Human Error Research Articles

Cybersecurity and critical care staff: A mixed methods study

IntroductionCyberattacks on healthcare organisations are becoming increasingly common and represent a growing threat to patient safety. The majority of breaches in cybersecurity have been attributed to human error. Intensive care departments are particularly vulnerable to cyberattacks. The aim of this study was to investigate cybersecurity awareness, knowledge and behaviours among critical care staff. MethodsThis was a multi-site cross-sectional survey study administered to critical care staff. Cybersecurity awareness was evaluated using the validated HAIS-Q instrument. Knowledge and behaviours were evaluated by direct questioning and scenario-based multiple-choice questions. Free text options were also offered to respondents. Thematic analysis was performed on free text sections. ResultsMedian scores of 12–15 in each of the HAIS-Q focus areas were achieved, indicating high levels of cybersecurity awareness among critical care staff. However, self-reported confidence in cybersecurity practices, especially identifying signs of cybersecurity breaches and reporting cybersecurity incidents, were relatively low. Participants responses to the scenarios demonstrated a lack of knowledge and awareness of some of the mechanisms of cyberattacks. Barriers to safe cybersecurity practices among staff that emerged from the qualitative analysis included: a lack of training and education; heavy workloads and staff fatigue; perceived lack of IT support and poor IT infrastructure. ConclusionCritical care staff appear to have a high-level cybersecurity awareness. However, in practice safe cybersecurity practices are not always followed. ICU departments and hospitals must invest in the human aspect of cybersecurity to strength their cyber-defences and to protect patients.

Effects of Task Proficiency and Automation Reliability on Pilot-ePilot Teaming in Intelligent Aircraft Cockpits

Intelligence of aircraft cockpits can be improved by creating an electronic pilot (ePilot) to establish a Pilot-ePilot teaming relationship. Effects of task proficiency and automation reliability on such kind of human-autonomy teaming were studied, including task performance, task load, situation awareness, trust in automation, and human errors. In a 2 × 3 within-subjects experiment (low, medium & high levels for task proficiency and automation reliability), 21 male participants completed simulated flight tasks within local airport traffic patterns. Participants were teamed up with a system which took over the flight control priority based on task requirements and cognitive states. Experimental data was analyzed by statistical tests on significance and effect sizes. Results indicated that automation reliability influenced subjective and objective measures more than task proficiency, while task proficiency partly compensated the loss of performance due to deteriorated automation reliability. Findings support that the pilot-centered design principle should still be adhered to for Pilot-ePilot teaming in intelligent aircraft cockpits.

Prototyping and evaluation of a novel machine vision system for real-time, automated quality grading of sweetpotatoes

Rapid advances in machine vision and artificial intelligence (AI) technologies have generated high interest in developing automated grading and sorting solutions for fresh produce of specialty crops. Sweetpotatoes are an economically important crop in the United States and beyond, but grading and sorting the commodity, especially for surface defects, remains a labor-intensive operation at commercial packing facilities. In addition to incurring high labor costs, manual grading is subjective and prone to human assessment error. This study was therefore aimed to develop automated grading technology based on machine vision and state-of-the-art AI to reduce labor dependence and improve quality assessment for sweetpotato packing lines. The developed grading system prototype consisted of a custom-designed roller conveyor to transport and rotate sweetpotatoes and an overhead color camera to perform full-surface quality assessment for size and surface defects. A YOLOv8-based computer vision pipeline was developed to segment and track each sweetpotato traveling on the conveyor and analyze sample quality conditions in real-time. The YOLOv8l model coupled with the BoT-SORT tracker achieved the overall Higher Order Tracking Accuracy (HOTA) score of 0.952. Based on the instance segmentation by YOLOv8l, the length and width of sweetpotatoes were estimated with percentage accuracies of 96.7 % and 90.3 %, respectively, while the instance segmentation for three classes of sweetpotatoes according to surface defect conditions yielded an overall accuracy of 82.6 %, regardless of samples. The final sample grading, which was realized by taking into account the quality information recorded from a sequence of multi-view images for each sample, achieved an overall accuracy of 91.7 % in grading four classes of sweetpotatoes. The developed prototype holds promise for next-stage integration with sorting mechanisms into a full-fledged sorting system and will be eventually beneficial for sweetpotatoes packers.

