An Unforeseen Hazard of Masks Being in Vogue.

Stability degradation and redundancy in damaged structures

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.

In February 1999 Shell examined the current state of AUV development and evaluated the usefulness of the technology for enhancing our ability to explore for and produce offshore oil and gas. Our study addressed four questions:Can AUV's resolve our present problems?Will AUV's deliver a business benefit?Does AUV technology lend itself to easy implementation in the near future? AndWhat actions can we take to help catalyze the market? Two separate types of AUV were identified for use within E&P: a survey class vehicle for data acquisition, and an engineering class vehicle for underwater intervention. For each type, this paper will describe the answers to the questions above; addressing the problems we hope to solve, our estimate of the feasibility of using AUV's to solve them, the business case for AUV's, and our analysis of the state of the market today. A description of desirable attributes will be given for each type of AUV for a minimal case and a realistic ideal case. Introduction In late 1998 and early 1999 Shell conducted an internal study of Autonomous Underwater Vehicle (AUV) technology. The purpose of the study was to evaluate the usefulness of the technology for enhancing our ability to find and produce offshore oil and gas. As the name implies, an AUV is a robotic vehicle capable of independent actions underwater. Using sophisticated artificial intelligence software integrated with sensors that give it an awareness of its surroundings, the vehicle can carry out pre-programmed tasks and react to unexpected conditions. We identified two separate types of AUVs for use within E&P. The first is the survey class vehicle for seabed data acquisition and the second is a work class vehicle for underwater inspection, repair, and maintenance (IRM). For each type of AUV, we examined four questions:Can AUV's resolve our present problems?Will AUV's deliver a business benefit?Does AUV technology lend itself to easy implementation in the near future (5 years)?What actions are necessary to catalyze the market? Seabed Survey Problem The use of appropriate seabed survey data affects the cost of offshore developments by providing information necessary for correct design and installation. Good survey data ensures that the local environment is fully understood so unnecessary safety factors are not incorporated into design. The data also helps to ensure minimal environmental impact and that there are no unforeseen hazards. Survey data should allow for interpretation and identification of seabed morphology, natural and man-made seabed features, seabed soils, subsurface soils and features, and subsurface hazards. Data necessary to make these interpretations are obtained from a variety of sensors such as swathe bathymetric systems, sidescan sonar, and subbottom profiler. Additionally, magnetometer data and coring or sample data are used. Other information about the sensor platform and immediate environment is also needed to correctly use the data obtained from these sensors. The location, orientation, and attitude of the sensor must be measured.

Many lifesaving implantable devices are equipped with wireless technology. This technology enables remote device checks and relieves patients from recurrent consultant visits. But this convenience is associated with unforeseen hazards. These hazards are the security and privacy of data. The labor needed to defend patients from exploits of stealing or nastiness gains more significance. This is especially so with increasing use of wireless telecommunication facilities and the services of global computer network or Internet by implanted devices. The susceptibilities of medical devices are of two types, viz., control or privacy susceptibilities. In control susceptibilities, an unauthorized person acquires control of device operation. The unlicensed person reprograms the device without the patients’ knowledge to disable its therapeutic services. In privacy susceptibilities, confidential patient data are disclosed to an unsanctioned party. Both vulnerabilities are detrimental to patient’s health outcome. Both are avoidable by incorporating well-thought-out measures in device design.

