Efficacy Of System Research Articles

Improving citizen-government interactions with generative artificial intelligence: Novel human-computer interaction strategies for policy understanding through large language models.

Effective communication of government policies to citizens is crucial for transparency and engagement, yet challenges such as accessibility, complexity, and resource constraints obstruct this process. In the digital transformation and Generative AI era, integrating Generative AI and artificial intelligence technologies into public administration has significantly enhanced government governance, promoting dynamic interaction between public authorities and citizens. This paper proposes a system leveraging the Retrieval-Augmented Generation (RAG) technology combined with Large Language Models (LLMs) to improve policy communication. Addressing challenges of accessibility, complexity, and engagement in traditional dissemination methods, our system uses LLMs and a sophisticated retrieval mechanism to generate accurate, comprehensible responses to citizen queries about policies. This novel integration of RAG and LLMs for policy communication represents a significant advancement over traditional methods, offering unprecedented accuracy and accessibility. We experimented with our system with a diverse dataset of policy documents from both Chinese and US regional governments, comprising over 200 documents across various policy topics. Our system demonstrated high accuracy, averaging 85.58% for Chinese and 90.67% for US policies. Evaluation metrics included accuracy, comprehensibility, and public engagement, measured against expert human responses and baseline comparisons. The system effectively boosted public engagement, with case studies highlighting its impact on transparency and citizen interaction. These results indicate the system's efficacy in making policy information more accessible and understandable, thus enhancing public engagement. This innovative approach aims to build a more informed and participatory democratic process by improving communication between governments and citizens.

Hybrid Blockchain Network for Drug Traceability in Secure Pharmaceutical Supply Chain

Several stakeholders are involved in the pharmaceutical supply chain (PSC), including those who provide raw materials, make and sell drugs, and oversee the industry, hospitals, pharmacies, and patients. An efficient traceability system is necessary to ascertain the present and all prior ownership of products because of the intricate nature of product and transaction flows in PSC. Furthermore, regulatory supervision and product safety can be significantly improved by digitizing the track and trace procedure. A permanent, trustworthy, responsible, and transparent system in the PSC can be achieved using blockchain-based medication tracing, which provides a distributed shared data platform. A hybrid blockchain network (HBN) based on secure pharmaceutical supply chain architecture (SPSCA) has been proposed for this research. The suggested system relies on smart contracts to record significant events and track how all the parties engaged in the supply chain perform their respective tasks. In this way, everyone concerned can remain informed of developments and protect the supply against manipulations of supply records and counterfeit pharmaceuticals. All parties involved benefit from a secure, immutable record of transactions due to the smart contract, which additionally guarantees the provenance of data and deals with mediators. Detailed methods and the system architecture that enable the solution's functioning are laid out here. We perform testing and validation to assess the system's efficacy in improving pharmaceutical supply chain traceability and provide cost and security evaluations.

Advanced functional connectivity analysis with integrated EEG signal enhancement and GTW-EEG-GAN for emotion detection

Emotion recognition is crucial in human-computer interaction and psychological research, utilizing modalities such as facial expressions, voice intonations, and EEG signals. This research investigates AI-driven techniques by employing Graph Neural Networks (GNNs) on functional connectivity matrices derived from EEG data to advance emotion recognition. Our approach integrates sophisticated preprocessing methods, including Integrated EEG Signal Enhancement (IESE) and novel augmentation techniques such as Gaussian Time Warping Generative Adversarial Network (GTW-EEG-GAN). This integration aims at enhancing data quality and diversity. Signal decomposition using EMD-Wavelet Hybrid Decomposition further refines feature extraction, enabling robust analysis of EEG signals. Functional connectivity metrics capture critical neural interactions intended for precise emotion characterization. The GNN architecture effectively processes these features, achieving significant accuracy improvements. Evaluations on the DEAP dataset demonstrate promising results, attaining 94.5% accuracy before hyperparameter tuning and further improving to 98.6% after tuning, highlighting the system's efficacy. The methodology showcases the integration of advanced signal processing techniques with AI, offering a comprehensive framework for future studies. The findings advocate potential applications in areas such as personalized mental health monitoring, adaptive learning environments, and responsive gaming experiences, demonstrating the broad impact and versatility of AI-driven EEG-based emotion recognition.

Development and evaluation of a panel of newly screened Y chromosome InDels for inferring paternal ancestry information in Southwest China.

