Benchmarking temporary traffic management (TTM) against risk analysis and uncertainty characterisation literature: a documentary analysis of New Zealand’s TTM system

Abstract. The preparation of the work is related to the low level of publication activity regarding the issues of research on the risks of scientific and technological development of agriculture, on the one hand, and the growing need for the formation of practical recommendations on risk management for the development of departmental information systems designed to implement state programs for the scientific and technological development of agriculture. This document presents the assessment and analysis of risk management elements in the Federal Scientific and Technical Program for Agricultural Development for 2017–2025 (FSTP). The purpose of the article is to analyze the range of risk management entities, the types of risks identified in the regulatory legal framework governing the scientific and technological support of agricultural development and procedural measures for their management, as well as the inclusion of risks of scientific and technological development of agriculture in the system of risks to food security. An analysis of food security risks was carried out in regulatory documents, new risk formulations in the updated Food Security Doctrine were clarified. Attention is paid to problematic issues of risk interpretation in the FSTP, which require scientific discussion and amendments to regulatory documents. For the first time, the definition of the risk of scientific and technological development of agriculture in the context of the FSTP was formulated. The work identified and considered the risks of the upper level of scientific and technical development of agriculture in connection with food security, the risks of achieving the results of the FSTP, the risks of achieving the result of the main activities of the FSTP subprogrammes. Methods. Methodically, the work is based on an expert method and analysis of documentation. The scientific novelty consists in developing an integrated approach to the analysis of risks of scientific and technological development of agriculture. The result of the work is the identified relationships of the analyzed risks and recommendations for improving the methodological apparatus of risk management in the area under consideration.

Learning logic programs with structured background knowledge

In the area of project construction industry, risk management has become an indispensable index of concern which needs to be focused on in order to ensure effective and successful execution of projects in the construction industry. This paper focuses on risk management within projects construction field and also to find out the opinions and ideas on the significance of the construction projects risks, and also to explore the risk analysis, risk response techniques and strategies as well as risk management processes and practices in construction industry. The questionnaire prepared for the survey was distributed both via e-mail and by sending questionnaire link onto a WhatsApp platform group of the respondents and 85 member respondents results were analyzed in the form of bar charts, column chart and radar chart. The survey results revealed that majority of the total respondents are in the capacity or position as Project Managers and also have over 15 years working experience, and as well attained Master’s degree qualification. It was also revealed that risk events are usually responsible for the poor delivery and quality of work, accompanied with delays and other associated losses in project construction and also risks that are associated with high probability and high impact are required to undergo further analysis, including quantification and thorough risk management. The most frequently used risk response technique according to the respondents is through the issuance of performance bonds, warranties and guarantees and also the most frequently used risk analysis practice is the Project Documented knowledge and Review Analysis. Risk management in project construction is therefore a constant learning process to constantly improve upon practices in order to adapt and increase the process efficiency of dealing with risk in construction projects as well as its successful execution.

ABSTRACT: Legal-Lens is an advanced AI-powered mobile application developed to address the challenges faced by legal professionals during contract review and document analysis. This platform simplifies the complex and tedious manual process by integrating technologies such as Natural Language Processing (NLP), Optical Character Recognition (OCR), and secure cloud infrastructure. Legal-Lens allows legal professionals to upload various types of documents, including scanned images, PDFs and Word files, which are then automatically processed to extract and analyze critical information such as clauses, obligations, risks, and summaries. The application provides additional support features like real-time analytics, secure authentication, interactive dashboards and collaboration tools to assist users in managing legal data efficiently and securely. This paper explores the underlying architecture, theoretical foundations, system design, implementation strategy, and comparative performance metrics of Legal-Lens while highlighting its significance in the evolving legal tech landscape. Keywords: Legal Document Analysis, NLP, OCR, Clause Detection, LegalTech, Risk Analysis, Contract summarization

This study aimed to indicate the identified risk factors, which is the first stage in the presented method for risk analysis in geodetic works. The experts’ opinion, analysis of available documentation, experience, subject literature review, and observation allowed for obtaining information due to which 20 risk factors were selected. The presented method for risk analysis was developed as a result of investigations and verification of existing risk analysis methods, as well as the market needs. The results of the study on the identification of risk factors and the presented risk analysis method are the first stage of the research on the given subject, the continuation of which will be presented in subsequent works.

