Probabilistic Tree Research Articles

Gene transfer among viruses substantially contributes to gene gain of giant viruses.

The phylum Nucleocytoviricota comprises a diverse group of double-stranded DNA viruses that display a wide range of gene repertoires. Although these gene repertoires determine the characteristics of individual viruses, the evolutionary processes that have shaped the gene repertoires of extant viruses since their common ancestor are poorly characterized. In this study, we aimed to address this gap in knowledge by using amalgamated likelihood estimation (ALE), a probabilistic tree reconciliation method that infers evolutionary scenarios by distinguishing origination, gene duplications, virus-to-virus horizontal gene transfer (vHGT), and gene losses. We analyzed over 4,700 gene families from 195 genomes spanning all known viral orders. The evolutionary reconstruction suggests a history of extensive gene gains and losses during the evolution of these viruses, notably with vHGT contributing to gene gains at a comparable level to duplications and originations. The vHGT frequently occurred between phylogenetically closely related viruses, as well as between distantly related viruses with an overlapping host range. We observed a pattern of massive gene duplications that followed vHGTs for gene families that was potentially related to host range control and virus-host arms race. These results suggest that vHGT represents a previously overlooked, yet important, evolutionary force that integrates the evolutionary paths of multiple viruses and affects shaping of Nucleocytoviricota virus gene repertoires.

The contribution of intimate partner violence to vertical HIV transmission: a modelling analysis of 46 African countries

Addressing gender inequities could be key to the elimination of vertical transmission of HIV. Women experiencing intimate partner violence (IPV) might be at an increased risk of vertical transmission due to their vulnerability to HIV acquisition and barriers to access to and retention in care. Sub-Saharan Africa, where IPV burden is among the highest globally, accounts for most new paediatric HIV infections. We aimed to examine the proportion of excess vertical transmission attributable to IPV in this region. In this modelling analysis, we created a probability tree model of vertical HIV transmission among women aged 15-49 years in 46 African countries. We estimated the proportion of vertical transmission attributable to past-year physical or sexual IPV, or both, as an age-standardised population attributable fraction (PAF) and as excess vertical transmission risk per 1000 births among women experiencing IPV. We incorporated perinatal and postnatal vertical transmission among women who acquired HIV before pregnancy, during pregnancy, and during breastfeeding. Fertility, HIV prevalence, HIV incidence, antiretroviral therapy (ART) uptake, and ART retention varied in the model by women's IPV experience. The model was parameterised using UNAIDS' 2023 Spectrum model data, WHO's Global Database on Violence Against Women, and the peer-reviewed literature. Uncertainty intervals (95% UI) were calculated through 1000 Monte Carlo simulations. Across 46 countries 13% (95% UI 6-21) of paediatric HIV infections in 2022 were attributed to IPV, corresponding to over 22 000 paediatric infections. The PAF ranged from 4% (2-7) in Niger to 28% (13-43) in Uganda. The PAF was highest among girls aged 15-19 years (20%, 8-33) and lowest among women aged 45-49 years (6%, 3-9). In southern Africa, where women's HIV prevalence is highest (23%), IPV led to 11 (5-20) additional infections per 1000 births among women affected by IPV. IPV might be responsible for one in eight paediatric HIV infections in sub-Saharan Africa. Ending IPV could accelerate vertical transmission elimination, especially among young women who bear the highest burden of violence. Canadian Institutes of Health Research, Canada Research Chair, and Fonds de recherche du Québec-Santé. For the French, Georgian and Spanish translations of the abstract see Supplementary Materials section.

