Miracle-dz/UniFAR-FTUs huggingface dataset:
Innovation, Entrepreneurship, and Solving Wicked Challenges through CSR and CSV

The Fourth Industrial Revolution has escalated innovation to new heights unseen, creating an evolution of innovation and corporate social responsibility (CSR), and as a result, a more Innovative CSR. With this evolution comes also the evolution of the ‘Preneur’ from social entrepreneur to corporate social entrepreneur and corporate social intrapreneur. It is therefore important to acknowledge that social entrepreneurship is not just for the social sector, or start-up entrepreneur – corporations can also be social entrepreneurs. This chapter establishes an understanding of this possibility alongside solving wicked problems and challenges, and how to provide collaborative networks and co-creation experiences to assist others on this journey. More importantly, the chapter discusses how corporates can assist millennials (and Generation Z) by funding and incubating their innovative or social enterprise idea under the umbrella of CSR strategy, until it is ready to be released to the world. The chapter is supported by academic literature and business publications with suggestions for future research opportunities.
CENTRAL EUROPEAN REVIEW OF ECONOMICS AND MANAGEMENT

Design / Research methods: Expeditious progress in industrialization and information techniques has made great advancement in developing the next span of production technology. Industry 4.0 is an imperative action where the intention is the alteration of modern production through digitalization and profiteering of the capabilities of new advancements. Today, the absence of powerful appliances still feigns a significant impediment for utilizing the ample potential of Industry 4.0. Notably, behavioral approaches are essential for understanding Industry 4.0, which professes novel trials. This paper briefly surveys the area of Industry 4.0 as it relates to behavioral operations by using sentiment analysis and social network analysis methods and tools by describing features of the relationship network either through numerical and visual representation
Characterization of Toxin/Immunity Complexes Involved in Bacterial Contact-Dependent Growth Inhibition

Author(s): Johnson, Parker McCormick | Advisor(s): Goulding, Celia W | Abstract: Bacteria have developed complex mechanisms to survive and propagate within their environments. Contact-dependent growth inhibition (CDI) is a recently discovered mechanism of inter-bacterial competition and communication widespread amongst Gram-negative bacteria. CDI+ cells inhibit the growth of neighboring cells upon delivery of a toxic protein (CdiA-CT) into the target cell cytosol. To prevent auto-inhibition, CDI+ bacteria express an immunity protein (CdiI) to specifically bind and inactivate cognate toxin. Here we provide further structural characterization of CDI toxin/immunity proteins to elucidate the diversity of CdiA-CT toxin activity, their potential activation, and neutralization to repurpose CDI systems for novel antimicrobials.Burkholderia pseudomallei isolates E479 and 1026b have been shown to utilize CDI to outcompete neighboring bacteria, signifying the pathway’s importance in growth and survival. The CdiA-CT toxins from these isolates are functional tRNases, yet have unique tRNA specificities and cleavage sites despite sharing no sequence similarity. We have solved the X-ray crystal structure of the B. pseudomallei E479 toxin/immunity protein complex and compared it to the previously solved 1026b complex. While the toxins are sequentially and functionally different, they share significant structural homology with nearly identical active sites, only differing in active site pocket size, correlating with the differing substrate cleavage sites of the two toxins. The toxin/tRNA interactions have been confirmed via molecular docking as well as small angle X-ray scattering. The CDI system from uropathogenic E. coli 536 (UPEC536) is unusual, CdiA-CT exhibits toxic tRNase activity only when bound to target cell CysK, termed a “permissive factor”, involved in cysteine biosynthesis. We have solved the X-ray crystal structure of the CysK/toxin/immunity ternary complex. The UPEC536 toxin inserts its C-terminus into the active site cleft of CysK, mimicking its natural interaction with CysE. A homologous toxin from the Gram-positive Ruminococcus lactaris has been identified to perform the same enzymatic function in a CysK-independent manner. Characterization of this toxin has shown that it’s significantly more thermostable than the UPEC toxin, and is capable of binding tRNA substrate in the absence of CysK. This demonstrates a chaperone-like function for CysK, stabilizing the UPEC toxin fold and promoting its association with tRNA substrate.
Relative expression of oxytocin receptor gene in buffalo endometrium in late luteal phase and pregnancy stages

ABSTRACT Molecular level information related to buffalo (Bubalus bubalis) reproduction and related genes is not present at appropriate level. If such exploration is made in the form of comparison between expression of genes is made between non-pregnant and pregnant phase, it may be helpful to aid manipulate the reproduction. Hence, the present study was carried out to reveal mRNA quantitative real time expression of oxytocin receptor (OTR) mRNA. IFN-τ is considered as the substance of maternal recognition of pregnancy and shut down the probable mechanisms which lead to luteolysis. Such mechanism includes shutting down of OTR. Therefore, relative expression of OTR was studied in endometrial tissue of three groups. The groups were non-pregnant late luteal phase, pregnancy stage I (pregnancy of <42 days) and pregnancy stage II (>42 days of pregnancy). With designed primer and GAPDH as house-keeping gene, relative mRNA expression was measured in Real-time PCR. After statistical analysis of results, the gene found to be expressed in all three stages with non-significant difference.
COMPARATIVE ANALYSIS OF SYSTEM LOGS AND STREAMING DATA ANOMALY DETECTION ALGORITHMS

