Hasil untuk "Environmental protection"

Shenggang Ren, Xiaolei Li, Baolong Yuan et al.

N. Klepeis, W. Nelson, W. Ott et al.

Qionghua Zhou, Qian Chen, Yilong Tong et al.

S. Chege, Daoping Wang

Abstract This paper evaluates the association between technology innovation, environmental sustainability and its impact on small business performance. Samples of 204 small businesses and hierarchical regression models were used in the analysis. The results of the survey show that technological innovation affects environmentally friendly owners who have a positive impact on the performance of the company. Successful companies that support environmental community projects and social well-being beyond their economic responsibilities can bring greater financial success. Innovation from management and employee participation in environmental protection practices can improve the company's performance and enhance its image to stakeholders. The findings of this paper enhance existing theories and contribute to the establishment of sustainable practices in developed and developing countries.

Kun-Min Zhang, Zongguo Wen

M. Bockarjova, L. Steg

Yaping Fu, Fuquan Wang, Zhengyuan Li et al.

Abstract Remanufacturing has become a mainstream sustainable manufacturing paradigm for energy conservation and environmental protection. Disassembly and reprocessing operations are two main activities in remanufacturing. This work proposes multiobjective integrated scheduling of disassembly and reprocessing operations considering product structures and random processing time. First, a stochastic programming model is developed to minimize maximum completion time and total tardiness. Second, a reinforcement learning-based multiobjective evolutionary algorithm is devised considering problem-specific knowledge. Three search strategy combinations are formed: crossover and mutation, crossover and key product-based iterated local search, mutation and key product-based iterated local search. At each iteration, a Q-learning method is devised to intelligently choose a combination of premium strategies. A stochastic simulation is incorporated to evaluate the objective values of the searched solutions. Finally, the formulated model and method are compared with an exact solver, CPLEX, and three well-known metaheuristics from the literature on a set of test instances. The results confirm the excellent competitiveness of the developed model and algorithm for solving the considered problem.

Alan R. Vincelette

Protection of the environment and its life forms has become a significant concern among philosophers and theologians alike in recent years. There is disagreement, however, over the best way to formulate the grounds of this concern. Some philosophers and theologians favor an instrumental or anthropocentric approach, claiming that adequate preservation of wildlife is warranted solely on the basis of benefits provided to humans, whether couched in terms of the satisfaction of material, medicinal, recreational, or psychological needs. Others claim that wild nature should be preserved for its own sake, due to its life forms possessing intrinsic value. How best to articulate and defend the intrinsic value of wildlife, however, has been much disputed. This paper first compares the adequacy of anthropocentric and non-anthropocentric approaches to environmental ethics. It concludes that a non-anthropocentric theory of the intrinsic value of living creatures is best suited to motivate care for and action on behalf of the environment, and, in addition, most accurately reflects the basis of human concern for the environment. This paper next goes on to examine the philosophical underpinnings required for a theory of the intrinsic value of nature. It argues that an objective account of the intrinsic value of nature, founded on some form of <i>non-naturalist ethics</i> or <i>minimal theism</i>, seems necessary to account for the intrinsic value of nature (in contrast with a purely subjective or naturalist approach). In particular, a sacramental view of nature wherein creation issues from a creator who is goodness itself seems ideal for grounding the intrinsic value of wildlife, along with motivating humans to contribute energy and resources to their conservation and even to sacrifice some of their interests in order to do so. This being the case, rather than being a hindrance to environmental ethics, religion, if properly formulated, can be a most helpful ally.

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.

Runlong Yu, Shengyu Chen, Yiqun Xie et al.

Modeling environmental ecosystems is essential for effective resource management, sustainable development, and understanding complex ecological processes. However, traditional methods frequently struggle with the inherent complexity, interconnectedness, and limited data of such systems. Foundation models, with their large-scale pre-training and universal representations, offer transformative opportunities by integrating diverse data sources, capturing spatiotemporal dependencies, and adapting to a broad range of tasks. This survey presents a comprehensive overview of foundation model applications in environmental science, highlighting advancements in forward prediction, data generation, data assimilation, downscaling, model ensembling, and decision-making across domains. We also detail the development process of these models, covering data collection, architecture design, training, tuning, and evaluation. By showcasing these emerging methods, we aim to foster interdisciplinary collaboration and advance the integration of cutting-edge machine learning for sustainable solutions in environmental science.

Wei Zhang, Senyou Chai, Changhui Duan et al.

