The study accessed the critical risk factors in public-private partnership (PPP)-procured mass housing project (MHP) delivery in Nigeria. The research design adopts a quantitative approach, using well-structured questionnaires distributed to stakeholders involved in PPP-MHPs i.e. consultants, in-house professionals, contractors, and the organized private sector (OPS) registered with PPP departments in the Federal Capital Territory Development Authority (FCDA) Abuja, Nigeria. The instrument relates to the background information of respondents and the risk peculiar to PPP-MHP. Sixty-three (63) risk factors were submitted for the respondents to rank using Mean Item score (MIS) for risk occurrence and its severity, while risk significance index (RI) was used to determine the risk impact. Fuzzy Synthetic Evaluation (FSE) method was subsequently applied to determine the risk criticality groups and the overall risk level in the sector. The fuzzy set theory deals with ambiguous, subjective and imprecise judgments peculiar to decision making in construction project risk assessment. It aims to provide a synthetic evaluation of an object relative to a fuzzy decision environment with multiple criteria that requires qualitative linguistic terms. The findings show that thirty-one (31) risk factors were critical in the sector while financial and micro-economic risk group is contributing most significantly to the overall risk level in PPP-MHPs in Nigeria. The top 10 risk factors in the sector include availability of finance, high finance cost, the unstable value of the local currency, lack of creditworthiness, influential economic events (boom/recession), high bidding cost, poor financial market, financial attraction to project investors, interest rate volatility, inflation rate volatility, corruption and lack of respect for the law, non-involvement of the host community and poor execution of housing policies. The implication for practice is that having known the risk group contributing most significantly to the overall risk level in PPP-MHPs, adequate financial and budgetary allocation should be made available before embarking on such venture so as to sustain the scheme in the country. The study is one of the recent researches conducted on housing, since the procurement option is novel in the sector. The study is of immense value to PPP actors in providing necessary information required to formulate risk response methods in minimize the identified risk impact sector.

This review explores the role of listening as a foundational component of communication and compassionate care in palliative settings. Drawing from ten scholarly articles published in 2025, the study examines how listening affects the experiences of patients, families, and healthcare providers. The findings emphasize that listening is not only a professional skill but a human act that reduces suffering, promotes dignity, and strengthens trust, especially where resources or standardized protocols are lacking. The review is organized into four main areas: the importance of listening in clinical decision making; its role in emotional support and team communication; its contribution to preserving patient dignity and comfort; and its impact on family involvement and closure. The study calls for future research to develop standardized tools for measuring listening in palliative care and to explore how cultural, economic, and technological contexts shape listening practices. Ultimately, the review concludes that listening must be at the heart of ethical, patient-centered care during the final stages of life.

Smallholder farmers face challenges when they lack information on their agricultural activities. To address this, we suggest a web-based system that can be used by farmers to help them in decision making considering the fact that all necessary information is provided by the system. Farmers can input crop type they want to grow and area. This data will help to recommend them the best crops that are suitable to be grown in that area and the necessary growing practices that can be done to produce high yield and have maximum profits, considering the average rainfall of that year. A persistent issue we face in Zimbabwe is the lack of access to reliable agricultural data. In the agricultural sector, one major uncertainty for farmers is the outlook of their future harvest. Once their produce is ready for sale, the presence of other potential buyers compels traders to offer prices that align closely with those in the formal market. However, without timely information, traders can take advantage of the situation by purchasing crops at unfairly low rates. Having data that tracks prices across various markets in near real-time would enable farmers to have a precise and complete understanding of their selling choices to maximize their profits.

Most people can't subscribe to every direct-to-consumer platform today, and the number is growing. The platform's content and the user's experience influence the decision to subscribe or buy. Today's consumers anticipate instantaneously curated content exploration, acquisition, and consumption. Media firms actively seek to increase both click-through rate and profitability by enhancing the user experience and enticing customers to subscribe or buy premium content through recommender systems. The direct-to-consumer platforms may maintain user engagement after consumers have visited the contents by providing suggestions that make the most of the site's rich content catalogs. By bringing it to the attention of viewers based on their viewing habits, for instance, effective recommendation systems might boost earnings for underappreciated "long tail" content. This research explores various recommender system types currently in widespread usage with an analysis of some of the fascinating breakthroughs.