This paper provides with the description, comparative analysis of multiple commonly used approaches of the analysis of system logs, and streaming data massively generated by company IT infrastructure with an unattended anomaly detection feature. An importance of the anomaly detection is dictated by the growing costs of system downtime due to the events that would have been predicted based on the log entries with the abnormal data reported. Anomaly detection systems are built using standard workflow of the data collection, parsing, information extraction and detection steps. Most of the document is related to the anomaly detection step and algorithms like regression, decision tree, SVM, clustering, principal components analysis, invariants mining and hierarchical temporal memory model. Model-based anomaly algorithms and hierarchical temporary memory algorithms were used to process HDFS, BGL and NAB datasets with ~16m log messages and 365k data points of the streaming data. The data was manually labeled to enable the training of the models and accuracy calculation. According to the results, supervised anomaly detection systems achieve high precision but require significant training effort, while HTM-based algorithm shows the highest detection precision with zero training. Detection of the abnormal system behavior plays an important role in large-scale incident management systems. Timely detection allows IT administrators to quickly identify issues and resolve them immediately. This approach reduces the system downtime dramatically.Most of the IT systems generate logs with the detailed information of the operations. Therefore, the logs become an ideal data source of the anomaly detection solutions. The volume of the logs makes it impossible to analyze them manually and requires automated approaches.
Financial Performances and Public Procurement Loss: Indonesia Ministries/State Study

Transparency of public acquisition is one of the core mechanisms in combating corruption. However, in Indonesia, despite the transparency of audit results of the Ministry/State Agency’s financial statement related to Unqualified Opinions published by the State Audit Agency has increased yearly, the trends of corruption cases increased as well. This research aims to investigate the determinants of financial performances and the state financial loss using panel data of 74 Indonesia ministries/state agencies between 2014-2016, further employing the random effects analysis to overcome serial correlation. The main findings indicate that unqualified opinion positively and significantly reduces the state financial loss in Indonesia. Besides, political party leadership (non-independent Minister) significantly influenced the state financial loss. Furthermore, capital expenditure and state expenditure allocation significantly influenced the state financial loss in Indonesia while accountability score was not important determinants of financial loss in Indonesia.
Sound Production in a Brazilian Cicada

1. Some aspects of the physiology of sound production in the Brazilian cicada Fidicana rana Walker have been investigated. 2. Three types of natural song are described. Singing can also be induced by electrical stimulation of the head. 3. The tymbal muscles are neurogenically activated and contract alternately. 4. The directional properties of the sound field produced by clicking of the tymbal are described, and the physical properties of the system necessary to produce this directionality are discussed. 5. Removal of the abdomen does not decrease the sound intensity during song produced by electrical stimulation, hence it is concluded that the abdominal air sac does not act as a resonant chamber. 6. The means whereby the sound intensity is varied are discussed. 7. A brief account is given of the occurrence of the different types of song in the natural habitat.
Investigation of Minimal Media and Chemical Stress on Antibiotic Sensitivity of Pseudomonas putida and Pseudomonas fluorescens

The genus Pseudomonas contains many species that range from harmless commensals to antibiotic resistant pathogens. The most dangerous organism is Pseudomonas aeruginosa, a leading cause of concern as the source of many antibiotic resistant infections. Pseudomonas putida and Pseudomonas fluorescens were used in this study as they exhibit properties similar to those of P. aeruginosa without the issue of pathogenicity. Previous studies with non-pathogenic organisms such as Escherichia coli and Staphylococcus epidermidis have shown an increase in antibiotic sensitivity when grown in the presence of sodium acetate, a known chemical stress agent, on complex media. This project is designed to study the effects of sodium acetate as the sole carbon source on antibiotic sensitivity of Pseudomonas putida and Pseudomonas fluorescens grown on M9 minimal medium. Antibiotic sensitivity is measured using the standard Kirby-Bauer method. Investigation of Minimal Media and Chemical Stress on Antibiotic Sensitivity of Pseudomonas putida and Pseudomonas fluorescens Rebekah Snyder, Jimmy Gao, Heather Aubihl and Dr. Donald Gerbig Kent State University Tuscarawas
The Sea Turtle Rookery at Gnaraloo Bay, Western Australia: Using Nocturnal Observations to Validate Diurnal Track Interpretations

