Miracle-dz/UniFAR-FTUs huggingface dataset:
Factors Affecting Members’ Satisfaction With High-Tech Agriculture Cooperatives in Vietnam’s Northern Key Economic Region

Purpose: This study aims to determine the factors affecting cooperates’ satisfaction when joining high-tech agricultural cooperatives in Vietnam’s Northern Key Economic Region. Theoretical framework: The study absorbed previous studies and expert opinions to determine the member' satisfaction coming to high-tech agricultural cooperatives. Design/methodology/approach: Primary data was collected through a direct survey of 395 members participating in high-tech agricultural cooperatives. Then, the study uses the method of partial least squares structural equation modeling to test the hypothesis and assess the satisfaction level of the members. Finding: The research results show that the factors affecting the satisfaction of cooperative members include 7 factors: working environment, trust, managers, level of participation, perceived benefits, support policies, and service capacity. Research, Practical & Social Implication: Based on the results of this study, several solutions to improve the current association activities with the role of the cooperative model as the core are proposed. People are encouraged to participate more in the association by improving the management skills of cooperatives, strengthening support activities, creating jobs, and raising incomes for people. Originality/value: Members' satisfaction will promote the activities of linking production and consumption with the main role of the cooperative, not just stopping at the immediate propaganda activities. Since then, these activities also improve people's confidence when participating in the association. Specifically, collective economic development must come from the needs of the people and participating organizations.
DNS Tunnelling, Exfiltration and Detection over Cloud Environments

The domain name system (DNS) protocol is fundamental to the operation of the internet, however, in recent years various methodologies have been developed that enable DNS attacks on organisations. In the last few years, the increased use of cloud services by organisations has created further security challenges as cyber criminals use numerous methodologies to exploit cloud services, configurations and the DNS protocol. In this paper, two different DNS tunnelling methods, Iodine and DNScat, have been conducted in the cloud environment (Google and AWS) and positive results of exfiltration have been achieved under different firewall configurations. Detection of malicious use of DNS protocol can be a challenge for organisations with limited cybersecurity support and expertise. In this study, various DNS tunnelling detection techniques were utilised in a cloud environment to create an effective monitoring system with a reliable detection rate, low implementation cost, and ease of use for organisations with limited detection capabilities. The Elastic stack (an open-source framework) was used to configure a DNS monitoring system and to analyse the collected DNS logs. Furthermore, payload and traffic analysis techniques were implemented to identify different tunnelling methods. This cloud-based monitoring system offers various detection techniques that can be used for monitoring DNS activities of any network especially accessible to small organisations. Moreover, the Elastic stack is open-source and it has no limitation with regards to the data that can be uploaded daily.
Changes in Germination and Seedling Traits of Sesame under Simulated Drought

Drought is considered one of the leading abiotic constraints to agricultural crop production globally. Present study was conducted to assess the effects of different drought treatments (viz. Control, 10% PEG, and 20% PEG) on seed germination, germination indices, seedling traits, and drought tolerance indices of sesame. Our results showed that maximum reduction in the studied parameters was observed at higher PEG concentration (i.e., 20% PEG). As compared to control, the drought treatments viz. 10% and 20% PEG decreased the values for germination indices, such as germination percentage, coefficient of variation of germination time, germination index, and seedling vigor index. Similarly, for seedling traits, the values were decreased for root length, shoot length, root shoot ratio, root fresh weight, shoot fresh weight, root dry weight and shoot dry weight under 10% and 20% PEG treatments significantly in comparison with control. Furthermore, relative to control, the values for drought tolerance indices, such as germination drought tolerance index, root length drought tolerance index, shoot length drought tolerance index, total seedling length drought tolerance index, root fresh weight drought tolerance index, shoot fresh weight drought tolerance index, total fresh weight drought tolerance index, root dry weight drought tolerance index, shoot dry weight drought tolerance index and total dry weight drought tolerance index were also reduced under 10% and 20% PEG treatments, respectively. Our results confirms that drought impact on seed germination and seedling traits could be quantified by using different indices which can further help to design drought adaptation and mitigation strategies. Based on these results it can be concluded that germination indices, seedling traits, and drought tolerance indices have great potential to simulate drought stress impacts on different crop traits thus they should be used in all kinds of stress related studies.
A sediment time‐angle spreading model

