Miracle-dz/UniFAR-FTUs huggingface dataset:
THESIS FOR THE DEGREE OF DOCTOR OF PHILOSOPHY Resource efficient products in a circular economy – The case of consumables From environmental and resource assessment to design guidelines

The circular economy (CE) is a concept to challenge the unsustainable production, consumption, and waste management of products, through the recirculation of resources and products through various means while reducing environmental impact. Within this concept, many measures are recommended for reducing environmental impact and resource use. However, to ensure and verify whether a measure is leading to its intended outcome, environmental and resource assessment is necessary. This thesis aims to investigate which measures are resource efficient and identify for which products different measures are suitable and under what circumstances they lead to their intended outcomes. Based on this, design methods are developed to enable the design of more resource-efficient products. Finally, this thesis aims to investigate more specifically the measures that are suitable for consumable products, as these products have not been examined thoroughly in the circular economy literature as durable products. The aims of this research were met by investigating which resource-efficiency measures exist and are applicable to products with different characteristics. This was done through life cycle assessment studies of specific consumable products. Further, a synthesis study was carried out in which lifecycle-based assessment studies of different products and measures were analysed. This research concludes that depending on a product’s characteristics, some measures are more relevant than others. In addition, the analysis shows that many measures lead to trade-offs between different types of environmental impacts and resources uses, as well as between different life cycle phases. For these findings to be practically useful, they were subsequently translated into design guidelines expressed as a design tool. Finally, a literature review was conducted of general product design guidelines in the CE and ecodesign literature to compile and analyse to what extent the design guidelines are applicable to different types of consumables. Among other factors, this review shows that, on average, less than half of the recommendations found in the general product design guidelines are possible to apply to consumables. Further, the CE literature was found to provide fewer relevant design considerations than the ecodesign literature. This work also identifies what aspects make product-types specific design guidelines transferable to other consumables.
GREEN SYNTHESIS, CHARACTERIZATION, AND ANTIMICROBIAL PROPERTIES OF COPPER NANOPARTICLES FROM NERIUM OLEANDER L EAF AQUEOUS EXTRACT

The advancement of Nanotechnology and the biogenic synthesis of Nanoparticles has emerging applications in the Medical field. Copper nanoparticle synthesis has been gaining attention due to its availability. Water-soluble cupric oxide nanoparticles are stable over a wide range of pH and temperature. This excellent stability in the form of aqueous colloidal suspensions makes the application of the water-soluble CuO nanoparticles easier in aqueous systems. In the present work, pure copper nanoparticles (CuNPs) were prepared from aqueous leaf extract of Nerium oleander which acts as a reducing agent in the presence of copper sulphate by biological means. The purity of the nanoparticles was authenticated using different optical (ultraviolet visible spectroscopy) and structural characterization techniques (transmission electron microscopy and Fourier transform infrared spectroscopy). The size of the copper nanoparticles obtained was in the range of 3.47 nm to 15.2 nm. The antibacterial activity of the nanoparticles were investigated using several microorganisms of interest, including Escherichia coli, Klebsiella pneumoniae, proteus vulgaris (Gram negative) and Staphylococcus aureus Bacillus subtilis, Micrococcus luteus (Gram positive) which showed that the nanoparticles have significant antibacterial activity against both Gram positive and Gram negative pathogenic bacterial strains and retains potential application in pharmaceutical and biomedical industries. The important outcome of the study will be the development of value-added products from medicinal plants for biomedical and nanotechnology based industries.
Synthesis Method of Flash Graphs for Optimal Search of Manufacturing Subsystems Recognised on Basis of Composite Bayes Task Solution

– The decision-making algorithm selection is one of the problems that modern economics, industrial management and situation identification process face. The Bayesian composite approach is considered to be the safest way for algorithm identification. However, the approach based on Bayesian composite rules is time-consuming and computationally intensive, that`s why it is important to develop the method, when combined with the Bayesian approach, will enable to scale back the computation. The article describes the synthesis method of flow graphs for optimal search within the member group (including the search within the industrial structure) and suggests this method as the main implementable algorithm for situation identification, based on Bayesian composite rule for decision-making. Such scientific methods as mathematical techniques of graph theory, algorithm and combination methods, probability theory method, and methods of statistical analysis were used in the research.
Neural networks for the N-Queens Problem : a review