The purpose of the study was to examine Social Studies teachers’ knowledge in the implementation of Social Studies lessons in formative assessment practices in Asante Akim North Municipality in the Ashanti Region of Ghana. The study employed both descriptive and interpretative techniques. The population for the study consisted of all Social Studies teachers and students in the Senior High Schools in the Asante Akim North Municipality of the Ashanti Region. Purposive, convenient and simple random sampling techniques were used to select the schools, teachers and students for the study in all, seventeen (17) Social Studies teachers and fifty (50) students were selected from six (6) Senior High Schools. The main instrument for data collection observation, interview and focus group discussion. The study revealed that not lessons presented were in line with the general objective of the subject (Social Studies). This affected the students understanding of concepts in the subject in helping them to right wrong their decision making. The study also indicates that teaching and learning Social Studies should not be one-man affair, with the teacher doing all the talking and the students doing all the listening. It is recommended that Ghana Education Service should organise workshops and in-service training for Social Studies teachers at the Senior High School level on how to present and evaluate social Studies lessons in order to realise the goals and objectives envisaged for national development. It is also recommended that teachers who have the exposure of the subject and are equally qualified to teach it at the Senior High Schools but are found teaching at the basic level, should be allowed by the Ghana Education Service to teach the subjects at the Senior High School level. The Government of Ghana should make it a laid down policy for the recruitment of qualified Social Studies teachers to teach at the Senior High School (SHS) level for effective assessment in the classroom.

The growing complexity of the operating environment urges pharmaceutical innovation. This essay addresses the need for the integration of advanced technologies in the pharmaceutical supply chain. It justifies the value proposition and presents a concrete use case for the integration of deep learning insights to make data-driven decisions. The supply chain has always been a priority for the pharmaceutical industry; research and development recognizes companies' increasing investment in big data strategies, with plans for a CAGR in big data tool adoption. The work presented herein has a preliminary explorative character to recuperate and integrate evidence from partly overlooked practical experience and know-how. The practical relevance of the essay is directed toward practitioners in pharmaceutical production, supply chain management, logistics, and regulatory agencies. The literature has shown a long-term concern for enhanced performance in the pharmaceutical supply chain network. This essay demonstrates the application of deep learning-driven insights to reveal non-evident flow dependencies. The main aim is to present a comprehensive insight into deep learning-driven decision support. The supply chain is portrayed in a holistic manner, seeking end-to-end visibility. Implications for public policy are discussed, such as data equity: many countries are protecting their populations and economic growth by building resilience and efficiency to ensure the capacity to move goods across supply chains. The implementation strategy is covered. The combined reduction of variability, efficiency as matured richness, reliability (on stochastic flows and their understanding through deep learning and data), and system noise (increased dampening through the inclusiveness of all stakeholders) results in increased responsiveness of supply chains for pharmaceutical products. Future work involves the integration of external data, closing the loop between planning and its application in reality.

Managing supply chains efficiently has become a major concern for organizations. One of the important factors to optimize in supply chain management is logistics. The advent of technology and the increase in data availability allow for the enhancement of the efficiency of logistics in a supply chain. This discussion focuses on the blending of analytics with innovation in logistics to improve the operations of a supply chain. An approach is presented on how predictive analytics can be used to improve logistics operations. In order to analyze big data in logistics effectively, an artificial intelligence computational technique, specifically deep learning, is employed. Two case studies are illustrated to demonstrate the practical employability of the proposed technique. This reveals the power and potential of using predictive analytics in logistics to project various KPI values ahead in the future based on the contemporary data from the logistics operations; sheds light on the innovative technique of employing deep learning through deep learning-based predictive analytics in logistics; suggests incorporating innovative techniques like deep learning with predictive analytics to develop an accurate forecasting technique in logistics and optimize operations and prevent disruption in the supply chain. The network of supply chains has become more complex, necessitating the need for the latest technological advancements. The sectors that have gained a fair amount of attention for the application of technology to optimize their operations are manufacturing, healthcare, aerospace, and the automotive industry. A little attention has been diverted to the logistics sector; many describe how analytics and artificial intelligence can be used in the logistics sector to achieve higher optimization. Currently, significant research has been done in optimizing logistics operations. Nevertheless, with the explosive volume of historical data being produced by the logistics operations of an organization, there is a great opportunity to learn valuable insights from the data accumulated over time for more long-term strategic planning. To develop the logistics operations in an organization, the use of historical data is essential to understand the trends in the operations. For example, regular maintenance planning and resource allocation based on trends are long-term activities that will not affect logistics operations immediately but can affect the business’s strategic planning in the long run. A predictive analysis technique employed on historical data of logistics can narrow down conclusions based on the future trends of logistics operations. Thus, the technique can be used to prevent the disruption of the supply chain.

