Miracle-dz/UniFAR-FTUs huggingface dataset:
Temporal availability of phosphorus and sulphur in acid Inceptisol as influenced by graded application of P and S under black gram (Vigna mungo L. Hepper) production

A pot culture experiment was conducted at School of Natural Resource Management, College of Post Graduate Studies in Agricultural Sciences, CAU, Umiam, Meghalaya to assess the temporal availability of phosphorus and sulphur in acid Inceptisol of Meghalaya as influenced by graded application of P and S under black gram (cv. SBC-47) production. The treatment consisted of four levels of P (20, 40, 60 and 80 mg P kg-1 soil) and four levels of S (10, 20, 30 and 40 mg S kg-1 soil). The experiment was laid out in factorial completely randomized design with three replications during kharif season of 2016. The experimental soil was deficient in available phosphorus (13.85 kg ha-1) and available sulphur (15.91 kg ha-1). The results revealed that combined application of P and S had significant effects on seed and stover yield of black gram. The optimum seed and stover yield were recorded with combined application of 60 mg kg-1 soil and 30 mg kg-1 soil, respectively which was statistically at par with combined application of 80 mg P kg-1 soil and 40 mg S kg-1 soil. The assessment of temporal availability of phosphorus and sulphur at 20, 40 DAS and after harvesting of black gram in acid Inceptisol of Meghalaya indicated that available phosphorus in soil increased with each successive increasing level of P application, however it showed decreasing trend with time intervals of 40 DAS and after harvesting of black gram at each successive dose of 40, 60 and 80 mg P kg-1 soil over 20 mg P kg-1 soil in the presence of S fertilization. Similarly, available sulphur in soil also increased with each successive increasing level of S application, whereas it also showed decreasing trend with time intervals of 40 DAS and after harvesting of black gram at each successive dose of 20, 30 and 40 mg S kg-1 soil over control as 10 mg S kg-1 soil in the presence of P fertilization.
Audio description in 360º videos : results from a focus group in Barcelona

Rapid developments in the field of virtual reality (VR) can be seen all around the world. Although the medium is new and its possibilities still need to be defined, immersive technologies are being applied in different industries. They are already used to entertain, inform and educate in video conferencing, language learning, e-commerce, architecture, in the medical field, filmmaking and video games. It is most probable that immersive technologies will permeate into other industries in the next years. The new technologies should provide accessible contents and accessible platforms to cater for the needs of multiple audiences. The aim of this presentation is to discuss the results of a focus group conducted in Barcelona both with professional audio describers and audio description end users. The study was conducted as part of my PhD in order to understand the user needs and expectations on how to implement audio description and audio subtitles in 360o media. This presentation will begin with an overview of immersive technologies. Then, previous work in the field of audio description and immersive environments will be discussed. Finally, the results of the focus group conducted in Barcelona in the early stages of the project will be reported.
EXPLORING COMPLEX FITNESS SURFACES: MULTIPLE ORNAMENTATION AND POLYMORPHISM IN MALE GUPPIES

Abstract. — The sexual ornamentation used by male guppies to attract females comprises many components, each of which varies considerably among males. Although natural and sexual selection have been shown to contribute to divergence among populations in male sexual ornaments, the role of sexual selection in maintaining polymorphism within populations is less clear. We used both parametric quadratic regression and nonparametric projection pursuit regression techniques to reveal the major axes of non‐linear sexual selection on male ornaments. We visualized the fitness surfaces defined by these axes using thin‐plate splines to allow a direct comparison of the two methodologies. Identification of the major axes of selection and their visualization was critical in determining the form and strength of nonlinear selection. Both types of analysis revealed fitness surfaces comprising three peaks, suggesting that there is more than one way to make an attractive guppy. Disruptive selection may be an important process underlying the presence of multiple sexual ornaments and may contribute to the maintenance of the high levels of polymorphism in male sexual ornaments found in guppy populations.
Data-driven classification of spectral profiles reveals brain region-specific plasticity

