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As technology continues to evolve, our society is becoming enriched with more intelligent devices that help us perform our daily activities more efficiently and effectively. One of the most significant technological advancements of our time is the Internet of Things (IoT), which interconnects various smart devices (such as smart mobiles, intelligent refrigerators, smartwatches, smart fire alarms, smart door locks, and many more) allowing them to communicate with each other and exchange data seamlessly. We now use IoT technology to carry out our daily activities, for example, transportation. In particular, the field of smart transportation has intrigued researchers due to its potential to revolutionize the way we move people and goods. IoT provides drivers in a smart city with many benefits, including traffic management, improved logistics, efficient parking systems, and enhanced safety measures. Smart transportation is the integration of all these benefits into applications for transportation systems. However, as a way of further improving the benefits provided by smart transportation, other technologies have been explored, such as machine learning, big data, and distributed ledgers. Some examples of their application are the optimization of routes, parking, street lighting, accident prevention, detection of abnormal traffic conditions, and maintenance of roads. In this paper, we aim to provide a detailed understanding of the developments in the applications mentioned earlier and examine current researches that base their applications on these sectors. We aim to conduct a self-contained review of the different technologies used in smart transportation today and their respective challenges. Our methodology encompassed identifying and screening articles on smart transportation technologies and its applications. To identify articles addressing our topic of review, we searched for articles in the four significant databases: IEEE Xplore, ACM Digital Library, Science Direct, and Springer. Consequently, we examined the communication mechanisms, architectures, and frameworks that enable these smart transportation applications and systems. We also explored the communication protocols enabling smart transportation, including Wi-Fi, Bluetooth, and cellular networks, and how they contribute to seamless data exchange. We delved into the different architectures and frameworks used in smart transportation, including cloud computing, edge computing, and fog computing. Lastly, we outlined current challenges in the smart transportation field and suggested potential future research directions. We will examine data privacy and security issues, network scalability, and interoperability between different IoT devices.

HighlightsThermosensitive fire alarms with various working mechanisms are overviewed.Different calculation methods for response time are discussed.Warning signal conversion types are provided.Limitations, challenges, and development direction are put forward.Early-stage fire-warning systems (EFWSs) have attracted significant attention owing to their superiority in detecting fire situations occurring in the pre-combustion process. Substantial progress on EFWSs has been achieved recently, and they have presented a considerable possibility for more evacuation time to control constant unintentional fire hazards in our daily life. This review mainly makes a comprehensive summary of the current EFWSs, including the working mechanisms and their performance. According to the different working mechanisms, fire alarms can be classified into graphene oxide-based fire alarms, semiconductor-based fire alarms, thermoelectric-based fire alarms, and fire alarms on other working mechanisms. Finally, the challenge and prospect for EFWSs are briefly provided by comparing the art of state of fire alarms. This work can propose a more comprehensive understanding of EFWSs and a guideline for the cutting-edge development direction of EFWSs for readers.

Early fire detection and notification techniques provide fire prevention and safety information to blind and visually impaired (BVI) people within a short period of time in emergency situations when fires occur in indoor environments. Given its direct impact on human safety and the environment, fire detection is a difficult but crucial problem. To prevent injuries and property damage, advanced technology requires appropriate methods for detecting fires as quickly as possible. In this study, to reduce the loss of human lives and property damage, we introduce the development of the vision-based early flame recognition and notification approach using artificial intelligence for assisting BVI people. The proposed fire alarm control system for indoor buildings can provide accurate information on fire scenes. In our proposed method, all the processes performed manually were automated, and the performance efficiency and quality of fire classification were improved. To perform real-time monitoring and enhance the detection accuracy of indoor fire disasters, the proposed system uses the YOLOv5m model, which is an updated version of the traditional YOLOv5. The experimental results show that the proposed system successfully detected and notified the occurrence of catastrophic fires with high speed and accuracy at any time of day or night, regardless of the shape or size of the fire. Finally, we compared the competitiveness level of our method with that of other conventional fire-detection methods to confirm the seamless classification results achieved using performance evaluation matrices.

