Miracle-dz/UniFAR-FTUs huggingface dataset:
A Bayesian Approach to Prediction and Variable Selection Using Nonstationary Gaussian Processes

This research proposes a Bayesian formulation of the Composite Gaussian Process (GP) of Ba and Joseph (2012). The composite Gaussian Process model generalizes the regression plus stationary GP model in both a stationary and nonstationary manner. The likelihood stage of the model combines two independent Gaussian processes and the remaining stages put priors on the means, variances, and correlation parameters of the Gaussian processes. Markov chain Monte Carlo methods are used to estimate posterior predictions and prediction intervals and are compared with predictions from the composite GP model, a treed GP model, and a universal kriging approach. This research also develops screening methodology for experiments with many inputs that is based on a hierarchical Bayesian Gaussian process model. This flexible model is able to describe output functions having varying range and patterns of fluctuation. Screening is accomplished by identifying inputs with small posterior probability of being correlated with the output by incorporating a Bayesian “variable selection” prior for the correlation parameters.
Optical, noncontact, automated experimental techniqes for three-dimensional reconstruction of object surfaces using projection moire, stereo imaging, and phase-measuring profilometry

Three optical, noncontact, automated experimental techniques for determining the topography of object surfaces were assessed. The main objective was to test the limitations of three experimental techniques: projection moire, stereo imaging, and phase-measuring profilometry. Phase-measuring profilometry is the most promising of the three techniques for mapping rock fracture surfaces automatically, accurately, quickly, and with high resolution. The experimental set-ups used to assess these different techniques are similar, and they require essentially the same equipment. It is relatively easy and inexpensive to go from one experimental set-up to another. Also, the experience gained in implementing one experimental technique is often applicable in another, although the basic principles of each experimental technique are sometimes very dissimilar. The first technique, projection moire, is an optical experimental technique that is useful for displaying and measuring the three-dimensional form of large objects. Manual analysis of
Performance and non-destructive evaluation methods of airborne radome and stealth structures

In the past few years, great effort has been devoted to the fabrication of highly efficient, broadband radome and stealth (R&S) structures for distinct control, guidance, surveillance and communication applications for airborne platforms. The evaluation of non-planar aircraft R&S structures in terms of their electromagnetic performance and structural damage is still a very challenging task. In this article, distinct measurement techniques are discussed for the electromagnetic performance and non-destructive evaluation (NDE) of R&S structures. This paper deals with an overview of the transmission line method and free space measurement based microwave measurement techniques for the electromagnetic performance evaluation of R&S structures. In addition, various conventional as well as advanced methods, such as millimetre and terahertz wave based imaging techniques with great potential for NDE of load bearing R&S structures, are also discussed in detail. A glimpse of in situ NDE techniques with corresponding experimental setup for R&S structures is also presented. The basic concepts, measurement ranges and their instrumentation, measurement method of different R&S structures and some miscellaneous topics are discussed in detail. Some of the challenges and issues pertaining to the measurement of curved R&S structures are also presented. This study also lists various mathematical models and analytical techniques for the electromagnetic performance evaluation and NDE of R&S structures. The research directions described in this study may be of interest to the scientific community in the aerospace sectors.
Ecological Diversity of Bacterial Rhizomicrobiome Core during the Growth of Selected Wheat Cultivars

