This study delves into the evaluation of daily profits within the network of Sephora cosmetics branches, employing distributed systems, the K-means algorithm, and WEKA visualization techniques. The objective is to analyze the profitability patterns across Sephora branches and uncover underlying clusters to optimize strategic decision-making. The research methods encompassed the utilization of distributed systems to collect and integrate daily profit data from multiple Sephora branches. The K-means clustering algorithm was applied to segment the dataset, discerning distinct clusters based on profit margins and performance metrics. The WEKA visualization tool was instrumental in depicting these clusters in a comprehensive and interpretable manner, offering insights into profit distributions and potential areas for enhancement. This research revealed intricate profitability structures within the Sephora branch network. The application of the K-means algorithm, distinct clusters of branches with varying profitability profiles were identified. WEKA visualization facilitated the graphical representation of these clusters, elucidating the relationships between different branches based on daily profits. Such insights can aid decision-makers in strategizing resource allocation, marketing initiatives, and operational improvements tailored to each cluster's profitability dynamics. The amalgamation of distributed systems, K-means algorithm, and WEKA visualization offered a robust framework for assessing and optimizing the financial performance of  Sephora branches. This research contributes valuable insights into the evaluation of daily profits across  Sephora cosmetics branches, showcasing the potential of distributed systems and advanced analytical tools in understanding profitability patterns. The findings provide a foundation for strategic interventions aimed at enhancing profitability and operational efficiencies within the Sephora branch network.

A new robust blind image watermarking method based on lifting wavelet transform (LWT), and Schur decomposition in combination with discrete cosine transform (DCT), and entropy analysis for copyright protection of multimedia information is introduced in this paper. At first, the 2-level LWT is applied to the original image to improve the invisibility of the watermarking method and then the high-frequency sub-band of 2-level LWT is decomposed by DCT. Then, the DCT coefficients are divided into 4×4 non-overlapping blocks. After that, Schur decomposition applies to each selected block, while the first row, the first column element of the upper triangular matrix is used to embed the watermark. For evaluation, the invisibility and robustness of the proposed watermarking method, the peak signal to noise ratio (PSNR), and normalized cross-correlation (NC) are used to measure the quality and the ability of the proposed watermarking method to be robust against signal processing operations and geometric attacks. Experimental results have demonstrated that the proposed method is achieved a very good tradeoff between invisibility and robustness. The comparisons with other scheme have shown that the proposed digital watermarking scheme has a superior performance in terms of invisibility than other. 

Abstract:

In this research study, we investigate the effectiveness of a proposed automated framework that leverages AI algorithms to improve the accuracy, scalability, and efficiency of data analysis. We compare the performance of the proposed framework to traditional data analysis methods in terms of accuracy, time and effort required for data analysis, and scalability. We conducted a questionnaire-based survey to collect data on the application of the proposed framework and traditional data analysis methods in real-world scenarios. We used statistical analyses, including t-tests and regression analyses, to analyze the data and draw conclusions. Our findings indicate that the proposed automated framework outperforms traditional data analysis methods in terms of accuracy, reduces the time and effort required for data analysis, and has better scalability for processing large datasets in real-time. Our results suggest that the proposed framework offers significant benefits for organizations seeking to improve their data analysis processes. However, we also note the importance of considering the costs and benefits of implementing the framework, as well as any ethical or legal implications. Further research is recommended to confirm the findings and to investigate the effectiveness of the proposed framework in different contexts and with different datasets.

Clostridioides difficile is the most important pathogen causing antimicrobial-associated diarrhea and has recently been recognized as a cause of community-associated C. difficile infection (CA-CDI). This study aimed to characterize virulence factors, antimicrobial resistance (AMR), ribotype (RT) distribution and genetic relationship of C. difficile isolates from diverse fecally contaminated environmental sources. C. difficile isolates were recovered from different environmental samples in Northern Germany. Antimicrobial susceptibility testing was determined by E-test or disk diffusion method. Toxin genes (tcdA and tcdB), genes coding for binary toxins (cdtAB) and ribotyping were determined by PCR. Furthermore, 166 isolates were subjected to whole genome sequencing (WGS) for core genome multi-locus sequence typing (cgMLST) and extraction of AMR and virulence-encoding genes. Eighty-nine percent (148/166) of isolates were toxigenic, and 51% (76/148) were positive for cdtAB. Eighteen isolates (11%) were non-toxigenic. Thirty distinct RTs were identified. The most common RTs were RT127, RT126, RT001, RT078, and RT014. MLST identified 32 different sequence types (ST). The dominant STs were ST11, followed by ST2, ST3, and ST109. All isolates were susceptible to vancomycin and metronidazole and displayed a variable rate of resistance to moxifloxacin (14%), clarithromycin (26%) and rifampicin (2%). AMR genes, such as gyrA/B, bla_CDD-1/2, aph(3′)-llla-sat-4-ant(6)-la cassette, ermB, tet(M), tet(40), and tetA/B(P), conferring resistance toward fluoroquinolone, beta-lactam, aminoglycoside, macrolide and tetracycline antimicrobials, were found in 166, 137, 29, 32, 21, 72, 17, and 9 isolates, respectively. Eleven “hypervirulent” RT078 strains were detected, and several isolates belonged to RTs (i.e., RT127, RT126, RT023, RT017, RT001, RT014, RT020, and RT106) associated with CA-CDI, indicating possible transmission between humans and environmental sources pointing out to a zoonotic potential.

