Miracle-dz/UniFAR-FTUs huggingface dataset:
Effects of Cry 1 Ab Bt maize straw return on bacterial community of earthworm Eisenia fetida

The eco-toxicological effects of Bacillus thuringiensis (Bt) maize on earthworm life-history traits were widely studied and the results were controversial, while their effects on earthworm bacterial community have been rarely studied. Here, effects of two hybrids of Bt maize [5422Bt1 (event Bt11) and 5422CBCL (MON810)] straw return on Eisenia fetida bacterial community were investigated by the terminal restriction fragment length polymorphism (T-RFLP) and polymerase chain reaction-denaturing gradient gel electrophoresis (PCR-DGGE) combing with DNA sequencing, compared to near-isogenic non-Bt maize (5422). Bt maize straw return had significant effects on soil nutrients, especially for available nitrogen (N). The significant differences were shown in soil bacterial community between Bt and non-Bt maize treatments on the 75th and 90th d, which was closely correlated with soil available N, P and K rather than Cry1Ab protein. There was no statistically significant difference in the bacterial community of earthworm gut contents between Bt and non-Bt maize treatments. The significant differences in the bacterial community of earthworm casts were found among three maize varieties treatments, which were closely correlated with Cry1Ab protein and N levels. The differentiated bacterial species in earthworm casts mainly belonged to Proteobacteria, including Brevundimonas, Caulobacter, Pseudomonas, Stenotrophomonas, Methylobacterium, Asticcacaulis and Achromobacter etc., which were associated with the mineralization, metabolic process and degradation of plants residues. Therefore, Bt maize straw return caused changes in the bacterial community of E. fetida casts, which was possibly caused by the direct (Cry1Ab protein) and non-expected effects (N levels) of Bt maize straw. © 2017 Elsevier Ltd. All rights reserved. lege of Natural Resources and angzhou 510642, China.
A novel systematic approach for analysing exploratory design ideation

Two kinds of design ideation process may be distinguished in terms of the problems addressed: (i) solution-focused, i.e. generating solutions to address a fixed problem specifying a desired output; and (ii) exploratory, i.e. considering different interpretations of an openended problem and generating associated solutions. Existing systematic analysis approaches focus on the former; the literature is lacking such an approach for the latter. In this paper, we provide a means to systematically analyse exploratory ideation for the first time through a new approach: Analysis of Exploratory Design Ideation (AEDI). AEDI involves: (1) open-ended ideation tasks; (2) coding of explored problems and solutions from sketches; and (3) evaluating ideation performance based on coding. We applied AEDI to 812 concept sketches from 19 open-ended tasks completed during a neuroimaging study of 30 professional product design engineers. Results demonstrate that the approach provides: (i) consistent tasks that stimulate problem exploration; (ii) a reliable means of coding explored problems and solutions; and (iii) an appropriate way to rank/compare designers’ performance. AEDI enables the benefits of systematic analysis (e.g. greater comparability, replicability, and efficiency) to be realised in exploratory ideation research, and studies using open-ended problems more generally. Future improvements include increasing coding validity and reliability. ARTICLE HISTORY Received 18 December 2018 Accepted 28 August 2019
Unsupervised Chinese Word Sense Disambiguation Based on Equivalent Pseudowords

This paper presents the concept of equivalent pseudowords and develops a new unsupervised Chinese word sense disambiguation method. This method, after statistical learning from the equivalent pseudowords, realizes unsupervised word sense disambiguation by calculating mutual information to measure the degree of collocation information between the ambiguous words and their context. In our experiment, we test ten real ambiguous words and get the highest accuracy of 97%. The mean accuracy was 88.52%. The experimental results have proved the feasibility of this method.
Chest Imaging in CF Studies : What Counts , and Can be Counted ?

