Miracle-dz/UniFAR-FTUs huggingface dataset:
Effect of heat treatment and heat treatment in combination with lignosulfonate on in situ rumen degradability of canola cake crude protein, lysine, and methionine

Abstract: The aim of this study was to determine the effect of heat treatment alone or in combination with the addition of lignosulfonate (LSO3) on canola cake protein, lysine, and methionine degradation in the rumen. Cold-pressed canola cake was left untreated, heated at 90, 110, 130, or 150 °C, or processed with 5% of LSO3 (in dry matter) and then heated. Effective rumen degradability of crude protein (CP), lysine, and methionine was less for treated than untreated canola cake (P < 0.05) and decreased with increased temperature of heating, but particularly when canola cake was heated at 150 °C (quadratic, P < 0.01). In general, effective rumen degradability of CP, lysine, and methionine was less for canola cake heated at 130 °C in combination with LSO3 compared with canola cake heat treated only (quadratic × LSO3 interaction, P ≤ 0.07). Results of this study indicate that high temperature heating (130 °C or greater for 60 min) may be necessary to protect canola cake protein from degradation in the rumen, and the combination of heat treatment and LSO3 may be more effective in protecting canola cake protein, lysine, and methionine from degradation in the rumen than the use of heat treatment only.
Laser Cladding Height Prediction Based on Neural Network

Real-time detection and closed-loop control of laser cladding height is necessary for forming high quality parts. Technological parameters are coupled and the forming process is a non-linear process. A large number of laser parameters affect the quality of the laser cladding surface. Based on the analysis of the influence of laser parameters on cladding height, the BP (Back propagation) neural network prediction model of cladding height is build. The neural network arithmetic is designed and the samples are acquired by laser forming experiment. The training samples are used to train the network to accomplish the mapping relation between input and output of the network. The test samples are used to verify the performance of the trained network. Simulation results indicate that the prediction model has sufficient accuracy. The BP neural network prediction model of cladding height is feasible and valid in theory and in practice. The laser cladding height BP neural network prediction model lays the foundation for real-time height prediction and closed-loop control in laser forming process, and it has great significance for improving the quality of formed parts.
Uncertainty, budget deficit and economic growth in OPEC member countries

ABSTRACT In this research, the effect of uncertainty and the government budget deficit on economic growth has been investigated in 12 OPEC member countries from 1997 to 2013. Given the structure of OPEC member countries, production uncertainty and oil revenue uncertainty were introduced as the proxy for the uncertainty variable, and the PP-GARCH method was used to estimate the two mentioned uncertainties. To investigate the relationship between research variables, the panel ARDL model was used based on two methods of PMG and MG for without and with uncertainty situations. The results of the research showed that under without uncertainty conditions, the government deficit and tax revenue had a significant negative effect on the economic growth of OPEC member countries. Also, in the uncertainty scenarios, both variables of production uncertainty and oil revenue uncertainty had a significant negative effect on the economic growth of countries in both short and long run.
Indonesian News Classification Using Naïve Bayes and Two-Phase Feature Selection Model

Since the rise of WWW, information available online is growing rapidly. One of the example is Indonesian online news. Therefore, automatic text classification became very important task for information filtering. One of the major issue in text classification is its high dimensionality of feature space. Most of the features are irrelevant, noisy, and redundant, which may decline the accuracy of the system. Hence, feature selection is needed. Maximal Marginal Relevance for Feature Selection (MMR-FS) has been proven to be a good feature selection for text with many redundant features, but it has high computational complexity. In this paper, we propose a two-phased feature selection method. In the first phase, to lower the complexity of MMR-FS we utilize Information Gain first to reduce features. This reduced feature will be selected using MMR-FS in the second phase. The experiment result showed that our new method can reach the best accuracy by 86%. This new method could lower the complexity of MMR-FS but still retain its accuracy.
Progressive Joint Framework for Chinese Question Entity Discovery and Linking With Question Representations

