Hazardous Conditions Research Articles

One‐Pot Transition‐Metal‐Free Synthesis of Alkynyl Amides

AbstractAlkynyl amides play crucial roles in organic synthesis in the production of bioactive compounds and valuable heterocycles. Despite numerous studies on their synthesis, challenges persist due to the necessity of harsh or hazardous conditions and the use of costly or unstable reagents. Herein, we present a one‐pot method for the synthesis of all three bonds of the alkyne under transition‐metal‐free conditions. An important feature of this chemistry is the use of readily available feedstock chemicals, such as methyl esters and acetamides. This approach offers efficient access to a wide range of aryl and alkyl alkynyl amides and demonstrates excellent tolerance towards various functional groups in a sustainable and cost‐effective manner.

Robotics in Disaster Management: AI-Driven Rescue, Aerial Mapping, and Emerging IoT Applications for Effective Response

This review paper explores the growing role of robotics in disaster management, focusing on the integration of artificial intelligence (AI), Internet of Things (IoT) applications, and advanced simulation environments. Highlighting lessons from events like the Fukushima nuclear disaster and Wenchuan earthquake, it examines the deployment of aerial, ground, and underwater robots in tasks such as data collection, search and rescue, and hazard monitoring. AIoT frameworks enable these robotic systems to classify objects and make decisions on-site, supporting timely and efficient responses in hazardous conditions. Virtual platforms like the DARPA Virtual Robotics Challenge, using simulators such as Gazebo and CloudSim, facilitate realistic testing of robotic capabilities in disaster scenarios, advancing operational readiness through virtual training. This review synthesizes recent progress in AI-driven disaster robotics, identifies challenges, and discusses potential improvements for future applications.

Substrate Directed Highly Diastereoselective Synthesis of Eliglustat: A Drug for Gaucher Disease

AbstractA highly diastereoselective total synthesis of Eliglustat has been accomplished starting from a readily available D‐serine. This novel route involves a four‐step telescoped process to afford the keto intermediate (4) in 74% overall yield. The diastereoselective reduction of 4 using sodium borohydride provides the alcohol, a key intermediate 5, with excellent selectivity (>99 dr) and yield (95%). The total synthesis of Eliglustat from D‐serine has been accomplished in ten steps with an overall yield of 21%. This process not only gives a desirable yield but also avoids the use of hazardous conditions.

Will the removal of carbon, nitrogen and mixed disinfectants occur simultaneously: The key role of heterotrophic nitrification-aerobic denitrification strain

The capacity and mechanism of heterotrophic nitrification-aerobic denitrification (HNAD) strain (H1) to remove carbon, nitrogen, disinfectants chloroxylenol (PCMX) and benzethonium chloride (BEC) were investigated in this study. PCMX was removed via metabolism and chemical oxygen demand co-metabolism process. BEC was eliminated through bacterial adsorption, which greatly inhibited the removal of other pollutants. Carbon source optimization tests revealed that glucose was the optimal carbon source for co-removal of pollutants under mixed disinfectants circumstances (PCMX + BEC). Comparing the groups without (G1) and with disinfectants (G2), the content of extracellular polymeric substances was higher, and hydrophobicity was enhanced under the hazardous conditions of G2. All the nitrogen metabolism functional genes in G2 were up-regulated, and the electron transport system activity was also improved. At the same time, G2 had lower reactive oxygen species content, which reduced the probability of resistance genes dissemination, but the abundance of most quantified resistance genes was elevated in G2. Toxicity assessment assays found a dramatic reduction in the virulence of G2’s effluent compared with the mixed disinfentants. The results confirmed that H1 strain could be used to treat the disinfectant-containing wastewater, which may aid in the application of HNAD process.