Y-InDels (insertions/deletions) are genetic markers which are extremely understudied. It is unknown whether this type of markers can be utilized for genetic ancestry inference. We have developed an innovative Y chromosome ancestry inference system tailored for forensic applications. This panel amplifies 21 Y chromosome loci, encompassing Y-InDels and Y-SNPs (Single Nucleotide Polymorphism), utilizing the capillary electrophoresis (CE) platform. The system performed well at DNA concentrations greater than 0.125 ng/ul and produced accurate results at a 1:100 mixing ratio of male and female DNA. The Cumulative probability of matching (CPM) was between 0.95 and 0.97 in the experimental population. The system's efficacy in inferring ancestral origins was demonstrated through intercontinental population discrimination, revealing high discrimination power between African and East Asian populations. Population genetic analyses conducted on Han, Qiang and Hui populations in Southwest China, where the smallest FST value was 0.0002 between Han Chinese in Beijing (from 1000 Genomes Project) and Qiang Chinese from Sichuan (CQSC). Phylogenetic tree construction further illuminated distinct haplotypes among populations, with ethnically unique haplotypes observed in 34.6% of Hui and 7.1% of Qiang populations. K-fold cross-validation show the system's inference abilities at the intercontinental level. In addition, our investigations identified potential associations between the Y-InDel locus Y: 15,385,547 (GRCh37) and haplogroup R1a1a1b2a2- Z2124, as well as locus Y: 13,990,180 (GRCh37) and haplogroup F-M89. In conclusion, we have established a Y-chromosome inference system tailored for grassroots-level application, underscoring the value of incorporating Y-InDel markers in forensic analyses.

The dual power of Alma Hybrid™: integrating ablative and non-ablative lasers for superior outcomes

The Alma Hybrid™ not only bridges the gap between traditional ablative and non-ablative laser treatments but also introduces enhanced transdermal delivery through the Impact Handpiece, utilising ultrasound technology. This integration allows for the effective treatment of a wide range of dermatological concerns, including scar revision, skin rejuvenation, and resurfacing ( Arellano-Huacuja, 2023 ; Arellano et al, 2023b ). This article aims to provide an examination of the clinical evidence supporting the Alma Hybrid™ system, with a focus on its integration into treatment plans for aesthetic and dermatological practices. Through an analysis of patient case studies and a review of the literature, this paper will demonstrate how the Alma Hybrid™ can be employed to enhance patient outcomes, particularly in challenging cases such as hypertrophic and atrophic scars. Furthermore, we will explore the system's efficacy across diverse patient demographics, with an emphasis on its safety profile in treating different Fitzpatrick skin types.

Biomechanical properties analysis of posterior lumbar interbody fusion with transpedicular oblique screw fixation

ObjectiveAn alternative to conventional posterior lumbar interbody fusion (PLIF) is a PLIF with transpedicular oblique screw fixation system. An assessment of new fixation system's viability and efficacy is conducted through a comparison of its biomechanical properties with those of conventional PLIF. MethodA comprehensive finite element model (FEM) of the lumbar regions L1-L5 was developed and the surgical segment L3-L4 was chosen to comprise the surgical models of both traditional PLIF and new PLIF. In new PLIF model, an analysis was conducted on segmental range of motion (RoM), cage stress, inferior endplates stress, vertebral stress, and internal fixation stress. Three-dimensional printers are utilized to fabricate and assemble the fusion cage and vertebrae, and compression test machines are employed to execute physiological load and extreme load experiments on new PLIF, so as to verify the accuracy of the FEM analysis and the mode of fatigue exhibited by new PLIF. ResultsIn new PLIF, the maximum stress on the inferior endplates under physiological loads was reduced in comparison to conventional PLIF. While the maximum stress on the cage, vertebral body, and screw increased, it remained within an acceptable range. The experimental data indicates that new fixation system can endure a vertical load exceeding 2800 N and an ultimate bending moment of 77 Nm. ConclusionThe new PLIF exhibits a comparable RoM to its predecessor, simultaneously mitigating inferior endplate stress and accommodating physiological loads, which reduce the amount of surgical incision and fusion fixation instruments. Consequently, it emerges as a sanguine surgical approach to fuse the degenerative lumbar spine.