Justice : A Predicting Criminal Acts According To IPC Section

The AI-driven IPC Section Prediction for Crime Classification project is a groundbreaking initiative with far- reaching implications for the legal and law enforcement sectors in India. Traditional crime classification and the assignment of the appropriate IPC section are often time-consuming and prone to human error. Our web application addresses these challenges by offering an efficient, accurate, and user-friendly solution. One of the key strengths of our application lies in its adaptability. It can process a wide range of crime descriptions, including those involving complex legal language or colloquial terms, ensuring its utility in diverse scenarios. Additionally, our system is designed to continuously learn and evolve. It adapts to changes in legal terminology, updates in the IPC, and emerging crime trends, thereby maintaining its relevance and precision over time. The social impact of this project cannot be overstated. By streamlining crime classification, it empowers law enforcement agencies to allocate resources more efficiently and prioritize cases based on severity and relevance. It also aids legal professionals by expediting case preparation and documentation. Moreover, it facilitates greater public engagement with the legal system, enabling citizens to better understand and navigate the complexities of the IPC. In conclusion, our AI-driven IPC Section Prediction web application is a pioneering tool that has the potential to revolutionize crime classification and legal processes. Its adaptability, continuous improvement, and positive societal impact make it an asset for law enforcement, legal practitioners, and the general public alike.

Analyzing human reliability for the operation of cargo oil pump using fuzzy CREAM extended Bayesian Network (BN)

Crude oil cargo discharging operations can be performed by cargo oil pumps in tanker ships. Considering the harmful effects of crude oil, any failure during the cargo oil pump operation may pose acute hazards such as temperature and pressure increase in pipelines, leakage, fire, and following undesired events that lead to marine environmental pollution and toxic effects to humans. The process is managed by the ship's crew, and it requires following the operational steps in order. This paper aims at performing a detailed human reliability analysis (HRA) for the operational process of cargo oil pumps on a tanker ship. In the paper, the Cognitive Reliability and Error Analysis Method (CREAM) is used to calculate human error probability under a fuzzy set which deals with the uncertainty and ambiguity of the CPC (Common Performance Condition). The Bayesian Network (BN) approach is adopted to determine the probability distribution of the control modes in reliability assessment. In view of the findings, the human reliability for the entire process of cargo oil pump operation on tanker ship is found 8.82E-01. The outcomes of the paper provide valuable insight into the improvement of tanker safety in shipboard operation and performance reliability assessment.

Investigating the Effects of Music and Temperature Changes on Heart Function and Human Error

Introduction: Exposure to music and temperature variations can influence physiological state and impact human error. This study aims to investigate the combined and independent effects of music and temperature changes on heart rate (HR) and human error.
 Materials and Methods: This interventional study was conducted on 14 participants (7 men and 7 women) under controlled conditions in 2018. The subjects experienced four types of music (Rock, Rap, Pop, and Iranian traditional music) at the noise pressure level of 75 dB, along with temperatures of 20, 25, and 30 °C independently and in combination, within an acoustic room with controlled temperature conditions. HR and human error rate were measured using a HR meter and a researcher-made text with spelling mistakes, respectively.
 Results: Using SPSS 19, data analysis revealed that exposure to traditional Iranian and rock music in temperatures deviating by 5 °C from 25 °C was associated with increased errors in detecting spelling mistakes (p < 0.05). Interestingly, traditional and rock music did not significantly affect HR across temperatures of 20, 25, and 30 °C (p > 0.05).
 Conclusion: Listening to traditional Iranian and rock music in conditions with approximately 5 °C cooler or warmer than room temperature can have a positive and constructive effect on HR and reduce human error. The use of this type of music has potential implications for optimizing environmental conditions, especially in workplaces where such factors are critical.

