Miracle-dz/UniFAR-FTUs huggingface dataset:
Improvement in Convolutional Neural Network for CIFAR-10 Dataset Image Classification

Image classification requires the generation of features capable of detecting image patterns informative of group identity. The objective of this study was to classify images from the public CIFAR10 image dataset by leveraging combinations of disparate image feature sources from deep learning approaches. The majority of regular convolutional neural networks (CNN) are based on the same structure: modification of convolution and the process of max-pooling layers connected with a number of entirely linked layers. In this paper, the prime objective is to improve the effectiveness of simple convolutional neural network models. The Artificial Neural Network (ANN) algorithm is applied on a Canadian Institute For Advanced Research dataset (CIFAR-10) using two different CNN structures. The result of the improved model achieves 88% classification accuracy rate by running for 10 hours. The deep learning models are implemented with the use of Keras library available for Python programming language.
Anticonvulsant activity of (+)‐5‐methyl‐10, 11‐dihydro‐5H‐dibenzo[a, d]cyclohepten‐5, 10‐imine (MK‐801), a substance with potent anticonvulsant, central sympathomimetic, and apparent anxiolytic properties

MK‐801 prevented tonic extensor seizures in the rat induced by bicuclline with the ED50 being 23 μg/kg p.o. Clonazepam, phenobarbital, diazepam, phenytoin, γ‐acetylenic GABA, sodium valproate, and trimethadione were all less potent. In mice, MK‐801 was likewise the most potent (ED50 = 0.35 mg/kg p.o.) compound in protecting against tonic seizures induced by electroshock. Clonazepam (ED50 = 0.41 mg/kg p.o.) and MK‐801 (ED50 = 0.67 mg/kg p.o.) were by far more potent than any of the other anticonvulsants tested versus bicuculline‐elicited seizures in mice. Besides being a potent anticonvulsant, MK‐801 demonstrated selectivity, since much higher doses were required in mice to block clonic convulsions produced by pentylenetetrazol (ED50 = 11 mg/kg p.o.) and tonic seizures caused by strychnine (ED50 > 15 mg/kg p.o.) than were needed against electroshock or bicuculline.
Review of the research on the effects of exercising on insulin resisting the AMPK/SIRT1/PGC-1α axis in skeletal muscle

Silent information regulation factor 2 related enzyme I(SIRT1) is a coenzyme NAD+ dependent deacetylase,not only related to aging,dying and DNA repairing,but also correlative with cellular metabolic transformation in different nutrition stress states.SIRT1 and AMPK are energy receptors in cells,regulating the activity of PGC-1α by means of phosphorylation and deacetylation.The regulation of the activity of the AMPK/SIRT1/PGC-1α axis directly affects the expression of its downstream genes related,being closely related to the occurrence and development of insulin resistance.
Arabic Opinion Mining Using Combined CNN - LSTM Models

: In the last few years, Sentiment Analysis regarding customers' reviews in order to comprehend the opinion polarity on social media has received considerable attention. However, the improvement of deep learning for sentiment analysis relating to customer reviews in Arabic language has received less attention. In fact, many users post and jot down their reviews in Arabic daily, so we ought to shed more light on Arabic sentiment analysis. Most likely all previous work depends on conventional classification techniques, such as KNN, Naïve Bayes (NB), etc. But in this work, we implement two deep learning models: Long Short Term Memory (LSTM) and Convolution Neural Networks (CNN), in addition to three traditional techniques: Naïve Bayes, K-Nearest Neighbor (KNN), Decision trees for sentiment analysis and compared the experimental results. Also, we offer a combined model from CNN and Recurrent Neural Network (RNN) architecture where this model collects local features through CNN as the input for RNN for Arabic sentiment analysis of short texts. An appropriate data preparation has been conducted for each utilized dataset. Our Conducted experiments for each dataset against traditional machine learning classifier; KNN, NB, and decision trees and regular deep learning models; CNN and LSTM, has resulted in impressive performance using our proposed combined (CNN-LSTM) model with an average accuracy of 85,83%, 86,88% for HTL and LABR datasets respectively
Proposed definition and classification of cerebral palsy, April 2005

INTRODUCTION Executive Committee for the Definition of Cerebral Palsy Martin Bax DM FRCP, Co-chairman, Emeritus Reader in Child Health, Division of Paediatrics, Obstetrics and Gynaecology, Imperial College, London, UK. Murray Goldstein* DO, Co-chairman, Medical Director, United Cerebral Palsy Research & Educational Foundation, Washington DC, USA. Peter Rosenbaum MD, CanChild Centre for Childhood Disability Research, Hamilton, Ontario, Canada. Alan Leviton MD, Neuroepidemiology Unit, Children’s Hospital, Carnegie, Boston, MA; Nigel Paneth MD, Department of Epidemiology, Michigan State University, East Lansing, MI, USA.
Study on the Training Mode of Network Security Practical Talent in Vocational and Technical College Based on Gamification and Leveling up

