Miracle-dz/UniFAR-FTUs huggingface dataset:
Molecular Identification and Characterization of Plant Growth Promoting Rhizobacteria and their Effect on Seed Germination and Vigour Index of Barley (Hordeum vulgare L.)

Plant growth promoting rhizobacteria (PGPR) are a group of useful bacteria that colonize the plant roots and significantly enhances the plant growth promotion. Keeping this in mind, an investigation was performed for the screening of potent PGPR strains for enhancing seed germination and vigour index of Hordeum vulgare (commonly called barley). Rhizobacterial strains were isolated and screened for various plant growth promoting traits, their effect on seed germination and vigour index of barley plant through pot trial, and resistant ability under various temperature and pH range. Based upon 16S rRNA sequencing data, Pseudomonas punonensis LMT03 (R1), Pseudomonas plecoglossicida R4, Pseudomonas aeruginosa DSM50071 (R2) and Alcaligenes faecalis (DBHU5) isolates were selected and showed positive result for IAA production, Phosphate solubilization, ammonia production, catalase activity, siderophore production and MR-VP test. Barley plants treated with P. punonensis and P. plecoglossicida both showed 94.44% of highest seed germination %, while P. aeruginosa and A. faecalis showed 83.11% and 77.33% respectively in comparison to the control plant shows which 49.99% seed germination only. These respective isolates also showed 2.57, 2.37, 2.0 and 1.69 fold of increase in vigour index as compare to the control plants. The above increase in fold in vigour index and seed germination is much higher as compared to earlier reports. Collectively, the data of current study underpin that addition of these PGPRs to barley rhizosphere appears a promising strategy to enhance root and shoot biomass of this important agriculture crop. To the best of our knowledge this is the first report demonstrating the effect of P. punonensis and P. plecoglossicda on barley crop.
Assessment of Current and Future Generated Selected E-Waste Quantities in Abu Dhabi

E-waste is a term used to describe electrical and electronic products that enter the waste stream. Currently, it is the fastest growing stream of municipal waste. Informal disposal of E-waste may cause serious health and pollution problems to humans as well as the environment. Although E-waste may represent less than 5% of the total waste discarded into landfills, yet it includes more than 70% of overall toxic waste. Not only the exact amount of E-waste generated in the UAE is unknown, but it is continuously increasing with the advent of new electronic products. Therefore, the main aim of this thesis is to assess the current and future quantities of E-waste in Abu Dhabi emirate. A questionnaire was developed to estimate the generated Ewaste quantities for three products; namely, laptops, desktops and mobile phones. Copies of this questionnaire have been distributed to the public in Al-Ain and Abu Dhabi cities to collect data about the Ewaste. This data was used to estimate the current and future quantities of E-waste. The estimated quantities of generated Ewaste in Abu Dhabi are for three products 1,151,000 mobile phones, 464,000 laptops, and 300,000 desktops. The future quantities of E-waste are predicted based on the life span of each product. They were estimated to be 827,000 laptops, 223,000 desktops, and 1,618,000 mobile phones. The results also indicated that 78% of residents in Abu Dhabi are aware of the adverse effects of E-waste and they are ready to dispose E-waste in an environmental friendly manner. This study will help government and agencies to plan for E-waste management based on the estimated quantities of E-waste.
Research on the optimal placement of light for face recognition

The current national standards of face recognition do not put the placement of light for a clear description, only said that the light should be uniform when face recognition, no overexposure and underexposure. In this paper, we present a novel database and use different face recognition algorithms to determine the placement of light during face recognition. For this study, we have chosen ten widely-used face recognition algorithms: principal component analysis(PCA), linear discriminant analysis(LDA), independent component analysis(ICA), nonnegative matrix factorization (NMF), histogram of oriented gradient(HOG), scale-invariant feature transform(SIFT), face recognition, CosFace, ArcFace, and Combined Margin. We use a novel database that contains face images with a wide range of illumination angles to analyze the relationship between these algorithms and illumination angles. The results of this experiment indicate that the horizontal illumination angle of the light should be controlled in the range from -10° to 10° and the vertical illumination angle should be controlled in the range from -10° to 10°.
Investigation of influence of loss function weights of cycle-consistent adversarial networks on generation of pareidolia stimuli

