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This paper introduces a new extension of the Gompertz function for estimating the survival rates. The actual survival rates from USA life tables 2015 is considered for assessment process under the ordinary least squares method. A real data application is presented under the maximum likelihood method. The new Gompertz function is compared with many other competitive ones such as the Gompertz, the exponentiated Gompertz, the Rayleigh Gompertz, Weibull Gompertz, the Burr type X Gompertz and Rayleigh generalized Gompertz models.

The autoregressive model is a representation of a certain kind of random process in statistics, insurance, signal processing, and econometrics; as such, it is used to describe some time-varying processes in nature, economics and insurance, etc. In this article, a novel version of the autoregressive model is proposed, in the so-called the partially autoregressive (PAR(1)) model. The results of the new approach depended on a new algorithm that we formulated to facilitate the process of statistical prediction in light of the rapid developments in time series models. The new algorithm is based on the values of the autocorrelation and partial autocorrelation functions. The new technique is assessed via re-estimating the actual time series values. Finally, the results of the PAR(1) model is compared with the Holt-Winters model under the Ljung-Box test and its corresponding p-value. A comprehensive analysis for the model residuals is presented. The matrix of the autocorrelation analysis for both points forecasting and interval forecasting are given with its relevant plots.

The main task of paper studied new ciphering and deciphering techniques reported previously. Computer programs are designed in C language to perform theses algorithms and to assess their performance. Out of these algorithms, it is found that the BLOWFISH is the more sophisticated one. Therefore, a development for this algorithm is introduced to enhance its operation. This has been carried out by using the Microcontroller in the decrypting operation to support fast and more accurate operation. From the results of simulation, it is found that the expansion and permutation operations require most of the computing time. Therefore, software was designed and implemented to execute the expansion and permutation operations. A series of experiments was connected using the new developed algorithm as applied to different types of data (e.g. text, graphics and geographic maps). In all paper experiment, the encrypted and decrypted files were exactly the same.

Cost overruns are common on long-term construction projects. This is mostly because of inaccurate early estimates and unexpected changes in the economy and finances. In Egypt, the costs of materials like steel, cement, bricks, sand, and aggregates make up a large part of the cost of building. These costs are greatly affected by the state of the economy and the financial markets. Even though the Construction Cost Index (CCI) is a widely used economic indicator around the world, Egypt has not yet made its own CCI official. This study creates a predictive model just for Egypt’s construction industry that aims to predict a localized CCI to improve financial planning and lower risk. The framework uses two deep learning models, Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU), to make predictions about Egypt’s CCI. The models include a wide range of macroeconomic, monetary, foreign exchange market, commodity/energy market and equity market indicators, as well as technical indicators. In Python, advanced statistical methods like correlation analysis, multicollinearity, and stepwise regression are used to make sure that the best features are chosen. The GRU is better at keeping things in balance because it wins on the calibration (Weighted Absolute Percentage Error (WAPE), Bias (mean error)), the absolute error metrics (Mean Absolute Error, Mean Absolute Percentage Error, Symmetric Mean Absolute Percentage Error, and median error), while LSTM is better at squared-loss/association and turning points (Root Mean Squared Error, Mean Squared Error, Coefficient of determination, Directional Accuracy) because it has a slightly tighter variance fit and sign tracking. There is a permutation feature importance analysis for six features in both the GRU Model and the LSTM Model that shows that oil is the most important thing that affects the construction cost index (CCI). The study shows that deep learning models can accurately predict economic indicators. This gives Egypt’s construction industry a useful, data-driven tool for estimating costs ahead of time. They make a big difference in Egypt’s construction industry and meet the need for localized forecasting models in markets.

