Hasil untuk "Industrial hygiene. Industrial welfare"

F. Patty

Nigar Hashimzade, Haoran Sun

Industrial policy has returned to the centre of economic governance, particularly in the high-tech sectors where positive network externalities in demand make market dominance self-reinforcing. This paper studies the welfare effects of an industrial policy targeting a sector with network externalities in a two-country model with strategic trade and R&D investment. We show how the welfare consequences of this policy are determined by the interaction between the strength of the externality, the type of R&D, and the degree of product differentiation between the home and the imported goods. When externalities are weak or the goods are close substitutes, the business-stealing effect produces a race to the bottom that dissipates more surplus than it creates. Under sufficiently strong externalities and weak substitutability or complementarity of the goods, industrial policy competition can make both countries simultaneously better off compared to the laissez-faire outcome because of the mutual business-enhancement effect. The case is stronger for the product innovation than for the process innovation, as the former directly affects the demand and triggers a stronger network effects than the latter which operates indirectly through the supply. Thus, the network externalities create an opportunity for a win-win industrial policies, but its realisation depends on the market structure and the nature of innovation.

Puyu Zeng, Zhaoxi Wang, Zhixu Duan et al.

Code generation and comprehension by Large Language Models (LLMs) have emerged as core drivers of industrial intelligence and decision optimization, finding widespread application in fields such as finance, automation, and aerospace. Although recent advancements have demonstrated the remarkable potential of LLMs in general code generation, existing benchmarks are mainly confined to single domains and languages. Consequently, they fail to effectively evaluate the generalization capabilities required for real-world industrial applications or to reflect the coding proficiency demanded by complex industrial scenarios. To bridge this gap, we introduce IndustryCode, the first comprehensive benchmark designed to span multiple industrial domains and programming languages. IndustryCode comprises 579 sub-problems derived from 125 primary industrial challenges, accompanied by rigorous problem descriptions and test cases. It covers a wide range of fields, including finance, automation, aerospace, and remote sensing-and incorporates diverse programming languages such as MATLAB, Python, C++, and Stata. In our evaluation, the top-performing model, Claude 4.5 Opus, achieved an overall accuracy of 68.1% on sub-problems and 42.5% main problems. The benchmark dataset and automated evaluation code will be made publicly available upon acceptance.

Jacob M. Cowley, Cassandra E. Deering-Rice, John G. Lamb et al.

Abstract Background Climate change and human activities have caused the drying of marine environments around the world. An example is the Great Salt Lake in Utah, USA which is at a near record low water level. Adverse health effects have been associated with exposure to windblown dust originating from dried lakebed sediments, but mechanistic studies evaluating the health effects of these dusts are limited. Results Monitoring data and images highlight the impact of local crustal and Great Salt Lake sediment dusts on the Salt Lake Valley/Wasatch front airshed. Great Salt Lake sediment and derived PM< 3.1 (quasi-PM2.5 or qPM2.5) contained metals/salts, natural and anthropogenic chemicals, and bacteria. Exposure of mice via inhalation and oropharyngeal aspiration caused neutrophilia, increased expression of mRNA for Il6, Cxcl1, Cxcl2, and Muc5ac in the lungs, and increased IL6 and CXCL1 in bronchoalveolar lavage. Inhaled GSLD qPM2.5 caused a greater neutrophilic response than coal fly ash qPM2.5 and was more cytotoxic to human airway epithelial cells (HBEC3-KT) in vitro. Pro-inflammatory biomarker mRNA induction was replicated in vitro using HBEC3-KT and differentiated monocyte-derived (macrophage-like) THP-1 cells. In HBEC3-KT cells, IL6 and IL8 (the human analogue of Cxcl1 and Cxcl2) mRNA induction was attenuated by ethylene glycol-bis(β-aminoethyl ether)-N, N,N′,N’-tetraacetic acid (EGTA) and ruthenium red (RR) co-treatment, and by TRPV1 and TRPV3 antagonists, but less by the Toll-like Receptor-4 (TLR4) inhibitor TAK-242 and deferoxamine. Accordingly, GSLD qPM2.5 activated human TRPV1 as well as other human TRP channels. Dust from the Salton Sea playa (SSD qPM2.5) also stimulated IL6 and IL8 mRNA expression and activated TRPV1 in vitro, but inhibition by TRPV1 and V3 antagonists was dose dependent. Alternatively, responses of THP-1 cells to the Great Salt Lake and Salton Sea dusts were partially mediated by TLR4 as opposed to TRPV1. Finally, “humanized” Trpv1 N606D mice exhibited greater neutrophilia than C57Bl/6 mice following GSLD qPM2.5 inhalation. Conclusions Dust from the GSL playa and similar dried lakebeds may affect human respiratory health via activation of TRPV1, TRPV3, TLR4, and oxidative stress.

