Hasil untuk "Industrial hygiene. Industrial welfare"

Mingchun Sun, Rongqiang Zhao, Zhennan Huang et al.

In industrial scenarios, data augmentation is an effective approach to improve model performance. However, its benefits are not unidirectionally beneficial. There is no theoretical research or established estimation for the optimal sample size (OSS) in augmentation, nor is there an established metric to evaluate the accuracy of OSS or its deviation from the ground truth. To address these issues, we propose an information-theoretic optimal sample size estimation (IT-OSE) to provide reliable OSS estimation for industrial data augmentation. An interval coverage and deviation (ICD) score is proposed to evaluate the estimated OSS intuitively. The relationship between OSS and dominant factors is theoretically analyzed and formulated, thereby enhancing the interpretability. Experiments show that, compared to empirical estimation, the IT-OSE increases accuracy in classification tasks across baseline models by an average of 4.38%, and reduces MAPE in regression tasks across baseline models by an average of 18.80%. The improvements in downstream model performance are more stable. ICDdev in the ICD score is also reduced by an average of 49.30%. The determinism of OSS is enhanced. Compared to exhaustive search, the IT-OSE achieves the same OSS while reducing computational and data costs by an average of 83.97% and 93.46%. Furthermore, practicality experiments demonstrate that the IT-OSE exhibits generality across representative sensor-based industrial scenarios.

Dian Afif Arifah, Ratih Andhika Akbar Rahma, Triana Harmini et al.

Introduction: As the center-cultured region in Indonesia, Ponorogo Regency is dominated by traditional manufacturing industries which support regional economic growth. Most production in this sector is labor-intensive and depends on manual handling processes, which may increase the risk of work-related musculoskeletal disorders (WMSDs). This study aims to develop a model to evaluate and predict ergonomic hazards using a neural network algorithm, focusing on the relationship between manual handling postures and musculoskeletal pain in 12 body regions. Method: A cross-sectional study involved data of 250 workers measured using used Nordic Musculoskeletal questionnaire and manual handling exposure checklist based on SNI 9011:2021. A neural network model was developed based on GLM’s output to explore the complex interrelationships between manual handling postures (X variables) and musculoskeletal pain across 12 body regions (Y variables). Result: The outputs identified carrying object over 9 meters (X10), one-handed lifting (X3), and trunk twisting (X2), with X10 confirmed as the most predictor for multiple outcomes, affecting six regions. Neural network models demonstrated adequate learning capacity with stable architecture, proved by average CEE values ranging from 0.21 to 0.54. The models showed improved predictive accuracy across epochs. Conclusion: The finding shows that NN modelling may be expanded to include broader industries in Indonesia's traditional manufacturing sector as an integrated data-based information system application. However, further validation using external datasets is recommended to enhance generalizability.

Marcos Lima Romero, Ricardo Suyama

The recent development of Agentic AI systems, empowered by autonomous large language models (LLMs) agents with planning and tool-usage capabilities, enables new possibilities for the evolution of industrial automation and reduces the complexity introduced by Industry 4.0. This work proposes a conceptual framework that integrates Agentic AI with the intent-based paradigm, originally developed in network research, to simplify human-machine interaction (HMI) and better align automation systems with the human-centric, sustainable, and resilient principles of Industry 5.0. Based on the intent-based processing, the framework allows human operators to express high-level business or operational goals in natural language, which are decomposed into actionable components. These intents are broken into expectations, conditions, targets, context, and information that guide sub-agents equipped with specialized tools to execute domain-specific tasks. A proof of concept was implemented using the CMAPSS dataset and Google Agent Developer Kit (ADK), demonstrating the feasibility of intent decomposition, agent orchestration, and autonomous decision-making in predictive maintenance scenarios. The results confirm the potential of this approach to reduce technical barriers and enable scalable, intent-driven automation, despite data quality and explainability concerns.

Jiaqi Li, Xizhong Guo, Yang Zhao et al.

Rapid industrial digitalization has created intricate cybersecurity demands that necessitate effective validation methods. While cyber ranges and simulation platforms are widely deployed, they frequently face limitations in scenario diversity and creation efficiency. In this paper, we present SpiderSim, a theoretical cybersecurity simulation platform enabling rapid and lightweight scenario generation for industrial digitalization security research. At its core, our platform introduces three key innovations: a structured framework for unified scenario modeling, a multi-agent collaboration mechanism for automated generation, and modular atomic security capabilities for flexible scenario composition. Extensive implementation trials across multiple industrial digitalization contexts, including marine ranch monitoring systems, validate our platform's capacity for broad scenario coverage with efficient generation processes. Built on solid theoretical foundations and released as open-source software, SpiderSim facilitates broader research and development in automated security testing for industrial digitalization.