Statistical Analysis of Cataract Disease: A Case Study of 15,221 Patients at the Niger Delta University Teaching Hospital, Okolobiri

Introduction: This research is on the statistical analysis of cataract disease between male and female patients. The objectives were to estimate the distribution of cataract disease over the years with respect to gender and measures the rate of patients with cataract disease (i.e., calculate the annual patients with cataract per 1000 patients) during the same year and test for significant difference between the rate of cataract patients in term of male and female that visited to the hospital.Material and Methods: A retrospective study design was adopted and a total number of 15,221 patients records from Niger Delta University Teaching Hospital eye clinic were collected. Out of which 7,362(48%) were male and 7859(52%) were female. The methods adopted for data analysis were Trend analysis, Vital statistics, Paired Samples t-Test, Chi-Square, Probability tree diagram and Conditional probability.Results: Results showed that male patients’ rate with cataract diseases was slightly higher than female per thousand visitations. Testing for significant difference between the rate of male and female cataract patients using paired t-test, showed no significant difference between them at 5% level. However, there was a moderate correlation between them with r= (0.552), p= 0.199. The result for test for association showed that there is no evidence of an association between gender and disease status. Results showed that probability of a male having the disease was slightly higher than female.Conclusion: This research is on the statistical analysis of cataract disease, its occurrence and determinants between male and female patients.

The impact of state merging on predictive accuracy in probabilistic tree automata: Dietze's conjecture revisited

Dietze's conjecture concerns the problem of equipping a tree automaton M with weights to make it probabilistic, in such a way that the resulting automaton N predicts a given corpus C as accurately as possible. The conjecture states that the accuracy cannot increase if the states in M are merged with respect to an equivalence relation ∼ so that the result is a smaller automaton M∼. Put differently, merging states can never improve predictions. This is under the assumption that both M and M∼ are bottom-up deterministic and accept every tree in C. We prove that the conjecture holds, using a construction that turns any probabilistic version N∼ of M∼ into a probabilistic version N of M, such that N assigns at least as great a weight to each tree in C as N∼ does.

Magnetic tunnel junction random number generators applied to dynamically tuned probability trees driven by spin orbit torque

Perpendicular magnetic tunnel junction (pMTJ)-based true-random number generators (RNGs) can consume orders of magnitude less energy per bit than CMOS pseudo-RNGs. Here, we numerically investigate with a macrospin Landau–Lifshitz-Gilbert equation solver the use of pMTJs driven by spin–orbit torque to directly sample numbers from arbitrary probability distributions with the help of a tunable probability tree. The tree operates by dynamically biasing sequences of pMTJ relaxation events, called ‘coinflips’, via an additional applied spin-transfer-torque current. Specifically, using a single, ideal pMTJ device we successfully draw integer samples on the interval [0, 255] from an exponential distribution based on p-value distribution analysis. In order to investigate device-to-device variations, the thermal stability of the pMTJs are varied based on manufactured device data. It is found that while repeatedly using a varied device inhibits ability to recover the probability distribution, the device variations average out when considering the entire set of devices as a ‘bucket’ to agnostically draw random numbers from. Further, it is noted that the device variations most significantly impact the highest level of the probability tree, with diminishing errors at lower levels. The devices are then used to draw both uniformly and exponentially distributed numbers for the Monte Carlo computation of a problem from particle transport, showing excellent data fit with the analytical solution. Finally, the devices are benchmarked against CMOS and memristor RNGs, showing faster bit generation and significantly lower energy use.

Generation of Visual Representations for Multi-Modal Mathematical Knowledge

In this paper we introduce MaRE, a tool designed to generate representations in multiple modalities for a given mathematical problem while ensuring the correctness and interpretability of the transformations between different representations. The theoretical foundation for this tool is Representational Systems Theory (RST), a mathematical framework for studying the structure and transformations of representations. In MaRE’s web front-end user interface, a set of probability equations in Bayesian Notation can be rigorously transformed into Area Diagrams, Contingency Tables, and Probability Trees with just one click, utilising a back-end engine based on RST. A table of cognitive costs, based on the cognitive Representational Interpretive Structure Theory (RIST), that a representation places on a particular profile of user is produced at the same time. MaRE is general and domain independent, applicable to other representations encoded in RST. It may enhance mathematical education and research, facilitating multi-modal knowledge representation and discovery.

Screening strategy to advance HCV elimination in Italy: a cost-consequence analysis.