Zainab Alwaisi, Simone Soderi, Rocco De Nicola

Internet of Everything (IoE) is a newly emerging trend, especially in homes. Marketing forces toward smart homes are also accelerating the spread of IoE devices in households. An obvious risk of the rapid adoption of these smart devices is that many lack controls for protecting the privacy and security of end users from attacks designed to disrupt lives and incur financial losses. Today the smart home is a system for managing the basic life support processes of both small systems, e.g., commercial, office premises, apartments, cottages, and largely automated complexes, e.g., commercial and industrial complexes. One of the critical tasks to be solved by the concept of a modern smart home is the problem of preventing the usage of IoE resources. Recently, there has been a rapid increase in attacks on consumer IoE devices. Memory corruption vulnerabilities constitute a significant class of vulnerabilities in software security through which attackers can gain control of an entire system. Numerous memory corruption vulnerabilities have been found in IoE firmware already deployed in the consumer market. This paper aims to analyze and explain the resource usage attack and create a low-cost simulation environment to aid in the dynamic analysis of the attack. Further, we perform controlled resource usage attacks while measuring resource consumption on resource-constrained victims' IoE devices, such as CPU and memory utilization. We also build a lightweight algorithm to detect memory usage attacks in the IoE environment. The result shows high efficiency in detecting and mitigating memory usage attacks by detecting when the intruder starts and stops the attack.

Shiying Zhang, Jun Li, Long Shi et al.

As an emerging technology that enables seamless integration between the physical and virtual worlds, the Metaverse has great potential to be deployed in the industrial production field with the development of extended reality (XR) and next-generation communication networks. This deployment, called the Industrial Metaverse, is used for product design, production operations, industrial quality inspection, and product testing. However, there lacks of in-depth understanding of the enabling technologies associated with the Industrial Metaverse. This encompasses both the precise industrial scenarios targeted by each technology and the potential migration of technologies developed in other domains to the industrial sector. Driven by this issue, in this article, we conduct a comprehensive survey of the state-of-the-art literature on the Industrial Metaverse. Specifically, we first analyze the advantages of the Metaverse for industrial production. Then, we review a collection of key enabling technologies of the Industrial Metaverse, including blockchain (BC), digital twin (DT), 6G, XR, and artificial intelligence (AI), and analyze how these technologies can support different aspects of industrial production. Subsequently, we present numerous formidable challenges encountered within the Industrial Metaverse, including confidentiality and security concerns, resource limitations, and interoperability constraints. Furthermore, we investigate the extant solutions devised to address them. Finally, we briefly outline several open issues and future research directions of the Industrial Metaverse.

Md Bokhtiar Al Zami, Shaba Shaon, Vu Khanh Quy et al.

