Safety Applications Research Articles

Advanced radiation detector design for applications in food safety and national security

We describe a new concept for a radiation detector based on extruded scintillator technology and commercially available solid-state photo-detectors. The detector is simple in construction, robust, very efficient, cost-effective and easily scalable in size from tens of cm2 to tens of m2. We describe two possible applications: flagging radioactive food contamination and detection of illicit radioactive materials, such as those potentially used in a dirty bomb.

Mechanical and vibration characteristics of hybrid natural fibre-reinforced composite using taguchi grey relational analysis

In recent scenarios, the need for lightweight components in aerospace and safety applications results in the formation of new composite materials. The current investigation mainly emphasized the influence of a multi-wall carbon nanotube (0, 0.5 and 1 wt%) with different wt% of natural banana fibre (0, 3 and 6 wt%) / three different resins (Epoxy, Vinylester and General purpose) reinforced in composite using Taguchi’s L9 approach. Tensile, impact and impulsive excitation strengths (Youngs Modulus, Shear Modulus and Poisson’s ratio) are performed to predict the mechanical characteristics of the reinforced composites. ASTM standards are followed for preparation and testing. The outcomes illustrate that the mechanical characteristics are improved by the inclusion of 1 wt% CNT and 6 wt% of banana fibre reinforced in epoxy resin with an increase in impact strength of 5.41 kJ m−2, a tensile strength of 11.25 MPa, Young’s Modulus of 3.54 GPa and Poisson’s ratio of 0.404 when compared to the other composition. The optimal process constraints are found using Hybrid Taguchi-Grey Relationship Analysis. The experiments are carried out using the L9 orthogonal matrix. The results show that experimental trial no 3 could give optimized output. To evaluate the outcomes of the Taguchi optimization and analysis of variance, experimental trials are also carried out. This research aims to increase the strength of the industrial helmet by using this combination.

Development And Utilization of An Innovative Industrial Automated PLC Electro-Pneumatic Feeder and Pick and Place System: An Industrial Process Simulator for Teaching Electrical and Electronics Courses

The main purpose of this study is to develop and evaluate an innovative industrial automated PLC electro-pneumatic feeder and pick-and-place system to improve technical instruction and address the inadequacy of electrical and electronics training equipment in the BS Industrial Technology program of Isabela State University, City of Ilagan Campus. Likewise, this study was conducted to evaluate the functionality, durability, safety, and instructional applicability of an innovative industrial automated PLC electropneumatic feeder and pick-and-place system. The descriptive method of research was used, with sets of questionnaire checklists supplemented by unstructured interviews and observations as the main gathering instruments. A five-point Likert rating scale was used to determine the descriptive meanings of the indicators of the variables used. The study respondents were third-year electronics and electrical students of the Industrial Technology program, electrical and electronics teachers, engineers, and industry experts. The statistical tools used in the study were percentage and weighted mean. The developed project mean was 4.82, which suggests that the project is “highly acceptable.” This implies that the developed instructional equipment is highly suitable for instruction and simulation in industrial automation processes, and is highly preferred by evaluators. It can also be said that the constructed industrial automated PLC electro-pneumatic feeder and pick-and-place system is well designed, durable, functional, safe to use, and easy to maintain.

Safety and Security-Specific Application of Multiple Drone Sensors at Movement Areas of an Aerodrome

Nowadays, the public service practice applicability of drones and remote sensing sensors is being explored in almost all industrial and military areas. In the present research, in collaboration with different universities, we investigate the applicability of drones in airport procedures, assessing the various potential applications. By exploiting the data from remote sensing sensors, we aim to develop methodologies that can assist airport operations, including managing the risk of wildlife threats to runway safety, infrastructure maintenance, and foreign object debris (FOD) detection. Drones equipped with remote sensing sensors provide valuable insight into surface diagnostics, helping to assess aprons, taxiways, and runways. In addition, drones can enhance airport security with effective surveillance and threat detection capabilities, as well as provide data to support existing air traffic control models and systems. In this paper, we aim to present our experience with the potential airport applications of UAV high-resolution RGB, thermal, and LiDAR sensors. Through interdisciplinary collaboration and innovative methodologies, our research aims to revolutionize airport operations, safety, and security protocols, outlining a path toward a safer, more efficient airport ecosystem.

