Miracle-dz/UniFAR-FTUs huggingface dataset:
Evaluation of botanicals and synthetic insecticides against major sucking pests of brinjal and its impacts on natural enemies

The present investigation entitled, “Evaluation of botanicals and synthetic insecticides against major pests of brinjal”, was conducted during kharif 2017-18 on the field of Department of Entomology Dr. Panjabrao Deshmukh Krishi Vidhyapeeth, Akola. The effects of botanicals along with synthetic insecticides were assessed against pests of brinjal to find out the cost effective treatment. The experiment was laid out in randomized block design (RBD) with twelve treatments replicated thrice. Total five sprays of the above treatments were done at an interval of 15 days commencing first application at 30 days after transplanting. The treatment Thiamethoxam 25 WG@ 0.4 g/L followed by Imidacloprid 17.8 SL @ 0.25 ml/L, Triazophos 40 EC @ 2 ml/Land NSE (Neem Seed Extract) @7% were found significantly effective in recording lower population of sucking pests i.e. leafhopper, whitefly and mites. Deleterious effect of treatments either botanical or synthetic insecticides were not observed on predator’s viz., ladybird beetle, Chrysopids and spider on brinjal throughout the season.
Credit Card Fraud Detection using Ensemble Learning

---------------------------------------------------------------------***--------------------------------------------------------------------- Abstract – This paper elaborates on the implementation of a Credit card fraud detection system using Ensemble Learning techniques. It provides information regarding the System design, architecture, and model. Credit card frauds are increasing considerably with an increase in the number of digital transactions. Credit card frauds cause huge financial loss to companies and consumers however, there is a lack of published literature on credit card fraud detection techniques. The major contribution to this is the confidentiality of data used to work with. We decided to construct the fraud detection system using Ensemble Learning. We studied various Machine Learning algorithms such as KNN, Random Forest, and GaussianNB(Naive Bayes). In this paper, we worked with publicly available European union credit card fraud dataset.
Performance Analysis of Improved Swarm Intelligence Based Classifier for Fabric Defect Detection

Automatic defect detection in fabrics is one of the most essential systems used in the textile industry to check the quality of the fabric. In most of the existing systems, a learning based approach is implemented for defect detection in simple patterned fabrics. In this paper, swarm intelligence-based Backpropagation Neural Network (BPNN) classifiers are implemented for defect detection in complex patterned fabrics. But the problem with existing Binary Particle Swarm Optimization (BPSO) based BPNN classifier is premature convergence. To offset this problem an evolutionary state-based greedy reset is proposed to promote an effective and efficient search of the particles in the search space of the BPSO algorithm. The proposed system comprises of feature extraction phase followed by a performance evaluation phase. The combinations of features namely (i)Gray Level Co-occurrence Matrix (GLCM), (ii)Discrete Wavelet Transform (DWT) and GLCM (W-GLCM), (iii)DWT, Local Binary Pattern (LBP), and GLCM (WL-GLCM) are extracted from the complex patterned fabrics and their performances are evaluated by employing swarm intelligence-based Backpropagation Neural Network (BPNN) classifier. The proposed system is validated with fabric datasets taken from the TILDA fabric database. From the results, it is observed that proposed system classification accuracy is 99.75% and it is better than the existing work with 77% reduced features.
What Possible Use Could Consciousness Be?

Consciousness is a widely studied phenomenon, but is still poorly understood. In this very preliminary work, we consider properties of consciousness in the context of systems made up of largely uniform computational elements, such as neurons or Deep Learning units. We argue that the phenomenology of consciousness may be, in part, a consequence of this uniformity and the brain’s need to compensate for it in identifying sources of information. We performed some initial experiments to show that the resulting representations can be useful in improving downstream tasks. This utility may explain in part why consciousness has been conserved in humans.
Hospitalization for the Initiation of Ketogenic Diet for the Treatment of Intractable Seizures

