Volume 13 Issue 9

Abstract: The purpose of this study was to investigate the impact that organizational culture has on employee well-being within a case-study organization. The study employed a quantitative research design and adopted the use of a questionnaire to collect the necessary data from the 20 participants. The collected data was analyzed using descriptive statistics and multiple linear regression analysis. The findings of the study indicated that the organization operates mainly on a hierarchy culture with the elements of other culture types of notably culture clan and market culture mixed into the overall organizational culture. It was inferred that these culture types exist as a subculture within the overarching culture of the organization. The findings suggest that employee well-being is not a point of critical concern for the organization. This suggests that within the case study organization, employee well-being is perceived as a moral obligation as against a strategic factor that can yield considerable benefits to the organization. The findings also indicated that organizational culture exerts a degree of influence on employee well-being. Organizational culture has an impact on employee well-being, therefore improving employee well-being can ensure optimal productivity and performance of employees.

Abstract: The research paper introduced improvements to PeeCheck, a portable urine analyzer, addressing the need for versatile, rapid, and low-cost urine analysis tools, particularly in resource-constrained rural healthcare settings. The focus was on evaluating the accuracy and speed of reading the urine strip and the electrical characteristics of the portable urine analyzer device across various parameters. PeeCheck 2.0 represented enhancements in medical diagnostics using urine as a non-invasive sample. By incorporating the integration of technologies such as versatility in using variations of urine strips and data cloud storage using the Raspberry Pi Pico W microcontroller, PeeCheck 2.0 was designed to analyze 14 colorimetric urine characteristics, aiding in the pre-diagnostic of health issues associated with kidney and metabolic-related diseases. The study evaluated PeeCheck 2.0's performance through the analysis of urine samples collected from 3rd- and 4th-year electrical engineering students and compared the results with those obtained from standard laboratory urinalysis. The findings highlighted PeeCheck 2.0's potential to enhance health outcomes and reduce healthcare disparities in rural communities by providing healthcare providers with a low-cost and reliable solution for rapid detection and addressing health concerns in areas with limited resources. The study findings demonstrated that PeeCheck 2.0 achieved significant accuracy and speed in analyzing urine samples across 10 colorimetric parameters compared to standard laboratory methods. PeeCheck 2.0 obtained an overall accuracy of 92.470 percent in its analyses. Results from the evaluation with urine samples collected from electrical engineering students showed a high correlation with laboratory urinalysis, validating PeeCheck 2.0's efficacy in detecting key indicators of kidney and metabolic-related diseases. However, continuous programming-based calibration optimized PeeCheck 2.0 by adjusting parameters in real-time with data and sensor feedback, ensuring consistent accuracy and reliability in urine parameter testing. This research underscored PeeCheck 2.0's role in advancing medical diagnostics through innovation in urine analysis technology, contributing to improved healthcare delivery and outcomes in rural and underserved communities.

Abstract -The concentration of CO2 in atmosphere is increase gradually, CO2 as greenhouse gas has impact to the climate change.  It’s mostly CO2 gas come from the burning of fossil fuels. Fossil fuels release more carbon dioxide compared to the renewable energy. The concentration of CO2 in atmosphere nowadays is double to the concentration of CO2 before the industrial age. Reducing the entering of CO2 gas into atmosphere by substitution of fuel with low carbon contain and renewables energy are necessary.  Energy mixed has been dominated with fossil energy, the CO2 emissions are still high.  Energy mixed with   more renewable and less fossil fuels energy should be designed for the next scenario.  Energy mixed formula is depend on the availability of energy source.

Abstract: The article examines the nexus between Artificial Intelligence (AI)-driven tax frameworks and sustainable economic growth, with a focus on enhancing business efficiency and prosperity. The research made use of explorative method. As governments and businesses face challenges like climate change, resource depletion, and income inequality, AI offers transformative potential in optimizing tax frameworks. By leveraging AI technologies such as machine learning, data analytics, and natural language processing, tax systems can become more efficient, equitable, and transparent. The paper proposed optimized tax collection strategies driven by artificial intelligence. The paper recommended the need to address technical, regulatory, and operational challenges by focusing on strategies targeting each of them can tax authorities and businesses employ the full potential of AI-driven tax frameworks.

