Unforeseen Hazards Research Articles

Verification and Validation for a Digital Twin for Augmenting Current SORA Practices with Air-to-Air Collision Hazards Prediction from Small Uncooperative Flying Objects

Future autonomous Unmanned Aerial Vehicles (UAV) missions will take place in highly cluttered urban environments. As a result, the UAV must be able to autonomously evaluate risks and react to unforeseen hazards. The current regulatory framework for missions implements SORA guidelines for hazard detection, but its application to air-to-air collision is limited. This research defined a rigorous verification and validation framework (V&V) for digital twins for use in future autonomous UAV missions. The researchers designed a sentry mission for a UAV to evaluate its capacity to detect small uncooperative flying objects. A digital twin of the DJI M300 vision system was built using a game engine and a V&V framework was developed to assure the quality of results in both virtual and real-world scenarios. The results showed the capability of the digital twin to identify vulnerabilities and worst-case scenarios in UAV mission operations, and how it can assist remote pilots in identifying air-to-air collision hazards. Furthermore, the probability of air-to-air collision was calculated for three sentry patterns, and the results were validated in the field. This research demonstrated the capability to identify vulnerabilities and worst-case scenarios in UAV mission operations. We present how the digital twin of an operational theatre can be exploited to assist remote pilots with the identification of air-to-air collision hazards of small uncooperative objects. Furthermore, we discuss how these results can be used to enhance current SORA-based risk assessment practices.

Unpacking candidate selection in authoritarian regimes: Evidence from Cameroon

Why do autocratic ruling parties adopt certain candidate selection processes, and what effects do they have on political outcomes? This article argues that many contemporary autocracies democratize candidate selection to address the demands of multiparty elections, but that this creates unforeseen hazards. Autocrats centralize candidate selection to reassert control over the composition of legislatures, but not necessarily to recruit more effective representatives. These ideas are explored in Cameroon, a longstanding authoritarian regime that has altered its candidate selection twice. Using an original biographical dataset and elite survey that measures legislative role orientations, the article shows how changes to candidate selection allowed the Cameroonian regime to retain more partisan legislators and to recruit new candidates who were more oriented to the party than the constituency. The article expands the comparative study of candidate selection to new settings and provides insights into how candidate selection helps shape and sustain authoritarian regimes.

A Double-Layer Indemnity Enhancement Using LSTM and HASH Function Technique for Intrusion Detection System

The Intrusion Detection System (IDS) is the most widely used network security mechanism for distinguishing between normal and malicious traffic network activities. It aids network security in that it may identify unforeseen hazards in network traffic. Several techniques have been put forth by different researchers for network intrusion detection. However, because network attacks have increased dramatically, making it difficult to execute precise detection rates quickly, the demand for effectively recognizing network incursion is growing. This research proposed an improved solution that uses Long Short-Term Memory (LSTM) and hash functions to construct a revolutionary double-layer security solution for IoT Network Intrusion Detection. The presented framework utilizes standard and well-known real-time IDS datasets such as KDDCUP99 and UNSWNB-15. In the presented framework, the dataset was pre-processed, and it employed the Shuffle Shepherd Optimization (SSO) algorithm for tracking the most informative attributes from the filtered database. Further, the designed model used the LSTM algorithm for classifying the normal and malicious network traffic precisely. Finally, a secure hash function SHA3-256 was utilized for countering the attacks. The intensive experimental assessment of the presented approach with the conventional algorithms emphasized the efficiency of the proposed framework in terms of accuracy, precision, recall, etc. The analysis showed that the presented model attained attack prediction accuracy of 99.92% and 99.91% for KDDCUP99 and UNSWNB-15, respectively.

Innovate and Stockpile: Respiratory Protection for Essential Workers in a Catastrophic Pandemic.

Innovate and Stockpile: Respiratory Protection for Essential Workers in a Catastrophic Pandemic.

