Hasil untuk "Environmental protection"

Saifeng PEI, Chaoye SHEN, Chao YU et al.

BackgroundBenzothiazoles and benzotriazoles are ubiquitously detected in drinking water, posing potential health risks. Developing a reliable and sensitive analytical method is critical for assessing their exposure levels and associated risks.ObjectiveTo establish an automated solid-phase extraction (SPE) coupled with liquid chromatography-tandem mass spectrometry method for the simultaneous determination of benzothiazole, 2-aminobenzothiazole, benzotriazole, and 5-chlorobenzotriazole in drinking water.MethodsKey parameters were systematically optimized. Three mobile phase systems were compared to evaluate their effects on chromatographic peak shape and separation; three SPE cartridges were tested for extraction efficiency; the influences of water pH on extraction recoveries and matrix effects were investigated; the contributions of pretreatment steps to benzothiazole blank were analyzed, and control measures were established; nitrogen evaporation temperatures were tested to ensure analyte stability. The optimized parameters were used to develop the method, validate its performance, and analyze drinking water samples.ResultsDrinking water samples were first adjusted to pH 6.0 with formic acid, and then extracted using HLB cartridges. After elution with 6 mL methanol, the eluate was concentrated under nitrogen evaporation at 45 °C, followed by separation on a C18 column using a gradient elution of acetonitrile-0.1% formic acid solution, and tandem mass spectrometry detection. Avoiding nitrogen blow-drying during SPE and eliminating the use of polypropylene materials during nitrogen evaporation can effectively minimize benzothiazole contamination. Good calibration linearity was obtained for the target analytes in the concentration range of 5.0-250 μg·L−1, with a correlation coefficient of r > 0.995. The method detection limits were in the range of 1.0-5.0 ng·L−1. The recoveries of the target analytes in pure water and tap water were 80.2%-119.5% and 72.2%-115.6%, respectively, with relative standard deviations of were 3.2%-12.8% and 2.3%-11.6%, respectively. When applying this method to actual water samples, benzotriazole was detected in 100% of treated and tap water samples, with median concentrations of 79.4 ng·L−1 and 114 ng·L−1, respectively. Benzothiazole was detected in 83.3% of treated water samples and 100% of tap water samples, with median concentrations of 48.3 ng·L−1 and 65.4 ng·L−1, respectively. In addition, 5-chloro-benzotriazole exhibited low detection rates and concentrations, while 2-amino-benzothiazole was undetected.ConclusionThe developed method demonstrates high accuracy, reliability, and sensitivity, making it suitable for the analysis of trace levels of benzothiazoles and benzotriazoles in drinking water.

Haris Khan, Sadia Asif

The proliferation of generative AI has transformed creative workflows, yet current systems face critical challenges in controllability and content protection. We propose a novel multi-agent framework that addresses both limitations through specialized agent roles and integrated watermarking mechanisms. Unlike existing multi-agent systems focused solely on generation quality, our approach uniquely combines controllable content synthesis with provenance protection during the generation process itself. The framework orchestrates Director/Planner, Generator, Reviewer, Integration, and Protection agents with human-in-the-loop feedback to ensure alignment with user intent while embedding imperceptible digital watermarks. We formalize the pipeline as a joint optimization objective unifying controllability, semantic alignment, and protection robustness. This work contributes to responsible generative AI by positioning multi-agent architectures as a solution for trustworthy creative workflows with built-in ownership tracking and content traceability.

