Spline smoothing is a technique used to filter out noise in time series observations when predicting nonparametric regression models. Its performance depends on the choice of the smoothing parameter. Most of the existing smoothing methods applied to time series data tend to over fit in the presence of autocorrelated errors. This study aims to determine the optimum performance value, goodness of fit and model overfitting properties of the proposed Smoothing Method (PSM), Generalized Maximum Likelihood (GML), Generalized Cross-Validation (GCV), and Unbiased Risk (UBR) smoothing parameter selection methods. A Monte Carlo experiment of 1,000 trials was carried out at three different sample sizes (20, 60, and 100) and three levels of autocorrelation (0.2, 05, and 0.8). The four smoothing methods' performances were estimated and compared using the Predictive Mean Squared Error (PMSE) criterion. The findings of the study revealed that: for a time series observation with autocorrelated errors, provides the best-fit smoothing method for the model, the PSM does not over-fit data at all the autocorrelation levels considered ( the optimum value of the PSM was at the weighted value of 0.04 when there is autocorrelation in the error term, PSM performed better than the GCV, GML, and UBR smoothing methods were considered at all-time series sizes (T = 20, 60 and 100). For the real-life data employed in the study, PSM proved to be the most efficient among the GCV, GML, PSM, and UBR smoothing methods compared. The study concluded that the PSM method provides the best fit as a smoothing method, works well at autocorrelation levels (ρ=0.2, 0.5, and 0.8), and does not over fit time-series observations. The study recommended that the proposed smoothing is appropriate for time series observations with autocorrelation in the error term and econometrics real-life data. This study can be applied to; non – parametric regression, non – parametric forecasting, spatial, survival, and econometrics observations.

Avian influenza (bird flu) occurs sporadically in American poultry flocks, decimating these flocks and causing substantial economic losses. Avian influenza also impacts the beliefs of food handlers and preparers in the home (home cooks). Although those who properly handle and prepare processed poultry products cannot succumb to avian influenza, there is a widespread belief that one can contract the bird flu from these foods. Beliefs about getting avian influenza from poultry products and intentions to avoid consuming poultry products are the focus of this study of 285 home cooks. False beliefs about getting avian influenza from handling, preparing, and consuming poultry products are apparent in this sample. Correlational analysis also shows that those holding the false beliefs intend to act upon those beliefs by planning not to consume poultry products. Moreover, the false beliefs about contracting avian influenza from poultry products are correlated with a bias to see oneself as less likely to produce food that contains foodborne diseases. These findings are consistent with, and contribute to, the research literatures on belief formation and change, behavioral intentions, and with research showing how guilt by association thought processes underlie false beliefs related to food safety. This research has important implications for poultry and other food processing industries, and for campaigns to persuade the public about real and imaginary risks associated with particular food products.

In this paper, we will examine a rather complex case of the paradoxical nature of certain conclusions that may arise when studying the numerical convergence of a specific nonlinear recursive sequence, known in the literature as Muller’s sequence. To analyze the peculiar computational behavior of this sequence, it is necessary to employ a powerful mathematical framework in order to understand the nontrivial issues that can arise when the software implementation of this seemingly simple mathematical problem. These challenges often stem from the limitations of numerical methods and the inherent errors in computer arithmetic, which can affect the accuracy and stability of the results, particularly when dealing with iterative methods like Muller's sequence.

This study examines concord errors in academic writing among first-year students at Offinso College of Education in Ghana, aiming to identify common errors and propose remedial strategies for improvement. The population sample consists of first-year students at the college, reflecting a gender-sensitive distribution. The study adopts a mixed-methods research design, combining qualitative and quantitative analyses to explore the effects of concord errors on academic writing. Sampling techniques include purposive, quota sampling, and simple random sampling methods. Research instruments include questionnaires, interviews, and writing assessments to evaluate students' language skills. Data analysis involves identifying concord errors in students' writing and assessing the impact on their academic performance. The study concludes by recommending strategies to mitigate concord errors, such as targeted language practice, timely feedback, and awareness of grammatical conventions, to enhance students' writing proficiency and academic success.