Real-Time Monitoring of Toxic Gases using Artificial Neural Networks with SnO2-based Thick Film Gas Sensors

The present study aims to develop an advanced system for real-time identification and measurement of harmful gases using Pd-doped SnO2-based thick film gas sensors integrated with Artificial Neural Networks. These sensors, renowned for their exceptional sensitivity and selectivity, undergo testing in a meticulously controlled gas chamber environment. Here, gas concentrations, temperature, and humidity are meticulously controlled. The gas chamber setup allows for mixing gases like carbon monoxide, nitrogen dioxide, and sulfur dioxide with nitrogen gas to achieve desired concentrations ranging from to . Temperature is kept at and relative humidity is maintained between to . The sensitivity analysis of the sensor demonstrates its adeptness in detecting low concentrations of target gases, with sensitivity increasing as gas concentrations rise, also the selectivity assessments highlight their ability to accurately differentiate between target gases and common interferents, ensuring precise detection even in complex gas mixtures. Response time testing indicates rapid detection capabilities, crucial and useful for emergencies. The Artificial Neural Network model, trained via the backpropagation algorithm, demonstrates remarkable accuracy, precision, recall, and F1 scores in predicting gas concentrations. The findings indicate that this integrated system offers a reliable and efficient solution for toxic gas detection, with potential applications in industrial safety and environmental monitoring.

Integrated Image Processing and Gas Sensing for Enhanced Hazard Detection.

In industrial and public safety applications, detecting hazardous situations in a timely manner is critical for averting accidents and maintaining human safety. This work offers a novel method for identifying dangerous environments by using cutting-edge image processing algorithms. Using sophisticated image processing algorithms, the technology extracts pertinent information from visual data that is recorded by cameras or other sensors. Gas sensors are simultaneously used to track the chemical composition of the surrounding air, with a special emphasis on chemicals that may pose a threat. The integration of gas and optical sensor data allows for a more thorough analysis, which enhances the system's precision in identifying and categorizing dangerous situations.

Isolation, selection, and evaluation of characteristics of potential Bacillusstrains for probiotic products

In the human digestive tract, there resides a very large number of microorganisms, including more than 400 different species of bacteria. Probiotics are defined as live bacteria that when introduced into the body in sufficient quantities will produce beneficial health effects. Probiotics (commonly known as digestive enzymes) are not drugs but are classified as nutritional supplements. In this study, we aim to investigate the probiotic properties of 2 strains of Bacillus bacteria isolated from fecal samples of healthy people, without the use of antibiotics or probiotic products for at least 1 month. The basic properties investigated include the hemolytic ability of bacteria, tolerance to artificial gastric acid and bile salt, and sensitivity to antibiotics. Out of a total of 72 isolates, 2 potential Bacillus strains were selected that did not cause hemolysis, were able to survive and survive in the harsh environment of the digestive system, and were sensitive to antibiotics commonly used for humans. On that basis, we initially evaluated the safety and potential application of these 2 strains to probiotic products.

Reduction Jammer Detection and Recovery Algorithms for DSRC Safety Application in VANET

Intelligent Transportation Systems (ITS) encompasses technologies, services, and applications facilitating communication between vehicles (V2V) and between vehicles and fixed infrastructure (V2I and I2V). This mutual interaction constitutes a Vehicular Ad-Hoc Network (VANET) which supports a plethora of applications targeting critical transportation aspects, such as safety, mobility, and environmental considerations. Dedicated Short Range Communications (DSRC), operating on the 5.9 GHz band, is pivotal for such exchanges. We introduced an innovative algorithm designed to identify jamming attacks and transition the Safety Application to a secure fail-safe mode. This algorithm leverages a dual-metric strategy, incorporating both distance and PDR measurements. Field tests confirm that our algorithm adeptly recognizes the activities of deceptive jammers, ensuring a prompt shift of the safety application into its fail-safe state. This paper delves into these countermeasures, evaluating their efficiency via mathematical modeling, simulations, and on-ground testing. Findings acknowledge that these strategies bolster the reliability of safety applications in jamming scenarios. Furthermore, the approaches propounded align with ongoing standardization endeavors by relevant authorities, ensuring communication mediums remain unhindered.

