Watermarking techniques have been considered as one of the best efficacious methods 

to solve the issues of protecting copyright due to the proliferation of digital multimedia 

information on the internet. The digital multimedia information is such as image, 

audio, and video which can today be easily copied, distributed, and modified without 

loss in fidelity. As a result, it is necessary to utilize some kind of copyright protection 

techniques to avoid illegal copying, distribution, and digital copyright modification. 

The digital watermarking techniques have engaged the researchers’ attention as a 

technique for protecting copyright in the digital multimedia information. This 

technique includes a subcategory of information-hiding technologies in which the 

ownership information is hidden into the original multimedia information without 

impacting the original multimedia information quality. Despite the advances in digital 

watermarking techniques, the main objective of many types of research in digital 

watermarking topic is to improve transparency and resistance to attacks and the present 

digital watermarking schemes are not sufficiently stable or strong against various 

attacks. This study aims to develop state-of-the-art digital watermarking techniques 

for different multimedia information through improving the imperceptibility and 

robustness to geometrical attacks and signal processing operations. Four novel robust 

blind digital watermarking schemes are introduced in this thesis for copyright 

protection of multimedia information that have not been previously introduced. The 

first robust blind digital watermarking scheme is applied for colour images utilizing 

discrete wavelet transform in combination with Walsh Hadamard transform and 

Hessenberg decomposition. The second robust blind digital watermarking scheme is 

applied for grayscale images utilizing discrete wavelet transform in combination with 

Walsh Hadamard transform and Hessenberg decomposition. The third robust blind 

digital watermarking scheme is applied for colour videos utilizing discrete wavelet 

transform in combination with Walsh Hadamard transform and Hessenberg 

decomposition. Forth robust blind digital watermarking scheme is applied for audio 

signals utilizing discrete wavelet transform in combination with Walsh Hadamard 

transform and Hessenberg decomposition. Several experiments are performed in order 

to present the effectiveness of the proposed digital watermarking schemes in terms of 

imperceptibility and robustness against attacks related to each scheme. The proposed 

digital watermarking schemes achieve good imperceptibility and robustness against 

geometrical attacks and signal processing operations. The proposed digital 

watermarking schemes have a superior performance in terms of imperceptibility and 

robustness than other reported schemes in the literature.

Keywords: Watermarking, Copyright Protection, Discrete Wavelet Transform, Walsh 

Hadamard Transform, Hessenberg Decomposition.

A new record for Abutilon theophrasti is recorded for the first time in the flora of Libya. This widespread agricultural weed was collected from El-Hamra region (El-Qabel) in the Nafusa Mountain south of Tripoli. To facilitate further identification and future detection, a full description, habitat information, distribution map and distribution data are provided. A brief discussion about the most important threats posed by this species is presented.

This study investigate the removal efficiency of chromium (VI) from water by using (R-AgCl) Silver chloride resin (SCR) as selective sorbent material for removal chromium (VI) from aqueous solution; the study was carried out by sorption batch experiments at room temperature(25°C). Efficiency of Cr(VI) removal was determined by measuring the chromium concentration before and after the sorption process. Atomic absorption spectroscopy (AAS) method has been applied as analytical method for determination of Cr concentration in water. Governing factors for removal efficiency were proposed, analyzed and compared. It was conducted that the process is mostly affected by pH value of solution, mass of sorbent and concentration of Cr(VI) , which was the most efficient sorbent showed maximal efficiency and maximal sorption capacity, 1.5g of SCR after 60 min at 25 °C and pH 6.5 recovered 98% of Cr(VI) from 100 mL of water polluted with 20 mg of Cr(VI).

High antibiotic releases from manufacturing facilities have been identified as a risk factor for antibiotic resistance development in bacterial pathogens. However, the role of antibiotic pollution in selection and transferability of antibiotic resistance genes (ARGs) is still limited. In this study, we analyzed effluents from azithromycin-synthesis and veterinary-drug formulation facilities as well as sediments from receiving river and creek taken at the effluent discharge sites, upstream and downstream of discharge. Culturing showed that the effluent discharge significantly increased the proportion of antibiotic resistant bacteria in exposed sediments compared to the upstream ones. Quantitative real-time PCR revealed that effluents from both industries contained high and similar relative abundances of resistance genes [sul1, sul2, qacE/qacEΔ1, tet(A)], class 1 integrons (intI1) and IncP-1 plasmids (korB). Consequently, these genes significantly increased in relative abundances in receiving sediments, with more pronounced effects being observed for river than for creek sediments due to lower background levels of the investigated genes in the river. In addition, effluent discharge considerably increased transfer frequencies of captured ARGs from exposed sediments into Escherichia coli CV601 recipient as shown by biparental mating experiments. Most plasmids exogenously captured from effluent and polluted sediments belonged to the broad host range IncP-1ε plasmid group, conferred multiple antibiotic resistance and harbored class 1 integrons. Discharge of pharmaceutical waste from antibiotic manufacturing sites thus poses a risk for development and dissemination of multi-resistant bacteria, including pathogens.

