Miracle-dz/UniFAR-FTUs huggingface dataset:
Dynamics of soil properties and bacterial community structure by mulched fertigation system in semi-arid area of Northeast China

The agricultural irrigation and fertigation systems have a non-negligible impact on the soil microenvironment in arid and semi-arid areas. Therefore, studying the processes and changes of soil microenvironment under different plastic mulch drip irrigation systems can reveal the “soil-microbe” mechanism and provide a theoretical support for the optimal irrigation and nutrition management of maize in the semi-arid area of Northeast China. Three treatments were used for this study in the semi-arid area of northeast China, namely; mulched fertigation system (MF), drip irrigation system (DI), and farmers’ practices system (FP). We used high-throughput sequencing to study the soil bacterial community structure targeting the 16S rRNA gene. The agricultural irrigation and fertigation systems significantly affected soil properties. MF significantly increased bacterial abundance and bacterial diversity and richness. Moreover, MF and DI markedly increased some relative abundance of beneficial bacterial. The bacterial network in MF was more conducive to the health and stability of the agroecosystem and the relationships among species in MF bacterial network were more complex. The agricultural irrigation and fertigation systems had indirect effects on community composition and bacterial diversity through soil organic carbon (SOC), ammonium nitrogen (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}${\mathrm{NH}}_{4}^{+}$\end{document}NH4+-N), nitrate nitrogen (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}${\mathrm{NO}}_{3}^{-}$\end{document}NO3−-N), pH, moisture, \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}${\mathrm{NH}}_{4}^{+}$\end{document}NH4+-N and \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}${\mathrm{NO}}_{3}^{-}$\end{document}NO3−-N had indirect effects on yield through bacterial community composition, bacterial diversity and bacterial abundance. These findings suggested that MF was the most effective treatment to improve soil bacterial abundance and diversity, and stabilize the functional quality of soil biological processes.
Deep reinforcement learning in dynamic positioning control: by rewarding small response of riser angles

ABSTRACT Dynamic positioning (DP) is one of the key technologies in the various offshore operations. This paper investigates machine learning (ML) techniques, more specifically reinforcement learning (RL) in DP applications for deepwater drilling rigs. An RL-based DP control algorithm with the deep Q-learning network is proposed. To guarantee the safe operation of the underwater riser, a comprehensive reward function with respect to the top and bottom angles of the riser is designed. Compared to traditional control methods, the proposed RL-based algorithm shows good positioning performance while maintaining the operating limits of the underwater riser.
Biocompatibility of 3D-Printed PLA, PEEK and PETG: Adhesion of Bone Marrow and Peritoneal Lavage Cells

Samples in the form of cylindrical plates, additively manufactured using the fused deposition modelling (or filament freeform fabrication, FDM/FFF) technology from polylactide (PLA), polyethylene terephthalate glycol (PETG) and polyetheretherketone (PEEK), were studied in series of in-vitro experiments on the adhesion of rat bone-marrow cells and rat peritoneal cells. Methods of estimation of the absolute number of cells and polymer samples’ mass change were used for the evaluation of cells adhesion, followed by the evaluation of cell-culture supernatants. The results of experiments for both types of cells demonstrated a statistically significant change in the absolute number of cells (variation from 44 to 119%) and the weight of the polymer samples (variation from 0.61 to 2.18%), depending on roughness of sample surface, controlled by a nozzle diameter of a 3D printer as well as printing layer height. It was found that more cells adhere to PLA samples with a larger nozzle diameter and layer height. For PETG samples, the results did not show a clear relationship between cell adhesion and printing parameters. For PEEK samples, on the contrary, adhesion to samples printed with a lower nozzle diameter (higher resolution) is better than to samples printed with a larger nozzle diameter (lower resolution). The difference in results for various polymers can be explained by their chemical structure.
A Symmetry Evaluation Method, Using Elevation Angle, for Lower Limb Movement Patterns during Sitting-to-Standing

In this paper, we propose a method that uses the femoral and tibial elevation angles to quantitatively evaluate the symmetry of lower limb movement during the transition from a sitting position to a standing position. In kinematic analysis of the transition from sitting to standing, the angles of the three joints of the lower limb are often measured. However, due to the large number of variables, it is difficult to evaluate the symmetry of the lower limb movement by comparing data from the six joints of the left and right lower limbs. In this study, therefore, we measured the femoral and tibial elevation angles of healthy participants and rehabilitation patients and visually and numerically evaluated the symmetry and asymmetry of the movement of the left and right lower limbs. We were able to identify the kinematically major lower limbs in the transition from sitting to standing and quantify the symmetry of the movement patterns of the left and right lower limbs. Furthermore, we examined the possibility that the method could be effectively used in the rehabilitation field to evaluate the motor co-ordination that constitutes the lower limb movement pattern in the transition from the sitting to standing position, such as the gait plane rule.
Computer Vision and Internet Meme Genealogy: An Evaluation of Image Feature Matching as a Technique for Pattern Detection

