Financial model for private finance initiative projects applied to school buildings

Private Finance Initiative (PFI) has become a major procurement method in the UK and worldwide. The number of signed PFI deals is growing, but competition is restricted to those companies that are able to afford the initial investment. The bidding cost of PFI projects are high, and bidding companies are not compensated if the client does not award them the project. This is the reason behind several recent high-profile tender xvithdra« als. and is considered a major barrier for private companies wanting to take part in the bidding process. There is an obvious need for a tool to enable construction organizations to participate in PFI projects; one that can support these organizations in a decision-making process that is compatible with their project selection strategies, and will allow them to bid for PFI projects with clearer goals and reduced costs. A computer-based financial model was developed to predict the cost and cash flow of PFI projects, enabling project teams to assess investment decisions at the tendering stage. The proposed model consists of four modules to identify the required building area, predict the construction cost, distribute the occupancy cost, and predict the cash flow of the project. The output of the model provides the project investment results, such as the Net Present Value (NPV), Internal Rate of Return (IRR), Debt Service Coverage Ratio (DSCR), payback period and investment growth ratio. The model can predict the unitary payment but also allows the user to define the unitary payment. The reports of the model contain the cash flow and investment ratio for both types of unitary payment. The model attempts to provide the information required to assess the feasibility and affordability of the project. It gives the private sector the chance to assess the project before they spend unrecoupable funds on the project. It allows the public sector to determine the project cost, cash flow, unitary charge, and provide the information to be used for the Public Sector Comparator. The data required for the development of the model was collected from different sources. The model was initially developed on spreadsheet software: the final version was transformed into a web-based model using the Hypertext Preprocessor (PHP) and Javascript programming languages. The completed model was then sent to many practitioners for validation and assessment of both the concept and numerical application. The responses received show the valuable role the model could play in PFI projects.Ministry of Higher Education, Saudi Arabi

Familial reciprocal translocation t (8; 17)(p23; q21) in a woman with recurrent spontaneous abortion

This work presents the results of cytogenetic analysis of a couple referred to our genetics laboratory with ten first trimester abortions and one IVF failure. The male showed a normal (46, XY) karyotype whereas the female was found to carry an apparently balanced reciprocal translocation [46, XX, t (8; 17)(p23; q21)]. Two sisters and two brothers of the eight siblings of the female proved to have the same translocation. Although the female's father is deceased and his sample was not available for investigation. The origin of this translocation must be paternal since the female's mother harbored a normal karyotype. It is concluded that the history of recurrent pregnancy losses in the couple is due to the production of unbalanced gametes in the female as a result of the reciprocal translocation she has and the couple was advised to undergo a PGD for embryo selection prior to their future IVF trials. The authors also recommend that all RSA couples with normal routine work-up results should be offered chromosomal analysis without delay

Magec: An image searching tool for detecting forged images in forensic investigation

© 2016 IEEE. Manipulation of digital images for the purpose of forgery is a rapidly growing phenomenon that poses a challenge for cyber-crime investigators. Distinguishing original images from duplicates and the number of original copies within the same media are some examples of challenges presented by duplicate digital images. In this paper, we present a new image-searching tool called, Magec, to detect duplicate image(s) on digital media, using the original image modification attributes as a signature. First, we describe the tool and the methods used to detect duplicate images, then we evaluate the tool\u27s performance based on the number of folders it searches and the number of files it searches for. Later, we present the analysis of the tool using different operating system attributes. The goal is to find copies of the same object that is hidden; compressed images, or images saved with different attributes and demonstrates which one is the original image and thereby deduce which ones are copies. This research helps in better utilization of small/limited capacity devices, where limited storage capacity may be a problem. The experimental results prove that the presented search tool provides faster and accurate results. Finally, the conducted tests on the Magec tool analyzed, and verified, and the results are presented alongside with challenges identified