This paper mainly reviews the fate of microplastics, released from used face masks, in the water environment. Through previous experiments, the amount of fiber microplastics released from used face masks into aqueous environments was not negligible, with the maximum microplastics releasing amount reaching 10,000 piece·day⁻¹ for each mask. Microplastic derived from these masks often occurred in the shape of polymeric fibers that resulted from the breakage of the chemical bonds in the plastic fibers by the force of water flow. The potential contact forces between microplastics (originating from face masks) with other pollutants, primarily encompass hydrophobic and electrostatic interactions. This critical review paper briefly illustrates the fate of microplastics derived from disposable face masks, further devising effective strategies to mitigate the environmental impact of plastic particle release from the used personal protective equipment.

Chaoqun Li, Huanqian Yan, Lifeng Zhou et al.

Adversarial attacks in the physical world pose a significant threat to the security of vision-based systems, such as facial recognition and autonomous driving. Existing adversarial patch methods primarily focus on improving attack performance, but they often produce patches that are easily detectable by humans and struggle to achieve environmental consistency, i.e., blending patches into the environment. This paper introduces a novel approach for generating adversarial patches, which addresses both the visual naturalness and environmental consistency of the patches. We propose Prompt-Guided Environmentally Consistent Adversarial Patch (PG-ECAP), a method that aligns the patch with the environment to ensure seamless integration into the environment. The approach leverages diffusion models to generate patches that are both environmental consistency and effective in evading detection. To further enhance the naturalness and consistency, we introduce two alignment losses: Prompt Alignment Loss and Latent Space Alignment Loss, ensuring that the generated patch maintains its adversarial properties while fitting naturally within its environment. Extensive experiments in both digital and physical domains demonstrate that PG-ECAP outperforms existing methods in attack success rate and environmental consistency.

Patrick Chwalek, Sailin Zhong, Nathan Perry et al.

This study presents a comprehensive dataset capturing indoor environmental parameters, physiological responses, and subjective perceptions across three global cities. Utilizing wearable sensors, including smart eyeglasses, and a modified Cozie app, environmental and physiological data were collected, along with pre-screening, onboarding, and recurring surveys. Peripheral cues facilitated participant engagement with micro-EMA surveys, minimizing disruption over a 5-day collection period. The dataset offers insights into urban comfort dynamics, highlighting the interplay between environmental conditions, physiological responses, and subjective perceptions. Researchers can utilize this dataset to deepen their understanding of indoor environmental quality and inform the design of healthier built environments. Access to this dataset can advance indoor environmental research and contribute to the creation of more comfortable and sustainable indoor spaces.

Fu-Hsuan Chen, Hao-Ren Liu

This manuscript focuses on analyzing the growth dynamics of the Central Taiwan Science Park (CTSP) and Silicon Glen in Scotland with a specific emphasis on their approaches to energy, environmental conservation, and economic management. The objective is to provide insights into their sustainable development strategies. In terms of energy, CTSP addresses Taiwan’s energy security and green transformation challenges, while Silicon Glen concentrates on Scotland’s wind energy generation technologies. Both regions prioritize the advancement of renewable energy sources and smart grid technologies. In the realm of environmental conservation, both CTSP and Silicon Glen prioritize environmental protection and sustainability by implementing rigorous environmental monitoring measures. Regarding economic management, CTSP and Silicon Glen serve as vital technology industry hubs in Taiwan and Scotland, respectively, attracting a multitude of high-tech and startup enterprises. This growth is facilitated through various means, including policy support, access to research resources, and robust infrastructure. This manuscript presents a comparative analysis of these two industrial parks, focusing on their environmental and economic management strategies. It aims to elucidate the principles underpinning the sustainable development and economic growth of industrial parks, offering valuable insights to decision-makers and stakeholders involved in the planning of sustainable industrial parks.

Zhiwei Li, Feng Lu, Mengmeng Liu et al.