Graphene oxide-based fire alarm sensors have received the most extensive research. Various organic and inorganic flame retardants and reducing agents as well as conductive fillers have been used to improve the temperature sensitivity of graphene oxide-based fire sensors. However, the effect of graphene oxide (GO) itself has sometimes been neglected by many researchers, since the synthesis of different structures of GO has received little attention. Here, we prepared two sensors, including the fire alarm based on GO synthesized at low temperatures (LGO) and the fire alarm based on GO synthesized by the conventional Hummers method (HGO). Compared with the HGO-based fire alarms, the improved temperature sensitivity of the LGO-based fire alarms is mainly due to the lower thermal decomposition temperature of the oxygen-containing functional groups in the LGO sheets. Interestingly, the LGO-based fire alarm has a response time of ~ 148 s at 150 °C, while the HGO does not respond. This finding will provide new ideas for the design of advanced GO-based fire alarm sensors. Graphical abstract The low-temperature synthesized graphene oxide/nano-graphite (LGO/G) composite film can be applied as desirable smart fire early warning sensor material.

Graphene-like 2D nanomaterials, such as graphene, MXene, molybdenum disulfide, and boron nitride, present a promising avenue for eco-friendly flame retardants. Their inherent characteristics, including metal-like conductivity, high specific surface area, electron transport capacity, and solution processability, make them highly suitable for applications in both structural fire protection and fire alarm systems. This review offers an up-to-date exploration of advancements in flame retardant composites, utilizing pristine graphene-like nanosheets, versatile graphene-like nanosheets with multiple functions, and collaborative systems based on these nanomaterials. Moreover, graphene-like 2D nanomaterials exhibit considerable potential in the development of early fire alarm systems, enabling timely warnings. This review provides an overview of flame-retarding and fire-warning mechanisms, diverse multifunctional nanocomposites, and the evolving trends in the development of fire alarm systems anchored in graphene-like 2D nanomaterials and their derivatives. Ultimately, the existing challenges and prospective directions for the utilization of graphene-like 2D nanomaterials in flame retardant and fire-warning applications are put forward.

The main object of this research was to develop a security system to evaluate the intrusion into an object through a glass pane. More specifically, this study deals with sensing and evaluating signals from a contact glass-break detector, which is part of an intruder alarm system. Each alarm detector in an alarm system must accomplish certain security level requirements that strictly describe the requirements for the area of use and the detector’s reliability. To date, no contact glass-break detector has been developed and fully tested to meet the stringent requirements of the highest security level. A contact glass-break detector was developed whose main part is an accelerometer that transmits signals from the glass pane. These signals were evaluated according to the developed methodology. It was verified that the proposed system can distinguish at the highest security level between false alarms and situations where the building has been intruded.

Background Alarm fatigue, a multifactorial desensitization of staff to alarms, can harm both patients and health care staff in intensive care units (ICUs), especially due to false and nonactionable alarms. Increasing amounts of routinely collected alarm and ICU patient data are paving the way for training machine learning (ML) models that may help reduce the number of nonactionable alarms, potentially increasing alarm informativeness and reducing alarm fatigue. At present, however, there is no publicly available dataset or process that routinely collects information on alarm actionability (ie, whether an alarm triggers a medical intervention or not), which is a key feature for developing meaningful ML models for alarm management. Furthermore, case-based manual annotation is too slow and resource intensive for large amounts of data. Objective We propose a scalable method to annotate patient monitoring alarms associated with patient-related variables regarding their actionability. While the method is aimed to be used primarily in our institution, other clinicians, scientists, and industry stakeholders could reuse it to build their own datasets. Methods The interdisciplinary research team followed a mixed methods approach to develop the annotation method, using data-driven, qualitative, and empirical strategies. The iterative process consisted of six steps: (1) defining alarm terms; (2) reaching a consensus on an annotation concept and documentation structure; (3) defining physiological alarm conditions, related medical interventions, and time windows to assess; (4) developing mapping tables; (5) creating the annotation rule set; and (6) evaluating the generated content. All decisions were made based on feasibility criteria, clinical relevance, occurrence frequency, data availability and quantity, structure, and storage mode. The annotation guideline development process was preceded by the analysis of the institution’s data and systems, the evaluation of device manuals, and a systematic literature review. Results In a multidisciplinary consensus-based approach, we defined preprocessing steps and a rule-based annotation method to classify alarms as either actionable or nonactionable based on data from the patient data management system. We have presented our experience in developing the annotation method and provided the generated resources. The method focuses on respiratory and medication management interventions and includes 8 general rules in a tabular format that are accompanied by graphical examples. Mapping tables enable handling unstructured information and are referenced in the annotation rule set. Conclusions Our annotation method will enable a large number of alarms to be labeled semiautomatically, retrospectively, and quickly, and will provide information on their actionability based on further patient data. This will make it possible to generate annotated datasets for ML models in alarm management and alarm fatigue research. We believe that our annotation method and the resources provided are universal enough and could be used by others to prepare data for future ML projects, even beyond the topic of alarms.