A. Member has failed to respond to anti-convulsant medications (monotherapy and polytherapy) or is intolerant to anti-convulsant medications. There must have been an adequate trial of drug therapy (specifically, the correct anti-convulsant medications have been used in the correct dosage, the member has been carefully monitored for treatment effects and the member has been compliant with drug therapy for at least 1 year); and B. Member must be younger than 18 years old; and C. Since strict adherence to this dietary regimen is tantamount to its effectiveness, parents/family
Stimulatory Role of Low viscous Sodium Alginate Extracted from Turbinaria decurrens (Phaeophyta) on Immune Responses and Gene Expression in Litopenaeus vannamei

This study assessed the stimulatory role of supplementation of low viscous sodium alginate extracted from Turbinaria decurrens (Phaeophyta) on the immune response of the white shrimp Litopenaeus vannamei. The total haemocytes count, phenoloxidase activity, respiratory burst and lysozyme activity of shrimp fed with diets containing sodium alginate was significantly higher than that of control (without sodium alginate). An increase in resistance against V. parahaemolyticus was observed in shrimps under laboratory conditions. An enhanced activity of βGBP, PE, proPO, cyt-SOD, PA-5 and SWDP mRNA transcriptions was observed in shrimps when fed with sodium alginate. In addition to that, expression fold of PE, pro PO and SWDP was higher with low viscous sodium alginate when compared to higher viscous sodium alginate. Our results suggest that low viscous sodium alginate supplementation plays a significant role in the immune responses and gene expression in Litopenaeus vannamei.
Budgeted Opinion Dynamics with Varying Susceptibility to Persuasion

The Opinion Susceptibility Problem is an optimization problem in opinion dynamics. It explores the optimal way to guide the aggregate opinions in a social network to an extreme by modifying the agents’ susceptibility to persuasion. Based on the opinion dynamics model proposed by Abebe et al. [1], the L1-budgeted variant of the problem is deﬁned and analysed in this project. This variant imposes an upper bound on the sum of the absolute changes of the agents’ resistance parameters. In this project, the NP-hardness of the L1-budgeted variant is established through a reduction to the vertex cover problem for regular graphs. Moreover, by analysing the possible locations of the optimal solution, the Projected Gradient Algorithm is constructed and implemented to search for the optimum within a conﬁned search space on the feasible region boundary. The algorithm employs the Optimistic Local Search strategy, originally proposed in Chan et al. [2] to solve the unbudgeted variant of the problem, to locate a starting point. Moreover, the objective and the gradient approximations by iterative processes are used to enhance the efﬁcient of the algorithm. From the algorithm experiments, it is found that for almost all agents, the budget allocated to each agent is either zero or is adequate to reach his upper/lower bound of resistance, meaning that the optimal point found is usually a vertex of the feasible region that uses up the budget.
Model-Based Novelty Adaptation for Open-World AI

One hallmark of human cognition is our ability to function in an open-world. People navigate to previously unseen places, perform new tasks, and integrate new technology into their lives. In games, human flexibility supports inventing new strategies along with adapting to changing rules (e.g., consider chess players who play bughouse1). While current AI systems perform superhuman in many game domains, each of these domains is a closed-world, and minor perturbations of the game can lead to significant drops in performance. Witty et al. demonstrated that even changes which made the game easier could cause catastrophic results for superhuman performing deep Qlearning agents (Witty et al., 2018). This mismatch between human cognitive abilities and machine capabilities indicates that adapting to novelty is a cognitive systems problem. In this paper, we introduce Hypothesis-Guided Model Revision over Multiple Aligned Representations (HYDRA), our approach to model-based novelty response. We take the cognitive systems view that learning is a goal-oriented activity undertaken when predictions from models differ from observations in the environment. We define the novelty problem in the Science Birds domain and outline our system design. Central to our design is the use of mixed continuous-discrete planning formalism, namely PDDL+ (Fox & Long, 2006), to model the Science Birds domain. We demonstrate how this enables HYDRA to play the game as well as adapt to many types of novelty by making localized modifications to the domain theory. Next, we present a case study demonstrating how HYDRA adapts its domain theory to changing dynamics in ballistic flight. We close with a discussion of different issues we expect to address in the course of this project.
What is the relationship between domestic saving and investment in Latin America and the Caribbean? Re-Estimating Feldstein-Horioka

