Hasil untuk "Industrial hygiene. Industrial welfare"

Haonan Li, Elizabeth A. Holzhausen, Devendra Paudel et al.

Abstract This study investigates independent and joint effects of fine particulate matter (PM2.5) components on early childhood neurodevelopment and explores emission sources of key toxic components. We included 165 mother-infant dyads from Southern California. Annual average concentrations of 15 PM2.5 components, including carbonaceous components, secondary inorganic salts, and trace elements, were estimated for the birth year. Neurodevelopment across cognitive, language, motor, social-emotional, and adaptive behavior domains was assessed at age 2 using Bayley-III Scales. Mixture effects and key contributors were evaluated using weighted quantile sum (WQS) and Bayesian kernel machine regression (BKMR). Source inference was conducted through inter-component clustering and spatial analysis. Linear regression showed PM2.5, sulfate (SO4 2−), nitrate (NO3 −), ammonium (NH4 +), copper (Cu), nickel (Ni), lead (Pb), and vanadium (V) were inversely, while calcium (Ca) and zinc (Zn) were positively, associated with adaptive behavior scores (p < 0.05). WQS showed negative associations between the mixture and adaptive behavior (p = 0.02–0.06), with Ni, Cu, V, and SO₄²⁻ as key contributors. BKMR showed similar trends. Ni, V, and SO4 2− likely originate from heavy oil combustion, and Cu from brake wear. Findings suggest that PM2.5 components, particularly from traffic and marine fuel combustion, may adversely affect adaptive behavior in early childhood.

Kasra Ekrad, Bjarne Johansson, Inés Alvarez Vadillo et al.

Interoperability across industrial automation systems is a cornerstone of Industry 4.0. To address this need, the OPC Unified Architecture (OPC UA) Publish-Subscribe (PubSub) model offers a promising mechanism for enabling efficient communication among heterogeneous devices. PubSub facilitates resource sharing and communication configuration between devices, but it lacks clear guidelines for mapping diverse industrial traffic types to appropriate PubSub configurations. This gap can lead to misconfigurations that degrade network performance and compromise real-time requirements. This paper proposes a set of guidelines for mapping industrial traffic types, based on their timing and quality-of-service specifications, to OPC UA PubSub configurations. The goal is to ensure predictable communication and support real-time performance in industrial networks. The proposed guidelines are evaluated through an industrial use case that demonstrates the impact of incorrect configuration on latency and throughput. The results underline the importance of traffic-aware PubSub configuration for achieving interoperability in Industry 4.0 systems.

Petr A. Popov, A. Smirnov, N. K. Gunenkova et al.

The article presents the results of research for 2025 aimed at ensuring sustainable veterinary and sanitary welfare of livestock farming, biological and food safety of livestock products and feed, and environmental protection from pollution by ecotoxicants. The state task for 2025 has been fully completed. 8 technologies and instructions, 8 methodological recommendations, and technological scheme for disinfection of veterinary surveillance facilities have been developed and approved in accordance with the established procedure; 5 monographs, 3 textbooks for universities and one lecture have been published. They were awarded a Silver medal and a Diploma of the Russian Agro-industrial Exhibition “Golden Autumn 2025”.The monographs “On the 80th Anniversary of Victory in the Great Patriotic War” dedicated to the employees of the All-Russian Research Institute of Veterinary Sanitation, Hygiene, and Ecology, who contributed to the Great Victory,” “Biological Safety: Means and Methods of Protection against Pathogenic Biological Agents,” “Commodity Science and Standardization of Veterinary Medicines,” and “Veterinary and Sanitary Requirements and Measures to Ensure the Safety of Poultry Eggs for Food Purposes” were awarded the Silver Medal and Diploma of the Russian Agro-Industrial Exhibition “Golden Autumn 2025.” State Standard Samples (SSS) of mycotoxins were produced for 136 applications, and ELISA test systems were developed for 26 applications from various republics and regions of the Russian Federation. Feed was tested for four applications. In the reporting year, the institute’s staff published 145 scientific papers, including 112 within the framework of the State Contract, 9 in Scopus, 75 in RSCI, 93 in RINTS, 6 in AGRIS, 102 in the White List, and 84 in the VAK list. 4 issues of the Russian Journal “Problems of Veterinary Sanitation, Hygiene and Ecology” (included in RSCI, RSCI, RSCI core, AGRIS, White List, Metaphor System, HAC list) and the collection of scientific papers “Problems of Veterinary Sanitation, Hygiene and Ecology”, volume 123 (included in RSCI) have been published.