Joint applicability test of software for laboratory assessment and risk analysis

Risk analysis is the identification and documentation of risks with respect to an organisation or a target system. Established risk analysis methods and guidelines typically focus on a particular system configuration at a particular point in time. The resulting risk picture is then valid only at that point in time and under the assumptions made when it was derived. However, systems and their environments tend to change and evolve over time. In order to appropriately handle change, risk analysis must be supported with specialised techniques and guidelines for modelling, analysing and reasoning about changing risks. In this paper we introduce general techniques and guidelines for managing risk in changing systems, and then instantiate these in the CORAS approach to model-driven risk analysis. The approach is demonstrated by a practical example based on a case study from the Air Traffic Management (ATM) domain.KeywordsRisk managementrisk analysischange managementAir Traffic Managementsecurity

Route risk analysis of hazardous materials transportation by railroad is receiving considerable new attention from industry and government. Such analyses are necessary for effective public policy and development of rational risk management strategies. However, route risk analysis is complex and generates results that can be difficult to properly interpret. Risk analyses are intended to provide risk managers with objective information about how to effectively manage risk and the most effective options to reduce it. This paper uses results from a quantitative risk analysis of hazardous materials shipped by rail to develop and illustrate several new techniques to present, interpret, and communicate risk results more effectively. The analysis accounted for the major factors affecting risk: infrastructure quality, traffic volume, and population exposure along shipment routes, as well as tank car design and product characteristics. Approaches for system-level and route-specific analyses are presented. Both absolute and normalized estimates of risk provide useful information. The question of interest and the user affects which type of information is most useful for effective decision making. Various graphical techniques enable risk metrics to be compared and contrasted, either in a geographic context or independent of it, depending on which is most useful. Identifying the locations that account for the highest concentration of risk and understanding the contributing factors will also clarify the mutual roles of carriers, shippers, and municipalities along a route in regard to risk management, reduction, and mitigation options. In addition, the techniques presented in this paper may also be useful for regulators and researchers who might be interested in a broader view of risk analysis at the network level.

The relevance of political prestudies for implementation studies of cognitive services in community pharmacies

In order to meet the needs of group enterprises for data privacy protection processing in the internal private network environment, this paper proposes an optimization design method of Privacy Protection System (PPS) based on cloud native. The main goal of this method is to transform structured data using data anonymization techniques to mitigate attacks that could lead to privacy breaches. From the perspective of logical architecture, this method supports the anonymization of data using the privacy protection model and related parameters selected by the user. First, the identifiers are removed from the dataset to be processed, and constraints are imposed on the quasi-identifiers. Further, the algorithms for protecting sensitive properties that require certain assumptions about the attacker’s goals and background knowledge are also supported. In particular, in the process of anonymization, the scheme introduces the methods of utility analysis and risk analysis, so that the anonymization results can be accurately evaluated. Finally, the proposed method allows users to iteratively update the privacy-preserving model and related parameters according to the results of the anonymization evaluation. From the perspective of technical architecture, the proposed method uses Spring Boot as the back-end framework and MyBatis as the persistence layer framework. At the same time, in order to ensure system security requirements, Json Web Token is also used for user authentication. Finally, when designing the system deployment scheme, cloud native technology is introduced to encapsulate system functions into microservice containers, and cluster management tools are used to dynamically manage microservice containers to ensure high availability of the system.KeywordsPrivacy protectionAnonymizationCloud nativeMicroservice