ABSTRACT Internet memes are a fundamental aspect of digital culture. Despite being individual expressions, they vastly transcend the individual level as windows into and vehicles for wide-stretching social, cultural, and political narratives. Empirical research into meme culture is thriving, yet particularly compartmentalized. In the humanities and social sciences, most efforts involve in-depth linguistic and visual analyses of mostly handpicked examples of memes, begging the question on the origins and meanings of those particular expressions. In technical disciplines, such as computer science, efforts are focused on the large-scale identification and classification of meme images, as well as patterns of “viral” spread at scale. This contribution aims to bridge the chasm between depth and scale by introducing a three-step approach suitable for “computational grounded theoretical” studies in which (1) an automated procedure establishes formal links between meme images drawn from a large-scale corpus paving the way for (2) network analysis to infer patterns of relatedness and spread, and (3) practically classifying visually related images in file folders for the purpose of further local, hermeneutic analysis. The procedure is demonstrated and evaluated on two datasets: an artificially constructed, structured dataset and a naturally harvested unstructured dataset. Future horizons and domains of application are discussed.
Ocean tide models for satellite geodesy and Earth rotation

A theory is presented which predicts tides in turbulent, self-gravitating, and loading oceans possessing linearized bottom friction, realistic bathymetry, and continents (at coastal boundaries no-flow conditions are imposed). The theory is phrased in terms of spherical harmonics, which allows the tide equations to be reduced to linear matrix equations. This approach also allows an ocean-wide mass conservation constraint to be applied. Solutions were obtained for 32 long and short period luni-solar tidal constituents (and the pole tide), including the tidal velocities in addition to the tide height. Calibrating the intensity of bottom friction produces reasonable phase lags for all constituents; however, tidal amplitudes compare well with those from observation and other theories only for long-period constituents. In the most recent stage of grant research, traditional theory (Liouville equations) for determining the effects of angular momentum exchange on Earth's rotation were extended to encompass high-frequency excitations (such as short-period tides).
Identificação de Ocorrências do Cnidário Physalia physalis em Dados Extraídos de Mídias Sociais

As necessidades de conhecimento da biodiversidade são constantes, enquanto recursos para pesquisa, sejam financeiros, de tempo e humanos são escassos. Por outro lado, a Internet oferece um enorme volume de dados que podem ser explorados em favor da ciência da conservação. As caravelas-portuguesas (Physalia physalis) oferecem risco à população, e dados sobre sua ocorrência nem sempre estão disponíveis para estudo da espécie. Neste trabalho é proposto o treinamento de modelos de aprendizagem de máquina como ferramenta para classificar dados extraídos de uma mídia social e assim possibilitar a geração de uma base de dados sobre ocorrências de caravelas-portuguesas no litoral brasileiro.
Competing Risk Model for Time to Development of Tuberculosis among Adults on Combination Antiretroviral Treatment

The purpose of this study was to identify factors affecting the time to development of tuberculosis in the presence of competing risks. In this case death before developing tuberculosis was deemed a competing risk because it al-tered the occurrence of the outcome of interest being time to development of tuberculosis from baseline. We used data from a randomized longitudinal clinical trial study called the “Tshepo” study. The “Tshepo” study was a 3-year randomized clinical study following 650 ART-naïve adults (69.4% female) from Botswana who initiated first-line NNRTI-based ART. Participants were assigned in equal proportions (in an open-label, unblinded fashion) to one of 6 initial treatment arms and one of two adherence arms using permuted block randomization. Randomization was stratified by CD4+ cell count (less than 200 cells/mm 3 , 201 - 350 cells/mm 3 ) and by whether the participants had an adherence assistant. Classical methods such as the Kaplan-Meier method and standard Cox proportional hazards regression were used to analyze survival data ignoring the competing event(s) which may have been in-appropriate in the presence of competing risks. The idea was to use competing risk models to investigate how different treatment regimens affect the time to the development of TB and compare the results to those obtained using the classical survival CD4, Hemoglobin and gender. Similarly, after accounting for competing risks the hazard ratio for treatment C was about 1.89 implying that the risk of developing TB amongst those taking treatment C was about 89% higher as compared to those taking treatment A. From the obtained results it was thus concluded that the standard Cox model of time to event data in the presence of competing risks underestimated the hazard ratios hence when dealing with data with multiple failure events it is important to account for competing events.
Electrospun Poly(3-Hydroxybutyrate-Co-3-Hydroxyvalerate)/Olive Leaf Extract Fiber Mesh as Prospective Bio-Based Scaffold for Wound Healing