Innovative Soft Cryptosystem for Encrypting and Decrypting Messages with IDEA

This paper introduces an advanced cryptosystem for message encryption and decryption, integrating the International Data Encryption Algorithm (IDEA) with soft set and soft matrix principles. Soft sets, conceptualized by Molodtsov, are adept at managing uncertainty, making them ideal for cryptographic frameworks. Our approach utilizes inverse and characteristic products of soft sets and matrices, combined with the robust IDEA algorithm, to significantly enhance security. The IDEA algorithm operates on 64 -bit data blocks with a 128 -bit key, ensuring strong encryption. Additionally, we incorporate the symmetric to introduce complex permutations, further strengthening the cryptographic process by ensuring unique encryptions for identical plaintexts. This layered approach drastically improves resilience against unauthorized decryption. Practical applications demonstrate the system's efficacy, highlighting its superior security and reliability for data transmission in environments with high uncertainty. This innovative cryptosystem provides a formidable solution for contemporary encryption challenges.

Design and Development of Lightning Detection System Utilizing Slow Atmospheric Electric Field Waveform at Legoland Malaysia

Conventional lightning localization and detection techniques, including Magnetic Direction Finder (MDF), Time of Arrival (TOA), Interferometer (ITF), and Distance of Arrival (DOA), predominantly rely on fast atmospheric electric fields, magnetic fields, and very high frequency (VHF) signals. This paper pioneers a novel approach by delving into the analysis of the slow atmospheric electric field, aiming to develop a waveform analysis utilizing this field to estimate the distance and radius of lightning occurrences at LEGOLAND Malaysia Resort. The newly implemented lightning detection system at LEGOLAND leverages a straightforward and cost-effective setup, incorporating a capacitive antenna, slow and fast atmospheric electric field sensors, and dedicated data analysis software. The system's efficacy and accuracy have undergone a rigorous comparison with LEGOLAND's existing online lightning detection service. Achieving accurate data necessitates proper grounding and isolation from electrical noise, as signal interference from power lines, towers, or machinery can potentially trigger false signals. This research has contributed detailed documentation on the analysis of slow atmospheric electric field data, encompassing waveform patterns and key characteristics. This documentation is expected to serve as a valuable resource for future research endeavors and the continuous refinement of lightning detection systems. The comparative evaluation between the novel system and the current online service at LEGOLAND Malaysia Resort has shed light on the efficiency and capabilities of the newly introduced methodology.

Iris Detection Using Deep Learning and OpenCV

The proposed system's efficacy is evaluated through extensive simulations and real- world testing, demonstrating high accuracy in accident detection and timely activation of the emergency response. This integration of deep learning with OpenCV and an automated alert system aims to reduce response times and enhance the chances of survival and support in the critical moments following a car accident. Keywords--- Deep Learning, OpenCV

A comprehensive investigation of an LTE-enabled smart door system using the Arduino UNO

Traditional residential security systems frequently lack remote access and real-time reaction capabilities, necessitating home automation security technology developments. With the rise of the Internet of Things (IoT), solutions such as the Smart Door are prepared to transcend these constraints by leveraging modern communication technology. The article aims to develop, implement, and evaluate an LTE-enabled Smart Door system built on the Arduino Uno platform. The goal is to improve home security by offering keyless entry, real-time surveillance, and remote operation, increasing user convenience and system reliability. We created a Smart Door system with an Arduino Uno microcontroller and LTE connectivity. The system's performance was measured using a variety of measures, including response time, reliability, and security against unwanted access. Testing included simulation environments and real-world scenarios to ensure the system's operational efficacy and security. The Smart Door system performed admirably, with a reaction time of less than two seconds in 95% of commands and strong resilience to brute force attacks. It effectively incorporated keyless entry, live video streaming, and real-time alarms, all controlled by a smartphone app, demonstrating the system's dependability and efficiency in live scenarios. The LTE-enabled Smart Door system considerably improves over traditional home security options. It provides enhanced security, simple operation, and more outstanding remote management capabilities by utilizing IoT technology. These enhancements fill essential holes in existing systems, providing a safer, dependable, and convenient answer for modern home security requirements.