The human brain exhibits rhythms that are characteristic for anatomical areas and presumably involved in diverse perceptual and cognitive processes. Visual deprivation results in behavioral adaptation and cortical reorganization, particularly affecting sensory cortices. Whether these plasticity-related changes are accompanied by altered spectral properties of neural signals and whether certain brain areas are particularly targeted by these changes is unknown. With a recently introduced approach, we analyzed MEG resting state data of a group of congenitally blind and matched sighted individuals. First, using clustering procedures (k-means and Gaussian Mixture Models) we identified brain region-specific spectral clusters. Second, a classifier was employed to test the specificity of the spectral profiles within and the differences between groups. We replicated the previously reported finding of area-specific spectral profiles, indicated by high classification performance in the sighted. Additionally, we found high classification performance in the blind, suggesting that after deprivation-related restructuring, area-specific spectral profiles can be consistently identified. Crucially, in the cross-group classification (sighted vs. blind), several sensory (visual and auditory) and right frontal brain areas were classified significantly worse compared to the control condition. Overall the spectral profiles of those brain areas showed increased neuronal power in higher frequency-bands, possibly reflecting acceleration of the regionally prevalent brain rhythms in the blind compared to the sighted. We provide evidence that visual deprivation-related plasticity selectively alters the spectral profiles of right frontal and sensory brain areas, possibly reflecting increased temporal processing capabilities (auditory, frontal cortices) and changes in the visual inhibitory-excitatory circuits in the blind.
Human T-cell malignancies: Correlative clinical, histopathologic, immunologic, and cytochemical analysis of 23 cases.

Abstract Twenty-three T-cell neoplasms were investigated for their reactivity with the OKT monoclonal antibodies and expression of certain cytochemical markers. Fourteen neoplasms with diverse histopathologic features, T-cell chronic lymphocytic leukemia, mycosis fungoides, the Sezary syndrome, T-immunoblastic sarcoma, and a pleomorphic large-cell lymphoma, expressed the T helper cell phenotype, OKT3+T4+. Nine other neoplasms displayed marked inter- and intra- tumor heterogeneity. Seven of these cases, lymphoblastic lymphoma, T-cell acute lymphoblastic leukemia, and tumors with feature of T-immunoblastic sarcoma or the multilobated lymphoma of Pinkus, expressed intrathymic phenotypes. The other 2 cases, a lymphoblastic lymphoma and a so-called Lennert's lymphoma, expressed the previously undescribed OKT3+T10+ phenotype. These studies demonstrate that the T-cell malignancies are divisible into phenotypes corresponding to normal maturational stages of T-cell differentiation and functionally distinct T-cell subsets. Such studies should provide a basis for understanding the biologic heterogeneity, clinical diversity, and significance of the variable cytomorphologic characteristics of T-cell malignant tumors and assist in the further delineation of normal human T-cell heterogeneity.
OXIDATIVE STRESS AND DIABETES: AN OVERVIEW

Oxidative stress is well known to be involved in the pathogenesis of lifestyle related diseases. Oxidative stress contributes to many pathological conditions including cancer, asthma, atherosclerosis, hypertension, and diabetes. It is a state in which oxidation exceeds the antioxidant systems in the body secondary to a loss of balance between them. Reactive oxygen species (ROS) are produced from molecular oxygen as a result of normal cellular metabolism and environmental factors such as air pollutants, cigarette smoke and sedentary lifestyle. ROS are highly reactive molecules that can damage carbohydrates, nucleic acids, lipids and proteins. There is considerable evidence that induction of oxidative stress is a key process in the onset of diabetes. Lipid peroxidation owing to free radical activity plays an important role in complications of diabetes. Increased levels of lipid peroxidation are a consequence of free radical activity in both Type 1 and Type 2 diabetes. The human body has several mechanisms to counter the effects of these reactive species by the production of antioxidant enzymes like glutathione and catalase. Antioxidants can also be taken exogenously through the diet. In this review article, we summarize the effect of oxidative stress in the development of diabetes. Keywords: Diabetes, Oxidative stress, Lipid peroxidation, Reactive oxygen species.
Parallel Volume Rendering Algorithm of Volume Mineralization Model based on GPU

