Miracle-dz/UniFAR-FTUs huggingface dataset:
Natural Gas Condensate Desulfurization via Polyacrylonitrile/Ag Nanocomposite Nanofibers: Optimization and Kinetics/isotherms Studies

In the present study for the first time, a novel polyacrylonitrile-silver nanocomposite nanofiber was synthesized by electrospinning for adsorbing sulfur compounds in natural gas condensate. The synthesized material was characterized by SEM, EDAX, and XRD. The presence of C, N, O, and Ag proved that the composite was synthesized successfully. In order to achieve the best adsorption conditions, the effect of contact time, adsorbent dosage and initial sulfur compound concentration was examined. Under optimal values, efficiency of greater than 90% was found. In addition, different isotherms and kinetics models were tested to describe the sorption process. It was found that Freundlich (0.9900) was superior to Langmuir (0.9688), Temkin (0.9648) and Dubinin-Radushkevich (0.8273) models, revealing that sulfur compounds tend to for, multilayers on the heterogeneous surface of polyacrylonitrile-silver nanocomposite nanofiber. The energy value of the adsorption was 23.57 kJ/mol, indicating chemisorption reactions. Based on kinetics studies, the desulfurization by nanofibers followed Pseudo-second-order and Elovich kinetics. Finally, the desulfurization function of nanocomposite was studied and validated using adsorbent columns. The obtained results demonstrate polyacrylonitrile-silver nanocomposite nanofiber as a promising material in the field of desulfurization.
Thermal Energy Shifting Using Thermal Energy Storage with Solar Assisted System for Space Cooling Application

Due to the instability and irregular of national electric power suppled to residence sector in Iraq for long term history, attracted researchers interest to strive for solutions, and associated challenge dry and very hot summer season in Iraq on air conditioning application, A test room full size prototype was constructed in Baghdad, its size 33.5m3, the room is built from very good thermal insulation Autoclave Aerated Concrete AAC with white panted Concrete roof, test room is exposed to solar radiation during entire day, thermal energy shifted by time using thermal energy storage TES containing PCM, PCM is soft paraffin its phase inversion temperature (29 to 27)°C, thermal energy was shifted from night timing by cooling down TES (Discharging PCM) to peak time 11:00 am to 02:00 pm, the testes were carried out over entire summer season April to October, the results showed thermal energy can shift to by any quantity and time based on mass of PCM and enthalpy, electrical energy saved at peak time 52.5% of total power spent over season 2.7KW/day, Only 27% of electric energy utilized to discharge PCM during night, about 43% of heat lose is sourced from exposed roof, melting and solidification of PCM temperature must be within indoor comfort range 23 to 28 ˚C to release or absorb the latent heat 41kJ/kg.
Pharmacognostic and Elemental Comparative Studies of the Leaf of Hygrophila auriculata (K. Schum) Heine (Acanthaceae) Grown in Industrial and Non-Industrial Areas of Jos-South Local Government Area of Plateau State, Nigeria

Exposure to Industrial wastes and leakages can cause prolong damage to humans and plants due to the presence of chemical wastes and heavy metals. This study was conducted to compare the pharmacognostic profile, presence and effects of heavy metals and determine the adverse effects of using medicinal plants from Industrial Area to manage diseases in humans with that of the plant grown in Non-Industrial Area. Standard methods for pharmacognostic and elemental analysis such as macro- and microscopical evaluation, Physico-chemical, phytochemical and elemental analysis were used. Statistical analyses were done using student t-test and Analysis of Variance (ANOVA). Hygrophila auriculata leaf grown in Industrial Area was found to be slightly sour in taste, light green in color with yellowish patches and musty odor as compared to the tasteless, deep green coloration and characteristic odor in Non-Industrial Area. Results of the physicochemical analysis showed that, moisture content, total ash, acid insoluble and water soluble ash, alcohol and water soluble extractive values showed accumulation of extraneous organic matters, impurities and retained much water in Hygrophila auriculata leaf grown in Industrial Area than the one grown in Non-Industrial Area. Hygrophila auriculata leaf grown in Industrial Area was screened positive for steroids/terpenes, cardiac glycosides and saponins while that grown in Non-Industrial Area was positive for steroids/terpenes, cardiac glycosides, saponins, tannins, flavonoids and alkaloids. Hygrophila auriculata leaf grown in Industrial Area was found to contain a significant amount of Cobalt, Lead and Zinc at (p<0.05) as compared to the values obtained from those grown in Non-Industrial Area. Plants from industrial areas should not be used as drug.
Polymerisation onto Biological Templates. A New Way to Obtain Bioartificial Polymeric Materials