Simple Summary The rhizosphere is the narrow region of soil that is directly influenced by root secretions and their associated microorganisms, named as the root microbiome. The rhizosphere is known as the niche most rich in microorganisms. From a microbiological point of view, the identification of these microorganisms and the discovery of the functions they perform by living, for example, in symbiosis with plants, is of interest. Consequently, this study aims to analyze the diversity of soil bacteria inhabiting the rhizospheric zone of wheat (Triticum aestivum L.), represented by four wheat cultivars (Tytanika, Nordcap, Hondia, Rotax), employing three methods of microbiome determination: (1) membership, (2) composition, and (3) functionality. Wheat cultivation in Poland is cropped on about 20% of all arable land. The studied cultivars are characterized by good yielding parameters and are highly popular for growing in Poland. A technique independent of cultivation (NGS—Next Generation Sequencing) was applied for microbial biodiversity determination, whereas for prediction of their ecological functionality, the FAPROTAX database was utilized. By performing lab analyses, we evidenced that molecular identification of the core rhizomicrobiome is particularly important for understanding the general principles regarding the selection of microorganisms around living roots and for harnessing the power of the microbiome in agricultural practice. Abstract One of the latest ecological concepts is the occurrence of a biased rhizosphere of microorganisms recruited mostly through interactions among various components of the rhizosphere, including plant roots and the bulk soil microbiome. We compared the diverse attributes of the core microbiome of wheat rhizosphere communities with wheat (W) and legume (L) forecrops determined by three different methods in this study (membership, composition, and functionality). The conclusions of the three methods of microbiome core definition suggest the presence of generalists, i.e., some representative microorganisms from Proteobacteria, Actinobacteria, Hypomicrobiaceae, Bradyrhizobiaceae, Sphingomonas sp. , in the wheat rhizomicrobiome. The relative abundance of the core microbiome accounted for 0.1976% (W) and 0.334% (L)—membership method and 6.425% (W) and 4.253% (L)—composition method. Additionally, bacteria of the specialist group, such as Rhodoplanes sp. , are functionally important in the rhizomicrobiome core. This small community is strongly connected with other microbes and is essential for maintenance of the sustainability of certain metabolic pathways.
Hydropodia silvae-nipponicae (Porotheleaceae), a new species from evergreen broad-leaved forests in Japan

Hydropodia silvae-nipponicae, a new agaric species was described based on the specimens collected from evergreen broad-leaved forests in Japan. The present new species is distinct from H. subalpina, the type species of the genus by morphological characteristics and nuclear rDNA sequences. Hydropodia silvae-nipponicae is characterized by its nodulose cystidium-like terminal elements of pileipellis, cylindrical to clavate pileocystidia with irregular excrescences, and dimorphic pleurocystidia. It is the second species belonging to the genus Hydropodia.
Interdisciplinary Approaches in Business Studies: Applications and Challenges

Monodisciplinary research confines itself to expanding the literature, knowledge, and methodologies of a single discipline. In contrast, interdisciplinary studies adopt a holistic approach to solve current problems and create new knowledge informed by different disciplinary perspectives. While there are some debates and challenges regarding the position of interdisciplinary research within academia, interdisciplinary approaches have potential for creating new integrative research domains by combining various skill sets and expertise from various fields or disciplines to tackle critical problems across several areas, including in business studies. A particular application of interdisciplinary approaches will be discussed in connection with a larger research study. This research involves theories, literature, and methodologies from these practical and rapidly-changing fields: business studies, information systems, and consumer psychology.
Indoor Localisation Based on Wi-Fi Infrastructure

Over the last decade, indoor positioning has played an increasing role in the navigation market. In this work we present an indoor attempt for localization based on radio-environment properties. Our research concerns implementation of machine learning algorithms for a Wi-Fi fingerprints-based positioning system. The algorithms we chose are kNN, NB and RF. The method was evaluated using several different mobile devices, with samples collected in different locations of a school building. The results we achieved are very promising.
Toward Reaching a Consensus on the Concept of Power System Resilience: Definitions, Assessment Frameworks, and Metrics

