review discusses the usefulness of considering gender and sex in dental research, whose guidelines have been provided so far on this topic, and whether the top 20 dental scientific journals promote the analysis of sex and gender in their guidelines. Sex and gender analysis in dental research could be important both for analyzing biological differences such as those in the immune or neuro-immune system, cardiovascular physiology, developmental anomalies or deformities, and psychosocial differences such as lifestyle, pain experience and prevalence of chronic pain, eating behavior, and access to healthcare services. As for the specific policies for sex and gender analysis and reporting, only five out of 20 biomedical journals have included them in their editorial policy, which refers mainly to the correct use of the terms “sex” and “gender. ” In conclusion, we found that no specific and differentiated sex and gender analysis and reporting are required in dental journals. Their integration, which is still not routinely applied, may be improved in the future by updating editorial guidelines and developing more specific methodological recommendations.
Computational and Experimental Study of Sprays from the Breakup of Water Sheets

This thesis presents an Eulerian multi-fluid CFD model for sprays, which is able to describe droplet breakup and coalescence and size polydispersion as well as the associated size-conditioned dynamics. In order to model the evolution of the polydisperse droplet phase, the population balance equation (PBE) is coupled to the continuity and momentum balance equations. The direct quadrature method of moments (DQMOM) is implemented to simulate the evolution of the droplet size distribution (DSD) due to breakup and coalescence. The DQMOM-multi-fluid model uses source terms for the first 2N moments of the droplet number distribution as parameters to determine the source terms of the transport equations of the N droplet volume fractions and the N droplet diameters. Transport equations are also solved for phase-average velocities for each droplet phase. Submodels are designed to capture the effects of droplet breakup and droplet-droplet collisions. The model is applied to calculate local values of droplet sizes and velocities produced by diesel-type, Y-jet, and hollow-cone sprays. The droplet velocity results for the diesel-type spray are well predicted. The droplet size and velocity results for the Y-jet water sprays are less accurate, although this is considered to be due to inaccuracies in the boundary conditions at the nozzle, rather than an error in the model. The collapse of the hollow-cone spray is evident in the predictions, and droplet sizes and velocities are in good agreement with experimental data. The model has successfully predicted the main features of the sprays, but there are aspects of the model in which improvements can be made.