Search Results (7)

By 2040, more than 80 million Americans will be aged ≥65, yet contemporary senior living communities still operate on a hospitality-first model developed for healthier cohorts three decades ago. This commentary argues that the next generation of senior living must pivot from hotel-style amenities to person-centric health platforms that proactively coordinate medical, functional, and social support. We outline four mutually reinforcing pillars. (1) Data infrastructure that stitches together clinical, functional, and social determinants of health enables continuous risk stratification and early intervention. (2) Ambient and conversational artificial-intelligence tools can extend sparse caregiving workforces while preserving resident autonomy. (3) Value-based contractual arrangements—for example, Medicare Advantage special-needs plans embedded within senior living sites—can realign financial incentives toward prevention rather than occupancy. (4) Targeted policy levers, including low-income housing tax credits for the “forgotten middle” and outcomes-based regulatory frameworks, can catalyze adoption at scale. Ultimately, re-architecting senior living around integrated technology, value-based financing and supportive regulation can transform these communities into preventive-care hubs that delay nursing home entry, improve quality of life, and reduce total cost of care. Full article

This study focuses on examining the shift of an application system from a traditional monolithic architecture to a cloud-native microservice architecture (MSA), with a specific emphasis on the impact of this transition on resource efficiency and cost reduction. In order to evaluate whether artificial intelligence (AI) and application performance management (APM) tools can surpass traditional resource management methods in enhancing cost efficiency and operational performance, these advanced technologies are integrated. The research employs the refactor/rearchitect methodology to transition the system to a cloud-native framework, aiming to validate the enhanced capabilities of AI tools in optimizing cloud resources. The main objective of the study is to demonstrate how AI-driven strategies can facilitate more sustainable and economically efficient cloud computing environments, particularly in terms of managing and scaling resources. Moreover, the study aligns with model-based approaches that are prevalent in sustainable systems engineering by structuring cloud transformation through simulation-supported frameworks. It focuses on the synergy between endogenous AI integration within cloud management processes and the overarching goals of Industry 5.0, which emphasize sustainability and efficiency that not only benefit technological advancements but also enhance stakeholder engagement in a human-centric operational environment. This integration exemplifies how AI and cloud technology can contribute to more resilient and adaptive industrial and service systems, furthering the objectives of AI and sustainability initiatives. Full article

The coronavirus disease-19 (COVID-19) pandemic, declared in early 2020, has left an indelible mark on global health, with over 7.0 million deaths and persistent challenges. While the pharmaceutical industry raced to develop vaccines, the emergence of mutant severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) strains continues to pose a significant threat. Beyond the immediate concerns, the long-term health repercussions of COVID-19 survivors are garnering attention, particularly due to documented cases of cardiovascular issues, liver dysfunction, pulmonary complications, kidney impairments, and notable neurocognitive deficits. Recent studies have delved into the pathophysiological changes in various organs following post-acute infection with murine hepatitis virus-1 (MHV-1), a coronavirus, in mice. One aspect that stands out is the impact on the skin, a previously underexplored facet of long-term COVID-19 effects. The research reveals significant cutaneous findings during both the acute and long-term phases post-MHV-1 infection, mirroring certain alterations observed in humans post-SARS-CoV-2 infection. In the acute stages, mice exhibited destruction of the epidermal layer, increased hair follicles, extensive collagen deposition in the dermal layer, and hyperplasticity of sebaceous glands. Moreover, the thinning of the panniculus carnosus and adventitial layer was noted, consistent with human studies. A long-term investigation revealed the absence of hair follicles, destruction of adipose tissues, and further damage to the epidermal layer. Remarkably, treatment with a synthetic peptide, SPIKENET (SPK), designed to prevent Spike glycoprotein-1 binding with host receptors and elicit a potent anti-inflammatory response, showed protection against MHV-1 infection. Precisely, SPK treatment restored hair follicle loss in MHV-1 infection, re-architected the epidermal and dermal layers, and successfully overhauled fatty tissue destruction. These promising findings underscore the potential of SPK as a therapeutic intervention to prevent long-term skin alterations initiated by SARS-CoV-2, providing a glimmer of hope in the battle against the lingering effects of the pandemic. Full article