We estimate the “saving retention” coefficient for Latin America and the Caribbean (LAC). Using panel cointegration techniques; we find a positive and significant correlation between domestic savings and investment in LAC over the period 1980-2012. The estimated saving retention in the region is approximately 0.39; i.e., for every 1 percentage point increase in domestic savings, domestic investment increases by 0.39 percentage points on average. There are however, three nuances to the headline result: (1) the estimated saving retention has been declining over time; (2) the regional average hides a large degree of intra-regional heterogeneity; and (3) the regional dynamics are driven more by the smaller countries in Central America and the Caribbean than by the larger Southern Cone countries. JEL Codes: C23, E2, F36
Predicting Banking Sector Crisis using ANNs: Indian context

The purpose of this study is to develop an early warning system for the Indian banking sector. In this study, a data set covering the period of February 2001 – March 2017 of the Indian economy is used and an early warning system is constructed using two different artificial neural networks(ANNs). A banking sector fragility index is calculated to identify the period of crisis composed of variables like net bank reserves, credit, deposits, and foreign currency borrowings. This variable is converted to a dummy binary variable on the basis of the threshold, which is used as the dependent variable. The independent variables are the 15 leading indicators which are selected based on a thorough literature review. The study takes into account an Elman recurrent neural network and a Multilayered Feedforward Network(MLFN) which are trained using different transfer functions. The ANNs are evaluated based on their accuracy and calibration using Quadratic Probability Score(QPS) and Global Squared Bias(GSB) for both within the sample and out of sample. The scores depict results with Elman recurrent network outperforming the MLFN. The uniqueness of this study lies in that it uses diverse macroeconomic variables to anticipate the banking sector fragility for the Indian economy.
Virtual environment for robotic assistance application

A virtual environment presents several advantages in the training of assistive robots based on artificial intelligence techniques, it let iterative probes, tunings, and establish previous conditions for real environments. Convolutional neural network (CNN) is the most representative architecture employ to object recognition, this is necessary for a machine vision system for assistive robots. A Fast R-CNN is trained in a virtual environment for an application of tools ordering with an assistive robot obtaining 98% precision in the execution of this task, with five virtual tools.
A Cooperative Rotational Sweep Scheme to Bypass Network Holes in Wireless Geographic Routing

Geographic routing in wireless ad hoc networks is characterized by routing decisions made from locally available position information, which entails network scalability. However, it requires an effective recovery approach to sending a packet bypassing network holes whenever the simple greedy forwarding fails. Among well-known approaches, rotational sweep routing algorithms based on a circular arc are able to achieve packet delivery guarantee as well as low routing path stretch under the impractical assumption that a wireless link exists between two nodes if and only if their distance is less than one unit. Instead, we propose a cooperative rotational sweep algorithm taking into account practically imperfect wireless connections. The algorithm involves a regular rotational sweep procedure and a cooperative one both making use of iterative sweeps with circular arcs of decreasing size subjective to a minimum size constraint. Essentially, the cooperative rotational sweep procedure resolves hidden node issues through exploiting packet header overheads for memory of the latest routing path while iterative sweeps reduce the possibility of missing pivotal re-lays. Simulation results demonstrate that the proposed scheme presents the beneﬁt of using packet header overheads to support high end-to-end routing success probabilities without sacriﬁcing the inherent feature of localized routing.
Not Quite Any Way You Slice It: How Different Analogical Constructions Affect Raven’s Matrices Performance