The radical polymerisation of synthetic monomers onto biological templates can be recognised as a technique for the straightforward preparation of bioartificial polymeric materials, polymer blends based materials in which interactions at the molecular level lead to enhanced properties. In the first part the polymerisations of different acrylic monomers in the presence of synthetic templates are presented as study models. The second part deals with preliminary experiments carried out using natural polymers as templates. Dilatometric and conductimetric measurements were used to study the kinetics of the template polymerisation reactions. Results indicate that the reactions proceed according the different mechanisms depending on the interactions between functionla groups present in the systems. In many instances the results of the characterisation analyses have shown that the polymer complexes obtained by template polymerisation have a more ordered structure than the complexes prepared by simple mixing the two polymers. In addition conductimetry has revealed as a very simple diagnostic tool for identifying the template reaction mechanism.
Evaluation of detection of koi herpesvirus disease by conventional polymerase chain reaction (PCR)

The method of detection of koi herpesvirus disease continues to be developed to obtain a high level of sensitivity and specificity. One common test method uses conventional PCR. This method resulting not only relatively inexpensive cost analysis but also rapid analysis. This experiment is using four types of commercial kits, the primers design of TK and Sph as well as some annealing temperatures. The sample used was the result of DNA extraction from koi gills, positive control of the collection of KHV 0301 FRA, positive control KHV with dilution concentration of 10000; 1000; 100; 10 and 1, to regulate the presence of contamination on amplification process, a negative control must be included, ddH2O. The annealing temperatures used are 52 C; 55 C; 56 C; 60 C and 63 C. This experiment showed different results of KHV detection. The sensitivity and specificity of the results are different for each kit, primers pairs and annealing temperature. Using a one-degree difference in annealing temperature will affect the detection of KHV although using the same kind of kits and primer pairs. The user is obliged to do optimization method regarding the kits usage, primers pairs and various temperatures to obtain a diagnostic method that has high level sensitivity and specificity detection. The sensitivity detection of KHV virus in this experiment could detect up to 10 copies. The user or analyst have to know and understand about all the information in the manual kits.
“Flash” Synthesis of CdSe/CdS Core–Shell Quantum Dots

We report on the “flash” synthesis of CdSe/CdS core–shell quantum dots (QDs). This new method, based on a seeded growth approach and using an excess of a carboxylic acid, leads to an isotropic and epitaxial growth of a CdS shell on a wurtzite CdSe core. The method is particularly fast and efficient, allowing the controllable growth of very thick CdS shells (up to 6.7 nm in the present study) in no more than 3 min, which is considerably shorter than in previously reported methods. The prepared materials present state-of-the-art properties with narrow emission and high photoluminescence quantum yields, even for thick CdS shells. Additionally, Raman analyses point to an alloyed interface between the core and the shell, which, in conjunction with the thickness of the CdS shell, results in the observed considerable reduction of the blinking rate.
TASKS FOR SERVICE MODULARIZATION PLANNING

