Hasil untuk "Industrial hygiene. Industrial welfare"

Lissa Aoki, Juliana de Carvalho Rodrigues, Ingrid Bertollini Lamy et al.

Introduction: The COVID-19 pandemic disrupted social interactions, family dynamics, and economic stability, disproportionately affecting vulnerable populations. Tuberculosis and leprosy perpetuate poverty and, once manifested, hinder socioeconomic development due to their high potential for disability. Methodology: This study analyzed the impact of the COVID-19 pandemic using DATASUS health data and assessed the influence of socioeconomic interventions (SAGICAD data) on tuberculosis and leprosy case notifications in Brazil. A correlation analysis was performed between regional diagnoses and variables such as Bolsa Família (a national social welfare program), BCG vaccination coverage, and COVID-19 immunization rates, applying Pearson’s correlation test. Results: No significant correlations were found between COVID-19 vaccination rates and tuberculosis/leprosy diagnoses. However, a strong negative correlation (<i>p</i> < 0.05) was observed between BCG (Bacillus Calmette–Guérin) vaccination coverage and leprosy incidence in the Northern region. The findings also suggest that social assistance programs such as Bolsa Família play a pivotal role in preventing infectious diseases in vulnerable areas. Conclusions: Understanding the complex interplay between socioeconomic determinants and public health outcomes is essential for guiding future research and informing health policies, including potential revisions to social programs and vaccination protocols.

Dimitris Kallis, Moysis Symeonides, Marios D. Dikaiakos

The widespread adoption of IoT has driven the development of cyber-physical systems (CPS) in industrial environments, leveraging Industrial IoTs (IIoTs) to automate manufacturing processes and enhance productivity. The transition to autonomous systems introduces significant operational costs, particularly in terms of energy consumption. Accurate modeling and prediction of IIoT energy requirements are critical, but traditional physics- and engineering-based approaches often fall short in addressing these challenges comprehensively. In this paper, we propose a novel methodology for benchmarking and analyzing IIoT devices and applications to uncover insights into their power demands, energy consumption, and performance. To demonstrate this methodology, we develop a comprehensive framework and apply it to study an industrial CPS comprising an educational robotic arm, a conveyor belt, a smart camera, and a compute node. By creating micro-benchmarks and an end-to-end application within this framework, we create an extensive performance and power consumption dataset, which we use to train and analyze ML models for predicting energy usage from features of the application and the CPS system. The proposed methodology and framework provide valuable insights into the energy dynamics of industrial CPS, offering practical implications for researchers and practitioners aiming to enhance the efficiency and sustainability of IIoT-driven automation.

Vade Shah, Yohan John, Ethan Freifeld et al.

Industrial refrigeration systems have substantial energy needs, but optimizing their operation remains challenging due to the tension between minimizing energy costs and meeting strict cooling requirements. Load shifting--strategic overcooling in anticipation of future demands--offers substantial efficiency gains. This work seeks to rigorously quantify these potential savings through the derivation of optimal load shifting policies. Our first contribution establishes a novel connection between industrial refrigeration and inventory control problems with convex ordering costs, where the convexity arises from the relationship between energy consumption and cooling capacity. Leveraging this formulation, we derive three main theoretical results: (1) an optimal algorithm for deterministic demand scenarios, along with proof that optimal trajectories are non-increasing (a valuable structural insight for practical control); (2) performance bounds that quantify the value of load shifting as a function of cost convexity, demand variability, and temporal patterns; (3) a computationally tractable load shifting heuristic with provable near-optimal performance under uncertainty. Numerical simulations validate our theoretical findings, and a case study using real industrial refrigeration data demonstrates an opportunity for improved load shifting.

Francesc Sabater, Ouns El Harzli, Geert-Jan Besjes et al.