The electric power system plays an integral part in the well-being of the modern society. Because of climate change, the ageing power system infrastructure is under threat due to the ever-increasing intensity and frequency of high-impact, low-probability (HILP) events. Although, in most cases, these events are area-specific, the impact of such events, if unaddressed, can lead to cascading failures. Therefore, it is vital for the grid of tomorrow to not only be reliable but also be resilient in view of the broad inter-dependencies. Despite being a widely researched topic, the applicability of the concept of resilience, especially in power systems terms, is not a straightforward task due to the lack of consensus on a consistent definition, or a set of robust metrics. This paper starts with an analysis of different definitions, frameworks, and metrics related to resilience proposed by multiple researchers and research organizations which is then followed by determination of the damage cost and risk associated with an extreme event which is pivotal in resilience enhancement decisions. We then present two case studies: 1) for determining the customer damage cost that underpins the increase in customer cost as a result of major event, 2) for estimating the risk index of the network that helps support resilience-oriented decision making. We also summarize some of the guidelines and standard practices followed by electric utility companies concerning extreme weather events in terms of preparedness and recovery actions, resilience improvement plans, etc. Moreover, to ascertain the improvement in the grid resilience indices, as a result of resilience enhancement application, a case study (Case Study 3) that evaluates three resilience improvement techniques is presented.
Short-term wind speed prediction based on combinatorial prediction model

Improving the accuracy of wind speed forecast can increase wind power generation and better achieve wind energy grid connection. Therefore, a two-stage wind speed prediction model based on Ensemble Empirical Modal Decomposition (EEMD) and the combination prediction of Recurrent Neural Network (RNN), Long Short Term Memory (LSTM), eXtreme Gradient Boosting (XGBOOST), Gate Recurrent Unit (GRU), Temporal Convolutional Network (TCN) is proposed. First, the original wind speed series is separated into Intrinsic Mode Functions (IMFs) using EEMD. Then, RNN, LSTM, XGBOOST, GRU, TCN multiple prediction models are established to learn features from each subsequence and superimpose the prediction results of subsequences. Finally, Particle Swarm Optimization (PSO) is applied to the results of multiple prediction models to assign weights, combined with weight superimposing sequences to achieve higher accuracy and more robust wind speed prediction. Simulation analysis using data from St. Thomas, Virgin Islands wind measurement station to validate the validity of the combined prediction model. The experimental simulation results show that the model proposed in this paper has a good result on increasing wind speed prediction accuracy.
Primary Thyroid Lymphoma: An Interesting Case Emphasising Early Diagnosis

Introduction: Primary thyroid lymphoma is defined as a lymphoma involving only the thyroid gland, or thyroid gland with regional lymph nodes without contiguity or metastasis to other areas at the time of diagnosis. Hashimoto’s thyroiditis is a known risk factor for the disease. It responds well to chemotherapy but since it is a rare clinical entity, the diagnosis is often missed, thereby subjecting the patient to extensive surgery. Case report: A 69 years old male presented with rapidly progressive swelling in front of neck for 10 days associated with dysphagia, odynophagia and dyspnoea. He had right lobe thyromegaly with multiple cervical lymphadenopathy with ultrasonographic risk stratification of TIRADS 5. Diagnosis was clinched by core biopsy report on immunohistochemistry as Diffuse Large B Cell Non-Hodgkin’s Lymphoma. Positron emission tomography computerized scan revealed 18-fluorodeoxyglucose avid grossly enlarged thyroid lobes and isthmus with multiple bilateral cervical lymph nodes. Documenting the stage of disease as III E, he was managed with Chemo-immunotherapy consisting of RCVP regime with complete remission. Conclusion: Primary thyroid lymphoma with extra-thyroid involvement can be rapidly progressing and life threatening. However, timely investigation leads to correct diagnosis, appropriate curative medical management and can avoid unwarranted surgery.
Airway Epithelial-Derived Immune Mediators in COVID-19