This paper introduces the Web-Based Intelligent Packaging Evaluation (WIPE) platform, a novel approach to assess the performance of product and packaging systems within the e-commerce distribution sector. Unlike traditional methods that primarily rely on laboratory evaluations under controlled conditions, WIPE addresses the unique challenges posed by e-commerce distribution, such as increased handling points and unforeseen hazards that standard physical tests may not capture. Leveraging advanced machine learning algorithms and association rule mining, WIPE extracts insights about packaging defects directly from customer reviews on e-commerce platforms. Analyzing both images and text from these reviews establishes connections between frequently used words and the predicted damages, causes, and effects. This innovative approach was exemplified in two case studies involving laundry detergent liquid bottles and pods sold on the Amazon. The findings from these studies demonstrate WIPE’s capability to extract pertinent information from customer feedback and identify specific packaging defects and predict their potential causes. This integration of sentiment analysis and association rule mining into the packaging evaluation process marks a significant advancement in the field. The introduction of WIPE represents a transformative step in packaging evaluation, offering a more dynamic, real-world analysis that can significantly enhance product and packaging design, ultimately leading to improved customer satisfaction in the rapidly evolving e-commerce landscape.

Identifying 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 non-obvious emergent behavior, which may result in unforeseen hazards. Current hazard analysis techniques either focus on small slices of failure scenarios (fault trees and 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 will be identified as hazardous by hazard rules specified by the engineer, but the goal is to identify scenarios representing unknown hazards. A clustering method is applied repetitively to the large set of failure propagation results. Then, an 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 rules. The collaborative process of computer rating and human judgment will identify previously unknown hazards. The feasibility of this methodology is being tested on a relatively simple functional model of an electrical power system. Related work applying function failure reasoning to a team of robotic rovers will provide data from a more complex system.

В статье рассматриваются проблемы нехватки рабочей силы и обеспечения безопасности на строительных площадках. Обеспечение безопасности рабочих должно быть главным приоритетом на каждой рабочей площадке. Условия на площадке могут быстро меняться, и в любой момент могут возникнуть непредвиденные опасности, создав неожиданные риски для проекта. Крупные аварии могут привести к серьезным травмам или гибели сотрудников. Цель в каждом проекте должна заключаться в том, чтобы избежать несчастных случаев и гарантировать, что каждый работник благополучно вернется домой к своей семье. Гораздо дешевле инвестировать в обучение, технический контроль и СИЗ для предотвращения несчастных случаев, чем бороться с их последствиями. Убедитесь, что субподрядчики понимают приверженность безопасности и проводят обучение своих сотрудников перед началом работы. The article examines the problems of labor shortages and safety at construction sites. Keeping workers safe should be a top priority at every jobsite. Site conditions can change rapidly and unforeseen hazards can arise at any time, creating unexpected risks for the project. Major accidents can result in serious injury or death to employees. The goal in every project should be to avoid accidents and to ensure that every worker returns safely home to their family. It is much cheaper to invest in training, technical control and PPE to prevent accidents than to deal with their consequences. Make sure subcontractors understand the commitment to safety and provide training to their employees before starting work.

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.

Nanomaterials play an important role in science and in every day products. This is due to their varied and specific properties, whereby especially engineered nanoparticles (ENPs) have shown various beneficial properties for a wide range of application in consumables (e.g. cosmetics, drinks, food and food packaging). Silver nanoparticles for instance are hidden in meat packaging materials or in deodorants. Reasons for this can be found in the antibacterial effect of silver, which leads to high applicability in consumer products. However, ENPs are under permanent discussion due to their unforeseen hazards and an unknown disposition in living organisms and the environment. So far, there is a lack of methods, which allows for the fast and effective characterization and quantification of such nanoparticles in complex matrices (e.g. creams, fruit juice), since matrix components can impede a specific detection of the analyte. It was the objective of project INSTANT to address this topic and compose a method to detect nanoparticles as a first step. Therefore, the development of a sensor system with an upstream sample preparation for the characterization and quantification of specific nanoparticles in complex matrices using a label free optical sensor array in combination with novel recognition elements was developed. The promising optical technology iRIfS (imaging reflectometric interference sensor) was used for this purpose. As a recognition element, functionalized carbon nanotubes can be effectively used. Owing to their excellent electronical, mechanical and chemical properties, CNTs have already been used for extracting ENPs from complex matrices as sorbent material by filtration. After successful immobilization of CNTs on microscope glass slides e.g. the detection of stabilized silver nanoparticles extracted by a sample preparation unit using the iRIfS technology was performed.