Background—The dawn of precision medicine and CFTR modulators require more detailed assessment of lung structure in cystic fibrosis (CF) clinical studies. High-resolution chest computed tomography (CT) scoring has yielded sensitive markers for the study of CF disease progression and therapeutic effectiveness. Similarly, Magnetic Resonance Imaging (MRI) is in development to generate structural as well as functional markers.
Information Technology and Quantitative Management ( ITQM 2016 ) Prototyping Business Models for IoT Service

The Internet of Things (IoT) generates new business opportunities by connecting physical objects with a multitude of sensors. IoT research mainly focused on technology and business models are relatively unexplored, although developing IoT business models is important for successful IoT service. The existing literature on IoT business models are industry or context-dependent. The aim of this research is to develop a generic business model framework for IoT business through literature analysis and interviews. To test the proposed business model framework, we undertake case studies of current IoT companies. The findings suggest that capability for data analytics is an essential element for IoT service. Also, open ecosystems help companies provide new integrated service and offer greater value for consumers. This research acts as a starting point for designing or developing business models for IoT services. © 2016 The Authors. Published by Elsevier B.V. Selection and/or peer-review under responsibility of the organizers of ITQM 2016
Floor Localization Based on Seq2Seq Model

Due to increasing smartphone penetration and micro-electromechanical systems (MEMS) technology advances, smartphone-based location service is becoming an increasingly important research topic. This paper presents a Seq2Seq model-based floor localization scheme, which is infrastructure-free and tracks the floor level according to each step's action. In addition, a floor decision algorithm was developed to eliminate atmospheric pressure drift over time, identify the floor transition type, and estimate the floor number. The proposed scheme was tested in university buildings and achieves a 94.2% average floor number accuracy, with all floor transition types successfully identified.
Molecular cloning , gene expression pro fi ling and in silico sequence analysis of vitamin E biosynthetic genes from the oil palm Kong

a Laboratory of Plantation Crops, Institute of Tropical Agriculture, Universiti Putra Malaysia, 43400, UPM, Serdang, Selangor, Malaysia b Department of Agriculture Technology, Faculty of Agriculture, Universiti Putra Malaysia, 43400, UPM, Serdang, Selangor, Malaysia c Department of Cell and Molecular Biology, Faculty of Biotechnology and Biomolecular Sciences, University Putra Malaysia, 43400, UPM, Serdang, Selangor, Malaysia d Advanced Biotechnology and Breeding Centre, Malaysian Palm Oil Board, No. 6, Persiaran Institusi, B. B. Bangi, 43000 Kajang, Selangor, Malaysia
Machine learning algorithms for inter-cell interference coordination

Los despliegues actuales de LTE y LTE-A requieren mayor esfuerzo para la gestion de recursos radio debido al incremento de usuarios y a la gran demanda de servicios; en ese escenario, la optimizacion automatica es un punto clave para evitar problemas como la interferencia inter-celda. El presente trabajo recopila propuestas de algoritmos de aprendizaje automatico [machine learning] enfocados en resolver este problema. Las investigaciones buscan que los sistemas celulares consigan su auto-optimizacion, un concepto que se enmarca dentro del area de redes auto-organizadas [Self-Organized Networks, SON], cuyo objetivo es lograr que las redes respondan de forma automatica a las necesidades de los escenarios dinamicos de trafico de red.
Determinants of sales force technology adoption among insurance sales agents in Kenya

Salespersons are adopting and using a variety of technologies to increase their selling productivity and efficiency at different rates. This study identifies various factors that can influence the adoption of sales force automation and analyzes their effect on technology adoption. An explanatory research design was used and data collected by means of self-administered questionnaires to the target population. Reliability and correlation analysis were conducted to establish relationships between the research variables. Logit regression showed that social factors, system characteristics, organizational factors and salesperson characteristics significantly affect technology adoption. The major reason for such failure rates seems to be that the experienced salespersons frequently reject the new sales technologies. The study recommends that insurance companies should create an enabling environment for sales agents to adopt technology and improve their performance and gives further research directions.
INCULATION OF SELENIUM NANOPARTICLES IMPROVES PRODUCTIVE PERFORMANCE , BLOOD BIOCHEMICAL PROFILE , ANTIOXIDANT STATUS AND IMMUNE RESPONSE OF HATCHED CHICKS [ 81 ]