To identify potential categories of alarm fatigue among ICU nurses and to explore the differences in characteristics and influencing factors among different categories. Using convenience sampling, 597 ICU nurses from 12 tertiary public hospitals across 8 cities in the Inner Mongolia Autonomous Region of China were recruited from September 2024 to December 2024. A cross-sectional survey was conducted using the General Information Questionnaire, ICU Nurses' Alarm Fatigue Scale, Stanford Presenteeism Scale: Health Status and Employee Productivity, and Nurses' Emotional Labor Scale. Potential profiles of nurse alarm fatigue were analyzed, and the influencing factors of different profiles were explored by univariate analysis and multivariate logistic regression analysis. The median alarm fatigue scale score was 26(IQR = 19.75-31), and the alarm fatigue of ICU nurses could be categorized into low fatigue-robust tolerance group (30.8%), moderate fatigue (54.4%), and high fatigue-negative coping group (14.9%). The regression analyses showed that the number of children, the frequency of night shifts, the health status and employee productivity score, and the emotional labor score were the main factors of the ICU factors influencing different potential categories of nurse alarm fatigue (P < 0.05). ICU nurses alarm in Inner Mongolia exhibited moderate-to-high alarm fatigue with notable subgroup heterogeneity. Nursing managers should implement tailored interventions addressing profile-specific factors, such as workload adjustments and emotional support strategies, to mitigate alarm fatigue.

Photoelectric smoke detectors are the most cost-effective devices for very early warning fire alarms. However, due to the different light intensity response values of different kinds of fire smoke and interference from interferential aerosols, they have a high false-alarm rate, which limits their popularity in Chinese homes. To address these issues, an embedded spatial–temporal convolutional neural network (EST-CNN) model is proposed for real fire smoke identification and aerosol (fire smoke and interferential aerosols) classification. The EST-CNN consists of three modules, including information fusion, scattering feature extraction, and aerosol classification. Moreover, a two-dimensional spatial–temporal scattering (2D-TS) matrix is designed to fuse the scattered light intensities in different channels and adjacent time slices, which is the output of the information fusion module and the input for the scattering feature extraction module. The EST-CNN is trained and tested with experimental data measured on an established fire test platform using the developed dual-wavelength dual-angle photoelectric smoke detector. The optimal network parameters were selected through extensive experiments, resulting in an average classification accuracy of 98.96% for different aerosols, with only 67 kB network parameters. The experimental results demonstrate the feasibility of installing the designed EST-CNN model directly in existing commercial photoelectric smoke detectors to realize aerosol classification.

Bed and chair alarms have been included in many multifaceted falls prevention interventions. None of the randomised trials of falls alarms as sole interventions have showed significant effect on falls or falls with injury. Further, use of bed and chair alarms did not change patients’ fear of falling, length of hospital stay, functional status, discharge destination or health related quality of life. The aim of this study was to explore nurses’ experiences of using bed and chair alarms. A qualitative descriptive study using semi-structured interviews with a purposive sample of 12 nurses was conducted on a 32-bed Geriatric Evaluation and Management ward in Melbourne, Australia. Participants were interviewed between 27 January and 12 March 2021.Transcribed audio-recordings of interviews were analysed using inductive thematic analysis. NVIVO 12.6 was used to manage the study data. Three major themes and four subthemes were constructed from the data: i) negative impacts of falls alarms (subthemes: noisy technology, imperfect technology), ii) juggling the safety-risk conflict, and iii) negotiating falls alarm use (subthemes: nurse decision making and falls alarm overuse). Nurses’ experience of using falls alarms was predominantly negative and there was tension between falls alarms having limited impact on patient safety and risks associated with their use. Nurses described a need to support nurse decision making related to falls alarms use in practice and policy, and a desire to be empowered to manage falls risk in other ways.