Manure application to agricultural soil introduces antibiotic residues and increases the abundance of antibiotic-resistant bacteria (ARB) carrying antibiotic resistance genes (ARGs), often located on mobile genetic elements (MGEs). The rhizosphere is regarded as a hotspot of microbial activity and gene transfer, which can alter and prolong the effects of organic fertilizers containing antibiotics. However, not much is known about the influence of plants on the effects of doxycycline applied to soil via manure. In this study, the effects of manure spiked with or without doxycycline on the prokaryotic community composition as well as on the relative abundance of ARGs and MGEs in lettuce rhizosphere and bulk soil were investigated by means of a polyphasic cultivation-independent approach. Samples were taken 42 days after manure application, and total community DNA was extracted. Besides a pronounced manure effect, doxycycline spiking caused an additional enrichment of ARGs and MGEs. High-throughput quantitative PCR revealed an increase in tetracycline, aminoglycoside, and macrolide–lincosamide–streptogramin B (MLS_B) resistance genes associated with the application of manure spiked with doxycycline. This effect was unexpectedly lower in the rhizosphere than in bulk soil, suggesting a faster dissipation of the antibiotic and a more resilient prokaryotic community in the rhizosphere. Interestingly, the tetracycline resistance gene tetA(P) was highly enriched in manure-treated bulk soil and rhizosphere, with highest values observed in doxycycline-treated bulk soil, concurring with an enrichment of Clostridia. Thus, the gene tetA(P) might be a suitable marker of soil contamination by ARB, ARGs, and antibiotics of manure origin. These findings illustrate that the effects of manure and doxycycline on ARGs and MGEs differ between rhizosphere and bulk soil, which needs to be considered when assessing risks for human health connected to the spread of ARGs in the environment.

 Human milk contains an abundant supply and diverse array of oligosaccharides that are known to impart significant health benefits to the nursing infant including establishment and maintenance of a healthy gut microflora, immune development and protection against gastro intestinal infections. When breastfeeding is not possible or insufficient, infant formulas are commonly used as an alternative. However, limited information is available about the presence of naturally occurring oligosaccharides in these infant formulas and their likely health benefits. The present study examined the presence of naturally occurring oligosaccharides in commercial goats’ milk-based stage 1 and stage 2 infant formulas and their prebiotic and anti-infection properties. LC/MS was used to detect and quantify oligosaccharides and their prebiotic potential was assessed by their ability, at concentrations present in reconstituted ready-to-use infant formula, to promote the growth of Bifidobacterium animalis subsp. lactis BB12, B. longum BB536, Lactobacillus acidophilus 4461 and L. casei 2607 in vitro. For anti-infection properties, the ability of goat milk oligosaccharides to prevent the adhesion of Escherichia coli NCTC 10418 and a Salmonella typhimurium isolate to Caco-2 cells was investigated. The results showed the presence off ourteen quantifiable oligosaccharides instage1andstage2goats’milk-basedinfantformula.Thiswassimilartothenumberofoligosaccharidesdetectedinthefreshgoats’milk.Of these, five were structurally similar to those foundinhumanmilk.Theseoligosaccharideswereshowntosignificantlyenhancethegrowthof bifidobacteria and lactobacilli and reduce the adhesion of E. coli NCTC 10418 and S. typhimurium to Caco-2 cells. Together, these results suggest that oligosaccharides naturally present in goats’ milk-based infant formula exhibit strong prebiotic and anti-pathogen adhesion properties and may confer gut health benefits to infants.

Understanding diversity is important in our inclusive society to hedge against ignorance and accommodate plural perspectives. Diversity nowadays can be observed in online social spaces. People from different backgrounds (e.g. gender, age, culture, expertise) are interacting every day around online digital objects (e.g. videos, images and web articles) leaving their social content in different format, commonly as textual comments and profiles. The social clouds around digital objects (i.e. user comments, user profiles and other metadata of digital objects) offer rich source of information about the users and their perspectives on different domains. Although, researchers from disparate disciplines have been working on understanding and measuring diversity from different perspectives, little has been done to automatically measure diversity in social clouds. This is the main objective of this research. This research proposes a semantic driven computational model to systematically represent and automatically measure diversity in a social cloud. Definitions from a prominent diversity framework and Semantic Web techniques underpin the proposed model. Diversity is measured based on four diversity indices - variety, balance, coverage and (within and across) disparity with regards to two perspectives – (a) domain, which is captured in user comments and represented by domain ontologies, and (b) user, which is captured in profiles of users who made the comments and represented by a proposed User Diversity Ontology. The proposed model is operationalised resulting in a Semantic Driven Diversity Analytics Tool (SeDDAT), which is responsible for diversity profiling based on the diversity indices. The proposed approach of applying the model is illustrated on social clouds from two social spaces - open (YouTube) and closed (Active Video Watching (AVW-Space)). The open social cloud shows the applicability of the model to generate diversity profiles of a large pool of videos (600) with thousands of users and comments. Closed social clouds of two user groups around same set of videos illustrate transferability and further utility of the model. A list of possible diversity patterns within social clouds is provided, which in turn deepen the understanding of diversity and open doors for further utilities of the diversity profiles. The proposed model is applicable in similar scenarios, such as in the social clouds around MOOCs and news articles.

Age prediction is an active study field that can substantially affect many computer vision problems like object recognition. In this paper, an accurate system with extensive experiments is proposed to provide an efficient and accurate approach for age range prediction of people from their facial images. Histogram Equalization technique is used to reduce the illumination effects on all facial images taken from FG-NET and UTD aging databases, and image resizing is used to unify all image sizes. Moreover, Histogram of Oriented Gradient (HOG) and Local Binary Pattern (LBP) are used to extract the features of these images. Then, Support Vector Machine (SVM) and k-Nearest Neighbor (k-NN) are used for the classification processes. To evaluate the performance of the proposed system, both Leave-One-Out (LOO) and Confusion Matrix (CM) are used. The extensive and intensified experiments improved the age range predicting performance up to 98.6%.