Review on Clinical Research

Clinical trials are crucial to the practise of evidence-based medicine and health care reform, as demonstrated by the recent federal funding focus on comparative effectiveness research. Clinical trials have an impact on society as a whole by raising the quality of healthcare offered, in addition to having an impact on the individual patient by creating a wider range of effective therapies. Clinical trials may, however, also expose participants to unforeseen hazards, and skewed knowledge drawn from problematic clinical studies may unintentionally damage patients. A well-designed clinical trial's execution may seem simple, but it is based on meticulous procedures and oversight that adhere to fundamental ethical norms. I give an overview of the moral principles in this project.

Examining Coroners’ Recommendations for Health and Safety Management of Ageing Heavy Vehicle Drivers: A STAMP Analysis

Recommendations were analysed from coronial cases involving ageing heavy vehicle drivers (≥55 years) and mapped onto a Systems Theoretic Accident Model and Processes (STAMP) control structure to identify the controllers and control actions influential in the heavy vehicle industry with regard to health and safety. A National Coronial Information System (NCIS) database search revealed 38 coroners' recommendations arising from 14 unique cases of ageing driver involvement. There were no ageing themes identified in the analysis of coroners' findings and recommendations. An examination of the STAMP control structure identified that the highest concentration of recommendations was in the level of regulation, the second most senior level of control, although safety constraints were advised for all five levels of the system. In regard to identifying themes of control flaws in the recommendations, the study found that "unidentified hazards" were the most common type of safety failure in the analysis of cases of ageing drivers, concentrated at the regulatory level, which indicates that additional risk identification methods by upper levels of control are needed. Therefore, a recommendation arising from the current study is that additional controls in safety intervention are necessitated in the upper and middle levels of the road freight transportation system; in particular, formalising health and safety education for organisational managers, with a focus on identifying ageing issues, would fill a gap in the system for managing ageing heavy vehicle drivers. In conclusion, this study has found that improving the health and safety of ageing heavy vehicle drivers necessitates additional safety constraints with a focus on formalised safety education for organisational managers, in addition to a means to detect emerging and unforeseen hazards in the road freight transportation industry.

An open workflow to gain insights about low‐likelihood high‐impact weather events from initialized predictions

AbstractLow‐likelihood weather events can cause dramatic impacts, especially when they are unprecedented. In 2020, amongst other high‐impact weather events, UK floods caused more than £300 million damage, prolonged heat over Siberia led to infrastructure failure and permafrost thawing, while wildfires ravaged California. Such rare phenomena cannot be studied well from historical records or reanalysis data. One way to improve our awareness is to exploit ensemble prediction systems, which represent large samples of simulated weather events. This ‘UNSEEN’ method has been successfully applied in several scientific studies, but uptake is hindered by large data and processing requirements, and by uncertainty regarding the credibility of the simulations. Here, we provide a protocol to apply and ensure credibility of UNSEEN for studying low‐likelihood high‐impact weather events globally, including an open workflow based on Copernicus Climate Change Services (C3S) seasonal predictions. Demonstrating the workflow using European Centre for Medium‐Range Weather Forecasts (ECMWF) SEAS5, we find that the 2020 March–May Siberian heatwave was predicted by one of the ensemble members; and that the record‐shattering August 2020 California‐Mexico temperatures were part of a strong increasing trend. However, each of the case studies exposes challenges with respect to the credibility of UNSEEN and the sensitivity of the outcomes to user decisions. We conclude that UNSEEN can provide new insights about low‐likelihood weather events when the decisions are transparent, and the challenges and sensitivities are acknowledged. Anticipating plausible low‐likelihood extreme events and uncovering unforeseen hazards under a changing climate warrants further research at the science‐policy interface to manage high impacts.

Transient electromagnetic characteristics of coal seams intruded by magmatic rocks.

