Miracle-dz/UniFAR-FTUs huggingface dataset:
Phosphorus Waste Production in Fish Farming a Potential for Reuse in Integrated Aquaculture Agriculture

— The development of aquaculture in recent years to become the fastest growing food production in the world is accompanied by a secondary effect on the environment, since considerable quantities of waste can be produced and discharged into the environment, as these phosphorus-rich effluents, over time, can contribute to eutrophication phenomena in the aquatic environment. This pollutant is essentially of food origin and is a necessary macro-mineral for fish. However, current scientific and technical means are far from offering the solution to the environmental problems posed by aquaculture development. However, this effluent is a compound that is necessary for the soil as a fertiliser and has great potential for reuse. In this context, aquaculture systems must therefore be well managed to ensure the environmental sustainability of the sector by exploiting these phosphorus-rich discharges in the system of integrating aquaculture with agriculture. The integration of agricultural and aquaculture production systems is seen as a sustainable alternative and as a way to rationalise the use of water and fertilisers. However, for the optimisation of this integrated system to be justifiable in terms of the exploitation of phosphorus from aquaculture effluents, it is necessary to take ownership of the processes involved in the presence of food-borne phosphorus in these effluents and the possibility of its advantageous use both in aquaponics and in agricultural irrigation, the aim of which is to increase the efficiency and sustainability of both aquaculture and agriculture.
University of Birmingham Achievement goals, behavioural engagement, and mathematics achievement

Previous studies have shown that mastery-approach and performance-approach goals can be positively associated with adaptive educational outcomes. Few studies, however, have examined links with behavioural engagement. The aim of the present study was to examine whether behavioural engagement mediated relations between mastery-approach and performance-approach goals and subsequent achievement in mathematics. Data were collected from 1,057 students aged 9 to 11 years in a longitudinal design over the course of a single school year. Results showed that a mastery-approach, but not a performance-approach, goal predicted behavioural engagement. Behavioural engagement, in turn, predicted mathematics achievement. Furthermore, behavioural engagement mediated relations between mastery-approach and subsequent mathematics achievement. This study contributes to the evidence base for the adaptive role of mastery-approach which can be encouraged by students setting personal best goals, and teachers ensuring that feedback is task-focused, and that the classroom climate is mastery-focused.
Statistically Speaking: methodological madness Comparing two methods – agreeing to disagree?

Dear Ms. Method Matters, In one of your recent articles, you mentioned using the BlandAltman test for comparison between two methods, rather than using Spearman’s (or Pearson’s correlation) [1]. The statistic of interest, when using correlation, is the rvalue. If a high r-value (for example, 0.998) is obtained, we may be tempted into thinking that there is good agreement between the two methods. But r measures the strength of a relationship, not agreement, and even if two sets of values were obtained using different scales (or expressed in different units), the strength of the relationship would be high. We know that the BlandAltman analysis is the correct method if we want to look at agreement, however, you didn’t go into details with regard to how the results from a Bland-Altman test should be interpreted. For example, in an Anaesthesia paper comparing the results from a non-invasive monitor of cardiac output (Nexfin , BMEYE, Amsterdam, The Netherlands) with the gold-standard thermodilution technique, it states that the mean (SD) bias was 0.60 (1.62) l.min 1 with limits of agreement 2.67 to 3.86 l.min 1 [2]. The precision error was 46%. How are these results to be interpreted? If I were to conduct a similar study, how many subjects would I be required to recruit?
Compact Dual Band-Notched UWB MIMO Antenna for USB Dongle Application with Pattern Diversity Characteristics