Chinese question entity discovery and linking (QEDL) may encounter short texts and small-scale annotated datasets, which may invalidate certain machine learning algorithms. In this paper, we propose a progressive joint framework for Chinese QEDL, which leverages the mutual dependency information of these two tasks to enhance the performance with each other. The framework uses the candidate entity generation (CEG) of entity linking to iteratively augment the overall process of entity discovery that consists of mention generation, filtering and merging modules. In mention generation module, to reduce the hand-crafted effort of the rule-based entity discovery, we develop a question representation method to generate domain-independent entity discovery rules, and use CEG to check the extracted mentions in priority order. This module can embed extracted mentions into other entity discovery methods as one feature or as extra mentions to alleviate insufficiencies of annotated datasets. The mentions filtering module leverages the joint features of extracted mentions and CEG’s entities to build a voting model and filter out low-confidence mentions. Moreover, the mentions merging module merges different patterns’ mention-entity pairs and check their corresponding candidate entities with CEG. During entity linking, we incorporate the joint features of questions, extracted mentions and CEG’s entities into a ranking model for entity disambiguation. Finally, we conduct experiments on two real datasets and compare our approach with other state-of-the-art methods. The results illustrate that the proposed framework can reduce error accumulation and flexibly combine different entity discovery methods, which significantly improves the performance on small-scale datasets.
Privacy and Security Challenges and Solutions in IOT: A review

The Internet of Things (IoT) is a revolutionary concept that heavily relies on the network infrastructure to connect large number of devices whose main purpose is to collect data and communicate among one another in the bid to enhance their decision-making capabilities. However, with this great evolution comes various challenges that threatens the information technology industry; these include security and privacy issues. The presence of a large number of interconnected technologies open a grey area in the IT field which has been worsened by the lack of capacity of various institutions to identify, assess and monitor important components to ensure compliance with security policies. Thus, there is need for a clear understanding of the issues at hand and how they can be solved in order to fully harness this capability.
A Quantitative Exploration of Collaborative Pruning and Approximation Computing Towards Energy Efficient Neural Networks

Editor’s note: This work has the goal of minimizing digital neural network computation energy consumption with little loss in accuracy. The authors describe a Dynamic Network Surgery based approach to network pruning, after which weights are incrementally selected for approximate multiplication. Considering which network components are necessary and determining the needed level of accuracy for them enables greater energy savings than solving either problem independently. — Robert P. Dick, University of Michigan
The Integration of Memory and Visual Perception in the Human Medial Temporal Lobe

1 1 General Introduction 3 1.1 Traditional models of the medial temporal lobe 3 1.1.1 The medial temporal lobe memory system 3 1.1.2 Functional segregations within the MTL 5 1.2 A perceptual role of the MTL 8 1.2. 1 The effect of MTL lesions on visual discrimination 8 1.2. 2 fMRI studies of visual discrimination in the MTL 10 1.2. 3 The importance of working memory 11 1.2. 4 The role of the fornix 12 1.3 MTL and category learning 13 1.3. 1 The SUSTAIN model 13 1.3. 2 Neural correlates of category learning 14 1.4 A representational view of the MTL 15 1.5 Aims of the thesis 16 1.5. 1 Studies 17
NeuroImage: Clinical