Abstract : Nigeria as a multi ethnic nationalities country is faced with tremendous task of integrating a country that is deeply depleted by mirage of problems, including among others ethno-religious and cultural crises and the constitutional imperfection. Regrettably, the manner the country practices federalism has resulted to the country's inability to foster or promote national integration, and unity in diversity. The main objective of the study was to investigate the role of federalism in promoting national integration in Nigeria. Four states comprising of AkwaIbom, Bayelsa, Delta and Rivers   in south south, Nigeria were selected for the study. To achieve this objective, survey correlation/research design was adopted for the study and a sample size of 384 was drawn from the population of 20,787,224, using purposive and systematic sampling techniques respectively. Data were selected using researchers developed questionnaire titled Federalism and National Integration Questionnaire (FNIQ) the instrument was administered and retrieved by the researchers with the help of two research assistants. Three research questions and three hypotheses were formulated for the study. Pearson Product Moment Correlation (PPMC) was used in testing the hypotheses at 0.5 level of significance. Three null hypotheses of the study were rejected leading to the decision to accept the alternative hypotheses. It was concluded that there was significant relationship between ethnicity, supremacy of power of having, and the Federal Constitution and the national integration in south south Nigeria. It was recommended among others, that, to foster possible breakup by the ethnic nationalities in Nigeria, government should device a federal principle through accommodation, consultation, negotiation and inclusion, instead of exclusion and denial of their rights. The sharing of political offices and opportunities should be equitable and based on the spirit of federal character principle and true federalism.

Abstract: The season is a pivotal representative in ascertaining the health status of a coastal ecosystem in a changing climate condition. The physico-chemical parameters of Badagry Creek were spatially and temporally investigated for twelve months (July 2017 to May 2018 for dry and wet seasons). Water samples were collected bi-monthly from nine stations, grouped into five activity zones during the water flow, covering upper, middle and lower courses. Seasonal variations in the physicochemical characteristics of Badgry Creek were found to be significantly different (<0.05) in salinity, phosphate, total suspended solids (TSS), total dissolved solids (TDS), depth, and dissolved oxygen (DO). Data were analysed using descriptive statistics, ANOVA and Spearman correlation at α₀.₀₅. The wet season had greater values of conductivity (551.27±79.09µS/cm; 412.77±42.7µS/cm) , pH (7.43±0.39: 7.41±0.41), salinity (5.04±1.11ppt: 1.22±0.40ppt), DO (6.72±0.26: 5.39±0.58mg/l), and chloride (Cl̉ˉ, 684.51±82.50 µmol/L: 674.22±73.44 µmol/L) than the dry season respectively. An increase in salinity during the wet season indicates the seawater intrusion and the waste discharge effluents from domestic and aquaculture processes into the stream from the higher course. Significant differences were observed between the Aquaculture (AQ) and Aquaculture combined Dredging (AQ_DG) zone for Sulphate and Chloride (Clˉ). The Biological Oxygen Demand (BOD) varied significantly between zones; they were 3.12±0.74mg/l in the Aquaculture zone (AQ) to 4.00±0.42 mg/l in the Domestic Waste (DW) zone and 2.10± 0.5 mg/l in the Aquaculture and Dredging (AQ_DG) zone to 19.2±9.44 mg/l in the Domestic Waste (DW) zone, respectively. The Highest value of TOM in DW station indicates anthropogenic effluents from domestic waste in this zone.  The seasons were used to describe variations in Badagry Creek's physicochemical parameter values because the wet season noted higher parameter values, which may be related to an influx of water from the upper to the lower course.

Abstract: Background: Brain tumors are a significant global health concern impacting both adults and children. Tumors are characterized by abnormal or excessive growth resulting from uncontrolled cell division. Diagnosing brain tumors poses various challenges, including limited funding, a shortage of qualified professionals, and insufficient access to medical facilities in remote regions. Different learning techniques for detecting brain tumors have been developed due to their ease of use, cost-effectiveness, and non-invasive nature, in contrast to other invasive methods. Methods: This research conducts a systematic literature review to explore modern trends and concepts of machine learning in healthcare, aiming to identify effective techniques for brain tumor detection. It also compares and analyzes the most efficient machine learning methods currently in use, focusing on aspects such as machine learning algorithms, image augmentation, evaluation metrics, and the sizes of datasets employed. Results: The findings indicate that non-invasive methods, such as machine learning algorithms for brain tumor detection, are cost-effective and provide quick results. Conclusions: This systematic literature review offers a technical overview, demonstrating the efficiency and effectiveness of machine learning techniques in making brain tumor detection feasible. The study utilizes deep learning and machine learning methods to comprehensively analyse diagnosis, imaging, and clinical evaluations in medical fields related to brain tumor detection.