Abstract The association between CO and chronic obstructive pulmonary disease (COPD) has been widely reported; however, the association among patients with type 2 diabetes mellitus (T2DM) or hypertension has remained largely unknown in China. Over‐dispersed generalized additive model was adopted to quantity the associations between CO and COPD with T2DM or hypertension. Based on principal diagnosis, COPD cases were identified according to the International Classification of Diseases (J44), and a history of T2DM and hypertension was coded as E12 and I10‐15, O10‐15, P29, respectively. A total of 459,258 COPD cases were recorded from 2014 to 2019. Each interquartile range uptick in CO at lag 03 corresponded to 0.21% (95%CI: 0.08%–0.34%), 0.39% (95%CI: 0.13%–0.65%), 0.29% (95%CI: 0.13%–0.45%) and 0.27% (95%CI: 0.12%–0.43%) increment in admissions for COPD, COPD with T2DM, COPD with hypertension and COPD with both T2DM and hypertension, respectively. The effects of CO on COPD with T2DM (Z = 0.77, P = 0.444), COPD with hypertension (Z = 0.19, P = 0.234) and COPD with T2DM and hypertension (Z = 0.61, P = 0.543) were insignificantly higher than that on COPD. Stratification analysis showed that females were more vulnerable than males except for T2DM group (COPD: Z = 3.49, P < 0.001; COPD with T2DM: Z = 0.176, P = 0.079; COPD with hypertension: Z = 2.48, P = 0.013; COPD with both T2DM and hypertension: Z = 2.44, P = 0.014); No statistically significant difference could be found between age groups (COPD: Z = 1.63, P = 0.104; COPD with T2DM: Z = 0.23, P = 0.821; COPD with hypertension: Z = 0.53, P = 0.595; COPD with both T2DM and hypertension: Z = 0.71, P = 0.476); Higher effects appeared in cold seasons than warm seasons on COPD (Z = 0.320, P < 0.001). This study demonstrated an increased risk of COPD with comorbidities related to CO exposure in Beijing. We further provided important information on lag patterns, susceptible subgroups, and sensitive seasons, as well as the characteristics of the exposure‐response curves.

Federico Martinelli-Orlando, Shishir Mundra, Ueli M. Angst

Cathodic protection (CP) was introduced two centuries ago and since has found widespread application in protecting structures such as pipelines, offshore installations, and bridges from corrosion. Despite its extensive use, the fundamental working mechanism of CP remains debated, particularly for metals in porous media such as soil. Here, we offer resolution to the long-standing debate by employing in-situ and ex-situ characterisation techniques coupled with electrochemical measurements to characterise the spatio-temporal changes occurring at the steel-electrolyte interface. We show that upon CP, the interfacial electrolyte undergoes alkalinisation and deoxygenation, and that depending on polarisation conditions, an iron oxide film can simultaneously form on the steel surface. We further demonstrate that these changes in interfacial electrolyte chemistry and steel surface state result in altered anodic and cathodic reactions and their kinetics. We propose a mechanism of CP that integrates the long debated theories, based on both concentration and activation polarisation, complimentarily. Implications of this coherent scientific understanding for enhancing corrosion protection technologies and the safe, economic, and environmental-friendly operation of critical steel-based infrastructures are discussed.

Adrian Helmling-Cornell, Philippe Nguyen, Robert Schofield et al.

The extreme sensitivity required for direct observation of gravitational waves by the Advanced LIGO detectors means that environmental noise is increasingly likely to contaminate Advanced LIGO gravitational wave signals if left unaddressed. Consequently, environmental monitoring efforts have been undertaken and novel noise mitigation techniques have been developed which have reduced environmental coupling and made it possible to analyze environmental artifacts with potential to affect the 90 gravitational wave events detected from 2015-2020 by the Advanced LIGO detectors. So far, there is no evidence for environmental contamination in gravitational wave detections. However, automated, rapid ways to monitor and assess the degree of environmental coupling between gravitational wave detectors and their surroundings are needed as the rate of detections continues to increase. We introduce a computational tool, PEMcheck, for quantifying the degree of environmental coupling present in gravitational wave signals using data from the extant collection of environmental monitoring sensors at each detector. We find that PEMcheck's automated analysis identifies only a small number of gravitational waves that merit further study by environmental noise experts due to possible contamination, a substantial improvement over the manual vetting that occurred for every gravitational wave candidate in the first two observing runs. With the validation provided herein; PEMcheck will play a critical role in event validation during LIGO's fourth observing run as an integral part of the data quality report produced for each gravitational wave candidate.

Christian Rathgeb, Jascha Kolberg, Andreas Uhl et al.

Today, deep learning represents the most popular and successful form of machine learning. Deep learning has revolutionised the field of pattern recognition, including biometric recognition. Biometric systems utilising deep learning have been shown to achieve auspicious recognition accuracy, surpassing human performance. Apart from said breakthrough advances in terms of biometric performance, the use of deep learning was reported to impact different covariates of biometrics such as algorithmic fairness, vulnerability to attacks, or template protection. Technologies of biometric template protection are designed to enable a secure and privacy-preserving deployment of biometrics. In the recent past, deep learning techniques have been frequently applied in biometric template protection systems for various purposes. This work provides an overview of how advances in deep learning take influence on the field of biometric template protection. The interrelation between improved biometric performance rates and security in biometric template protection is elaborated. Further, the use of deep learning for obtaining feature representations that are suitable for biometric template protection is discussed. Novel methods that apply deep learning to achieve various goals of biometric template protection are surveyed along with deep learning-based attacks.