In recent years, with the advancement of information and intelligence, the importance of network information security has become increasingly prominent, and the shortage of network security personnel has become increasingly serious. Vocational and Technical colleges mainly train practical network security talents, but network security requires a solid foundation of IT knowledge, a wide range of knowledge, part of the course learning boring, students have no motivation to learn. Therefore, Information Security and Management teaching and research section of Wenzhou Polytechnic company with the related security vendor, explore a training Mode of network Security practical talent in Vocational and Technical College based on gamification and leveling up. the basic knowledge learning task in traditional courses, the security service projects from companies and the CTF (Capture The Flag) network security competition contents are converted to corresponding grade leveling task, students can obtain corresponding experience points after complete the tasks. When students get enough experience points and meet the certain requirements, they will level up and get the skills’ badges. After the required skills badges are all obtained, the student can trade-in course credits and scores or get the corresponding enterprise certification. In the actual teaching practice, the students respond positively to the gamification and gradation learning mode inspired by the clear goal, and gets better learning effect.
Everything is imperfect: Studies on point defects in insulators

Point defects can be detrimental or absolutely necessary for the performance of a material. Due to the implications defects have on a material, defects have been widely studied over the last century. However, there are plenty of open questions that concerns the effect of point defects in materials but also properties of defects that are common across materials. This thesis consists of studies on equilibrium properties of oxygen vacancies and optical lineshapes of transitions on point defects. Using first principles calculations, general properties of oxygen vacancies in wide band gap oxides are revealed. The induced defect level of the oxygen vacancy is found to be constant in a number of materials. It is often located in the middle of the band gap and associated with a large structural distortion upon a change in charge state. Due to the large structural distortion, oxygen vacancies may play a role in luminescence quenching in oxide based phosphors and recombination processes in general. A possible recombination mechanism involving oxygen vacancies is proposed in the oxide phosphor Ce:YAG, which could explain the luminescence quenching. In connection with luminescence quenching in Ce:YAG, the origin of the phonon side bands of the optical transition is elucidated. Due to the connection between the optical lineshape and the geometry of a point defect, the optical lineshape can be used to identify point defects. The point defect emission in hexagonal boron nitride is addressed and several point defects can be matched to observed emission lines.
Application of Biogeography-Based Optimization to Antennas and Wireless Communications

The purpose of this chapter is to briefly describe the BBO algorithm and present its application to antenna and wireless communications design problems. This chapter presents results from design cases that include patch antenna, linear antenna array, and a partial transmit sequence (PTS) scheme for OFDM signals based on BBO. The chapter is supported with an adequate number of references. This chapter is subdivided into five sections. The “background” section presents the issues, problems, and trends with BBO. Then the authors briefly present the main BBO algorithm. In the next section, they describe the design cases and present the numerical results. An outline of future research directions is provided in the following section while in the “conclusion” section the authors conclude the chapter and discuss the advantages of using a BBO-based approach in the design and optimization of wireless systems and antennas. Finally, an “additional reading section” gives a list of readings to provide the interested reader with useful sources in the field.
Cost Aware Feature Elicitation

Motivated by clinical tasks where acquiring certain features such as FMRI or blood tests can be expensive, we address the problem of test-time elicitation of features. We formulate the problem of cost-aware feature elicitation as an optimization problem with trade-off between performance and feature acquisition cost. Our experiments on three real-world medical tasks demonstrate the efficacy and effectiveness of our proposed approach in minimizing costs and maximizing performance.
Notes on Hazard-Free Boolean Circuits

The problem of constructing hazard-free Boolean circuits (those avoiding electronic glitches) dates back to the 1940s and is an important problem in circuit design and even in cybersecurity. We show that a DeMorgan circuit is hazard-free if and only if the circuit produces (purely syntactically) all prime implicants as well as all prime implicates of the Boolean function it computes. This extends to arbitrary DeMorgan circuits a classical result of Eichelberger [ IBM J. Res. Develop. , 9 (1965)] showing this property for special depth-two circuits. We also show that the hazard-free circuit complexity of any Boolean function f p x q is lower-bounded by the monotone circuit complexity of the monotone Boolean function which accepts an input x iﬀ f p z q “ 1 for some vector z ď x . This interesting reduction to monotone circuits was recently proved by Ikenmeyer et al. [ J. ACM , 66:4 (2019), Article 25]; our contribution to it is an amazing simplicity of the short and direct proof. Finally, we show that hazard-free circuit complexity of a very simple (non-monotone) Boolean function is super-polynomially larger than its unrestricted circuit complexity. This function accepts a Boolean n ˆ n matrix iﬀ every row and every column has exactly one 1-entry.
FCA-based Approach for Interactive Query Refinement with IR-chatbots