Industrial networks are undergoing rapid transformation driven by the convergence of emerging technologies that are revolutionizing conventional workflows, enhancing operational efficiency, and fundamentally redefining the industrial landscape across diverse sectors. Amidst this revolution, Digital Twin (DT) emerges as a transformative innovation that seamlessly integrates real-world systems with their virtual counterparts, bridging the physical and digital realms. In this article, we present a comprehensive survey of the emerging DT-enabled services and applications across industries, beginning with an overview of DT fundamentals and its components to a discussion of key enabling technologies for DT. Different from literature works, we investigate and analyze the capabilities of DT across a wide range of industrial services, including data sharing, data offloading, integrated sensing and communication, content caching, resource allocation, wireless networking, and metaverse. In particular, we present an in-depth technical discussion of the roles of DT in industrial applications across various domains, including manufacturing, healthcare, transportation, energy, agriculture, space, oil and gas, as well as robotics. Throughout the technical analysis, we delve into real-time data communications between physical and virtual platforms to enable industrial DT networking. Subsequently, we extensively explore and analyze a wide range of major privacy and security issues in DT-based industry. Taxonomy tables and the key research findings from the survey are also given, emphasizing important insights into the significance of DT in industries. Finally, we point out future research directions to spur further research in this promising area.

Jinfan Liu, Yichao Yan, Junjie Li et al.

Video anomaly detection (VAD) is a challenging task aiming to recognize anomalies in video frames, and existing large-scale VAD researches primarily focus on road traffic and human activity scenes. In industrial scenes, there are often a variety of unpredictable anomalies, and the VAD method can play a significant role in these scenarios. However, there is a lack of applicable datasets and methods specifically tailored for industrial production scenarios due to concerns regarding privacy and security. To bridge this gap, we propose a new dataset, IPAD, specifically designed for VAD in industrial scenarios. The industrial processes in our dataset are chosen through on-site factory research and discussions with engineers. This dataset covers 16 different industrial devices and contains over 6 hours of both synthetic and real-world video footage. Moreover, we annotate the key feature of the industrial process, ie, periodicity. Based on the proposed dataset, we introduce a period memory module and a sliding window inspection mechanism to effectively investigate the periodic information in a basic reconstruction model. Our framework leverages LoRA adapter to explore the effective migration of pretrained models, which are initially trained using synthetic data, into real-world scenarios. Our proposed dataset and method will fill the gap in the field of industrial video anomaly detection and drive the process of video understanding tasks as well as smart factory deployment.

Xiao Wang, Yutong Wang, Jing Yang et al.

As the concept of Industries 5.0 develops, industrial metaverses are expected to operate in parallel with the actual industrial processes to offer ``Human-Centric" Safe, Secure, Sustainable, Sensitive, Service, and Smartness ``6S" manufacturing solutions. Industrial metaverses not only visualize the process of productivity in a dynamic and evolutional way, but also provide an immersive laboratory experimental environment for optimizing and remodeling the process. Besides, the customized user needs that are hidden in social media data can be discovered by social computing technologies, which introduces an input channel for building the whole social manufacturing process including industrial metaverses. This makes the fusion of multi-source data cross Cyber-Physical-Social Systems (CPSS) the foundational and key challenge. This work firstly proposes a multi-source-data-fusion-driven operational architecture for industrial metaverses on the basis of conducting a comprehensive literature review on the state-of-the-art multi-source data fusion methods. The advantages and disadvantages of each type of method are analyzed by considering the fusion mechanisms and application scenarios. Especially, we combine the strengths of deep learning and knowledge graphs in scalability and parallel computation to enable our proposed framework the ability of prescriptive optimization and evolution. This integration can address the shortcomings of deep learning in terms of explainability and fact fabrication, as well as overcoming the incompleteness and the challenges of construction and maintenance inherent in knowledge graphs. The effectiveness of the proposed architecture is validated through a parallel weaving case study. In the end, we discuss the challenges and future directions of multi-source data fusion cross CPSS for industrial metaverses and social manufacturing in Industries 5.0.