In this paper a compact planar dual band-notched ultra-wideband (UWB) multiple input multiple output (MIMO) antenna is presented for universal synchronous bus (USB) dongle application with pattern diversity characteristic. The MIMO configuration has orthogonally placed elements with overall size of 16× 37.6mm2, and the common ground plane of the MIMO antenna is further extended by 20mm for its practical use. The measured and simulated reflection coefficients of the antenna show good impedance bandwidth matching over the range from 3GHz to 12GHz excluding the dual notched bands. Both elements show good band-reject property at the notched bands. The simulated results are verified through measurements and calculations. Moreover, absence of decoupling network makes circuit less complex and very compact. Radiation pattern of the MIMO antenna is almost omnidirectional. Furthermore, diversity performance of MIMO antenna is validated with its envelope correlation coefficient (ECC) and pattern diversity characteristic. These characteristics demonstrate its candidacy as a compact dual band-notched UWB MIMO antenna for USB dongle application.
Image Reconstruction Using Priority Patch Algorithm

: In this paper, a simple Priority Patch Algorithm (PPA) is presented for exemplar image completion. This novel algorithm is developed for identifying a patch of picture from the surrounding pixels of an image and it fills the unknown regions using the technique of priority patch - matching. Quality picture is required by the people, whenever they take a photograph. Unfortunately, if large unwanted object hides the necessary background image, then it can be filled by the known surrounding pixels to enhance the image. The matching score is evaluated by selecting few coefficients from decomposed frequency coefficients. A simple priority patch algorithm is developed to fill the unknown pixels in a patch image block. The Priority Patch Algorithm is based on a search - array technique, and can be implemented very efficiently. The search - array technique runs well on the Graphics Processing Units (GPU) and good experimental results are obtained on real photographs.
EFFECTS OF AMINOREDUCTONE AGAINST THE GROWTH OF FOOD-BORNE BACTERIA IN MEDIUM AND IN MILK

The aim of this study was to determine the antimicrobial potential of aminoreductone (AR), a product formed in the initial stages of the Maillard reaction, against common food-borne bacteria included food-spoilage and food-pathogenic bacteria ( Staphylococcus aureus ( S. aureus ), Bacillus subtilis ( B. subtilis ), Enterococcus faecalis ( E. faecalis ), Listeria innocua ( L. innocua ), Listeria monocytogenes ( L. monocytogenes ), Escherichia coli ( E. coli ), Salmonella Typhimurium ( S. Typhimurium )). The result indicated that AR was a strong inhibitor against all bacteria by disc susceptibility assay with inhibition zones ranged from 17 mm ( B. subtilis ) to 24.3 mm ( S. aureus ) in diameter. The minimum inhibitory concentrations (MIC) of AR ranged from 20 to 26 mM. Among test strains, AR possessed bactericidal action to five species ( S. aureus, B. subtilis, L. innocua, L. monocytogenes and S. Typhimurium ) at the concentration of less than 10 · MIC. AR also showed strong inhibitory effect against food-borne bacteria even in milk (UHT milk and raw milk). These findings suggest that AR, a naturally formed antimicrobial agent present in thermally processed foods, has a promising potential for food preservation.
Sentiment Analysis on the Basis of Tweeter Comments of Application of Drugs by Customary Language Toolkit and TextBlob Opinions of Distinct Countries

Twitter is now one of the greatest podiums all over the world through which anyone can present his opinion regarding a particular issue. It is a social networking site. Every day, it generates almost 500 million of tweets and the total volume of tweets contains 8TB of data. This data is very much significant if examined, because we are able to draw out salient facts through opinion mining. We can keep an eye at the augmentation of a product or any important affairs or events or a novel fashion in twitter data. The prime objective of this sentiment analysis or opinion mining is to explore emotion, opinion, subjectivity and perspective from a customary text on application of a medicine for the treatment of COVID-19(Corona Virus Disease 2019). We can classify the tweets into positive and negative sentiments in twitter sentiment analysis. The expression ‘Cluster’ refers to an accomplished method where homogeneous substances are kept in a distinct class and thus a pack of cluster is created. We went on with an analysis and concluded that the application of clustering can hurriedly and precisely differentiate tweets on account of their sentiment scores and thus it gets weekly and firmly positive or negative tweets, if they are clustered, with outcomes of distinct dictionaries. The objective of this paper is to scrutinize discrete viewpoints of clustering sentiment analysis and thus devises a method to make connection with the tweets of the statesmen of some first world, second world and third world countries. The frame of reference in this case is the polarity and subjectivity on the application of drugs for the treatment of COVID-19.
Copper-64 PET imaging of the CXCR4 chemokine receptor using a cross-bridged cyclam bis-tetraazamacrocyclic antagonist