A 71-year-old man attempted suicide by self-inflicting a cranial injury with a metallic chopstick following a family dispute. CT imaging showed penetration through the frontal sinus and lobe, yet the patient experienced no significant neurological deficits. Mini-craniotomy revealed a dural deficit without active bleeding. The favorable outcome is attributed to anatomical factors and the chopstick’s limited penetrative ability. Successful recovery was achieved through minimal debridement and appropriate antibiotic therapy. This case highlights both the dangers of common objects and the importance of precise preoperative imaging and conservative surgical approaches in traumatic brain injuries.

Semantic segmentation renders a unified way of surrounding perception, where most of driving scene detection tasks can be covered by running a single efficient ConvNet through a forward pass. However, current frameworks posit the closed-world paradigm expressed as a single source of distribution over a predetermined set of visual classes, forgetting that a deep model must be deployed in the wild facing unseen domains and unforeseen hazards. In spite of being accurate in its comfort zone, the segmentation model may not generalize well to a new domain. In addition, a model trained with single dataset is heavily limited in terms of recognizable classes. In this paper, we propose an omni-supervised learning framework for semantic segmentation which is able to leverage heterogeneous data sources. Our omni-supervised training framework incorporates all available labeled and unlabeled data, meanwhile bridges multiple training sets to be capable of recognizing more classes that are needed for autonomous navigation application at hand in the new domain. A comprehensive variety of experiments shows that with the proposed multi-source omni-supervised learning solution, an efficient ConvNet like our ERF-PSPNet attains significant robustness gains in open domains that are of critical relevance to real deployment of vision algorithms. Our approach surpasses the state of the art on the highly unconstrained PASS and IDD20K datasets.

After ploughing over a mouse's nest in his field, a farmer laments, “But Mousie, thou art no thy‐lane [not alone], in proving foresight may be vain; The best‐laid schemes o’ mice an’ men gang aft agley [often go wrong], an’ lea‘e us nought but grief an’ pain, for promis‘d joy!” (Williams, 1952). The farmer's words, penned by eighteenth century poet Robert Burns, aptly summarize one of the primary challenges facing risk managers: despite best intentions and efforts to eliminate negative consequences, technological progress creates new and unforeseen hazards. Such is the nature of risk and, some might say, the cost of progress. > …scientists are as much ‘in the dark’ about how individuals, groups and other stakeholders will respond to the new and emerging risks of nanotechnology and other technologies as the public is ignorant about the science The delicate balance between risks and benefits is evident today in ongoing public debates about biotechnology, gene therapy and now nanotechnology. Scientists and engineers are abuzz with excitement about the substantial promise of nanotechnology to improve food, agricultural, medical and security systems; yet we already see growing public concern about the real and potential ethical implications associated with this new technology, the possibilities of its misuse and other unforeseen risks or consequences. Popular media accounts and bestselling novels do their part in fuelling the public's perceptions of risks and hazards, but, as with any emerging technology, a shortage of public knowledge and a great deal of uncertainty about the science also exists. It is equally likely, however, that scientists are as much ‘in the dark’ about how individuals, groups and other stakeholders will respond to the new and emerging risks of nanotechnology and other technologies, as the public is ignorant about the science. Today, it is essentially a truism in risk communication that …

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.

We consider the problem of detecting, in the visual sensing data stream of an autonomous mobile robot, semantic patterns that are unusual (i.e., anomalous) with respect to the robot's previous experience in similar environments. These anomalies might indicate unforeseen hazards and, in scenarios where failure is costly, can be used to trigger an avoidance behavior. We contribute three novel image-based datasets acquired in robot exploration scenarios, comprising a total of more than 200k labeled frames, spanning various types of anomalies. On these datasets, we study the performance of an anomaly detection approach based on autoencoders operating at different scales.

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.