ADVANCED SMART PARKING BARRIERS: DESIGN, IMPLEMENTATION, AND IMPACT ON URBAN INFRASTRUCTURE

This study investigates the creation and execution of an intelligent individual parking barrier system aimed at resolving urban parking difficulties in Malaysia. The project adheres to a systematic methodology that includes conceptual design, detailed design, prototype creation, testing and validation, data analysis and refinement, and implementation and deployment. The system's lifetime and efficacy are guaranteed by the inventive use of durable materials and advanced sensor technologies. Additionally, convenience and security are enhanced by a user-friendly wireless control system. The data analysis confirms that the system is highly reliable, with sensors that have a 95% success rate in detecting events and low delay in wireless control operation. The concept is in line with larger smart city projects, such as the Kuala Lumpur Smart City Blueprint, which aims to promote parking options that are both efficient and secure. In addition, the system promotes environmental sustainability by minimizing emissions related to the search for parking. It also aligns with the United Nations Sustainable Development Goals, namely SDG 11 (Sustainable Cities and Communities) and SDG 13 (Climate Action). This study showcases the possibility of implementing the proposed system on a large scale in different urban areas in Malaysia, therefore leading to the development of more intelligent and effective urban infrastructure.

Assessing PEM fuel cell performance in a geothermal Cogeneration system for peak-time energy storage

In this investigation, a novel geothermal power plant was designed, combining absorption chiller units, PEMFC, EPEM, and ORC. We conducted a comprehensive case study across four continents—Asia, Oceania, Europe, and America—leveraging weather data from various cities to evaluate the system's efficacy. Our modeling utilized EES (Engineering Equation Solver) software, optimizing the setup via the RSM with three key objective functions: exergy efficiency, hydrogen production, and cost rate. The primary focus was on delivering electricity to residential properties during peak demand periods. Exploring six scenarios for organic fluids in organic Rankine cycles, we gauged energy, exergy, and overall system performance. The TOPSIS method led us to select scenario 3, employing R123 and R134a refrigerants, as the optimal choice. The results of the optimization showcased impressive figures: an exergy efficiency of 81.816%, a hydrogen production rate of 25.119 kg/h, and a cost rate of 15.967 $/h for the system's most efficient configuration. Economic analysis highlighted the organic Rankine cycle units 1 and 2 as the components with the highest costs. Our evaluation extended to various cities—Aomori, Grosseto, Lhasa, Wellington, and San Diego—assessing the electricity, heating, and cooling needs of residential complexes based on the system's performance.

Nucleic acid-functionalized gold nanoparticles as intelligent photothermal therapy agents for precise cancer treatment.

We present an intelligent photothermal therapy agents by functionalizing gold nanoparticles with specific nucleic acid sequences. Hairpin nucleic acids are modified to the nanoparticles, forming AuNPs-1 and AuNPs-2. Upon infiltrating cancer cells, these nanoparticles undergo catalytic hairpin assembly in the presence of target miRNA, leading to aggregation and subsequent photothermal conversion. Under near-infrared laser irradiation, aggregated gold nanoparticles exhibit efficient photothermal conversion, selectively damaging cancer cells. This approach offers heightened selectivity, as nanoparticles only aggregate in environments with cancer biomarkers present, sparing normal cells. Cytotoxicity assays confirm minimal toxicity to normal cells. In vivo studies on mice bearing solid tumors validate the system's efficacy in tumor regression. Overall, this study highlights the potential of nucleic acid-functionalized gold nanoparticles in intelligent and selective cancer photothermal therapy, offering insights for targeted diagnosis and treatment development.

Synthesis of α-hydroxy ketones through novel one-pot multi-enzyme cascade system from racemic epoxides

α-Hydroxy ketones, highly versatile building blocks, present challenges for their green and efficient synthesis. Based on thermodynamic analysis, a novel in vitro one-pot multi-enzyme cascade system was designed for synthesizing 2-hydroxyacetophenone (1c) from racemic styrene oxide (1a). This system incorporated a Rhodotorula paludigena epoxide hydrolase (RpEHL360V) for the enantioconvergent hydrolysis of rac-1a to (R)-phenyl-1,2-ethanediol (1b), a Bacillus subtilis (2,3)-butanediol dehydrogenase (BsBDHAI49L/V266L/G292A) for the subsequent oxidation to 1c, and a Lactobacillus casei lactate dehydrogenase (LcLDH1) for the reduction of pyruvate to lactate, thereby facilitating coenzyme recycling and shifting the reaction's thermodynamic equilibrium. After optimizing the reaction conditions, the system reached its highest efficiency using cell-free extracts of RpEHL360V (4.0 U/mL), BsBDHAI49L/V266L/G292A (0.3 U/mL), and LcLDH1 (0.1 U/mL), along with 4 mM NADH and 200 mM sodium pyruvate as cofactor and cosubstrate, respectively, in a Na2HPO4-NaH2PO4 buffer (100 mM, pH 8.0) at 30 °C. Under these conditions, the system catalyzed the conversion of 50 mM rac-1a to 1c with a total yield of 73.1 % and a maximum space-time yield of 10.7 mmol/L/h within 24 h. Furthermore, this one-pot system was applied to catalyze 20 mM of rac-4-chlorostyrene oxide and 1,2-epoxyhexane, successfully synthesizing two corresponding α‑hydroxy ketones, each with a space-time yield of 0.7 mmol/L/h, demonstrating the system's efficacy for the production of α‑hydroxy ketones.