Analogical reasoning fundamentally involves exploiting redundancy in a given task, but there are many different ways an intelligent agent can choose to deﬁne and exploit redundancy, often resulting in very different levels of task performance. We explore such variations in analogical reasoning within the domain of geometric matrix reasoning tasks, namely on the Raven’s Standard Progressive Matrices intelligence test. We show how different analogical constructions used by the same basic visual-imagery-based computational model—varying only in how they “slice” a matrix problem into parts and do search and optimization within/across these parts—achieve very different levels of test performance, ranging from 13/60 correct all the way up to 57/60 correct. Our ﬁndings suggest that the ability to select or build effective high-level analogical constructions can be as important as an agent’s competencies in low-level reasoning skills, which raises interesting open questions about the extent to which building the “right” analogies might contribute to individual differences in human matrix reasoning performance, and how intelligent agents might learn to build or select from among different analogical constructions in the ﬁrst place.
Determination of Acetaldehyde and Acrolein in Raw Spirits by Capillary Isotachophoresis After Derivatization

Abstract This paper describes the optimization of a new isotachophoretic (ITP) method for determining acetaldehyde and acrolein in raw sprits. Aldehydes as nonionic species are separated and determined by ITP methods following derivatization with sodium bisulfite after forming 1‐hydroxy‐1‐sulfonic acid derivatives. The method showed sufficient sensitivity, especially for acrolein, with a limit of detection at 0.03 µg/cm3 100% EtOH. A limit of detection for acetaldehyde, the dominant carbonyl compound in ethanolic solutions is 0.54 µg/cm3 100% EtOH. The relative standard deviations do not exceed 11% for low concentration range. The total analysis time without the derivatization step required about 20 minutes. The suitability of the method is shown for real samples of raw spirits.
Design and Fabrication of Microstrip Patch Antenna for Cognitive Radio Application

: This paper presents a rectangular microstrip patch antenna the antenna designed over the operating frequency is 2.4GHz using the substrate material as FR-4 epoxy which has the dielectric constant of 4.3 the designed antenna has low profile, low cost, easy fabrication and good isolation. The antenna is designed by using HFSS software.
Underwater Wireless Sensor Network Challenges and Applications : A Review

Wireless sensor network is a group of spatially distributed and dedicated tiny nodes that monitor and sense the environmental phenomena like temperature, pressure, humidity, or object location etc. Wireless sensor network can be of many types like terrestrial WSN and underwater WSN. Underwater WSN has emerged as a promising technique for various applications of underwater like pollution monitoring, prevention of disaster, detection of submarine and so on. UWSN is a network which consists of a range of sensor nodes which are spatially scattered to detect the water related properties like excellence, temperature, pollution tracking. The detected information can be implemented to a wide variety of area. The focus of this survey paper is to provide an overview on different applications of underwater wireless sensor network. Various design challenges for deployment of UWSN are also discussed.
Investigating Geochemical Characterization of Asmari and Bangestan Reservoir Oils and the Source of H2S in the Marun Oilfield

Abstract Geochemical evaluation is one of the most important and applicable methods for optimization of hydrocarbon exploration and production. In this article, the geochemistry of Asmari and Bangestan reservoir oils of Marun oil field was experimentally studied. Marun oil field is one of the giant oil fields in southwest Iran and has two oil reservoirs (Asmari and Bangestan) and one gas reservoir (Khami). The main goal of this study is to investigate the genetic behavior of the above oil reservoirs, focusing mainly on hydrogen sulfide pollutants. Biomarkers of saturated and aromatic fractions were studied on five polluted, three unpolluted Asmari, and two Bangestan reservoir oils. A triangular diagram was used to determine the chemical composition of the studied oil. The results show relatively higher oil maturity for both reservoirs with no biodegradation. The carbon preference index of both reservoir oils was also around 1, which indicates mature oil samples. The pristane-to-phytane ratio, Pri/nC17 versus Phy/nC18, terrigenous/aquatic ratio (TAR), and geochemical data all show that the source rock for both Asmari and Bangestan reservoirs is the same. This source rock was deposited in a reducing environment with algae (kerogen type II) organic matter and without any higher plants. Genetic potential studies of probable source rocks, by means of Rock-Eval VI analysis in Marun oilfield, present Kazhdomi and Garu as main source rocks. Biomarkers of sulfur compounds and structural analysis of Marun oil field revealed that hydrogen sulfide gas pollution in the Asmari reservoir originated from the Bangestan reservoir. In addition, thermal sulfate reduction was a possible main process for hydrogen sulfide formation.
PREPARATION AND CHARACTERIZATION OF RESVERATROL/HYDROXYPROPYL‐β‐CYCLODEXTRIN INCLUSION COMPLEX USING SUPERCRITICAL ANTISOLVENT TECHNOLOGY

