Quantitatively, this study aimed to determine the abundance and diversity of the insect fauna that visits the staminate and pistillate flowers of Cocos nucifera. The study was conducted at an experimental plantation belonging to the Coconut Research Programme (CRP) of the Oil Palm Research Institute (OPRI) of the Council for Scientific and Industrial Research (CSIR), to provide diagnostic support for the Cape St. Paul Wilt Disease (CSPWD) at Asebu in the Central Region of Ghana. The populations of coconut palms represented the dwarf type with few tall ecotypes. Five Insects were randomly chosen with newly opened inflorescences. Observations and collections of insect visitors to coconut flowers were made once a week on 30 newly opened inflorescences, five from each batch within the plantation. Specimens of the data were deposited in the official insect collection and processed at the laboratory of the Entomology Museum of the Department of Conservation Biology and Entomology, University of Cape Coast, Ghana. The study indicated that 9 different species of insects were identified to be the true fauna that visited the staminate and pistillate flowers of C. nucifera Ethiosciapus sp., Sarcophaga sp., Scolia dubia, Lucilia sp., Ornidia sp., Apis melifera, Dactylurina standingeri, Red Ant and Black Ant. These insects were observed in all the six batches considered and were available at all times of the day. Most of the insects were observed in the early morning from 6 am - 9 am followed by the evening 4 pm –7 pm. The abundance of insect visitors was low during the mid-day (11 a.m. to 3 p.m.) in all six batches during high temperatures. The results of this study revealed that there were abundances of Ethioscipus sp. was the least abundant in all the batches followed by Scolia dubia then Sarcophaga sp. Red Ants had the highest abundance in most of the Batches thus becoming the most abundant insect that forage the coconut inflorescence at the Asebu plantation. The bees, Apis melifera and Dactylurina standingeri were the most abundant species after the Red Ants. All these groups of insects were not considered in the study and it is recommended that further studies consider such visitors to observe which insects are doing what on the inflorescence. The range for the ‘time of day for’ of the study was mostly diurnal (morning 6 am-9 am, afternoon 11 am-2 pm and evening 4 pm7 pm). There was no observation made of the pollination system or activities of these insect visitors nocturnally. There may be high pollination activities of these insects during the late evenings. It is recommended that future work should incorporate the late evening period to observe an abundance of diurnal insect visitors of the coconut inflorescences.Keywords: Insect, Fauna, Staminate, Pistillate Flowers, Cocos nuciferaFull article

This experimental study was carried out to evaluate the effect of three selected pollinator-friendly practices on the African eggplant (Solanum aethiopicum) at Mankessim in the Central region of Ghana. The study focused on determining how the practices affect the production and yield of garden eggs. The three pollinator-friendly practices were the use of mulch, cassava hedgerow/marigold plants and controlled pesticide application in garden egg farms. Experimental-control group design was used. Mulching positively influenced the number of flowers, fruits and height of garden eggplants. Cassava hedgerow/marigold plants influenced the number of flowers, but had no significant effect on the number of fruits and plants’ height. There was no effect on the number of flowers, fruits, and height of garden eggplants when pesticide application was controlled or uncontrolled. No significant influence was observed in fruit weight in all treatment and control plots. The growth and yield trends observed in this research indicated that practicing the three pollinator-friendly practices may encourage flower visitors leading to effective pollination and increased yields. It is recommended that mulching be practised in garden egg farming to increase the growth and productivity of garden eggplants.Full article

The development of ChatGPT is a topical subject of reflection. This short paper focuses on the (possible) use of ChatGPT in academia and some of its (possible) ramifications for users’ cognitive abilities and, dramatically put, their existence.Full article

This charge-ahead paper suggests that transitioning the automotive industry towards a zero-carbon ecosystem from material to end-of-life can be accomplished through disruptive zero-carbon manufacturing in the broad area of all-electric vehicle production technology. To accomplish zero carbon emission automotive manufacturing in the vehicle assembly domain, future paradigms must converge on the decoupling of carbon dioxide emissions from automobile manufacturing and use the design, processing, and manufacturing conditions. The envisioned zero carbon emission vehicle manufacturing domain consists of two complementary components: (a) making more efficient use of energy and (b) reducing carbon in energy use. This paper presents the status of key scientific and technological advancements to bring the manufacturing model of today to a zero-carbon ecosystem for the entire automotive industry of tomorrow. This paper suggests the groundbreaking application of dynamic and distributed predictive scheduling algorithms and open sensing and visualization technology to meet the zero carbon emission vehicle manufacturing goals. Power-aware high-performance computing clusters have recently become a viable solution for sustainable production. Advances in scalable and self-adaptive monitoring, predictive analytics, timeline-based machine learning, and digital replica of cyber-physical systems are also seen co-evolving in the zero carbon manufacturing future. These methods are inspired by initiatives to decouple gross domestic product growth and energy-related carbon dioxide emissions. Stakeholders could co-design and implement shared roadmaps to transition the automotive manufacturing sector with relevant societal and environmental benefits. The automated mobility sector offers a program, an industry-leading example of transforming an automotive production facility to carbon neutrality status. The conclusions from this paper challenge automotive manufacturers to engage in industry offsetting and carbon tax programs to drive continuous improvement and circular vehicle flows via a multi-directional zero-carbon smart grid.Full article