Mahdiyeh Bakhtiyari-Ramezani, Narges Ziveh, Navid Ghaemi

Organic pollutants originating from industrial discharges pose significant threats to human health and ecological balance. Conventional pretreatment methods face challenges due to high costs, limited efficiency, and the generation of residual sludge. Non-thermal plasma (NTP) technology, a promising advanced oxidation process, has attracted substantial research interest for its potential to rapidly and effectively treat industrial wastewater. This study employed a dielectric barrier discharge (DBD) reactor to investigate the feasibility of low-cost, efficient industrial wastewater treatment through NTP-mediated pollutant degradation. NTP generates reactive oxygen and nitrogen species (RONS), capable of complete organic pollutant oxidation. Wastewater samples from Kaveh Industrial City underwent treatment in a DBD reactor to induce the formation of reactive agents. Water quality parameters, including turbidity, total dissolved solids (TDS), total suspended solids (TSS), chemical oxygen demand (COD), biochemical oxygen demand (BOD5), dissolved oxygen (DO), electrical conductivity (EC), and pH, were measured before and after synergetic plasma treatment. The combination of aeration/filtration and 90 min of plasma treatment significantly reduced turbidity compared to untreated wastewater. A 30-min NTP treatment coupled with aeration/filtration demonstrated superior efficiency in removing TDS and TSS, attributed to NTP-generated active species. Optimal COD and BOD5 removal was achieved through a 24-h aeration, adsorbent filtration, and 30-min NTP process. While standalone 30-min NTP treatment exhibited lower efficiency, the combined aeration/filtration system reduced EC and increased pH with extended plasma exposure. A comparative study of advanced oxidation processes showed that plasma treatment effectively reduced COD by 65 %. Plasma offers a cost-effective and efficient solution for wastewater treatment, despite slightly higher energy consumption.These findings underscore the potential of NTP as a viable strategy for industrial wastewater treatment. The integration of NTP with conventional pretreatment methods offers promising prospects for enhancing wastewater quality and environmental protection.

Toni Beth Guatato-Lopez, Alvie Asuncion-Astronomo, Gil Nonato C. Santos

Exposure to mid-energy radiation poses significant health risks, necessitating the development of effective shielding materials. Traditional lead-based shields, while effective, have significant drawbacks including toxicity and environmental concerns. This study investigates the potential of lead-free epoxy resin nanocomposites, incorporating bismuth oxide, nickel oxide, and cerium oxide, for mid-energy radiation protection. Nanocomposites were fabricated using an open mold casting technique, and their physical properties were characterized via scanning electron microscopy (SEM) and energy dispersive X-ray (EDX) analyses. Further morphological analysis was conducted using a compound microscope and image processing software, ImageJ, to investigate the distribution of the particles on the polymer matrix. The radiation shielding effectiveness of the composites was evaluated using Na-22, Cs-137, and Mn-54 gamma sources in a gamma spectroscopy setup in Philippine Nuclear Research Institute. The results revealed that pure epoxy resin exhibited higher attenuation coefficients compared to the modified composites, primarily due to the challenges in achieving uniform dispersion of metal oxides within the polymer matrix. Agglomeration of nickel oxide nanoparticles was particularly noted, leading to reduced shielding performance. Average mass attenuation coefficients obtained in this experimental setup reached up to 0.08-0.1 cm2/g for energy range 500-900 keV. Radiation protection efficiency (RPE) measurements indicated that pure epoxy resin achieved an RPE of approximately 6% across different sources, highlighting its potential for practical applications in medical imaging, industrial radiography, environmental monitoring, and nuclear power plants. This study underscores the importance of nanoparticle dispersion and provides insights into the development of lightweight, lead-free, and efficient radiation shielding materials. Future work should focus on optimizing synthesis methods to improve homogeneity and radiation protection efficacy of polymer-based composites.

P. Z. Zhao, Lei Qiao

In addition to the traditional two-level system, the three-level system serves as another important elemental building block for the manipulation of qubits. However, the quantum information processing in the three-level system is also subject to the decoherence induced by the interaction between the quantum system and its environment or by the crosstalk between different qutrits. In this work, we construct a sequence of physically feasible dynamical decoupling operators for the three-level system to mitigate not only the transverse dephasing between the excited state and ground states but also the longitudinal relaxation among them. Combining the Hamiltonian engineering and our constructed dynamical decoupling sequence, we further realize the dynamical decoupling protection of qutrit-based quantum gates. Our scheme can effectively enhance the fidelity of three-level-based quantum gates through filtering out the environmental noises, which may provide a new horizon to improve the accuracy of three-level-based quantum manipulation.