SmokeNav: Millimeter‐Wave‐Radar/Inertial Measurement Unit Integrated Positioning and Semantic Mapping in Visually Degraded Environments for First Responders

First responders often face hazardous and life‐threatening situations in environments filled with smoke, posing significant risks to their safety. The existing perception solutions, such as camera or light detection and ranging (LiDAR)‐based methods, are inadequate when faced with visually degraded conditions caused by smoke. In this work, SmokeNav, a novel system that combines data from an inertial sensor and millimeter‐wave (mmWave) radar, is proposed to enhance situational awareness for first responders in smoky environments. SmokeNav utilizes an inertial positioning module that exploits the human motion constraints with a foot‐mounted inertial measurement unit to provide accurate user localization. By integrating this location information with mmWave radar data, it employs a probabilistic occupancy map construction to reconstruct an accurate metric map. To enable semantic understanding of the environment, a DNN‐based semantic segmentation model that incorporates radar reflectivity and employs focal loss to improve performance is introduced. Herein, extensive real‐world experiments in smoky environments is conducted to demonstrate that SmokeNav precisely localizes the user and generates detailed maps with semantic segmentation. In this work, potentials are held for enhancing the safety and effectiveness of first responders in hazardous conditions.

Indoor Air Quality Monitoring System

The awareness of the risks associated with contaminated indoor air quality is often lacking among individuals. This study employs a prototyping method, utilizing an Arduino Uno integrated with various sensors to monitor air quality. A sound buzzer alerts users when the sensor readings indicate hazardous conditions. Additionally, a Wi-Fi module connects the Arduino Uno to the internet, facilitating data uploads to the IoT platform ThingSpeak.

IoT based smart emergency response system (SERS) for monitoring vehicle, home and health status

This paper presents an innovative Internet of Things (IoT)-based smart emergency response system that aims to improve safety and efficiency across vehicles, homes and healthcare settings. The proposed system combines advanced sensors, communication technologies, and intelligent algorithms to provide a comprehensive solution for real-time data monitoring and emergency management. Here, the vehicle device is equipped with temperature, gas, flame, GPS, piezo, and accelerometer sensor and the system can detect accidents, hazardous conditions, precise location automatically notifying emergency services. The home devices incorporate Flame Sensor, humidity sensor, DHT11, MQ-2 gas sensor, MQ-4 Gas Sensor, MQ-135 gas sensor, and intrusion detection to ensure immediate alerts and response to potential security threats. Lastly the health device is equipped with temperature, humidity, stress levels, heart rate, oxygen saturation, and fetal heart rate sensor which can monitor vital signs and detect anomalies, triggering alerts for medical emergencies and facilitating timely intervention. The novelty of the proposed study is it incorporates the three devices, processes the data and automatically notifies the emergency responders for further emergency assistance. Moreover, the system utilizes a cloud-based platform to collect, analyze, and disseminate data, enabling seamless coordination between emergency responders such as ambulance for health and accident emergency, fire brigade for fire emergency and affected individuals. We have also developed a MySQL-PHP-Laravel based live dashboard and emergency response system where both the user and relevant stakeholder will have the access which could mitigate the emergency situation in the quickest amount of time without human intervention. The findings of our study reveals that incorporation of IoT and smart infrastructure offers significant improvement of server response time 3 ms, accuracy > 99% and overall effectiveness of emergency management. This integrated approach not only enhances individual safety but also contributes to broader public safety goals, showcasing the potential of IoT technologies in creating smarter, safer environments.

Perception of stress among healthcare workers in hospitals in Belgrade during COVID-19 pandemic

Abstract Background The COVID-19 pandemic has affected healthcare institutions and workers, exposing them to unfamiliar and hazardous conditions, increased workloads, and changing directives. This has led to work-related stress, resulting in burnout, depression, anxiety disorders, and other health issues among healthcare workers. Methods This cross-sectional study utilized an anonymous questionnaire with closed-ended questions. To assess stress levels among healthcare workers, a Likert-type scale was used. Over 6.000 healthcare workers completed the questionnaire in 2022. The relationship between the dependent variable (perceived stress level) and predictors was modelled using generalized ordinal univariate and multivariate logistic regression. Results Almost a third (30.7%) of respondents felt a very high amount of tension, stress, or pressure while performing their jobs under the conditions of the COVID-19 epidemic, while 422 (6.8%) did not feel tension, stress, or pressure at all. In the multivariate logistic regression model, statistically significant predictors of higher levels of perceived stress in the conditions of the COVID-19 epidemic include female sex (OR = 0,80), older age (OR = 1.19), being in a managerial position (OR = 1.32), lower satisfaction with available work time (OR = 0.82), patient relations (OR = 0.81), financial compensation (OR = 0.83), and cooperation with colleagues (OR = 0.93). Conclusions Through the identification of potential stress predictors, valuable insights are gained to aid policymakers in the planning and enhancement of healthcare personnel’s work during pandemics, as well as in situations characterized by heightened stress levels. Key messages • These findings emphasize supporting healthcare workers during the pandemic. • Strategies for reducing stress should be directed towards healthcare professionals who experience higher stress levels.