Yinuo Chen, Yiyang Nan, Lang Xu et al.

Abstract Background Recent studies emphasize the significance of copper dyshomeostasis in neurodegenerative diseases, such as Alzheimer’s and Parkinson’s, thereby highlighting the role of copper in neurotoxicity. Cuproptosis, a novel mechanism of copper-dependent cell death, remains underexplored, particularly concerning environmental pollutants like polystyrene nanoplastics (PS-NPs). While PS-NPs are recognized for inducing neurotoxicity through various forms of cell death, including apoptosis and ferroptosis, their potential to trigger neuronal cuproptosis has not yet been investigated. This study aims to determine whether exposure to PS-NPs induces neurotoxicity via cuproptosis and to explore the preliminary molecular mechanisms involved, thereby addressing this significant knowledge gap. Methods Seven-week-old male C57BL/6 mice were exposed to PS-NPs at dose of 12.5 mg/kg, and were co-treated with the antioxidant N-acetylcysteine (NAC). Complementary in vitro experiments were conducted using SH-SY5Y neuronal cells exposed to PS-NPs at a concentration of 0.75 mg/mL, with interventions that included the copper chelator tetrathiomolybdate (TTM), NAC, and the MAPK inhibitor PD98059. Results Exposure to PS-NPs significantly increased cerebral copper accumulation (P < 0.05) and induced cuproptosis, characterized by lipid-acylated DLAT oligomerization, dysregulation of cuproptosis regulators (FDX1, LIAS, HSP70), and mitochondrial damage. In murine models, PS-NPs elicited neurotoxicity, as evidenced by neuronal loss, decreased Nissl body density, impaired synaptic plasticity, and suppressed oxidative stress markers (GSH, SOD, Nrf2), alongside activation of the ERK-MAPK pathway, ultimately resulting in deficits in learning and memory. Treatment with NAC alleviated these adverse effects. In SH-SY5Y cells, exposure to PS-NPs resulted in reduced cell viability (p < 0.01), an effect that was mitigated by TTM. Furthermore, NAC and PD98059 were found to reverse elevated copper levels, cuproptosis markers, and mitochondrial anomalies (p < 0.05). Conclusion This study presents preliminary evidence indicating that PS-NPs may induce neuronal cuproptosis, potentially through the oxidative stress-mediated activation of the ERK-MAPK pathway, which contributes to cognitive dysfunction in mice. These findings provide insights into the potential mechanisms underlying PS-NPs neurotoxicity and highlight possible therapeutic targets, such as copper chelation or MAPK inhibition, for mitigating the neurological risks associated with nanoplastic exposure, pending further validation in human-relevant models.

Jiayu Cao, Yuhui Yang, Xi Liu et al.

Abstract Background The advancement of nanotechnology underscores the imperative need for establishing in silico predictive models to assess safety, particularly in the context of chronic respiratory afflictions such as lung fibrosis, a pathogenic transformation that is irreversible. While the compilation of predictive descriptors is pivotal for in silico model development, key features specifically tailored for predicting lung fibrosis remain elusive. This study aimed to uncover the essential predictive descriptors governing nanoparticle-induced pulmonary fibrosis. Methods We conducted a comprehensive analysis of the trajectory of metal oxide nanoparticles (MeONPs) within pulmonary systems. Two biological media (simulated lung fluid and phagolysosomal simulated fluid) and two cell lines (macrophages and epithelial cells) were meticulously chosen to scrutinize MeONP behaviors. Their interactions with MeONPs, also referred to as nano-bio interactions, can lead to alterations in the properties of the MeONPs as well as specific cellular responses. Physicochemical properties of MeONPs were assessed in biological media. The impact of MeONPs on cell membranes, lysosomes, mitochondria, and cytoplasmic components was evaluated using fluorescent probes, colorimetric enzyme substrates, and ELISA. The fibrogenic potential of MeONPs in mouse lungs was assessed by examining collagen deposition and growth factor release. Random forest classification was employed for analyzing in chemico, in vitro and in vivo data to identify predictive descriptors. Results The nano-bio interactions induced diverse changes in the 4 characteristics of MeONPs and had variable effects on the 14 cellular functions, which were quantitatively evaluated in chemico and in vitro. Among these 18 quantitative features, seven features were found to play key roles in predicting the pro-fibrogenic potential of MeONPs. Notably, IL-1β was identified as the most important feature, contributing 27.8% to the model’s prediction. Mitochondrial activity (specifically NADH levels) in macrophages followed closely with a contribution of 17.6%. The remaining five key features include TGF-β1 release and NADH levels in epithelial cells, dissolution in lysosomal simulated fluids, zeta potential, and the hydrodynamic size of MeONPs. Conclusions The pro-fibrogenic potential of MeONPs can be predicted by combination of key features at nano-bio interfaces, simulating their behavior and interactions within the lung environment. Among the 18 quantitative features, a combination of seven in chemico and in vitro descriptors could be leveraged to predict lung fibrosis in animals. Our findings offer crucial insights for developing in silico predictive models for nano-induced pulmonary fibrosis.