Single-step batch fabrication of microfluidic paper-based analytical devices with a 3D printer and their applications in nanoenzyme-enhanced visual detection of dopamine.

Wax printing is the most widely used method for fabricating microfluidic paper-based analytical devices (μPADs), but it still suffers from disadvantages like discontinuation of wax printers and need for additional equipment for heating treatment. To address these issues, this work initially describes a new class of wax printing approach for high-precision, batch fabrication of μPADs using a household 3D printer. It only involves a one patterning step of printing polyethylene wax into rice paper body. Under optimized parameters, a fabrication resolution, namely the minimum hydrophilic channel width, down to ~189 ± 30 μm could be achieved. In addition, the analytical applicability of such polyethylene wax-patterned μPADs was demonstrated well with enhanced colorimetric detection of dopamine as a model analyte by combining metal-organic framework (MOF) based nanoenzymes (ZIF-67) with a smartphone (for portable quantitative readout). The developed nanosensor could linearly detect dopamine over a concentration range from 10 to 1000 μM, with a detection limit of ca. 2.75 μM (3σ). The recovery results for analyzing several real samples (i.e., pig feed, chicken feed, pork and human serum) were between 91.82 and 102.79%, further validating its good detection accuracy for potential practical applications in food safety and medical diagnosis.

Continuity of GNSS as a critical attribute for safety applications in land transport

The Global Navigation Satellite System (GNSS) is widely used for air traffic management—more than 150,000 aircraft and 5000 airports worldwide are equipped with SBAS (Satellite-based augmentation system) technology, which contributes to safer and more efficient air operations. The next challenge is to extend GNSS positioning to maritime, autonomous cars and railway control systems preserving their safety requirements. The main parameter is the integrity of the GNSS positioning, although the time for which the integrity is guaranteed, defined by continuity, the most demanding requirement for aviation applications, has not been sufficiently investigated for land transportation. The aim of this paper is to close this gap by clarifying: (1) where the requirement for GNSS continuity comes from, (2) why GNSS continuity is needed in land transport, and (3) how GNSS-based applications can be made more reliable when needed. Using a comparative analysis, the continuity requirements in aviation, rail, maritime, and road transport have been investigated showing their importance for railways and automotive control, paving the way to eventually update the current EN 50126 (RAMS) and ISO/TR 4804 standards respectively for railways and automated cars. One of the main findings, through Markov modeling, is the improvement of the Mean Time to System Failure (MTTFsys) that for the railway safety-of-life applications can be significantly increased from about 521 h up to 5 × 105 h. These results can contribute to accelerating the adoption of GNSS positioning for automated land transportation, by exploiting the extensive experience brought by the aviation sector where GNSS was introduced 20 years ago.

Design & Development of Smart Fire Safety System For e-Vehicles Using IOT

The project concept of An autonomous fire safety mechanism for EVs is an inventive way of considering a less cost-effective design and a more convenient application for e-vehicle safety. Day by day, the number of fatalities in electric vehicles increases. In this project, our main aim is to reduce the number of accidents with electric vehicles by making a smart fire safety system for electric vehicles. With the rapid adoption of electric vehicles (EVs), ensuring their safety, particularly concerning fire hazards, has become paramount. This abstract presents a conceptual framework for a sophisticated fire safety system tailored specifically for EVs. The proposed system integrates advanced technologies such as battery monitoring systems (BMS), temperature and smoke sensors, machine learning algorithms, active fire suppression mechanisms, emergency shutdown features, remote monitoring capabilities, user alerts, and compliance with safety standards. By combining these elements into a cohesive system, EV manufacturers can significantly mitigate the risks associated with fires in electric vehicles, enhancing overall safety for drivers, passengers, and emergency responders. With this technique, the fire extinguisher automatically puts out the fire when it detects flames or smoke coming from the vehicle's fire zones. Numerous factors can cause an electric vehicle to catch fire. The lithium-ion battery is the primary cause of flames in electric vehicles used within them. Installing an autonomous fire extinguishing system with Internet of Things (IoT) technology can reduce potential financial loss from a fire while also raising vehicle and human safety standards. Additionally, as soon as an accident is discovered, the nearby authorities or emergency services are notified, allowing them to get to you sooner. KEYWORDS:- Accident prevention, emergency alert, controller, fire incident, IOT technology, etc.