Expression of the chemokine receptor chemokine C-X-C motif receptor 4 (CXCR4) plays an important role in cancer metastasis, autoimmune diseases and during stem cell based repair processes after stroke and myocardial infarction. Previously reported positron emission tomography (PET) imaging agents targeting CXCR4 suffer from either high non-specific uptake or only bind to the human form of the receptor. The objective of this study is to develop a high stability copper-64 labelled small molecule PET agent for imaging both human and murine CXCR4 chemokine receptors. Methods: Synthesis, radiochemistry, stability and radioligand binding assays were performed for the novel tracer 64 Cu-CuCB-Bicyclam. In vivo dynamic PET studies were carried out on mice bearing U87 (CXCR4 low ) and U87.CXCR4 (CXCR4 high ) tumors. Biodistribution and receptor blocking studies were carried out on CD1-IGS immunocompetent mice. CXCR4 expression on tumor and liver disaggregates was confirmed using a combination of immunohistochemistry, quantitative polymerase chain reaction (qPCR) and western blot. Results: 64 Cu-CuCB-Bicyclam has a high affinity for both the human and murine variants of the CXCR4 receptor (IC 50 = 8 nM (human)/ 2 nM (murine)) and can be obtained from the parent chelator that has low affinity. In vitro and in vivo studies demonstrate specific uptake in CXCR4 expressing cells that can be blocked by >90% using a higher affinity antagonist, with limited uptake in non-CXCR4 expressing organs and high in vivo stability. The tracer was also able to selectively displace the CXCR4 antagonists AMD3100 and AMD3465 from the liver. Conclusions: The application of the tetraazamacrocyclic small molecule 64 Cu-CuCB-Bicyclam is demonstrated as an imaging agent for the CXCR4 receptor that is likely to be applicable across a range of species. It has high affinity and stability and is suitable for preclinical research in immunocompetent murine models.
Edinburgh Research Explorer Redefine statistical significance

Citation for published version: Benjamin, DJ, Berger, JO, Johannesson, M, Nosek, BA, Wagenmakers, E-J, Berk, R, Bollen, KA, Brembs, B, Brown, L, Camerer, C, Cesarini, D, Chambers, CD, Clyde, M, Cook, TD, De Boeck, P, Dienes, Z, Dreber, A, Easwaran, K, Efferson, C, Fehr, E, Fidler, F, Field, AP, Forster, M, George, EI, Gonzalez, R, Goodman, S, Green, E, Green, DP, Greenwald, AG, Hadfield, JD, Hedges, LV, Held, L, Hua Ho, T, Hoijtink, H, Hruschka, DJ, Imai, K, Imbens, G, Ioannidis, JPA, Jeon, M, Jones, JH, Kirchler, M, Laibson, D, List, J, Little, R, Lupia, A, Machery, E, Maxwell, SE, McCarthy, M, Moore, DA, Morgan, SL, Munafó, M, Nakagawa, S, Nyhan, B, Parker, TH, Pericchi, L, Perugini, M, Rouder, J, Rousseau, J, Savalei, V, Schönbrodt, FD, Sellke, T, Sinclair, B, Tingley, D, Van Zandt, T, Vazire, S, Watts, DJ, Winship, C, Wolpert, RL, Xie, Y, Young, C, Zinman, J & Johnson, VE 2018, 'Redefine statistical significance', Nature Human Behaviour, vol. 2, no. 1, pp. 6-10. https://doi.org/10.1038/s41562-017-0189-z
Output Feedback Stabilization of Switching Discrete-Time Linear Systems with Parameter Uncertainties