Selenium is a trace element essential in animal nutrition and exerts multiple actions related to enhance animal production, fertility, immune response and antioxidant defense system of chickens. The aim of the present study is investigating the benefit effects of selenium nanoparticles (SEN) in-ovo injection on productive performance, express stimulate antioxidant defense system and immune response of hatched chicks. A total of 210 broiler breeder eggs ( Habbard Star-Bro) were divided into three in-ovo injection treatment groups,( 0, 5 and 10 ppm SEN) and incubated. Hatchability traits , productive performance, biochemical profile, antioxidant status and immune response of hatched chicks were estimated. Results indicated significant increase in HDLcholesterol, T3, GSR, GSH, IGA, IGM and IGG as affected by in-ovo inoculated SEN levels. However feed conversion ratio, triglycerides and MDA significantly decreased by in-ovo treatments. No significant alternations were recorded in hatched chicks weight, feed intake, body weight, body weight gain, carcass characteristics, and serum levels of protein fractions, cholesterol, LDLcholesterol, ALT, AST, ALP, uric acid, creatinine and glucose in in-ovo treated groups compared with the control one. It is summarized that, in-ovo inoculation of different levels of SEN can improve feed conversion ratio, lipid profile, antioxidants status and immunity of broiler hatched chicks.
kcgi : securing CGI web applications

With privilege separation, sandboxes, capabilities, and jails, the BSD systems have gained increasingly sophisticated tools to protect developers from themselves. These tools constrain the resources available to processes whether operating normally or exploited by an adversary. In this paper, I’ll introduce a library, kcgi, that brings these constraints to bear on a special class of applications: web applications. Web applications (“CGI scripts”) are broad enough to have only one common characteristic: a complex, non-interactive input channel. Connected either directly to the Internet or proxied through a web server, they often expose precious resources in response to their input. kcgi helps by parsing and proxying input to calling applications in a sandboxed child.
Effects of Technical and Organizational Activities on Redundancy Allocation Problem with Selecting Redundancy Strategies Using Memetic Algorithm

Redundancy allocation problem is one of the most important problems in reliability area. This problem involves the suitable redundancy levels under certain strategies to maximize system reliability under some constraints. However, it has undergone many changes to come closer to the real situations. Selecting the redundancy strategy and using different system configuration are some of these changes. In this paper, we studied the effects of technical and organizational activities on this problem and showed the difference between the system reliability with and without using these activities. Also, we worked on a system containing s subsystems connected serially. Each subsystem contains ni , i = 1, 2, . , s parallel components that can be selected from mi , i = 1, 2, . , s different component types and all subsystem components must be identical. Because redundancy allocation problem belongs to Np. Hard problems, we used a new metaheuristic algorithm called memetic competition algorithm for solving the presented problem and compared the results of this algorithm with other solving methods.
Formal Proofs for Broadcast Algorithms

Standard distributed algorithmic solutions to recurring distributed problems are commonly specified and described informally. A proper understanding of these distributed algorithms that clarifies ambiguities requires formal descriptions. However, formalisation tends to yield complex descriptions. We formally study two broadcast algorithms and present an encoding framework using a process descriptive language and formalise these algorithms and their specifications using this framework. Following these new formal encodings we discuss correctness proofs for the same algorithms.
Dynamic Ad Hoc Social Networks in Improvised Intelligence/Counter-Intelligence Exercises: A Department of Homeland Security Red-Team Blue-Team Live-Action Roleplay