While several application domains are exploiting the added-value of analytics over various datasets to obtain actionable insights and drive decision making, the public policy management domain has not yet taken advantage of the full potential of the aforementioned analytics and data models. To this end, in this paper authors present an overall architecture of a cloud-based environment that facilitates data retrieval and analytics, as well as policy modelling, creation and optimization. The environment enables data collection from heterogeneous sources, linking and aggregation, complemented with data cleaning and interoperability techniques. An innovative approach for analytics as a service is introduced and linked with a policy development toolkit, which is an integrated web-based environment to fulfil the requirements of the public policy ecosystem stakeholders [1]. Large information databases on various public issues exist, but their usage for public policy formulation and impact analysis has been limited so far, as no cloud-based service ecosystem exists to facilitate their efficient exploitation. With the increasing availability and importance of both public big and traditional data, the need to extract, link and utilize such information efficiently has arisen. Current data-driven web technologies and models are not aligned with the needs of this domain, and therefore, potential candidates for big data, cloud-based and service-oriented public policy analysis solutions should be investigated, piloted and demonstrated [2]. This paper presents the conceptual architecture of such an ecosystem based on the capabilities of state-of-the-art cloud and web technologies, as well as the requirements of its users.

The automation of IT Service Management (ITSM) workflows using explicit rules and data has been established for years. Domain-specific rule engines interpret rules written in declarative rule modelling languages and generate forwarding arrows to process event streams and support decision making. Such automation is augmented by rule-driven Quality Assurance for correctness, safety, and risk management. The service desk is the onshore base of an ITSM supply chain. An end-to-end incident response service resolves incidents using only onshore resources and employs back office teams to help with unresolvable incidents. The forward factories of rule-based automation for ticket processing service are identified. Several rule-based workflows in incident and change management have been published. Further glimpses of the future across all ITSM workflows are provided based on training in an online ITSM service with automated operations. Rule engines are specialised components that direct the processing of data flows according to pre-defined rules. Decision factories complement the more common event-driven rule engines. While event processing occurs below the polling frequency of the source, rules in decision factories are triggered based on the arrival of data. These factories are applied in ITSM for risk and safety evaluation and quality assurance. Rule-enriched architectures incorporate domain-specific modelling languages to ensure correctness with respect to qualitative quality attributes. Dedicated factories provide resilience, detect slack or over-utilisation, and offer point-in-time assurance and testing.

Healthcare is increasingly recognized as a data-intensive industry. Multi-hospital networks, among other organizations, face mounting operational and governance challenges because of rigid data-integration pipelines that support all data sources and destinations in the network. These pipelines have become difficult to modify, causing them to lag behind the changing needs of the clinical operation. Scalable data-pipeline architectures better support clinical decision making, optimize hospital operations, ease data quality and compliance concerns, and contribute to improved patient outcomes. Meeting scalability goals requires breaking up monolithic data-integration pipelines into smaller decoupled components and aligning service-level agreements of pipeline components and source systems. Parallelization and adoption of distributed data-warehouse technology mitigate the burden of ingesting data into a multi-hospital network. However, latency requirements still warrant the construction of separate pipelines for data ingress from clinical devices, electronic health records, and external laboratory-information systems. Healthcare associations recommend near real-time data availability for a growing list of clinical and operational applications. Mishandling the real-time ingestion of data from clinical devices, in particular, compromises availability and performance. Scalable architectural patterns for real-time streaming Ingestion from heterogeneous data sources, transport processes, and back-end processing structures are detailed.