Articles

<b>Objective:</b> This integrative review aims to critically examine the clinical management of periprosthetic joint infections (PJI) in total hip arthroplasty (THA), emphasizing decision-making strategies, diagnostic advancements, and therapeutic innovations. The study focuses on the complexity of infection control, microbial resistance, and individualized treatment planning. <b>Methods:</b><b> </b>A systematic review of the literature was conducted using PubMed, Scopus, Web of Science, and Google Scholar, targeting studies published between 2015 and 2025. Articles were selected based on their contribution to understanding the clinical, microbiological, and surgical aspects of PJI in THA. Fifty-five studies met the inclusion criteria and were analyzed descriptively.<b> </b><b>Results:</b><b> </b>PJI in THA is influenced by multifactorial risk profiles, including obesity, diabetes, and immunosuppression. Staphylococcus aureus, particularly MRSA, remains the most frequently isolated pathogen, followed by Gram-negative organisms and fungal species. Diagnostic innovations such as next-generation sequencing have enhanced pathogen detection, while two-stage revision remains the gold standard for chronic infections. Emerging strategies—such as antimicrobial coatings, tailored antibiotic protocols, and multidisciplinary care models—demonstrate promise in improving clinical outcomes. <b>Conclusion:</b><b> </b>Managing PJI in THA necessitates a comprehensive and individualized approach, integrating early and accurate diagnosis, pathogen-specific treatment, and advanced preventive measures. The integration of emerging technologies and personalized care pathways is critical to optimizing outcomes and reducing the clinical and economic burden of PJI.

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.

The quality of communication between healthcare providers and pregnant women is a topic of paramount importance within the realm of maternal healthcare. It is not merely an aspect of medical interaction; rather, it is the prerequisite that influences various critical dimensions of maternal care, including maternal satisfaction, health outcomes, and shared decision-making. Effective communication between healthcare providers and pregnant women is essential for optimal maternal care during pregnancy and childbirth. Maternal satisfaction is a fundamental metric of patient-centered care, and improved communication, characterized by empathy, information sharing, and active listening, cultivates trust and enhances women's contentment with their care experiences. Positive provider-patient interactions are associated with improved emotional well-being, reduced stress levels, and increased adherence to prenatal recommendations, contributing to positive health outcomes for both mother and fetus.<b> </b>Shared decision-making is impacted by open and transparent dialogue between healthcare providers and pregnant women. Inclusive discussions about available interventions, risks, and benefits empower women to make informed choices aligned with their preferences and values. This shared decision-making promotes autonomy, self-efficacy, and a collaborative care partnership, potentially influencing the birthing experience and postpartum adaptation. However, challenges persist in communication quality, such as variability in healthcare provider communication styles, cultural considerations, and system-level factors. Addressing these challenges through targeted interventions, training, and policy implementation can further enhance the overall maternal care experience. Further research is needed to explore innovative strategies that optimize communication and promote positive outcomes throughout the continuum of maternal care.

This paper brings to light efforts of male institutional leaders in the University of Education, Winneba in promoting the gender equality agenda and effects of their efforts in bridging the gender gaps in staff and students’ levels. It sheds further light on involving men in the gender mainstreaming efforts. Both primary and secondary data on gender equality and equity measures were used in this study. The study found that male Vice-Chancellors have over the years been devoted to promoting gender equality agenda in the University. The effects of their efforts are largely seen in the increase in the number of females that have benefited from the institutional mentorship and scholarship programmes, which has impacted positively on addressing the gender gaps in the University. It is recommended that new strategies be adopted in promoting gender equality agenda. These strategies include revision in gender policies to involve men as agents of change in gender mainstreaming activities; gender training for male staff and students of all levels and categories for effective gender work, offering specific trainings and interactive discussions on gender issues for men as volunteers for gender mainstreaming. The study further suggests among other things, the need to engage the university community in entertaining activities like cycling for the equality agenda to be made more real to men and court their interest to promote men’s involvement in gender work.

Artificial intelligence systems have been previously used to predict post-operative complications in small studies and single institutions. Here we developed a robust artificial intelligence model that predicts the risk of having cardiac, pulmonary, thromboembolic, or septic complications after elective, non-cardiac, non-ambulatory surgery. We combined structured and unstructured electronic health record data from 3.5 million surgical encounters from 25 medical centers between 2009 and 2017. Our neural network model predicted postoperative comorbidities 15 to 80 times faster than classical models. As such, our model can be used to assess the risk of having a specific complication postoperatively in a fraction of a second. With our model, we believe clinicians will be able to identify high-risk surgical patients and use their good judgment to mitigate upcoming risks, ultimately improving patient outcomes [1].

