Predicting and Controlling Fertility Using Family Planning Methods

Without a real reduction in population fertility rates, developing societies will push for more spending on their infrastructure and more demand for basic services for new-born, and more dependency and crowding, and the attendant ills and various social, economic and cultural problems, which will push these countries towards Directing a large part (if not most) of development revenues to meet the growing population. In general, the importance of this study lies in how to predict fertility rates using the rates of family planning methods (practice rates, years of protection) and to identify the method of neural networks and its accuracy in dealing with fertility data in particular. The study concluded that the prevalence of family planning methods (PR) and protection rate (CYP) are used to estimate and predict the total fertility rate (TFR) very efficiently, and artificial neu6ral networks have reached a high rate and high accuracy in estimating and predicting the total fertility rate (TFR) is highly and reliable (99.6%)

Incremental materialization of object-oriented views

We present an approach to handle incremental materialization of object-oriented views. Queries that define views are implemented as methods that are invoked to compute corresponding views. To avoid computation from scratch each time a view is accessed, we introduce some deferred update algorithms that reflect for a view only related modifications introduced into the database while that view was inactive. A view is updated by considering modifications performed within all classes along the inheritance and class-composition subhierarchies rooted at every class used in deriving that view. To each class, we add a modification list to keep one modification tuple per view dependent on that class. Such a tuple acts as a reference point that marks the start of the next update to the corresponding view. © 1999 Elsevier Science B.V. All rights reserved

Effect of Chloride Ions on the Electrochemical Performance of Magnesium Metal‐Organic‐Frameworks‐Based Semi‐Solid Electrolytes

The majority of research on magnesium (Mg) electrolytes has focused on enhancing reversible Mg deposition, often employing chloride-containing electrolytes. However, there is a notable gap in the literature regarding the influence of chloride ions in semi-solid Mg electrolytes. In this study, we systematically explore the impact of chloride ions on Mg deposition/ dissolution on a copper (Cu) anode using a semi-solid electrolyte composed of Mg-based mixed metal-organic frameworks, MgCl$_2$ and Mg[TFSI]$_2$. We separate the Mg deposition/dissolution process from changes in the anode’s surface morphology In this respect, the morphological and compositional transformations in the electrolyte and electrode following galvanostatic cycling are meticulously investigated. Initial potential cycling reveals the feasibility of Mg deposition/dissolution on Cu electrodes, albeit with reduced reversibility in subsequent cycles. Extending the upper potential limit to 4.0 V vs. Mg/Mg2$^{+}$ enhances Mg dissolution, attributed to chloride ions facilitating Cu surface dissolution. Our findings provide insights into optimizing semi-solid electrolytes for advanced Magnesium battery technologies

Simulation and performance assessment of a modified throttled load balancing algorithm in cloud computing environment

Load balancing is crucial to ensure scalability, reliability, minimize response time, and processing time and maximize resource utilization in cloud computing. However, the load fluctuation accompanied with the distribution of a huge number of requests among a set of virtual machines (VMs) is challenging and needs effective and practical load balancers. In this work, a two listed throttled load balancer (TLT-LB) algorithm is proposed and further simulated using the CloudAnalyst simulator. The TLT-LB algorithm is based on the modification of the conventional TLB algorithm to improve the distribution of the tasks between different VMs. The performance of the TLT-LB algorithm compared to the TLB, round robin (RR), and active monitoring load balancer (AMLB) algorithms has been evaluated using two different configurations. Interestingly, the TLT-LB significantly balances the load between the VMs by reducing the loading gap between the heaviest loaded and the lightest loaded VMs to be 6.45% compared to 68.55% for the TLB and AMLB algorithms. Furthermore, the TLT-LB algorithm considerably reduces the average response time and processing time compared to the TLB, RR, and AMLB algorithms

Three-dimensional transvaginal ultrasound: clinical implementation in assessing uterine cavity