Abstract Critical gaps remain in our understanding of many sea turtle nesting aggregations in remote or undeveloped regions. Here, we summarize the first 8 yrs of systematic monitoring of the rookery at Gnaraloo Bay, Western Australia. Diurnal track surveys on this approximately 7-km mainland beach were conducted daily during nesting seasons 2008/09 to 2015/16. The total number of emergences (i.e., nests and failed nesting attempts) recorded per season ranged from 480 to 813 (mean = 679.0, SE = 49.1), whereas the number of nests ranged from 305 to 522 (mean = 376.0, SE = 26.7). Peak nesting activity occurred between mid-December and late January, with approximately 70 emergences and 35 nests recorded on average per week during this time. The majority (97%) of emergences and nests were from loggerhead turtles (Caretta caretta), whereas the remainder (3%) were from green turtles (Chelonia mydas). The number of loggerhead turtle emergences recorded per season declined significantly over the course of the study, wheras the number of nests did not, although we suspect that nest detection errors contributed to the difference between trends. We conducted nocturnal surveys (i.e., direct observations) during parts of seasons 2010/11 to 2015/16 to validate diurnal track interpretations and assess potential biases in the diurnal data set. Diurnal nest counts for loggerhead turtles were underestimates in all seasons but one, with an average nest detection bias of −13.0% (SE = 3.0). After accounting for this bias, we estimate that approximately 405 nests are dug by 85 female loggerhead turtles in the Gnaraloo Bay survey area annually. A similar or slightly lower amount of loggerhead turtle nesting activity occurs at the Cape Farquhar survey area, also located on the Gnaraloo coast; thus, this region contains previously underreported nesting aggregations of this species. The Gnaraloo rookeries may play an important role in the dynamics of the southeast Indian Ocean loggerhead turtle subpopulation and may still be depleted relative to historic levels due to historical predation by introduced foxes. Monitoring, research, and the protection of Gnaraloo beaches are, therefore, critical at this juncture.
Changes in plant taxonomic and functional diversity patterns following treeline advances in the South Urals

Background: Treeline ecotones represent environmental boundaries that fluctuate in space and time and thus induce changes in plant taxonomic and functional diversity. Aims: To study changes through time in taxonomic and functional plant diversity patterns along the treeline ecotone. Methods: In 2002, vegetation was sampled along a gradient from upper montane forest to the treeline–alpine transition in the South Ural Mountains, Russia. In 2014, vegetation was resampled and plant functional traits were collected. We studied spatial and temporal changes in plant species composition, functional composition and functional diversity. Results: Species composition and diversity changed along the elevational gradient. The functional composition in height, leaf area, specific leaf area and leaf nitrogen content decreased with elevation, whereas functional composition of leaf carbon content increased. We found a temporal shift towards shorter plants with smaller leaves in treeline sites. Functional richness varied in several traits along the elevational gradient, while functional dispersion showed a trend towards increased functional dispersion in height, specific leaf area and leaf nitrogen in the treeline–tundra transition. Conclusions: Tree encroachment across the treeline ecotone has resulted in a shift in plant species relative abundances and functional diversity, possibly affecting plant community assembly patterns.
Constructing Non-Periodic Tiling Patterns with P System

Parametric Tile Pasting P System (PTPPS) is a new computational model, based on the structure and functioning of living cells, to generate tiling patterns, using simple rules of gluing tiles (pasting rule) at their edges and geometric operations on the tiles. In this paper, a variant of PTPPS, namely Tissue-like Parametric Tile Pasting P System, with new geometric operations, to generate non-periodic patterns is introduced. The computational complexity of the system and the power of the system with diﬀerent geometric operation are also discussed.
Seismic Isolation, monitoring, identification and modelling of the “Our Lady of Tears Shrine” LOSANNO DANIELE

This paper describes the monitoring system, the dynamic identification and the modelling of the “Our Lady of Tears Shrine” in Syracuse, a seismically isolated structure made of reinforced and prestressed concrete. The monitoring system has some innovative features, because it allows to manage with one and the same dedicated hardware and software both the slow (thermal variations, relative humidity, wind direction and velocity) and the fast acquisitions, namely those dynamic caused by environmental vibrations and earthquake. The monitoring system has been recently inserted among those structures maintained and controlled by the National Department of Civil Protection within the Seismic Observatory of Structures. The first records of low seismic events allowed to validate the correct behavior of the whole structure, as well as to make a first dynamic identification of the complex construction and to calibrate a detailed finite element model of the Sanctuary. Trough this model, the behavior of the isolated structure under the maximum probable earthquake has been predicted according to the current building code.
Insect herbivory is associated with microsite quality1