Fish Tank: An IoT-Based Mini Aquarium Control System

The manual management of aquarium systems is a meticulous process involving hands-on monitoring of various critical parameters such as water quality, feeding, lighting, and maintenance. However, this approach comes with challenges, including significant time consumption, the potential for human errors, and a lack of remote monitoring capabilities. To overcome these challenges, the development of automated smart aquarium systems has emerged as a transformative solution. These systems aim to simplify maintenance, enhance the well-being of aquatic life, provide remote accessibility, and contribute valuable data for research and education in the field. In the context of this evolutionary shift, this paper proposes a comprehensive Fish Tank system built upon the innovative IoT solution, IoT talk. This system utilizes sensors to drive actuators in real-time, enabling intelligent control of various water conditions within the aquarium. A notable feature of this proposed system is the implementation of a precise fish-feeding mechanism, enhancing the overall care provided to aquatic life. The paper goes further to present an analytic model, simulation, and measurement experiments that delve into the effects of IoT message delays and loss on water condition control, contributing valuable insights to the efficiency of the proposed system. Moreover, the proposed Fish Tank system incorporates advanced monitoring tools seamlessly connected to a robust database. This integration allows for efficient data storage and retrieval, enhancing the overall performance and reliability of the system. By extending this connectivity to a mobile application, the solution not only reduces human effort and errors in aquarium management but also provides users with convenient remote access. This transformative approach ensures continuous monitoring and control of the aquarium environment, ultimately contributing to the sustained health and well-being of aquatic life

Treatment of excessive gingival display using conventional esthetic crown lengthening versus computer guided esthetic crown lengthening: (a randomized clinical trial)

BackgroundSurgical guides have been proposed in an attempt to reach more predictable outcomes for esthetic crown lengthening. The objective of the present study was to evaluate the effectiveness of esthetic crown lengthening using 3D-printed surgical guides in the management of excessive gingival display due to altered passive eruption type 1B.Materials and methodsSixteen patients diagnosed with altered passive eruption type 1B, were divided into two groups. In the control group, the procedure was carried out conventionally, and in the study group, a dual surgical guide was used. The parameters of wound healing (swelling, color, probing depth, bleeding index, and plaque index), pain scores, gingival margin stability, and operating time were assessed at 1 week, 2 weeks, 3 months, and 6 months postoperatively.ResultsThere was no statistically significant difference in terms of wound healing, pain scores, and gingival margin stability between both groups at different time intervals (P = 1), however, there was a statistical difference between both groups in terms of operating time with the study group being significantly lower (P < 0.001).ConclusionDigitally assisted esthetic crown lengthening helps shorten the operating time and reduces the possibility of human errors during the measurements. This will be useful in helping practitioners achieve better results.Practical implicationsThe conventional method remains to be the gold standard. However, shorter operating time and lower margins for errors will help reduce costs as the chair side time is reduced as well as the possibility for a second surgery is lower. This will improve patient satisfaction as well.

Estimation of Temperature and Salinity from Marine Seismic Data—A Two-Step Approach

Ocean-water temperature and salinity are two vital properties that are required for weather-, climate-, and marine biology-related research. These properties are usually measured using disposable instruments at sparse locations, typically from tens to hundreds of kilometers apart. Laterally interpolating these sparse measurements provides smooth temperature and salinity distributions within the oceans, although they may not be very accurate. Marine seismic data, on the other hand, show visible reflections within the water-column which are primarily controlled by subtle sound-speed variations. Because these variations are functions of the temperature, salinity, and pressure, estimating sound-speed from marine seismic data and relating them to temperature and salinity have been attempted in the past. These seismically derived properties are of much higher lateral resolution (less than 25 m) than the sparse measurements and can be potentially used for climate and marine biology research. Estimating sound-speeds from seismic data, however, requires running iterative seismic inversions, which need a good initial model. Currently practiced ways to generate this initial model are computationally challenging, labor-intensive, and subject to human error and bias. In this research, we outline an automated method to generate the initial model which is neither computational and labor-intensive nor prone to human errors and biases. We also use a two-step process of, first, estimating the sound-speed from seismic inversion data and then estimating the salinity and temperature. Furthermore, by applying this method to real seismic data, we demonstrate the feasibility of our approach and discuss how the use of machine learning can further improve the computational efficiency of the method and make an impact on the future of climate modeling, weather prediction, and marine biology research.