ABSTRACT The inclusion complex of resveratrol (RV) and hydroxypropyl-β-cyclodextrin (HP-β-CD) prepared by supercritical antisolvent (SAS) process was investigated to improve its aqueous solubility. The phase solubility diagram with HP-β-CD was classified as AL type, and the calculated apparent complexation constant (KC) was 5,988 M−1 at 298K. Inclusion complexes were analyzed by differential scanning calorimetry (DSC), powder X-ray diffractometry (PXRD), Fourier-transform infrared spectroscopy (FT-IR) and scanning electron microscopy (SEM). No endothermic and characteristic diffraction peaks corresponding to RV were observed for the inclusion complex in DSC and PXRD. FT-IR study demonstrated the presence of intermolecular hydrogen bonds between RV and HP-β-CD in inclusion complex, resulting in the formation of amorphous form. Furthermore, the SAS-processed RV/ HP-β-CD powders showed a significant enhancement in the solubility of RV (up to 25.13 mg/mL) in an aqueous medium. The percent dissolution of RV increased considerably and about 100% of RV was dissolved within 5–10 min. PRACTICAL APPLICATIONS In this study, the supercritical antisolvent (SAS) process was effectively utilized to prepare solid-state inclusion complexes of resveratrol (RV) and hydroxypropyl-β-cyclodextrin (HP-β-CD). The SAS-processed RV/HP-β-CD powders showed a significant enhancement in the solubility of RV (up to 25.13 mg/mL) in an aqueous medium. The percent dissolution of RV increased considerably and about 100% of RV was dissolved within 5–10 min. This work thoroughly overcomes the high hydrophobicity and sensitivity of resveratrol to air and light, so that obviously enlarges the application in pharmaceutical, chemical, food and cosmetic industries.
BAYESIAN VALUE-OF-INFORMATION ANALYSIS

A framework is presented that distinguishes the conceptually separate decisions of which treatment strategy is optimal from the question of whether more information is required to inform this choice in the future. The authors argue that the choice of treatment strategy should be based on expected utility, and the only valid reason to characterize the uncertainty surrounding outcomes of interest is to establish the value of acquiring additional information. A Bayesian decision theoretic approach is demonstrated through a probabilistic analysis of a published policy model of Alzheimer's disease. The expected value of perfect information is estimated for the decision to adopt a new pharmaceutical for the population of patients with Alzheimer's disease in the United States. This provides an upper bound on the value of additional research. The value of information is also estimated for each of the model inputs. This analysis can focus future research by identifying those parameters where more precise estimates would be most valuable and indicating whether an experimental design would be required. We also discuss how this type of analysis can also be used to design experimental research efficiently (identifying optimal sample size and optimal sample allocation) based on the marginal cost and marginal benefit of sample information. Value-of-information analysis can provide a measure of the expected payoff from proposed research, which can be used to set priorities in research and development. It can also inform an efficient regulatory framework for new healthcare technologies: an analysis of the value of information would define when a claim for a new technology should be deemed substantiated and when evidence should be considered competent and reliable when it is not cost-effective to gather any more information.