Pareidolia is the psychological tendency of perceiving non-facial objects as faces. The pareidolia test utilizes this tendency for diagnosis, to identify patients suffering from Lewy body dementia. A typical symptom of Lewy body dementia consists of visual-hallucination of non-existing individuals, which can be created artificially by pareidolia stimuli. No research has been conducted on how dementia progression relates to the pareidolia test, primarily because it is difficult to systematically generate test stimuli with different strengths of pareidolia-inducing power. To overcome this difficulty, we utilize the cycle-consistent adversarial networks (CycleGAN). Two loss functions are associated with CycleGAN. In this paper, the influence of the weight of one of the CycleGAN loss functions, the “cycle consistency loss”, is investigated. The results demonstrate that, as expected, there are systematic differences in inducing pareidolia like facial perception.
Improving Classification Performance for Malware Detection Using Genetic Programming Feature Selection Techniques

Abstract Malware is the term used to describe any malicious software or code that is harmful to systems. From day to day, new malicious programs appear. To classify malware according to its characteristics, machine learning is now being used; this is because most new malware contains patterns that are similar to old ones. This paper proposes two feature selection methods based on Genetic Programming (GP) for predicting malware; the first is called Genetic Programming-Mean (GPM), and the second is called Genetic Programming-Mean Plus (GPMP). The results of these two methods were compared with three state-of-the-art popular feature selection techniques: filter-based, wrapper-based, and Chi-square. In this work, we compare the two proposed methods (GPM and GPMP) with these three widely used feature selection techniques. The results demonstrate that the proposed techniques beat these state-of-the-art ones in terms of accuracy and F-score. The results also revealed that the proposed methods employed less computation time and hence an enhanced performance when compared with filter-based, and wrapper-based feature selection. The proposed methods were evaluated using four datasets. Two classifiers were used to evaluate the proposed feature selection methods: Random Forest and Decision Tree. When a Random Forest classifier is used, our results showed that it outperformed the Decision Tree classifier in indicators, such as F1-score, recall, and precision. The analysis of results using Random Forest and Decision Tree proves that the proposed method is highly efficient.
Current Pathology Model of Pancreatic Cancer

Simple Summary Pancreatic cancer is a very aggressive and lethal malignant neoplasm with overall 5-year survival rates below 10%. The field of pancreatic cancer research is rapidly evolving. Reports of newly revealed pathomechanisms of the nature of these tumors are published daily. Nevertheless, many aspects of a pathologic evaluation are still uncertain. It is crucial to be able to pull out practical information that impacts the diagnostic process, called a pathologic evaluation. In this review, we comprehensively summarize some of the recent papers from the pathologists’ and clinicians’ points of view. We specifically focus on pathology assessment and reporting, to make them meaningful for clinical and research purposes. Lastly, we highlight novel diagnostic and research approaches, point out some missing pieces in the field, and suggest further study directions. Abstract Pancreatic cancer (PC) is one of the most aggressive and lethal malignant neoplasms, ranking in seventh place in the world in terms of the incidence of death, with overall 5-year survival rates still below 10%. The knowledge about PC pathomechanisms is rapidly expanding. Daily reports reveal new aspects of tumor biology, including its molecular and morphological heterogeneity, explain complicated “cross-talk” that happens between the cancer cells and tumor stroma, or the nature of the PC-associated neural remodeling (PANR). Staying up-to-date is hard and crucial at the same time. In this review, we are focusing on a comprehensive summary of PC aspects that are important in pathologic reporting, impact patients’ outcomes, and bring meaningful information for clinicians. Finally, we show promising new trends in diagnostic technologies that might bring a difference in PC early diagnosis.
Detecting Buildings and Nonbuildings from Satellite Images Using U-Net