Brain Tumor Detection and Categorization with Segmentation of Improved Unsupervised Clustering Approach and Machine Learning Classifier.

There is no doubt that brain tumors are one of the leading causes of death in the world. A biopsy is considered the most important procedure in cancer diagnosis, but it comes with drawbacks, including low sensitivity, risks during biopsy treatment, and a lengthy wait for results. Early identification provides patients with a better prognosis and reduces treatment costs. The conventional methods of identifying brain tumors are based on medical professional skills, so there is a possibility of human error. The labor-intensive nature of traditional approaches makes healthcare resources expensive. A variety of imaging methods are available to detect brain tumors, including magnetic resonance imaging (MRI) and computed tomography (CT). Medical imaging research is being advanced by computer-aided diagnostic processes that enable visualization. Using clustering, automatic tumor segmentation leads to accurate tumor detection that reduces risk and helps with effective treatment. This study proposed a better Fuzzy C-Means segmentation algorithm for MRI images. To reduce complexity, the most relevant shape, texture, and color features are selected. The improved Extreme Learning machine classifies the tumors with 98.56% accuracy, 99.14% precision, and 99.25% recall. The proposed classifier consistently demonstrates higher accuracy across all tumor classes compared to existing models. Specifically, the proposed model exhibits accuracy improvements ranging from 1.21% to 6.23% when compared to other models. This consistent enhancement in accuracy emphasizes the robust performance of the proposed classifier, suggesting its potential for more accurate and reliable brain tumor classification. The improved algorithm achieved accuracy, precision, and recall rates of 98.47%, 98.59%, and 98.74% on the Fig share dataset and 99.42%, 99.75%, and 99.28% on the Kaggle dataset, respectively, which surpasses competing algorithms, particularly in detecting glioma grades. The proposed algorithm shows an improvement in accuracy, of approximately 5.39%, in the Fig share dataset and of 6.22% in the Kaggle dataset when compared to existing models. Despite challenges, including artifacts and computational complexity, the study's commitment to refining the technique and addressing limitations positions the improved FCM model as a noteworthy advancement in the realm of precise and efficient brain tumor identification.

Utilizing ASReview in screening primary studies for meta-research in SLA: A step-by-step tutorial

Meta-research, including meta-analyses and systematic methodological reviews, has proven to be a useful tool for obtaining a comprehensive understanding of research questions by numerically summarizing data and methodological features in a given literature. As part of the review procedure, researchers select primary studies to be included in their analysis. However, this process is resource-intensive and prone to human error. In this tutorial, we introduce a practical application of artificial intelligence (AI), known as ASReview, that can facilitate the screening process. Using a simulated data set derived from a published meta-analysis, we offer step-by-step guidance on how to incorporate the tool into the screening process. We cover the essential steps, including the preparation of the data set, the import of the data set, the labeling of the study as relevant or irrelevant (for inclusion or not), as well as the saving of the results for the researcher's record and sharing for transparency in the spirit of open science. In addition, the tutorial addresses essential factors to consider in the AI-aided screening process, such as stopping rules. We acknowledge potential limitations of the tool and provide a couple of alternatives for interested readers. Our overall goal is to contribute to advancing and promoting meta-research in SLA by facilitating the screening process in the era of AI.