The intrusion of magmatic rocks into coal seams affects the coal quality and leads to unforeseen hazards in safety of the coal mines’ production. This paper summarizes the mechanism of magmatic rocks intruding into coal seams, depicts the electromagnetic characteristics of the coal seams intruded by magmatic rocks, briefly describes the characteristics of transient electromagnetic method (TEM), and introduces the method of detection by TEM and the data processing steps. Then, the effectiveness of TEM in detecting the ranges of the coal seams intruded by magmatic rocks is theoretically analysed. It is observed that after the intrusion of magmatic rocks in the coal seams, the electromagnetic characteristics (secondary field potential and resistivity) will be dramatically affected, namely high secondary field potential and low resistivity. For experimental verification purposes, this study selects the test project of the Tongxin Minefield in the Datong Coalfield in Shanxi, China as an example, and the accuracy for the detection of the ranges of the coal seams intruded by magmatic rock using TEM is successfully verified.

Avoiding Regrettable Substitutions: Green Toxicology for Sustainable Chemistry.

Green chemistry seeks to design less hazardous chemicals, but many of the efforts to replace chemicals have resulted in so-called "Regrettable Substitutions", when a chemical with an unknown or unforeseen hazard is used to replace a chemical identified as problematic. Here, we discuss the literature on regrettable substitution and focus on an oft-mentioned case, Bisphenol A, which was replaced with Bisphenol S-and the lessons that can be learned from this history. In particular, we focus on how Green Toxicology can offer a way to make better substitutions.

Real-Time Fusion Network for RGB-D Semantic Segmentation Incorporating Unexpected Obstacle Detection for Road-Driving Images

Semantic segmentation has made striking progress due to the success of deep convolutional neural networks. Considering the demands of autonomous driving, real-time semantic segmentation has become a research hotspot these years. However, few real-time RGB-D fusion semantic segmentation studies are carried out despite readily accessible depth information nowadays. In this letter, we propose a real-time fusion semantic segmentation network termed RFNet that effectively exploits complementary cross-modal information. Building on an efficient network architecture, RFNet is capable of running swiftly, which satisfies autonomous vehicles applications. Multi-dataset training is leveraged to incorporate unexpected small obstacle detection, enriching the recognizable classes required to face unforeseen hazards in the real world. A comprehensive set of experiments demonstrates the effectiveness of our framework. On Cityscapes , Our method outperforms previous state-of-the-art semantic segmenters, with excellent accuracy and 22 Hz inference speed at the full 2048 × 1024 resolution, outperforming most existing RGB-D networks.

A geographic meta-database of caves and underground structures in Turkey

Caves and underground structures are voids that can be natural or man-made. Besides this geological meaning, these voids, with peculiar environmental features, are important for many disciplines hosting diverse natural and cultural heritage. Having large karstic rock areas, different geomorphological structures, and climatic conditions, Turkey has a wide variety of caves. The relevant studies report that there are more than 40,000 caves in Turkey. Due to this large number, it is vital that all the spatial and non-spatial information on caves must be collected and stored correctly within a database structure. Through storing, retrieving, mapping, displaying, and analyzing geographic data capabilities, the Geographic Information System (GIS) can be used to manage the location-based information. The purpose of this study was to collect information about the unexamined caves and underground structures of Turkey as well as create a dedicated cave inventory. Therefore, a new GIS application called Geographic Information and Management System for Caves and Underground Structures (GIMSCUS) was developed to gather information from volunteers as a voluntary mapping source. For this purpose, the PostGIS database was used to store all the location-based information; GIS was used to analyze and map spatial data; and a virtual globe application was used to visualize the spatial data. Running on the Intranet network that is widespread all over Turkey and known to a user-friendly design, GIMSCUS was easily accessed by many volunteers. Thus, considerable new data (47.64% of the total data) were collected by the volunteers and provided to users within a short time. It is expected that these data will play an important role in: (1) land management and urban planning for preventing unforeseen hazards; (2) culture and tourism which is an important part of Turkey’s cultural heritage; and (3) hydrology for hydraulic structure building.

Case Study: Application of a Systematic Quality Improvement Methodology to a Medical Device Safety Alerts Process.