: Neural networks can be successfully applied to solving certain types of combinatorial optimization problems. In this paper several neural approaches to solving constrained optimization problems are presented and their properties discussed. The main goal of the paper is to present various improvements to the well known Hopfield models which are intensively used in combinato rial optimization domain. These improvements include determin istic modifications (binary Hopfield model with negative self-feed back connections and Maximum Neural Network model), stochastic modifications (Gaussian Machine) , chaotic Hopfield-based models (Chaotic Neural Network and Transiently Chaotic Neural Network) , hybrid approaches (Dual-mode Dynamic Neural Network and Har mony Theory approach) and finally modifications motivated by dig ital implementation feasibility (Strictly Digital Neural Network). All these models are compared based on a commonly used bench mark problem-the N-Queens Problem (NQP). Numerical results in dicate that each of modified Hopfi.eld models can be effectively used to solving the NQP. Convergence to solutions rate of these methods is very high-usually close to 100%. Experimental time requirements are generally low - polynomial in most cases. Some discussion of non-neural, heuristic approaches to solving the NQP is also presented in the paper.
On an Univalent Integral Operator

The integral operator denoted by I(f1, f2, . . . , fm) given in Definition 2 was introduced in [8]. Also, certain sufficient conditions of univalence of this operator were given there. In this paper, a different approach for proving the univalence of this operator is taken.
University of Birmingham A robust and efficient statistical method for genetic association study using case and control samples from multiple cohorts

Background: The theoretical basis of genome-wide association studies (GWAS) is statistical inference of linkage disequilibrium (LD) between any polymorphic marker and a putative disease locus. Most methods widely implemented for such analyses are vulnerable to several key demographic factors and deliver a poor statistical power for detecting genuine associations and also a high false positive rate. Here, we present a likelihood-based statistical approach that accounts properly for non-random nature of case – control samples in regard of genotypic distribution at the loci in populations under study and confers flexibility to test for genetic association in presence of different confounding factors such as population structure, non-randomness of samples etc. Results: We implemented this novel method together with several popular methods in the literature of GWAS, to re-analyze recently published Parkinson ’ s disease (PD) case – control samples. The real data analysis and computer simulation show that the new method confers not only significantly improved statistical power for detecting the associations but also robustness to the difficulties stemmed from non-randomly sampling and genetic structures when compared to its rivals. In particular, the new method detected 44 significant SNPs within 25 chromosomal regions of size < 1 Mb but only 6 SNPs in two of these regions were previously detected by the trend test based methods. It discovered two SNPs located 1.18 Mb and 0.18 Mb from the PD candidates, FGF20 and PARK8 , without invoking false positive risk. Conclusions: We developed a novel likelihood-based method which provides adequate estimation of LD and other population model parameters by using case and control samples, the ease in integration of these samples from multiple genetically divergent populations and thus confers statistically robust and powerful analyses of GWAS. On basis of simulation studies and analysis of real datasets, we demonstrated significant improvement of the new method over the non-parametric trend test, which is the most popularly implemented in the literature of GWAS.
Virtual Mouse Using Eye Tracking Technique

— nowing that user’s point of gaze has long held the promise of being a useful methodology for human computer interaction. However, a number of barriers have stood in the way of the integration of eye tracking into everyday applications, including the intrusiveness, robustness, availability, and price of eye-tracking systems. The goal of this thesis is to lower these barriers so that eye tracking can be used to enhance current human computer interfaces. The paper presents the real time system interface between computer and human. —. This technology is able to replace the standard interface which is the computer mouse or we can say traditional mouse with the human eyes, as a new way to interact with computer. Thus, mouse working with the help of human eyes is known as Virtual mouse. The system we described is fast and an affordable technique for tracking facial features. We are trying to detect facial features i.e eyes through Viola Jones algorithm, with the help of Matlab’13. The accuracy of eye tracking was found to be approximately one degree of visual angle. It is expected that the availability of this system will facilitate the development of eye-tracking applications and the eventual integration of eye tracking into the next generation of everyday human computer interfaces.
Goal Orientation and Health and Wellbeing outcomes in Nurses

The present study distinguishes between two sources of predictors of job strain in nurses: the psychosocial work environment and nurses’ work related goal orientation. Regulatory Focus Theory stresses the importance of goal orientation by distinguishing between a prevention focus and a promotion focus. The ﬁrst aim of this study is to investigate the inﬂuence of prevention and promotion oriented goals on nurses’ job satisfaction, emotional exhaustion, psychological distress and somatic complaints, over and above the inﬂuence that job stressors have on these stress indicators. The second aim was to explore the interactions between job stressors and goal orientation in the explanation of stress outcomes. The goals of 575 nurses were categorized into a “prevention focus”, a “promotion focus” or “other” category. Hierarchical regression analyses were performed to examine the relative inﬂuence of work environmental variables, goal orientation and interactions between work environment and goal orientation on the stress outcomes. Goal orientation added to the prediction of job stress over and above work environmental variables. Interaction eﬀects were found. The results of the present study suggest that it is worthwhile for future strain research to incorporate regulatory processes such as goal orientation goal orientation, the work environment, and strain outcomes, the present study shows that it is worthwhile to incorporate goal variables based on regulatory focus theory in studies on job strain.
GENETIC ATTRIBUTES OF F 3 POPULATIONS AND THEIR PARENTAL LINES IN UPLAND COTTON