Abstract: This reviewed literature on electricity forecasting covers its history, terminology, and techniques. A systematic review of existing studies highlighted key findings and future research opportunities. Conventional statistical techniques and MLA can predict electricity demand over time with various techniques and forecasting windows tailored to data and problem specifics. Most studies focused on STLF, often without testing techniques on MTLF and LTLF. The key findings include: Many studies (26%) used conventional statistical methods like ARIMA, ARIMAX, and SARIMAX for electricity forecasting, often without benchmarking algorithms. Various factors, such as time, weather, electricity price, population, and economy, influence ELF. Weather parameters were the most commonly used predictors, though performance varied across studies. A global increase in electricity demand has driven numerous studies, though less research has been done in low- and middle-income countries. Deep neural networks like LSTM have been underutilised in electricity forecasting. LSTM's ability to store memory and address the vanishing gradient problem makes it promising for future research, particularly in hybrid models combining CNN and LSTM for forecasting peak load demand based on economic and environmental factors.

Abstract: Malaria is one of life’s threatening disease that contributed significantly to morbidity and mortality in Nigeria. The variability effects of meteorological factors between the Northern and Southern region of Nigeria on malaria transmission remain unclear and not well researched. This research was aimed at comparing the non-linear effects of seasonal, trend and meteorological variables on monthly malaria cases in Kano and Lagos State of Nigeria. The monthly malaria cases for age group above 5-year for Kano and Lagos State were obtained from DHIS for the period of January, 2016 to December, 2022. Also, the predictors/meteorological variables (Rainfall, Relative Humidity, Minimum Temperature and Maximum Temperature) were obtained from Nigeria Meteorological Agent. Generalized Additive Model was fitted using Negative Binomial to apply smooth function to estimate and compare the effects of the selected predictors on the malaria transmission in Kano and Lagos States.  Rainfall and Maximum temperature were found not significant in explaining the effect of malaria transmission. Malaria transmission was found to be seasonal and varying in Kano State with highest transmission between September and October while the seasonal effect was reduced to linear in Lagos State. The long term trend had a significant increasing effect on malaria transmission in Kano Sate compared to non-significant effect in Lagos State. The effect of Relative Humidity was reduced to linear and increasing in the both State. Paying attention to the variability in the influence of season, trend and meteorological variables on malaria transmission will enhance respective State Government to develop an early warning and awareness that will help in controlling the malaria outbreak in their State.

Annotation: The level of employment and unemployment are important macroeconomic indicators in order to characterize the effect of the economic policy implemented in the Republic of Azerbaijan and the degree of economic development of the country. The high level of unemployment in the economy causes various complications, which negatively affects the state of socio-economic progress. It is appropriate to consider a number of directions in the field of state regulation of employment and unemployment. These are the regulation of the demand and development for workplaces, the implementation of a successful state policy in the field of education, vocational training and personnel retraining. In the study of the scientific basis of employment issues, it is selected due to the importance of maintaining the continuity of workplaces and regulating its market relations based on the relevance of objective development laws. In this field, it is also worth noting the need for specialization. Measures in the field of policy regulation in the direction of employment and unemployment mostly act as efficiency indicators of the state's social policy as well. Implementation of various projects, representation of people from relevant classes in those projects and other necessary issues carry the strategic essence of employment-oriented social policy. The paper clarifies various issues regarding employment and unemployment in the Republic of Azerbaijan and deals with the state regulation in this direction. In the study, the approach to both problems on the current national economic priorities is more concrete and systematic. The creation of industrial parks and districts in different regions of the country will have a positive effect on the reduction of unemployment and, in parallel, on the preservation of the increasing dynamics of employment indicators. If we take into account the prospects of the territories freed from occupation in recent years, we can say that substantial results will be achieved based on the reforms and strategies carried out in the future. As it is known, state programs for the development of regions are adopted continuously and gradually in Azerbaijan. The implementation of these programs in stages will allow the establishment of production enterprises and the creation of new jobs. This, in turn, will lead to a decrease in the level of unemployment and further increase in employment opportunities.

Abstract: Frankincense, derived from the resin of Boswellia species, has been valued for millennia across diverse cultures for its aromatic, medicinal, and economic significance. This review provides an in-depth analysis of frankincense, encompassing its historical development, geographical distribution, and trade dynamics. The paper examines the economic aspects of frankincense production, including its impact on local economies and global markets. Detailed attention is given to the plantation and harvesting practices, production statistics, and the chemical composition of frankincense. Various types and colours of frankincense are discussed, along with quality assessment criteria. The review explores the diverse properties and applications of frankincense products, including their benefits and potential side effects. Additionally, it addresses sustainable harvesting practices, conservation efforts, and regulatory considerations. The paper concludes with a summary of recent scientific research, current challenges in the industry, and future directions for exploration and improvement.