Italy has the greatest burden of hepatitis C virus (HCV) infection in Western Europe. The screening strategy represents a crucial prevention tool to achieve HCV elimination in Italy. We evaluated the cost-consequences of different screening strategies for the diagnosis of HCV active infection in the birth cohort 1948-1968 to achieve the HCV elimination goal. We designed a probabilistic model to estimate the clinical, and economic outcomes of different screening coverage uptakes, considering the direct costs of HCV management according to eachliverfibrosis stage, in the Italian context. A decision probabilistic tree simulates 4years of HCV testing of the 1948-1968 general population birth cohort, (15,485,565 individuals to be tested) considering different coverage rates. A No-screening scenario was compared with two alternative screening scenarios that represented different coverage rates each year: (1) Incremental approach (coverage rates equal to 5%, 10%, 30%, and 50% at years 1, 2, 3, and 4, respectively) and (2) Fast approach (50% coverage rate at years 1, 2, 3 and 4). Overall 106,200 cases were previouslyestimated to have an HCV active infection. A liver disease progression Markov model was considered for an additional 6years (horizon-time 10 years). The highest increased number of deaths and clinical events are reported for the No-screening scenario (21,719 cumulative deaths at the end of ten years; 10,148 cases with HCC and/or 7618 cases with Decompensated Cirrhosis). Following the Fast-screening scenario, the reductions in clinical outcomes and deaths were higher compared with No-screening and Incremental-screening. At ten years time horizon, less than 5696 liver deaths (PSA CI95%: -3873 to 7519), 3,549 HCC (PSA CI95%: -2413 to 4684) and less than 3005 liver decompensations (PSA CI 95%: -2104 to 3907) were estimated compared with the Incremental-scenario. The overall costs of the Fast-screening, including the costs of the DAA and liver disease management of the infected patients for 10years, are estimated to be € 43,107,543 more than no-investment in screening and € 62,289,549 less compared with the overall costs estimated by the Incremental-scenario. It is necessary to guarantee dedicated funds and efficiency of the system for the cost-efficacious screening of the 1948-1968 birth cohort in Italy. A delay in HCV diagnosis and treatment in the general population, yet not addressed for the HCV free-of-charge screening, will have important clinical and economic consequences in Italy.

Computing Optimal Leaf Roots of Chordal Cographs in Linear Time

A graph $G$ is a $k$-leaf power, for an integer $k \geq 2$, if there is a tree $T$ with leaf set $V(G)$ such that, for all distinct vertices $x, y \in V(G)$, the edge $xy$ exists in $G$ if and only if the distance between $x$ and $y$ in $T$ is at most $k$. Such a tree $T$ is called a $k$-leaf root of $G$. The computational problem of constructing a $k$-leaf root for a given graph $G$ and an integer $k$, if any, is motivated by the challenge from computational biology to reconstruct phylogenetic trees. For fixed $k$, Lafond [SODA 2022] recently solved this problem in polynomial time. In this paper, we propose to study optimal leaf roots of graphs $G$, that is, the $k$-leaf roots of $G$ with minimum $k$ value. Thus, all $k'$-leaf roots of $G$ satisfy $k \leq k'$. In terms of computational biology, seeking optimal leaf roots is more justified as they yield more probable phylogenetic trees. Lafond’s result does not imply polynomial-time computability of optimal leaf roots, because, even for optimal $k$-leaf roots, $k$ may (exponentially) depend on the size of $G$. This paper presents a linear-time construction of optimal leaf roots for chordal cographs (also known as trivially perfect graphs). Additionally, it highlights the importance of the parity of the parameter $k$ and provides a deeper insight into the differences between optimal $k$-leaf roots of even versus odd $k$.

Analýza variant vlastní činnosti v plánovacím a rozhodovacím procesu s využitím válečné hry

Decision-making is one of the basic activities of commanders in combat. Commanders' decision-making is a process of choosing between at least two possible options for the actions of their own units. The creation of variants of one's own activity is characterized by the effort of the staff to develop the widest possible set of conceptually different variants, which are selected at a certain stage of the process and inadmissible variants are subsequentlyexcluded. The content of the article is a description of the principles and ways of using the war game with the application of selected methods (systematic-analytical methods of the probability tree, supplemented by the scenario method) and managerial decision-making methodology (Kepner Tregoe methodology).