Polyhydroxyalkanoates (PHAs) are a family of biopolyesters synthesized by various microorganisms. Due to their biocompatibility and biodegradation, PHAs have been proposed for biomedical applications, including tissue engineering scaffolds. Olive leaf extract (OLE) can be obtained from agri-food biowaste and is a source of polyphenols with remarkable antioxidant properties. This study aimed at incorporating OLE inside poly(hydroxybutyrate-co-hydroxyvalerate) (PHBHV) fibers via electrospinning to obtain bioactive bio-based blends that are useful in wound healing. PHBHV/OLE electrospun fibers with a size of 1.29 ± 0.34 µm were obtained. Fourier transform infrared chemical analysis showed a uniform surface distribution of hydrophilic -OH groups, confirming the presence of OLE in the electrospun fibers. The main OLE phenols were released from the fibers within 6 days. The biodegradation of the scaffolds in phosphate buffered saline was investigated, demonstrating an adequate stability in the presence of metalloproteinase 9 (MMP-9), an enzyme produced in chronic wounds. The scaffolds were preliminarily tested in vitro with HFFF2 fibroblasts and HaCaT keratinocytes, suggesting adequate cytocompatibility. PHBHV/OLE fiber meshes hold promising features for wound healing, including the treatment of ulcers, due to the long period of durability in an inflamed tissue environment and adequate cytocompatibility.
Selecting Refining and Evaluating Properties for Program Analysis

This research proposes and evaluates techniques for selecting predicates for conditional program properties — that is, implications such as p ⇒ q whose consequent must be true whenever the predicate is true. Conditional properties are prevalent in recursive data structures, which behave differently in their base and recursive cases, in programs that contain branches, in programs that fail only on some inputs, and in many other situations. The experimental context of the research is dynamic detection of likely program invariants, but the ideas are applicable to other domains. Trying every possible predicate for conditional properties is computationally infeasible and yields too many undesirable properties. This paper compares four policies for selecting predicates: procedure return analysis, code conditionals, clustering, and random selection. It also shows how to improve predicates via iterated analysis. An experimental evaluation demonstrates that the techniques improve performance on two tasks: statically proving the absence of run-time errors with a theorem-prover, and separating faulty from correct executions of erroneous programs.
Geospace Concussion: Global Reversal of Ionospheric Vertical Plasma Drift in Response to a Sudden Commencement

An interplanetary shock can abruptly compress the magnetosphere, excite magnetospheric waves and field‐aligned currents, and cause a ground magnetic response known as a sudden commencement (SC). However, the transient (<∼1 min) response of the ionosphere‐thermosphere system during an SC has been little studied due to limited temporal resolution in previous investigations. Here, we report observations of a global reversal of ionospheric vertical plasma motion during an SC on 24 October 2011 using ∼6 s resolution Super Dual Auroral Radar Network ground scatter data. The dayside ionosphere suddenly moved downward during the magnetospheric compression due to the SC, lasting for only ∼1 min before moving upward. By contrast, the post‐midnight ionosphere briefly moved upward then moved downward during the SC. Simulations with a coupled geospace model suggest that the reversed E⃗×B⃗ $\vec{E}\times \vec{B}$ vertical drift is caused by a global reversal of ionospheric zonal electric field induced by magnetospheric compression during the SC.
Ending on a familiar note: Perceived endings motivate repeat consumption.

People fill their free time by choosing between hedonic activities that are new and exciting (e.g., exploring a buzzed-about restaurant) versus old and familiar (e.g., revisiting the same old spot). The dominant psychological assumption is that people will prefer novelty, holding constant factors like cost, availability, and convenience between acquiring such options ("variety is the spice of life"). Eight preregistered experiments (total N = 5,889) reveal that people's attraction to novelty depends, at least in part, on their temporal context-namely, on perceived endings. As participants faced a shrinking window of opportunity to enjoy a general category of experience (even merely temporarily; e.g., eating one's last dessert before starting a diet), their hedonic preferences shifted away from new and exciting options and toward old favorites. This relative shift emerged across many domains (e.g., food, travel, music), situations (e.g., impending New Year's resolutions, COVID-19 shutdowns), and consequential behaviors (e.g., choices with financial stakes). Using both moderation and mediation approaches, we found that perceived endings increase the preference for familiarity because they increase people's desire to ensure a personally meaningful experience on which to end, and returning to old favorites is typically more meaningful than exploring novelty. Endings increased participants' preference for familiarity even when it meant sacrificing other desirable attributes (e.g., exciting stimulation). Together, these findings advance and bridge research on hedonic preferences, time and timing, and the motivational effects of change. Variety may be the "spice of life," but familiarity may be the spice of life's endings. (PsycInfo Database Record (c) 2022 APA, all rights reserved).
Polynomial Signal Processing