For years, risk assessment and financial calculations have been based on mathematical, statistical, and actuarial studies of existing and historical data. The manual process of building datasets, processing data, deriving trends, identifying periodicities, and analyzing diagnostics is extremely expensive and time-consuming. With the automation and evolution of data science technologies, organizations are now bringing in niche data, such as unstructured data, which contain more disruptive and precise signals for decision-making—thereby making predictions and derivative valuations more robust. This discussion highlights how investment decision-making and financial ecosystem activities are set to be transformed with the power of technical automation, data, and artificial intelligence. A noted trend in the financial investment sector is that financial valuations are highly predictive and highly non-linear in long-term occurrences. To understand these robust evolving signals and execute profitable strategies upon them, the investment management process needs to be very dynamic, open, smart, and technically deep. However, with current manual processes, reaching a high-end asset prediction still seems like a shot in the dark. In parallel, open and democratically developed financial ecosystems query relatively riskless premium opportunities in high-finance valuation and perception. The process of evolving financial ecosystems or the use of automated tools and data to move to unique frontiers could make high-yield profiting opportunities very safe and entirely riskless. Financial economic theories and realistic approximation models support this.

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.

The evolution of supply chains (SC) from a linear to a network structure created an opportunity for new processes, product/service offerings, and provider-business. Rising customer service expectations have led to the need for innovative SC designs to develop and sustain competitive performance globally. Firms are forced to respond and adapt accordingly, thereby leading to design, network, operational, and performance dynamics. Traditionally, SCs are treated as static structures, focusing solely on design and/or operational optimization. Such perspectives are not viable options for SC domains, as they address only a portion of the dynamic problem space, use a deterministic assumption of dominant design variables, capitalize on past data to predict future decisions, and offer pre-classified forecasting options complemented with a limited comprehension of systemic SC elasticity. Novel self-learning agentic systems are proposed that blend the sciencematics of SC decisions and dynamics. The designs guide firms seeking to build adaptive SCs using operational decision processes. The designs address the agentic nature of SC, embedding computational interaction models of firm SC networks. The designs contrast the stochastic action-taking and thereby the performance outcomes, discovering opportunities for adaptive operational designs of SC tasks. Fine-tuning and meta-learning are new design capabilities that adapt to evolving dynamic environments. Frameworks for behavioral customization and systematic exploration of the design space are provided as user guides. Exemplar designs are also provided to serve as a translation template for users to express operational models of their own contexts. To account for the dynamics of supply chains (SC), agent-based models are increasingly adopted. Such models exhibit SC structure and/or formulation dynamics. Though existing efforts commence adjacent-only structural changes, dynamism with respect to tasks is crucial for SC design and operational strategy development. Proposed is a process modeling library and workflow for discovering intricate designs of adaptive agentic systems. The library revises Dataflow and Structure, concealing sequencing and context designs of processes. Prompted specifications describe and enact designs. Applications in SC formulation discovery are provided.

The multitude of services and resources available in multi-cloud environments has increased the importance of analytics applications in cloud brokering. These applications can orchestrate services and resources that reside in different domains and require inputs that a single cloud provider could not easily acquire. Yet, despite their distinct characteristics, multi-cloud analytics users have no voice in the ranking of the services in brokerage marketplaces. In this chapter, we introduce the concept and propose the implementation of explainable analytics to increase transparency and user satisfaction in multi-cloud environments. The criteria that we have identified and measured in order to summarize them in explainable results allow cloud users to acquire an understanding of the ranking rules, a crucial requirement in trustful decision-making. Our proposal accounts for a set of regulations for intelligent systems and targets their specific adaptation and use in multi-cloud environments.

This study aims to achieve a new understanding of how, why, and when consumer behavior is shaped, enacted, and experienced inside and across integrated digital ecosystems related to large-scale trackable goods, all in service of helping marketers optimize their business performance in the new economy. The pioneering understanding begins by exploring what motivates the choices of a homogeneous group of consumers to organize their consumption of national and store brand varieties of consumer package goods in a certain manner. Thereafter, the essay explores how, if at all, the other digital activities of consumers across various product-related digital spaces and on various platforms build expertise and interest in these products such that they exert an effect on the purchase choices for these products. The essay then advances to asking how online information seeking, in various product-related digital spaces, on various platforms, and from various sources, and taking place at various points in the purchase journey affects online-offline dynamics in purchasing these products. Thereafter, the research examines how paid digital communication in various product-related digital spheres and forms, enabled by consumer advertising engagement on various platforms, boosts the offline sales of these products. Finally, by employing a new methodology that combines consumer scanning data, self-reported online activity data, and transaction data collected from an ad-tech partner, the research presents a fresh set of marketing action levers and performance outcomes on chosen products. Along the way, progress is made on four under-investigated topics in the advertising literature – the role of consumer actors and their expertise in the online-offline purchasing dynamics for ads, advertising engagement, consumer digital spaces, and consumer digital activity investment.