The management of supply chains is increasingly complex. This study provides a comparative analysis of the cost-benefit analysis for managing various risks. It identifies the financial implications of leveraging artificial intelligence in supply chains to better address risk. Empirical results show a business case for managing some sources of risk more proactively facilitated through predictive modeling techniques offered by AI. Across investigation streams, the use of AI results in an average total cost saving ranging from 41,254 to 4,099,617. Findings from our research can be used to inform managers and theorists about the implications of integrating AI technologies to manage risks in the supply chain. Our work also highlights areas for future research. Given the growing interest in studying sub-second forecasting, our research could be a point of departure for future investigations aimed at considering the impact of forecasting horizons such as an intra-day basis. We formulate a conceptual framework that considers how and to what extent performance evaluation metrics vary according to differences in the fidelity of predictive models and factor importance for identifying risks. We also utilize a mixed-method approach to demonstrate the applicability of our ideas in practice. Our results illustrate the financial implications of integrating AI predictive tools with business processes. Results suggest that real-world companies can circumvent inefficiencies associated with trying to manage many classes of risk via the use of AI-enhanced predictive analytics. As managers need to justify investment to top management, our work supports decision-making by providing a means of conducting a trade-off analysis at the tactical level.Full article

Modern organizations leverage highly distributed, global deployments to provide high availability and resiliency for cloud-first applications. By hosting these applications across multiple geographic locations and relying on highly available services, organizations can prevent disruption to their business and reduce complexity by employing the scale of infrastructure offered by major cloud providers. Global deployments in the cloud are built on well-known models such as failover, load balancing, and scalability. However, traditional methods used to recover from regional failure—while effective—can be complex. Typical multi-region recovery and high availability system architectures have latency and cost risks that should be considered when facing other limitations such as deployment models in the cloud. This document describes the different traffic management techniques that can be applied to multi-region strategies, focusing on trade-offs and costs. The introduction of new traffic management techniques being applied to the traditional global architectures now allows organizations to adopt cloud services more efficiently. Traffic management is much more straightforward in some environments, while others have started to leverage their traffic management platform via routing. In multi-region deployments, active-active and active-passive are the most common architectural models, allowing organizations to seamlessly handle failover, scalability, and global distribution based on business goals and requirements. However, traffic management for these infrastructures is critical to ensure just data distribution and efficiency, maintaining costs under control and workloads rerouted when necessary. Using the new traffic management techniques will allow organizations to evolve system architectures easily based on business requirements, taking advantage of cost benefits from multiple infrastructures. In these scenarios, traffic management becomes a crucial backbone of success to ensure that traffic is being efficiently and intelligently distributed [1].Full article

M&A is a strategic concept of business growth through consolidation, gaining market access, increasing strategic positions, and increasing operational efficiency. To understand the dynamics of M&A, this paper looks at aspects such as targeted firm identification, evaluation, bidding for the target firm, and post-acquisition integration. All forms of M&A, including horizontal, vertical, conglomerate, and acquisitions, are discussed in terms of goals and values, including synergy, cost reduction, competitive advantages, and access to better technology. However, issues such as cultural assimilation, adhesion to regulations, and calculating an inaccurate value are also resolved. The paper then goes deeper to provide insight into how predictive analytics applies to M&A, using ML to improve decision-making with forecasting benefits. Including healthcare, education, and construction industries, the presented predictive models using regression analysis, neural networks, and ensemble techniques help to make decisions. Through time series and real-time data, PDA enables sound M&A strategies, effective risk management and smooth integration.Full article

The current financial services sector is realising considerable changes in its operations due to development in technology and embracing of digital platforms. This evolution is changing the established concepts of business, consumers and channels of delivery of services. Financial services firms are changing the way they work through digital transformation due to developments in technology, changes in customer needs, and an increase in emphasis on sustainability. Understanding the opportunities, risks, and new trends in digital transformation is the focus of this paper. Opportunities include efficient real-time decision-making processes, increased transparency and better process controls, which are balanced by the threats of change management, dubious organization-technology fit, and high implementation costs. The study also examines recent advancements, including the application of machine learning and artificial intelligence, developments in mobile and online banking, integration of blockchain, and increasing focus on security and personalised banking. A literature review yields some findings from different studies on rural financial services, the evolution of the blockchain, drivers of digital transformation, cloud-based learning approaches, and emerging sustainability practices. All of these results suggest that more strategic planning, analytics, and more focus on ensuring that organisational objectives are met with transformations should be pursued. Hence, this research findings add to the existing literature in determining how innovative and digital technologies are likely to transform the financial services sector and advance sustainability.Full article

Machine learning operations (MLOps) comprises the practices, methods, and tooling that facilitate the deployment of reliable ML models in production environments. While many aspects of cloud data platforms are designed to enable reliability, only some managed ML services support the MLOps goals of continuous integration, continuous delivery, data lineage tracking, associated reproducibility, governance, and security. Furthermore, reliability encompasses not only the fulfillment of service-level objectives, but also systematic monitoring, alerting, and incident response automation. Architectural patterns are proposed to enable reliable deployment in cloud data platforms, focusing on the implementation of continuous integration and testing pipelines for ML models and the formulation of continuous delivery and rollout strategies. Continuous integration pipelines reduce the risk of regressions and ensure sufficient model performance at the time of deployment, while continuous delivery pipelines enable rapid updates to production models within acceptable risk profiles. The landscape of publicly available MLOps frameworks, tools, and services is also examined, emphasizing the pros and cons of established and rising solutions in containerization, orchestration, model serving, and inference. Containerization and orchestration contributes to the building of reliable deployment pipelines in cloud data platforms, whether general-purpose tools (e.g. Docker and Kubernetes) or solutions tailored for ML workloads. Containerized serving frameworks designed for high-throughput, low-latency inference can benefit a wide range of business applications, while auto-scaling and model versioning capabilities enhance the ease of use of cloud-native ML services.Full article