How can we measure labour exploitation and develop interventions to protect migrant workers’ health? Insights from intervention-focused research on cattle ranching in Brazil and child domestic work in Liberia and Nigeria

Abstract Much of our food and services rely on the work of migrants, which is often carried out under exploitative terms. Exploitative work results in work-related injuries and illnesses and mental ill-health. Yet, the global health community has been negligent in detecting migrant workers in the general population, and in measuring the effects of their work terms on their health. For instance, the International Labour Organization’s global labour force and forced labour surveys capture few health data. Conversely, health surveys, such as the Demographics and Health Survey (DHS), include few questions on exploitative and hazardous work conditions or aspects of migration status. Studies are even more remiss when it comes to developing evidence for work-related health interventions. This presentation will draw on two recent mixed-methods studies - on cattle ranching in the Amazon region of Brazil (Pará State) (n = 1,241), and on child domestic work in Liberia (n = 1,088) and Nigeria (n = 1,088). Findings suggest some necessary improvements to the measurement of migrant labour exploitation and its health effects, and approaches to gathering intervention-focused evidence. Each study highlights the value of re-thinking data collection beyond disciplinary silos and narrow dichotomous measures (e.g., forced/unforced labour), towards generating a more specific understanding of the different dimensions of labour exploitation (e.g., pay conditions, hours, occupational health and safety training and PPE), various types of associated harm, and how they compare across sectors and contexts. Further, each study demonstrates the benefits of intervention-focused research methods that are designed to feed directly into participatory intervention development processes. On this basis, we will suggest shifts in measurement and research designs to foster operationalizable evidence on migrant workers’ working conditions, health, and worker-informed interventions.

The effects of expanding worker rights to children

One out of two working children worldwide works in hazardous conditions. We study the effects of a law that introduced benefits and protections for child workers and temporarily lowered the de facto legal working age from 14 to 10 in Bolivia. We employ a difference-in-discontinuity approach that exploits the variation in the law’s application to different age groups. Work decreased for children under 14, whose work was newly legalized and regulated under the law, particularly in areas with a higher threat of inspections. The effects disappear after the law is reversed. We do not find evidence of improvements in work safety. Thus, the effects do not appear to be driven by increased hiring costs to ensure worker safety. Instead, the effects appear to be driven by a reduction in the most visible forms of child work, suggesting that firms and parents (households) may have reduced employment of young children to minimize the risk of being subject to legal and social sanctions.

The Safety Management System (SMS) As A Tool for Building Safety Culture in Aviation: A Qualitative Research

Safety culture is conceptualized as the combination of beliefs, values, norms, attitudes, roles, and practices—both social and technical—within an organization, aimed at mitigating the exposure to adverse or hazardous conditions for both internal and external individuals. In entities where a safety culture is deeply ingrained, efforts are made to formulate a framework that encourages employee adherence to safety regulations, promoting safe behaviours, particularly in high-risk environments. The efficacy of safety culture in substantially reducing accidents, incidents, errors, hazards, and risks, especially those related to human factors, is pivotal in attaining elevated safety standards. At the heart of fostering such a culture lies the implementation of a robust Safety Management System (SMS), which, through its effective and dynamic execution, paves the way for cultivating a safety-centric ethos. This study conducts a detailed empirical analysis of aviation enterprises in Türkiye to evaluate the hypothesis that the Safety Management System (SMS) is a crucial instrument in cultivating a safety culture within the aviation industry. The study employs qualitative research methodologies to discern the correlation between the effectiveness of SMS and its components and the level of safety culture within these organizations. Findings from the study reveal a direct relationship where entities with inadequately implemented SMS and its elements exhibit lower levels of safety culture. Conversely, enterprises that demonstrate effective implementation of SMS are characterized by a high level of safety culture, affirming the critical role of SMS in fostering safety within aviation organizations. This inquiry not only substantiates the integral role of SMS in developing a safety culture in aviation but also highlights the necessity for meticulous implementation of SMS components and processes to achieve a superior safety standard in the industry.