This paper deals with observer-based control design for a class of switched discrete-time linear systems with parameter uncertainties. The main contribution of the paper is to propose a convenient way based on Finsler’s lemma to enhance the synthesis conditions, expressed in terms of Linear Matrix Inequalities (LMIs). Indeed, this judicious use of Finsler’s lemma provides additional decision variables, which render the LMIs less conservative and more general than all those existing in the literature for the same class of systems. Two numerical examples followed by a Monte Carlo evaluation are proposed to show the superiority of the proposed design technique.
Exploiting WordNet glosses to disambiguate nouns through verbs

—This paper presents an unsupervised graph-based algorithm for word sense disambiguation based on WordNet glosses. The algorithm exploits the contributions of verbs in identifying the correct senses of nouns. Due to the complexity of WordNet’s semantic network, we have defined disambiguation as a similarity optimization problem and solved it through a genetic algorithm. Testing compares the performance of our algorithm with that of a traditional method based on Wu-Palmer similarity measure. Our approach shows an overall precision of about 68% and a statistically significant average increase of precision of about 3% with respect to the traditional algorithm.
Higher-Order Conditional Random Field established with CNNs for Video Object Segmentation

We perform the task of video object segmentation by incorporating a conditional random field (CRF) and convolutional neural networks (CNNs). Most methods employ a CRF to refine a coarse output from fully convolutional networks. Others treat the inference process of the CRF as a recurrent neural network and then combine CNNs and the CRF into an end-to-end model for video object segmentation. In contrast to these methods, we propose a novel higher-order CRF model to solve the problem of video object segmentation. Specifically, we use CNNs to establish a higher-order dependence among pixels, and this dependence can provide critical global information for a segmentation model to enhance the global consistency of segmentation. In general, the optimization of the higher-order energy is extremely difficult. To make the problem tractable, we decompose the higher-order energy into two parts by utilizing auxiliary variables and then solve it by using an iterative process. We conduct quantitative and qualitative analyses on multiple datasets, and the proposed method achieves competitive results.
Not Imprinted during Thymic Development IL-9 Expression by Invariant NKT Cells Is

Invariant NKT (iNKT) cell thymic development can lead to distinct committed effector lineages, namely NKT1, NKT2, and NKT17. However, following identiﬁcation of IL-9–producing iNKT cells involved in mucosal inﬂammation, their development remains unaddressed. In this study, we report that although thymic iNKT cells from naive mice do not express IL-9, iNKT cell activation in the presence of TGF- b and IL-4 induces IL-9 secretion in murine and human iNKT cells. Acquisition of IL-9 production was observed in different iNKT subsets deﬁned by CD4, NK1.1, and neuropilin-1, indicating that distinct functional subpopulations are receptive to IL-9 polarization. Transcription factor expression kinetics suggest that regulatory mechanisms of IL-9 expression are shared by iNKT and CD4 T cells, with Irf4 and Batf deﬁciency deeply affecting IL-9 production. Importantly, adoptive transfer of an enriched IL-9 + iNKT cell population leads to exacerbated allergic inﬂammation in the airways upon intranasal immunization with house dust mite, conﬁrming the ability of IL-9–producing iNKT cells to mediate proinﬂammatory effects in vivo, as previously reported. Taken together, our data show that peripheral iNKT cells retain the capacity of shaping their function in response to environmental cues, namely TGF- b and IL-4, adopting an IL-9–producing NKT cell phenotype able to mediate proinﬂammatory effects in vivo, namely granulocyte and mast cell recruitment to the lungs. The Journal of Immunology , 2015, 195: 000–000.
Advanced Engineering Informatics

