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The cyber threat landscape is highly dynamic, posing a significant risk to the operations of systems and organisations. An organisation should, therefore, continuously monitor for new threats and properly contextualise them to identify and manage the resulting risks. Risk identification is typically performed manually, relying on the integration of information from various systems as well as subject matter expert knowledge. This manual risk identification hinders the systematic consideration of new, emerging threats. This paper describes a novel method to promote automated cyber risk identification: OnToRisk. This artificial intelligence method integrates information from various sources using formal ontology definitions, and then relies on these definitions to robustly frame cybersecurity threats and provide risk-related insights. We describe a successful case study implementation of the method to frame the threat from a newly disclosed vulnerability and identify its induced organisational risk. The case study is representative of common and widespread real-life challenges, and, therefore, showcases the feasibility of using OnToRisk to sustainably identify new risks. Further applications may contribute to establishing OnToRisk as a comprehensive, disciplined mechanism for risk identification.

The development of engineered systems is a complex process, involving many activities and development artifacts. The challenges introduced by this complexity calls for a model-based design environment. The PROVE methodology is an artifact-centric, model-based development process design methodology. In this paper, we present the first, fully model-based design environment of the PROVE. Furthermore, our environment incorporates additional process design perspectives that are applied in real-time and in a completely consistent manner, based on a formally defined information model. We discuss the details of our environment's design as well as highlight its potential benefits.

Achieving cyber resilient critical infrastructure poses a significant engineering management challenge. Society relies on infrastructure and services that extend beyond the managerial boundaries of a specific organizational entity, yet existing cybersecurity maturity models typically aim to assess a single organization. We offer a systems thinking approach to cyber resilience. Specifically, we relate to critical infrastructure and services in their sectoral system context, reimagining them as a system of systems. We then suggest exploring cyber resilience as a system property, with its expressions relating to the multiple dimensions of operation of the sector and to the different domains of practice. We discuss the dimensions of operation and domains of practice concepts that are embedded into a sectoral cyber resilience maturity model, which is under development. We demonstrate how these concepts frame a set of expressions that is designed to probe the sectoral design space; and propose how they may be further used as design considerations for improving the sector's cyber resilience.

In research, process descriptions provide causal mechanisms and context as a basis for establishing phenomena and enabling reproducibility and generalization, and are therefore considered essential to scientific progress. Process descriptions are a principle form of describing systems. However, process descriptions are typically unstructured and complex, presenting challenges with respect to the assessment of their quality, and eventually to their usability. Inspired by modern systems engineering approaches, we propose a model-based analysis approach to analyze complex process descriptions, and consequently improve the communicability and the applicability of said processes. We apply the approach to real-life case studies, and detail its contribution. Finally, we discuss how using the model-based approach can promote the quality of research-related process descriptions, specifically for supporting evaluation of research publications as well as for describing processes by the researchers themselves.

Radar systems are mainly used for tracking aircraft, missiles, satellites, and watercraft. In many cases, information regarding the objects detected by a radar system is sent to, and used by, a peripheral consuming system, such as a missile system or a graphical user interface used by an operator. Those systems process the data stream and make real-time operational decisions based on the data received. Given this, the reliability and availability of information provided by radar systems have grown in importance. Although the field of cyber security has been continuously evolving, no prior research has focused on anomaly detection in radar systems. In this paper, we present an unsupervised deep-learning-based method for detecting anomalies in radar system data streams; we take into consideration the fact that a data stream created by a radar system is heterogeneous, i.e., it contains both numerical and categorical features with non-linear and complex relationships. We propose a novel technique that learns the correlation between numerical features and an embedding representation of categorical features in an unsupervised manner. The proposed technique, which allows for the detection of the malicious manipulation of critical fields in a data stream, is complemented by a timing-interval anomaly-detection mechanism proposed for the detection of message-dropping attempts. Real radar system data were used to evaluate the proposed method. Our experiments demonstrated the method’s high detection accuracy on a variety of data-stream manipulation attacks (an average detection rate of 88% with a false -alarm rate of 1.59%) and message-dropping attacks (an average detection rate of 92% with a false-alarm rate of 2.2%).

Model-based Systems Engineering (MBSE) approaches are a step forward in the evolution of computer-aided engineering, and yet, they often incorporate deficiencies that may jeopardize their practical utility and usability, as well as the validity of the resulting models. We demonstrate how a domain-specific modeling approach can relieve some hurdles in adopting MBSE, and how it can be used in tandem with a general-purpose modeling approach to augment and introduce rigor to models. Specifically, we demonstrate the consequences of theoretical issues that were previously identified in Object Process Methodology and suggest an approach to solve them. We use a generalized case-study—derived from extensive process modeling in both academia and industry—to show that a domain-specific model can significantly relax the user’s modeling effort. This demonstration is based on two quantitative metrics: the number of representational elements and available modeling tactics. We discuss the contribution of our approach to model quality, particularly with respect to its rigor and communicability.