The genetic potential, inbreeding depression, heritability, selection response and correlation were studied during 2011-12 in parental cultivars and their F 3 populations of upland cotton at the University of Agriculture, Peshawar, Pakistan. Eight upland cotton genotypes i.e., SLH-284, CIM-446, CIM-473, CIM-496, CIM-499, CIM-506, CIM-554 and CIM-707 and their 56 F 3 populations were sown in a randomized complete block design with three replications. Mean squares revealed significant differences among the parental lines and F 3 populations for all the characters. On average, the F 3 populations showed best performance than parental cultivars for yield contributing traits and eventually increased seed cotton yield. Majority of the F 3 populations revealed inbreeding depression for various traits. However, some of the F 3 populations revealed negative values for inbreeding depression by transgressing their F 2 populations in performance. The PCV and GCV values were comparable for almost all the traits which revealed that genotypes played greater role rather than environment and indicating less influence of environment on majority of the traits. Heritabilities (broad sense) were low to high with desirable selection response for all the traits. Seed cotton yield had significant positive correlation with all the traits, except days to first flowering where the association was non-significant. Parental cultivars viz., CIM-496, SLH-284, CIM-554 and CIM-707 and their F 3 populations i.e. CIM-506 × SLH-284, CIM-707 × CIM-554, CIM-473 × CIM-496, SLH-284 × CIM-707, SLH-284 × CIM-473 and CIM-473 × CIM-707 revealed maximum values for seed cotton yield and major yield contributing traits. The promising F 3 populations authenticated stability even after segregation which can be further studied for enhancement in seed cotton and lint yields.
Influence of ceramic thickness and cavity design optimization on fracture resistance of partial coverage restorations.

Background and Objective: Literature suggests the use of thinner indirect partial coverage restorations. The purpose of this in vitro study was to evaluate the influence of cavity design optimisation and bio-substitution with various cuspal and fissure designs, for indirect lithium disilicate partial coverage restorations. The influence on the initial crack formation and subsequently the catastrophic fracture force were evaluated. Method: Forty molars were prepared and randomly divided into four test groups (A, B, C and D). Indirect partial coverage restorations were milled with different cuspal and fissure thicknesses of lithium disilicate ceramic. Group A (cusp 1; fissure 0.7 mm thickness) and group B (cusp 0.8; fissure 0.5 mm) received a composite restoration in the class I preparation additionally. Group C (cusp 1; fissure 1.7 mm) and group D (cusp 0.8; fissure 1.5 mm) had ceramic included in the class I cavity. The restored specimens were thermo-cycled and force application was completed with a Universal testing machine in two phases. Phase one was the determination of initial first crack formation with various pre-set forces. Phase two was the maximum catastrophic fracture force. Statistical analysis was completed with the Kruskal-Wallis test and confirmed by the Dunn-Sidak multiple comparison test (p < 0.05). Results: The catastrophic fracture force in group A (2460N) was significantly greater than group B (1523N), with no difference between group C (3142N) and group D (2591N). Conclusion: The cuspal thicknesses of 1 mm for indirect partial coverage restorations were advisable, with or without composite in the class I cavity.
Improvement of sucrose tolerance of baker’s yeast, Saccharomyces cerevisiae by mating

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Soluble CD14: genome-wide association analysis and relationship to cardiovascular risk and mortality in the older adults

Objective— CD14 is a glycosylphosphotidylinositol-(GPI)-anchored membrane glycoprotein expressed on neutrophils and monocytes/macrophages that also circulates as a soluble form (sCD14). Despite the well-recognized role of CD14 in inflammation, relatively little is known about the genetic determinants of sCD14 or the relationship of sCD14 to vascular- and aging-related phenotypes. Methods and Results— We measured baseline levels of sCD14 in over 5,000 European-American (EA) and African-American (AA) adults 65 years and older from the Cardiovascular Health Study, who were well-characterized at baseline for atherosclerotic risk factors and subclinical cardiovascular disease (CVD), and who have been followed for clinical CVD and mortality outcomes up to 20 years. At baseline, sCD14 generally showed strong positive correlations with traditional cardio-metabolic risk factors and with subclinical measures of vascular disease such as carotid wall thickness and ankle-brachial index (independently of traditional CVD risk factors), and was also inversely correlated with body mass index. In genome-wide association analyses of sCD14, we (a) confirmed the importance of the CD14 locus on chromosome 5q21 in EA; (b) identified a novel African ancestry-specific allele of CD14 associated with lower sCD14 in AA; (c) identified a putative novel association in EA of a nonsynonymous variant of PIGC, which encodes an enzyme required for the first step in GPI anchor biosynthesis. Finally, we show that, like other acute phase inflammatory biomarkers, sCD14 predicts incident CVD, and strongly and independently predicts all-cause mortality in older adults. Conclusion— CD14 independently predicts risk mortality in older adults.
Monitoring and Managing IoT Applications in Smart Cities Using Kubernetes