Volume rendering is an important technique in data visualization, but it has some problems, such as low sampling efficiency and slow drawing speed. This is especially true for the model of body visualization calculation, where the scene rendering speed is too large and the throughput rate is too high. To improve the drawing speed of the volume rendering algorithm, a method of parallel GPU computing is proposed to optimize the algorithm. A parallel volume rendering algorithm based on GPU is implemented to improve the rendering speed, and the experimental results show that the algorithm can improve the speed without affecting the image quality
Analyzing Android Code Graphs against Code Obfuscation and App Hiding Techniques

Abstract Malware creators have been very innovative when it comes to creating versions of existing malware to evade detection by anti-malware tools. Obfuscation has been the all-time favorite weapon used in malwares to hide the malicious code from being detected by analysts. These techniques are becoming more complex and sophisticated, and this motivated us to analyze Android code graphs against obfuscation. In this paper, we provide a framework for evaluating code graphs extracted from Android apps against code obfuscation. We have broadened the criteria for evaluation by employing three state-of-the-art obfuscation tools as well as app hiding techniques dedicated to the Android platform. The experimental results show that code graphs can strongly confront single level obfuscation but are vulnerable to multi-level obfuscations. Another finding is that code graphs can detect various techniques of hiding malicious app within a benign app. We also compare the code graph extraction and evaluation methodology with Androguard. This study also serves as a detailed framework for the rigorous evaluation of graph-based features against obfuscation.
Operator Workload Reduced in Unmanned Aerial Vehicles: Making Command and Control (C2) Maps More Useful

Command and control (C2) maps in military unmanned aerial vehicles (UAVs) are often cluttered beyond the needs of operators. Unfortunately, information overload increases the operators’ mental effort and mission performance suffers. To make C2 maps more useful and improve operator performance, this study proposes a triangular approach to highlighting mission-critical information. First, the underlying value of map information and its relevance to mission success are examined. Second, algorithms based on machine learning are developed to facilitate information integration and generate visualization items, via tagging in time and space, where the appropriate area of relevance for each item is defined. Third, the algorithms are improved to dynamically update the visualizations. The proposed approach and developed algorithms are being evaluated based on four experiments with professional operators in simulated UAV and C2 environments. Hopefully, it would be possible to generalize the algorithms developed in this research-in-progress to other spatial and temporal domains where workload must be reduced.
Biodiversity amongst cultivable polycyclic aromatic hydrocarbon-transforming bacteria isolated from an abandoned industrial site

The characterisation of a microbial community of a polycyclic aromatic hydrocarbons (PAHs) contaminated site (formerly Carbochimica, Trento, Italy) was carried out. A preliminary evaluation of the heterogeneity and the metabolic activity of the microbial community were attempted by denaturing gradient gel electrophoresis (DGGE) and reverse transcriptase-denaturing gel electrophoresis (RT-DGGE). The presence of a heterogeneous and metabolically active microbial community was found. To evaluate the PAH-transforming potential of the soil bacterial community, enrichment cultures were set up. Taxonomically diverse bacteria, showing different biochemical PAH-transforming pathways were obtained. Some of the isolates showed not nah-homologous PAH-transforming genotypes.
Improvement in Carbonization Efficiency of Cellulosic Fibres Using Silylated Acetylene and Alkoxysilanes

Comparative studies of the structure and thermal behavior of cellulose and composite precursors with additives of silyl-substituted acetylene and alkoxysilanes were carried out. It is shown that the introduction of silicon-containing additives into the cellulose matrix influenced the thermal behavior of the composite fibers and the carbon yield after carbonization. Comparison of the activation energies of the thermal decomposition reaction renders it possible to determine the type of additive and its concentration, which reduces the energy necessary for pyrolysis. It is shown that the C/O ratio in the additive and the presence of the Si–C bond affected the activation energy and the temperature of the beginning and the end of the pyrolysis reaction.
ATTRACTED EFFECTS OF THE SUBSTANCES EXTRACTED FROM POPUTUS CANADENSIS MOENCH ON HELICOVERPA ARMIGERA