A Numerical Investigation of Mixed Thermal Elastohydrodynamic Lubrication in Tilting-Pad Journal Bearing

Abstract The tilting-pad journal bearing in the cooling system of the nuclear power plant is equipped below the ground and vertically positioned to accomplish its function for water transfer. Usually, the loading conditions are relatively stable since the required water volume almost remains the same level during the operation, but the loading direction cannot be known in advance. Furthermore, the bearing is designed with several separate pads, which allows the bearing to support the loading flexibly. The safety application of nuclear energy requires the bearing to have a reliable ability to maintain the rotating motion of gear sets. This study develops a numerical model to simulate the mixed thermo-elastohydrodynamic lubrication for the tilting-pad journal bearing in the nuclear plant. The elastic and thermal fields are properly determined, and the induced displacement is taken into account for an accurate description of film thickness. The asperity contact due to misaligned journal is well evaluated in the local area where the lubrication film cannot separate the surfaces. A parametric study is undertaken in detail to reveal the aspects that influence bearing lubrication. The conclusions potentially provide fundamentals for further lubrication optimization of the bearing system.

Multi-Domain Smart Safety Helmet

Motorcycle accidents and head injuries are critical concerns globally, especially where helmet non-compliance is prevalent. To address this, a multi-domain smart safety helmet is proposed for rider safety and accident prevention through advanced sensor technology. This smart helmet integrates sensors like an MQ-3 alcohol sensor and helmet detection sensors to ensure safety conditions before the motorcycle engine starts. If alcohol levels exceed a threshold, the ignition system disables, preventing intoxicated riding. Helmet detection promotes helmet use, reducing head injury risk. The scalable sensor infrastructure enables multi-domain applications beyond motorcycles. This helmet can integrate into industries like coal mining and firefighting. In coal mining, it monitors environmental conditions and worker vital signs. In firefighting, it detects hazardous gases and monitors firefighter status. During motorcycle operation, the helmet continuously monitors critical parameters—speed, tilt, and environment—providing immediate feedback on unsafe behaviours. In accidents, the helmet's accelerometer detects impacts, activating GPS to pinpoint the location and GSM to alert emergency contacts. This smart helmet aims to enhance motorcycle safety, prevent accidents, and expedite emergency responses. It represents progress towards reducing motorcycle-related injuries and fatalities, with adaptable features for broader safety applications across industries and domains.

Enhancing the efficiency of polypyrrole-dodecylbenzene sulfonic acid in-tube solid-phase microextraction coating for analysis of nitrogen-containing pesticides in water environments

Background: With the growing use of nitrogen-containing pesticides in agriculture, their residues in the environment have raised significant public health concerns.Objective: This study aimed to develop a novel PPy-DBSA/IT-SPME coating to enhance the detection efficiency of polar nitrogen-containing pesticides in water matrices.Methods: The preparation conditions were optimized, including pyrrole concentration at 7.0 mol/L, DBSA concentration at 0.014 mol/L, oxidant concentration at 0.35 mol/L, and a coating cycle repeated 10 times. High-performance liquid chromatography-tandem mass spectrometry (HPLC-MS/MS) was employed for detection.Results: The novel coating effectively adsorbed 19 different categories of polar nitrogen-containing pesticides, including sulfonylureas, triazolopyrimidines, diphenyl ether herbicides, benzoylurea insecticides, and phenylurea herbicides, with unadsorbed rates below 10%. The analytical method achieved an average recovery rate of 61.92% to 115.21%, with an RSD below 5.0%. Detection and quantification limits ranged from 0.012 to 0.524 μg/L and 0.127 to 5.243 μg/L, respectively.Conclusion: The developed method is green, efficient, simple, and cost-effective. It offers an environmentally friendly and user-friendly approach for the detection of polar nitrogen-containing pesticide residues, demonstrating good recovery and precision. This method holds potential for wide application in environmental monitoring and food safety.