The airway epithelium, which lines the conducting airways, is central to the defense of the lungs against inhaled particulate matter and pathogens such as SARS-CoV-2, the virus that causes COVID-19. Recognition of pathogens results in the activation of an innate and intermediate immune response which involves the release of cytokines and chemokines by the airway epithelium. This response can inhibit further viral invasion and influence adaptive immunity. However, severe COVID-19 is characterized by a hyper-inflammatory response which can give rise to clinical presentations including lung injury and lead to acute respiratory distress syndrome, viral pneumonia, coagulopathy, and multi-system organ failure. In response to SARS-CoV-2 infection, the airway epithelium can mount a maladaptive immune response which can delay viral clearance, perpetuate excessive inflammation, and contribute to the pathogenesis of severe COVID-19. In this article, we will review the barrier and immune functions of the airway epithelium, how SARS-CoV-2 can interact with the epithelium, and epithelial-derived cytokines and chemokines and their roles in COVID-19 and as biomarkers. Finally, we will discuss these immune mediators and their potential as therapeutic targets in COVID-19.
Local Competition and Enhanced Defense: How Metarhizium brunneum Inhibits Verticillium longisporum in Oilseed Rape Plants

Metarhizium brunneum is a soil-borne fungal entomopathogen that can be associated with plant roots. Previous studies have demonstrated that root colonization by beneficial fungi can directly affect soil-borne pathogens through competition and antibiosis and can activate a systemic response in plants, resulting in a primed state for a faster and/or stronger response to stressors. However, the mechanisms by which Metarhizium inoculation ameliorates symptoms caused by plant pathogens are not well known. This study evaluated the ability of M. brunneum to protect oilseed rape (Brassica napus L.) plants against the soil-borne pathogen Verticillium longisporum and investigated whether the observed effects are a result of direct interaction and/or plant-mediated effects. In vitro and greenhouse experiments were conducted to measure fungal colonization of the rhizosphere and plant tissues, and targeted gene expression analysis was used to evaluate the plant response. The results show that M. brunneum delayed pathogen colonization of plant root tissues, resulting in decreased disease symptoms. Direct competition and antibiosis were found to be part of the mechanisms, as M. brunneum growth was stimulated by the pathogen and inhibited the in vitro growth of V. longisporum. Additionally, M. brunneum changed the plant response to the pathogen by locally activating key defense hormones in the salicylic acid (SA) and abscisic acid (ABA) pathways. Using a split-root setup, it was demonstrated that there is a plant-mediated effect, as improved plant growth and decreased disease symptoms were observed when M. brunneum was in the systemic compartment. Moreover, a stronger systemic induction of the gene PR1 suggested a priming effect, involving the SA pathway. Overall, this study sheds light on the mechanisms underlying the protective effects of M. brunneum against soil-borne pathogens in oilseed rape plants, highlighting the potential of this fungal entomopathogen as a biocontrol agent in sustainable agriculture.
An Incremental Shared Nearest Neighbour Clustering Approach For Numerical Data Using An Efficient Distance Measure

Clustering is one of the prominent fields of data mining. A major drawback of traditional clustering algorithms is that they perform clustering on static databases. But in real time databases are dynamic. Therefore incremental clustering algorithms have become an interesting area of research wherein clustering is performed on the incremental data without having to cluster the entire data from scrape. In this paper, a new incremental clustering algorithm called Incremental Shared Nearest Neighbor Clustering Approach (ISNNCA) for numeric data has been proposed. This algorithm performs clustering based on a similarity measure which is obtained from the number of nearest neighbors that two points share. In order to identify nearest neighbors, a distance measure is used. A distance measure that performs well with this algorithm has been identified in this work. This algorithm could find clusters of different shapes, sizes and densities.
Semi-Automatic Medical Syringe Pump Development: Interface, Control, Alarm, and Feedback

The syringe pump is used to inject liquids for a long period of time and on a small volume scale. The presence of air inside the hose whilst injection into the human body is dangerous. Early detection can be done using a bubble sensor. The SPICAF is equipped with a gearbox capable of injecting long-term injections on a small scale, and a bubble sensor capable of detecting air by comparing light intensity on a clear hose which automatically notifies the user if dangerous conditions occur. The SPICAF is successfully designed, developed, and tested to WHO specifications. It’s injection has 98% accuracy and is able to detect air bubbles properly.
A NOVEL METHOD FOR BLIND SEGREGATION OF SPEECH AND IMAGE DATA USING INDEPENDENT VECTOR ANALYSIS.

