Continuous Verification of Network Security Compliance

Continuous verification of network security compliance is an accepted need. Especially, the analysis of stateful packet filters plays a central role for network security in practice. But the few existing tools which support the analysis of stateful packet filters are based on general applicable formal methods like Satifiability Modulo Theories (SMT) or theorem prover and show runtimes in the order of minutes to hours making them unsuitable for continuous compliance verification.<br>In this work, we address these challenges and present the concept of state shell interweaving to transform a stateful firewall rule set into a stateless rule set. This allows us to reuse any fast domain specific engine from the field of data plane verification tools leveraging smart, very fast, and domain specialized data structures and algorithms including Header Space Analysis (HSA). First, we introduce the formal language FPL that enables a high-level human-understandable specification of the desired state of network security. Second, we demonstrate the instantiation of a compliance process using a verification framework that analyzes the configuration of complex networks and devices - including stateful firewalls - for compliance with FPL policies. Our evaluation results show the scalability of the presented approach for the well known Internet2 and Stanford benchmarks as well as for large firewall rule sets where it outscales state-of-the-art tools by a factor of over 41.

ALCOHOL ABUSE AS AN UNDERESTIMATED CAUSE OF NON-TRAUMATIC OSTEONECROSIS OF THE FEMORAL HEAD (DESCRIPTION OF CLINICAL CASES)

Osteonecrosis of the femoral head is a severe degenerative-dystrophic disease, which is characterized by the destruction of the bone substance in the head in the most vulnerable (loaded) parts. Due to a rapid progression in the absence of proper surgical treatment, osteonecrosis of the femoral head, as a rule, results in disability. There is a wide range of causes that cause the development of osteonecrosis. According to the authors, alcohol abuse should be attributed to the underestimated causal factors of this pathology. The article presents two clinical observations which convincingly demonstrate the role of alcohol abuse as a cause of osteonecrosis. In a 44-year-old man prolonged alcohol abuse led to a progressive (within a year) advance of osteonecrosis in the heads of both femurs, and in a 26-year-old woman, along with a short-term intake of glucocorticoids, it contributed to the formation of multifocal non-traumatic osteonecrosis with damage to the pelvic, right knee, shoulder joints. The authors explain underestimated role of alcohol abuse as a causal factor of osteonecrosis as follows. Firstly, patients, as a rule, try not to display their addiction, and therefore, unlike other predictors of developing femoral bone osteonecrosis (injuries, serious somatic diseases, taking glucocorticoids), this fact is not reflected in medical documentation. Secondly, patients with idiopathic forms of osteonecrosis often do not have an "assigned" doctor who could dynamically follow up the patient and, consequently, monitor more carefully his condition and risk factors for osteonecrosis. When consulting such a patient, a subspecialist, as a rule, sets himself the task first of all to exclude "his disease", which makes the patient vulnerable in terms of finding out the causes of osteonecrosis. These clinical cases demonstrate the need to take into account alcohol abuse as a significant causal factor in the development of osteonecrosis.

The Food Safety Risks of Harvesting Dropped and Drooping Produce: A Review

The Food Safety Modernization Act’s Produce Safety Rule sets forth minimum standards for fruit and vegetable production in the U.S. One provision states that growers must not harvest dropped produce, as damage or ground contact may contaminate produce. An unpublished survey of 2020 food safety inspections conducted by the Northeast Center to Advance Food Safety identified handling of dropped covered produce as a common misunderstanding and non-compliance issue among Northeast growers. In considering this provision’s on-farm practicality, this review was conducted to evaluate the risks associated with dropped and drooping produce to guide growers in making informed risk management decisions, and to answer the following questions: 1) What are the risk factors that influence transferability of pathogens from touching the ground to produce?; and 2) What are the risks associated with harvesting dropped or drooping produce covered under the Rule? A search of online databases found twelve relevant publications, which highlight moisture, contact time, and crop features as affecting contamination rates from a ground surface to a crop surface. Soil and mulch pose a differential risk, with bare soil generally presenting a lower risk than plastic mulch. The effects of other mulch types is unclear. Mulches may promote pathogen persistence in soil, though they may also protect produce from contaminated soils. These studies are limited in their scope and applicability and most do not directly address dropped produce. Future research is needed to clarify the varying effects of dropped and drooping produce, the impact of ground surface type on pathogen survivability and transfer, soil and crop features that facilitate contamination, and post-harvest risks of harvesting dropped or drooping produce. A comprehensive understanding will guide growers in implementing preventive measures and better managing risk in a way practicable to their farm's unique conditions.