Data mining of arsenic-based small molecules geometrics present in Cambridge structural database

Arsenic, ubiquitous in various industrial processes and consumer products, presents both essential functions and considerable toxicity risks, driving extensive research into safer applications. Our investigation, drawing from 7182 arsenic-containing molecules in the Cambridge Structural Database (CSD), outlines their diverse bonding patterns. Notably, 51% of these molecules exhibit cyclic connections, while 49% display acyclic ones. Arsenic forms eight distinct bonding types with other elements, with significant interactions observed, particularly with phenyl rings, O3 and F6 moieties. Top interactions involve carbon, nitrogen, oxygen, fluorine, sulfur, and arsenic itself. We meticulously evaluated average bond lengths under three conditions: without an R-factor cut-off, with R-factor ≤0.075, and with R-factor ≤0.05, supporting the credibility of our results. Comparative analysis with existing literature data enriches our understanding of arsenic's bonding behaviour. Our findings illuminate the structural attributes, molecular coordination, geometry, and bond lengths of arsenic with 68 diverse atoms, enriching our comprehension of arsenic chemistry. These revelations not only offer a pathway for crafting innovative and safer arsenic-based compounds but also foster the evolution of arsenic detoxification mechanisms, tackling pivotal health and environmental challenges linked to arsenic exposure across different contexts.

Advancements in Predictive Microbiology: Integrating New Technologies for Efficient Food Safety Models.

Predictive microbiology is a rapidly evolving field that has gained significant interest over the years due to its diverse application in food safety. Predictive models are widely used in food microbiology to estimate the growth of microorganisms in food products. These models represent the dynamic interactions between intrinsic and extrinsic food factors as mathematical equations and then apply these data to predict shelf life, spoilage, and microbial risk assessment. Due to their ability to predict the microbial risk, these tools are also integrated into hazard analysis critical control point (HACCP) protocols. However, like most new technologies, several limitations have been linked to their use. Predictive models have been found incapable of modeling the intricate microbial interactions in food colonized by different bacteria populations under dynamic environmental conditions. To address this issue, researchers are integrating several new technologies into predictive models to improve efficiency and accuracy. Increasingly, newer technologies such as whole genome sequencing (WGS), metagenomics, artificial intelligence, and machine learning are being rapidly adopted into newer-generation models. This has facilitated the development of devices based on robotics, the Internet of Things, and time-temperature indicators that are being incorporated into food processing both domestically and industrially globally. This study reviewed current research on predictive models, limitations, challenges, and newer technologies being integrated into developing more efficient models. Machine learning algorithms commonly employed in predictive modeling are discussed with emphasis on their application in research and industry and their advantages over traditional models.

Achieving Room-Temperature ppb-Level H2S Detection in a Au-SnO2 Sensor with Low Voltage Enhancement Effect.

Although semiconductor metal oxide-based sensors are promising for gas sensing, low-power and room temperature operation (24 ± 1 °C) remains desirable for practical applications particularly considering the request of energy saving or net zero emission. In this study, we demonstrate a Au/SnO2-based ultrasensitive H2S gas sensor with a limit of detection (LOD) of 2 ppb, operating at very low voltages (0.05 to 0.5 V) at room temperature. The Au/SnO2-based sensor showed approximately 7 times higher response (the ratio of change in the current to initial current) of ∼270% and 4 times faster recovery (126 s) compared to the pure SnO2-based sensor when exposed to 500 ppb H2S gas concentration at 0.5 V operating voltage at relative humidity (RH) 17.5 ± 2.5%. The enhancement can be attributed to the catalytic characteristics of AuNPs, increasing the number of adsorbed oxygen species on sensing material surfaces. Additionally, AuNPs aid in forming flower-petal-like Au/SnO2 nanostructures, offering a larger surface area and more active sites for H2S sensing. Moreover, at low voltage (<1 V), the localized dipoles at the Au/SnO2 interface may further enhance the absorption of polar oxygen molecules and hence promote the reaction between H2S and oxygen species. This low-power, ultrasensitive H2S sensor outperforms high-powered alternatives, making it ideal for environmental, food safety, and healthcare applications.