Juha-Matti Runtti, Usman Virk, Pekka Kyosti et al.

6G radio access architecture is envisioned to contain a network of short-range in-X subnetworks with enhanced capabilities to provide efficient and reliable wireless connectivity. Short-range communications in industrial environments are actively researched at the so-called mid-bands or FR3, e.g., in the EU SNS JU 6G-SHINE project. In this paper, we analyze omni-directional radio channel measurements at 10--12 GHz frequency band to estimate large-scale channel characteristics including power-delay profile, delay spread, K-factor, and pathloss for 254 radio links measured in the Industrial Production Lab at Aalborg University, Denmark. Moreover, we perform a comparison of estimated parameters with those of the 3GPP Indoor Factory channel model.

Aaditya Baranwal, Abdul Mueez, Jason Voelker et al.

Large-scale Vision-Language Models (VLMs) have transformed general-purpose visual recognition through strong zero-shot capabilities. However, their performance degrades significantly in niche, safety-critical domains such as industrial spill detection, where hazardous events are rare, sensitive, and difficult to annotate. This scarcity -- driven by privacy concerns, data sensitivity, and the infrequency of real incidents -- renders conventional fine-tuning of detectors infeasible for most industrial settings. We address this challenge by introducing a scalable framework centered on a high-quality synthetic data generation pipeline. We demonstrate that this synthetic corpus enables effective Parameter-Efficient Fine-Tuning (PEFT) of VLMs and substantially boosts the performance of state-of-the-art object detectors such as YOLO and DETR. Notably, in the absence of synthetic data (SynSpill dataset), VLMs still generalize better to unseen spill scenarios than these detectors. When SynSpill is used, both VLMs and detectors achieve marked improvements, with their performance becoming comparable. Our results underscore that high-fidelity synthetic data is a powerful means to bridge the domain gap in safety-critical applications. The combination of synthetic generation and lightweight adaptation offers a cost-effective, scalable pathway for deploying vision systems in industrial environments where real data is scarce/impractical to obtain. Project Page: https://synspill.vercel.app

Kessia Nepomuceno, Fabio Petrillo

Context: Fairness in systems has emerged as a critical concern in software engineering, garnering increasing attention as the field has advanced in recent years. While several guidelines have been proposed to address fairness, achieving a comprehensive understanding of research solutions for ensuring fairness in software systems remains challenging. Objectives: This paper presents a systematic literature mapping to explore and categorize current advancements in fairness solutions within software engineering, focusing on three key dimensions: research trends, research focus, and viability in industrial contexts. Methods: We develop a classification framework to organize research on software fairness from a fresh perspective, applying it to 95 selected studies and analyzing their potential for industrial adoption. Results: Our findings reveal that software fairness research is expanding, yet it remains heavily focused on methods and algorithms. It primarily focuses on post-processing and group fairness, with less emphasis on early-stage interventions, individual fairness metrics, and understanding bias root causes. Additionally fairness research remains largely academic, with limited industry collaboration and low to medium Technology Readiness Level (TRL), indicating that industrial transferability remains distant. Conclusion: Our results underscore the need to incorporate fairness considerations across all stages of the software development life-cycle and to foster greater collaboration between academia and industry. This analysis provides a comprehensive overview of the field, offering a foundation to guide future research and practical applications of fairness in software systems.

Aitor Toichoa Eyam, Jose L. Martinez Lastra

Photo-realistic avatar is a modern term referring to the digital asset that represents a human in computer graphic advanced systems such as video games and simulation tools. These avatars utilize the advances in graphic technologies in both software and hardware aspects. While photo-realistic avatars are increasingly used in industrial simulations, representing human factors such as human workers psychophysiological states, remains a challenge. This article contributes to resolving this issue by introducing the novel concept of MetaStates which are the digitization and representation of the psychophysiological states of a human worker in the digital world. The MetaStates influence the physical representation and performance of a digital human worker while performing a task. To demonstrate this concept, this study presents the development of a photo-realistic avatar enhanced with multi-level graphical representations of psychophysiological states relevant to Industry 5.0. This approach represents a major step forward in the use of digital humans for industrial simulations, allowing companies to better leverage the benefits of the Industrial Metaverse in their daily operations and simulations while keeping human workers at the center of the system.