Volterra Series Expansions. Realization of Truncated Volterra Filters. Multidimensional Volterra Filters. Parameter Estimation. Frequency-Domain Methods for Volterra System Identification. Adaptive Truncated Volterra Filters. Recursive Polynomial Systems. Inversion and Time Series Analysis. Applications of Polynomial Filters. Some Related Topics and Recent Developments. Appendices. References. Index.
A New Compact Method Based on a Convolutional Neural Network for Classification and Validation of Tomato Plant Disease

With recent advancements in the classification methods of various domains, deep learning has shown remarkable results over traditional neural networks. A compact convolutional neural network (CNN) model with reduced computational complexity that performs equally well compared to the pretrained ResNet-101 model was developed. This three-layer CNN model was developed for plant leaf classification in this work. The classification of disease in tomato plant leaf images of the healthy and disease classes from the PlantVillage (PV) database is discussed in this work. Further, it supports validating the models with the images taken at “Krishi Vigyan Kendra Narayangaon (KVKN),” Pune, India. The disease categories were chosen based on their prevalence in Indian states. The proposed approach presents a performance improvement concerning other state-of-the-art methods; it achieved classification accuracies of 99.13%, 99.51%, and 99.40% with N1, N2, and N3 models, respectively, on the PV dataset. Experimental results demonstrate the validity of the proposed approach under complex background conditions. For the images captured at KVKN for predicting tomato plant leaf disease, the validation accuracy was 100% for the N1 model, 98.44% for the N2 model, and 96% for the N3 model. The training time for the developed N2 model was reduced by 89% compared to the ResNet-101 model. The models developed are smaller, more efficient, and less time-complex. The performance of the developed model will help us to take a significant step towards managing the infected plants. This will help farmers and contribute to sustainable agriculture.
Relationship Extraction and Processing for Knowledge Graph of Welding Manufacturing

Acquiring welding domain relationships and forming a knowledge graph can positively impact complex engineering problem solving and intelligent manufacturing applications. However, relationships are lacking in the welding domain. The relationship extraction and processing solution are designed to handle data with different characteristics in welding fabrication. The BiLSTM+Attention and CR-CNN models are employed to extract relations in unstructured documents. The neighborhood rough set-based association rule model is proposed for project-specific documents to accomplish relationship acquisition, in which invalid attributes are removed via neighborhood rough sets and attribute values are related via association rules. In addition, the knowledge graph is built based on extracted relationships, and unique empirical relationships are handled by introducing relational nodes and databases. The results show that BiLSTM+Attention gets a good score with Macro-average metrics (0.788 for Precision, 0.846 for Recall, and 0.816 for F1-score). The relational rules obtained via the proposed model are consistent with the production experience. The constructed knowledge graph effectively handles empirical relationships while positively impacting knowledge retrieval, intelligent question and answer, and decision-making for complex engineering problems.
Working Conditions and Quality of Life of Cancer Survivors: A Cross-Sectional Study

International Agency for Research on Cancer, there were 18 million new cancer cases worldwide in 2018, and 43.8 million cancer patients who had been diagnosed within the previous 5 years were still alive. 1 Thanks to the increase in life expectancy, working age, and survival rates in cancer patients, the labor force participation rates have also increased for people with chronic illnesses such as cancer. 3 Approximately 50% of those newly diagnosed with cancer and more than one third of cancer BACKGROUND/AIMS: Work ability, which is based on the self-report of individuals while they work, is the strongest predictor of cancer survivors’ (CS) return to work. This study aimed to investigate the working conditions and quality of life (QoL) of individuals with cancer who survived after receiving cancer treatment. MATERIALS AND METHODS: The sample of this cross-sectional and correlational survey type study consisted of 262 CSs. Data were collected using the Socio-Demographic and Health Information Questionnaire, the SF-36 Health-Related Quality of Life (HRQoL) and the Work Ability Index (WAI). RESULTS: Of the 262 CSs who participated in this study, 21.4% were employed. The mean score obtained from the WAI by the employed CSs was 33.20. The mean scores the employed CSs obtained from the physical functioning and role physical subscales of the SF-36 HRQoL were higher than those of the unemployed CSs (p<0.05). There was a correlation between the mean score of the employed CSs obtained from the overall WAI and the mean scores they obtained from all the sub-dimensions of the SF-36 QoL scale (p=0.01). The strongest correlation was found between the WAI and the Social Functioning subscale of the HRQoL (SF-36). CONCLUSION: The QoL of the employed CSs was better than that of the unemployed CSs and there was a correlation between their work ability and QoL.