A stochastic matching model on hypergraphs

Advances in computer science prompt the creation of new economic models. One of them in urban life is Sharing Economy. This new economic model receives high attentions and has been applied to a wide range of fields in recent years. We argue that sharing economy, and its applications, may be taken as a way for researchers to understand human behavior, and thus this paper targets a growing issue on the relationship between sharing economy and its impacts on human behavior. It especially looks at the change of behaviors while the sharing economy comes into our daily lives. The Airbnb, one of the most significant examples of sharing economy, is taken as a starting point of this survey, and results are revealed that young generation tends to accept this new economic experience as well as willing to be involved, not only as traveler but host, in this new phenomenon.

Research background: Globalization, the development of technology, demographic change are phenomena that radically change the nature of work. The advent of modern information technologies represents a worldwide transition to the information society, and the ubiquitous technological development brings ever newer forms of working. The development of a sharing (collaborative) economy is global in nature. A wide range of models for carrying out work with the help of digital collaborative platforms is created and a natural person in the position of a service provider performs activities with a high degree of independence and flexibility. There are several collaborative platforms but not all of them show the characteristics of a pure sharing economy, and there are considerations as to whether, in some cases, there is no concealment of employment relationships. Purpose of the article: The author points out various models of collaborative economics, compares them and assesses the nature of the work performed. Based on the findings, the author concludes whether the person performs work in a commercial relationship or whether this work can be perceived as dependent work in terms of labour law. Methods: Models of collaborative economics need to be analyzed and compared. The induction method is used in the examination of individual court decisions. The conclusion is a summary of the acquired knowledge. Findings & Value added: The issue is highly topical due to the ambiguity of the nature of work relationships. In practice, this is evidenced by several lawsuits. The article will contain proposals de lege ferenda.

Funds sharing regulation in the context of the sharing economy: Understanding the logic of China's P2P lending regulation

Climate change is predicted to exert further stress on already exploited ecologies and healthcare systems, added to this, the coronavirus SARS-CoV-2 (Covid-19) has signified a stimulus to revolutionize existing models of sustainable development in broad, and sustainable supply chains in the healthcare sector in particular. The Kruger to Canyons Biosphere Region in South Africa and the Amazonas state in Brazil serve as geographic case studies for this paper. The impact of economic globalization, natural catastrophes such as droughts, economic and geopolitical stresses, deforestation, economic and healthcare access inequalities in these two biomes converge with issues of climate change, and undercut coping mechanisms that are customarily used to oversee extreme events such as pandemics. The Covid-19 pandemic has deepened many of the economic and social difficulties which South Africa and Brazil are already facing. In comparison to Brazil, South Africa’s overall response to the pandemic can be regarded as a standout. By borrowing from best practices from prior public health responses to heath emergencies in South Africa, such as the tuberculosis and the HIV/AIDS crisis, South Africa has demonstrated its comparatively successful means of dealing with Covid-19.

Background: Despite Canada’s universal healthcare system, Black Canadians continue to experience inequitable access to services. While much of the literature has examined urban centres such as Toronto and Montreal, little is known about experiences in mid-sized cities where healthcare systems are less resourced and culturally diverse. This study explores how structural racism and intersecting barriers shape healthcare access for Black Canadians in Kingston and London, Ontario. Methods: We conducted a qualitative secondary analysis of 25 semi-structured interviews with Black parents and caregivers (2023–2024) from a mixed-methods study on early learning and childcare. Although the parent study focused on childcare, participants frequently described healthcare challenges, which prompted a re-analysis of these narratives. Using a hybrid inductive and deductive thematic analysis, transcripts were coded through the lens of Critical Race Theory (CRT) and Structural Violence Theory (SVT), emphasizing both systemic inequities and community-based strategies of resilience. Results: Five overarching themes emerged: (1) Institutional Racism and Cultural Incompetence, where participants reported dismissal, disbelief, and lack of culturally responsive care; (2) Structural Violence and Delayed Access, where immigration-linked exclusions, long wait times, and economic barriers limited access; (3) Intersectionality, where race, gender, immigration, and socioeconomic status compounded disadvantage, with Black women describing gendered racism in maternal care; (4) Informational and Communication Barriers, where newcomers relied on informal networks and Google due to weak institutional guidance; and (5) Community-Based Resilience, where churches, diaspora networks, and midwifery care provided culturally safe alternatives to formal systems. Conclusion: Findings demonstrate that healthcare inequities for Black Canadians in smaller cities are not incidental but structurally embedded. CRT and SVT reveal how systemic racism and bureaucratic inaction reproduce harm while communities develop parallel infrastructures of care. Addressing these inequities requires race-conscious health policies, investment in culturally safe services outside metropolitan centres, and intentional inclusion of Black voices in healthcare planning.