Amaratou Mahamadou Saley, Thierry Moyaux, Aïcha Sekhari et al.

The convergence of the Internet of Things (IoT) and Industry 4.0 has significantly enhanced data-driven methodologies within the nuclear industry, notably enhancing safety and economic efficiency. This advancement challenges the precise prediction of future maintenance needs for assets, which is crucial for reducing downtime and operational costs. However, the effectiveness of data-driven methodologies in the nuclear sector requires extensive domain knowledge due to the complexity of the systems involved. Thus, this paper proposes a novel predictive maintenance methodology that combines data-driven techniques with domain knowledge from a nuclear equipment. The methodological originality of this paper is located on two levels: highlighting the limitations of purely data-driven approaches and demonstrating the importance of knowledge in enhancing the performance of the predictive models. The applicative novelty of this work lies in its use within a domain such as a nuclear industry, which is highly restricted and ultrasensitive due to security, economic and environmental concerns. A detailed real-world case study which compares the current state of equipment monitoring with two scenarios, demonstrate that the methodology significantly outperforms purely data-driven methods in failure prediction. While purely data-driven methods achieve only a modest performance with a prediction horizon limited to 3 h and a F1 score of 56.36%, the hybrid approach increases the prediction horizon to 24 h and achieves a higher F1 score of 93.12%.

Qiyu Chen, Huiyuan Luo, Haiming Yao et al.

Anomaly detection plays a vital role in the inspection of industrial images. Most existing methods require separate models for each category, resulting in multiplied deployment costs. This highlights the challenge of developing a unified model for multi-class anomaly detection. However, the significant increase in inter-class interference leads to severe missed detections. Furthermore, the intra-class overlap between normal and abnormal samples, particularly in synthesis-based methods, cannot be ignored and may lead to over-detection. To tackle these issues, we propose a novel Center-aware Residual Anomaly Synthesis (CRAS) method for multi-class anomaly detection. CRAS leverages center-aware residual learning to couple samples from different categories into a unified center, mitigating the effects of inter-class interference. To further reduce intra-class overlap, CRAS introduces distance-guided anomaly synthesis that adaptively adjusts noise variance based on normal data distribution. Experimental results on diverse datasets and real-world industrial applications demonstrate the superior detection accuracy and competitive inference speed of CRAS. The source code and the newly constructed dataset are publicly available at https://github.com/cqylunlun/CRAS.

Debasree Dhar

This article elaborates on the new wellbeing discourse developed from the mid-1940s that concerned itself with providing human amenities to mineworkers to improve their health, morale, and, in turn, efficiency on the Indian coal mines. This wellbeing practice brought about new institutional machinery, called Coal Mines Labour Welfare Organisation, which was jointly managed by the representatives of the state, organised labour and colliery management since 1946. The wellbeing practice prioritised the issue of housing, drinking water, sanitation and hygiene, crèche, canteen, education and awareness campaign. Some of these measures registered impressive advancement while others remained more on paper with actual provision of the above taking much longer. Mineworkers saw that the record of human amenities as a whole was insufficient, unsatisfactory and half-hearted during 1946 to 1972. The financial anxiety and unfavourable social priority of labour-intensive colliery firms, on one side, and the humane desire of workers and the promise of state welfare were at loggerheads. This manifested in the problem of allocation of funds and its improper utilisation, anxiety-related to the material cost, and the very loophole in the ‘protective’ labour laws.