Background: Three-dimensional transvaginal ultrasonography (3D TVS) represents a new technique of imaging and provides a unique diagnostic tool for non-invasive examination of the uterine morphology and diagnosis of congenital uterine anomalies. In this study the clinical value of 3D TVS in diagnosis of uterine cavity abnormalities were evaluated.Methods: A prospective of diagnostic accuracy study included 226 patients with various clinical presentations; infertility, recurrent pregnancy loss, menstrual disorders and post-menopausal bleeding with suspected uterine cavity lesions or abnormality on two-dimensional (2D) TVS or hysterosalpingography (HSG). After taking consent, all patients were subjected to history taking, clinical examination, 3D TVS evaluation, magnetic resonance imaging (MRI) and finally endoscopic examination.Results: The 3D has 98% accuracy in infertile women in comparison to 87% for MRI. While with recurrent pregnancy loss, Concordance was 96% correct for 3D and 78% for MRI. The women with abnormal uterine bleeding, the accuracy of 3D was 100%, while with MRI was 74%. The sensitivity of 3D TVS was 97.8% and 100% specificity, positive and negative predictive value. While the sensitivity, specificity, positive and negative predictive values for MRI were 89.3%, 64%, 70.4% and 86.3% respectively.Conclusions: 3D TVS appears to be extremely accurate, less expensive and a rapid examination for the diagnosis and classification of uterine anomalies, more than MRI. Thus it may become the only mandatory step in the assessment of the uterine cavity

Probing the putative α7 nAChR/NMDAR complex in human and murine cortex and hippocampus: Different degrees of complex formation in healthy and Alzheimer brain tissue

α7 nicotinic acetylcholine receptors (nAChRs) and N-methyl-D-aspartate receptors (NMDARs) are key mediators of central cholinergic and glutamatergic neurotransmission, respectively. In addition to numerous well-established functional interactions between α7 nAChRs and NMDARs, the two receptors have been proposed to form a multimeric complex, and in the present study we have investigated this putative α7 nAChR/NMDAR assembly in human and murine brain tissues. By α-bungarotoxin (BGT) affinity purification, α7 and NMDAR subunits were co-purified from human and murine cortical and hippocampal homogenates, substantiating the notion that the receptors are parts of a multimeric complex in the human and rodent brain. Interestingly, the ratios between GluN1 and α7 levels in BGT pull-downs from cortical homogenates from Alzheimer’s disease (AD) brains were significantly lower than those in pull-downs from non-AD controls, indicating a reduced degree of α7 nAChR/NMDAR complex formation in the diseased tissue. A similar difference in GluN1/α7 ratios was observed between pull-downs from cortical homogenates from adult 3xTg-AD and age-matched wild type (WT) mice, whereas the GluN1/α7 ratios determined in pull-downs from young 3xTg-AD and age-matched WT mice did not differ significantly. The observation that pretreatment with oligomeric amyloid-β1–42 reduced GluN1/α7 ratios in BGT pull-downs from human cortical homogenate in a concentration-dependent manner provided a plausible molecular mechanism for this observed reduction. In conclusion, while it will be important to further challenge the existence of the putative α7 nAChR/NMDAR complex in future studies applying other methodologies than biochemical assays and to investigate the functional implications of this complex for cholinergic and glutamatergic neurotransmission, this work supports the formation of the complex and presents new insights into its regulation in healthy and diseased brain tissue.</div

Evaluation of IR Spectral Analysis and Dyeing Parameters for Plasma and /or Nano-Silver Treatments of Polyester and Nylon Fabrics

In our work of this paper, we study the effect of surface modification of polyester and nylon fabrics induced by DC plasma discharge and/ or nano-siliver treatments .DC plasma discharge was employed at first, as a function of plasma device parameters including different time, different current and different hydrostatic pressure using chemically inert working gas: argon or nitrogen. Optimization of the performance of the applied DC plasma discharge with various applied conditions were performed using Fourier Transform Infra-Red (FTIR) Spectroscopy spectral analysis, by following up the changes in the peak intensity values of the characteristic functional groups that characterize polyester fabric. Then the dyeing properties of different pretreated fabrics with plasma by the best conditions are subjected to nano-silver treatment by concentration 50 ppm under the effect of different dye concentrations, different dyeing temperature and different dyeing time. Finally, the fastness properties to light and washing for the treated samples were studied. The results obtained showed that both of the dyeing parameters and fastness properties were highly improved by the treatment of fabrics by either individual plasma treatment or combined DC cold plasma and nano-silver treatments

Deep learning for Covid-19 forecasting: State-of-the-art review.