This month's Current Topics essay, by Kara Morgan and Ellen Peters, discusses risk perception and communication issues raised by a January article in Science that excited considerable debate by its discussion of the role of random cell divisions in carcinogenesis. Some commentators expressed concern that emphasizing the substantial contribution of randomness in cancer risk could undermine efforts to persuade people to take sensible precautions to reduce avoidable cancer risks. Morgan and Peters propose some possible ways to find out how, if at all, people's mental models and behaviors were changed by news stories emphasizing the “bad luck” component of carcinogenesis rather than the “partly preventable risk” aspect. They also wonder whether such stories might change beliefs and behaviors about the efficacy of other measures intended to protect health and risks of other diseases, and point out that diet and lifestyle choices that reduce risk of cancer may also protect against other diseases. The editors welcome future submissions that examine empirically how news stories that frame risk information in different ways, emphasizing avoidable or unavoidable components of risks, affect mental models, intents to take precautions, actual behaviors, and health outcomes. Six papers in this issue address risks, vulnerabilities, uncertainties, optimization, and resilience of electrical power, transportation, and other network infrastructures. Infrastructure improvement and network resilience-building are crucially important topics in the intersection of risk analysis, operations research, and homeland security, since functioning transportation, communications, and power networks provide preconditions for responding effectively to a wide range of natural disasters, disease outbreaks, and attacks on multiple geographic scales and time scales. An introduction by the editors of this special series of papers, Seth Guikema, Laura McLay, and Jim Lambert, outlines key research gaps and opportunities for advancing and applying risk analysis to help reduce risks and vulnerabilities and improve resilience of key infrastructures. The need for robust and resilient transportation infrastructure is heightened when threats include deliberate attacks as well as random failures. Although a great deal of thought and modeling effort have already been devoted to the problems of allocating limited defensive resources to protect various transportation modes, hubs, and links, against potential attacks, the problem of how best to defend against intelligent adversary attacks is far from being solved. Barnett introduces a valuable empirical analysis of all 87 successful attacks worldwide against air and rail transport systems (defined as attacks that killed more than one passenger) between 1982 and 2011. He finds a clear shift over that interval from attacks on airplanes to attacks on trains, especially subway and commuter rail trains. Rail attacks may be relatively difficult to thwart once they begin, so that early detection and prevention of attacks, rather than on-board defenses, may be crucial to protecting against this shifting attack pattern. In addition to network infrastructures such as transportation, energy (e.g., oil and natural gas pipelines), water, electric power grid, and telecommunications networks, other key components of our national infrastructure include processing, manufacturing, and production plants. Failures at such plants can send disruptions cascading through supply networks, with substantial economic consequences. Zadakbar et al. consider how to quantify the economic consequences of deviations of chemical (or other) processes from normal operating conditions by applying loss functions to scenarios to describe the losses (adverse consequences) over time of process malfunctions, releases, or accidents (e.g., fires and explosions). They illustrate their proposed methodology with case studies in the petroleum industry. In 2012, the International Institute for Research on Cancer (IARC), following a long practice of using expert opinions to make judgments about the causal interpretation of epidemiological associations and other evidence, strengthened its classification of diesel engine exhaust (DEE) to “carcinogenic to humans.” However, it is becoming increasingly well recognized that statistical associations do not provide a reliable basis for establishing causation. Crump et al. reexamine one of the epidemiological data sets that was influential in IARC's determination. They find that whether DEE has any statistical effect on lung cancer mortality depends on how exposures are estimated and on how and whether radon exposure is adjusted for. Once exposure uncertainties and model uncertainties are accounted for, it is no longer clear that DEE exposures are associated with increased lung cancer risks in this data set. In a companion piece, Moolgavkar et al. argue that modeling the effect of exposure duration and other temporal factors shows that DEE has a statistical association with lung cancer mortality in only one of four types of mines (limestone), but not in other mines, undermining any clear causal interpretation of this association. These findings can be viewed as raising important questions about the usefulness and reliability of expert judgments about the causal interpretation of model-dependent associations in general, and about whether DEE is in fact carcinogenic to humans in these studies in particular. How best to interpret and use model-dependent risk estimates when different plausible models give very different results is a recurring theme in recent issues of Risk Analysis, and this application again illustrates its practical importance. Continuing with the theme of how best to characterize risks and uncertainties when different models and assumptions give very different results, Aven and Renn critically review the treatment of risks and uncertainties in the Fifth Assessment Reports (2013 and 2014) of the Intergovernmental Panel on Climate Change (IPCC). They argue that the IPCC defines “risk” and “confidence” too narrowly and vaguely, making heavy use of probabilities or likelihoods estimated by experts, which may be incorrect; and of confidence measures that indicate the degree of understanding and/or consensus in these expert judgments, but that do not necessarily reveal by how much reality is likely to differ from expert predictions. They conclude that the ICPP treatment of risk and uncertainties lacks a theoretically and conceptually convincing foundation on which to base risk management decisions. Aven and Renn propose several useful improvements, such as being more explicit about the data, models, and assumptions (background knowledge) on which probabilities are based or conditioned, and the extent to which this underlying knowledge implies broad or narrow ranges for the probabilities of consequences. A possible consequence of climate change is more frequent and/or more severe droughts. Van Duinen et al. analyze the factors that contribute to perceptions of drought risk among farmers in the southwest Netherlands. The find that both System 1 and System 2 (emotional/affect and rational/cognitive) factors shape risk perceptions, with those who would suffer more from a drought (due to the kinds of crops being grown or to current salinization issues in their fields) generally perceiving the risks as being higher. The issue ends with a review by Michael Greenberg of the recent book Sustainable Cities and Military Installations, a volume edited by Igor Linkov of papers from a NATO-funded and SRA-supported conference held in 2012. Although clearly defining and measuring sustainability remains a challenge, with many metrics proposed based on differing local needs, there is a clear need for better analytics to support decisions about how best to maintain and make more sustainable water, electricity, and other key infrastructures in military bases and facilities worldwide. Productive involvement of stakeholders, planning under current uncertainties, and experiences in managing water sustainability and other risks and challenges in small cities and military bases are key themes discussed in several of the book's 19 chapters. Michael finds the book well organized and a source of useful case studies illustrating how planners are coping with the challenges of increasing sustainability (and, in some cases, resilience) with tight budgets and considerable planning uncertainty