Integrating Web-Based Weather Data into Building Information Modeling Models through Robot Process Automation

The rapid evolution of digital technologies has revolutionized the architecture, engineering, and construction (AEC) industry, driving the wide-spread adoption of digital twins for structures. These virtual replicas, developed using Building Information Modeling (BIM) methodology, incorporate extensive information databases, proving indispensable for enhancing project management throughout a structure’s entire lifecycle and towards smart city development. As the impact of climate change continues to grow, hazardous weather alerts play a critical role as an early-warning system that notifies stakeholders of imminent threats, thereby influencing decision-making processes in construction projects. Surprisingly, despite its evident value, the integration of alert systems for hazardous weather conditions into BIM is often overlooked. To fill this gap, this paper proposes Robot Process Automation (RPA) protocols to automate the integration of real-time weather parameters into a structure’s BIM models. These very protocols are also used as alert systems, enabling the timely notification of stakeholders in the event of detected hazardous weather conditions. The effectiveness of the proposed methodology is demonstrated through its practical application in enhancing the safety of an actual building in Viña del Mar, Chile.

A conflict risk graph approach to modeling spatio-temporal dynamics of intersection safety

Intersections are among the most hazardous roadway spaces due to the complex and conflicting road users’ movements. Spatio-temporal modeling of conflict risks among road users can help to identify strategies to mitigate the exacerbation of safety risks and restore hazardous conditions to normal traffic situations. This paper proposes the 'Conflict Risk Graph' as a novel concept to infer real-time conflict risks at intersections at a fine-grained level by mapping conflict-prone locations to nodes within a network characterized by specific topological structures. A significant contribution of this work is the development of a Transformer-based Graph Convolutional Network (Trans-GCN), a model that synergistically combines the Transformer's proficiency in capturing global dependence with the GCN's ability to learn local correlations. The Trans-GCN adeptly models the complex evolution patterns of conflict risks at intersections. The evaluation in this paper against five common state-of-the-art deep learning approaches demonstrates the superior performance of the Trans-GCN in conflict risk inference and adaptability to node changes. Furthermore, extensive experiments with different node configurations reveal a correlation between node setup and model performance, showing that higher spatio-temporal resolution decreases inference accuracy. This insight informs the selection of an optimal node configuration that balances the detailed capture of spatio-temporal dynamics with modeling accuracy, enabling ideal conflict risk inferences at intersections. Ultimately, this work offers significant insights for the enhancement of proactive traffic safety management and the advancement of intelligent traffic systems.

Preparedness and Response to Flooding Risks by Kenya Wildlife Service in Hells Gate National Park, Nakuru County, Kenya

Floods are short-lived events that occur abruptly with little to no warning and are among the most dangerous disasters relating to weather due to the hazardous conditions caused for people, the destruction of property and the loss of life as well. Thus, minimizing flooding impacts timely and passing accurate information is critical in flood preparedness and response. Previous studies have focused on the probability of floods occurring and their magnitude, therefore leaving a knowledge gap on preparedness and response to flooding risks in national parks by government agencies in Kenya. Hell’s Gate National Park experiences significant flash flooding; therefore, it provided a good venue to study the levels of preparation and response mechanisms available in Kenyan national parks. The objectives of the study were to understand the level of preparedness and mechanisms to respond to flooding events by Kenya Wildlife Service in Hells Gate National Park. The study was guided by a descriptive research design which involved collecting quantitative and qualitative data from 118 park users and other key informants. The study found that the Kenya Wildlife Service plays a crucial role in flood preparedness and response, but there is room for improvement in early warning systems, hazard map distribution, and user education because only 36.6% of the respondents were aware of escape routes to be used during floods. In terms of recommendations, the study found that it is crucial to enhance the existing flood preparedness and response mechanisms within the park including investing in advanced technology for early warning systems, conducting regular drills, and fostering community engagement and partnerships, continuous improvement of flood preparedness and response plans, along with a robust communication strategy.