Optimization via decoded quantum interferometry (DQI) has recently gained a great deal of attention as a promising avenue for solving optimization problems using quantum computers. In this paper, we apply DQI to an industrial optimization problem in the automotive industry: the vehicle option-package pricing problem. Our main contributions are 1) formulating the industrial problem as an integer linear program (ILP), 2) converting the ILP into instances of max-XORSAT, and 3) developing a detailed quantum circuit implementation for belief propagation, a heuristic algorithm for decoding LDPC codes. Thus, we provide a full implementation of the DQI algorithm using Belief Propagation, which can be applied to any industrially relevant ILP by first transforming it into a max-XORSAT instance. We also evaluate the effectiveness of our implementation by benchmarking it against both Gurobi and a random sampling baseline.

Leila Ismail, Abdelmoneim Abdelmoti, Arkaprabha Basu et al.

With the increasing complexity of industrial systems, there is a pressing need for predictive maintenance to avoid costly downtime and disastrous outcomes that could be life-threatening in certain domains. With the growing popularity of the Internet of Things, Artificial Intelligence, machine learning, and real-time big data analytics, there is a unique opportunity for efficient predictive maintenance to forecast equipment failures for real-time intervention and optimize maintenance actions, as traditional reactive and preventive maintenance practices are often inadequate to meet the requirements for the industry to provide quality-of-services of operations. Central to this evolution is digital twin technology, an adaptive virtual replica that continuously monitors and integrates sensor data to simulate and improve asset performance. Despite remarkable progress in digital twin implementations, such as considering DT in predictive maintenance for industrial engineering. This paper aims to address this void. We perform a retrospective analysis of the temporal evolution of the digital twin in predictive maintenance for industrial engineering to capture the applications, middleware, and technological requirements that led to the development of the digital twin from its inception to the AI-enabled digital twin and its self-learning models. We provide a layered architecture of the digital twin technology, as well as a taxonomy of the technology-enabled industrial engineering applications systems, middleware, and the used Artificial Intelligence algorithms. We provide insights into these systems for the realization of a trustworthy and efficient smart digital-twin industrial engineering ecosystem. We discuss future research directions in digital twin for predictive maintenance in industrial engineering.

Qishan Wang, Shuyong Gao, Junjie Hu et al.

Multi-class Unsupervised Anomaly Detection algorithms (MUAD) are receiving increasing attention due to their relatively low deployment costs and improved training efficiency. However, the real-world effectiveness of MUAD methods is questioned due to limitations in current Industrial Anomaly Detection (IAD) datasets. These datasets contain numerous classes that are unlikely to be produced by the same factory and fail to cover multiple structures or appearances. Additionally, the defects do not reflect real-world characteristics. Therefore, we introduce the Heterogeneous Same-Sort Industrial Anomaly Detection (HSS-IAD) dataset, which contains 8,580 images of metallic-like industrial parts and precise anomaly annotations. These parts exhibit variations in structure and appearance, with subtle defects that closely resemble the base materials. We also provide foreground images for synthetic anomaly generation. Finally, we evaluate popular IAD methods on this dataset under multi-class and class-separated settings, demonstrating its potential to bridge the gap between existing datasets and real factory conditions. The dataset is available at https://github.com/Qiqigeww/HSS-IAD-Dataset.

Afifah Salsabilla Istisya, Hanifa Maher Denny, Yuliani Setyaningsih

Introduction:Palm oil workers are exposed to numerous hazards in the work environment associated with accidents and occupational diseases. Work-related accidents are typically influenced by a combination of internal and external factors. Therefore, this study aimed to analyze the potential hazards and influencing factors affecting the safety of workers in palm oil gardens. Method: PRISMA guidelines with Boolean operators were used alongside specified keywords "Safety Work ” OR " Health Work " AND " Hazard" AND " Palm Oil ” AND " Worker.” The inclusion criteria for the review included articles published from 2019 to 2023. Results: The results showed that factors psychologically influencing work accidents among palm oil workers included length of service, knowledge, attitudes, as well as the use of PPE, and overtime system. In terms of potential ergonomic hazards, the influencing factors identified were work posture, workload, and repetitive movements while working. From the aspect of potential biological and chemical hazards, the use of PPE when spraying pesticides and cleanliness were found to play a crucial role in the prevention of infection and exposure to chemical materials. Conclusion: Based on the results from several studies, smallholder palm oil workers have the potential to experience occupational accidents and diseases in the form of physical, biological, chemical, and ergonomic hazards. However, by adopting a comprehensive approach to mitigating these complex hazards, stakeholders can create a safer and more sustainable work environment.