To model time‐angle spreading from rough sediment layers, a large‐scale forward‐scattering approximation is applied to the correlation of the characteristic function in the expected value of the intensity. A composite distribution is derived for the transmitted and reflected portions of the acoustic wave through multiple closely spaced layers. Time spread data comparisons are used to set these composite variances at 20° on the consolidated layers and 2°–4° on unconsolidated surficial layers. These values are tested against some independent measurements of angle spreading and found to give good agreement. The model validation at low grazing angles implies that only a small number of layers are apparently participating in the scattering process at the surface of many sediments.
Prognostic biomarker GSTK1 in head and neck squamous cell carcinoma and its correlation with immune infiltration and DNA methylation

Background: Glutathione S-transferase kappa 1 (GSTK1) is critical in sarcoma and breast cancer (BRCA) development. However, the clinical significance of GSTK1 in head and neck squamous cell carcinoma (HNSC) remains unclear. This study is the first investigation into the role of GSTK1 in HNSC. Methods: All original data were downloaded from the Cancer Genome Atlas (TCGA) dataset and verified by R Base Package 4.2. 0. The expression of GSTK1 in various cancers was explored with TIMER and TCGA databases. Prognostic value of GSTK1 was analyzed via survival module of Kaplan-Meier plotter and Human Protein Atlas database and Cox regression analysis. The association between GSTK1 and clinical features was evaluated by Wilcoxon signed-rank test and logistic regression analysis. The relationship between GSTK1 and immune infiltration and methylation level was further explored. The expression of GSTK1 and its correlation with immune cell infiltration was verified by Immunohistochemical staining (IHC). Results: GSTK1 was lower in HNSC, BRCA, Lung squamous cell carcinoma, and Thyroid carcinoma than in para-carcinoma. Low GSTK1 expression was associated with worse overall survival in Bladder urothelial carcinoma, Kidney renal papillary cell carcinoma, BRCA, and HNSC. However, only in BRCA and HNSC, GSTK1 expression in tumors was lower than that in normal tissues. Cox regression analyses confirmed that GSKT1 was an independent prognostic factor of overall survival in HNSC patients. The decrease in GSTK1 expression in HNSC was significantly correlated with high T stage and smoker history. IHC showed that the expression level of GSTK1 in HNSC was lower than that in para-carcinoma. In addition, GSEA showed that three pathways related to immune infiltration were positively correlated, while two pathways related to DNA methylation were negatively correlated with expression of GSTK1. Further analysis showed that GSTK1 was moderately positively correlated with the infiltration level of T cells and Cytotoxic cells, which was further confirmed by IHC. The methylation level of GSTK1 was associated with prognosis in patients with HNSC. Conclusion: Low GSTK1 expression may be a potential molecular marker for poor prognosis in HNSC and provide new insight for the development of diagnostic marker or therapeutic target.
Magnetism in transition-metal-doped ZnO: A first-principles study

The magnetic properties and clustering behaviors of the transition-metal (TM)-doped ZnO have been studied by first-principles calculations. We demonstrate the relationship between the types of TM (Co, Fe, Ni, and Cu) pairs and the energetic most stable configurations in ZnO:TM. Further calculations show that Co pairs have no tendency to form clusters, showing paramagnetic (PM) state in ZnO:Co samples in an equilibrium state and without excessive defects, as evidenced by various experiments, while ferromagnetic (FM) state is possible for the samples prepared under non-equilibrium conditions. Finally, systematical studies of a series of TM elements reveal different types of TM pairs in terms of clustering behaviors determined by spin-polarized and non-spin-polarized contributions: Co pairs and Fe pairs have no tendency to form clusters, showing PM and FM states, respectively, while Ni pairs and Cu pairs have a clustering tendency and show intrinsic FM ordering.
Self-calibration for a multi-segment straight-line trajectory CT using invariant moment