Ahmed Gailani, Peter Taylor

Decarbonising the industrial sector is vital to reach net zero targets. The deployment of industrial decarbonisation technologies is expected to increase industrial electricity demand in many countries and this may require upgrades to the existing electricity network or new network investment. While the infrastructure requirements to support the introduction of new fuels and technologies in industry, such as hydrogen and carbon capture, utilisation and storage are often discussed, the need for investment to increase the capacity of the electricity network to meet increasing industrial electricity demands is often overlooked in the literature. This paper addresses this gap by quantifying the requirements for additional electricity network capacity to support the decarbonisation of industrial sectors across Great Britain (GB). The Net Zero Industrial Pathways model is used to predict the future electricity demand from industrial sites to 2050 which is then compared spatially to the available headroom across the distribution network in GB. The results show that network headroom is sufficient to meet extra capacity demands from industrial sites over the period to 2030 in nearly all GB regions and network scenarios. However, as electricity demand rises due to increased electrification across all sectors and industrial decarbonisation accelerates towards 2050, the network will need significant new capacity (71 GW + by 2050) particularly in the central, south, and north-west regions of England, and Wales. Without solving these network constraints, around 65% of industrial sites that are large point sources of emissions would be constrained in terms of electric capacity by 2040. These sites are responsible for 69% of industrial point source emissions.

Gareth S. Evans, Harrison Kloke, Steven D. Jahn

Abstract This review is about the ethical use of sensors to monitor occupational exposure to hazards. It considers whether the same or different, ethical measures apply to using sensors, compared to conventional hazard monitoring surveys. To undertake the review, subject experts developed a research question, identified suitable search terms, and set the scope of these searches. Candidate research papers dating from 2000 to mid-2022 that met inclusion criteria were identified and reviewed by each author. Ethical concerns were identified by the authors of studies in which sensors were used to monitor employee health and well-being, but most of the studies that used them to monitor employee exposure to hazards focused on the technical aspects of their deployment. These ethical concerns included questions about employee rights and privacy, the anonymity of the data collected with sensors, and how the security of this data is managed. The review considers ethical standards and codes of practice for occupational hygiene work and the ethical risks when sensors are used to gather data. Sensors may provide insight into occupational exposure to hazards, but their use is not always adequately explained to employees by those managing this monitoring work. The ethical concerns identified were relevant to many areas of industrial hygiene work, but more studies are required that consider the ethical use of sensors in workplaces. Studies that monitored employee health, well-being, and productivity, identified ethical risks in using sensors to monitor these endpoints. An ethical framework and checklist for hygienists are proposed including a set of questions that consider the risks of using sensors to monitor occupational hazards. Industrial hygiene professional bodies provide ethical codes of practice for their members but may also need to consider the implications of using sensors in workplaces. Ethical standards support the collection of industrial hygiene exposure data whilst maintaining the privacy rights of employees.