Self-Healing Grids: AI Techniques for Automatic Restoration after Outages

The reliability and resilience of power grids are paramount for sustaining modern society's energy demands. However, power outages resulting from natural disasters, equipment failures, or human errors remain persistent challenges. Traditional approaches to power grid restoration, relying heavily on manual intervention, often lead to delays and inefficiencies in restoring services. [1],[2] Recent advancements in artificial intelligence (AI) have spurred the development of self-healing grids capable of autonomously detecting, diagnosing, and restoring power after outages. This paper presents a comprehensive overview of AI techniques employed in self-healing grids and their applications in automatic restoration following outages. The traditional methods of power grid restoration, characterized by manual inspection and decision-making processes, are discussed, highlighting their limitations and challenges. Subsequently, the paper delves into various AI techniques employed in self-healing grids. Machine learning algorithms, such as supervised and unsupervised learning, are utilized for outage detection by analyzing historical data to identify patterns indicative of faults or anomalies. Fault diagnosis is facilitated through the application of Bayesian networks, neural networks, and fuzzy logic systems, enabling operators to accurately identify the root cause of outages and prioritize restoration efforts. Optimization algorithms, including evolutionary algorithms and reinforcement learning, play a crucial role in planning and coordinating restoration efforts to minimize downtime and maximize efficiency. The benefits of self-healing grids, including improved reliability, reduced downtime, and enhanced safety, are discussed alongside the challenges posed by data quality, scalability, and cybersecurity concerns. Finally, the paper outlines future directions, emphasizing advancements in AI techniques, integration with emerging technologies, and the importance of standardization and regulatory frameworks for the future of power grid management. DOI: https://doi.org/10.52783/pst.302

Internal multiple interactions-adsorption and external zwitterionic polymer-exclusion of restricted access materials as adsorbent for offline and online extraction of neonicotinoid pesticides in Goji samples

A method based on novel restricted access materials (RAMs) for the determination of neonicotinoid pesticides in Goji samples using offline and online solid phase extraction (SPE) coupled with high-performance liquid chromatography (LC). RAMs were synthesized using poly(chloromethylstyrene-co-divinylbenzene) (PVBC/DVB) microspheres as substrate, styrene (St) and n-vinylpyrrolidone (NVP) were first copolymerized on the interior to construct adsorption sites, and sulfobetaine methacrylate (SBMA) was then polymerized on the exterior to form exclusion sites via two-step surface initiated-atom transfer polymerization. The prepared PVBC/DVB@poly(St-co-NVP)@poly(SBMA) RAMs could efficiently extract neonicotinoid pesticides and automatically exclude proteins. Under the optimized conditions, the developed methods of offline (magnetic SPE and SPE column) and online extraction coupled with LC both using PVBC/DVB@poly(St-co-NVP)@poly(SBMA) RAMs as the extractant, exhibit a wide linearity, low limits of detection and limit of quantification and good inter-day and intra-day precision with satisfactory recoveries. Among these methods, online extraction coupled with LC based on novel RAMs exhibits clear advantages for the determination of neonicotinoid pesticides in Goji samples has clear advantages, such as simple operation by direct injection, short extraction times, and high accuracy with less human error.

EEG as a potential ground truth for the assessment of cognitive state in software development activities: A multimodal imaging study.

Cognitive human error and recent cognitive taxonomy on human error causes of software defects support the intuitive idea that, for instance, mental overload, attention slips, and working memory overload are important human causes for software bugs. In this paper, we approach the EEG as a reliable surrogate to MRI-based reference of the programmer's cognitive state to be used in situations where heavy imaging techniques are infeasible. The idea is to use EEG biomarkers to validate other less intrusive physiological measures, that can be easily recorded by wearable devices and useful in the assessment of the developer's cognitive state during software development tasks. Herein, our EEG study, with the support of fMRI, presents an extensive and systematic analysis by inspecting metrics and extracting relevant information about the most robust features, best EEG channels and the best hemodynamic time delay in the context of software development tasks. From the EEG-fMRI similarity analysis performed, we found significant correlations between a subset of EEG features and the Insula region of the brain, which has been reported as a region highly related to high cognitive tasks, such as software development tasks. We concluded that despite a clear inter-subject variability of the best EEG features and hemodynamic time delay used, the most robust and predominant EEG features, across all the subjects, are related to the Hjorth parameter Activity and Total Power features, from the EEG channels F4, FC4 and C4, and considering in most of the cases a hemodynamic time delay of 4 seconds used on the hemodynamic response function. These findings should be taken into account in future EEG-fMRI studies in the context of software debugging.