Multi-segment straight-line trajectory computed tomography (CT) requires accurate movement between each segment trajectories. In industrial applications for large objects, it is difficult and very cost-expensive for the mechanical and control system to ensure the accurate movement during the rotation process between two segment trajectories. Hence, precisely measuring the movement would be an alternative choice to acquire an exact reconstruction. In this work, we proposes a new method to measure the movement by using invariant moment of attenuation distribution images. Its accuracy improves as the number of projection data increases. The method is validated by both numerical and practical experiments. Reasonable CT reconstructions are obtained for a two-segment linear trajectory CT with coarse mechanical movement control modeled in our experiments.
Recurrent Neural Networks for Oil Well Event Prediction

We have conducted a comparison between three types of recurrent neural networks and their ability to predict anomalies occurring in oil wells using a publicly available dataset. We have included two types of well-known state-of-the-art recurrent neural networks and a new type with neurons evolved specifically for the dataset using automatic programming. We show that the new type of recurrent neuron offers a massive improvement over the state of the art. The overall test accuracy of the new network type is 94.6%, which is an improvement by 18.3%, or 14.6 percentage points. We also show that a network with the new neuron performs better than any other solution proposed for the dataset.
Deblurring of Poissonian images using BM3D frames

We propose a novel deblurring algorithm for Poissonian images. The algorithm uses data adaptive BM3D-frames for sparse image modeling. Reconstruction is formulated as a generalized Nash equilibrium problem, seeking a balance between the data fit and the complexity of the solution. Simulated experiments demonstrate numerical and visual superiority of the proposed algorithm over the current state-of-the-art methods.
Zika Vaccine Microparticles (MPs)-Loaded Dissolving Microneedles (MNs) Elicit a Significant Immune Response in a Pre-Clinical Murine Model

Although the global Zika epidemic in 2015–16 fueled vaccine development efforts, there is no approved Zika vaccine or treatment available to date. Current vaccine platforms in clinical trials are administered via either subcutaneous or intramuscular injections, which are painful and decrease compliance. Therefore, in the present study, we explored Zika vaccine microparticles (MPs)-loaded dissolving microneedles (MNs) with adjuvant MPs encapsulating Alhydrogel® and MPL-A® administered via the transdermal route as a pain-free vaccine strategy. We characterized the MNs for needle length, pore formation, and dissolvability when applied to murine skin. Further, we evaluated the in vivo efficacy of vaccine MPs-loaded MNs with or without adjuvants by measuring the immune response after transdermal immunization. The vaccine MPs-loaded dissolving MNs with adjuvants induced significant IgG, IgG1, and IgG2a titers in immunized mice compared to the untreated control group. After the dosing regimen, the animals were challenged with Zika virus, monitored for seven days, and sacrificed to collect spleen and lymph nodes. The lymphocytes and splenocytes from the immunized mice showed significant expressions of helper (CD4) and cytotoxic (CD8a) cell surface markers compared to the control group. Thus, this study puts forth a ‘proof-of-concept’ for a pain-free transdermal vaccine strategy against Zika.
Analysis of Training Deep Learning Models for PCB Defect Detection

Recently, many companies have introduced automated defect detection methods for defect-free PCB manufacturing. In particular, deep learning-based image understanding methods are very widely used. In this study, we present an analysis of training deep learning models to perform PCB defect detection stably. To this end, we first summarize the characteristics of industrial images, such as PCB images. Then, the factors that can cause changes (contamination and quality degradation) to the image data in the industrial field are analyzed. Subsequently, we organize defect detection methods that can be applied according to the situation and purpose of PCB defect detection. In addition, we review the characteristics of each method in detail. Our experimental results demonstrated the impact of various degradation factors, such as defect detection methods, data quality, and image contamination. Based on our overview of PCB defect detection and experiment results, we present knowledge and guidelines for correct PCB defect detection.
Some Acoustic Correlates of Word Stress in American English