Automatic building detection from high-resolution satellite imaging images has many applications. Understanding socioeconomic development and keeping track of population migrations are essential for effective civic planning. These civil feature systems may also help update maps after natural disasters or in geographic regions undergoing dramatic population expansion. To accomplish the desired goal, a variety of image processing techniques were employed. They are often inaccurate or take a long time to process. Convolutional neural networks (CNNs) are being designed to extract buildings from satellite images, based on the U-Net, which was first developed to segment medical images. The minimal number of images from the open dataset, in RGB format with variable shapes, reveals one of the advantages of the U-Net; that is, it develops excellent accuracy from a limited amount of training material with minimal effort and training time. The encoder portion of U-Net was altered to test the feasibility of using a transfer learning facility. VGGNet and ResNet were both used for the same purpose. The findings of these models were also compared to our own bespoke U-Net, which was designed from the ground up. With an accuracy of 84.9%, the VGGNet backbone was shown to be the best feature extractor. Compared to the current best models for tackling a similar problem with a larger dataset, the present results are considered superior.
Blister Testing for Adhesion Strength Measurement of Polymer Films Subjected to Environmental Conditions

A blister testing procedure combined with a numerical procedure can be used effectively for characterizing the adhesion strength. A challenge arises when it is implemented after subjecting samples to various environmental conditions. The concept of pseudoproperty is introduced to cope with the problem associated with property changes during environmental testing. The pseudoproperty set is determined directly from a deflection versus pressure curve obtained from a typical blister test. A classical energy balance approach is followed to evaluate the energy release rate from the critical pressure and pseudoproperty set. The proposed approach is carried out for an epoxy/copper interface after subjecting samples to full moisture saturation and a high temperature storage condition. In spite of significant change in property, the energy release rates are calculated accurately without extra tests for property measurements. [DOI: 10.1115/1.4034454]
Submarine pipeline inspection: The 12 years experience of TRANSMED and future developments

TRANSMED is a submarine pipeline gas transportation system crossing the Sicily Channel, in the Mediterranean Sea, at a maximum water depth of 610 m. It went into operation in 1981. Initially composed of three lines, it has been increased to five lines and to a total length of 780 Km in the last year. In the present paper criteria, methodology and equipment used for the periodic inspection surveys of TRANSMED and for the management of gathered data are presented. The twelve years inspection activity and the relevant results of data analysis, mainly the historical evolution of scour induced free spans and the incidence of other hazards, are illustrated. Moreover, the critical aspects concerning data acquisition, processing and management are analyzed and the planned developments are presented.
Nutrient Management on Dairy Farms - Development and Application of the Cornell University Nutrient Management Planning System - a Case Study

Most decisions made on dairy farms have traditionally revolved around the economic impact of the particular decision on the profitability of the enterprise. With expanded knowledge and an increase in herd size to maintain profitability and economic sustainability, environmental sustainability has become as important an issue as profitability. With a greater emphasis on environmental issues, decisions become much more global, and the amount of information that needs to be accounted for can be overwhelming. Integrating this knowledge to make economic and environmentally sustainable decisions requires new tools. This paper describes the development of some of these tools and how application of them to make environmentally sustainable nutrition and cropping decisions potentially increases the overall profitability of dairy farms in New York State.
English and Japanese Resultatives Resultative Constructions vs. Lexical Resultatives

Linguistic expressions describing a situation where an action brings about a result are called resultative expressions or resultatives. Of resultatives, those based on construction are resultative constructions. English resultative constructions are considered a family extending from core cases to peripheral and idiomatic instances. On the other hand, contrary to ordinary opinions, the Japanese language lacks explicit resultative constructions. Its resultative expressions are limited to pseudo-resultatives and resultative verbal compounds. Resultative expressions can be of two types; those whose causal relation derives from construction and those whose causal relation derives from a word. English has these two resultatives, but Japanese has only the latter. In other words, when viewed from the origin of where the causal relationship represented by CAUSE comes from, resultatives can be divided into those whose CAUSE originates lexically and those whose CAUSE originates from the construction. English has both types, while Japanese has only the lexically originated causal relationship. This paper examines this issue in detail.
PITPNA-AS1/miR-98-5p to Mediate the Cisplatin Resistance of Gastric Cancer