Abstract We discuss a Red Team-Blue Team (RT-BT) study conducted to examine the formation and efficacy of social networks in self-organizing, ad hoc, or crowd-sourced intelligence and counter-intelligence operations in grassroots, improvised communities. Student volunteers were sorted into two teams: one team (Blue) was asked to find puzzle pieces using clues provided by the organizers, with the goal of reconstructing a message contained therein, while the opposing team (Red) was tasked with disrupting this process. While the Blue Team quickly organized into an efficient, centrally-governed structure, the Red Team instead adopted a decentralized, distributed operational network to hinder puzzle completion, using creative and diverse infiltration and disruption methods to interfere in the more centralized, hierarchical organization of their opponents. This exercise shows how untrained, unaffiliated individuals may self-organize into different types of social organizations to accomplish common tasks when aware of potential adversarial organizations, and how these choices may affect their efficacy in accomplishing collaborative clandestine goals.
PyTorch Neural Networks and Track Analysis for Top Quark Tagging

The identification of top quarks is motivated by their high mass and strong coupling to the Higgs mechanism. Boosted top quarks also allow for improved measurements of the Standard Model in the high momentum tails of event feature distributions. Neural networks have been proven as an effective method for distinguishing top quarks from Quantum ChromoDynamic (QCD) events using jet constituent features from the ATLAS and CMS calorimeters. In this project Deep Neural Networks (DNN’s) and Long Short-Term Memory (LSTM) networks were built in PyTorch to compare their performances to previously tested Keras models. After applying similar preprocessing and optimization techniques, the performance of the PyTorch models was found to be highly comparable to the Keras models. Track features from the inner tracker offer promising new information to improve the performance of top tagging neural networks by utilizing information typically used in b-jet identification. Track features were analyzed and incorporated in processing scripts used to prepare the data for input to neural networks. It was found that keeping 100 pT ordered tracks with pT greater than 1 GeV could retain relevant information for jet classification while minimizing noise and computing time.
PROGRAM VERIFICATION STRATEGY AND EDGE RANKING OF GRAPHS

There are several criteria for testing program correctness. In this paper we deal with the problem of automatic software testing under the assumption that the set of tests (assertions) is given for selected blocks of code. We simplify the analysis by assuming that the program being tested contains exactly one bug, but this does not lead to loss of generality. We consider not only practical aspects of the above problem and a graphtheoretical model in general but some chromatic aspects of a graph searching model as well.
The role of the blaKPC gene in antimicrobial resistance of Klebsiella pneumoniae

Background and Objectives: Klebsiella pneumoniae isolates that produce K. pneumoniae carbapenemase (KPC) have become a grave concern for the treatment of infections. KPC-producing strains are not only able to hydrolyze carbapenems but are also resistant to a variety of β-lactam and non-β-lactam antibiotics. The present study evaluated the prevalence of blaKPC in K. pneumoniae infections and determined the antimicrobial susceptibility of the isolates. Materials and Methods: The K. pneumoniae isolates were identified by biochemical tests and confirmed by genotyping. The modified Hodge test (MHT) was performed to detect carbapenemases, and antimicrobial susceptibility was determined for all isolates by the disc diffusion method. Also, for MHT-positive isolates, supposed to carbapenemases isolates, broth microdilution method was used to measure the minimum inhibitory concentrations (MICs) of meropenem and colistin. Results: The blaKPC genotypic evaluation revealed that only 5 of 96 isolates carried blaKPC genes. Antimicrobial pattern showed that isolates carrying blaKPC were resistant to cefepime, ticarcillin/tazobactam, and aztreonam discs. Also, results of broth microdilution method showed that KPC-producing K. pneumoniae was resistant to meropenem and colistin, according to the CLSI and EUCAST. Conclusion: In this study nearly half the isolates showed carbapenemase activity as shown by MHT results, but only few of them were carrying blaKPC. Thus blaKPC gene is not the main cause of resistance spread to carbapenems in Isfahan, Iran.
Deep Neural Network for Deep Sea Plankton Classification