The COVID-19 pandemic caught hospitals unprepared. The need to treat patients remotely and with limited resources led hospitals to identify a gap in their operational situational awareness. During the pandemic, Israeli Aerospace Industries helped hospitals to address the gap by designing a system to support their effective operation, management and decision making. In this paper, we report on the development of a functional, working prototype of the system using model-based engineering approach and tools. Our approach relies on domain-specific modeling, incorporating metamodeling and domain-specific representations based on the problem domain’s ontology. The tools practiced are those embedded into the Eclipse Modeling Framework—specifically, Ecore Tools and Sirius. While these technological tools are typically used to create dedicated, engineering-related modeling tools, in this work, we use them to create a functional system prototype. We discuss the advantages of our approach as well as the challenges with respect to the existing tools and their underlying technology. Based on the reported experience, we encourage practitioners to adopt model-based engineering as an effective way to develop systems. Furthermore, we call researchers and tool developers to improve the state-of-the-art as well as the existing implementations of pertinent tools to support model-based rapid prototyping.

This multicentric study of 17 high-volume centers presents 12 benchmark values for liver transplantation. Those values, mostly targeting markers of morbidity, were gathered from 2024 "low risk" cases, and may serve as reference to assess outcome of single or any groups of patients.To propose benchmark outcome values in liver transplantation, serving as reference for assessing individual patients or any other patient groups.Best achievable results in liver transplantation, that is, benchmarks, are unknown. Consequently, outcome comparisons within or across centers over time remain speculative.Out of 7492 liver transplantation performed in 17 international centers from 3 continents, we identified 2024 low risk adult cases with a laboratory model for end-stage liver disease score ≤20 points, a balance of risk score ≤9, and receiving a primary graft by donation after brain death. We chose clinically relevant endpoints covering intra- and postoperative course, with a focus on complications graded by severity including the complication comprehensive index (CCI). Respective benchmarks were derived from the median value in each center, and the 75 percentile was considered the benchmark cutoff.Benchmark cases represented 8% to 49% of cases per center. One-year patient-survival was 91.6% with 3.5% retransplantations. Eighty-two percent of patients developed at least 1 complication during 1-year follow-up. Biliary complications occurred in one-fifth of the patients up to 6 months after surgery. Benchmark cutoffs were ≤4 days for ICU stay, ≤18 days for hospital stay, ≤59% for patients with severe complications (≥ Grade III) and ≤42.1 for 1-year CCI. Comparisons with the next higher risk group (model for end stage liver disease 21-30) disclosed an increase in morbidity but within benchmark cutoffs for most, but not all indicators, while in patients receiving a second graft from 1 center (n = 50) outcome values were all outside of benchmark values.Despite excellent 1-year survival, morbidity in benchmark cases remains high with half of patients developing severe complications during 1-year follow-up. Benchmark cutoffs targeting morbidity parameters offer a valid tool to assess higher risk groups.

The planning of system development efforts is crucial to the successful realization of projects. However, development planning typically lacks systematic, engineering discipline, and consequently risks project and business success. Model-based process design is a potential information systems approach to addressing the increasing complexity of such planning. We characterize the ontology of development process design, based on real-life observations and scientific publications. We then synthesize the required ontology with the desirable characteristics of models, and derive key requirements for model-based development process design. Next, these requirements are used to evaluate the adequacy of three prominent, standardized model-based process design approaches&mdash, BPMN, OPM and SPEM. The findings reveal that the surveyed models are a partial fit, and do not promote sound process design. Finally, by generalizing the categorical evaluation results, possible root causes for the identified inadequacies are proposed. A new model design, which should rely on the formulated requirements set, is called for, in pursuit of a wider adoption of model-based design paradigms and better information systems realization to support the development of complex systems.

As manmade systems become increasingly complex, a multitude of tasks needs to be performed in coordination throughout their development lifecycle. In this paper, we introduce a new framework – consisting of a method and a modeling notation – that can facilitate development planning within an organization and/or project, as well as improve the coordination of multiple development efforts in System of Systems development. Then, we describe a real life application of this framework to an organizational process and policy design situation of introducing intellectual property governance into a large enterprise which specializes in systems development.

To determine the impact of different flush and reperfusion techniques on postreperfusion syndrome (PRS) and postoperative graft function, 100 transplants were randomly assigned into four groups as follows: group 1 (n=31), portal vein flush, no vena caval venting; group 2 (n=21), hepatic arterial flush, no vena caval venting; group 3 (n=29), portal vein flush with vena caval venting; and group 4 (n=19), hepatic artery flush with vena caval venting. Donor and recipient characteristics were similar. Extensive intraoperative and postoperative monitoring was performed and measurements were documented immediately before reperfusion and at 1, 5, 15, and 30 min after reperfusion. PRS was defined by three criteria: mean arterial pressure (MAP)

Neutropenic typhlitis is an acute inflammation of the cecum occurring in neutropenic patients. Two cases are described, both presenting typical features of the disease. It is our experience that the treatment approach should consist of bowel rest, fluid therapy, and massive administration of broad spectrum antibiotics. In some cases, full recovery will follow. If abdominal signs persist, surgery should be deferred until hematologic convalescence occurs. At this time, cecostomy and drainage should be adequate. Only in very severe cases, with wide-spread necrosis, should a right hemicolectomy be performed.