Independent Vector Analysis (IVA) is a handy algorithm that is used to separate a Convolutive mixture into its constituent signals that are most common in acoustic surrounding. While postulating that sources are independent and mixing is linear. IVA strongly excludes the permutation issue in the frequency domain by the adoption of spherical dependency model to the entire frequency bins. Which was one of the dilemma with Independent Component Analysis (ICA) even though it was handled by several engineering solutions. Till now IVA algorithm segregates the mixture into its components that are generated by homogenous sources, while in this paper heterogeneous sources are introduced. Quality of splitting is determined utilizing signal-to-noise ratio (SNR).
Improved Sugarcane-Based Fermentation Processes by an Industrial Fuel-Ethanol Yeast Strain

In Brazil, sucrose-rich broths (cane juice and/or molasses) are used to produce billions of liters of both fuel ethanol and cachaça per year using selected Saccharomyces cerevisiae industrial strains. Considering the important role of feedstock (sugar) prices in the overall process economics, to improve sucrose fermentation the genetic characteristics of a group of eight fuel-ethanol and five cachaça industrial yeasts that tend to dominate the fermentors during the production season were determined by array comparative genomic hybridization. The widespread presence of genes encoding invertase at multiple telomeres has been shown to be a common feature of both baker’s and distillers’ yeast strains, and is postulated to be an adaptation to sucrose-rich broths. Our results show that only two strains (one fuel-ethanol and one cachaça yeast) have amplification of genes encoding invertase, with high specific activity. The other industrial yeast strains had a single locus (SUC2) in their genome, with different patterns of invertase activity. These results indicate that invertase activity probably does not limit sucrose fermentation during fuel-ethanol and cachaça production by these industrial strains. Using this knowledge, we changed the mode of sucrose metabolism of an industrial strain by avoiding extracellular invertase activity, overexpressing the intracellular invertase, and increasing its transport through the AGT1 permease. This approach allowed the direct consumption of the disaccharide by the cells, without releasing glucose or fructose into the medium, and a 11% higher ethanol production from sucrose by the modified industrial yeast, when compared to its parental strain.
On Vehicle Stabilization Under Saturation Constraints by Linear Quadratic Regulator

The stability of a four-wheel vehicle is an issue widely discussed. As an effort to reduce accidents involving four-wheel vehicles, many studies have been conducted to develop a vehicle stability control system. So far, all studies utilize vehicle lateral dynamics model as the reference to derive the desired states. However, defining the desired states using steady-state cornering values may yield instability due to changes in tire cornering stiffness. This paper proposes a simple vehicle stability control system that could stabilize the vehicle and allow tracking of a desired state under the case of vehicle instability due to reduced rear tire cornering stiffness. The controllers are designed using Linear Quadratic Regulator (LQR) technique and combined with the servo control system to track a reference state. Simulations are performed in MATLAB to validate the controller’s performance with and without controller constraint. Results show that the controller successfully stabilizes the vehicle and tracks the desired state.
Criterion for estimation of stress-deformed state of SD-materials

A criterion is proposed that determines the moment when the growth pattern of the monotonic numerical sequence varies from the linear to the parabolic one. The criterion is based on the comparison of squares of errors for the linear and the incomplete quadratic approximation. The approximating functions are constructed locally, only at those points that are located near a possible change in nature of the increase in the sequence.A criterion is proposed that determines the moment when the growth pattern of the monotonic numerical sequence varies from the linear to the parabolic one. The criterion is based on the comparison of squares of errors for the linear and the incomplete quadratic approximation. The approximating functions are constructed locally, only at those points that are located near a possible change in nature of the increase in the sequence.