Attention-de ﬁ cit/hyperactivity disorder (ADHD) is a highly prevalent and heterogeneous neurodevelopmental disorder, which is diagnosed using subjective symptom reports. Machine learning classi ﬁ ers have been utilized to assist in the development of neuroimaging-based biomarkers for objective diagnosis of ADHD. However, existing basic model-based studies in ADHD report suboptimal classi ﬁ cation performances and inconclusive results, mainly due to the limited ﬂ exibility for each type of basic classi ﬁ er to appropriately handle multi-dimensional source features with varying properties. This study applied ensemble learning techniques (ELTs), a meta-algorithm that combine several basic machine learning models into one predictive model in order to decrease variance, bias, or improve predictions, in multimodal neuroimaging data collected from 72 young adults, including 36 probands (18 remitters and 18 persisters of childhood ADHD) and 36 group-matched controls. All currently available optimization strategies for ELTs (i.e., voting, bagging, boosting and stacking techniques) were tested in a pool of semi ﬁ nal classi ﬁ cation results generated by seven basic classi ﬁ ers. The high-dimensional neuroimaging features for classi ﬁ cation included regional cortical gray matter (GM) thickness and surface area, GM volume of subcortical structures, volume and fractional anisotropy of major white matter ﬁ ber tracts, pair-wise regional connectivity and global/nodal topological properties of the functional brain network for cue-evoked attention process. As a result, the bagging-based ELT with the base model of support vector machine achieved the best results, with signi ﬁ cant improvement of the area under the receiver of operating characteristic curve (
ANTIOXIDANT AND HEPATOPROTECTIVE EFFECT OF Cinnamomum zeylanicum OIL AGAINST PARACETAMOL INDUCED HEPATOTOXICITY IN MALE RATE.

The main objective of this study is a trial to investigate the efficiency of cinnamomum zeylanicum (cinnamon) oil against paracetamol induced hepatotoxicity in male rats. The bark cinnamon was hydrodistilled using Clevenger,s apparatus to yield essential oil 2.3%. Total phenolic compounds and scavenging radical effect on 2, 2-diphenylpicrylhydrazyl (DPPH) were investigated. Gas chromatographic- mass spectroscopy fractionated cinnamon bark volatile oil to six components accounting for 100% of total amount. Cinnamaldehyde was found as the major component 68.01% followed by Eugenol 11.47, Cinnamyl alcohol 6.52% , α- murolene 5.24%, δ- cadinen 4.41% and Methyl eugenol 4.35%. Biological experiment was carried out for eight weeks. Results revealed that liver marker enzymes alanine transaminase (ALT) aspartate transaminase (AST), alkaline phosphatase (ALP) were increased. On the other hand serumtotal protein and serum albumin were decreased as well as, lipid profile of total lipids (TL), triglycerides (TAG), total cholesterol (TC), low density lipoprotein cholesterol (LDL-C) were increased and high density lipoprotein cholesterol (HDL-C)was decreased in paracetamol treated rats(group2). Lipid peroxidation malondialdehyde (MDA) was increased in liver tissue of rats treated with paracetamol, while activities of glutathione peroxidase(Gpx) and Glutathione-S-transferase (GST) were decreased. Cinnamon oil both dose and silymarin showed remarkable hepatoprotective and antioxidant activity against paracetamol induced hepatotoxicity as judged from the serum marker enzymes and antioxidant in liver tissue.
Influences of the Global Deterioration Scale according to Routine Blood Chemistry Results

Received July 19, 2019 Revised 1 July 30, 2019 Revised 2 August 14, 2019 Accepted August 15, 2019 Neurocognitive testing commonly uses the MMSE (Mini-Mental State Examination) to evaluate the overall cognitive function of patients at outpatient clinics, but the MMSE has recently been extensively used in the SNSB II (Seoul Neuropsychological Screening Battery II) for making diagnoses. We retrospectively investigated the results of routine neurocognitive tests and the results of the blood tests of 120 elderly patients who had been referred to a South Central Medical Center from 2017 to 2018 and who had been examined at a public health center. These subjects’ space-time capability was high on the sub-region of the global deterioration scale (GDS). GDS showed a significant increase as the Na decreased on the electrolyte analysis. The subjects’ concentration, their language-based orientation for space and time, their memory, and their scores for the frontal lobe function on GDS showed statistically significant reductions (P<0.001) For the normal and abnormal groups according to the ALT and creatinine levels, the frontal/execute function areas showed statistically significant differences (P<0.001) as well as negative correlation between GDS and ALT (P<0.01). In conclusion, this study provides basic information to develop test items that are important for patient screening and diagnosis, and several routine blood chemistry factors provide basic information for diagnosing and assessing the status and progress of cognitively impaired patients. Copyright © 2019 The Korean Society for Clinical Laboratory Science. All rights reserved.
Lightweight Deep Convolutional Neural Networks for Facial Expression Recognition