Sentagi Sesotya Utami, Winny Setyonugroho, Moch Zihad Islami et al.

Introduction: Ship-to-shore (STS) crane operators strive for efficiency in their work, but they must take a hard look at their high-risk jobs. It is necessary to learn how to improve occupational safety and health. This study aims to investigate the problems faced by STS crane operators working in container ports and to understand the importance of fit-for-work monitoring procedures, particularly for individuals working in high-risk industries such as STS operators. Methods: This study used a qualitative approach, and data were collected through interviews and observations of STS operators and in-house clinic staff. Nine STS operators, two in-house clinic staff, and two safety, health, and environment (SHE) staff were interviewed. Results: This study found that container terminal companies emphasise two critical aspects for STS operators: productivity and occupational safety and health. STS operators face health problems, including physical and psychological problems, due to the fast-paced work system, sleep patterns, daily activities, and thoughts that are difficult to control. Employees have coping mechanisms to deal with fatigue, and stakeholders have effectively communicated the company's safety and health culture. Most stakeholders in a container terminal company want a fit-for-work monitoring system to make the business efficient and sustainable. Conclusion: The STS industry faces a significant problem with operator fatigue, which can negatively impact safety and productivity. This issue requires a comprehensive strategy, including legislation to regulate working hours and shift patterns, technology to combat fatigue, and operator education and training.

Alka Verma, Amit Goel, Priyank Yadav et al.

Introduction: Limited information is available regarding effect of vaccination on protection against Covid-19 infections and their severity as well. Objectives: In the present study, we assessed the effect of Covid-19 vaccination on incidences and severity of break through Covid-19 infections. Method: This retrospective study was conducted at a tertiary care center in Northern India during one calendar year, 1st August 2021 to 31st July 2022. The study population included Health-care workers (HCWs) who were treated for Covid 19 infection and had already received at least 1 dose of Covishield TM (AZD1222) Covid-19 vaccine. Results: Out of 1868 health care workers enrolled for the study, 513 contracted Covid-19 infections. Amongst infected HCWs, number of single and double doses of CovishieldTM (AZD1222) recipients were 112 and 401 respectively. Out of the 513 covid positive HCWs, 459 (89.4%) had mild disease, whereas 54 (10.6%) had moderate disease. None of the HCWs developed severe disease and no mortality was noted in either group. Conclusion: In this study, we found that immunization with two doses of CovishieldTM (AZD1222) vaccine was associated with decline in number of cases with moderate or severe Covid-19. Moreover, immunization with even single dose of CovishieldTM (AZD1222) vaccine prevented development of severe disease. Henceforth, it is concluded that although, immunization with CovishieldTM (AZD1222) could not protect all recipients from SARS-Cov-2 infection, it did prevent the progress of disease to severe grades.

Idalmis Acosta Pérez , Fernando Marrero Delgado , José Ángel Espinosa Acosta et al.

Introducción: La inclusión de la gestión de riesgos de Seguridad y Salud en el Trabajo en la planificación estratégica constituye una herramienta necesaria para que la organización pueda anticiparse y mitigar posibles amenazas e identificar oportunidades para el crecimiento y la mejora continua. Objetivo: Diseñar un procedimiento, científicamente fundamentado, que permita la inclusión de la gestión de riesgos de Seguridad y Salud dentro de la proyección estratégica, en aras de mejorar el sistema de gestión y por ende, el entorno laboral y las condiciones de trabajo. Métodos: Incluyó el diseño de un procedimiento para la gestión estratégica de los riesgos de Seguridad y Salud, con una secuencia lógica de cuatro fases, que incluye métodos empíricos como el criterio de expertos y revisión de la documentación legal en materia de gestión de riesgo. Además, el procedimiento contiene pasos para la realización de un diagnostico estratégico donde se definen la política y la filosofía en materia de gestión de riesgos, se identifican los riesgos y se evalúan según la severidad, ocurrencia y detectatibilidad, para luego calcular el Nivel del Prioridad del Riesgo (NPR). Resultados: Se logró la inclusión de los riesgos de Seguridad y Salud desde la planificación estratégica, además, se conoce el Nivel de Percepción de Convergencia Estratégica en la Gestión de Riesgos de SST (NCERSST). Conclusiones: El procedimiento diseñado permitirá identificar las principales debilidades que presenta la organización relacionada con la gestión estratégica de sus riesgos Introduction: The inclusion of Occupational Health and Safety risk management in strategic planning constitutes a necessary tool so that the organization can anticipate and mitigate possible threats and identify opportunities for growth and continuous improvement. Objective: To design a scientifically based procedure, which allows the inclusion of Health and Safety risk management within the strategic projection, in order to improve the management system and therefore, the work environment and the working conditions. Methods: Included the design of a procedure for the strategic management of Health and Safety risks, with a logical sequence of four phases, which includes empirical methods such as expert judgment and review of legal documentation on risk management. In addition, the procedure includes steps to carry out a strategic diagnosis where the policy and philosophy regarding risk management are defined, risks are identified and evaluated according to severity, occurrence and detectability, and then calculate the Priority Level of Risk (PLR). Results: The inclusion of Health and Safety risks is achieved from the strategic planning, in addition, the Level of Perception of Strategic Convergence in (Security and health at work) Risk Management (NCERSST) is known. Conclusions: The designed procedure will allow identifying the main weaknesses that the organization presents related to the strategic management of its risks