Abstract: This research is centered on the purification of Spent Shell Helix (HX3 20W-50) lubricating oil using acidified clay treatment with three acids options: mineral sulfuric acid (H2SO4), organic acetic acid (CH3COOH), and organic citric acid (C6H8O7), (a novel washing agent). The clay mineral, spent, purified, and fresh lubrication oil samples were characterized to determine their physicochemical properties. The clay mineral was characterized using Energy-Dispersive X-Ray Fluorescence (EDXRF). The result indicated that the clay was bentonite clay and it was successfully acidified. The result also indicated that properties of the spent lubricating oil such as Kinematic viscosities at 40 oC and 100 oC increased from 118.9 cSt and 13.9 cSt for the spent oil to 169.7 cSt and 15.2 cSt, 178.2 cSt and 16.3 cSt, 183.4 cSt and 16.8 cSt for the oil samples purified with C6H8O7, CH3COOH and H2SO4 respectively Also, the flash point increased from 106 oC for the spent oil to 133 oC, 180 oC, 189 oC for the oil samples purified with C6H8O7, CH3COOH and H2SO4 respectively. The FTIR analysis of the spent oil revealed the presence of primary oxidized products, absent in spent engine oil purified with mineral sulphuric acid (H2SO4), organic acetic acid (CH3COOH), and fresh oil. Acidified clay effectively purified the spent engine oil, bringing its physicochemical properties closer to those of fresh oil. However, the effectiveness of the treatment varied depending on the specific acid used.

A considerable advancement of automation over the decades shows automation is very much essential in every sectors especially where time is a major factor. Hence there is a huge demand for automated notification or bell system which has high degree of accuracy and low cost. The main purpose of this project is to develop a smart bell system which can be used all kinds of educational institutions. Smart school bell implement a smart way to manage class periods and breaks without use of manually operated manpower.  There is a controller machine, where each class can be set up as desired and from this machine one speaker will be installed in each class. Bell will play in every room through the speakers. The machine will be equipped with a microphone that allows the headmaster or anyone to send the message to all classes simultaneously or to any class separately. The system is made by establishing a serial interface between PIC microcontroller (PIC16F877A) and a Real Time Clock (RTC) is designed with the help of PIC 16F72 module and the LCD display units provide real time and class time separately. The software coding part is done using micro C and hardware implementation is done using the components. The main advantage of this project is due to very easy circuit operation it is easy to operate, cost effective and can be password protected to avoid unauthorized access

Abstract: Quality of Experience (QoE) is a well-established methodology for measuring and understanding the overall level of customer satisfaction with a service, and has been presented as a way to improve telecommunication services. Even though QoE can be used to solve problems such as customer loyalty and optimisation of network resources in mobile networks, there is a great lack of knowledge on how mobile operators can take advantage of QoE and its potential benefits. This thesis explores the incorporation of QoE in mobile networks to improve their service offering from a technical, regulatory and business perspective. An important conclusion is that due to the nature of the challenges faced by the mobile industry, a QoE analysis cannot be limited to a technical discussion. A technical solution can be the first step to the first step to overcoming industry challenges. Mobile operators require new methods that integrate technical, market and business considerations to improve their service offer. A method analysed in this dissertation is a Customer Experience Management (CEM) platform. Given the technical, regulatory and business factors covered in this thesis, a CEM platform can be used by mobile operators to make a better use of QoE in their business operation. operators to make a better use of QoE in their business operation.

Abstract: In this article, we try to establish a relation between a nation’s population and its growth with the GDP growth by means of endogenous growth theories and attempt to explain India growth by means various empirical studies. India’s growth associated with an increase in total factor productivity associated to have a relation with human capital and knowledge capital formation in India which is debatable. We have a very low gross enrolment ratio (GER), Education expenditure is very low for general education and it is even low for higher education. Our education system is not R&D based to a very large extent and skill development is also questionable.  The government investment for R&D is low. The proper human resource management through upskilling human capital of the youth is vital through a PPP model. Macro synergy of innovation capital can be obtained only through firm level micro consistent innovation acts

Abstract: In recent times, a multitude of technologies have surfaced and are currently being extensively employed within the banking and financial industries. Among the various technologies available are robotic process automation (RPA), machine learning (ML), artificial intelligence (AI), big data, block chain integration, cyber security, cloud computing, and cryptocurrency. These technological advancements ease human lives. On the other hand, some people are not aware of them in the public domain. This study aims to educate readers on the benefits of these technologies in financial services and how they can be used to their advantage. The data from this study will assist different stakeholders in developing plans for growing their businesses and acquiring new clients while gaining a competitive edge. Since the descriptive technique was most suited for this topic, it was used to perform the research. Information was gathered from a variety of sources, including websites for listed businesses, RBI, SEBI, and published reports. Credit evaluation, check payments, money transfers, merchant services, currency exchange, wealth management, stock market, portfolio management, and insurance are only a few of the processes that heavily rely on the chosen technologies. Therefore, this is beneficial to all parties involved in these companies. One significant outcome of this study is the rapid growth of digital accounting, which in the future may totally replace human labor.