In this paper, we propose a method to reduce the number of parameters based on a pre-trained deep convolutional neural network (DNN) for a facial expression recognition (FER) task. In order to maintain the high level of accuracy of the pre-trained network and save computational resources through reducing the number of parameters, we first build and train a high accuracy DNN model and extract the layers which contain the most representative facial expression features; then, we connect these layers with lightweight depthwise separable convolutions and global max pooling layers as the re-training network. We train and compare the re-training network with different DNN architectures on two FER datasets (FER2013, AffectNet). The results demonstrate that our method retains its pre-trained high accuracy with fewer model parameters than current state-of-the-art DNN methods.
Electrochemistry

: With an objective to develop electrode materials with high specific capacitance and good stability, a completely new nanocomposite of Polypyrrole (PPY) and graphene quantum dots (GQD) was successfully obtained through in - situ polymerization of pyrrole in the presence of GQD suspension. The obtained composites with different mass ratios were characterized by X-ray diffraction(XRD), Fourier transformed infrared spectroscopy(FT-IR) and scanning electron microscopy (SEM). GQD enhanced electrochemical performance of PPY and, as supercapacitor electrodes, the PPY/GQD composites with the mass ratio of PPY to GQD at 50:1 showed a competitive specific capacitance of 485 F 窑 g -1 at a scan rate of 0.005 V 窑 s -1 . The attenuation of the specific capacitance is about 2% after 2000 cycles. The high specific capacitance and good stability of the PPY/GQD nanocomposites are promising for applications in electrochemical supercapacitors.
Comparison of Cache- and Scratch-Pad based Memory Systems with respect to Performance, Area and Energy Consumption

In this report we evaluate the options for low power on-chip memories during system design and configuration. Specifically, we compare the use of scratch pad memories with that of cache on the basis of performance, area and energy. The target architecture used in our experiments is the AT91M40400 microcontroller containing an ARM7TDMI core. A packing algorithm is used to map the memory objects of the benchmarks to the scratch pad. Area and energy for different scratch pad and cache sizes are computed using the CACTI tool while performance is derived using the trace results of the ARMulator. We observe area and performance improvements by using a scratch pad memory. For example, for bubble sort there is a performance improvement of 18% from a hardware which needs 34% less area. The scratch pad also needs less energy per access, due to the absence of tag comparison.
Recent progress in development of 2,3-diaminomaleonitrile (DAMN) based chemosensors for sensing of ionic and reactive oxygen species

2,3-Diaminomaleonitrile (DAMN) has proved to be a valuable organic π-conjugated molecule having many applications in the area of chemosensors for sensing of ionic and neutral species because of its ability to act as a building block for well-defined molecular architectures and scaffolds for preorganised arrays of functionality. In this article, we discussed the utilization of 2,3-diaminomaleonitrile (DAMN) for the design and development of chemosensor molecules and their application in the area of metal ion, anion and reactive oxygen species sensing. Along with these, we present different examples of DAMN based chemosensors for multiple ion sensing. We also discuss the ion sensing mechanism and potential uses in other related areas of research.
An Equivalent Time-Variant Storage Model to Harness EV Flexibility: Forecast and Aggregation