Yunyi Wang, Ke Xu, Xiao Gao et al.

Abstract Background Increasing attention is being paid to the environmental and health impacts of nanoplastics (NPs) pollution. Exposure to nanoplastics (NPs) with different charges and functional groups may have different adverse effects after ingestion by organisms, yet the potential ramifications on mammalian blood glucose levels, and the risk of diabetes remain unexplored. Results Mice were exposed to PS-NPs/COOH/NH2 at a dose of 5 mg/kg/day for nine weeks, either alone or in a T2DM model. The findings demonstrated that exposure to PS-NPs modified by different functional groups caused a notable rise in fasting blood glucose (FBG) levels, glucose intolerance, and insulin resistance in a mouse model of T2DM. Exposure to PS-NPs-NH2 alone can also lead the above effects to a certain degree. PS-NPs exposure could induce glycogen accumulation and hepatocellular edema, as well as injury to the pancreas. Comparing the effect of different functional groups or charges on T2DM, the PS-NPs-NH2 group exhibited the most significant FBG elevation, glycogen accumulation, and insulin resistance. The phosphorylation of AKT and FoxO1 was found to be inhibited by PS-NPs exposure. Treatment with SC79, the selective AKT activator was shown to effectively rescue this process and attenuate T2DM like lesions. Conclusions Exposure to PS-NPs with different functional groups (charges) induced T2DM-like lesions. Amino-modified PS-NPs cause more serious T2DM-like lesions than pristine PS-NPs or carboxyl functionalized PS-NPs. The underlying mechanisms involved the inhibition of P-AKT/P-FoxO1. This study highlights the potential risk of NPs pollution on T2DM, and provides a new perspective for evaluating the impact of plastics aging.

Eric Guiffo Kaigom

Metarobotics aims to combine next generation wireless communication, multi-sense immersion, and collective intelligence to provide a pervasive, itinerant, and non-invasive access and interaction with distant robotized applications. Industry and society are expected to benefit from these functionalities. For instance, robot programmers will no longer travel worldwide to plan and test robot motions, even collaboratively. Instead, they will have a personalized access to robots and their environments from anywhere, thus spending more time with family and friends. Students enrolled in robotics courses will be taught under authentic industrial conditions in real-time. This paper describes objectives of Metarobotics in society, industry, and in-between. It identifies and surveys technologies likely to enable their completion and provides an architecture to put forward the interplay of key components of Metarobotics. Potentials for self-determination, self-efficacy, and work-life-flexibility in robotics-related applications in Society 5.0, Industry 4.0, and Industry 5.0 are outlined.

Francisco de Arriba-Pérez, Silvia García-Méndez, Javier Otero-Mosquera et al.