Sueny Andrade Batista, Emanuele Batistela dos Santos, Gabriel Teles Câmara et al.

This study assessed raw vegetable sanitizing in Brazilian schools and identified barriers to standards. This experimental and quantitative study was conducted in 12 school food services in the Federal District (Brazil) public primary education institutions. Microbiological analyses were conducted with vegetable samples (before and after sanitizing) and water used in the sanitization process, collected before the process. The Petrifilm^®<i>E. coli</i>/Coliform Count Plates and COLIlert methods were used to evaluate vegetables and water samples, and a checklist of good practices was applied in each school food service to identify barriers to proper sanitization. Thirty-five samples of raw vegetables were offered to students, 32 samples of water, and 17 hygiene processes were evaluated. The results indicate that 76.5% (<i>n</i> = 13) of hygiene processes were considered unsatisfactory, with an average increase of 5.8 log CFU g⁻¹ (DV = 7.4) in the initial microbial load in 47.1% (<i>n</i> = 8) of the evaluated processes; moreover, 33.3% (<i>n</i> = 6) of the samples exceeded the tolerable limit, with an average value above 1.5 × 10³ CFU/g. Attention to food handler training and necessary organizational changes is essential to ensure safe food and promote healthy student eating habits, highlighting the importance of strengthening basic hygiene practices and following the parameters for sanitizing vegetables.

Axel Fruhauf, Gabriel Fernandez de Grado, Julie Scholler et al.

Objectives: In the protocol for cleaning and sterilizing dental handpieces (DHs), water retention within the instrument poses a challenge and may compromise the sterilization process. This study aimed to assess the reliability and reproducibility of the sterilization protocol regarding the dryness of DHs. It evaluated the presence of residual water in these instruments after various conditions of treatment through multiple dryness tests. Methods: This comparative study examined the dryness of seven different DHs following five washing–disinfection and/or sterilization protocols. Dabbing tests, shaking by hand, or compressed air tests through DHs and over absorbent paper were employed to ascertain the thorough dryness of DHs after treatment. As soon as the first sign of water appeared on the absorbent paper, the DH was deemed to be not dry. Results: Upon completion of the washing–disinfection protocol without sterilization, five out of seven DHs were deemed dry using the dabbing test, yet none were fully dry when subjected to shaking or compressed air. However, in the four protocols incorporating final sterilization, all DHs were dry according to the three drying tests. Conclusion: This study underscores the essential role of the sterilization step in eliminating residual water from DHs, thereby ensuring optimal conditions for effective sterilization in terms of dryness. Furthermore, the study recommends against relying solely on the dabbing drying test, emphasizing the importance of shaking or using compressed air to confirm instrument dryness.

Anju Rani, Daniel Ortiz-Arroyo, Petar Durdevic

In recent years, 3D point clouds (PCs) have gained significant attention due to their diverse applications across various fields, such as computer vision (CV), condition monitoring (CM), virtual reality, robotics, autonomous driving, etc. Deep learning (DL) has proven effective in leveraging 3D PCs to address various challenges encountered in 2D vision. However, applying deep neural networks (DNNs) to process 3D PCs presents unique challenges. This paper provides an in-depth review of recent advancements in DL-based industrial CM using 3D PCs, with a specific focus on defect shape classification and segmentation within industrial applications. Recognizing the crucial role of these aspects in industrial maintenance, the paper offers insightful observations on the strengths and limitations of the reviewed DL-based PC processing methods. This knowledge synthesis aims to contribute to understanding and enhancing CM processes, particularly within the framework of remaining useful life (RUL), in industrial systems.