—With the rapid urbanization, cities are transforming to smart cities with core objectives to maintain a safe, healthy and livable environment for the people. The current landscape of smart cities are continuously evolving with unique challenges and gaining ground with new technology-based solutions on the Internet of Things (IoT) and cloud computing. The efﬁcient integration of IoT and cloud computing can tackle the unprecedented growth of smart cities by supporting various smart services like healthcare, transportation systems, environment monitoring, smart grids, etc. Recent advances in cloud computing like containerization of applications are promising solutions to host, supervise and reform the diverse IoT applications in smart cities. In this paper, we have explored the possibilities to implement a secure, distributed and reliable cloud-based monitoring system for IoT applications for effective management of a smart city environment. We propose to build a container-based system with low latency, a reliable and secure communication among large scale deployment of IoT devices with a strong focus on horizontal interoperability among various IoT applications. Our experiment with Docker-based containerization techniques along with a Kubernetes container orchestration platform emphasizes an efﬁcient way to manage and monitor the status and events in IoT applications in the scale of smart cities.
Dielectric Shimming

Dielectric shimming is proven to be very useful in increasing the homogeneity of the B field in high field MRI. Current optimization and design techniques for dielectric pad parameters are slow. The goal of this thesis is to find a fast and accurate pad design and optimization technique. Two new techniques are proposed. The first, a method that simply uses inspection by solving the forward problem in a relatively fast way. The other proposed technique follows a more analytical approach to find the optimal permittivity and conductivity of a pad in a couple of iterative steps with a Gauss-Newton method. This last technique uses a new proposed approach to predict the phase of the B field in a direct fashion. These techniques provide fast and accurate simulation results for a two-dimensional abdominal body slice placed in a 3T MRI scanner for different pad scenarios. From these results it can be concluded that both proposed techniques generate comparable pads, which are able to increase the homogeneity of the B field. A comparison between the two techniques is made. The Gauss-Newton method provides a fast, robust and accurate optimization technique for large scale problems, but offers less flexibility and insight to the data compared to the method via inspection. The flexibility of the method via inspection and the insight it provides is shown for different scenarios (pad location, multiple pads, pad shape, pad thickness), where the effect of the optimal permittivity and conductivity on the homogeneity of the resulting B field is simulated. Even the maximum allowed SAR can be incorporated in this pad optimization technique.
Classifying Vehicles’ Behaviors using Global Positioning Systems Information

—This study presents a solution to enhance the cities’ trafﬁc control by classifying particular vehicles’ behaviors. A Support Vector Machine (SVM) approach is presented, enabling the system to classify cars that are looking to park and those that are simply transiting through a city. Through this paper, we also propose a new way of managing the high density of trafﬁc data using a grid. The results show that the system is able to distinguish the two different behaviors with an accuracy averaging 80%.
Fast Identification of Frequent Occurring Motorcades Based on Massive Traffic Data Streams

Under some circumstances, Frequent Occurring Motorcades, or FOMs, would be suspected to be a gang of criminals. However, huge amount of traffic data streams could hinder the fast identification of FOMs. This paper presents a novel approach to fast identification of FOMs based on multi-core processors. It combines the data parallelism and task parallelism, and obtains suspect FOMs by combining local candidates discovered in different processor cores. The case shows that the approach has higher performance and speedup when searching FOMs among massive passing records.
Comparative analysis of memristor models and memories design

The advent of the memristor breaks the scaling limitations of MOS technology and prevails over emerging semiconductor devices. In this paper, various memristor models including behaviour, spice, and experimental are investigated and compared with the memristor's characteristic equations and fingerprints. It has brought to light that most memristor models need a window function to resolve boundary conditions. Various challenges of availed window functions are discussed with matlab's simulated results. Biolek's window is a most acceptable window function for the memristor, since it limits boundaries growth as well as sticking of states at boundaries. Simmons tunnel model of a memristor is the most accepted model of a memristor till now. The memristor is exploited very frequently in memory designing and became a prominent candidate for futuristic memories. Here, several memory structures utilizing the memristor are discussed. It is seen that a memristor-transistor hybrid memory cell has fast read/write and low power operations. Whereas, a 1T1R structure provides very simple, nanoscale, and non-volatile memory that has capabilities to replace conventional Flash memories. Moreover, the memristor is frequently used in SRAM cell structures to make them have non-volatile memory. This paper contributes various aspects and recent developments in memristor based circuits, which can enhance the ongoing requirements of modern designing criterion.