Al-Bahir Journal for Engineering and Pure Sciences

In this study, we presented a modified SIR-SI model to investigate the dynamics and potential controls for cholera
transmission, with an incident rate equipped with a saturation factor to investigate the combined impact of three vital
measures which include effective surveillance, vaccination campaign and proper treatment in case severity. We established
among other things, the qualitative analysis of the model to validate the results. Furthermore, the reproduction
number (R0) was found to be less than unity (1), through the stability analysis. Additionally, finite different scheme was
utilized in solving the differential equations of the model. MATLAB software was used for the numerical simulation to
examine the effect of these control measures on the population density, findings from the graphical solutions depicts that
these measures will aid in flattening the curve Cholera propagation in the population if properly implemented

In this paper we introduce a new class of maps in the dual Soft topological space and study some of its basic properties
and relations among them, then we study dual Sof topen and dual Sof tclosed mapping

We are considering a novel method for analyzing time series data that relies on quasi-linear recurrence relations.
Unlike neural networks, this approach allows for directly formulating high-quality quasi-linear difference equations
that accurately represent the studied process. Techniques for determining the parameters of a single equation have been
devised and validated. This work discusses and tests a technique for identifying the parameters of a quasi-linear
recurrence equation. This approach is employed to tackle the issue of regression analysis including observable variables
that are mutually dependent. It enables the utilization of the Generalized Least Deviations Method (GLDM). This model
was utilized in a computational experiment to determine the parameters of quasi-linear differential equations that
describe the spread of Covid-19 infection. The model underwent testing on three distinct types of processes: (1)
monotonous (predicting cumulative cases); (2) oscillatory (predicting daily cases). The specified parameters allow the
model to generate long-term predictions

This research delineates the advancement of a refined predictive algorithm centered on the Generalized Least Deviation
Method (GLDM) specifically configured for analyzing COVID-19 infection trends in Dagestan using univariate
time series data. Our methodology is characterized by its enhancement of forecast precision through diligent minimization
of a bespoke loss function. The algorithm's innovation lies in its formulation, incorporating second-order relationships
within the time series data:
xt ¼
PvðWÞ j¼1 hjgjðfxtkgwk
¼1Þþ 3t ;
where hj are the computed weights ascribed to historical data, and 3t denotes the error component. Our empirical
analysis substantiates that by strategically accentuating pertinent coefficients and optimizing the loss function, there is a
significant elevation in the model's forecasting accuracy. Consequently, the refined second-order GLDM model emerges
as an advanced and applicable instrument for the prognostication of COVID-19 infection cases in Dagestan

This study investigates the effect of Alpha radiation on dye_sensitized solar cell. The DSSC was manufactured using a
TiO2 thin-layer anode, a conductive glass FTO (Fluorine-Doped Tin Oxide) glass substrate, a counter electrode that was
deposited with aluminium (Al)on the FTO glass, and two dyes (a natural dye, blackberry, and a synthetic dye, methylene
blue). It was detected that irradiation is positive, as we found that the longer the irradiation time, the higher the efficiency.
This research studied the J-V curve and UVeVis of the cell. The UVeVis results showed that the energy band
gap decreases with increasing irradiation and the energy gaps were (3.01,2.90 and 2.77) eV for (st, 30 min, 60 min) samples
respectively. The best result appeared for the cell using the synthetic dye methylene blue; the cell parameters were open
circuit voltage (0.69) volt, Jsc(5.85 mA/cm2), FF at (0.15), and efficiency 1c/o. The results showed that DSSC, which uses
radiation, is a promising method for future research.

In this study, cure models have been applied to model National Health Insurance Scheme (NHIS) claims payment data
with cured proportion using Pru District in the Bono East Region of Ghana as a case study. The covariates effects were
also modelled to investigate the effects of the covariates on the cured proportion. The estimates of the parameters of the
models were obtained by directly maximizing the observed likelihood functions. Most estimates of the parameters of the
cure models are significant at 5% significance level. The study revealed that the cured claims payments rate increases
over time with an estimated cured rate of 23.2%. Most covariates are significant at 5% significance level in most of the
cure regression models, thus, the covariates, number of NHIA staff vetting the claims and claims submission category
contribute to the claims payments cure rate. Weibull cure model modelled the data best over the competitive models. To
ensure the sustainability of the scheme and the attainment of Universal Health Coverage (UHC) by 2030 in Ghana, the
health policy makers should prioritise claims payments to Healthcare Providers (HCPs) at the primary health care (PHC)
levels within time over HCPs above PHC level.