Research on energy consumption optimization of predictive cruise control considering the state of the leading vehicle

To comprehensively assess the economic performance of electric vehicles (EVs) cruise planning and mitigate conflicts with leading vehicles, a dynamic programming-based predictive cruise speed optimization control system was developed. This system comprises two layers: an upper V2X-based LSTM-DP cruise speed planning system and a lower adaptive cruise control system based on sliding mode control (SMC), and the mode switching strategy of the speed and distance tracking controller is designed. Initially, a segmented uniform variable motion algorithm processed the historical driving data of the leading vehicle, significantly enhancing the LSTM neural network's acceleration prediction capability, particularly on inclines, with an improvement in accuracy of 37.8 %. Subsequently, an adaptive cruise tracking controller was implemented to track the planned vehicle speed. Simulation tests on actual roads demonstrated that the LSTM-DP approach not only ensures driving safety but also markedly improves fuel efficiency over conventional constant speed cruising methods when planning is effective. Moreover, even in scenarios where planning fails, the LSTM-DP strategy can still achieve energy savings of up to 37 % without compromising the safety distance between vehicles. Finally, hardware-in-the-loop (HIL) testing confirmed the system's efficacy under real-time operational conditions.

IoT-enabled fire detection for sustainable agriculture: A real-time system using flask and embedded technologies

In modern agriculture, the threat of fire poses significant economic and environmental risks. Additionally, traditional fire detection methods are inadequate and poorly integrated with advanced technologies. It is crucial to develop a more efficient and reliable fire detection system. Also, the lack of real-time and accurate monitoring in current systems compromises the resilience and sustainability of farming operations. This article details developing and implementing a real-time fire detection system tailored for smart agriculture. It integrates cutting-edge technologies, including the Internet of Things (IoT), embedded systems, and a Flask-based web application, fortified by cybersecurity measures such as login authentication and secure HTTP protocols. The system's primary aim is to monitor environmental conditions continuously in agricultural fields to detect signs of smoke or flame swiftly, facilitating preventive actions to safeguard crops and infrastructure. The architecture employs sensors distributed across fields to monitor conditions, a Raspberry Pi 3 B+ as the central processing unit for data acquisition and transmission, and a web interface developed using Flask, HTML, and CSS for secure and real-time visualization of detection data. Critical components like the MCP3208 analog-to-digital converter ensure the system's reliability and accuracy. Experimental results confirm the system's efficacy in early fire detection and real-time data visualization via the web browser. Enhanced security features, such as login authentication, protect sensitive information, while regular updates maintain data relevance. This study advances fire prevention and farm preservation efforts, offering a high-performance technological solution for proactive monitoring and quick response to fire risks, thereby supporting food security and sustainable agricultural practices.

Development and experimental evaluation of hybrid K-edge/X-ray fluorescence densitometer for uranium solution measurement