Information retrieval (IR) chatbot is a special class of virtual assistants, which is widely used nowadays in customer support services. However, the work of modern IR retrieval systems is limited by simple queries to the database, which does not utilize all the potential of interaction with the user. In this paper we implement an FCA-based approach to deliver the relevant information the user has requested. A developing approach integrates a concept-based model build upon the database and intelligent traversal through it. The proposed algorithm has been implemented as an additional function within the existing IR chatbot. In this paper we also enlighten the perspectives for further development of the proposed system. Formal Concept Analysis (FCA) technique and Pattern Structures as its extension are proposed to process unstructured data (objects with a text description), which has become a common way of presenting various items recently.
Role of plant growth promoting rhizobacteria in sustainable agriculture: A review

The use of external chemical inputs such as chemical fertilizers and pesticides undoubtedly resulted in huge increase in agricultural products in the past many decades in developing countries like India. Such indiscriminate use of agrochemicals has resulted not only in the deterioration of soil health but also has led to some major environmental disasters and other health related problems, besides increasing the input cost for crop production especially on the marginal farmers. So, the use of plant growth-promoting rhizobacteria (PGPR) as biofertilizers and/or as biocontrol agents to enhance plant growth and yield, suppress diseases in a wide range of agricultural crops and improve the socio-economic status of poor farmers is gaining momentum. The present review highlights the role of plant growth promoting rhizobacteria in the process of crop production and health, development of sustainable agriculture and their commercialization with global applicability.
Turbulence Decay Corrections for Transitional Flow Calculations

Transitional flow calculations are becoming increasingly common due to the emergence of applications operating at low Reynolds numbers and the appearance of mathematical models focused on modeling transition. From a physical perspective, transition is a complex phenomenon, non-linear and unsteady, in which flow disturbances from the freestream cause the laminar flow in the boundary layer to become unstable and transition to turbulent flow. As a result, the exact location for the transition region is dependent on the characteristics of the freestream flow and its disturbances. From the numerical standpoint, this sensitivity to the freestream conditions is obtained from inlet boundary conditions for turbulence. In calculations using the Reynolds-Averaged Navier-Stokes (RANS) equations, when transition models are not employed, transition is handled by the underlying turbulence model. This leads to transition occurring at too low Reynolds numbers, which originates turbulent flow close to the leading edge of a body, regardless of the specified turbulence quantities at the inlet. However, when transition modelling is desired, the inlet turbulence quantities have a strong influence on the transition location as shown by Eça et al. (2016). Despite being physically expected, this influence causes difficulties, since the specification of these values becomes a challenge as little information about turbulence is known in order to determine both variables (in the case of two-equation models). Additionally, common two-equation eddy-viscosity models such as the k − and k −ω turbulence models predict a very strong decay of the turbulence variables in the freestream, which is related to the value of the eddy-viscosity at the inlet according to Spalart and Rumsey (2007). Thus it is common to observe that calculations with transition modelling are accompanied by very high values of νt ν in order to maintain a ’reasonable’ decay of the turbulence intensity. This means that for practical applications, one must not only know the correct value for the turbulence intensity, but also has to estimate the eddy-viscosity value that will result in the correct evolution of the turbulence intensity along the flow. This situation severely hinders the predictive capability of transition models (Li et al. (2019)), and often results in awkward values for the eddy-viscosity at the inlet, which may become physically questionable. In this paper, we explore an alternative technique to control the decay of turbulence kinetic energy in the freestream that modifies dissipation in the k and ω transport equations. This modification is calibrated for the flow around a flat-plate and then subsequently tested on the flow around the NACA 0012 airfoil. The mathematical formulation is described in section 2. The test cases and numerical settings are described in sections 3 and 4 while the results are presented and discussed in section 5. Section 6 summarizes the conclusions of this work.
Association between XRCC1,DNA-PKcs,and PCNA and radiotherapeutic effect in non-small cell lung cancer

Objective To investigate expressions of XRCC1,DNA-PKcs and PCNA proteins in non-small cell lung cancer(NSCLC)and its relationship to the effect of radiotherapy.Furthermore,to analyze the predict value of radiotherapy effect.Methods There were 62 patients with NSCLC who were treated by 3D-radiotherapy.Before and after the radiotherpy,they were checked by chest enhanced-CT to evaluate the short-term effect.The expression of XRCC1,DNA-PKcs and PCNA in 62 patients with NSCLC were determined by immunohistochemical method.The expression levels of the three kinds of proteins with the short-term radiotherapy effect were analyzed.Results The expressions of XRCC1,DNA-PKcs and PCNA were closely related with short-term effect after radiotherapy in NSCLC.The short-term effect was descent along with enhancement of XRCC1 and DNA-PKcs,and was rised along with enhancement of PCNA.Conclusion These findings indicated that the expressions of XRCC1,DNA-PKcs and PCNA may predict short-term effect of radiotherapy.The three proteins may have relationship with radiotherapeutic sensitivity in some extent.
The Impact of Exchange Rate on Export in Nepal: The ARDL Bounds Test Approach