Safety alerts are published by manufacturers of medical devices when unforeseen hazards are identified in the post-market phase of a device's lifecycle. Multiple problems were identified in 2017 with Lower Mainland Biomedical Engineering's process for identifying, assessing and actioning safety alerts. A quality improvement (QI) project utilizing "The Model for Improvement" methodology, was carried out to improve the process. The original process was centralized and relied on a single individual to carry out all alert management responsibilities. A new hybrid process was developed which involves both centralized and decentralized components with responsibilities shared across multiple stakeholders. The QI project resulted in the closure time for assessing and actioning high priority alerts to improve from 200 days in 2017 to 14 days in 2019.

Recurring outburst floods from drained lakes: an emerging Arctic hazard

New development in regions with rapidly changing climates may experience unforeseen hazards. Thermokarst lakes (bodies of freshwater that collect in depressions formed by thawing permafrost) cover 20% of Arctic lowlands and are naturally prone to causing catastrophic flooding. Cumulative lake drainage over time has resulted in up to 60% landscape coverage by drained thermokarst lake basins (DTLBs). Although the impacts of lake drainage on ecosystems and infrastructure have long been considered a potential hazard, these occurrences are usually viewed as one‐time events. However, recent observations in northern Alaska highlight a previously unrecognized threat: annually recurring snow‐dam outburst floods from DTLBs. Each winter, blowing snow fills DTLB drainage gullies, impounding meltwater in the spring and – upon snow‐dam failure – releasing downstream floods. Data collected from 14 DTLBs in Alaska reveal a wide range in flood peaks, many of which exceed river flood events, providing a missing link in Arctic ecosystem hydrology. Projected snowier winters may result in even larger snow‐dam outbursts from DTLBs, adding uncertainty to future risk assessments. If climate change also drives enhanced lake drainage, we predict even more sporadic and higher magnitude floods, with implications for downstream ecosystems and human infrastructure as development pushes into the Arctic lowlands.

Sensors and gizmos and data, oh my

This study identifies communication design challenges associated with firefighters' personal protective equipment (PPE), an assemblage of wearable technologies that shield these workers from occupational hazards. Considering two components of modern firefighting PPE through Zuboff's (1998) theorization of information technology, we offer an extended case study that illustrates how these wearables, as interfaces, automate or informate firefighters' practice of safety. Often lauded for their abilities to augment firefighters' work capacities and increase safety outcomes, our analysis revealed that these wearables engender practices that expose firefighters to unforeseen hazards and displace the "tacit craft skills and knowledge" that these workers mobilize to mitigate workplace risk (Spinuzzi, 2005, p. 164). Drawing from these insights, we sketch four points of tension that communication designers, system architects, and practitioners may utilize to consider the informating potential of smart-firefighting PPE equipped with physiological sensors.

Safety-informed design: Using subgraph analysis to elicit hazardous emergent failure behavior in complex systems

AbstractIdentifying failure paths and potentially hazardous scenarios resulting from component faults and interactions is a challenge in the early design process. The inherent complexity present in large engineered systems leads to nonobvious emergent behavior, which may result in unforeseen hazards. Current hazard analysis techniques focus on single hazards (fault trees), single faults (event trees), or lists of known hazards in the domain (hazard identification). Early in the design of a complex system, engineers may represent their system as a functional model. A function failure reasoning tool can then exhaustively simulate qualitative failure scenarios. Some scenarios can be identified as hazardous by hazard rules specified by the engineer, but the goal is to identify scenarios representing unknown hazards. The incidences of specific subgraphs in graph representations of known hazardous scenarios are used to train a classifier to distinguish hazard from nonhazard. The algorithm identifies the scenario most likely to be hazardous, and presents it to the engineer. After viewing the scenario and judging its safety, the engineer may have insight to produce additional hazard rules. The collaborative process of strategic presentation of scenarios by the computer and human judgment will identify previously unknown hazards. The feasibility of this methodology has been tested on a relatively simple functional model of an electrical power system with positive results. Related work applying function failure reasoning to a team of robotic rovers will provide data from a more complex system.