Ayman M. Arfaj, Jose Lauro M. Llamas

Introduction: The assessment of dermal exposure is a complex task. The most commonly used methods have fundamental problems, and there are large gaps in the documentation and validation of the known assessment methods. This study aimed to determine the prevalence of self-reported skin problems in laboratory technicians. Additionally, to determine if there is an association between self-reported skin problems and work tasks and other exposure-related parameters, we developed a simple qualitative questionnaire that may be used for conducting qualitative dermal exposure assessments. Methods: A well-structured survey questionnaire was developed and 45 laboratory technicians were interviewed while conducting qualitative dermal exposure assessments in three selected laboratories. The sampling technique was a qualitative survey conducted through interviews. The examined variables included skin problems, work characteristics, and chemicals used. Results: This study indicated that 18% of technicians reported having skin problems, most notably inexperienced technicians or technicians with more than 6 years of experience. Skin problems were also identified in technicians who worked between one and eight hours, performed manual operations, and handled solvents. The prevalence of skin problems has also been associated with changing gloves. However, no significant differences were observed between the examined parameters and skin problems (p > 0.05). Conclusion: The prevalence of self-reported skin problems (18%) among laboratory technicians was not high. The prevalence of dry skin was low (11%). A well-structured questionnaire can be used to conduct a qualitative dermal risk assessment. As this was a cross-sectional study with a small sample size, it was not possible to establish a causative effect between exposure to workplace hazards and dermal problems.

Lucía Rodríguez Guzmán, Jesús Salvador Hernández Romero, Arlene Oramas Viera

Introducción: La labor docente tiene un reconocido carácter estresante y un potencial impacto negativo en la salud psicológica de quienes la desempeñan. El compromiso académico es un constructo que tomado del ámbito laboral expresa la relación del estudiante con su tarea, puede constituir un predictor del desempeño estudiantil, pero esencialmente su presencia es indicador de la obtención, durante esta etapa de prácticas frente a un aula, de la adquisición de recursos personales para el afrontamiento de las demandas emocionales de la tarea y proteger el bienestar del futuro docente. Objetivo: Describir los niveles de vigor, dedicación, absorción y compromiso académico en una muestra preprofesional que simultanea el rol de estudiante y de docente frente a aula. Métodos: Se realiza un estudio descriptivo con un diseño transversal en los tres grupos docentes del 6to semestre de la Licenciatura en Educación, de la Universidad de Guanajuato, México. La muestra fue de 64 sujetos de los tres grupos, grupo A: 19, grupo B: 16 y grupo C: 29. Se utilizó la versión abreviada en 9 ítems de la Escala de Engagement Académico (UWESS-9). Resultados: Predominan valores altos de dedicación, absorción, engagement total y medios de vigor. Existen diferencias significativas entre los grupos siendo el grupo B, significativamente, el de mayor absorción y el A el de menor. Conclusiones: Los resultados apuntan a un posible desbalance entre las demandas y los recursos disminuyendo la dimensión energética pero no la actitudinal. Introduction: The teaching job has a recognized stressful character and a potential negative impact on the psychological health of those who perform it. Academic commitment is a construct that, taken from the work environment, expresses the student's relationship with his or her task. It can be a predictor of student performance, but essentially its presence is an indicator of the acquisition, during this stage of classroom practice, of personal resources to cope with the emotional demands of the task and to protect the well-being of the future teacher. Objective: To describe the levels of vigor, dedication, absorption, and academic commitment in a pre-professional sample that simultaneously plays the role of student and classroom teacher. Methods: A descriptive study with a cross-sectional design was carried out in three groups of teachers in the 6th semester of the Bachelor's Degree in Education at the University of Guanajuato, Mexico. The sample consisted of 64 subjects from the three groups, group A: 19, group B: 16, and group C: 29. The abbreviated 9-item version of the Academic Engagement Scale (UWESS-9) was used. Results: High values of dedication, absorption, total engagement, and average vigor predominated. There are significant differences between groups with group B having significantly the highest absorption and group A the lowest. Conclusions: The results point to a possible imbalance between demands and resources decreasing the energetic dimension but not the attitudinal one. ; ; vigor, dedicación; absorción; ; docentes ; student engagement; vigor, ; ; ; teachers

Yuanbao Zhang, Yiqun Mo, Yue Zhang et al.

