Journal of Engineering Research and Reports

Artificial Intelligence, Big Data, and Cloud Infrastructures: Policy Recommendations for Enhancing Women's Participation in the Tech-Driven Economy

2024-05-02T11:08:21+00:00

This study investigates the underrepresentation of women in Artificial Intelligence (AI), Big Data, and Cloud Infrastructures, exploring the barriers and challenges they face and assessing the effectiveness of current policies and initiatives to promote gender diversity within the tech industry. Employing quantitative research methods, the study used a survey distributed to 572 female professionals in tech-related roles across various industries, achieving a 67.9% response rate. Multiple regression analysis was utilized to test four main hypotheses concerning barriers to entry and advancement, the inclusivity of educational programs, the impact of diverse teams on innovation and performance, and the effectiveness of gender-inclusive policies. Key findings indicate that the type of organization and specific tech sectors significantly influence the barriers experienced by women. Notably, gender diversity within teams correlates strongly with improved innovation and performance. However, educational and training programs often fail to be sufficiently inclusive, underscoring the need for programs better tailored to women's needs in tech fields. Moreover, the study confirms that implementing gender-inclusive policies substantially increases women's participation in tech roles, especially when these policies are applied long-term. Based on the findings, recommendations are made for adopting comprehensive, inclusive practices at organizational and educational levels, promoting diversity in team composition and leadership, committing long-term to effective policy implementation, and developing supportive networks through mentorship and sponsorship programs. These measures are aimed at reducing gender disparities and enhancing the integration of women into the high-tech economy. The study underscores the critical role that strategic policy-making and organizational change play in fostering an inclusive tech environment that not only addresses gender disparities but also enhances overall industry innovation and performance.

Developing and Assessing Performance of a Laboratory-Scale Fluidized Bed Dryer

2024-05-02T13:08:04+00:00

A laboratory-scale batch fluidized bed dryer with 75 mm bed diameter was designed, fabricated and evaluated to study the hydrodynamics of river sand as well as the drying of cassava mash and bitter kola particulates. The hydrodynamics properties such as minimum fluidization velocity, effect of bed height and pressure drop across the bed, effect of particle size and density on minimum fluidization velocity and stability of the bed column of river sand were studied. Drying of cassava mash to edible garri was carried out at in fluidized bed dryer at controlled temperatures. Drying characteristics of the laboratory fluidized bed dryer was compared with the laboratory WiseVen oven (model number: WOF - 105) using bitter kola particulate material sample. The experimental results from laboratory-scale fluidized bed dryer showed that the minimum fluidization velocity increases as material density increases and the value of the minimum fluidization velocity obtained from the fluidized bed gave good agreement with other empirical correlation such as Kozeny-Carman Equation. The fluidized bed dryer showed high rates of moisture removal over the conventional oven with the ratio of 1:29 under the same operating conditions. Drying of cassava mash to edible garri was achieved at lower drying temperatures of 83 ^oC \(\pm\) 3 ^oC at 55 minutes when compared to conventional frying temperatures of cassava mash to edible at 180 – 200 ^oC thereby saving energy cost. Hence, this fluidized bed dryer is recommended for use in demonstrating hydrodynamics and drying of particulate materials in the laboratory.

CyberFusion Protocols: Strategic Integration of Enterprise Risk Management, ISO 27001, and Mobile Forensics for Advanced Digital Security in the Modern Business Ecosystem

2024-05-04T06:48:38+00:00

This research paper explores the integration of Enterprise Risk Management (ERM), the ISO 27001 standard, and mobile forensics methodologies as a comprehensive framework for enhancing digital security measures within modern business ecosystems. Employing a quantitative research design, this paper utilized a survey methodology, gathering data from 372 professionals across various sectors including risk management, IT/security, and forensic analysis. The analysis was conducted using Partial Least Squares Structural Equation Modeling (PLS-SEM) to test the research hypotheses and assess the impact of the integrated approach on organizational digital security capabilities. The findings reveal a significant positive effect of integrating ERM, ISO 27001, and mobile forensics on an organization’s ability to manage digital risks effectively. Specifically, the integrated approach was found to enhance strategic digital security management, improve the identification, assessment, and mitigation of digital risks, strengthen information security management practices, and elevate the effectiveness and efficiency of digital crime investigation processes. These outcomes underscore the value of a cohesive strategy that leverages the strengths of ERM, ISO 27001, and mobile forensics in addressing the complex and interconnected digital threat landscape. Based on the results, the study recommends adopting a holistic security framework, investing in continuous professional development, leveraging technological advancements for proactive security management, and fostering a culture of security and collaboration. Such measures are crucial for organizations aiming to enhance their resilience against cyber threats and protect their digital assets in the face of sophisticated cyber-attacks. This research contributes to the field of cybersecurity by providing empirical evidence on the benefits of an integrated approach to digital security, offering practical guidelines for organizations seeking to improve their digital security measures, and highlighting the need for continuous adaptation and collaboration in the fight against cyber threats.

Assessment of the Spatial Distribution of Water Quality for Surface and Groundwater at Bonny Island, Nigeria

2024-05-04T12:27:02+00:00

The aim of the study was to assess the spatial distribution of water quality for surface and ground water at Bonny Island in Rivers State, Nigeria. Surface and Groundwater samples were collected from 9 sampling locations and analyzed for physicochemical parameters, heavy metals, and petroleum hydrocarbons. The contaminants in the surface and groundwater were measured using a variety of methods. Heavy metals in the water were measured using gas chromatography, PAHs were measured using Atomic Absorption Spectrophotometry (ASS) while THC were measured using HACH DR/890 colorimeter. The water quality index (WQI) was calculated using the Weighted Arithmetic Index Method while Principal Component Analysis was used to understand the distribution of the physicochemical parameters, heavy metals, and petroleum hydrocarbons. The results showed that the WQI for both surface and groundwater samples ranged from 55.22 – 125.39, indicating poor to unfit water quality for consumption. In contrast, a control location away from the industrial activities had good water quality with WQI between 35.19 and 45.77. Principal component analysis revealed that the surface water in the industrial area were contaminated with lead, Poly Aromatic Hydrocarbon (PAHs), Total Petroleum Hydrocarbon which indicate oil and gas activities. High chloride, nitrate and sulphate levels were also observed in surface water close to the NLNG plant. Limiting further contamination of the surface and groundwater can be achieved by constant effluent and plume stack monitoring by regulatory agencies. Industrial stakeholder should set up subsidized clinics to provide treatment for any gastrointestinal illnesses or renal damage suffered by residents.