English noun‐verb pairs, differing principally in their stress patterns, were spoken by native speakers of American English. The fundamental frequency and the relative amplitude and duration of the stressed and unstressed syllables were determined and correlated with the aurally perceptible stress patterns. Certain “trading” effects in the importance of these parameters with different speakers are discussed.
Numerical calculations of critical densities for lines and planes

Critical percolation densities have been calculated numerically for large systems of lines in two dimensions and planes in three dimensions. Results for lines of varying length have been successfully predicted from the constant length case. In special cases very large systems of up to 1/4 million lines have been studied. By calculating critical densities for a range of system sizes and using a finite-size scaling argument, a prediction of the infinite critical density is made. For the two-dimensional system with fixed length orthogonal lines this corresponds to 3.11 intersections per line. For the three-dimensional system of fixed size orthogonal planes, the prediction is 2.00 intersections per plane.
Baire paradoxical decompositions need at least six pieces

We prove that in certain cases, paradoxical decompositions of compact metric spaces using sets (or even $[0,1]$-valued functions) with the property of Baire modulo meager sets need more pieces than paradoxical decompositions with unrestricted pieces. In particular, any Baire paradoxical decomposition of the sphere $S^2$ using isometries needs at least six pieces.
Motivational Profiles and Associations With Physical Activity Before, During, and After the COVID-19 Pandemic: Retrospective Study

Background In March 2020, the World Health Organization declared the worldwide COVID-19 outbreak a pandemic, triggering many countries, including Canada, to issue stay-at-home orders to their citizens. Research indicates that these stay-at-home orders are associated with a decline in physical activity (PA), a behavior that can reduce disease risk and improve the quality of life. Many behavioral change theories, such as the self-determination theory (SDT) of motivation, state that PA engagement is mediated by psychological constructs, such as motivation. According to the SDT, motivation exists on a continuum from more controlled (external or coerced) to more autonomous (volitional) regulatory forms. Individuals move along the continuum from more controlled to more autonomous forms through the fulfillment of 3 psychological needs: autonomy, competence, and relatedness. Research indicates that moderate-to-vigorous physical activity (MVPA) is positively associated with the autonomous regulatory form of motivation. Recently, researchers have speculated that a better method to describe motivation than movement along the continuum is to generate motivational profiles, which represent combinations of differing levels of controlled and autonomous regulation existing simultaneously. Objective We aimed to identify distinct motivational profiles and determine their association with MVPA before, during, and after the COVID-19 pandemic. Methods Using a cross-sectional, retrospective design, we surveyed 977 Canadian adults. We assessed motivation for PA using the Behavioral Regulations in Exercise Questionnaire-3 (BREQ-3). We assessed PA pre–, during, and post–COVID-19 stay-at-home orders in Canada using the International Physical Activity Questionnaire (IPAQ). We derived motivational profiles using latent profile analysis (LPA). Using motivational profiles as an independent variable, we assessed their effect on PA at all 3 time points with multilevel models that included the participant ID as a random variable. Results We identified 4 profiles: high controlled and high autonomous (HCHA), low overall motivation (LOM), high autonomous and introjected (HAI), and high amotivation and external (HAE). The HCHA profile had the highest levels of weekly MVPA minutes at all 3 time points, followed by the HAI profile. Conclusions Our results suggest that a combination of both autonomous and controlled regulatory forms may be more effective in influencing MVPA than the controlled or autonomous forms alone, particularly during times of high stress, such as a worldwide pandemic. Although the odds of another global pandemic are low, these results may also be applied to other times of stress, such as job transitions, relationship changes (eg, change in marital status), or the death of a loved one. We suggest that clinicians and practitioners consider developing PA interventions that seek to increase both controlled and autonomous regulatory forms instead of aiming to reduce controlled forms.
Modulo-2 Pi Phase Sequence Estimation.