Deep sea plankton classification is a competition on Kaggle, named as ‘National Data Science Bowl’, aiming at automatically identifying plankton. In the project, we explored how to use deep convolutional neural network, together with data augmentation and regularization, to achieve the classification task. We also investigated using the ‘code vector’ (output of second last fully-connected layer) of CNN models to do model assembly. We introduced PCA, Ridge and Randomized Ridge model assembly methods and showed the Randomized Ridge model can outperform the simple probability averaging method. With single VGG-like [6 Conv + 3 FC] CNN model, we can get 73.90% accuracy and reduce log loss to 0.938 on test set. Using model assembly methods, we can further push to 75.80% accuracy and 0.857 log loss. Finally assembling 11 independent CNN models we achieved log loss 0.772707 on Kaggle.com for the full unlabeled test set and ranked 107th (1049 teams in total, top 1 log loss 0.560994).
Federated Deep Learning for RIS-assisted UAV-enabled Wireless Communications

Unmanned aerial vehicles (UAVs) are becoming more crucial to enhancing spectral efficiency in the 6G network. However, there are problems such as blockage and energy-hungry. To solve these problems, an UAV-assisted reconfigurable intelligent surface (RIS) communication has been proposed for wireless system. RIS is a two-dimensional meta-surface, that is composed of cost-effective and energy-efficient passive reflecting elements, which can enhance spectral and energy efficiency. This paper formulates the problem of maximizing the achievable data rate by using long short-term memory and federated learning. We proposed the method that consists of two stages: 1) training stage for a global model in UAV, and 2) resource allocation stage in RIS controller.
Retraction Retracted : Stimulatory Effects of Gamma Irradiation on Phytochemical Properties , Mitotic Behaviour , and Nutritional Composition of Sainfoin ( Onobrychis viciifolia Scop . ) The Scientific World

e Scientific World Journal has retracted the article titled “Stimulatory Effects of Gamma Irradiation on Phytochemical Properties, Mitotic Behaviour, and Nutritional Composition of Sainfoin (Onobrychis viciifolia Scop.)” [1]. As raised on PubPeer, several figures also appear in the authors’ publications in Journal of Agricultural Science and Technology [2] and Journal of Animal & Plant Sciences [3] and additional concerns with figures and tables were found in comparing the article to the first author’s thesis [4], as listed below. e authors’ responses to these ethical concerns (https://www.hindawi.com/journals/tswj/ethics/) were not considered adequate by the Editorial Board and therefore the article is being retracted. e first author maintains that the results in the article are correct, but he says the university has restricted the download of the thesis. Details of the main concerns are as follows:
Optimal Design of Steel Crossed Beams with Genetic Algorithm

The structural engineering design problem consists in an iterative process of design. Various authors have analyzed the optimization of steel structures using different algorithmic approaches. These techniques are generally grouped in deterministic or meta-heuristics. From the introduction of structural optimization in the 1960’ up to now, various methods have been proposed. A GA genetic algorithm, is built and applied in this study using MatLab soft R2017a to crossed beams systems. The structural analysis is done applying the direct stiffness method with constraints verification based on EuroCode 3:2005 criteria. Results are reported in the study.
WEAK-ODD EDGE-COLORING OF DIGRAPHS

A weak-odd edge-coloring of a digraph D is a (not necessarily proper) edge-coloring such that for each vertex v ∈ V (D) at least one color c satisfies the following requirement: if d(v) > 0 then c appears an odd number of times on the outgoing edges at v; and if d−(v) > 0 then c appears an odd number of times on the ingoing edges at v. The minimum number of colors sufficient for a weak-odd edge-coloring of D is the weak-odd chromatic index, denoted χwo(D). In this article we prove that χwo(D) ≤ 3 for every digraph D, and show that this bound is sharp. We study when does a graph admit an orientation so that the obtained digraph is weak-odd 1-edge-colorable. We also prove that every graph admits an orientation for which the obtained digraph is weak-odd 2-edge-colorable.