Abstract: This review aimed to investigate the effect of pH on morphology, and size of gold nanoparticles (AuNPs) biosynthesized using extracts of various plant parts and agricultural waste. By reviewing previous studies, it was found that the formation of AuNPs was more rapid in neutral and basic mediums than in acidic mediums. Furthermore, most of those studies indicated that the smallest sizes of biosynthesized AuNPs, produced by plant extracts, were in neutral and alkaline mediums. Regarding the morphology, it was found that multiple shapes of the AuNPs were obtained in an acidic medium of the reaction, while there was typically a dominant shape of the AuNPs in the basic medium. In conclusion, significant quantities of AuNPs with appropriate sizes and morphology could be obtained by controlling the medium of the reaction when AuNPs are biosynthesized using extracts of plant parts and agricultural waste.

The goal of this study was to investigate the vegetation composition and structure of the Al-Orban area in Gharyan district, Libya over two continues growing seasons from the first of January 2020 to the first of Jaunary 2022. During this period, a total of 309 plant species were identified and collected in the field. These species belonged to 43 families, with 39 families of dicotyledons and 4 families of monocotyledons. The most dominant family was Asteraceae with 60 species, followed by Fabaceae with 32 species, Poaceae with 33 species, and Brassicaceae with 35 species. The most dominant genera were Plantago and Euphorbia, each represented by 6 species, followed by Erodium and Chenopodiun with 5 species each, and Astragalus, Centaurea, Rumex, Concolvulus, and Stipa each represented by 4 species. Life-form spectrum analysis indicated that Therophytes were the most predominant with 189 species, followed by Hemicryptophytes with 47 species, and geophytes with 40 species. Furthermore, chorotype spectrum analysis showed the dominance of Mediterranean species with 128 species, followed by Mediterranean/Iranian-Turanian with 66 species. These findings provide valuable insight into the plant diversity and distribution in the Al-Orban area of Gharyan district, Libya.

Derivative-free optimization techniques are widely used for solving optimization problems. The

focus in this work is given to some variants of Nelder-Mead and particle swarm methods to tackle two

unconstrained optimization problems originated from optimal control, namely the pole assignment problem for

discrete and continuous-time positive systems. we present the Nelder-Mead and Particle Swarm optimization

methods to solve problems. Moreover comparing our results with benchmarks in the literature.

The extremely high speed offered by UWB systems allows the guard band to be extended to 100% instead of maximum of 25% as in broadband systems. This particular guard ensures this channel is completely free of spurious signals before transmitting next symbol. In this paper, the performance of non block transmission frequency domain UWB receiver with changeable zero-padding is presented. The receiver can be an alternate to Rake receivers, since clustering effect, common in UWB signals makes Rake receivers impractical and imposes low complexity rake receivers that are not optimum, either.

Biosynthesis techniques have a number of advantages over other methods for producing silver nanoparticles (AgNPs), which provide a wide range of applications. The present work highlights the biosynthesis of AgNPs by mixing pear leaf aqueous extract with silver nitrate, and the formation of AgNPs was observed by the change of mixture color from yellow to dark brown and visible spectrophotometry. Moreover, the effect of pH, reaction time, AgNO3 concentration, extract volume and temperature on the suggested approach was also studied. The results showed that pear leaf aqueous extract is an excellent material for the biosynthesis of AgNPs, and by controlling the mentioned parameters that influence synthesis, a large number of AgNPs with small sizes may be produced. Keywords: silver nitrate, silver nanoparticles, pear leaves, biosynthesis 

Biosynthesis techniques have a number of advantages over other methods for producing silver nanoparticles (AgNPs), which provide a wide range of applications. The present work highlights the biosynthesis of AgNPs by mixing pear leaf aqueous extract with silver nitrate, and the formation of AgNPs was observed by the change of mixture color from yellow to dark brown and visible spectrophotometry. Moreover, the effect of pH, reaction time, AgNO3 concentration, extract volume and temperature on the suggested approach was also studied. The results showed that pear leaf aqueous extract is an excellent material for the biosynthesis of AgNPs, and by controlling the mentioned parameters that influence synthesis, a large number of AgNPs with small sizes may be produced.