Alireza Barekatain, Hamed Rahimi Nohooji, Holger Voos

The aim of this study is to investigate an automated industrial manipulation pipeline, where assembly tasks can be flexibly adapted to production without the need for a robotic expert, both for the vision system and the robot program. The objective of this study is first, to develop a synthetic-dataset-generation pipeline with a special focus on industrial parts, and second, to use Learning-from-Demonstration (LfD) methods to replace manual robot programming, so that a non-robotic expert/process engineer can introduce a new manipulation task by teaching it to the robot.

Damian Hostettler

Purpose: Industrial robots allow manufacturing companies to increase productivity and remain competitive. For robots to be used, they must be accepted by operators on the one hand and bought by decision-makers on the other. The roles involved in such organizational processes have very different perspectives. It is therefore essential for suppliers and robot customers to understand these motives so that robots can successfully be integrated on manufacturing shopfloors. Methodology: We present findings of a qualitative study with operators and decision-makers from two Swiss manufacturing SMEs. Using laddering interviews and means-end analysis, we compare operators' and deciders' relevant elements and how these elements are linked to each other on different abstraction levels. These findings represent drivers and barriers to the acquisition, integration and acceptance of robots in the industry. Findings: We present the differing foci of operators and deciders, and how they can be used by demanders as well as suppliers of robots to achieve robot acceptance and deployment. First, we present a list of relevant attributes, consequences and values that constitute robot acceptance and/or rejection. Second, we provide quantified relevancies for these elements, and how they differ between operators and deciders. And third, we demonstrate how the elements are linked with each other on different abstraction levels, and how these links differ between the two groups.

Maksim Papenkov

Accurate industry classification is critical for many areas of portfolio management, yet the traditional single-industry framework of the Global Industry Classification Standard (GICS) struggles to comprehensively represent risk for highly diversified multi-sector conglomerates like Amazon. Previously, we introduced the Multi-Industry Simplex (MIS), a probabilistic extension of GICS that utilizes topic modeling, a natural language processing approach. Although our initial version, MIS-1, was able to improve upon GICS by providing multi-industry representations, it relied on an overly simple architecture that required prior knowledge about the number of industries and relied on the unrealistic assumption that industries are uncorrelated and independent over time. We improve upon this model with MIS-2, which addresses three key limitations of MIS-1 : we utilize Bayesian Non-Parametrics to automatically infer the number of industries from data, we employ Markov Updating to account for industries that change over time, and we adjust for correlated and hierarchical industries allowing for both broad and niche industries (similar to GICS). Further, we provide an out-of-sample test directly comparing MIS-2 and GICS on the basis of future correlation prediction, where we find evidence that MIS-2 provides a measurable improvement over GICS. MIS-2 provides portfolio managers with a more robust tool for industry classification, empowering them to more effectively identify and manage risk, particularly around multi-sector conglomerates in a rapidly evolving market in which new industries periodically emerge.

Andrés Felipe Posada-Moreno, Kai Müller, Florian Brillowski et al.

The industry 4.0 is leveraging digital technologies and machine learning techniques to connect and optimize manufacturing processes. Central to this idea is the ability to transform raw data into human understandable knowledge for reliable data-driven decision-making. Convolutional Neural Networks (CNNs) have been instrumental in processing image data, yet, their ``black box'' nature complicates the understanding of their prediction process. In this context, recent advances in the field of eXplainable Artificial Intelligence (XAI) have proposed the extraction and localization of concepts, or which visual cues intervene on the prediction process of CNNs. This paper tackles the application of concept extraction (CE) methods to industry 4.0 scenarios. To this end, we modify a recently developed technique, ``Extracting Concepts with Local Aggregated Descriptors'' (ECLAD), improving its scalability. Specifically, we propose a novel procedure for calculating concept importance, utilizing a wrapper function designed for CNNs. This process is aimed at decreasing the number of times each image needs to be evaluated. Subsequently, we demonstrate the potential of CE methods, by applying them in three industrial use cases. We selected three representative use cases in the context of quality control for material design (tailored textiles), manufacturing (carbon fiber reinforcement), and maintenance (photovoltaic module inspection). In these examples, CE was able to successfully extract and locate concepts directly related to each task. This is, the visual cues related to each concept, coincided with what human experts would use to perform the task themselves, even when the visual cues were entangled between multiple classes. Through empirical results, we show that CE can be applied for understanding CNNs in an industrial context, giving useful insights that can relate to domain knowledge.