Artificial intelligence applications in allergic rhinitis diagnosis: Focus on ensemble learning.

The diagnosis of allergic rhinitis (AR) primarily relies on symptoms and laboratory examinations. Due to limitations in outpatient settings, certain tests such as nasal provocation tests and nasal secretion smear examinations are not routinely conducted. Although there are clear diagnostic criteria, an accurate diagnosis still requires the expertise of an experienced doctor, considering the patient's medical history and conducting examinations. However, differences in physician knowledge and limitations of examination methods can result in variations in diagnosis. Artificial intelligence is a significant outcome of the rapid advancement in computer technology today. This study aims to present an intelligent diagnosis and detection method based on ensemble learning for AR. We conducted a study on AR cases and 7 other diseases exhibiting similar symptoms, including rhinosinusitis, chronic rhinitis, upper respiratory tract infection, etc. Clinical data, encompassing medical history, clinical symptoms, allergen detection, and imaging, was collected. To develop an effective classifier, multiple models were employed to train on the same batch of data. By utilizing ensemble learning algorithms, we obtained the final ensemble classifier known as adaptive random forest-out of bag-easy ensemble (ARF-OOBEE). In order to perform comparative experiments, we selected 5 commonly used machine learning classification algorithms: Naive Bayes, support vector machine, logistic regression, multilayer perceptron, deep forest (GC Forest), and extreme gradient boosting (XGBoost).To evaluate the prediction performance of AR samples, various parameters such as precision, sensitivity, specificity, G-mean, F1-score, and area under the curve (AUC) of the receiver operating characteristic curve were jointly employed as evaluation indicators. We compared 7 classification models, including probability models, tree models, linear models, ensemble models, and neural network models. The ensemble classification algorithms, namely ARF-OOBEE and GC Forest, outperformed the other algorithms in terms of the comprehensive classification evaluation index. The accuracy of G-mean and AUC parameters improved by nearly 2% when compared to the other algorithms. Moreover, these ensemble classifiers exhibited excellent performance in handling large-scale data and unbalanced samples. The ARF-OOBEE ensemble learning model demonstrates strong generalization performance and comprehensive classification abilities, making it suitable for effective application in auxiliary AR diagnosis.

The principle “like begets like” in algebra-matrix genetics and code biology

The article is devoted to analysis of emergent properties of the system of binary oppositions in the genetic code ensemble. The epochal model of the double helix of DNA by Watson and Crick showed that the multiple reproduction of genetic information on DNA strands uses the ancient principle “like begets like” based on the simple complementarity in pairs of nucleobases. Each of these pairs is built on the binary opposition “purine-pyrimidine”. But the system of DNA n-plet alphabets and genetic coding is much richer in types of binary oppositions, which also have some coding meanings related to this principle. The article contains the results of the application of the author’s “method of hierarchy binary stochastics” (HBS-method) to the analysis of the quasi-stochastic organization of binary sequences of hydrogen bonds in genomic single-stranded DNAs. This analysis revealed hidden probability rules related to dichotomous fractal-like probability trees. The relationship between inherited bodily dichotomies in living organisms and the discovered probability dichotomies in information sequences of genomic DNAs is discussed. The encoding properties of molecular binary oppositions in the DNA nucleotide system allows the algorithmic construction of (2n∗2n)-matrices of probabilities of n-plets in these binary sequences, which are matrix representations of 2n-dimensional hyperbolic numbers. Connections of these multidimensional numbers with some inherited physiological phenomena and deep neural networks are noted. A unified algebra-numeric certification of the DNAs of genomes and genes - based on these multidimensional numerical systems - is proposed.

Too hot, too cold, or just right: Can wildfire restore dry forests of the interior Pacific Northwest?