In this paper , the attracted effects on moths of Helicoverpa armigera were reported in the field and lab. The results showed that, 4.17 moths of Helicoverpa armigera were attracted by the Populus canadensis Moench per branches every day.The ratio of female and male attracted was 1.72. While the synthetic sex pheromones added in branches,5.5 moths were attracted,and the ratio was 1.86. The attracted efficiency of substances extracted from fresh leaves on female and not mated Helicoverpa armigera moths was 26.67%,while the attracted efficiency of substances extracted from leaves piled for two days was 44.44%.
A Model for the Optical/IR Emission from Magnetars

A number of Anomalous X-ray Pulsars (AXPs) have recently been detected in the optical/IR wavelengths. We use their inferred brightness to place general constraints on any model for this emission within the magnetar framework. We find that neutron-star surface emission cannot account for the observations and that the emission must be magnetospheric in origin. We propose a model for the optical/IR emission in which a distribution of energetic electrons in the neutron-star magnetosphere emits synchrotron radiation. This model can naturally reproduce the observed brightness and the rising spectra of AXPs as well as the observed pulsations at the stellar spin frequency and the correlation of the IR flux with their bursting activity.
Real-time video object detection and classification using hybrid texture feature extraction

ABSTRACT In video processing, feature extraction and classification are necessary steps to classify the video frames. To improve accuracy, an efficient texture-based feature extraction is required. Also, before improving the feature extraction, the background subtraction step is almost equal to the ground truth level. An efficient real-time video object recognition and classification utilizing hybrid texture feature extraction are proposed. A stationary wavelet transform-based joint bilateral filtering is used to remove the noise. An effective background subtraction is employed before extracting the features. In feature extraction, hybrid texture feature extraction is proposed where gray level co-occurrence matrix (GLCM) and discrete wavelet transform (DWT) features are combined and applied to the categorization process. The GLCM is used to extract the second-order statistical texture features for the estimation of motion in the videos. And, the DWT algorithm reduces the storage space in real-time video processing. The main objectives are to implement the real-time video object detection and classification using the hybrid texture feature extraction method called the GLCM-DWT technique, and investigate the performance evaluation of the proposed methodology and compare with the existing technique. The parameters, namely accuracy, sensitivity, specificity, and execution time are to be evaluated for the proposed algorithm in the MATLAB software.
Privacy Preserving Data Publishing

Recent years have witnessed increasing interest among researchers in protecting individual privacy in the big data era, involving social media, genomics, and Internet of Things. Recent studies have revealed numerous privacy threats and privacy protection methodologies, that vary across a broad range of applications. To date, however, there exists no powerful methodologies in addressing challenges from: high-dimension data, high-correlation data and powerful attackers. In this dissertation, two critical problems will be investigated: the prospects and some challenges for elucidating the attack capabilities of attackers in mining individuals private information; and methodologies that can be used to protect against such inference attacks, while guaranteeing significant data utility. First, this dissertation has proposed a series of works regarding inference attacks laying emphasis on protecting against powerful adversaries with auxiliary information. In the context of genomic data, data dimensions and computation feasibility is highly challenging in conducting data analysis. This dissertation proved that the proposed attack can effectively infer the values of the unknown SNPs and traits in linear complexity, which dramatically improve the computation cost compared with traditional methods with exponential computation cost. Second, putting differential privacy guarantee into high-dimension and high-correlation data remains a challenging problem, due to high-sensitivity, output scalability and signal-tonoise ratio. Consider there are tens-of-millions of genomes in a human DNA, it is infeasible for traditional methods to introduce noise to sanitize genomic data. This dissertation has proposed a series of works and demonstrated that the proposed differentially private method satisfies differential privacy; moreover, data utility is improved compared with the states of the arts by largely lowering data sensitivity. Third, putting privacy guarantee into social data publishing remains a challenging problem, due to tradeoff requirements between data privacy and utility. This dissertation has proposed a series of works and demonstrated that the proposed methods can effectively realize privacy-utility tradeoff in data publishing. Finally, two future research topics are proposed. The first topic is about Privacy Preserving Data Collection and Processing for Internet of Things. The second topic is to study Privacy Preserving Big Data Aggregation. They are motivated by the newly proposed data mining, artificial intelligence and cybersecurity methods. INDEX WORDS: Inference Attack, Data Sanitization, Differential Privacy, SNPTrait Association, Belief Propagation, Probabilistic Graphical Model PRIVACY PRESERVING DATA PUBLISHING
Twitter Sentiment Analysis and Visualization Using Apache Spark and Elasticsearch