The Covid-19 pandemic has galvanized scientists to apply machine learning methods to help combat the crisis. Despite the significant amount of research there exists no comprehensive survey devoted specifically to examining deep learning methods for Covid-19 forecasting. In this paper, we fill the gap in the literature by reviewing and analyzing the current studies that use deep learning for Covid-19 forecasting. In our review, all published papers and preprints, discoverable through Google Scholar, for the period from Apr 1, 2020 to Feb 20, 2022 which describe deep learning approaches to forecasting Covid-19 were considered. Our search identified 152 studies, of which 53 passed the initial quality screening and were included in our survey. We propose a model-based taxonomy to categorize the literature. We describe each model and highlight its performance. Finally, the deficiencies of the existing approaches are identified and the necessary improvements for future research are elucidated. The study provides a gateway for researchers who are interested in forecasting Covid-19 using deep learning. © 2022 Elsevier B.V.Dunarea de Jos” University of GalatiUmm Al-Qura Univer-sityUmm Al-Qura University, UQU, (22UQU4300274DSR01)Deanship of Scientific Research, King Saud UniversityFunding text 1: Deanship of Scientific Research at Umm Al-Qura University supported this work by Grant Code: (22UQU4300274DSR01).Funding text 2: Conceptualization, H.O.T., H.M.H.Z., A.M.A.M., G.A. and S.A.M.I.methodology, F.T.A. and H.O.T.software, D.S.B., A.H.A., H.M.H.Z. and A.E.validation, S.A.M.I., A.M.A.M., D.S.B., D.E.A., W.E., Y.S.R. and A.E.formal analysis, H.M.H.Z., and F.T.A.investigation, H.O.T., W.E., and G.A.resources, F.T.A. and D.S.B.data curation, S.A.M.I., A.H.A. and A.E.writing—original draft preparation, Y.S.R., D.E.A., H.O.T., D.E.A., F.T.A. and A.E.writing—review and editing, H.M.H.Z., S.I, A.M.A.M., A.H.A. and A.E.visualization, W.E. and A.E.supervision, H.M.H.Z., W.E., Y.S.R. and D.S.B.project administration, H.O.T., A.E., Y.S.R. and S.A.M.I.funding acquisition A.E. (The APC was funded by “Dunarea de Jos” University of Galati, Romania). All authors have read and agreed to the published version of the manuscript.Funding text 3: The authors would like to thank the Deanship of Scientific Research at the Umm Al-Qura Univer-sity for supporting this work by grant code (22UQU4300274DSR01). The APC was covered by “Dunarea de Jos” University of Galati, Romania

Tailoring Cu Electrodes for Enhanced CO 2 Electroreduction through Plasma Electrolysis in Non‐Conventional Phosphorus‐Oxoanion‐Based Electrolytes

This study presents a green, ultra-fast, and facile technique for the fabrication of micro/nano-structured and porous Cu electrodes through in-liquid plasma electrolysis using phosphorous-oxoanion-based electrolytes. Besides the preferential surface faceting, the Cu electrodes exhibit unique surface structures, including octahedral nanocrystals besides nanoporous and microporous structures, depending on the employed electrolyte. The incorporation of P-atoms into the Cu surfaces is observed. The modified Cu electrodes display increased roughness, leading to higher current densities for CO2 electroreduction reaction. The selectivity of the modified Cu electrodes towards C2 products is highest for the Cu electrodes treated in Na2HPO3 and Na3PO4 electrolytes, whereas those treated in Na2H2PO2 produce the most H2. The Cu electrode treated in Na3PO4 produces ethylene (23 %) at −1.1 V vs. RHE, and a comparable amount of acetaldehyde (15 %) that is typically observed for Cu(110) single crystals. The enhanced selectivity is attributed to several factors, including the surface morphology, the incorporation of phosphorus into the Cu structure, and the formation of Cu(110) facets. Our results not only advance our understanding of the influence of the electrolyte\u27s nature on the plasma electrolysis of Cu electrodes, but also underscores the potential of in-liquid plasma treatment for developing efficient Cu electrocatalysts for sustainable CO2 conversion