As contemporary wildfire activity intensifies across the western United States, there is increasing recognition that a variety of forest management activities are necessary to restore ecosystem function and reduce wildfire hazard in dry forests. However, the pace and scale of current, active forest management is insufficient to address restoration needs. Managed wildfire and landscape-scale prescribed burns hold potential to achieve broad-scale goals but may not achieve desired outcomes where fire severity is too high or too low. To explore the potential for fire alone to restore dry forests, we developed a novel method to predict the range of fire severities most likely to restore historical forest basal area, density, and species composition in forests across eastern Oregon. First, we developed probabilistic tree mortality models for 24 species based on tree characteristics and remotely sensed fire severity from burned field plots. We applied these estimates to unburned stands in four national forests to predict post-fire conditions using multi-scale modeling in a Monte Carlo framework. We compared these results to historical reconstructions to identify fire severities with the highest restoration potential. Generally, we found basal area and density targets could be achieved by a relatively narrow range of moderate-severity fire (roughly 365-560 RdNBR). However, single fire events did not restore species composition in forests that were historically maintained by frequent, low-severity fire. Restorative fire severity ranges for stand basal area and density were strikingly similar for ponderosa pine (Pinus ponderosa) and dry mixed-conifer forests across a broad geographic range, in part due to relatively high fire tolerance of large grand (Abies grandis) and white fir (Abies concolor). Our results suggest historical forest conditions created by recurrent fire are not readily restored by single fires and landscapes have likely passed thresholds that preclude the effectiveness of managed wildfire alone as a restoration tool.

Costs, Health Benefits, and Cost-Effectiveness of Chlamydia Screening and Partner Notification in the United States, 2000-2019: A Mathematical Modeling Analysis.

Chlamydia remains a significant public health problem that contributes to adverse reproductive health outcomes. In the United States, sexually active women 24 years and younger are recommended to receive annual screening for chlamydia. In this study, we evaluated the impact of estimated current levels of screening and partner notification (PN), and the impact of screening based on guidelines on chlamydia associated sequelae, quality adjusted life years (QALYs) lost and costs. We conducted a cost-effectiveness analysis of chlamydia screening, using a published calibrated pair formation transmission model that estimated trends in chlamydia screening coverage in the United States from 2000 to 2015 consistent with epidemiological data. We used probability trees to translate chlamydial infection outcomes into estimated numbers of chlamydia-associated sequelae, QALYs lost, and health care services costs (in 2020 US dollars). We evaluated the costs and population health benefits of screening and PN in the United States for 2000 to 2015, as compared with no screening and no PN. We also estimated the additional benefits that could be achieved by increasing screening coverage to the levels indicated by the policy recommendations for 2016 to 2019, compared with screening coverage achieved by 2015. Screening and PN from 2000 to 2015 were estimated to have averted 1.3 million (95% uncertainty interval [UI] 490,000-2.3 million) cases of pelvic inflammatory disease, 430,000 (95% UI, 160,000-760,000) cases of chronic pelvic pain, 300,000 (95% UI, 104,000-570,000) cases of tubal factor infertility, and 140,000 (95% UI, 47,000-260,000) cases of ectopic pregnancy in women. We estimated that chlamydia screening and PN cost $9700 per QALY gained compared with no screening and no PN. We estimated the full realization of chlamydia screening guidelines for 2016 to 2019 to cost $30,000 per QALY gained, compared with a scenario in which chlamydia screening coverage was maintained at 2015 levels. Chlamydia screening and PN as implemented in the United States from 2000 through 2015 has substantially improved population health and provided good value for money when considering associated health care services costs. Further population health gains are attainable by increasing screening further, at reasonable cost per QALY gained.