N. Krempa, O. Kozenko, B. Gutyj et al.

The market situation regarding the breeding of decorative dogs of the Pomeranian breed and providing consumers with appropriate services in this field of activity dictate the rules and create the conditions of competition between market participants, which encourages improvement, development, and constant monitoring of it in terms of novelties and needs that provide opportunities for comfortable care and decent maintenance of pupils. The work aimed to analyze the impact of sanitary and hygienic factors and ensuring healthy housing conditions on the functional state of the body and mental health of Pomeranian Spitz dogs in the conditions of the private breeding kennel Fluffy Luxury Pom in the suburbs of Lviv. To fulfill this task, a study of the hygienic parameters of the air environment, compliance with the hygiene of feeding and drinking of the wards, peculiarities of the care of the wool coat, and compliance with the concept of animal welfare – the principle of “Five Freedoms” was carried out. It has been established that the technological solutions for keeping Pomeranian Spitz dogs in the conditions of this kennel are characterized by compliance with hygienic requirements for the air environment at the proper level and meeting sanitary standards. Thus, the air temperature was 17 °C, and its movement speed was 0.15 m/s. In the absence of the smell of ammonia, the illuminance ratio corresponded to 1:2. The animals are fed with industrial complete ration super premium 1st Choice feed, the use of which, according to recommendations, helps dogs strengthen the immune system, slow down the aging process, maintain health and activity. This type of food requires constant free access of dogs to clean water, and the containers are kept clean by changing the filling every day. Regarding the quality characteristics of the water, it was established that it fully meets the standard. Dogs of this breed are characterized by a beautiful, lush coat requiring special care. Evaluating the level of compliance with individual hygienic procedures, it was established that they are carried out at a high level, as evidenced by the appearance of the animals. According to the statements of John Webster, the welfare of animals is defined by advocating the main three conditions - to live a natural life, to be healthy, and to be happy. The dogs in this kennel are provided with a good life; the behavioral reactions of the wards themselves confirm this. During the research, the dogs behaved calmly, naturally, and playfully, which indicates the socialization of dogs; the purpose is to make the pet feel comfortable and calm in various life situations without causing trouble to its owner and his surroundings.

Aleida Fernández Moreno

Se presenta un sencillo caso hipotético sobre la presencia y la puntualidad de profesionales en los servicios de salud y, derivadas de este, algunas consideraciones sobre la ética en Terapia Ocupacional. La autora invita a pensar en el tema y a realizar una revisión ética desde tres aristas: la ley profesional vigente, los principios de la bioética y la deliberación ética. En las dos primeras no se logra resolver el caso, la salida será planteada desde la deliberación ética. La Terapia Ocupacional colombiana necesita la renovación de su ley profesional, empezar la formación en ética desde los primeros semestres y generar espacios interdisciplinares de discusión, especialmente a nivel de las prácticas profesionales.

James S. Brown, Gary L. Diamond

Abstract Inhalation is a portal-of-entry for aerosols via the respiratory tract where particulate burden accumulates depending on sites of particle deposition, normal clearance mechanisms, and particle solubility. The time available for dissolution of particles is determined by the balance between the rate of particle clearance from a region and their solubility in respiratory solvents. Dissolution is a function of particle surface area divided by particle volume or mass (i.e., dissolution is inversely proportional to the physical diameter of particles). As a conservative approach, investigators commonly assume the complete and instantaneous dissolution of metals from particles depositing in the alveolar region of the respiratory tract. We derived first-order dissolution rate constants to facilitate biokinetic modeling of particle clearance, dissolution, and absorption into the blood. We then modeled pulmonary burden and total dissolution of particles over time as a function of particle size, density, and solubility. We show that assuming poorly soluble particle forms will enter the blood as quickly as highly soluble forms causes an overestimation of concentrations of the compound of interest in blood and other extrapulmonary tissues while also underestimating its pulmonary burden. We conclude that, in addition to modeling dose rates for particle deposition into the lung, physiologically based pharmacokinetic modeling of pulmonary and extrapulmonary tissues concentrations of moderately and poorly soluble materials can be improved by including estimates of lung burden and particle dissolution over time.

Philku Lee, Jin Kwon Kim, Mi Seong Jo et al.