Traditional images classification methods are frequently utilized in practical applications, but they have many disadvantages, including disappointing effects, low classification accuracy, and a lack of adaptability. In this paper, we propose an image classification model by the Integration of Convolution Neural Network and modified Bag of Visual Word (MBoVW). A BoVW is capable of picking features in image classification; however, it is only useful if the feature extractor provided is well-matched. in this paper image classification, BoVW uses ScaleInvariant Feature Transform (SIFT) and Oriented Fast and Rotated BRIEF (ORB) descriptors as a result, the SIFT-ORB-BoVW model that was created has highly discriminant features, which improves classification performance. We've also looked into using a fuzzy bag of visual words to find relevant photos and solve the problem of visual code words being hard to assign to local image properties. Adding fuzziness to an image improves its qualities. Convolutional neural networks (CNNs)/ Support vector machines (SVM) are also used in this paper to increase the suggested feature extractor's potential to learn more meaningful visual vocabulary from the image. In different datasets containing a variety of image categories, our method outperforms traditional BoVW as a tool for image classification.
INFRARED SPECTRA OF SOME LIPOPROTEINS AND RELATED LIPIDS

The infrared absorption spectra of some ultracentrifugally isolated serum lipoproteins and those of some related lipids have been presented and discussed. In a lipoprotein spectrum the relative intensities of the ester carbonyl band and certain strong bands of prote_ins can serve as a rough gauge of the lipid/protein ratio. Other absorption bands have been correlated with those o,f the principal component molecules making up the lipoprotein. A rudimentary method for quantitative analysis of these molecules and of lipid mixtures by infrared methods has been described. Needed improve ments have been suggested.
Clinical target volume automatic segmentation based on lymph node stations for lung cancer with bulky lump lymph nodes

The lack of standardized delineation of lymph node station in lung cancer radiotherapy leads to nonstandard clinical target volume (CTV) contouring, especially in patients with bulky lump gross target volume lymph nodes (GTVnd). This study defines lymph node region boundaries in radiotherapy for lung cancer and automatically contours lymph node stations based on the International Association for the Study of Lung Cancer (IASLC) lymph node map.
SPCBPT

Bidirectional path tracing (BDPT) can be accelerated by selecting appropriate light sub-paths for connection. However, existing algorithms need to perform frequent distribution reconstruction and have expensive overhead. We present a novel approach, SPCBPT, for probabilistic connections that constructs the light selection distribution in sub-path space. Our approach bins the sub-paths into multiple subspaces and keeps the sub-paths in the same subspace of low discrepancy, wherein the light sub-paths can be selected by a subspace-based two-stage sampling method, i.e., first sampling the light subspace and then resampling the light sub-paths within this subspace. The subspace-based distribution is free of reconstruction and provides efficient light selection at a very low cost. We also propose a method that considers the Multiple Importance Sampling (MIS) term in the light selection and thus obtain an MIS-aware distribution that can minimize the upper bound of variance of the combined estimator. Prior methods typically omit this MIS weights term. We evaluate our algorithm using various benchmarks, and the results show that our approach has superior performance and can significantly reduce the noise compared with the state-of-the-art method.
enacted within interpersonal student curriculum and of

: This paper offers a conceptual analysis of collusion, the often overlooked relative of plagiarism in debates on academic integrity. Considered as an inherently social phenomenon, we present the results of a systematic effort to understand the anatomy of collusion. The term’s meanings and associated governance practices are compared for contexts outside Higher Education (HE). These are considered alongside a thematic analysis of publicly available UK university academic integrity documentation that specifies for students what counts as collusion. We indicate how current guideline practice can (1) appear incomplete by concentrating on classroom peers, (2) create blurred boundaries around useful collaboration, peer review and dishonest practice and (3) may be so unrealistic as to have unwelcome, unintended consequences for students and staff. Taking an ecological perspective on the conditions of collusion emphasises how these guidelines - by seeking to constrain social interactions around assignment work - may create an uncomfortable incoherence between their prescriptions and well-established patterns of study.