Abstract Background Copper oxide nanoparticles (Nano-CuO) are one of the most produced and used nanomaterials. Previous studies have shown that exposure to Nano-CuO caused acute lung injury, inflammation, and fibrosis. However, the mechanisms underlying Nano-CuO-induced lung fibrosis are still unclear. Here, we hypothesized that exposure of human lung epithelial cells and macrophages to Nano-CuO would upregulate MMP-3, which cleaved osteopontin (OPN), resulting in fibroblast activation and lung fibrosis. Methods A triple co-culture model was established to explore the mechanisms underlying Nano-CuO-induced fibroblast activation. Cytotoxicity of Nano-CuO on BEAS-2B, U937* macrophages, and MRC-5 fibroblasts were determined by alamarBlue and MTS assays. The expression or activity of MMP-3, OPN, and fibrosis-associated proteins was determined by Western blot or zymography assay. Migration of MRC-5 fibroblasts was evaluated by wound healing assay. MMP-3 siRNA and an RGD-containing peptide, GRGDSP, were used to explore the role of MMP-3 and cleaved OPN in fibroblast activation. Results Exposure to non-cytotoxic doses of Nano-CuO (0.5 and 1 µg/mL) caused increased expression and activity of MMP-3 in the conditioned media of BEAS-2B and U937* cells, but not MRC-5 fibroblasts. Nano-CuO exposure also caused increased production of cleaved OPN fragments, which was abolished by MMP-3 siRNA transfection. Conditioned media from Nano-CuO-exposed BEAS-2B, U937*, or the co-culture of BEAS-2B and U937* caused activation of unexposed MRC-5 fibroblasts. However, direct exposure of MRC-5 fibroblasts to Nano-CuO did not induce their activation. In a triple co-culture system, exposure of BEAS-2B and U937* cells to Nano-CuO caused activation of unexposed MRC-5 fibroblasts, while transfection of MMP-3 siRNA in BEAS-2B and U937* cells significantly inhibited the activation and migration of MRC-5 fibroblasts. In addition, pretreatment with GRGDSP peptide inhibited Nano-CuO-induced activation and migration of MRC-5 fibroblasts in the triple co-culture system. Conclusions Our results demonstrated that Nano-CuO exposure caused increased production of MMP-3 from lung epithelial BEAS-2B cells and U937* macrophages, which cleaved OPN, resulting in the activation of lung fibroblasts MRC-5. These results suggest that MMP-3-cleaved OPN may play a key role in Nano-CuO-induced activation of lung fibroblasts. More investigations are needed to confirm whether these effects are due to the nanoparticles themselves and/or Cu ions.

Luana Argote Ravelo, Lucía Fariñas Rodríguez, Judith Pupo Balboa et al.

Resumen Introducción: La obesidad es un padecimiento crónico complejo de etiología multifactorial, se ha descrito su relación con el estrés oxidativo. Objetivo: Determinar la relación entre el estado nutricional y algunos marcadores de estrés oxidativo de una población trabajadora del sector agrícola, del municipio habanero Arroyo Naranjo, en 2019. Métodos: Estudio descriptivo de corte transversal. Se obtuvo una muestra de 68 trabajadores mediante la aplicación de un muestreo al azar, los cuales fueron clasificados según el índice de masa corporal en tres grupos: normopeso, sobrepeso y obesos. Se estudió la edad, sexo, evaluación nutricional por índice de masa corporal e índice cintura/cadera y marcadores de daño oxidativo. Resultados: Se observó un predominio del sobrepeso global con una mayor prevalencia de la obesidad central en ambos sexos. Los obesos, con relación al resto del grupo, presentaron promedios superiores de colesterol, triglicéridos y lipoproteínas de alta, baja y de muy baja densidad, así como cifras de triglicéridos por encima de los valores de referencia. Los promedios de malondialdehido, de los productos avanzados de la oxidación de proteínas y de peróxido totales estuvieron por encima de los valores de referencia en los tres grupos. Conclusiones: Las concentraciones plasmáticas promedio de los marcadores de estrés oxidativo estudiados estuvieron aumentadas, sin diferencias significativas entre los sujetos normopeso, sobrepeso y obesos con respecto a estas variables. Abstract Introduction: Obesity is a complex chronic condition of multifactorial etiology, whose relationship with oxidative stress has been described. Objective: To determine the relationship between nutritional status and some oxidative stress markers in a working population from the agricultural sector, in the Havana municipality of Arroyo Naranjo, in 2019. Methods: A descriptive and cross-sectional study was carried out with a sample of 68 workers obtained by random sampling and classified, according to body mass index, in three groups: normal weight, overweight and obese. Age, sex, nutritional evaluation by body mass index and waist/hip index, as well as oxidative damage markers were studied. Results: A predominance of global overweight was observed, with a higher prevalence of central obesity in both sexes. The obese, compared to the rest of the group, had higher average values for cholesterol, triglycerides and high, low and very low density lipoproteins, as well as triglyceride figures above the reference values. The average values of malondialdehyde, advanced products of protein oxidation and total peroxide were above reference values in all three groups. Conclusions: The average plasma concentrations of the studied oxidative stress markers had increased, with no significant differences between normal weight, overweight and obese subjects with respect to these variables.