Level of blood and blood products in human body is very important. Therefor, managing supply of blood is critical issue in healthcare system specially when the system faced with high demand for the product. In natural disasters, demand for blood units increase sharply because of injuries. Hence, efficiency in blood supply chain management play a significant role in this situation in supplying blood for transfusion centers, it is vital to supply in right time to prevent from casualties. Present paper proposes an optimization model for designing blood supply chain network in case of an earthquake disaster. The proposed two-stage stochastic model is Programmed based on scenarios for earthquake in a populated mega-city. The designed network has three layers; first layer is donation areas, the second layer consists distribution centers and facilities and the last layer is transfusion centers. In proposed two-stage stochastic optimization model, decisions of locating permanent collection facilities and amount of each blood type pre-inventory are made in first stage and operation decisions that have dependent on possible scenarios are made in second stage. The model also considers the possibility of blood transfusion between different blood types and its convertibility to blood derivatives regarding medical requirements. In order to solve the proposed two-stage stochastic model, L-shaped algorithm, an efficient algorithm to solve scenario based stochastic models, has been used. In addition, application of the model and the algorithm tests with real data of likely earthquake in Tehran mega city (Densest city of Iran).

Degradation branching in systems with inter-dependent units is a common phenomenon in many real-life applications, particularly in biomechanical and healthcare systems. In such processes, the degradation of one unit may initiate spontaneously or be triggered by the degradation of the adjacent units, leading to spatially correlated failure behaviors. This paper develops a general stochastic degradation branching model that characterizes both the inter-dependency of degradation initiation times and the inter-dependency of degradation growth rates among units. Analytical formulations are derived for the probabilistic properties of the branching process, including the probability distribution of degradation of the system that has specific combination of degraded components, the overall system degradation, along with system reliability. The model is validated using longitudinal cervical spine degeneration data, demonstrating its ability to capture spontaneous and propagation-induced degradation patterns among vertebrae. Beyond theoretical modeling, the proposed framework provides clinically and socially relevant insights: it supports individualized degeneration risk assessment, optimization of surgical and rehabilitation timing, and evidence-based healthcare resource allocation, while also offering a quantitative basis for medical insurance policy design by estimating population-level degeneration trajectories. This unified stochastic framework thus strengthens the translational linkage between reliability theory, precision medicine, and healthcare management.

On May 23 1993 after years of colonization and oppression Eritrea was a free country. From 1961 to 1991 an armed struggle was fought against the occupying Ethiopian forces. Health has always been on the agenda of Eritrean Peoples Liberation Front (EPLF). From 1970 onwards they set up clinics with mobile teams to encompass primary health care and health education. In hospitals health centers and stations there is a shortage of qualified staff a lack of transportation for referral of patients and in some places inadequate drug supply. The main causes of morbidity and mortality are malaria malnutrition diarrheal diseases respiratory infections tuberculosis and vaccine preventable diseases. 30% of neonatal deaths are thought to result from tetanus. Anemia multiple closely spaced pregnancies and complications of child birth contribute to maternal mortality. Economic difficulties food insecurity and limited access to safe water exacerbate health problems faced by the Eritreans. Over a million people sought refuge in neighboring countries or in the West. Christian Outreach has been involved with Eritrean refugees in Sudan since 1986 providing health care in a camp on the Sudan/Eritrea border. Hospital out-patient and maternal and child health facilities were provided along with comprehensive community care and health education. Training courses for the Eritrean health workers have been established to encompass all aspects of primary health care. The Ministry of Health of Eritrea is to promote a system of primary health care throughout Eritrea by the extension of rural health services the prevention and control of disease and promotion of community involvement. The goal is the promotion of child survival and development maternal well-being and the improvement of overall access to health services. The school of nursing will provide courses to allow barefoot doctors to upgrade their training while training civilians as nurses midwives and health assistants. The aim is to provide one health station for every 10000 people instead of the current 72000.