Chenxu Wang, Huaping Liu

Adversarial attacks and robustness in Deep Reinforcement Learning (DRL) have been widely studied in various threat models; however, few consider environmental state perturbations, which are natural in embodied scenarios. To improve the robustness of DRL agents, we formulate the problem of environmental state perturbation, introducing a preliminary non-targeted attack method as a calibration adversary, and then propose a defense framework, named Boosted Adversarial Training (BAT), which first tunes the agents via supervised learning to avoid catastrophic failure and subsequently adversarially trains the agent with reinforcement learning. Extensive experimental results substantiate the vulnerability of mainstream agents under environmental state perturbations and the effectiveness of our proposed attack. The defense results demonstrate that while existing robust reinforcement learning algorithms may not be suitable, our BAT framework can significantly enhance the robustness of agents against environmental state perturbations across various situations.

Zeyi Liao, Guanqun Song, Ting Zhu

The technological transition of MacBook charging solutions from MagSafe to USB-C, followed by a return to MagSafe 3, encapsulates the dynamic interplay between technological advancement, environmental considerations, and economic factors. This study delves into the broad implications of these charging technology shifts, particularly focusing on the environmental repercussions associated with electronic waste and the economic impacts felt by both manufacturers and consumers. By investigating the lifecycle of these technologies - from development and market introduction through to their eventual obsolescence - this paper underscores the importance of devising strategies that not only foster technological innovation but also prioritize environmental sustainability and economic feasibility. This comprehensive analysis illuminates the crucial factors influencing the evolution of charging technologies and their wider societal and environmental implications, advocating for a balanced approach that ensures technological progress does not compromise ecological health or economic stability.

Natali Helberger, Frederik Zuiderveen Borgesius, Agustin Reyna

In modern markets, many companies offer so-called 'free' services and monetize consumer data they collect through those services. This paper argues that consumer law and data protection law can usefully complement each other. Data protection law can also inform the interpretation of consumer law. Using consumer rights, consumers should be able to challenge excessive collection of their personal data. Consumer organizations have used consumer law to tackle data protection infringements. The interplay of data protection law and consumer protection law provides exciting opportunities for a more integrated vision on 'data consumer law'.

Emmanuel Kwame Nti, Gordana Kranjac-Berisavljevic, Dzigbodi Adzo Doke

The increase in illegal gold mining (galamsey) activities that harm the environment by polluting water sources necessitates the need to take meaningful steps towards protecting the environment.The Jacobu water system in the Amansie Central district was selected as a case study. The purpose of the work is to assess the environmental sustainability of groundwater resource and whether surface water pollution has seeped into the water table to pollute groundwater resource. Methodologically, the study employed groundwater sustainability assessment framework, involving both linear and non-linear sustainability index functions, as well as analytical hierarchy process (AHP). Four groundwater sustainability aspects of quantity, quality, ecosystem and management were assessed. Also, thirteen groundwater sustainability indicators were created for the study. Using the linear sustainability index function, the environmental sustainability index was 0.845, which indicates an excellent level. For the assessment to properly reflect the reality in the study area, the combined linear and non-linear sustainability index function revealed a good level, 0.765, showing an environmentally good overview of Jacobu groundwater resource. This, therefore, suggests the need to ensure continuous protection of groundwater resource. Generally, the study area has not yet experience hydromorphic dispersion of polluted surface water into groundwater resources. For a sustainable groundwater in the future, strict control measures by enforcing environmental laws and regulations to prevent possible pollution of groundwater resource due to ‘‘galamsey’’ activities is critical to protect the invisible resource. This can be done through community engagement, and innovative approaches to monitoring and controlling illegal mining activities. This is to help sustain future generation's security of the ‘invisible’ groundwater resource in Ghana and contributes to Goal 6 (Ensuring Access to Clean Water and Sanitation) of the Sustainable Development Goals set by the United Nations.