Abstract Background Toxicokinetics of nanomaterials, including studies on the absorption, distribution, metabolism, and elimination of nanomaterials, are essential in assessing their potential health effects. The fate of nanomaterials after inhalation exposure to multiple nanomaterials is not clearly understood. Methods Male Sprague–Dawley rats were exposed to similar sizes of silver nanoparticles (AgNPs, 10.86 nm) and gold nanoparticles (AuNPs, 10.82 nm) for 28 days (6-h/day, 5-days/week for four weeks) either with separate NP inhalation exposures or with combined co-exposure in a nose-only inhalation system. Mass concentrations sampled from the breathing zone were AuNP 19.34 ± 2.55 μg/m3 and AgNP 17.38 ± 1.88 μg/m3 for separate exposure and AuNP 8.20 μg/m3 and AgNP 8.99 μg/m3 for co-exposure. Lung retention and clearance were previously determined on day 1 (6-h) of exposure (E-1) and on post-exposure days 1, 7, and 28 (PEO-1, PEO-7, and PEO-28, respectively). In addition, the fate of nanoparticles, including translocation and elimination from the lung to the major organs, were determined during the post-exposure observation period. Results AuNP was translocated to the extrapulmonary organs, including the liver, kidney, spleen, testis, epididymis, olfactory bulb, hilar and brachial lymph nodes, and brain after subacute inhalation and showed biopersistence regardless of AuNP single exposure or AuNP + AgNP co-exposure, showing similar elimination half-time. In contrast, Ag was translocated to the tissues and rapidly eliminated from the tissues regardless of AuNP co-exposure. Ag was continually accumulated in the olfactory bulb and brain and persistent until PEO-28. Conclusion Our co-exposure study of AuNP and AgNP indicated that soluble AgNP and insoluble AuNP translocated differently, showing soluble AgNP could be dissolved into Ag ion to translocate to the extrapulmonary organs and rapidly removed from most organs except the brain and olfactory bulb. Insoluble AuNPs were continually translocated to the extrapulmonary organs, and they were not eliminated rapidly.

Ankush Meshram, Markus Karch, Christian Haas et al.

Since 2010, multiple cyber incidents on industrial infrastructure, such as Stuxnet and CrashOverride, have exposed the vulnerability of Industrial Control Systems (ICS) to cyber threats. The industrial systems are commissioned for longer duration amounting to decades, often resulting in non-compliance to technological advancements in industrial cybersecurity mechanisms. The unavailability of network infrastructure information makes designing the security policies or configuring the cybersecurity countermeasures such as Network Intrusion Detection Systems (NIDS) challenging. An empirical solution is to self-learn the network infrastructure information of an industrial system from its monitored network traffic to make the network transparent for downstream analyses tasks such as anomaly detection. In this work, a Python-based industrial communication paradigm-aware framework, named PROFINET Operations Enumeration and Tracking (POET), that enumerates different industrial operations executed in a deterministic order of a PROFINET-based industrial system is reported. The operation-driving industrial network protocol frames are dissected for enumeration of the operations. For the requirements of capturing the transitions between industrial operations triggered by the communication events, the Finite State Machines (FSM) are modelled to enumerate the PROFINET operations of the device, connection and system. POET extracts the network information from network traffic to instantiate appropriate FSM models (Device, Connection or System) and track the industrial operations. It successfully detects and reports the anomalies triggered by a network attack in a miniaturized PROFINET-based industrial system, executed through valid network protocol exchanges and resulting in invalid PROFINET operation transition for the device.

Yuan Luo, Song Han, Jingjing Liu

Machine learning is expected to enable the next Industrial Revolution. However, lacking standardized and automated assembly networks, ML faces significant challenges to meet ever-growing enterprise demands and empower broad industries. In the Perspective, we argue that ML needs to first complete its own Industrial Revolution, elaborate on how to best achieve its goals, and discuss new opportunities to enable rapid translation from ML's innovation frontier to mass production and utilization.

Fouad Oubari, Raphael Meunier, Rodrigue Décatoire et al.

Generative design is an increasingly important tool in the industrial world. It allows the designers and engineers to easily explore vast ranges of design options, providing a cheaper and faster alternative to the trial and failure approaches. Thanks to the flexibility they offer, Deep Generative Models are gaining popularity amongst Generative Design technologies. However, developing and evaluating these models can be challenging. The field lacks accessible benchmarks, in order to evaluate and compare objectively different Deep Generative Models architectures. Moreover, vanilla Deep Generative Models appear to be unable to accurately generate multi-components industrial systems that are controlled by latent design constraints. To address these challenges, we propose an industry-inspired use case that incorporates actual industrial system characteristics. This use case can be quickly generated and used as a benchmark. We propose a Meta-VAE capable of producing multi-component industrial systems and showcase its application on the proposed use case.