Aisy Rahmania, Eka Rosanti, Ramadhan Saputra et al.

Introduction: industrial mining activities have the highest prevalence of NIHL due to operating a heavy vehicle. Dozer is one of the heavy vehicles with a high noise level. Methods: This study was descriptive research about risk factors related to hearing loss of 28 dozer operators at PT. X. The risk factors consisted of demographic factors, working behavior (listening to music, smoking), noise levels were analyzed with hearing loss using STS. Interviews were conducted with the workers and company representatives. Hearing loss examination used an audiometric test to determine the STS of the operators with the result that positive more than 10 dB and negative at 10 dB or less. All the data is secondary. Results: noise level of all dozers exceeds the TLV (>85dB) operated for 10 hours a day and six days a week. Half of the dozer operators had STS (+) occurred at age 40 years and older, working for more than five years, not use the PPE or misused, the habit of listening to music and smoking. Conclusion: noise and demographic factors can increase the risk of hearing loss in dozer operators. The company must control by combining plywood, foam, tray, and coir material in the dozer cabin, which can reduce 31.94 dB and apply for PPE double protection.

Rohit Gupta, Nico Jansen, Nikolaus Regnat et al.

Domain-specific modelling languages (DSMLs) help practitioners solve modelling challenges specific to various domains. As domains grow more complex and heterogeneous in nature, industrial practitioners often face challenges in the usability of graphical DSMLs. There is still a lack of guidelines that industrial language engineers should consider for improving the user experience (UX) of these practitioners. The overall topic of UX is vast and subjective, and general guidelines and definitions of UX are often overly generic or tied to specific technological spaces. To solve this challenge, we leverage existing design principles and standards of human-centred design and UX in general and propose definitions and guidelines for UX and user experience design (UXD) aspects in graphical DSMLs. In this paper, we categorize the key UXD aspects, primarily based on our experience in developing industrial DSMLs, that language engineers should consider during graphical DSML development. Ultimately, these UXD guidelines help to improve the general usability of industrial DSMLs and support language engineers in developing better DSMLs that are independent of graphical modelling tools and more widely accepted by their users.

Alexander Zeiser, Bas van Stein, Thomas Bäck

Anomaly detection describes methods of finding abnormal states, instances or data points that differ from a normal value space. Industrial processes are a domain where predicitve models are needed for finding anomalous data instances for quality enhancement. A main challenge, however, is absence of labels in this environment. This paper contributes to a data-centric way of approaching artificial intelligence in industrial production. With a use case from additive manufacturing for automotive components we present a deep-learning-based image processing pipeline. Additionally, we integrate the concept of domain randomisation and synthetic data in the loop that shows promising results for bridging advances in deep learning and its application to real-world, industrial production processes.

Longfei Chen, Yang Ouyang, Haipeng Zhang et al.