Antoaneta Angelacheva, Stanislava Stefanova

The article presents the results of an assessment of students’ science competencies in chemistry education in 7. grade in the school year 2022/2023. Didactic tests "Alkaline group" and "Halogen group" have been created to measure the students’ achievements. The tests are aligned with the State Educational Content Requirements and the 7th Grade Curriculum in Chemistry and Environmental Protection. The quality of the tests is evaluated by expert chemistry teachers. The expert evaluation results show that the test items have good statistical properties. The data from the testing are compared with the curriculum for 7th grade expected results. On this basis, relevant conclusions are formulated – students cope with tasks that are at the level of knowledge (reproduction) according to B. Bloom’s taxonomy; students have difficulty with comprehension and application level tasks involving the application of knowledge to new, unfamiliar cognitive situations. It is possible that the claim made is within the boundaries of the particular study and the particular sample of students.

Ysobel Sims, Alexandre Mendes, Stephan Chalup

Zero-shot learning enables models to generalise to unseen classes by leveraging semantic information, bridging the gap between training and testing sets with non-overlapping classes. While much research has focused on zero-shot learning in computer vision, the application of these methods to environmental audio remains underexplored, with poor performance in existing studies. Generative methods, which have demonstrated success in computer vision, are notably absent from zero-shot environmental sound classification studies. To address this gap, this work investigates generative methods for zero-shot learning in environmental audio. Two successful generative models from computer vision are adapted: a cross-aligned and distribution-aligned variational autoencoder (CADA-VAE) and a leveraging invariant side generative adversarial network (LisGAN). Additionally, we introduced a novel diffusion model conditioned on class auxiliary data. Synthetic embeddings generated by the diffusion model are combined with seen class embeddings to train a classifier. Experiments are conducted on five environmental audio datasets, ESC-50, ARCA23K-FSD, FSC22, UrbanSound8k and TAU Urban Acoustics 2019, and one music classification dataset, GTZAN. Results show that the diffusion model outperforms all baseline methods on average across six audio datasets. This work establishes the diffusion model as a promising approach for zero-shot learning and introduces the first benchmark of generative methods for zero-shot environmental sound classification, providing a foundation for future research.

Ummar Ahmed, Tuomo Sipola, Jari Hautamäki

Quantum computing is a cutting-edge field of information technology that harnesses the principles of quantum mechanics to perform computations. It has major implications for the cyber security industry. Existing cyber protection applications are working well, but there are still challenges and vulnerabilities in computer networks. Sometimes data and privacy are also compromised. These complications lead to research questions asking what kind of cyber protection applications of quantum computing are there and what potential methods or techniques can be used for cyber protection? These questions will reveal how much power quantum computing has and to what extent it can outperform the conventional computing systems. This scoping review was conducted by considering 815 papers. It showed the possibilities that can be achievedif quantum technologies are implemented in cyber environments. This scoping review discusses various domains such as algorithms and applications, bioinformatics, cloud and edge computing, the organization of complex systems, application areas focused on security and threats, and the broader quantum computing ecosystem. In each of these areas, there is significant scope for quantum computing to be implemented and to revolutionize the working environment. Numerous quantum computing applications for cyber protection and a number of techniques to protect our data and privacy were identified. The results are not limited to network security but also include data security. This paper also discusses societal aspects, e.g., the applications of quantum computing in the social sciences. This scoping review discusses how to enhance the efficiency and security of quantum computing in various cyber security domains. Additionally, it encourages the reader to think about what kind of techniques and methods can be deployed to secure the cyber world.

Wesley W. Ingwersen, Mo Li, Ben Young et al.

AbstractUSEEIO v2.0 is an environmental-economic model of US goods and services that can be used for life cycle assessment, footprinting, national prioritization, and related applications. This paper describes the development of the model and accompanies the release of a full model dataset as well as various supporting datasets of national environmental totals by US industry. Novel methodological elements since USEEIO v1 models include waste sector disaggregation, final demand vectors for US consumption and production, a domestic form of the model that can be used to separate domestic and foreign impacts, and price adjustment matrices for converting outputs to purchaser price and in various US dollar years. Improvements in modeling national totals of industry and environmental flows are described. The model is validated through reproduction of national totals from input data sources and through analysis of changes from the most recent complete USEEIO model that can be explained based on data updates or method changes. The model datasets can all be reproduced with open source software packages.