Background The inclusion of synthetic amino acids in poultry nutrition plays a crucial role in both enhancing the synthesis of immunoglobulins and elevating the overall comprehensiveness of the amino acid profile. Objectives This research examined the effects of consuming threonine (Thr) in various forms levels with low or high oil on broiler chickens' growth and immunity. Methods We investigate the growth performance, feed efficiency, immune response, intestinal morphology, absorptive capacity, and expression of some genes related to the feed intake (Pro‐opiomelanocortin [POMC]), fatty acid synthesis (Acetyl‐CoA Carboxylase [ACC]), immunity (lipopolysaccharide‐induced tumour necrosis like alpha factor [LITAF]), and heat shock protein 70 (HSP70). Eight groups of chicks were used, including four dietary Thr levels (100%, 115%, 130%, or 145%) with two oil levels (mixture of sunflower 50% and soybean oils 50%): (control) and high. Results The higher dietary Thr level (145%) with high oil inclusion significantly increased ACC and POMC gene expression, resulting in the lowest feed intake, body weight gain (BWG), and liver fat content. Combining high oil with 115% Thr was the optimum for the broilers. The birds have significant (p ≤ .05) growth performance, immune parameters, and intestinal health, as well as the lowest expression of ACC, POMC, HSP70, and LITAF, which was reflected in better feed conversion ratio and lower incidence of fatty liver, thermo‐resistance, and immune status of the birds. Conclusions The combination of high oil and 115% Thr levels optimises broiler health and productivity, enhancing growth, immune function, and gut health. This diet lowers the expression of genes associated with fatty liver and stress, leading to better feed efficiency, thermo‐resistance, and overall well‐being. Adopting these dietary adjustments can improve broiler performance and economic viability in poultry farming by enhancing essential productivity metrics. This research investigated the impact of varying threonine levels and dietary oil content on broiler chickens' growth and immunity. The results showed that combining high oil with a threonine level of 115% led to optimal outcomes for the birds. This combination improved growth performance, immune parameters, intestinal health, and gene expression related to feed intake and fatty acid synthesis. The findings suggest that carefully balanced threonine levels and dietary oil can enhance broiler chicken health and productivity.

Silver nanoparticles (AgNPs) have garnered significant interest due to their distinctive properties and potential applications. Traditional fabrication methods for nanoparticles often involve high-energy physical conditions and the use of toxic solvents. Various green synthesis approaches have been developed to circumvent these issues and produce environmentally benign nanoparticles. Our study focuses on the green synthesis of AgNPs using L-ascorbic acid and explores the modification of their properties to enhance antibacterial and anticancer effects. This is achieved by coating the nanoparticles with Zinc oxide (ZnO) and Silica oxide (SiO 2 ), which alters their optical properties in the visible spectrum. The synthesized formulations—AgNPs, zinc oxide-silver nanoparticles (Ag@ZnO), and silica oxide-silver nanoparticles (Ag@SiO 2 ) core/shell nanoparticles—were characterized using a suite of physicochemical techniques, including Transmission Electron Microscopy (TEM), Dynamic Light Scattering (DLS), Zeta potential measurement, UV–Vis spectroscopy, Refractive Index Measurements, and Optical Anisotropy Assessment. TEM imaging revealed particle sizes of 11 nm for AgNPs, 8 nm for Ag@ZnO, and 400 nm for Ag@SiO 2 . The Zeta potential values for Ag@ZnO and Ag@SiO 2 were measured at −17.0 ± 5 mV and −65.0 ± 8 mV, respectively. UV–Vis absorption spectra were recorded for all formulations in the 320 nm to 600 nm wavelength range. The refractive index of AgNPs at 404.7 nm was 1.34572, with slight shifts observed for Ag@ZnO and Ag@SiO 2 to 1.34326 and 1.37378, respectively. The cytotoxicity of the nanocomposites against breast cancer cell lines (MCF-7) was assessed using the MTT assay. The results indicated that AgNPs and Ag@ZnO exhibited potent therapeutic effects, with IC50 values of 494.00 µg/mL and 430.00 µg/mL, respectively, compared to 4247.20 µg/mL for Ag@SiO2. Additionally, the antibacterial efficacy of AgNPs was significantly enhanced under visible light irradiation. Ag@ZnO demonstrated substantial antibacterial activity both with and without light exposure, while the Ag@SiO2 nanocomposites significantly reduced the inherent antibacterial activity of silver. Conversely, the Ag@ZnO nanocomposites displayed pronounced antibacterial and anticancer activities. The findings suggest that silver-based nanocomposites, particularly Ag@ZnO, could be practical tools in water treatment and the pharmaceutical industry due to their enhanced therapeutic properties.