Lifetime quality-adjusted life years lost due to genital herpes acquired in the United States in 2018: a mathematical modeling study

Genital herpes (GH), caused by herpes simplex virus type 1 and type 2 (HSV-1, HSV-2), is a common sexually transmitted disease associated with adverse health outcomes. Symptoms associated with GH outbreaks can be reduced by antiviral medications, but the infection is incurable and lifelong. In this study, we estimate the long-term health impacts of GH in the United States using quality-adjusted life years (QALYs) lost. We used probability trees to model the natural history of GH secondary to infection with HSV-1 and HSV-2 among people aged 18-49 years. We modelled the following outcomes to quantify the major causes of health losses following infection: symptomatic herpes outbreaks, psychosocial impacts associated with diagnosis and recurrences, urinary retention caused by sacral radiculitis, aseptic meningitis, Mollaret's meningitis, and neonatal herpes. The model was parameterized based on published literature on the natural history of GH. We summarized losses of health by computing the lifetime number of QALYs lost per genital HSV-1 and HSV-2 infection, and we combined this information with incidence estimates to compute the total lifetime number of QALYs lost due to infections acquired in 2018 in the United States. We estimated 0.05 (95% uncertainty interval (UI) 0.02-0.08) lifetime QALYs lost per incident GH infection acquired in 2018, equivalent to losing 0.05 years or about 18 days of life for one person with perfect health. The average number of QALYs lost per GH infection due to genital HSV-1 and HSV-2 was 0.01 (95% UI 0.01-0.02) and 0.05 (95% UI 0.02-0.09), respectively. The burden of genital HSV-1 is higher among women, while the burden of HSV-2 is higher among men. QALYs lost per neonatal herpes infection was estimated to be 7.93 (95% UI 6.63-9.19). At the population level, the total estimated lifetime QALYs lost as a result of GH infections acquired in 2018 was 33,100 (95% UI 12,600-67,900) due to GH in adults and 3,140 (95% UI 2,260-4,140) due to neonatal herpes. Results were most sensitive to assumptions on the magnitude of the disutility associated with post-diagnosis psychosocial distress and symptomatic recurrences. GH is associated with substantial health losses in the United States. Results from this study can be used to compare the burden of GH to other diseases, and it provides inputs that may be used in studies on the health impact and cost-effectiveness of interventions that aim to reduce the burden of GH. The Center for Disease Control and Prevention.

Formalizing Attack Tree on Security Object for MySANi in Legal Metrology

Illegal software manipulation is one of the biggest issues in software security. This includes the legally relevant software which are now crucial modules in weight and measuring instruments such as weighbridges. Despite the advancement and complexity of weight and measuring instruments, the inspection methodology is weak and lacks of innovation. The conventional inspection method is merely based on the observation printed certificate of the software. This paper introduces Malaysia Software-Assisted Non-Automatic Weighing Instrument (NAWI) Inspection (MySANI), a method used to enhance the software inspection scheme in legal metrology. MySANI introduces security objects in order to assist and enhance the inspection process. The security evaluation is based on the best practices in IT in metrology, where the attack model on relevant assets of the security objects is simulated for the Attack Probability Tree. The attack tree is verified by integrating formal notation and comparison with finite state transition system domain to verify the correctness properties of the tree design before the model can be further used in a risk analysis procedure within the Attack Probability Tree framework. Results show that the designed attack tree is consistent with the designed simulation.

Toward Human Chromosome Knowledge Engine

Human chromosomes carry genetic information about our life. Chromosome classification is crucial for karyotype analysis. Existing chromosome classification methods do not take into account reasoning, such as: analyzing the relationship between variables, modeling uncertainty, and performing causal reasoning. In this paper, we introduce a knowledge engine for reasoning-based human chromosome classification that stores knowledge of chromosomes via a novel representation structure, the Chromosome Part Description (CPD), and reasons over CPDs by utilizing the probability tree model (PTM) for classification. Each CPD keeps information on a particular feature of chromosomes, while the PTM provides causal reasoning capability taking CPDs as nodes and dependencies between CPDs and types as edges. Experimental results show that the proposed knowledge engine’s performance increases when providing more CPDs and achieves 100% classification accuracy with more than three CPDs.

Biophysical Modeling of the Ionizing Radiation Influence on Cells Using the Stochastic (Monte Carlo) and Deterministic (Analytical) Approaches.