Comparative Analysis of the Various Statistical Techniques Applied to Assess Participants&amp;apos; Laboratory Performance in an Ordinary Portland Cement (OPC) Proficiency Testing Program

Outliers in data analysis pose both challenges and opportunities for researchers. On one hand, if not adequately addressed, outliers can distort statistical analyses and lead to flawed conclusions. Conversely, outliers can also offer valuable insights into underlying processes or factors at play. One commonly used method for identifying outliers is through the analysis of interquartile ranges (IQRs). By accurately detecting and treating these anomalies, researchers can ensure the accuracy and validity of their findings. The major causes of outliers in data analysis stem from measurement and sampling errors. These errors can arise from issues such as human errors in data collection or problems with measurement equipment. Researchers must comprehend these causes to appropriately address outliers and minimize their impact on the analysis. Treating outliers effectively can greatly enhance data analysis by providing a more precise representation of underlying patterns and relationships. Removal or adjustment of extreme values enables researchers to obtain a clearer and more reliable picture of the phenomena under investigation, leading to crucial insights and facilitating further analyses and decision-making. Addressing outliers also offers opportunities for additional research and a deeper understanding of the underlying processes or factors at play. By extensively investigating the reasons behind outliers, researchers can gain valuable insights that can guide future research efforts and contribute to more informed decision-making based on the data. An exemplary illustration of the significance of accurate assessment techniques in statistical analyses is the OPC fineness study. This study analyzed the impact of various assessment methods on scoring results by comparing data from different laboratories using z-scores. The findings of this study demonstrated that the choice of assessment technique significantly influenced the scoring outcomes. Therefore, careful consideration of assessment procedures is crucial for obtaining reliable and comparable results in statistical analyses. In conclusion, outliers in data analysis present both challenges and opportunities for researchers. Accurately detecting and addressing outliers is essential for obtaining reliable and meaningful results. A comprehensive understanding of the causes of outliers, such as measurement and sampling errors, is necessary for appropriate treatment. Effectively treating outliers enhances the accuracy and validity of analysis and provides avenues for further research and informed decision-making. The OPC fineness study exemplifies the importance of assessment techniques in statistical analyses. A nuanced understanding of outlier detection and treatment is indispensable for drawing valid statistical conclusions.

Systematic evaluation of common natural language processing techniques to codify clinical notes.

Proper codification of medical diagnoses and procedures is essential for optimized health care management, quality improvement, research, and reimbursement tasks within large healthcare systems. Assignment of diagnostic or procedure codes is a tedious manual process, often prone to human error. Natural Language Processing (NLP) has been suggested to facilitate this manual codification process. Yet, little is known on best practices to utilize NLP for such applications. With Large Language Models (LLMs) becoming more ubiquitous in daily life, it is critical to remember, not every task requires that level of resource and effort. Here we comprehensively assessed the performance of common NLP techniques to predict current procedural terminology (CPT) from operative notes. CPT codes are commonly used to track surgical procedures and interventions and are the primary means for reimbursement. Our analysis of 100 most common musculoskeletal CPT codes suggest that traditional approaches can outperform more resource intensive approaches like BERT significantly (P-value = 4.4e-17) with average AUROC of 0.96 and accuracy of 0.97, in addition to providing interpretability which can be very helpful and even crucial in the clinical domain. We also proposed a complexity measure to quantify the complexity of a classification task and how this measure could influence the effect of dataset size on model's performance. Finally, we provide preliminary evidence that NLP can help minimize the codification error, including mislabeling due to human error.

Design of a tracking controller for autonomous articulated heavy vehicles using a nonlinear model predictive control technique