This study explores the level and frequency of anxiety about COVID-19 infection in some Middle Eastern countries, and differences in this anxiety by country, gender, workplace, and social status. Another aim was to identify the predictive power of anxiety about COVID-19 infection, daily smartphone use hours, and age in smartphone addiction. The participants were 651 males and females from Jordan, Saudi Arabia, the United Arab Emirates, and Egypt. The participants’ ages ranged between 18 and 73 years (M 33.36, SD = 10.69). A questionnaire developed by the authors was used to examine anxiety about COVID-19 infection. Furthermore, the Italian Smartphone Addiction Inventory was used after being translated, adapted, and validated for the purposes of the present study. The results revealed that the percentages of participants with high, average, and low anxiety about COVID-19 infection were 10.3%, 37.3%, and 52.4%, respectively. The mean scores of anxiety about COVID-19 infection in the four countries were average: Egypt (M = 2.655), Saudi Arabia (M = 2.458), the United Arab Emirates (M = 2.413), and Jordan (M = 2.336). Significant differences in anxiety about COVID-19 infection were found between Egypt and Jordan, in favor of Egypt. Significant gender differences were found in favor of females in the Jordanian and Egyptian samples, and in favor of males in the Emirati sample. No significant differences were found regarding workplace and social status. The results also revealed a significant positive relationship between anxiety about COVID-19 infection, daily smartphone use hours, and age on the one hand, and smartphone addiction on the other. The strongest predictor of smartphone addiction was anxiety about COVID-19 infection, followed by daily use hours. Age did not significantly contribute to the prediction of smartphone addiction. The study findings shed light on the psychological health and cognitive aspects of anxiety about COVID-19 infection and its relation to smartphone addiction.

Background Patients with schizophrenia suffer from diffuse cognitive impairment and high prevalence of cardiovascular metabolic risks, associated with poor clinical outcomes. We aimed in this study to test the presence of cognitive impairment in a sample of patients with schizophrenia, and evaluate its possible relations to patients’ metabolic profile. We recruited forty patients diagnosed with schizophrenia and their matched controls from the inpatient departments and outpatient services from January to December 2016. Schizophrenia diagnosis was confirmed by the ICD10 criteria checklist. Symptoms profile and severity were assessed by the Positive and Negative Syndrome Scale. Cognitive profile was assessed through (1) Trail Making Test, Parts A and B and (2) Wechsler Memory Scale-Revised Visual Reproduction Test. Metabolic profile was assessed by measuring the body mass index, fasting blood glucose, and lipid profile. SPSS (V. 22.0, IBM Corp., USA, 2013) was used for data analysis. Results The patients group had a significantly higher means in the speed of processing, executive function, attention, and working memory scores on TMT-A ( p = 0.0), TMT-B ( p = 0.00), and WMS-R ( p = 0.029) and significantly higher FBG levels ( p = 0.00). Correlation studies showed that the increase in patients’ age, illness duration, treatments, number of hospitalizations, number of episodes and of ECT sessions received, symptoms severity, and deficits in cognitive function scores was associated with higher BMI and FBG. Conclusions Patients with schizophrenia have a higher prevalence of cognitive impairment and vascular risk factors than the general population. Close monitoring and early management of these risk factors can promote better cognitive abilities and overall functions.

Multidrug resistance (MDR) remains a burden in cancer chemotherapy. Several members of ATP-binding cassette (ABC transporters) are responsible for the efflux of anticancer drugs outside cells decreasing the drug’s effective intracellular concentration. Therefore, extensive efforts have been conducted by researcher to circumvent the activity of these transporters to enhance the success of chemotherapy. In the present study, we questioned the role played by two microRNAs, namely miR-98 and miR-214 in controlling their bioinformatics’ predicted ABC efflux transporter targets ABCC5 and ABCC10, in addition to ABCB1 and ABCC1 in doxorubicin-resistant HCC cells (HepG2/Dox). miRNA mimics and inhibitors transfection were utilized to explore the role of both candidate molecules in MDR in HepG2/Dox cells. QRT-PCR and western blotting were used for quantitative gene and protein analyses. The study revealed that miR-214 mimics significantly upregulated ABCC1 and ABCC5. While, miR-98 and miR-214 inhibitors significantly down regulated ABCC5 and ABCC10, respectively. These results introduced a possible negative role played by both miR-98 and miR-214 on drug sensitization. Moreover, these findings clarified that the predicted targets for miR-98 and miR-214 were not confirmed experimentally.