ABSTRACT
The manufacturing sector is globally considered a crucial engine room, which plays a strategic role in value addition, economic development/diversification, industrialization and employment generation. However, in Nigeria, this sector, operates far below optimal capacity, largely due to chronic deficiencies in energy supply and affordability. Manufacturers in Nigeria face energy expenditures that constitute a significant portion of total production costs, with resultant negative impacts on the economy. This study investigates the socio-economic consequences of electricity tariff adjustments on the manufacturing industry in South-South region of Nigeria, with particular emphasis on production costs and productivity. Empirical evidence indicates that manufacturing firms continue to experience unreliable electricity supply, compelling reliance on alternative power sources that substantially raise operational expenses. The results show that rising electricity tariffs significantly increase overhead and total production costs and adversely affect productivity, thereby constraining output efficiency and competitiveness within the sector. Inferential and regression analysis reveal a strong and statistically significant positive relationship between electricity tariff increases and manufacturing production costs, confirming that higher tariffs are closely associated with escalating production expenses. This substantial explanatory power underscores electricity tariffs as a dominant determinant of manufacturing cost structures in Nigeria. The findings establish that increased electricity tariffs, compounded by persistent supply unreliability, significantly elevate production costs and reduce productivity in the manufacturing sector. The study therefore, concludes that electricity tariff policies have direct and measurable implications for industrial performance and sustainability, highlighting the need for policy frameworks that balance cost recovery with the operational rea

ABSTRACT
The erroneous prediction of the speed of light in dispersive media has been looked upon historically as unequivocal proof that Newton's corpuscular theory is incorrect. Examination of his arguments shows that they were only directly applicable to the momentum of photons, however, leaving open the possibility that the cause of his mistake was the unavailability of a suitable mechanical theory to enable a correct light speed prediction, rather than his use of a particle model. It is shown that Hamilton's canonical equations of motion remove Newton's error quantitatively, and also lead to the most basic formulas of quantum mechanics without reference to any of the pioneering experiments of the late nineteenth century. An alternative formulation of the wave-particle duality principle is then suggested which allows the phenomena of interference and diffraction to be understood in terms of statistical distributions of large populations of photons or other particles.

ABSTRACT
The spiders, in comparison with the majority of others animals, it has the ability to access to that kind of environment where others animals or even the humans can’t. Those attributes of the spiders are taken into this project in order to design and develop a quadruped spider robot in conditions to move in all kind of directions and perform such movement like ascend or descend. The paper is presented the dynamic and kinematics model with the purpose of understand how, mathematically, a quadruped animal and a spider walk. In this case we have studied the movement of a real spider, so we can define a suitable bio-mimetic model for our robot. Similarly, the motion simulation was implemented and the results are shown.

ABSTRACT
Agriculture serves as a fundamental pillar of Nigeria's economy. A significant portion of the available freshwater resources is allocated to agricultural activities. In Northern Nigeria, irrigation systems are essential. Over the years, farming practices have remained largely primitive, particularly in sub-Saharan Africa. This situation arises from a lack of advanced technological knowledge that could enhance agricultural practices. Various challenges hinder agricultural practices, including reliance on traditional farming methods, limited understanding of concepts and practices, policy issues, environmental concerns, and financial constraints. The purpose of this study was to optimize an IoT-based model for smart agriculture and irrigation water management. The study aimed to design, implement, test, and evaluate the performance of this optimized IoT-based model. The proposed system utilized the prototyping model as its methodology. The design was created using the Balsamiq application. The system is intended to feature a login page, a dashboard, a system use case diagram, an actuators page, a sensor page, and an application interface design. The Justinmind tool was employed to illustrate the flow of information within the system, encompassing data input and output, data storage, and all subprocesses through which the data traverses. The optimized IoT model was developed using four primary platforms: the ReactJS frontend application development platform, Amazon Web Services IoT Core for the backend, the Arduino development platform for sensor node creation, and the Python programming language for the actuator node based on the Raspberry Pi board. When compared to existing systems using the specified parameters, the optimized model demonstrates superior performance, particularly in terms of measurement accuracy, irrigation water management, operational modes, platform accessibility, real-time video capabilities, user-friendliness, and overall efficiency. The perform

ABSTRACT
sickle cell disease is a genetic condition characterized by abnormal red blood cell morphologies. It can be quite challenging to identify and monitor its response to treatment. Although deep learning-based models exhibit great potential in medical image processing, existing approaches often fail to cope with variability in sickle cell morphology. Additionally, publicly available sickle cell datasets tend to have a few samples with imbalanced classes. To mitigate the above challenges, we propose using the synthetic minority sampling technique (SMOTE) mechanism to handle class imbalances and a deep CNN architecture that aims to capture complex patterns and descriptive features in a newly created low-resolution sickle cell dataset from hospitals in eastern Uganda. This could help improve the efficiency of the diagnosis and classification of the disease. We performed experiments and examined several algorithms in the literature for related tasks. Based on the evaluation results, the proposed SMOTE-based DL-SCD outperforms the best baseline, its variant without the SMOTE component, with a 2.06% increase in classification accuracy. SDL-SCD could help to conveniently and early detect sickle cell anemia, especially in low-developed settings where medical services are constrained. Our code is accessible at https://github.com/MarthaKJ/sickle-cell-detection-using-nvidia.

ABSTRACT
Leadership dashboards are valuable instruments for leaders' decision-making, converting complicated streams of data into actionable information to drive performance. These tools are increasingly utilized to solve simple business issues such as strategic misalignment, operational inefficiencies, and sluggish decision-making, offering minimal competitive advantage. A systematic approach is developed through gathering stakeholder requirements, validating a proof of concept, fitness for finance, and development using an agile process. Quality assurance processes, including tests and user acceptance of the dashboard, are conducted prior to deployment to ensure reliability and rigorous adoption. Production deployment requires consideration of data refreshing frequency, safe access, and backup to increase operational continuity. Governance post-deployment includes metric ownership, a monitoring process of performance, and a process for ongoing improvement to create sustained value. Development, including construction of the dashboard, is collaborative across functional teams through the roles of scrum masters, owners of the product, business analyst, and the owner of the dashboard. Ultimately, the delivered dashboard provides real-time knowledge and information to increase decision-making speed, improve risk management, and align organizational effort to strategic intent. Regardless of the delivery speed, data quality and system maintenance must continually remain as formal commitments to effective use.

ABSTRACT
There are two frequently used systems of units for physical properties. The mks System employs the meter (m) and kilogram (kg) as the units of distance and inertial mass, whereas the British System employs foot (ft) and pound (lb) in their place. In many cases, a conversion between the numerical values of a given object can be obtained by simply making use of the respective ratios of these quantities. It is helpful to realize that any physical property can be expressed as a product of the variables of distance, inertial mass and time. One can expand the applicability of the two Systems to the description of electromagnetic properties by adding the Coulomb (Coul) to this list. The situation is made more complicated by the fact that the British System employs a unit of force (lbf) which depends on the location of the object lying within a gravitational field with a particular value of g. As a consequence, 1 lbf = 4.44822 N = 32.173722 ft lb / s2 within the British System itself. Relationships between the units of energy and power are also disussed.