Automated Recognition of Snow-Covered and Icy Road Surfaces Based on T-Net of Mount Tianshan

The Tianshan Expressway plays a crucial role in China’s “Belt and Road” strategy, yet the extreme climate of the Tianshan Mountains poses significant traffic safety risks, hindering local economic development. Efficient detection of hazardous road surface conditions (RSCs) is vital to address these challenges. The complexity and variability of RSCs in the region, exacerbated by harsh weather, make traditional surveillance methods inadequate for real-time monitoring. To overcome these limitations, a vision-based artificial intelligence approach is urgently needed to ensure effective, real-time detection of dangerous RSCs in the Tianshan road network. This paper analyzes the primary structures and architectures of mainstream neural networks and explores their performance for RSC recognition through a comprehensive set of experiments, filling a research gap. Additionally, T-Net, specifically designed for the Tianshan Expressway engineering project, is built upon the optimal architecture identified in this study. Leveraging the split-transform-merge structure paradigm and asymmetric convolution, the model excels in capturing detailed information by learning features across multiple dimensions and perspectives. Furthermore, the integration of channel, spatial, and multi-head attention modules enhances the weighting of key features, making the T-Net particularly effective in recognizing the characteristics of snow-covered and icy road surfaces. All models presented in this paper were trained on a custom RSC dataset, compiled from various sources. Experimental results indicate that the T-Net outperforms fourteen once state-of-the-art (SOTA) models and three models specifically designed for RSC recognition, with 97.44% accuracy and 9.79% loss on the validation set.

Integrating environmental remediation with biodiesel production from toxic non-edible oil seeds (Croton bonplandianus) using a sustainable phyto-nano catalyst

In the current situation of the environmental uprising toxicology, rising global temperature, and energy-depleting urges to explore and discover more renewable and greener ecological-benefiting energy resources. Biobased renewable fuels generated by using waste products can help in waste management, climate change mitigation, and a low-carbon future. The main objective of this research is to produce environment-friendly and cost-effective biofuel. The potentiality of the novel, toxic, waste, and inedible feedstock Croton bonplandianus was evaluated for biodiesel synthesis through transesterification utilizing a Phyto-nano catalyst of potassium oxide prepared by Croton bonplandianus floral stalk's aqueous extract focusing on waste management. Phyto-nano catalyst characterization was done through innovative tools such as Scanning Electron Microscopy (SEM), Energy Dispersive Spectroscopy (EDS), Zeta Potential (ZP), X-Ray Diffraction (XRD), Fourier Transformed Infrared spectroscopy (FTIR), and Diffuse Reflectance Spectroscopy (DRS). The characterization results revealed that the potassium oxide phyto-nanocatalyst possesses an average nanoparticle size of 44.5 nm. This size is optimal for enhanced catalytic activity, indicating significant potential for efficient catalysis. The highest yield (94 %) of biodiesel was secured at optimized reaction conditions of catalyst quantity (0.50 wt%), reaction time (180 min), methanol: oil ratio (9:1), and reaction thermal point (70 °C). Transformation of triglycerides to methyl esters was confirmed by GC/MS, NMR, and FTIR techniques. A total of 21 methyl esters were observed in Croton bonplandianus biodiesel confirmed via GC/MS results. Evaluation of fuel properties was done and matched with international fuel standards. The conclusive remarks for the conducted research are that Croton bonplandianus has a high potential for biodiesel production by applying Phyto-nanocatalysts of potassium oxide while dealing with hazardous environmental conditions and waste management. Phyto nanocatalyst of potassium oxide can be reused and gives the same yield after several cycles of reusability, this reusability of heterogenous Phyto nanocatalyst can reduce to total cost of biodiesel production and can contribute towards circular economy.

Automated wireless system for monitoring the technical condition of chimneys

The present paper considers the technology of monitoring the technical condition of chimneys in order to identify areas of increased heat losses. The technology involves an automated wireless monitoring system that includes an unmanned aircraft with additional equipment. Aim. To study the operation safety of reinforced concrete chimneys used in hazardous industrial facilities. The presented monitoring technology tackles the task of prompt identification of defects and damages to reduce the risks of potentially hazardous conditions. While developing technology for monitoring the technical condition, special attention is paid to its autonomy and intelligence. At the first stage, sensors, coordinators and remote thermometry system are installed with the transmission of data on the temperature of the chimney trunk to the control center. At the next stage, the outer surface of the chimney is examined using an unmanned aicraft equipped with a thermal imaging camera to verify information about heat losses. The final stage involves creating a thermogram of the object and a scheme of the possible locations of the identified defects in order to plan repair works. In addition, the paper introduces main advantages of the suggested technology.