New technologies such as Machine Learning (ML) gave great potential for evaluating industry workflows and automatically generating key performance indicators (KPIs). However, despite established standards for measuring the efficiency of industrial machinery, there is no precise equivalent for workers' productivity, which would be highly desirable given the lack of a skilled workforce for the next generation of industry workflows. Therefore, an ML solution combining data from manufacturing processes and workers' performance for that goal is required. Additionally, in recent times intense effort has been devoted to explainable ML approaches that can automatically explain their decisions to a human operator, thus increasing their trustworthiness. We propose to apply explainable ML solutions to differentiate between expert and inexpert workers in industrial workflows, which we validate at a quality assessment industrial workstation. Regarding the methodology used, input data are captured by a manufacturing machine and stored in a NoSQL database. Data are processed to engineer features used in automatic classification and to compute workers' KPIs to predict their level of expertise (with all classification metrics exceeding 90 %). These KPIs, and the relevant features in the decisions are textually explained by natural language expansion on an explainability dashboard. These automatic explanations made it possible to infer knowledge from expert workers for inexpert workers. The latter illustrates the interest of research in self-explainable ML for automatically generating insights to improve productivity in industrial workflows.

Carlos Alberto Durigan Junior, André Taue Saito, Daniel Reed Bergmann et al.

The main objective of this paper is to Identify which macroe conomic factors and industrial indexes influenced the total Brazilian banking spread between March 2011 and March 2015. This paper considers subclassification of industrial activities in Brazil. Monthly time series data were used in multivariate linear regression models using Eviews (7.0). Eighteen variables were considered as candidates to be determinants. Variables which positively influenced bank spread are; Default, IPIs (Industrial Production Indexes) for capital goods, intermediate goods, du rable consumer goods, semi-durable and non-durable goods, the Selic, GDP, unemployment rate and EMBI +. Variables which influence negatively are; Consumer and general consumer goods IPIs, IPCA, the balance of the loan portfolio and the retail sales index. A p-value of 05% was considered. The main conclusion of this work is that the progress of industry, job creation and consumption can reduce bank spread. Keywords: Credit. Bank spread. Macroeconomics. Industrial Production Indexes. Finance.

Haiming Yao, Yunkang Cao, Wei Luo et al.

Image anomaly detection plays a pivotal role in industrial inspection. Traditional approaches often demand distinct models for specific categories, resulting in substantial deployment costs. This raises concerns about multi-class anomaly detection, where a unified model is developed for multiple classes. However, applying conventional methods, particularly reconstruction-based models, directly to multi-class scenarios encounters challenges such as identical shortcut learning, hindering effective discrimination between normal and abnormal instances. To tackle this issue, our study introduces the Prior Normality Prompt Transformer (PNPT) method for multi-class image anomaly detection. PNPT strategically incorporates normal semantics prompting to mitigate the "identical mapping" problem. This entails integrating a prior normality prompt into the reconstruction process, yielding a dual-stream model. This innovative architecture combines normal prior semantics with abnormal samples, enabling dual-stream reconstruction grounded in both prior knowledge and intrinsic sample characteristics. PNPT comprises four essential modules: Class-Specific Normality Prompting Pool (CS-NPP), Hierarchical Patch Embedding (HPE), Semantic Alignment Coupling Encoding (SACE), and Contextual Semantic Conditional Decoding (CSCD). Experimental validation on diverse benchmark datasets and real-world industrial applications highlights PNPT's superior performance in multi-class industrial anomaly detection.

Mykhailo Koshil, Tilman Wegener, Detlef Mentrup et al.

Visual inspection, or industrial anomaly detection, is one of the most common quality control types in manufacturing. The task is to identify the presence of an anomaly given an image, e.g., a missing component on an image of a circuit board, for subsequent manual inspection. While industrial anomaly detection has seen a surge in recent years, most anomaly detection methods still utilize knowledge only from normal samples, failing to leverage the information from the frequently available anomalous samples. Additionally, they heavily rely on very general feature extractors pre-trained on common image classification datasets. In this paper, we address these shortcomings and propose the new anomaly detection system AnomalousPatchCore~(APC) based on a feature extractor fine-tuned with normal and anomalous in-domain samples and a subsequent memory bank for identifying unusual features. To fine-tune the feature extractor in APC, we propose three auxiliary tasks that address the different aspects of anomaly detection~(classification vs. localization) and mitigate the effect of the imbalance between normal and anomalous samples. Our extensive evaluation on the MVTec dataset shows that APC outperforms state-of-the-art systems in detecting anomalies, which is especially important in industrial anomaly detection given the subsequent manual inspection. In detailed ablation studies, we further investigate the properties of our APC.

Caroline Carlé, Delphine Boucher, Luisa Morelli et al.