Haili Sun, Yan Huang, Lansheng Han et al.

The rapid development of Industry 4.0 has amplified the scope and destructiveness of industrial Cyber-Physical System (CPS) by network attacks. Anomaly detection techniques are employed to identify these attacks and guarantee the normal operation of industrial CPS. However, it is still a challenging problem to cope with scenarios with few labeled samples. In this paper, we propose a few-shot anomaly detection model (FSL-PN) based on prototypical network and contrastive learning for identifying anomalies with limited labeled data from industrial CPS. Specifically, we design a contrastive loss to assist the training process of the feature extractor and learn more fine-grained features to improve the discriminative performance. Subsequently, to tackle the overfitting issue during classifying, we construct a robust cost function with a specific regularizer to enhance the generalization capability. Experimental results based on two public imbalanced datasets with few-shot settings show that the FSL-PN model can significantly improve F1 score and reduce false alarm rate (FAR) for identifying anomalous signals to guarantee the security of industrial CPS.

Rindang Diannita

Introduction: One of the efforts to reduce the risk of occupational accident and occupational diseases is awareness regarding the importance of the safety and health of workers in hospitals, which is also a top priority in hospitals during a pandemic situation. The application of health protocols and the use of Personal Protective Equipment (PPE) are the main lines of defense against the risk of disease and occupational accident. So that the use of Personal Protective Equipment (PPE) is very important, especially for workers during a pandemic. The purpose of this study was to analyze the mapping of the use of Personal Protective Equipment (PPE) with the incidence of occupational accident. Methods: The research used was an analytic observational type using a cross sectional approach, besides that the researchers conducted a survey of the conditions in the hospital. With a sample of 179 respondents in all parts of the hospital. Results: There is an effect of the use of PPE on the incidence of work accidents and it is necessary to have a mapping of PPE, such as gloves, safety shoes, surgical glasses, surgical gown, apron, mask, face shield, head protection, safety helmet, safety shoes, body harness, fire-resistant clothing, fire-resistant helmet, fire-resistant goggles, and fire-resistant gloves. Conclusion: Control is needed in the form of procurement of Personal Protective Equipment at Hospital X, including face shields, aprons, gloves, masks, head protectors, and safety shoes.

Priskila Hananingrum, Ais Assana Athqia, Y. Denny A. Wahyudiono

Introduction: Mental workload is one of the most important aspects that affects the health and safety of workers. The Maintenance and Repair Division and Warship Division are divisions in PT. PAL which has a high job demand of the workers in it. Methods: This study was an observational analytic study with a cross-sectional design. The independent variables used in this study were age, gender, job placement, and social relationship, while the dependent variable was mental workload. The sample used was the total population of all managers in both divisions, totaling 12 respondents. The data was collected using a general questionnaire and the NASA-TLX method was used to measure mental workload. The data analysis technique used was the correlation test. Results: In the Maintenance and Repair Division, most managers were in the age range of 46 – 55 years old (50%) and 4 managers had an overloaded mental workload (66.7%). In the Warship Division, most of the managers were 46 – 55 years old (66.6%) and 4 managers (66.7%) had a moderate workload. Age has a relationship with mental workload in the Maintenance and Repair Division (0,612) and Warships Division (-0,316). Gender shows no relation with mental workload in the Warship Division (0,196). Job placement (-0.632) and social relationship (0.316) have a relation with mental workload in the Warship Division. Conclusion: Age has a relationship with mental workload in both divisions while there is no relationship between gender and mental workload among the managers in the Warship Division. Job placement has a strong negative relationship while social relationship has a strong positive with the mental workload in the Warship Division.