Research in Analyzing and Characterizing Nanoparticles in Aqueous Phase Using NMR Solvent Relaxation Method

Nanomaterials are playing a more and more important role in all fields, leading to increasing its release into the environment. Nanomaterials may pose a potential threat to environment and human health. Thus, there is a growing need for adaptable methods and technologies to analyze and characterize the properties of nanoparticle for purpose of understanding and minimizing the potentially unforeseen health or environmental hazards. Nuclear Magnetic Resonance (NMR) method, as a powerful analytical tool which based on the theory of solvent relaxation, breaks through some limitations of conventional methods, making it feasible in analysis and characterization of nanoparticles in aqueous phase. This paper has sketched recent research about specific surface area determination and competitive adsorption between components on the surface of nanoparticles. It also pointed out the pros and cons of NMR and conventional methods. As a result, NMR solvent relaxation will be a promising application in future.

Built-in resilience through disaster risk reduction: operational issues

It has been argued that the broad range of people responsible for the delivery, operation and maintenance of the built environment need to become more proactively involved in making the built environment resilient to a wide range of known and unforeseen hazards and threats. Accordingly, the (actual and potential) roles of a wide range of stakeholders associated with the integration of disaster risk reduction into the (re-)development of the built environment are examined. A review of literature, government data and interviews with key stakeholders in England highlights that despite regulatory intentions to increase local resilience through the use of public and private sector stakeholders, a number of structural and operational obstacles exist. A range of strategies can be employed to overcome these obstacles: revisions to building codes, tightening planning policy, improving professional training, clarifying roles and missions, enabling complementary bottom-up and top-down approaches, and the provision of good-practice guidance about the broad range of structural and non-structural risk reduction measures. Many of the operational challenges are non-structural and require a coherent, overarching strategy: changing and aligning the social understandings and practices in civil society, government and built environment stakeholders.

Development of Green Nanotechnology and Factors Influencing It in China

The rise of nanotechnology brings an unprecedented change to us, but its uncertainty may also causes unforeseen hazards to human health and the environment. Many countries around the world are actively researching and developing green nanotechnology which is non-toxic, environmentally friendly for the interests of humanity, and this systematic project needs the collaboration and efforts of government, research institutions, enterprises and public groups.

Assessment of worker safety in a pharmaceutical industry using FMEA

Safety is a relative quantity that is a function of the situation in which it is measured. This research has been conducted in a well established pharmaceutical company in Bangladesh practicing GMP (Good Manufacturing Practice), which has a tablet manufacturing facility. Tablet manufacturing comprises a number of manufacturing activities and machineries. Each of these activities, requiring amalgamated interaction of man, machine, equipment and environment has the potential of producing unforeseen hazards. Therefore, analysis of risk to assure worker safety is not only necessary here, it is also important to use an appropriate tool that can incorporate all the risk- producing parameters. The research work conducted here using a very effective tool named Failure Mode and Effect Analysis (FMEA), consisted of observing the works at different sections, breaking them down into sequential steps, noting the surrounding variables of each task, and using these data to perform risk analysis to find out the most hazardous works for the workers.

Interface Network Models for Complex Urban Infrastructure Systems

The reliability assessment of infrastructure systems providing power, natural gas, and potable water is an integral part of societal preparedness to unforeseen hazards. The topological properties of interface networks connecting electric substations to water pumping stations and natural gas compressors have received little attention, despite the key role these connections play in operation and failure propagation. This work introduces a performance assessment methodology for coupled infrastructures that links physical fragility modeling with the topology of realistic and ideal connecting interfaces. Distinct interfaces based on features such as betweenness, clustering, vertex degree, and Euclidean distance are assessed regarding their role in connecting utility systems and propagating failures from random and hurricane events in Harris County, Texas. The interface minimizing the Euclidean distance between electric substations and other utility nodes exhibits a slow performance decline as random failures increase, and retains the greatest functionality under hurricane events compared to alternative interfaces, although it suffers from limited efficiency and controllability during normal operation. A convenient hybrid interface using both betweenness and distance features shows adequate performance during normal operation while exhibiting tolerance to random failures and sufficient performance at increasing hurricane event levels. These findings provide utility owners and operators with new simple yet adequate strategies focused on the interface across complex systems to enhance routine operation and reduce the probability of widespread interdependent failures following disruptive events.