Cardiovascular medications and regulation of COVID-19 receptors expression

INTRODUCTION: Emerging epidemiological studies suggested that Renin–Angiotensin–Aldosterone system (RAAS) inhibitors may increase infectivity and severity of COVID-19 by modulating the expression of ACE2. METHODS: In silico analysis was conducted to compare the blood expression levels of SARS-CoV-2 entry genes between age and gender matched cohort of hypertensive patients versus control, and to determine the effect of common cardiovascular medications on the expression of COVID-19 receptors in vitro using primary human hepatocytes. RESULTS: The transcriptomic analysis revealed a significant increase of ACE2 and TMPRSS2 in the blood of patients with hypertension. Treatment of primary human hepatocytes with captopril, but not enalapril, significantly increased ACE2 expression. A similar pattern of ACE2 expression was found following the in vitro treatments of rat primary cells with captopril and enalapril. Telmisartan, a second class RAAS inhibitors, did not affect ACE2 levels. We have also tested other cardiovascular medications that may be used alone, or in combination with RAAS inhibitors. Some of these medications increased TMPRSS2, while others, like furosemide, significantly reduced COVID-19 receptors. CONCLUSIONS: The increase in ACE2 expression levels could be due to chronic use of RAAS inhibitors or alternatively caused by other hypertension-related factors or presence of other comorbidities. Treatment of common co-morbidities often require chronic use of multiple medications, which may result in an additive increase in the expression of ACE2 and TMPRSS2. Our data suggest that more research is needed to determine the effect of different medications, as well as medication combinations, on COVID-19 receptors

Enhanced expression of immune checkpoint receptors during SARS-CoV-2 viral infection

The immune system is tightly regulated by the activity of stimulatory and inhibitory immune receptors. This immune homeostasis is usually disturbed during chronic viral infection. Using publicly available transcriptomic datasets, we conducted in silico analyses to evaluate the expression pattern of 38 selected immune inhibitory receptors (IRs) associated with different myeloid and lymphoid immune cells during coronavirus disease 2019 (COVID-19) infection. Our analyses revealed a pattern of overall upregulation of IR mRNA during severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection. A large number of IRs expressed on both lymphoid and myeloid cells were upregulated in nasopharyngeal swabs (NPSs), while lymphoid-associated IRs were specifically upregulated in autopsies, reflecting severe, terminal stage COVID-19 disease. Eight genes (BTLA, LAG3, FCGR2B, PDCD1, CEACAM1, CTLA4, CD72, and SIGLEC7), shared by NPSs and autopsies, were more expressed in autopsies and were directly correlated with viral levels. Single-cell data from blood and bronchoalveolar samples also reflected the observed association between IR upregulation and disease severity. Moreover, compared to SARS-CoV-1, influenza, and respiratory syncytial virus infections, the number and intensities of upregulated IRs were higher in SARS-CoV-2 infections. In conclusion, the immunopathology and severity of COVID-19 could be attributed to dysregulation of different immune inhibitors. Targeting one or more of these immune inhibitors could represent an effective therapeutic approach for the treatment of COVID-19 early and late immune dysregulations

Effect of Common Medications on the Expression of SARS-CoV-2 Entry Receptors in Kidney Tissue

Besides the respiratory system, severe acute respiratory syndrome-coronavirus 2 (SARS-CoV-2) infection was shown to affect other essential organs such as the kidneys. Early kidney involvement during the course of infection was associated with worse outcomes, which could be attributed to the direct SARS-CoV-2 infection of kidney cells. In this study, the effect of commonly used medications on the expression of SARS-CoV-2 receptor, angiotensin-converting enzyme (ACE)2, and TMPRSS2 protein in kidney tissues was evaluated. This was done by in silico analyses of publicly available transcriptomic databases of kidney tissues of rats treated with multiple doses of commonly used medications. Of 59 tested medications, 56% modified ACE2 expression, whereas 24% modified TMPRSS2 expression. ACE2 was increased with only a few of the tested medication groups, namely the renin-angiotensin inhibitors, such as enalapril, antibacterial agents, such as nitrofurantoin, and the proton pump inhibitor, omeprazole. The majority of the other medications decreased ACE2 expression to variable degrees with allopurinol and cisplatin causing the most noticeable downregulation. The expression level of TMPRSS2 was increased with a number of medications, such as diclofenac, furosemide, and dexamethasone, whereas other medications, such as allopurinol, suppressed the expression of this gene. The prolonged exposure to combinations of these medications could regulate the expression of ACE2 and TMPRSS2 in a way that may affect kidney susceptibility to SARS-CoV-2 infection. Data presented here suggest that we should be vigilant about the potential effects of commonly used medications on kidney tissue expression of ACE2 and TMPRSS2