Chia-Chi Ho, Wei-Te Wu, Yi-Jun Lin et al.

Abstract Background Exposure to ambient fine particulate matter (PM2.5) is associated with vascular diseases. Polycyclic aromatic hydrocarbons (PAHs) in PM2.5 are highly hazardous; however, the contribution of PM2.5-bound PAHs to PM2.5-associated vascular diseases remains unclear. The ToxCast high-throughput in vitro screening database indicates that some PM2.5-bound PAHs activate the aryl hydrocarbon receptor (AhR). The present study investigated whether the AhR pathway is involved in the mechanism of PM2.5-induced vascular toxicity, identified the PAH in PM2.5 that was the major contributor of AhR activation, and identified a biomarker for vascular toxicity of PM2.5-bound PAHs. Results Treatment of vascular smooth muscle cells (VMSCs) with an AhR antagonist inhibited the PM2.5-induced increase in the cell migration ability; NF-κB activity; and expression of cytochrome P450 1A1 (CYP1A1), 1B1 (CYP1B1), interleukin-6 (IL-6), and osteopontin (OPN). Most PM2.5-bound PAHs were extracted into the organic fraction, which drastically enhanced VSMC migration and increased mRNA levels of CYP1A1, CYP1B1, IL-6, and OPN. However, the inorganic fraction of PM2.5 moderately enhanced VSMC migration and only increased IL-6 mRNA levels. PM2.5 increased IL-6 secretion through NF-κB activation; however, PM2.5 and its organic extract increased OPN secretion in a CYP1B1-dependent manner. Inhibiting CYP1B1 activity and silencing OPN expression prevented the increase in VSMC migration ability caused by PM2.5 and its organic extract. The AhR activation potencies of seven PM2.5-bound PAHs, reported in the ToxCast database, were strongly correlated with their capabilities of enhancing the migration ability of VSMCs. Benzo(k)fluoranthene (BkF) contributed the most to the AhR agonistic activity of ambient PM2.5-bound PAHs. The association between PM2.5-induced vascular toxicity, AhR activity, and OPN secretion was further verified in mice; PM2.5-induced intimal hyperplasia in pulmonary small arteries and OPN secretion were alleviated in mice with low AhR affinity. Finally, urinary concentrations of 1-hydroxypyrene, a major PAH metabolite, were positively correlated with plasma OPN levels in healthy humans. Conclusions The present study offers in vitro, animal, and human evidences supporting the importance of AhR activation for PM2.5-induced vascular toxicities and that BkF was the major contributor of AhR activation. OPN is an AhR-dependent biomarker of PM2.5-induced vascular toxicity. The AhR activation potency may be applied in the risk assessment of vascular toxicity in PAH mixtures.

Sankarshan Damle, Manisha Padala, Sujit Gujar

Civic Crowdfunding (CC) uses the ``power of the crowd'' to garner contributions towards public projects. As these projects are non-excludable, agents may prefer to ``free-ride,'' resulting in the project not being funded. For single project CC, researchers propose to provide refunds to incentivize agents to contribute, thereby guaranteeing the project's funding. These funding guarantees are applicable only when agents have an unlimited budget. This work focuses on a combinatorial setting, where multiple projects are available for CC and agents have a limited budget. We study certain specific conditions where funding can be guaranteed. Further, funding the optimal social welfare subset of projects is desirable when every available project cannot be funded due to budget restrictions. We prove the impossibility of achieving optimal welfare at equilibrium for any monotone refund scheme. We then study different heuristics that the agents can use to contribute to the projects in practice. Through simulations, we demonstrate the heuristics' performance as the average-case trade-off between welfare obtained and agent utility.