This article proposes a design of a tracking controller for autonomous articulated heavy vehicles (AAHVs) using a nonlinear model predictive control (NLMPC) technique. Despite economic and environmental benefits in freight transportation, articulated heavy vehicles (AHVs) exhibit poor directional performance due to their large sizes, multi-unit vehicle configurations, and high centers of gravity (CGs). AHVs represent a 7.5 times higher risk of traffic accidents than single-unit vehicles (e.g. rigid trucks, cars, etc.) in highway operations. Human driver errors cause about 94% of traffic collisions. However, little attention has been paid to autonomous driving control of AHVs. To increase the safety of AHVs, we design a novel NLMPC-based tracking controller for an AHV, that is, a tractor/semi-trailer combination, and this tracking controller is distinguished from others with the feature of controlling both the lateral and longitudinal motions for both the leading and trailing units. To design the tracking controller, a new prediction AHV model is developed, which represents both the lateral and longitudinal dynamics of the vehicle and captures its rearward amplification feature over high-speed evasive maneuvers. With the proposed tracking controller, the AAHV tracks the predefined reference path and follows a planned forward-speed scheme. Co-simulation demonstrates the effectiveness and robustness of the proposed NLMPC tracking controller.

Ensuring the resilience of multi-unit residential buildings (MURBs): a building information modeling (BIM)-based evaluation approach

ABSTRACT Residential infrastructure, particularly multi-unit residential buildings (MURBs), lacks sufficient attention to resilience. This oversight is attributed to the absence of specific guidelines for assessing MURBs’ resilience, with existing literature primarily concentrating on singular hazards. Additionally, current frameworks for resilience evaluation necessitate manual interpretation, leading to time and cost inefficiencies and potential human errors. The present study, therefore, developed a comprehensive framework and an automatic rule-based checking system on MURB resilience that can be utilized as a decision support system for practitioners. A literature review revealed 44 resilience indicators, categorized into four based on the general characteristics, i.e., technical, organizational, geographical positioning, and economic. The resilience indicators were benchmarked and defined as a building information modeling (BIM) ruleset. A case study was conducted to demonstrate the execution of the developed BIM ruleset using a MURB design. The proposed framework and rule-based checking system help ensure that MURBs comply with resilience requirements.

Investigating the impacts of autonomous vehicles on crash severity and traffic safety

Traffic accidents are a nationwide public health concern, but autonomous vehicles (AVs) have the potential to significantly reduce accident severity and frequency by eliminating their most common cause, human error. By analyzing the data published by California’s Department of Motor Vehicles, researchers have identified the factors that influence AV crash severity, however, none do so through a literature review. This paper’s aims are multi-faceted: to understand AVs’ operation on public roadways by identifying and classifying the factors contributing to accident severity, to develop a list of strategies that address the public’s safety concerns, and to acknowledge the ethics of unavoidable collisions. To fulfill these objectives, a comprehensive literature review was conducted based on a keyword search. Following a multi-step screening and exclusion process, detailed review was performed of 107 relevant publications, and the factors contributing to increased crash severity were classified into 14 categories. The literature revealed that AVs are not at fault in most accidents, although they have a higher propensity than conventional cars to be involved in rear-end collisions, and they are specifically designed to minimize the number of accidents, but may face unavoidable ones. For the benefit of policymakers and manufacturers, 11 strategies that address the moral dilemma of these accidents and 7 strategies that address concerns about AV safety to improve public perception were identified.

Smart contract challenges and drawbacks for SME digital resilience

PurposeThis study aims to explore the key challenges and drawbacks of smart contracts (SCs) and how they impact digital resilience within small and medium enterprises (SMEs). Whilst this type of technology is seen as a step forward in terms of traceability, transparency and immutability to increase digital resilience, we argue that it should be approached with trepidation.Design/methodology/approachIn developing this paper, the authors conduct a systematic literature search using the Scopus database. Through this, we identified 931 relevant articles, of which 30 were used as the focus of this article. Thematic analysis was used as the analytical approach to develop themes and meaning from the data.FindingsIn this paper, there is an emphasis on the importance of understanding the potential risks associated with SC implementation, as well as identifying appropriate strategies for mitigating any negative impact. In our findings, we puts forward three key themes, namely legality, security and human error, which we argue are key smart contract challenges that impact SME digital resilience.Originality/valueIn this paper, we propose the notion of “centralised control in decentralised solutions”. This comes from the research highlighting SC weaknesses in digital resilience for SMEs. We argue that there is a need for standards, regulations and legislation to address these issues, advocating, ironically, a centralised approach to decentralised technology.