Sentiment analysis on Twitter data has paying more attention recently. The system’s key feature, is the immediate communication with other users in an easy, fast way and user-friendly too. Sentiment analysis is the process of identifying and classifying opinions or sentiments expressed in source text. There is a huge volume of data present in the web for internet users and a lot of data is generated per second due to the growth and advancement of web technology. Nowadays, Internet has become best platform to share everyone's opinion, to exchange ideas and to learn online. People are using social network sites like facebook, twitter and it has gained more popularity among them to share their views and pass messages about some topics around the world. As tweets, notices and blog entries, the online networking is producing a tremendous measure of conclusion rich information. This client produced assumption examination information is extremely helpful in knowing the supposition of the general population swarm. At the point when contrasted with general supposition investigation the twitter assumption examination is much troublesome because of its slang words and incorrect spellings. Twitter permits 140 as the most extreme cutoff of characters per message. The two procedures that are mostly utilized for content examination is information base approach and machine learning approach. In this paper, we investigated the twitter created posts utilizing Machine Learning approach. Performing assumption examination in a particular area, is to distinguish the impact of space data in notion grouping. we ordered the tweets as constructive, pessimistic and separate diverse people groups' data about that specific space. In this paper, we developed a novel method for sentiment learning using the Spark coreNLP framework. Our method exploits the hashtags and emoticons inside a tweet, as sentiment labels, and proceeds to a classification procedure of diverse sentiment types in a parallel and distributed manner.
Analytic Extensions to the Data Model for Management Analytics and Decision Support in the Big Data Environment

Analytic Extensions to the Data Model for Management Analytics and Decision Support in the Big Data Environment by Nsikak E. Akpakpan MBA, University of Illinois at Chicago, 1996 MPH, University of Illinois at Chicago, 1996 DA, University of Calabar Teaching Hospital, 1989 MB, BS, University of Nigeria, 1985 Dissertation Submitted in Partial Fulfillment of the Requirements for the Degree of Doctor of Philosophy Applied Management and Decision Sciences
Design of Shape Memory Alloy-Based Soft Wearable Robot for Assisting Wrist Motion

In this paper, we propose a shape memory alloy (SMA)-based wearable robot that assists the wrist motion for patients who have difficulties in manipulating the lower arm. Since SMA shows high contraction strain when it is designed as a form of coil spring shape, the proposed muscle-like actuator was designed after optimizing the spring parameters. The fabricated actuator shows a maximum force of 10 N and a maximum contraction ratio of 40%. The SMA-based wearable robot, named soft wrist assist (SWA), assists 2 degrees of freedom (DOF) wrist motions. In addition, the robot is totally flexible and weighs 151g for the wearable parts. A maximum torque of 1.32 Nm was measured for wrist flexion, and a torque of larger than 0.5 Nm was measured for the other motions. The robot showed the average range of motion (ROM) with 33.8, 30.4, 15.4, and 21.4 degrees for flexion, extension, ulnar, and radial deviation, respectively. Thanks to the soft feature of the SWA, time cost for wearing the device is shorter than 2 min as was also the case for patients when putting it on by themselves. From the experimental results, the SWA is expected to support wrist motion for diverse activities of daily living (ADL) routinely for patients.