We consider this discussion, as there is a lot of confusion about the definition of the risk and the reliability of flexible manufacturing system analysis, both being risk analysts and decision makers. Thinking of risk and reliability analysis of flexible construction robotized systems (FCRS’s) from a probabilistic perspective, we come to the conclusion that probability is a measure of expressing uncertainty about the process seen through the point of view of the assessor (i.e. the controller of a process), and based on some background information and knowledge that we have at the time we quantify our uncertainty. A sharp distinction between objective, real risk, and perceived risk cannot be made, simply because complete knowledge about the world does not exist in most cases, and the analysis provides a tool for dealing with these uncertainties based on coherence by using the rules of probabilities. If sufficient data become available, consensus in probability assignments may be achieved, but not necessarily, as there are always subjective elements involved in the assessment process. Risk is primarily a judgement, not a fact. As risk expresses uncertainty about the world, i.e. about consequences and outcomes of an activity, risk perception has a role to play to guide decision makers. It is, however, not obvious how such a thinking should be implemented in practice, in a decision making context, and different frameworks can be established. This approach emphasises the so-called observable quantities and their prediction (Aven, 2004). Examples of observable quantities are the number of facilities and production volumes. The starting point is an activity or a system that we would like to analyse now, to provide decision support for investments, design, operation etc. Therefore, the interesting quantities for risk and reliability analysis of an FCRS are the performances of that system, for example measured by production, production loss, number of fatalities, and so on. Unfortunately, in most cases, we are led to predictions of these quantities that reflect our expectations. But these predictions will normally not provide sufficient information; assessment of uncertainties is required. In order to express the uncertainties, we need a measure, and we choose the probability for measuring uncertainties.

The data-driven approach prioritises operational data and does not require in-depth knowledge of system background; nevertheless, it requires considerable amounts of data. Obtaining faulty building data is a significant challenge for researchers. As a result, employing simulated data can be beneficial in data-driven faults detection and diagnosis (FDD) analysis because it is inexpensive and can run multiple sorts of faults with varying severities and time periods. The predominant implementation of FDD techniques within the building sector is done at the system level. However, as useful as system-level analysis is, typical buildings are comprised of multiple systems with their peculiar characteristics. Also, individualised system level-based analysis makes it challenging and sometimes impossible to visualise system-to-system interactions. However, there is a glaring underrepresentation of literatures that explore the development of whole building models that diagnose faults over the entire building energy performance sphere. Therefore, this paper presents a work to detect and diagnose building systems (HVAC, lighting, exhaust fan) faults in whole building energy performance within hot climate areas, using energy consumption and weather data. The detection process on the main building meter was conducted using LSTM-Autoencoders, and different multi-class classification methods were compared for the diagnosis phase. Moreover, feature extraction approaches were included in the comparison to quantify their performance in improving the diagnosis.

Chapter 10 - The BXM Method

Minimization of disclosure risks is a key challenge in publicly available visualizations that can potentially reveal personal information. Such risks are inherently dependent on the amount of information that adversaries can gain by manipulating visual representations and by using their background knowledge. Conventional risk quantification models proposed in the field of privacy-preserving data mining suffer from a lack of transparency in letting data owners control privacy parameters and understand their implications for disclosure risks. To fill this gap, we propose a visual uncertainty model for letting data owners understand the relationships between privacy parameters and vulnerable visualization configurations. Our main contribution is a probabilistic analysis of the disclosure risks associated with vulnerabilities in privacy-preserving parallel coordinates and scatter plots. We quantify the relationship among attack scenarios, adversarial knowledge, and the inherent uncertainty in cluster-based visualizations that can act as defense mechanisms. We present examples and a case study to demonstrate the effectiveness of the model.