This review article describes our simplified biophysical model for the response of a group of cells to ionizing radiation. The model, which is a product of 10years of studies, acts as (a) a comprehensive stochastic approach based on the Monte Carlo simulation with a probability tree and (b) the thereof derived detailed deterministic models describing the selected biophysical and radiobiological phenomena in an analytical manner. Specifically, the presented model describes effects such as the risk of neoplastic transformation of cells relative to the absorbed radiation dose, the dynamics of tumor development, the priming dose effect (also called the Raper-Yonezawa effect) based on the introduced adaptive response approach, and the bystander effect. The model is also modifiable depending on users' potential needs.

Using Value-Based Potentials for Making Approximate Inference on Probabilistic Graphical Models

The computerization of many everyday tasks generates vast amounts of data, and this has lead to the development of machine-learning methods which are capable of extracting useful information from the data so that the data can be used in future decision-making processes. For a long time now, a number of fields, such as medicine (and all healthcare-related areas) and education, have been particularly interested in obtaining relevant information from this stored data. This interest has resulted in the need to deal with increasingly complex problems which involve many different variables with a high degree of interdependency. This produces models (and in our case probabilistic graphical models) that are difficult to handle and that require very efficient techniques to store and use the information that quantifies the relationships between the problem variables. It has therefore been necessary to develop efficient structures, such as probability trees or value-based potentials, to represent the information. Even so, there are problems that must be treated using approximation since this is the only way that results can be obtained, despite the corresponding loss of information. The aim of this article is to show how the approximation can be performed with value-based potentials. Our experimental work is based on checking the behavior of this approximation technique on several Bayesian networks related to medical problems, and our experiments show that in some cases there are notable savings in memory space with limited information loss.

Supply Chain Risk Diffusion Model Considering Multi-Factor Influences under Hypernetwork Vision

Considering the problem of risk diffusion in increasingly complex supply chain networks, we propose using the supply chain risk diffusion model, under the hypernetwork vision, to study the influence of certain factors on risk diffusion, including the herd mentality, self-vigilance, talent recruitment, and enterprise management. First of all, the state transition probability tree is constructed to represent the state transition of each enterprise, then the Microscopic Markov Chain Approach (MMCA) is used to analyze the scale of risk spread, and the diffusion threshold of risk is discussed. We find that the herd mentality, self-vigilance, talent recruitment, and enterprise management will effectively curb the spread of risks. Directly recruiting talents and strengthening enterprise management is more effective than increasing vigilance. This study helps professionals to understand the mechanism of risk diffusion, and it provides effective suggestions on how to suppress risk diffusion in the real world.

Cost-minimisation analysis of polysomnography and pulse oximetry in a risk stratification protocol for paediatric adenotonsillectomy.

Severe obstructive sleep apnoea (OSA) is associated with increased risk of respiratory compromise in the post-operative period following adenotonsillectomy (AT). This study analyses the economic cost of polysomnography or overnight oximetry as part of pre-operative risk stratification in paediatric AT, supplementing previously published research demonstrating the efficacy of this protocol in predicting respiratory complications. This cost-minimisation analysis examines costs associated with pre-operative overnight oximetry and polysomnography in triaging paediatric patients older than 2 years old, with no major comorbidities except for OSA, undergoing AT for OSA (n=1801) to either a secondary or quaternary Australian hospital. Decision analysis modelling via probability trees were utilized to estimate pre- and peri-operative costs. A third hypothetical 'no investigation' model based upon conducting all AT at a secondary hospital was performed. Costs are derived from the financial year 2020-2021, censored at discharge. The total cost per patient of AT including pre-operative investigations of oximetry and polysomnography, and associated inpatient costs, were AUD4181.34 and 5013.99 respectively. This is more expensive compared to a hypothetical no-investigation model (AUD3958.98). Within the scope of this partial economic evaluation, this study finds a small additional cost for a model of care involving overnight oximetry as a pre-operative triage tool, balanced by the reduced cost of care in a lower acuity centre for low-risk patients and potential high cost of complications if all children are treated in a low acuity centre. This supports oximetry in peri-operative risk stratification for paediatric AT from a financial perspective.