Abstract: This research investigates the transformative potential of incorporating blockchain technology into computer science education. In light of the rapid evolution of the digital landscape, traditional educational frameworks often fail to meet the demands of the industry, resulting in a significant skills gap among graduates. This paper analyzes how the integration of blockchain can revolutionize computer science curricula by enhancing learning experiences and equipping students to navigate future technological challenges. The study highlights the advantages of blockchain in educational contexts, including increased security and transparency of academic records, streamlined credentialing processes, and the establishment of decentralized learning platforms that promote collaboration and innovation. It presents case studies of institutions that have successfully implemented blockchain, along with strategies for educators to effectively integrate this technology into their pedagogical approaches. Additionally, the research addresses the challenges and limitations associated with blockchain integration, such as the requisite learning curve and infrastructure demands. The findings indicate that the incorporation of blockchain in computer science education can significantly boost student engagement, provide verifiable skill sets, and align academic outcomes more closely with industry requirements. This study contributes to the ongoing discourse on educational innovation and offers a strategic framework for institutions aiming to utilize blockchain technology in preparing students for the future job market.

Abstract: The dynamics of red blood cells (RBCs) is one of the major aspects of the cardiovascular system that has been studied intensively in the past few decades. Using computational fluid dynamics, complex nonlinear fluid flows have been modeled. The dynamics of biconcave RBCs are thought to have major influences in cardiovascular diseases and other problems associated with cardiovascular flow behaviour, and the determination of blood rheology and properties. Most reported computational models have been confined to the behaviour of a single RBC in 2-dimensional domains, under physiological flow conditions. This work investigates a particular stacking pattern in analyzing the RBC flow behavior under physiological flow conditions, using the D2Q9 lattice Boltzmann numerical method created using Matlab. Prior to the analysis the Matlab script was benchmarked using the Poiseuille flow and the flow around the cross-section of a cylinder, after which the accuracy of the method used was determined. The benchmarks showed that the lattice Boltzmann code had minimal error. The accuracy was determined using the data obtained from Matlab and a created excel program. It also showed that the lattice Boltzmann method was of the first order, which corresponds with results existing in literatures. The analysis of the stacking pattern showed how RBC flows through the chosen stacking pattern, and the results are shown.

Abstract: The purpose of a fire alarm system is to warn us of an emergency so that we can take precautions to safeguard ourselves, our employees, and the public. However, modern fire alarm systems require a significant amount of wiring and labor during installation. The most frequent issues that was encountered during monitoring commercial and residential fire alarm systems are false alarms, inappropriate installation, lost connections, malfunctioning or aged equipment, and careless maintenance that discouraged users from putting them in their houses as a result. Hence, the researcher developed an IoT-based fire alarm system connected to several types of sensors and micro-controller distributed over the building and house. Each of these nodes is made up of an Arduino microcontroller that is coupled with smoke, temperature, humidity, flame, methane, and carbon monoxide (CO) sensors. Thus, an innovative system is designed with the help of Internet of Things (IoT) that records live reading of sensors and stored in cloud interface which also lessens the investigation process of the authorities concerned. Results shows that majority of the evaluators responded to the functionality of the proposed system to be Superior and provide all the functionality required for the system to detect and prevent fire occurrences.

Abstract: The shift to electric vehicles (EVs) is vital for reducing greenhouse gas (GHG) emissions, but Thailand faces challenges in adopting new EVs due to high costs and limited infrastructure. This study investigates the feasibility of converting internal combustion engine (ICE) vehicles to EVs over the next decade, examining the economic, environmental, and social impacts. Findings show that EV conversions can reduce operational costs by 40-50% and lower GHG emissions by up to 70%, even when accounting for electricity-related emissions. In addition, the transition could significantly improve air quality, leading to public health benefits. However, barriers such as high upfront conversion costs, limited access to conversion kits, and the need for skilled labor remain. Addressing these challenges through government incentives, infrastructure development, and workforce training will be essential for scaling up EV conversions, enabling Thailand to accelerate its shift toward sustainable transportation and meet its climate goals.