BUILDING THE HEALTH SERVICE

Deterministic and stochastic Boolean network models are built for the dynamics of negative feedback loops of the p53 pathways. It is shown that the main function of the negative feedback in the p53 pathways is to keep p53 at a low steady state level, and each sequence of protein states in the negative feedback loops, is globally attracted to a closed cycle of the p53 dynamics after being perturbed by outside signal (e.g., DNA damage). Our theoretical and numerical studies show that both the biological stationary state and the biological oscillation after being perturbed are stable for a wide range of noise level. Applying the mathematical circulation theory of Markov chains, we investigate their stochastic synchronized dynamics and by comparing the network dynamics of the stochastic model with its corresponding deterministic network counterpart, a dominant circulation in the stochastic model is the natural generalization of the deterministic limit cycle in the deterministic system. Moreover, the period of the main peak in the power spectrum, which is in common use to characterize the synchronized dynamics, perfectly corresponds to the number of states in the main cycle with dominant circulation. Such a large separation in the magnitude of the circulations--between a dominant, main cycle and the rest--gives rise to the stochastic synchronization phenomenon.

To evaluate the cost-effectiveness of surgical treatment pathways for apical prolapse. We constructed a stochastic Markov model to assess the cost-effectiveness of vaginal apical suspension, laparoscopic sacrocolpopexy, and robotic sacrocolpopexy. We modeled over 5 and 10 years, with 9 pathways accounting for up to 2 separate surgical repairs, recurrence of symptomatic apical prolapse, reoperation, and complications, including mesh excision. We calculated costs from the health care system's perspective. Over 5 years, compared with expectant management, all surgical treatment pathways cost less than the willingness-to-pay threshold of US $50,000 per quality adjusted life-years. However, among surgical treatments, all but 2 pathways were dominated. Of the remaining 2, laparoscopic sacrocolpopexy followed by vaginal repair for apical recurrence was not cost-effective compared with the vaginal-only approach (incremental cost-effectiveness ratio [ICER], >$500,000). Over 10 years, all but the same 2 pathways were dominated. However, starting with the laparoscopic approach in this case was more cost-effective with an ICER of US $6,176. If the laparoscopic approach was not available, starting with the robotic approach similarly became more cost-effective at 10 years (ICER, US $35,479). All minimally invasive surgical approaches for apical prolapse repair are cost-effective when compared with expectant management. Among surgical treatments, the vaginal-only approach is the only cost-effective option over 5 years. However, over a longer period, starting with a laparoscopic (or robotic) approach becomes cost-effective. These results help inform discussions regarding the surgical approach for prolapse.

APhA advances provider status initiative

Stochastic Flow Models (SFMs) form a class of hybrid systems used as abstractions of complex Discrete Event Systems (DES) for the purpose of deriving performance sensitivity estimates through Infinitesimal Perturbation Analysis (IPA) techniques when these cannot be applied to the original DES. In this paper, we establish explicit connections between gradient estimators obtained through a SFM and those obtained in the underlying DES, thus providing analytical evidence for the effectiveness of these estimators which has so far been limited to empirical observations. We consider DES for which analytical expressions of IPA (or finite difference) estimators are available, specifically G/G/1 and G/G/1/K queueing systems. In the case of the G/G/1 system, we show that, when evaluated on the same sample path of the underlying DES, the IPA gradient estimators of states, event times, and various performance metrics derived through SFMs are, under certain conditions, the same as those of the associated DES or their expected values are asymptotically the same under large traffic rates. For G/G/1/K systems without and with feedback, we show that SFM-based derivative estimates capture basic properties of finite difference estimates evaluated on a sample path of the underlying DES.

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The COVID-19 pandemic has highlighted the need to effectively manage hospital resources: ICU beds and ventilators. These resources are significant for sustaining life, especially in severe cases. Traditional deterministic models often fall short in addressing the uncertainties associated with patient inflows and resource availability. This paper develops a novel two-stage stochastic programming model which aims to dynamically allocate resources to deal with the variability of inpatient admissions. To this end, the scenarios are developed using Monte Carlo simulation based on the probabilities estimated from the historical data. The model is created in Python language and solved using the Gurobi optimizer in 0.05s, a large-scale scenario optimization analysis problem with 42 variables and 35 constraints. The KPIs show the highest utilization of ventilators at 66. 67% and the average reduction of 53.5 in the number of offers an ICU practical shortfall leading to better patient care and shorter wait times. This research presents a data-driven tool to enhance the decision-making process and the healthcare system's overall readiness to maintain its strategic reserves by implementing flexible staffing models to improve preparation for disasters such as the pandemic. Its stochastic optimization framework makes hospital resource allocation more efficient, offering a scalable, resilient solution for tackling future pandemic challenges.