Gastric cancer (GC) is the most deadly gastrointestinal malignancy with high incidence and mortality. Although, molecular mechanisms which drive gastric cancer progression are extensively investigated, the roles of long noncoding RNA (lncRNA) in gastric cancer growth and drug sensitivity remain unclear. Platinum is a mainstay to treat gastric cancer, and platinum resistance always leads to the local recurrence of gastric cancer. Therefore, it is important to identify biomarkers or therapeutic targets to sensitize gastric cancer to platinum. In this study, we employ noncoding RNA sequencing and found that lncRNA PITPNA-AS1 is overexpressed in gastric cancer tissues and associated with poor survival of gastric cancer patients. Kockdown of PITPNA-AS1 in gastric cancer cells significantly inhibited cell growth and triggered apoptotic cell death in gastric cancer cells. Also, cisplatin treatment could decrease PITPNA-AS1 levels in gastric cancer cells through inhibiting H3K27ac. Besides, PITPNA-AS1 is elevated in cisplatin-resistant gastric cancer cells and tissues, PITPNA-AS1 knockdown could sensitize gastric cancer cells to cisplatin treatment. Furthermore, we identified that PITPNA-AS1 directly interacts and inhibits miR-98-5p. Therefore, PITPNA-AS1 could be served as a potential biomarkers and curative therapeutic targets for gastric cancer progression.
Methodological strategies for the development of emotional intelligence in upper basic school

The main objective of the research was to determine the methodological strategies that guarantee the development of emotional intelligence at the level of higher basic education, according to the Ecuadorian educational reality. For this scientific work, the application of different research methods that served to guarantee an adequate informative development was extremely necessary. The analytical-synthetic methods and the descriptive method were used, where they were considerably useful in the aspects of analyzing in depth the characteristics and positive and negative elements of the methodological models, this to apply for the type of emotional intelligence and general form within Ecuadorian education. The bibliographic review was used as a technical tool, with this it was possible to collect the largest number of updated authors and their models of methodological strategies, In addition, the descriptive method for the adequate development of the investigation. 20 methodological strategies were determined with the aim of establishing the most appropriate ways to guarantee the development of emotional intelligence, where it was considered that teachers should apply it in the classroom, as a significant contribution in the student body of the upper basic level of the Ecuador and for their social and mental well-being.
Effect of combined treatment with benazepril and spironolactone on diabetic nephropathy and serum levels of IL-6, CRP and TNF-α

Purpose: To study the therapeutic effect of a combination of benazepril and spironolactone on diabetic nephropathy patients, and also to determine the influence of the combined treatment on serum IL-6, CRP and TNF-α. Methods: 100 diabetic nephropathy patients admitted to The Affiliated Hospital of Inner Mongolia Medical University from April 2019 to October 2020 were randomly chosen and divided into groups E and F by drawing lots (n = 50 each). Group E received benazepril, while group F received a combined treatment of benazepril and spironolactone. Therapeutic efficacy, incidence of adverse drug reactions, and renal function after treatment, as well as IL-6, CRP and TNF-α levels in serum pre- and posttreatment, were determined. Fasting blood glucose (FBG) levels were also measured pre-treatment, and at 7 days and 14 days post-exposure to drugs.Results: Therapeutic efficacy and renal functions were significantly better in group F than in group E, while post-treatment incidence of adverse drug reactions, and expression levels of CRP, IL-6, and TNF-α levels were significantly reduced in group F, relative to group E (p < 0.05). Following treatment, the inflammatory factor levels were decreased in both groups.Conclusion: The combination of benazepril and spironolactone produces higher treatment effect in diabetic nephropathy patients than those that received benazepril only. The combined treatment is recommended in the management of diabetic nephropathy patients.
Cine phase-contrast magnetic resonance imaging evaluation of cerebrospinal fluid flow dynamics in healthy pediatric subjects