Abstract The nonrandom distribution of insect herbivory among plants is generally thought to reflect variation in plant-defense strategies and/or insect preferences for particular plant genotypes, where less-defended plants suffer the most damage. However, we postulated that aggregated distributions of herbivory may occur when (a) plants support greater herbivore densities under conditions with better resources (plant's choice), (b) plants with more nutrient-rich leaves are preferred more by herbivores (herbivore's choice), or (c) both, where plants living in better conditions can support more herbivores and have higher-quality leaves that herbivores also prefer. We tested these hypotheses using a population of black tupelo, Nyssa sylvatica Marshall (Cornales: Nyssaceae) and the tupelo leafminer, Antispila nysaefoliella Clemens (Lepidoptera: Heliozelidae). We found that the among-tree variation in herbivore density was positively associated with nutrient-cycling rate and not with leaf-nitrogen content, which supports the plant's choice hypothesis. Our results suggest that environmental conditions may have a more important role than host plant quality per se in determining the distribution of herbivores among trees.
Smart Home Automation Using IOT and its Low Cost Implementation

: The use of automation is emerging with the help of internet providing possibility of objects to work it-self. With recent advancements to the fast speed internet, IoT will be playing a vital role in our daily tasks in present and future. IoT is offering feasibility and effectiveness to the system that are based upon it. These modern technologies are creating comfort and standard way of living because of its time, energy and cost efficiency. In this modern world, where things are going to be on our finger tips, our daily household appliances will also be controlled with our smartphones. This will allow us to manage the usage smartly, and can help in building of an eco-friendly environment. This paper will conduct a study based on how household appliances may be automated smartly with software applications that are integrated with hardware board. It presents the complete architecture of the system with its working capabilities. Also, it explains the internal mechanism of the system which mainly considers the software application and hardware board interaction. As we know the smart home automation is a costly process, so in this paper we would be looking at its low-cost implementation.
Recent advances in molecular techniques for the identification of phytopathogenic fungi – a mini review

ABSTRACT At present, 1.5 million species of fungi are estimated. Among these less than 5% have been described. Many fungal species cause disease in plants. These diseases cause major economic and production losses in the agricultural industry worldwide. Monitoring plant health and detecting the pathogen early are essential to reduce the disease spread, and facilitate effective management practices. DNA-based methods now provide essential tools for accurate plant disease diagnosis. Recently, effective amplification platforms, probe development, various quantitative PCR, DNA barcoding and RNA-Seq-based next-generation sequencing have revolutionized the research in fungal detection field, and differentiation area. Although the molecular diagnostics techniques have grown extensively over the last couple of decades but still there is a long way to go in the development and application of molecular diagnostics to assist the plant disease diagnosticians. Finally, molecular diagnostic techniques used in plant disease diagnostic clinics need to be robust, reliable, inexpensive and easy to be used that they can compete with, and complement traditional techniques. Challenge now remains residue with the researchers to develop the practical techniques used for diagnostic setting. Examples of the recent advancement in the molecular techniques for diagnosing the fungi causing plant disease are discussed in the review. Abbreviations: PCR: polymerase chain reaction; LAMP: loop-mediated isothermal amplification; RCA: rolling circle amplification; NASBA: nucleic acid sequence-based amplification; ITS: internal transcribed spacer; RT: reverse transcriptase; FEB: Fusarium ear blight; qPCR: real-time PCR; SNPs: single nucleotide polymorphism; HRCA: hyper-branched RCA; FIP: forward inner primer; BIP: backward inner primer; COX I: cytochrome c oxidase I; MBs: molecular beacons.
TLGAN: document Text Localization using Generative Adversarial Nets

Text localization from the digital image is the first step for the optical character recognition task. Conventional image processing based text localization performs adequately for specific examples. Yet, a general text localization are only archived by recent deep-learning based modalities. Here we present document Text Localization Generative Adversarial Nets (TLGAN) which are deep neural networks to perform the text localization from digital image. TLGAN is an versatile and easy-train text localization model requiring a small amount of data. Training only ten labeled receipt images from Robust Reading Challenge on Scanned Receipts OCR and Information Extraction (SROIE), TLGAN achieved 99.83% precision and 99.64% recall for SROIE test data. Our TLGAN is a practical text localization solution requiring minimal effort for data labeling and model training and producing a state-of-art performance.
Fine-tuned Pre-trained Mask R-CNN Models for Surface Object Detection

This study evaluates road surface object detection tasks using four Mask R-CNN models as a pre-study of surface deterioration detection of stone-made archaeological objects. The models were pre-trained and fine-tuned by COCO datasets and 15,188 segmented road surface annotation tags. The quality of the models were measured using Average Precisions and Average Recalls. Result indicates substantial number of counts of false negatives, i.e. left detection and unclassified detections. A modified confusion matrix model to avoid prioritizing IoU is tested and there are notable true positive increases in bounding box detection, but almost no changes in segmentation masks.