Experimental and numerical study of micropitting initiation in real rough surfaces in a micro-elastohydrodynamic lubrication regime

Micropitting is a form of surface fatigue damage that happens at the surface roughness scale in lubricated contacts in commonly used machine elements, such as gears and bearings. It occurs where the specific film thickness (ratio of smooth surface film thickness to composite surface roughness) is sufficiently low for the contacts to operate in the mixed lubrication regime, where the load is in part carried by direct asperity contacts. Micropitting is currently seen as a greater issue for gear designers than is regular pitting fatigue failure as the latter can be avoided by control of steel cleanliness. This paper describes the results of both theoretical and experimental studies of the onset of micropitting in test disks operated in the mixed lubrication regime. A series of twin disk mixed-lubrication experiments were performed in order to examine the evolution of micropitting damage during repeated cyclic loading of surface roughness asperities as they pass through the contact. Representative measurements of the surfaces used in the experimental work were then evaluated using a numerical model which combines a transient line contact micro-elastohydrodynamic lubrication (micro-EHL) simulation with a calculation of elastic sub-surface stresses. This model generated time-history of stresses within a block of material as it passes through the contact, based on the instantaneous surface contact pressure and traction at each point in the computing mesh at each timestep. This stress time-history was then used within a shear-strain-based fatigue model to calculate the cumulative damage experienced by the surface due to the loading sequence experienced during the experiments. The proposed micro-EHL model results and the experimental study were shown to agree well in terms of predicting the number of loading cycles that are required for the initial micropitting to occur

Location prediction based on a sector snapshot for location-based services

In location-based services (LBSs), the service is provided based on the users' locations through location determination and mobility realization. Most of the current location prediction research is focused on generalized location models, where the geographic extent is divided into regular-shaped cells. These models are not suitable for certain LBSs where the objectives are to compute and present on-road services. Such techniques are the new Markov-based mobility prediction (NMMP) and prediction location model (PLM) that deal with inner cell structure and different levels of prediction, respectively. The NMMP and PLM techniques suffer from complex computation, accuracy rate regression, and insufficient accuracy. In this paper, a novel cell splitting algorithm is proposed. Also, a new prediction technique is introduced. The cell splitting is universal so it can be applied to all types of cells. Meanwhile, this algorithm is implemented to the Micro cell in parallel with the new prediction technique. The prediction technique, compared with two classic prediction techniques and the experimental results, show the effectiveness and robustness of the new splitting algorithm and prediction technique

Assembly of Smart Microgels and Hybrid Microgels on Graphene Sheets for Catalytic Reduction of Nitroarenes

Poly (N-isopropylacrylamide-acrylic acid) [p(NIPAM-AAc)] microgel was successfully fabricated using the precipitation polymerization method. Silver (Ag) nanoparticles and graphene oxide (G) were used to fabricate the following hybrid microgels: Ag-p(NIPAM-AAc) (Ag-HMG), Ag-G-p(NIPAM-AAc) (Ag-G-HMG), and G-p(NIPAM-AAc) (G-HMG). Ag-HMG, Ag-G-HMG, and G-HMG were characterized using a Zetasizer and UV-Vis spectroscopy. The reduction of a series of different compounds with comparable and distinct chemical structures was catalyzed by synthesized Ag-HMG, Ag-G-HMG, and G-HMG hybrid microgels. The average size of Ag nanoparticles was found to be ~50 nm. Ag nanoparticles were synthesized within microgels attached to G sheets. Ag-p(NIPAM-AAc), Ag-G-p(NIPAM-AAc), and G-p(NIPAM-AAc) hybrid microgels were used for the catalytic reduction of nitroarenes and dyes. By comparing their apparent rate constant (kapp), reduction duration, and percentage reduction, the activity of HMG (hybrid microgel) as a catalyst towards different substrates was investigated. Graphene sheets play role in electron relay among Ag nanoparticles and microgels.publishedVersio