Abstract Background Perinatal exposure to titanium dioxide (TiO2), as a foodborne particle, may influence the intestinal barrier function and the susceptibility to develop inflammatory bowel diseases (IBD) later in life. Here, we investigate the impact of perinatal foodborne TiO2 exposure on the intestinal mucosal function and the susceptibility to develop IBD-associated colitis. Pregnant and lactating mother mice were exposed to TiO2 until pups weaning and the gut microbiota and intestinal barrier function of their offspring was assessed at day 30 post-birth (weaning) and at adult age (50 days). Epigenetic marks was studied by DNA methylation profile measuring the level of 5-methyl-2′-deoxycytosine (5-Me-dC) in DNA from colic epithelial cells. The susceptibility to develop IBD has been monitored using dextran-sulfate sodium (DSS)-induced colitis model. Germ-free mice were used to define whether microbial transfer influence the mucosal homeostasis and subsequent exacerbation of DSS-induced colitis. Results In pregnant and lactating mice, foodborne TiO2 was able to translocate across the host barriers including gut, placenta and mammary gland to reach embryos and pups, respectively. This passage modified the chemical element composition of foetus, and spleen and liver of mothers and their offspring. We showed that perinatal exposure to TiO2 early in life alters the gut microbiota composition, increases the intestinal epithelial permeability and enhances the colonic cytokines and myosin light chain kinase expression. Moreover, perinatal exposure to TiO2 also modifies the abilities of intestinal stem cells to survive, grow and generate a functional epithelium. Maternal TiO2 exposure increases the susceptibility of offspring mice to develop severe DSS-induced colitis later in life. Finally, transfer of TiO2-induced microbiota dysbiosis to pregnant germ-free mice affects the homeostasis of the intestinal mucosal barrier early in life and confers an increased susceptibility to develop colitis in adult offspring. Conclusions Our findings indicate that foodborne TiO2 consumption during the perinatal period has negative long-lasting consequences on the development of the intestinal mucosal barrier toward higher colitis susceptibility. This demonstrates to which extent environmental factors influence the microbial-host interplay and impact the long-term mucosal homeostasis.

Oluwatosin Ogundare, Srinath Madasu, Nathanial Wiggins

Large Language Models (LLMs) have shown great potential in solving complex problems in various fields, including oil and gas engineering and other industrial engineering disciplines like factory automation, PLC programming etc. However, automatic identification of strong and weak solutions to fundamental physics equations governing several industrial processes remain a challenging task. This paper identifies the limitation of current LLM approaches, particularly ChatGPT in selected practical problems native to oil and gas engineering but not exclusively. The performance of ChatGPT in solving complex problems in oil and gas engineering is discussed and the areas where LLMs are most effective are presented.

Yuting Sun, Tong Chen, Quoc Viet Hung Nguyen et al.

Monitoring and detecting abnormal events in cyber-physical systems is crucial to industrial production. With the prevalent deployment of the Industrial Internet of Things (IIoT), an enormous amount of time series data is collected to facilitate machine learning models for anomaly detection, and it is of the utmost importance to directly deploy the trained models on the IIoT devices. However, it is most challenging to deploy complex deep learning models such as Convolutional Neural Networks (CNNs) on these memory-constrained IIoT devices embedded with microcontrollers (MCUs). To alleviate the memory constraints of MCUs, we propose a novel framework named Tiny Anomaly Detection (TinyAD) to efficiently facilitate onboard inference of CNNs for real-time anomaly detection. First, we conduct a comprehensive analysis of depthwise separable CNNs and regular CNNs for anomaly detection and find that the depthwise separable convolution operation can reduce the model size by 50-90% compared with the traditional CNNs. Then, to reduce the peak memory consumption of CNNs, we explore two complementary strategies, in-place, and patch-by-patch memory rescheduling, and integrate them into a unified framework. The in-place method decreases the peak memory of the depthwise convolution by sparing a temporary buffer to transfer the activation results, while the patch-by-patch method further reduces the peak memory of layer-wise execution by slicing the input data into corresponding receptive fields and executing in order. Furthermore, by adjusting the dimension of convolution filters, these strategies apply to both univariate time series and multidomain time series features. Extensive experiments on real-world industrial datasets show that our framework can reduce peak memory consumption by 2-5x with negligible computation overhead.