Abstract : The probabilistic evolution of random walk on the circle is studied and the results used to derive a MAP sequence estimator for phase. The sequence estimator is a Viterbi tracker for tracking phase on a finite-dimensional grid in (-Pi, Pi). The algorithm is shown to provide a convenient method for obtaining fixed-lag phase sequence estimates. Performance characteristics are presented and compared with several other nonlinear filtering algorithms. The results indicate superior performance over the range of parameter values usually considered and excellent performance in parameter ranges corresponding to high signal-to-noise ratio and rapidly fluctuating phase. Applications to coherent data communication systems are outlined. (Author)
Current Icing Potential: Algorithm Description and Comparison with Aircraft Observations

Abstract The “current icing potential” (CIP) algorithm combines satellite, radar, surface, lightning, and pilot-report observations with model output to create a detailed three-dimensional hourly diagnosis of the potential for the existence of icing and supercooled large droplets. It uses a physically based situational approach that is derived from basic and applied cloud physics, combined with forecaster and onboard flight experience from field programs. Both fuzzy logic and decision-tree logic are applied in this context. CIP determines the locations of clouds and precipitation and then estimates the potential for the presence of supercooled liquid water and supercooled large droplets within a given airspace. First developed in the winter of 1997/98, CIP became an operational National Weather Service and Federal Aviation Administration product in 2002, providing real-time diagnoses that allow users to make route-specific decisions to avoid potentially hazardous icing. The CIP algorithm, its individual c.
BIFURCATION ANALYSIS OF A DELAYED EPIDEMIC MODEL WITH DIFFUSION

In this paper, a class of delayed epidemic model with diffusion is investigated. By analyzing the associated characteristic transcenden- tal equation, its linear stability is investigated and Hopf bifurcation is demonstrated. Some explicit formulae determining the stability and the direction of the Hopf bifurcation periodic solutions bifurcating from Hopf bifurcations are obtained by using the normal form theory and center manifold theory. Some numerical simulation are also carried out to sup- port our analytical findings. Finally, biological explanations and main conclusions are given.
Disease Classification Model Based on Multi-Modal Feature Fusion

Aiming at the application of classification tasks in deep learning in disease classification, this paper proposes a disease classification model based on multi-modal feature fusion. In this model, chest X-ray images of patients are used as image modal data, and corresponding disease descriptions are used as text modal data. By innovatively pro-posing an adaptive multi-modal attention mechanism, the feature vectors extracted from the two modal data are fused for classification by classifier. In order to verify the effectiveness of the proposed model in disease classification, this paper uses Chest X-ray dataset in open I database. In order to solve the problem of small sample size and unbalanced sample categories in this dataset, SMOTE algorithm is used to expand the samples and ablation study is designed to compare the model effects. The results show that the model based on both image and text modes and sample expansion by SMOTE algorithm can solve the problems of overfitting, low recall and F1 value due to small samples and unbalanced samples. In addition, the classification accuracy of multi-modal model using image and text is improved by about 0.55% and 2.69% respectively compared with single-modal model using only image or text. Similarly, the addition of adaptive multi-modal attention mechanism also improves the classification effect of the model by about 0.41% compared with the feature fusion method using vector concatenation simply.
Investigation of a changing range genetic algorithm in noisy environments

This paper analyses the effect of noise on the performance of a changing range genetic algorithm (CRGA). CRGA adaptively shifts and shrinks the size of the search space of the feasible region by employing feasible and infeasible solutions in the population to reach the global optimum. An additional modification of CRGA was introduced to reduce the effects of noise on the performance of the algorithm. Several test cases demonstrated the ability of the improved CRGA to deal with an additive noise. Copyright © 2007 John Wiley & Sons, Ltd.