The working principle of a self-balancing robot is similar to that of an inverted pendulum, with the mobile robot's
controller playing a crucial part in both self-balancing and stabilization. It is the kind that constantly modifies itself to
keep balance when it rides on two wheels. This review will center on the basic construction of the suggested robot,
summarize the recent studies relative to control methods and the building of the two-wheeled self-balancing robot, and
discuss the outcomes of control experiments conducted on hardware systems. It will assist researchers in building and
designing two-wheeled mobile robots in the future.

With the increasing availability of textual information in various languages via the Internet in homes and companies
through Internet and intranet services, there is an urgent need for the technologies and tools necessary to process this
information, phonetic representation, and voice interaction. For example voice to voice machine translation need to
phonetic mapping and similarity among the languages especially for names and foreign words. This one example of the
importance of phonetic mapping and similarity. This article aims to describe, in detail, the recent surge in interest and
advancements in phonetic similarity (PS), phonetic representation, and phonetic mapping researches. PS and phonetic
representation are a fundamental elements in information technology, supporting applications such as search engines,
speech recognition and voice to voice MT systems. The importance of PS is demonstrated, the main characteristics of
phonetic processing are highlighted, and the standardization aspects in converting text to phonetic representation are
clarified. The current study presented a survey of previous studies for a period of time (2000/2024). By utilizing advanced
AI algorithms and diverse linguistic datasets, new avenues for comprehending the dynamics and alterations of extinct
languages over time, as well as, their pronunciation mechanisms, can be explored. This change presents a breakthrough
in language learning and cross-cultural communication in addition to being a technical advancement. Additionally,
various linguistic resources and approaches used in the PS field are explained. Also, the features of common tools are
described, and standard evaluation metrics are illustrated. The article also reviews the current state of art for PS research
and converting text to phonetic representation. Finally, we present our analysis and conclusions. Disseminating these
findings is crucial for scholars focused on phonetic similarity and its related methodologies

The tremendous development in various industrial and agricultural fields has led to an increase in pollution of soil
and water bodies, which has been reflected in the quality of agricultural products and their quantity, as well as the
damage that this pollution causes to human health and the growth system of other living organisms. Many soils are
polluted with heavy metal elements like lead (Pb), zinc (Zn), cadmium (Cd), chromium (Cr), copper (Cu), iron (Fe),
aluminum (Al), and mercury (Hg), which makes the environmental situation more difficult. As part of this sensitive
stage, monitoring and analysis have become essential to assess the risk resulting from pollution and determine current
environmental trends accurately and promptly. Established on the laser's produced plasma, a novel element analysis
way called laser-induced breakdown spectroscopy (LIBS) has been developed. This method involves applying laser
pulses to the sample to ablate it. This process causes the material to evaporate and ionize in a hot plasma, which the
spectrometer then analyzes. The concentrations of various elements of environmental importance, like copper, cadmium,
calcium, barium, magnesium, chromium, manganese, titanium, phosphorus, iron, zinc, and others, are determined using
this technique because it has shown to be a reliable and efficient tool. Elements are detected by their spectroscopic
fingerprint. The last twenty years have witnessed the presentation of much research in this field, which has dealt with
various technical and environmental aspects. In this study, provided the basic principles of LIBS for soil analysis
included The formation of plasma mainly consists of heating, melting, evaporation, ionization, and excitation processes,
many of these studies will be reviewed to find out the latest developments, identify differences between researchers, and
discuss several aspects related to these studies. This understanding will help optimize conditions for improved signal
quality and stability. Another aim is to develop effective noise decreasing methods, such as signal averaging and
advanced data processing techniques, to enhance the analytical features of LIBS and ensure accurate readings. The study
will also explore the pollutants detection mechanisms inherent in LIBS, and will address the challenges associated with
sample preparation, striving for minimal processing to facilitate direct analysis of soil samples. By achieving these
objectives, the study aims to establish LIBS as a robust and reliable tool for assessing soil health and monitoring
environmental contamination. It will make it easier for readers to comprehend the state of the LIBS technique's research
in soil analysis today