Service modularization is gaining increasing attention from scholars and practitioners. However, it must be noted that the essence of service modularization is still not well understood, and its purposefulness of practical application has not yet been well explored. This conceptual paper presents the results of ongoing research addressing service modularization planning issues. The study examines the relationships between the premises and the outcomes of service modularisation. Based on the obtained results, the authors provide the purpose statement for service modularisation. Further, the work outlines the activities that are critical to planning as a management function, analyses their content in the context of service modularization, and identifies the problems that require management decisions. The paper employs comparative analysis, systemic analysis, abstraction, synthesis, abductive reasoning for achieving the research results.
High-resolution Sparse Representation and Its Applications in Radar Moving Target Detection

: To address difficulties in radar signal processing, the effective and efficient detection of low-observable moving targets in complex environments is an ongoing research hotspot. On the one hand, a signal may be extremely weak due to strong clutter and the complex motion of a target, making it hard to separate them in the time and frequency domains. On the other hand, complex coherent integration methods and the heavy computational burden of long-time integration represent challenges for improving radar detection performance with limited resources. High-resolution sparse representation can separate clutter from a moving target with respect to signal sparsity, and can be regarded as an extension of traditional transform-based moving target detection methods. This method has promising application prospects due to the advantages of its high time-frequency resolution, anti-noise property, robustness, and suitability for the analysis of multi-signals. In this paper, we systematically review conventional radar moving target detection methods. Then, we
AN ASSESSMENT OF VIRTUAL MACHINE ASSAILS

Virtual machine plays a typical role in assisting the organizations to decrease the processing cost, pick up the effectiveness, enhanced operation and litheness of accessible hardware. This paper presented an empirical study on security issues in virtualization technologies. Our study focus on various security threads which are common to all virtual machine technologies. The security assails by the intruders have compromised virtual machine infrastructures permit them to way in other virtual machines on the same system and even at the host. Providentially these security apprehensions are being monitored and users can prevent the majority intrusions by taking conventional measures.
AutoSynPose: Automatic Generation of Synthetic Datasets for 6D Object Pose Estimation

We present an automated pipeline for the generation of synthetic datasets for six-dimension (6D) object pose estimation. Therefore, a completely automated generation process based on predefined settings is developed, which enables the user to create large datasets with a minimum of interaction and which is feasible for applications with a high object variance. The pipeline is based on the Unreal 4 (UE4) game engine and provides a high variation for domain randomization, such as object appearance, ambient lighting, camera-object transformation and distractor density. In addition to the object pose and bounding box, the metadata includes all randomization parameters, which enables further studies on randomization parameter tuning. The developed workflow is adaptable to other 3D objects and UE4 environments. An exemplary dataset is provided including five objects of the Yale-CMU-Berkeley (YCB) object set. The datasets consist of 6 million subsegments using 97 rendering locations in 12 different UE4 environments. Each dataset subsegment includes one RGB image, one depth image and one class segmentation image at pixel-level.
MSc. Econometrics and Management Science Business Analytics and Quantitative Marketing Master’s Thesis Convolutional Neural Networks for Multiclass Predictions of Alzheimer’s Disease Progression

Alzheimer’s disease (AD) is a neurodegenarative disorder that is increasingly affecting the worldwide population. There are currently no disease-modifying treatments available, however, promising drugs are being tested in multi-cohort clinical trials. Accurate early-stage forecasts of the development of AD progression are helpful in improving cohort selection. In this study, I investigate the performance of a multi-input convolutional neural network (CNN) for predictions of AD progression. I specifically focus on the generalisability of these forecasts to previously unseen individuals. The multiclass predictions distinguish between cognitively normal (CN) patients, patients with a mild cognitive impairment (MCI), and AD patients. The model is based on grey matter density maps of brain MRI scans and two non-image features, i.e. current diagnosis and follow-up time. One of the main challenges of predicting future diagnosis is to outperform a simple model that always predicts current diagnosis. I therefore introduce a custom loss function that differentiates between non-converters, converters to MCI, and converters to AD. Furthermore, the Grad-CAM tool is used to visualise model activations in the brain and the external performance of the methods is validated on a separate dataset. This study shows that the progression of Alzheimer’s disease can be predicted quite accurately based on a single MRI scan and corresponding current diagnosis. When converters are of interest, a custom loss function is beneficial for tackling the imbalance in the data. A model that uses such a custom loss function generalises quite well to predictions further in the future and to an external dataset. The Grad-CAM results show that model activations in the brain are not always consistent, however, the cerebellum appears to be an important brain region for MRIbased predictions of Alzheimer’s disease progression.
Challenging and opportunities in clinical implementation of circulating cardiac biomarkers in diabetes mellitus: the narrative review