Restructuring the regional industrial structure (RIS) has the potential to halt economic recession and achieve revitalization. Understanding the current status and dynamics of RIS will greatly assist in studying and evaluating the current industrial structure. Previous studies have focused on qualitative and quantitative research to rationalize RIS from a macroscopic perspective. Although recent studies have traced information at the industrial enterprise level to complement existing research from a micro perspective, the ambiguity of the underlying variables contributing to the industrial sector and its composition, the dynamic nature, and the large number of multivariant features of RIS records have obscured a deep and fine-grained understanding of RIS. To this end, we propose an interactive visualization system, RISeer, which is based on interpretable machine learning models and enhanced visualizations designed to identify the evolutionary patterns of the RIS and facilitate inter-regional inspection and comparison. Two case studies confirm the effectiveness of our approach, and feedback from experts indicates that RISeer helps them to gain a fine-grained understanding of the dynamics and evolution of the RIS.

Nina Townsend, Garrett Brown, Mark Katchen

Abstract Industrial hygienists have a moral responsibility, and, if certified or a member of an OHS professional organization, a professional obligation to act and perform their work in an ethical manner. According to ethicist Rushworth Kidder, the single largest problem in ethics is the inability to recognize ethical issues. Ethical challenges for individual industrial hygienists and the companies that employ them grew substantially with the development of the global economy starting in the 1990s. The growth of corporate social responsibility has not improved working conditions around the world; however, a handful of examples of worker‐driven social responsibility show potential. A code of ethics provides for a common understanding and minimum expectations. A professional code of ethics also provides evidence to those outside of the organization or discipline of the members' value and repute. The American Board of Industrial Hygiene code is enforceable among CIH candidates and diplomats. American Industrial Hygiene Association (AIHA) has a Code of Conduct, AIHA and the American Conference of Governmental Industrial Hygienists have joint ethical principles and the International Commission on Occupational Health has the more comprehensive International Code of Ethics for Occupational Health Professionals. Case studies provide an opportunity to work through common and complex ethical issues faced by industrial hygienists.

Pernille Høgh Danielsen, Katja Maria Bendtsen, Kristina Bram Knudsen et al.

Abstract Background Pulmonary exposure to high doses of engineered carbonaceous nanomaterials (NMs) is known to trigger inflammation in the lungs paralleled by an acute phase response. Toll-like receptors (TLRs), particularly TLR2 and TLR4, have recently been discussed as potential NM-sensors, initiating inflammation. Using Tlr2 and Tlr4 knock out (KO) mice, we addressed this hypothesis and compared the pattern of inflammation in lung and acute phase response in lung and liver 24 h after intratracheal instillation of three differently shaped carbonaceous NMs, spherical carbon black (CB), multi-walled carbon nanotubes (CNT), graphene oxide (GO) plates and bacterial lipopolysaccharide (LPS) as positive control. Results The LPS control confirmed a distinct TLR4-dependency as well as a pronounced contribution of TLR2 by reducing the levels of pulmonary inflammation to 30 and 60% of levels in wild type (WT) mice. At the doses chosen, all NM caused comparable neutrophil influxes into the lungs of WT mice, and reduced levels were only detected for GO-exposed Tlr2 KO mice (35%) and for CNT-exposed Tlr4 KO mice (65%). LPS-induced gene expression was strongly TLR4-dependent. CB-induced gene expression was unaffected by TLR status. Both GO and MWCNT-induced Saa1 expression was TLR4-dependent. GO-induced expression of Cxcl2, Cxcl5, Saa1 and Saa3 were TLR2-dependent. NM-mediated hepatic acute phase response in terms of liver gene expression of Saa1 and Lcn2 was shown to depend on TLR2 for all three NMs. TLR4, in contrast, was only relevant for the acute phase response caused by CNTs, and as expected by LPS. Conclusion TLR2 and TLR4 signaling was not involved in the acute inflammatory response caused by CB exposure, but contributed considerably to that of GO and CNTs, respectively. The strong involvement of TLR2 in the hepatic acute phase response caused by pulmonary exposure to all three NMs deserves further investigations.