RFOR-DQHFEM: Hybrid relaxed first-Order reliability and differential quadrature hierarchical finite element method for multi-physics reliability analysis of conical shells

In this current work, a hybrid reliability analysis and theoretical frequency technique are suggested for the reliability response of conical shells. Two levels of analyses are proposed as the main loop of the reliability method for finding the failure probability and the second level applied in the main loop for giving the performance function of frequency applied in conical shell structures with multi-physics vibration analysis. A dynamical adjusting procedure is proposed for computing the relaxed factor using the enough descent condition inside the reliability method. The superior convergence rate is considered for selecting the relaxed factor of the proposed first-order reliability method named RFORM. An elastic-electro-mechanical model based on the Higher-Order Shear Deformation Theory (HSDT) is extended for frequency analysis of conical shells. The innovative numerical procedure named Differential Quadrature Hierarchical Finite Element Method (DQHFEM) as a robust framework for giving the vibration behavior of studied mechanical structures is applied for solving motion equations. The developing DQHFEM and RFORM are applied for the laminated, nanocomposite, and piezoelectric conical shell structures with multi-source uncertainties. Increasing the volume percentage of nanoparticles from 0% to 10% significantly enhances the reliability index, with carbon nanoparticles showing a 132% increase, silica nanoparticles showing a 97% increase, and other nanoparticles showing an approximate 40% increase. Also, as moisture content increases from 0% to 30%, the reliability index for a thickness-to-large-radius ratio of 0.2 drops by about five times. Excessive moisture levels (above 20%) result in a negative reliability index, indicating a hazardous condition.

Political Regimes and Climate Change: Learning from Past Civilisations

As the world is threatened by new and powerful climate-driven hazards, how are states and societies likely to react? In this paper, I explore the role of regime type in determining the likelihood of state survival under extreme environmental conditions. I begin with a theoretical and empirical analysis of public goods provision under different regime types, finding no evidence for the contention that any one particular regime is superior to others. Following from this, I argue that the survival of the state under increased hazard conditions will at least partly be a function of political flexibility, with more democratic regimes better able to weather crisis than non-democratic ones. I explore this argument by analysing two historical cases of climate change: the non-democratic Maya civilisation in the first millennium and the quasi-democratic Icelandic state at the start of the second millennium. These historical cases highlight potential advantages to the democratic system in allowing states to survive a world with increased hazards but also underscore how rising competition and political instability can negatively impact those same democratic institutions. This article was published open access under a CC BY licence: https://creativecommons.org/licences/by/4.0 .

Enhancing Skin Disease Diagnosis with TFFNet: A Two-Stream Feature Fusion Network Integrating CNNs and Self Attention Block

The skin of an individual serves as the primary defense mechanism for safe guarding vital organs in the body. Although this barrier effectively protects internal organs from a variety of threats, it is still prone to damage from viral, fungal, or dust-related illnesses. Even minor skin injuries possess the potential to escalate into more severe and hazardous conditions. A prompt and precise skin disease diagnosis becomes crucial in expediting the healing process for individuals grappling with skin-related issues. The objective of this study is to develop a system based on Convolutional Neural Network (CNN) that can accurately identify various skin diseases. The proposed architecture, known as TFFNet (Two-Stream Feature Fusion Network), integrates two simultaneous modules featuring a Self-Attention (SA) block. We employ Self Attention-Convolutional Neural Networks (SACNNs) and Depthwise Separable Convolution (DWSC) to establish a diagnostic system for skin diseases. In this method, two separate CNN models are joined together, and two parallel modules (M1 and M2) are added. This greatly reduces the total number of trainable parameters. In comparison to other deep learning methods outlined in existing literature, the proposed CNN exhibits a notably lower number of learned parameters, specifically around 7 million for classification purposes. The skin disease classification was carried out on three datasets—ISIC2016, ISIC2017, and HAM10000. The model achieved testing accuracies of 89.70%, 90.52%, and 90.12% on each respective dataset.