Gias Oktaruly Sinaga, Clariza Vioito Sinaga

Introduction: Safety performance refers to an individual safety behavior that can be determined by two groups of factors, environmental and individual. Each company has its own safety performance program for its employees. The company's role in occupational health and safety is to create a positive organizational climate by implementing an occupational safety and health management system. This relates to the organization's commitment to prevent accidents and occupational diseases, and to improve the level of work productivity. This research aims to analyze the relationship between the Working Period, safety knowledge, and safety performance among the workforce of the LRT construction project. Method: This research used the quantitative research approach which emphasizes data in the form of numbers and processing by statistical methods. The research design was observational with a cross-sectional approach. The population of this research was 97 respondents who filled in the questionnaires. The independent variables were Working Period relationship and safety knowledge while the dependent variable was safety performance. Result: The results show that the Working Period has a negative relationship with safety performance. Safety knowledge has a positive relationship with safety performance. The individual characteristics of the Jabodebek LRT station construction project are based on a Working Period of < 1 year for 38 people. Conclusion: Working Period has a weak relationship with safety performance and has criteria which relate to negative relationships. However, the relationship between safety knowledge and safety performance has a positive and significant relationship because the broad knowledge of safety of the employees improves their safety performance.

Alessio Sampieri, Guido D'Amely, Andrea Avogaro et al.

Pushing back the frontiers of collaborative robots in industrial environments, we propose a new Separable-Sparse Graph Convolutional Network (SeS-GCN) for pose forecasting. For the first time, SeS-GCN bottlenecks the interaction of the spatial, temporal and channel-wise dimensions in GCNs, and it learns sparse adjacency matrices by a teacher-student framework. Compared to the state-of-the-art, it only uses 1.72% of the parameters and it is ~4 times faster, while still performing comparably in forecasting accuracy on Human3.6M at 1 second in the future, which enables cobots to be aware of human operators. As a second contribution, we present a new benchmark of Cobots and Humans in Industrial COllaboration (CHICO). CHICO includes multi-view videos, 3D poses and trajectories of 20 human operators and cobots, engaging in 7 realistic industrial actions. Additionally, it reports 226 genuine collisions, taking place during the human-cobot interaction. We test SeS-GCN on CHICO for two important perception tasks in robotics: human pose forecasting, where it reaches an average error of 85.3 mm (MPJPE) at 1 sec in the future with a run time of 2.3 msec, and collision detection, by comparing the forecasted human motion with the known cobot motion, obtaining an F1-score of 0.64.

Ashis Bhattacharjee, M. Bhowmik, Chiranjit Paul et al.

With the growing awareness and sensitivity towards green energy, it makes an excellent opportunity to promote rubber trees [Hevea brasiliensis (Willd. ex A.Juss.) Muell.Arg.] as an alternate source for sustainable development and cleaner production. In recent years, rubber tree has extensively been used only for its latex. The most neglected yet abundant by-product of the rubber tree is the seed biomass. The rubber tree seeds can yield a constant source of biomass which would need a little care, financial investment, and time. This biomass efficiently can be used in several small-scale or medium-scale industries, and one of the positive aspects of using this biomass is its zero or neutral CO2 emission. A little effort, if given into its resource utilization, can go a long way in augmenting the income and improving the livelihood of the rubber tree cultivators, as well as help to develop a cleaner and sustainable environment. Instead of using the seeds as mere propagating material, these can otherwise be used in organic farming. Rubber tree seeds being protein-rich, the seed cakes and seed meal after the oil extraction can act as an abundant source of organic manure and a potential natural fertilizer. The seeds have an intrinsic property of acting as herbicides and fungicides. The seed cake, owing to its nutritional value, can be used as cattle and poultry feed. The problem of food scarcity is a major contributing factor to malnutrition in the underprivileged sections of society. The rubber tree seeds can play an influential role by supplementing protein and as an alternate source of food, thereby helping the developing nations combat the food crisis. The few available reports regarding rubber tree seeds consumed by the tribes inhabiting the Amazon basin are worth mentioning. Rubber tree seeds contain cyanogenic compounds, and extensive research is needed to develop a methodology to eliminate such toxic compounds, which can help humanity overcome the challenges of food shortages in underdeveloped and developing nations. In today’s COVID-19 pandemic scenario, there is a multiple-fold increase in demand for soaps and handwash for personal hygiene. This demand shall continue to grow in the coming days, and the inclusion of rubber tree seed oil (RSO) in producing soaps may ease the burden on vegetable oil, which can be used for human consumption. Rubber tree seed oil can also be used effectively in the cosmetics industry. Several encouraging reports support the idea of the inclusion of RSO in cosmetics to minimize the detrimental effects of harmful chemicals on the human body. The use of rubber tree seed oil as an alternate energy source cannot be downplayed. Rubber tree seed oil not only has a high ability to become an excellent source of biofuel but also has enormous potential in other industrial sectors. Products derived from RSO are used in several industrial sectors, including the paint industry, soap industry, and engineering sector as core binders. All products obtained from rubber trees can clearly show a path for cleaner green energy production and help to establish a rapport between nature and livelihood generation. The present review investigates the overall possibilities of rubber tree seed utilization as an alternate source of biomass and its venture into the various fields of biological utility and possible means of revenue generation in developing countries.