The hybrid K-edge/X-ray fluorescence densitometer (HKED) is a combination of K-edge absorption technology (KED) and characteristic X-ray fluorescence (XRF), which has the advantages of direct, fast and non-destructive determination, and is an ideal non-destructive measurement technology for uranium and plutonium concentrations. In this paper, a new HKED was developed, primarily utilizing an X-ray tube from COMET, alongside high-purity germanium (HPGe) and cadmium telluride (CdTe) detectors from AMETEK ORTEC. This manuscript delves into several variables that influence measurement outcomes under predefined experimental conditions and operational prerequisites to pinpoint critical parameters. It was discerned that the adoption of a 160 kV high voltage setting markedly diminishes experimental interferences, while the beam current, optimally set at 2 mA, not only ensures a linear correlation with the count rate but also maximizes the effective count detected. The incorporation of a 2 cm fixed-length iron rod along the trajectory between the sample and the detector, complemented by an additional 3 mm external absorber before the KED detector, effectively mitigates direct X-ray exposure, thereby enhancing transmittance values to attainable extents. Subsequent to the determination of these pivotal parameters, validation of the HKED system's efficacy was conducted via performance evaluation tests on a laboratory-scale HKED setup. Measurements undertaken for both KED and XRF across an interval ranging from 300 to 3000 s fell within the 2σ boundary, affirming the system's stability. Repeated measurements of 50 g/L and 150 g/L uranium solutions yielded KED precision rates of 0.56% and 0.19%, respectively. Moreover, linear regression analyses linking transmittance, characteristic X-ray fluorescence, and uranium concentrations across a spectrum of 0–150 g/L underscored the laboratory HKED instrument's robust analytical capabilities. Notably, the relative discrepancy between theoretical predictions and empirical findings for the 150 g/L uranium sample was minimized to a commendable 0.58%.

ProtectaBEE® and bee vectoring: innovating hive-based health with inspensing technology for sustainable apiculture

Apivectoring, or bee vectoring, employs managed bees to distribute powders containing disease and pest-fighting biocontrol agents during pollination flights to crops. Our research introduces a novel application of this concept, termed inspensing, which leverages bee vectoring for hive-based benefits. In inspensing, bees traverse through a carrier powder combined with products aimed at combating pathogens or pests within the hive. To facilitate this, we developed the ProtectaBEE® system, an innovative beehive-entrance technology that guides bees through a compartment inoculated with an inspensing powder. This system facilitates the application of beneficial agents into the hive without the need for beekeepers to open the hive, thereby streamlining the treatment process and reducing hive disturbance. To analyze the effectiveness of the system, we employed a fluorescent tracer in a powder formulation for tracking distribution throughout the hive. Complementing this, we inspensed a living dry powder-formulated biocontrol agent, Beauveria bassiana, an entomopathogenic fungus known to reduce Varroa mite populations, and detected its presence in the hive using PCR. The fluorescent powder was detected in 78.8% of the samples while B. bassiana was confirmed in up to 86.2% of larvae and 91.7% of mites. Our results underscore the system's efficacy in delivering material throughout the hive and affirm the potential for inspensing dry-powder-formulated biocontrol agents to manage Varroa destructor. Inspensing paves new paths for optimizing bee health and pest control strategies, streamlining disease management, simplifying hive maintenance, and minimizing beekeeper intervention, all contributing to sustainable apiculture.

GRANTEES INFORMATION SYSTEM (GIS)

The aim of the study is to provide grantees with, scholarship in-charges, and bookkeepers tools to help them manage their daily tasks while interacting with a grantees information system. The study also intends to assess the system's adoption and efficacy using the TAM theory. A 5-point Likert Scale questionnaire is used by the researchers. In addition, there are thirtyseven (37) respondents from various year levels and courses at St. Peters College. Respondents were encouraged to use the grantees' information system. As a result of the evaluation, the majority of respondents accepted the Grantees Information System. The results show that the system has a favorable impact and benefits grantees, scholarship in-charges, and bookkeepers.

Design and implementation of a mechatronic system for monitoring worker exposure in cold storage facilities

In response to the increasing need for enhanced occupational safety in cold storage facilities, this study presents the design and validation of a mechatronic time control device specifically developed for monitoring the exposure times of warehouse operators in a multinational company. Over the past two decades, Ecuador has significantly increased regulations to safeguard worker health and safety. In compliance with these regulations, this research addresses the challenges operators face when exposed to sub-zero temperatures during their work shifts. The proposed system integrates mechanical, electronic, and control components to systematically track and manage operators' time within cold rooms, ensuring compliance with predefined safety thresholds. A NodeMCU microcontroller, digital temperature sensors, and a robust communication interface form the device's core. This device records entry and exit times, monitors ambient conditions, and triggers alerts when exposure limits are approached. Extensive validation was conducted using a 3D-printed resin prototype, demonstrating the device's capability to function effectively in harsh environments. The system's efficacy was evaluated through real-world testing, where operators used the prototype during their shifts. Data collected confirmed the device's reliability in tracking exposure times and enhancing worker safety.