Spesolimab: A Review of the First IL-36 Blocker Approved for Generalized Pustular Psoriasis.

This article reviews clinical trial data that assesses the safety, efficacy, and clinical application of spesolimab, an interleukin-36 (IL-36) blocker, for the treatment of generalized pustular psoriasis (GPP). A review of the literature was conducted using the search terms: "spesolimab," "BI 655130," and "spevigo" in MEDLINE (PubMed) and Clinicaltrials.gov from January 1, 1950 to October 31, 2023. Relevant articles in English relating to the pharmacodynamics, pharmacokinetics, efficacy, and safety of spesolimab were included. In one phase 2 clinical trial evaluating single dose IV spesolimab for GPP flares at day 8, 54% of patients receiving spesolimab had a GPP physician global assessment (GPPGA) pustulation subscore of 0, and 43% had a GPPGA total score of 0 compared with 6% and 11% for the placebo group, respectively. Another phase 2 clinical trial assessing subcutaneous spesolimab found 23% of patients in low-dose, 29% in medium-dose, and 10% of high-dose spesolimab had flares by week 48 compared with 52% of the placebo group. Hazard ratios for time to GPP flare compared with placebo were 0.16 (P = 0.0005), 0.35 (P = 0.0057), and 0.47 (P = 0.027) for the spesolimab groups, respectively. Infection rates were similar across treatment and placebo groups, and severe adverse events such as drug reactions with eosinophilia and systemic symptom (DRESS), cholelithiasis, and breast cancer occurred with spesolimab. Spesolimab is a first-in-class IL-36 monoclonal antibody receptor antagonist approved for the treatment of acute GPP flares. It is a safe and effective therapeutic agent in preventing future GPP flares, with no current comparator trials with other GPP agents. Spesolimab is a safe and effective treatment for acute GPP flares in adults. Future clinical trials can establish safety and efficacy compared with other agents.

Understanding the Role of Filamentous Actin in Food Quality: From Structure to Application.

Actin, a multifunctional protein highly expressed in eukaryotes, is widely distributed throughout cells and serves as a crucial component of the cytoskeleton. Its presence is integral to maintaining cell morphology and participating in various biological processes. As an irreplaceable component of myofibrillar proteins, actin, including G-actin and F-actin, is highly related to food quality. Up to now, purification of actin at a moderate level remains to be overcome. In this paper, we have reviewed the structures and functions of actin, the methods to obtain actin, and the relationships between actin and food texture, color, and flavor. Moreover, actin finds applications in diverse fields such as food safety, bioengineering, and nanomaterials. Developing an actin preparation method at the industrial level will help promote its further applications in food science, nutrition, and safety.

Harnessing artificial intelligence in microbial food safety: global progress and implications in the ASEAN region

SummaryFood safety remains one of the major concerns in ASEAN, with many of the recent developmental plans and published policies in the region being focused on the topic. Most recent WHO data indicate that over 90% of the food safety burden in ASEAN is due to microbial foodborne diseases. However, conventional systems for controlling FBDs are resource‐intensive and require significant infrastructure which may not yet be present in ASEAN. Prior work on the use of Artificial intelligence (AI) in food safety application has shown its potential to reduce costs and increase efficiency. However, there remains a paucity in such research specific for the ASEAN region. In this review, the state of microbial food safety and the unique challenges in the ASEAN region are presented. The global state‐of‐the‐art of microbial food safety applications of AI are presented and possible steps for its adaptation to the ASEAN context are then discussed.