Siran Zhu, Jinglei Si, Huijie Zhang et al.

Identifying and verifying appropriate biomarkers is instrumental in improving the prediction of early-stage pig production performance while reducing the cost of breeding and production. The main factor that affects the production cost and environmental protection cost of the pig industry is the feed efficiency of pigs. This study aimed to detect the differentially expressed proteins in the early blood index determination serum between high-feed efficiency and low-feed efficiency pigs and to provide a basis for further identification of biomarkers using the isobaric tandem mass tag and parallel reaction monitoring approach. In total, 350 (age, 90 ± 2 d; body weight, 41.20 ± 4.60 kg) purebred Yorkshire pigs were included in the study, and their serum samples were obtained during the early blood index determination. The pigs were then arranged based on their feed efficiency; 24 pigs with extreme phenotypes were grouped as high-feed efficiency and low-feed efficiency, with 12 pigs in each group. A total of 1364 proteins were found in the serum, and 137 of them showed differential expression between the groups with high- and low-feed efficiency, with 44 of them being upregulated and 93 being downregulated. PRM (parallel reaction monitoring) was used to verify 10 randomly chosen differentially expressed proteins. The proteins that were differentially expressed were shown to be involved in nine pathways, including the immune system, digestive system, human diseases, metabolism, cellular processing, and genetic information processing, according to the KEGG and GO analyses. Moreover, all of the proteins enriched in the immune system were downregulated in the high-feed efficiency pigs, suggesting that a higher immune level may not be conducive to improving feed efficiency in pigs. This study provides insights into the important feed efficiency proteins and pathways in pigs, promoting the further development of protein biomarkers for predicting and improving porcine feed efficiency.

Levent Aksoy, Quang-Linh Nguyen, Felipe Almeida et al.

A digital Finite Impulse Response (FIR) filter is a ubiquitous block in digital signal processing applications and its behavior is determined by its coefficients. To protect filter coefficients from an adversary, efficient obfuscation techniques have been proposed, either by hiding them behind decoys or replacing them by key bits. In this article, we initially introduce a query attack that can discover the secret key of such obfuscated FIR filters, which could not be broken by existing prominent attacks. Then, we propose a first of its kind hybrid technique, including both hardware obfuscation and logic locking using a point function for the protection of parallel direct and transposed forms of digital FIR filters. Experimental results show that the hybrid protection technique can lead to FIR filters with higher security while maintaining the hardware complexity competitive or superior to those locked by prominent logic locking methods. It is also shown that the protected multiplier blocks and FIR filters are resilient to existing attacks. The results on different forms and realizations of FIR filters show that the parallel direct form FIR filter has a promising potential for a secure design.

Jinyin Chen, Mingjun Li, Mingjun Li et al.

Federated learning (FL), an effective distributed machine learning framework, implements model training and meanwhile protects local data privacy. It has been applied to a broad variety of practice areas due to its great performance and appreciable profits. Who owns the model, and how to protect the copyright has become a real problem. Intuitively, the existing property rights protection methods in centralized scenarios (e.g., watermark embedding and model fingerprints) are possible solutions for FL. But they are still challenged by the distributed nature of FL in aspects of the no data sharing, parameter aggregation, and federated training settings. For the first time, we formalize the problem of copyright protection for FL, and propose FedRight to protect model copyright based on model fingerprints, i.e., extracting model features by generating adversarial examples as model fingerprints. FedRight outperforms previous works in four key aspects: (i) Validity: it extracts model features to generate transferable fingerprints to train a detector to verify the copyright of the model. (ii) Fidelity: it is with imperceptible impact on the federated training, thus promising good main task performance. (iii) Robustness: it is empirically robust against malicious attacks on copyright protection, i.e., fine-tuning, model pruning, and adaptive attacks. (iv) Black-box: it is valid in the black-box forensic scenario where only application programming interface calls to the model are available. Extensive evaluations across 3 datasets and 9 model structures demonstrate FedRight's superior fidelity, validity, and robustness.