The demand for vehicle charging will require large investments in power distribution, transmission, and generation. However, this demand is often also flexible in time, and can be actively managed to reduce the needed investments, and to better integrate renewable electricity. Harnessing this flexibility requires forecasting and controlling electric vehicle (EV) charging at thousands of stations. This paper addresses the problem of forecasting and management of the aggregate flexible demand from tens to thousands of EV supply equipment (EVSEs). First, it presents an equivalent time-variant storage model for flexible demand at an aggregation of EVSEs. The proposed model is generalizable to different markets, and also to different flexible loads. Model parameters representing multiple EVSEs can be easily aggregated by summation, and forecasted using autoregressive models. The forecastability of uncontrolled demand and storage parameters is evaluated using data from 1341 nonresidential EVSEs located in Northern California. The median coefficient of variation is as low as 24% for the forecast of uncontrolled demand at the highest aggregation and 10–15% for the storage parameters. The benefits of aggregation and forecastability are demonstrated using an energy arbitrage scenario. Purchasing energy day ahead is less expensive than in the real-time market, but relies on a uncertain forecast of charging availability. The results show that the forecastability significantly improves for larger aggregations. This helps the aggregator make a better forecast, and decreases the cost of charging in comparison to an uncontrolled case by 60% with respect to an oracle scenario.
Soil Biological Quality Assessment to Improve Decision Support in the Wine Sector

Biodiversity is an increasingly important aspect of wine production. The assessment of agro-ecosystem biodiversity is highly complex due to the heterogeneity of the elements involved in the evaluation. For this reason, wine companies have expressed a need for a decision support system (DSS) capable of dealing with this complexity, integrating assessments referring to the whole production system within a single tool. In this study a DSS developed for wine sector biodiversity management assessment is introduced. The DSS, called BIOPASS®, is made up of different sections relating to three compartments in the winemaking process (the soil, the vine and wine). Assessment of the physical, chemical and biological components of soil is a key element of the DSS. We investigate the relationship between biological soil quality (represented by the QBS-ar index), environmental conditions and the type of farming (organic or conventional). 70 soil samples were analysed in different Italian viticultural contexts. The model highlighted the relationships between QBS-ar and meteorological variables (air temperature and precipitation) as well as a positive relationship with organic farming systems. These results provide useful information for understanding agroecosystem biodiversity and will be integrated within the DSS for assessment of soil quality.
An Accurate Method Based on Deep Learning Architectures for Real-world Object Detection

Object detection and recognition play an important role in blind navigation. However, in real-world shooting, images are degraded and deviate from the statistical distribution of academic datasets, which have a bad impact on object detection. Here, using deep neural networks and door detection as an example, we simulate problems that the blind may encounter when shooting images, such as lack of illumination in the imaging environment, relative motion with the object in exposure moment, rotation and jitter of the photographic apparatus and the like. After establishing a mathematical model of image degradation, we compare and demonstrate the impact of various image degradation on door detection. By degenerating the training set, we train a robust model that improves the average precision (AP) of the door detection in real scenes and outperforms other training methods.
Single Cell Spatial Chromatin Analysis of Fixed Immunocytochemically Identified Neuronal Cells

Assays examining the open-chromatin landscape in single cells require isolation of the nucleus, resulting in the loss of spatial/microenvironment information. Here we describe CHEX-seq (CHromatin EXposed) for identifying single-stranded open-chromatin DNA regions in paraformaldehyde-fixed single cells. CHEX-seq uses light-activated DNA probes that binds to single-stranded DNA in open chromatin. In situ laser activation of the annealed probes’ 3’-Lightning Terminator™ in selected cells permits the probe to act as a primer for in situ enzymatic copying of single-stranded DNA that is then sequenced. CHEX-seq is benchmarked with human K562 cells and its utility is demonstrated in dispersed primary mouse and human brain cells, and immunostained cells in mouse brain sections. Further, CHEX-seq queries the openness of mitochondrial DNA in single cells. Evaluation of an individual cell’s chromatin landscape in its tissue context enables “spatial chromatin analysis”. One Sentence Summary A new method, CHEX-seq (CHromatin eXposed), identifies the open-chromatin landscape in single fixed cells thereby allowing spatial chromatin analysis of selected cells in complex cellular environments.