Yong Ho Kim, Sarah H. Warren, Ingeborg Kooter et al.

Abstract Background Open burning of anthropogenic sources can release hazardous emissions and has been associated with increased prevalence of cardiopulmonary health outcomes. Exposure to smoke emitted from burn pits in military bases has been linked with respiratory illness among military and civilian personnel returning from war zones. Although the composition of the materials being burned is well studied, the resulting chemistry and potential toxicity of the emissions are not. Methods Smoke emission condensates from either flaming or smoldering combustion of five different types of burn pit-related waste: cardboard; plywood; plastic; mixture; and mixture/diesel, were obtained from a laboratory-scale furnace coupled to a multistage cryotrap system. The primary emissions and smoke condensates were analyzed for a standardized suite of chemical species, and the condensates were studied for pulmonary toxicity in female CD-1 mice and mutagenic activity in Salmonella (Ames) mutagenicity assay using the frameshift strain TA98 and the base-substitution strain TA100 with and without metabolic activation (S9 from rat liver). Results Most of the particles in the smoke emitted from flaming and smoldering combustion were less than 2.5 µm in diameter. Burning of plastic containing wastes (plastic, mixture, or mixture/diesel) emitted larger amounts of particulate matter (PM) compared to other types of waste. On an equal mass basis, the smoke PM from flaming combustion of plastic containing wastes caused more inflammation and lung injury and was more mutagenic than other samples, and the biological responses were associated with elevated polycyclic aromatic hydrocarbon levels. Conclusions This study suggests that adverse health effects of burn pit smoke exposure vary depending on waste type and combustion temperature; however, burning plastic at high temperature was the most significant contributor to the toxicity outcomes. These findings will provide a better understanding of the complex chemical and combustion temperature factors that determine toxicity of burn pit smoke and its potential health risks at military bases.

Dominique Lison, Saloua Ibouraadaten, Sybille van den Brule et al.

Abstract Background In vitro models are widely used in nanotoxicology. In these assays, a careful documentation of the fraction of nanomaterials that reaches the cells, i.e. the in vitro delivered dose, is a critical element for the interpretation of the data. The in vitro delivered dose can be measured by quantifying the amount of material in contact with the cells, or can be estimated by applying particokinetic models. For carbon nanotubes (CNTs), the determination of the in vitro delivered dose is not evident because their quantification in biological matrices is difficult, and particokinetic models are not adapted to high aspect ratio materials. Here, we applied a rapid and direct approach, based on femtosecond pulsed laser microscopy (FPLM), to assess the in vitro delivered dose of multi-walled CNTs (MWCNTs). Methods and results We incubated mouse lung fibroblasts (MLg) and differentiated human monocytic cells (THP-1) in 96-well plates for 24 h with a set of different MWCNTs. The cytotoxic response to the MWCNTs was evaluated using the WST-1 assay in both cell lines, and the pro-inflammatory response was determined by measuring the release of IL-1β by THP-1 cells. Contrasting cell responses were observed across the MWCNTs. The sedimentation rate of the different MWCNTs was assessed by monitoring turbidity decay with time in cell culture medium. These turbidity measurements revealed some differences among the MWCNT samples which, however, did not parallel the contrasting cell responses. FPLM measurements in cell culture wells revealed that the in vitro delivered MWCNT dose did not parallel sedimentation data, and suggested that cultured cells contributed to set up the delivered dose. The FPLM data allowed, for each MWCNT sample, an adjustment of the measured cytotoxicity and IL-1β responses to the delivered doses. This adjusted in vitro activity led to another toxicity ranking of the MWCNT samples as compared to the unadjusted activities. In macrophages, this adjusted ranking was consistent with existing knowledge on the impact of surface MWCNT functionalization on cytotoxicity, and might better reflect the intrinsic activity of the MWCNT samples. Conclusion The present study further highlights the need to estimate the in vitro delivered dose in cell culture experiments with nanomaterials. The FPLM measurement of the in vitro delivered dose of MWCNTs can enrich experimental results, and may refine our understanding of their interactions with cells.