Biological Algorithms for Digital Manufacture

<p>Computational simulations are generally built upon a form or design that is near or mostly complete. Agent-based simulations are ones where the rules and behaviours are designed, creating an unpredictable output. In this research, these rules are derived from the complex systems in nature, utilising cross-disciplinary principles between architecture and biology. The abstraction of data and rules from biological structures are used to inform computational rule-sets for modelling 3D printed structures. The simulations in this paper explore the concept of emergence: where systems have an irreducible complexity and adaptability - a series of smaller parts combined acting as a whole. The concept of agent-based simulations as a form of emergence is a tool used greatly within many areas of research as a speculative method to build form and space. Computation rule-sets define a design intent for each simulation, demonstrating the ability to use agent-based systems and a spatial design driver. Informing the agents with design intent, allows them to adapt to their environment and to the ability and limitations of a freeform 3D printer. The focus in this project is the design of emergent principles in nature and how they can be applied to optimize structures for use with digital fabrication methods, thus producing a new approach to designing fabricated forms. Using a design by research approach, this research demonstrates the potential of free-form 3D printing as a technique for an integrated fabrication system. It outlines computational design techniques including the simulation of emergent phenomena to define a digital workflow that supports the integration of both emergent structures and free-form printing.</p>

Biological Algorithms for Digital Manufacture

<p>Computational simulations are generally built upon a form or design that is near or mostly complete. Agent-based simulations are ones where the rules and behaviours are designed, creating an unpredictable output. In this research, these rules are derived from the complex systems in nature, utilising cross-disciplinary principles between architecture and biology. The abstraction of data and rules from biological structures are used to inform computational rule-sets for modelling 3D printed structures. The simulations in this paper explore the concept of emergence: where systems have an irreducible complexity and adaptability - a series of smaller parts combined acting as a whole. The concept of agent-based simulations as a form of emergence is a tool used greatly within many areas of research as a speculative method to build form and space. Computation rule-sets define a design intent for each simulation, demonstrating the ability to use agent-based systems and a spatial design driver. Informing the agents with design intent, allows them to adapt to their environment and to the ability and limitations of a freeform 3D printer. The focus in this project is the design of emergent principles in nature and how they can be applied to optimize structures for use with digital fabrication methods, thus producing a new approach to designing fabricated forms. Using a design by research approach, this research demonstrates the potential of free-form 3D printing as a technique for an integrated fabrication system. It outlines computational design techniques including the simulation of emergent phenomena to define a digital workflow that supports the integration of both emergent structures and free-form printing.</p>

A New Method to Compare the Interpretability of Rule-Based Algorithms

Interpretability is becoming increasingly important for predictive model analysis. Unfortunately, as remarked by many authors, there is still no consensus regarding this notion. The goal of this paper is to propose the definition of a score that allows for quickly comparing interpretable algorithms. This definition consists of three terms, each one being quantitatively measured with a simple formula: predictivity, stability and simplicity. While predictivity has been extensively studied to measure the accuracy of predictive algorithms, stability is based on the Dice-Sorensen index for comparing two rule sets generated by an algorithm using two independent samples. The simplicity is based on the sum of the lengths of the rules derived from the predictive model. The proposed score is a weighted sum of the three terms mentioned above. We use this score to compare the interpretability of a set of rule-based algorithms and tree-based algorithms for the regression case and for the classification case.

Cost Control of Treatment for Cerebrovascular Patients Using a Machine Learning Model in Western China

Background. Cerebrovascular disease has been the leading cause of death in China since 2017, and the control of medical expenses for these diseases is an urgent issue. Diagnosis-related groups (DRG) are increasingly being used to decrease the costs of healthcare worldwide. However, the classification variables and rules used vary from region to region. Of these variables, the question of whether the length of stay (LOS) should be used as a grouping variable is controversial. Aim. To identify the factors influencing inpatient medical expenditure in cerebrovascular disease patients. The performance of two sets of classification rules, and the effects of the extent of control of unreasonable medical treatment, were compared, to investigate whether the classification variables should include LOS. Methods. Data from 45,575 inpatients from a Healthcare Security Administration of a city in western China were used. Kruskal–Wallis H tests were used for single-factor analysis, and multiple linear stepwise regression was used to determine the main factors. A chi-squared automatic interaction detector (CHAID) algorithm was built as a decision tree model for grouping related data. The intensity of oversupply of service was controlled step by step from 10% to 100%, and the performance was calculated for each group. Results. The average hospitalization cost was 1,284 US dollars, and the total was 51.17 million US dollars. Of this, 43.42 million were paid by the government, and 7.75 million were paid by individuals. Factors including gender, age, type of insurance, level of hospital, LOS, surgery, therapeutic outcomes, main concomitant disease, and hypertension significantly influenced inpatient expenditure ( P < 0.05 ). Incorporating LOS, the patients were divided into seven DRG groups, while without LOS, the patients were divided into eight DRG groups. More clinical variables were needed to achieve good results without LOS. Of the two rule sets, smaller coefficient of variation (CV) and a lower upper limit for patient costs were found in the group including LOS. Using this type of economic control, 3.35 million US dollars could be saved in one year.

Memory over matter?—a conceptual framework to integrate social–ecological l legacies in agricultural NCP co-production

To better account for how social–ecological legacies of social and ecological systems jointly shape the current composition, the quality and quantity of nature’s contribution to people (NCPs), we integrate the concept of NCP co-production into social–ecological system thinking. Our expanded framework highlights how NCP co-production is frequently entangled within its social–ecological context, such as legacies, current resources and social activities. Additionally, we underline the relevance of non-material and material dimensions of resources in NCP co-production. To illustrate the potential of this expanded framework, we explore its application to an agricultural system of the French Northern Alps. We conclude that this framework (1) facilitates the understanding of society–ecosystem interactions in a specific regional social–ecological context; (2) helps to better conceptualise the interdependencies between resources and social activities; (3) demonstrates how current rule sets to organise social–ecological legacies affect the entire NCP co-production chain. The framework’s further implementation requires more research to better understand the complex interlinkages between the social and the ecological subsystems that underpin socioeconomic activities.