Diabetes mellitus (DM) is a powerful factor contributing cardiovascular (CV) morbidity and mortality in general population. The purpose of the narrative review was to summarize the knowledge with respect to clinical perspectives of the implementation of the circulating biomarkers to stratify patients with type 2 DM (T2DM) at higher CV risk. The bibliographic database of life science and biomedical information MEDLINE, EMBASE, Medline (PubMed), the Web of Science, and the Cochrane Central were searched for English publications satisfying the following of key words: [diabetes mellitus], [type 2 diabetes mellitus], [cardiovascular risk], [cardiovascular risk factors], [cardiac biomarkers]; [circulating biomarkers]; [prognosis]. Conventional biomarkers of biomechanical myocardial stress (natriuretic peptides, cardiac high sensitive troponin T and I), fibrosis (soluble suppressor tumorigenicity-2), and inflammation (growth differential factor-15) may assist in the risk stratification and prediction of T2DM-related complications. Novel circulating biomarkers focused on several specific pathological processes accompanying T2DM, such as extracellular matrix remodeling [matrix metalloproteinases (MMPs), collagen turn-over and bone-related proteins], altered vascular reparation [the number and function of the endothelial progenitor cells (EPCs)], and profiling of cell-to-cell cooperation [endothelial cell derived extracellular vesicles (EVs)], continue to be developed and show promise in the prognosis of the T2DM patient. Conventional approach is based on the use of NPs and cardiac troponins in routine clinical practice, whereas there is no evidence that these biomarkers have equal diagnostic and predictive values for patients with prediabetes and established T2DM.
CONFIRMATION THEORETICAL MODEL OF SUSTAINABLE CONSUMPTION

The organic food and product market is a reality in more than 130 countries, including Mexico. The aim of this research was to identify if sustainable consumption may be explained by three factors: Marketing Strategies, Consumer Behavior and Demographics data. In this study, the test designed by Salgado-Beltrán and Beltrán-Morales (2011) consisting of 10 questions with a Likert scaling was replicated. The statistical technique to test whether the proposed theoretical model fits the data was structural equations due to its potential to further develop the theory. The result show that the hypothetical model fits the data very well, as shown by the CFI (0.970) and RMSEA (0.071) values.
COSMECEUTICALS FOR HAIR AND NAILS SHORT REVIEW

The principal focal point of this review article is to show the cosmeceuticals of hair and nails, it shows how the cosmeceuticals perform in the hair cycle where there are supposed to perform in the improvement of hair appearance by promoting growth and influencing hair color, volume, and conditioning. Even if there are natural growth factors of hair that performs in the hair cycle such as insulin like growth factor and Hormones such as androgens, estrogen and thyroxine which also influence the hair growth. Cosmeceuticals do there apart by speeding up the wellbeing of the hair fast. The article shows also the important of cosmeceuticals on nails as there are to hair, nail cosmeceuticals perform in the protection of nails from getting damaged by environmental factors such as water, soap, and detergents. That’s why there are difference nail cosmeceuticals such as cuticle and nail care products, cuticle removers and nail hardeners.
UNIVERSITY OF VAASA SCHOOL OF TECHNOLOGY AND INNOVATIONS COMMUNICATION AND SYSTEMS ENGINEERING RAJA VARDHAN REDDY MARTHALA WIFI BASED INDOOR POSITIONING A MACHINE LEARNING APPROACH