The role of enterprise applications in pharmaceutical industries is driving the digital transformation of various critical processes, and one process benefiting from this innovation is pharmaceutical drug traceability. This industry grapples with challenges like a lack of transparency, difficulties in tracking products, a deficit of trust, and issues related to shipping expired products. To address these concerns, blockchain technology as an enterprise application has been harnessed as a solution. Notably, counterfeit drug prevention emerged as the most prevalent category, aligning with the pharmaceutical industry's primary objective. Blockchain technology is an emerging innovation that is finding enterprise applications in various industries, including healthcare. In the healthcare sector, Blockchain networks are being utilized to securely store and exchange patient data across hospitals, diagnostic laboratories, pharmacies, and medical practitioners. These enterprise applications can effectively identify and mitigate critical errors, including potentially hazardous ones within the realm of healthcare. Consequently, this enterprise technology holds the promise of enhancing the efficiency, security, and transparency of medical data sharing within the healthcare system. Moreover, it offers valuable tools for medical institutions to gain insights and improve the analysis of medical records. It visually represents the diverse capabilities, enablers, and the unified workflow process of Blockchain technology in supporting healthcare on a global scale. Additionally, the paper presents a thorough discussion of fourteen significant applications of Blockchain in healthcare, underscoring its pivotal role in addressing issues like deception in clinical trials.

The paper presented the results of the research related to the analysis of the reliability of computer calculations. Relevant examples of incorrect program operation were demonstrated: both quite simple and much less obvious, such as S. Rump's example. In addition to mathematical explanations, authors focused on purely software capabilities for controlling the accuracy of complex calculations. For this purpose, examples of effective use of the functionality of the decimal and fraction modules in Python 3.x were given.

A hydrographic survey vessel shows three -dimensional movements (Roll, Pitch and Heave) misalignment with respect to the vessel reference unit (VRU) due to environmental effects, such as wind, current, other vessel wakes, etc. These motions if ignored, cause errors in measured depth and in the positioning of the sounding. Hence the need of a motion sensor and gyroscope. However, the alignment of the multi-beam sonar head to the motion sensor and gyro (Octant) is critical to the accuracy of the determined depths. It is not possible to install the sonar head in perfect alignment with the motion sensor and gyroscope to the accuracy required. The synchronization of the GPS time with the Motion sensor and gyro, the latency of the position, as reported by the GPS as well as the velocity of sound in water are important parameters to account for the misalignment of the motion senor and the multi beam sonar head; this is called the Patch Test. In view of this, a patch test was done to ascertain the mounting angles of EMB 2058 Multi-beam sonar with Octan V installed onboard a survey vessel (Bitam). The result of the Patch test gives a row, pitch and heading value of -1.242˚, -4.92˚, and -0.48˚respectively. The speed of sound in water as measured ranges from; 1531.47m/s to 1531.60m/s within a minimum cast depth of 0.49m and maximum cast depth of 16.00m. The statistical analysis gives and average error of 2.642cm/m2 which was within acceptable standard as define by the International Hydrographic Organization (IHO).

The deflection of the vertical is an important parameter that combines both physical (astronomic) and geometric (geodetic) quantities. It is critical in such areas as datum transformation, reduction of astronomic observation to the geodetic reference surface, geoid modelling and geophysical prospecting. Although the deflection of the vertical is a physical property of the gravitational field of the earth; which almost all terrestrial survey measurements, with the exception of spatial distances, made on the earth surface are with respect to the Earth’s gravity vector, because a spirit bubble is usually used to align survey instruments. It has been ignored in most geodetic computation and adjustment. This research work is therefore aimed at computing the component of the deflection of the vertical component for part of Rivers State using a geometric method. This method involves the integration of Global Positioning System (GPS) to obtain the geodetic coordinate of points, precisely levelling to obtain the orthometric height of this point located within the study area. By least square using MATLAB program, the estimated deflections of vertical component parameters for the test station SVG/GPS-002 were; -0.0473” and 0.0393” arc seconds for the north-south and east-west components respectively. The associated standard errors of the North-south and East-west components were ±0.0093” and ±0.0060” arc seconds, respectively. The deflection of the vertical was also computed independently from gravimetric models of the earth as: ξ = 0.0204” ±0.0008814”, η = -0.0345” ±0.0014”; ξ =0.0157” ±0.000755”, η = -0.0246” ±0.0012”; ξ = -0.0546±0.0006014, η = -0.0208±0.0006014 for EGM 2008, EGM 1996 and EGM 1984 respectively. The two-tailed hypothesis test reveals that the estimated deflection component is statistically correct at 95% confidence interval. It was observed that the effect of the deflection of the vertical is directly proportional to the distance of the geodetic baseline. Therefore, including the derived component of deflection of the vertical to the ellipsoidal model will yield high observational accuracy since an ellipsoidal model is not tenable due to its far observational error in the determination of high-quality job. It is important to include the determined deflection of the vertical component for Rivers State, Nigeria.