The main objective of this study is to examine the potential impact of exchange rate on exports in Nepalese context by using annual time series data over the period from 1974 to 2011. This particular study incorporates real interest rate, investment and inflation rate as control variables. This study empirically investigates the impact of real exchange rate over exports in Nepal by applying the Autoregressive Distributed Lag approach (ARDL). In part of checking the unit root properties of the time series data, the variables were diagnosed with the Augmented Dickey-Fuller (ADF) and Philips Perron (PP) test. Results obtained from the study show that exchange rate has a significant positive relationship with exports in Nepal.
Designing a Real-Time Data-Driven Customer Churn Risk Indicator for Subscription Commerce

: One of the main goals of customer relationship management is to reduce or eliminate “customer churn”, i.e. loss of existing customers. This paper introduces a prototype algorithm to estimate a continuously updated indicator of the probability of an existing customer to cease purchasing from a subscription commerce business. The investigation is focused on the case of repeat consumers of subscription commerce products which require regular replacement or replenishment. The motivation is to help marketers to make targeted proactive retention actions by categorizing regular customers into groups of similar estimated churn risk. The proposed algorithm re-computes the probability of churn for each customer at regular intervals using past purchase transaction data and incorporating subscription-based business logic. We describe the detailed process from data collection and feature engineering to the algorithm’s design. We also present evaluation results of the algorithm’s performance based on a pilot test that took place on a consumables e-commerce business. The results suggest a significant capability of the proposed algorithm in capturing the purchasing intentions of repeat customers, regardless of the risk group they belong to.
Use of Remote Sensing and Soils database for Sustainable Management of Irrigation Water in Desert Landforms

The scarceness of water resources in dry regions imposes considerable attention in both cropping pattern and irrigation management. The main objective of this study is to use the thermal band of the Landsat ETM+ and the soil database for setting a sustainable management of irrigation in the desert landforms. A newly reclaimed area located to the west of the Nile Delta was selected for this study. A Landsat ETM+ image acquired during the year of 2009 and a Shuttle Radar Topography Mission (SRTM) data were processed using ENVI 4.7 software to identify the main landforms in the area. The recognized landforms comprised; an old deltaic plain, Aeolian plain and depression with alluvial deposits. Twenty four soil profiles and thirty six observation points were undertaken to represent the different mapped units. The morphological description of the soil profiles is carried out and representative disturbed soil samples are collected and analyzed. A GIS based soil database is created using both the landform map and the results from the soil analysis. The irrigation management is discussed with respect to the soils properties. The crop evapotranspiration (ET), of the main crops in the area under investigation, was estimated from Landsat ETM+ thermal band using the surface energy balance algorithm for land (SEBAL). Results indicate that the calculated ET of all crops varies from one landform to another; the highest crop ET values are associated with the high terraces, eroded terraces, gently undulating, and almost flat alluvial deposits. Considering the crop ET and soil properties, two cropping patterns are identified. Wheat, Beans, Sugar beet and Groundnut pattern is proposed for the winter season while, Maize, Sunflower, Tomato and Sesame, for the summer.
Structural Model of Job Satisfaction in Agricultural Research, Education and Extension Organization (AREEO) Staff Based on Herzberg Motivational Theory

Jamshid Abdi, Sahar Dehyouri*, Azita Zand, Masoomeh Arfaee Agricultural Department, Islamshahr Branch, Islamic Azad University, Islamshahr, Iran Environmental Science Research Center, Agricultural Department, Islamshahr Branch, Islamic Azad University, Islamshahr, Iran 3 Agricultural Departments, Ashtiyan Branch, Islamic Azad University, Ashtiyan, Iran *Corresponding Author Email: Dehyouri.s@gmail.com, Cell phone: 09123009169
Improved multi-objective particle swarm optimization algorithm that can give “ ideal ” solution

: One problem of using multi-objective particle swarm optimization algorithm(MOPSO) is how to ﬁnd a recommendable solution within the non-inferior solution set for decision-makers. Therefore, the technique for order preference by similarity to ideal solution(TOPSIS) strategy is introduced into the MOPSO algorithm. In order to improve the accuracy and uniformity of solutions, two other strategies are also proposed: the mutation operator according to Pbest, and the modiﬁed method to calculate 𝑘 neighbor distance. The results show that the solution uniformity of the algorithm which chooses Gbest using the TOPSIS method only is not satisﬁed, while the algorithm that uses all improvement strategies has better performance. An ideal solution can be found for decision-makers according to the TOPSIS strategy.