Navigation has become much easier these days mainly due to advancement in satellite technology. The current navigation systems provide better positioning accuracy but are limited to outdoors. When it comes to the indoor spaces such as airports, shopping malls, hospitals or office buildings, to name a few, it will be challenging to get good positioning accuracy with satellite signals due to thick walls and roofs as obstacles. This gap led to a whole new area of research in the field of indoor positioning. Many researches have been conducting experiments on different technologies and successful outcomes have been seen. Each technology providing indoor positioning capability has its own limitations. In this thesis, different radio frequency (RF) and non-radio frequency (Non-RF) technologies are discussed but focus is set on Wi-Fi for indoor positioning. A demo indoor positioning app is developed for the Technobothnia building at the University of Vaasa premises. This building is already equipped with Wi-Fi infrastructure. A floor plan of the building, radio maps and a fingerprinting database with Wi-Fi signal strength measurements is created with help of tools from HERE technology. The app provides real-time positioning and routing as a future visitor tool. With the exceeding amounts of available data, one of the highly popular fields is applying Machine Learning (ML) to data. It can be applied in many disciplines from medicine to space. In ML, algorithms learn from the data and make predictions. Due to the significant growth in various sensor technologies and computational power, large amounts of data can be stored and processed. Here, the ML approach is also taken to the indoor positioning challenge. An open-source Wi-Fi fingerprinting dataset is obtained from Tampere University and ML algorithms are applied on it for performing indoor positioning. Algorithms are trained with received signal strength (RSS) values with their respective reference coordinates and the user location can be predicted. The thesis provides a performance analysis of different algorithms suitable for future mobile implementations.
Modelling the Relationship between Hotel Perceived Value, Customer Satisfaction, and Customer Loyalty Abstract

Modelling the Relationship between Hotel Perceived Value, Customer Satisfaction, and Customer Loyalty Abstract This study using structural equation modelling (SEM) investigates the relationship between the dimensions of customer perceived value, customer satisfaction, and customer loyalty in the context of hotels. The main procedure of this study was to conceptualise hotel perceived value as a multidimensional construct of seven dimensions with both cognitive and affective aspects. Five out of these seven dimensions; specifically, the self-gratification, price, quality, transaction, hedonic dimensions were then found to have a significant direct positive effect on customer satisfaction and/or customer loyalty. Two dimensions of hotel perceived value (aesthetics, prestige) were found to have no significant direct positive effect either on customer satisfaction or customer loyalty. It was also found that four hotel perceived value dimensions (hedonic, price, quality, transaction) had an indirect significant positive effect on customer loyalty through customer satisfaction as a mediator. Finally, customer satisfaction was found to have a direct positive effect on customer loyalty.
Unifying the Analysis in Control and Optimization via Semidefinite Programs Lecturer:

In this lecture, we review some stability analysis tools in the controls literature, and then tailor them to analyze the convergence rates of some simple optimization methods. Hopefully you will be convinced that there are similarities between the analysis problems in control and optimization, and hence it is not too surprising that some analysis tools developed in the controls field can be applied to study large-scale optimization.
A Computationally Efficient 11 Band Non-Uniform Filter Bank for Hearing Aids Targeting Moderately Sloping Sensorineural Hearing Loss

A computationally efficient 11 band non-uniform filter bank addressing low or moderately sloping sensorineural hearing loss - the most common type of hearing problem- is proposed. This structure is suitable for low cost, small area implementations of hearing aids. The computational efficiency is achieved by adopting the Frequency Response Masking technique, which uses only two prototype filters with a total of 19 multipliers at 80dB stopband attenuation for the design of entire non-uniform filter bank. The computational complexity analysis shows that the proposed method provides about a 70-90% reduction in computational resources compared to non-FRM methods and about a 40-80% reduction in computational resources compared to the other FRM methods. The audiogram matching performance analysis shows that the matching error of the proposed filter bank is negligible even without optimization. The delay performance of the filter bank is acceptable for both Closed Canal Fittings and Open Canal Fittings.