Objective To evaluate cerebrospinal fluid dynamics, using cine phase-contrast magnetic resonance imaging (cine-PC MRI), in healthy pediatric subjects, determining the normal flow values in this population, as well as identifying differences related to age, sex, and body surface area. Materials and Methods This was a descriptive cross-sectional study involving 32 healthy children and adolescents, in whom the flow of cerebrospinal fluid through the cerebral aqueduct was evaluated quantitatively with cine-PC MRI. We used specialized software to analyze the images obtained with cine-PC MRI, drawing a region of interest on the aqueduct. A flow-time curve was obtained, as were automated measurements of the various parameters. Results The following normal (mean) values were obtained: net flow, 1.10 ± 0.99 mL/m; stroke volume, 12.2 ± 10.1 µL/cycle; mean velocity, 0.72 ± 1.00 cm/s; peak systolic velocity, 5.28 ± 2.30 cm/s; peak diastolic velocity, 4.51 ± 1.77 cm/s. These values were not affected by age or sex. In addition, body surface area was not found to correlate significantly with mean velocity or stroke volume. Conclusion In children and adolescents, the basic cerebrospinal fluid flow parameters, as determined by cine-PC MRI, appear to be independent of age and sex.
Multi-step Coupled Graph Convolution with Temporal-Attention for Traffic Flow Prediction

Forecasting traffic flow is significant for intelligent transportation systems (ITS), such as urban road planning, traffic control, traffic planning, and many more. A flow prediction model aims at forecasting the traffic flow of future time slices at certain regions by learning the historical traffic flow data and environmental information. However, due to the complicated traffic network topology and the dynamicity of traffic patterns in the real world, it is difficult to capture the multi-level spatial dependencies (e.g. global and local impacts to the traffic) and temporal dependencies (e.g. long-term and short-term impacts to the traffic). In this paper, we propose a Multi-step Coupled Graph Convolution Neural network (MCGCN) with temporal attention to capture the spatial and temporal dependencies of different levels in a traffic network, simultaneously, to predict traffic flow. First, a Multi-step Coupled Graph Convolution module (MCGC) is designed to learn the representation of a traffic network by coupling learning the relationship matrices, to capture the different levels’ information of a traffic network. Then, the traffic network information extracted by MCGC is fed into a Multi-step Coupled Graph Gated Recurrent Unit (MCGRU) module to realize the fusion of traffic network information and temporal features. Finally, a Multi-step Coupled Graph Attention mechanism (MCGCAtt) is used to extract the temporal information of historical time steps to predict the future traffic flow. The experiments are conducted on the NYCTaxi and NYCBike datasets, and the evaluation results demonstrate that our proposed model performs better than the eight compared methods.
Improving Sinkhole Attack Detection Rate through Knowledge-Based Specification Rule for a Sinkhole Attack Intrusion Detection Technique of IoT

– The Internet of Things (IoT) is a technology that enables various IoT devices to collect data through sensors or sensor networks and to allow devices to share the collected data in an internet environment. Therefore, most communication is made wirelessly, and it is very vulnerable to a blackhole, selective forwarding, and sinkhole attacks that can occur in the network. One of the destructive attacks is the sinkhole attack, which compromises the integrity and reliability of data in a network. In general, the sinkhole attack detection method used by ad hoc networks and WSNs is less effective than the method used for IoT because of environmental differences. Therefore, the Intrusion detection of SiNkhole attack on 6LoWPAN for InterneT of Things (INTI) method can detect sinkhole attacks occurring in IoT. In this study, rules are defined using a specification-based approach of intrusion detection technology based on the number of input/output transmissions collected in the monitoring phase of INTI. Knowledge base rules were defined to thresholds of normal operation, and different rules were defined according to the role each node plays in improving sinkhole attack detection rates.