Distributed computing paradigms have evolved towards low latency and highly virtualized environments. Fog Computing, as its latest iteration, enables the usage of Cloud-like services closer to the generators and consumers of data. The processing in this layer is performed by Fog Applications, which are decomposed into smaller components following the microservice paradigm and encapsulated into containers. Current state-of-the-art container orchestrators can manage hundreds of simultaneous containers. However, Kubernetes, being the de facto standard, does not consider the application itself as a top-level entity, which limits its orchestration capabilities. This raises the need to rearchitect Kubernetes to benefit from application-awareness, which refers to an orchestration method optimized for managing the applications and the set of components that comprise them. Thus, this paper proposes an application-aware and OpenFog-compliant architecture that manages applications as first-level entities during their lifecycle. Furthermore, the proposed architecture allows the definition of organizational structures to group subordinated applications based on user-defined hierarchies. This logical structuring makes it possible to outline how orchestration should be shaped to reflect the operating model of a system or an organization. The proposed architecture is implemented as a Kubernetes extension and provided as an operator. Full article

On-demand service is the main feature of the 5G network, and Network Function Virtualization (NFV) provides it by virtualizing the existing 5G network infrastructure. NFV crafts various virtual networks on a shared physical network, but one of the core challenges in future 5G networks is to automate the modeling of Virtualized Network Functions (VNFs) and end-to-end Network Service (NS) orchestration with less human interaction. Traditionally, the descriptor of VNF and NS is created manually, which requires expert-level skills. This manual approach has a big threat of human error, which can be avoided by using the Intent-Based Networking (IBN) approach. The IBN approach eliminates the requirement of expertise for designing VNFs and NS by taking users’ intentions as an input. In this paper, the proposed system presents the Intent Management System for VNF modeling and end-to-end NS orchestration for multi-platforms. This system takes the high-level information related to a specific service, configures it accordingly, and converts it into the selected platform. The proposed system is tested using Mobile Central Office Re-architected as Data Center (M-CORD) and Open-Source Management and Orchestration (OSM) orchestrators. The results section shows that the proposed system reduces the effort of the end-user in creating network slices and provides seamless end-to-end service orchestration. Full article

Most of the healthcare facilities (HFs) have to face the nosocomial infections (NIs), which increase the rates of morbidity, mortality, and financial burden on the HFs and the patients. The control of the NIs is a global issue and requires additional effort. Because the pathogenic microbes can be transmitted among all the HF departments, the layout and design of the HFs (or the department configuration) is considered to play a significant role in control of the NIs. A few of the departments transmit the microbes more than other departments, called ‘cause’, while some other departments are more infected than others, called ‘effect’. Here, the researchers have stated that both the cause and effect departments are risky. This research tried to propose a comprehensive mathematical algorithm for choosing the high-risk department(s) by applying the NI and the managerial criteria to minimize NIs through rearchitecting of the HFs. To develop the algorithm, the researchers applied the multiple criteria decision-making (MCDM) methods. They used Decision-Making Trial and Evaluation Laboratory (DEMATEL) and modified weighted sum method (WSM) methods, and their hybrid, along with a modified nominal group technique (NGT) for data collection. The proposed algorithm was later validated by implementation in a HF as a case study. Based on all results of the algorithm, the high-risk departments in the HF were identified and proposed to be eliminated from the HF in such a way that the facility would retain its functionality. The algorithm was seen to be valid, and the feasibility of the algorithm was approved by the top managers of the HF after the algorithm was implemented in the case study. In conclusion, the proposed algorithm was seen to be an effective solution for minimizing the NIs risk in every HF by eliminating the high-risk departments, which could simplify the HF manager’s decisions. Full article

We present Value Prediction for Security (VPsec), a novel hardware-only framework to counter fault attacks in modern microprocessors, while preserving the performance benefits of Value Prediction (VP.) VP is an elegant and hitherto mature microarchitectural performance optimization, which aims to predict the data value ahead of the data production with high prediction accuracy and coverage. Instances of VPsec leverage the state-of-the-art Value Predictors in an embodiment and system design to mitigate fault attacks in modern microprocessors. Specifically, VPsec implementations re-architect any baseline VP embodiment with fault detection logic and reaction logic to mitigate fault attacks to both the datapath and the value predictor itself. VPsec also defines a new mode of execution in which the predicted value is trusted rather than the produced value. From a microarchitectural design perspective, VPsec requires minimal hardware changes (negligible area and complexity impact) with respect to a baseline that supports VP, it has no software overheads (no increase in memory footprint or execution time), and it retains most of the performance benefits of VP under realistic attacks. Our evaluation of VPsec demonstrates its efficacy in countering fault attacks, as well as its ability to retain the performance benefits of VP on cryptographic workloads, such as OpenSSL, and non-cryptographic workloads, such as SPEC CPU 2006/2017. Full article