The disordered assembly and low conductivity of carbon nanotubes are the main problems that limit the application of electromagnetic interference (EMI) shielding. In this work, an ordered lamellar assembly structure of multiwalled carbon nanotube/Ti3C2Tx (MWCNT/Ti3C2Tx) hybrid films was achieved by vacuum-assisted filtration through the hybridization of Ti3C2Tx nanosheets and carbon nanotubes, where carbon nanotubes were tightly sticking on the surface of Ti3C2Tx nanosheets via physical adsorption and hydrogen bonding. Compared with the pure carbon nanotubes films, the hybrid MWCNT/Ti3C2Tx films achieved a significant improvement in conductivity of 452.5 S/cm and EMI shielding effectiveness (SE) of 44.3 dB under 50 wt% Ti3C2Tx with a low thickness (8.6 μm) and orderly lamellar stacking structure, which finally resulted in high specific SE (SSE/t, SE divided by the density and thickness) of 55,603.1 dB∙cm2∙g−1.

Urban parking around the world faces similar challenges of inadequate space, pollution, and carbon emissions. Although various smart parking technologies have been tested and implemented, they primarily aim to reduce the time spent searching for parking, without considering the impact on air quality. In this study, the air quality in three urban garages was investigated with portable instruments at the entrance and exit gates and inside the garages. Garage emissions measured include CO2, PM2.5, PM10, NO2, and total VOCs. The results suggested that the PM2.5 levels in these garages tend to be higher than the ambient levels. The emissions also exhibit seasonal variations, with the highest concentrations occurring in the summer, which are 20.32 µg/m3 in Campus Green, 14.25 µg/m3 in CCM, and 15.23 µg/m3 in Washington Park garages, respectively. PM2.5 measured from these garages is strongly correlated (with an R2 of 0.64) with ambient levels. CO2 emissions are higher than ambient levels but within the indoor air quality limit. This suggests that urban garages in Cincinnati tend to enrich ambient air concentrations, which can affect garage users and garage attendants. Portable sensors are capable of long-term emission monitoring and are compatible with other technologies in smart garage development. With portable air sensors becoming increasingly accessible and affordable, there is an opportunity to integrate these devices with smart garage management systems to enhance the sustainability of parking garages.

Polychlorinated biphenyls (PCBs), including all 209 congeners, are designated as persistent organic pollutants (POPs) due to their high toxicity and bioaccumulation in human bodies and the ecosystem. The need for PCB remediation still remains long after their production ban. In this study, a catalytic hydro-dechlorination (HDC) method was employed to dechlorinate 2,4,4'-trichlorobiphenyl (PCB 28), a congener found ubiquitously in multiple environmental media. The HDC of PCB 28 was experimentally studied at mild temperatures viz. ~20, 50, and ~77°C and atmospheric pressure. Et3N (triethylamine) was added as a co-catalyst. The dechlorination rates increased with temperature as well as Et3N dosage, and the HDC pathway was hypothesized based on the product and intermediates observed. The less chlorinated intermediates suggested that the position of the chlorine strongly impacted HDC rates, and the preference of HDC at para positions can be orders of magnitudes higher than the ortho. The activation energy was estimated in the range of 12.4-13.9 kJ/mole, indicating a diffusion-controlled HDC system.Implications: The remediation need for polychlorinated biphenyls (PCBs) still remains long after their production ban around the world. The development of low-cost methods is highly desirable, especially for developing countries, in response to the Stockholm Convention. In this study, the dechorination of a ubiquitously present PCB congener was studied using a catalytic hydro-dechlorination (HDC) method in low temperatures up to ~77°C and was able to achieve near 100% dechlorination in 6 hr. Results indicated that the HDC process can be performed under mild temperatures and atmospheric conditions and can be a potential solution to real world PCB contamination issues.

With urbanization and increased vehicle usage, understanding the exposure to air pollutants inside the vehicles is vital for developing strategies to mitigate associated health risks. In-vehicle air quality influences the comfort of the driver during long commutes and has gained significant interest. This study focuses on studying in-vehicle air quality in the San Francisco Bay Area in California, an urban setting with significant traffic congestion and varied emission sources and road conditions. Each trip is about 80.5 km (50 miles) in length, with commute times of approximately one hour. Two low-cost portable sensors were employed to simultaneously measure in-vehicle pollutants (PM2.5, PM10, and CO2) during morning and evening rush hours from May 2023 to December 2023. Seasonally averaged PM2.5 varied from 5.07 µg/m3 to 6.55 µg/m3 during morning rush hours and from 4.38 µg/m3 to 4.47 µg/m3 during evening rush hours. In addition, the impacts of local PM2.5, vehicle ventilation settings, and speed of the vehicle on in-vehicle PM concentrations were also analyzed. CO2 buildup in vehicles was studied for two scenarios: one with inside recirculation enabled (RC on) and the other with circulation from outside (RC off). With RC off, CO2 concentrations are largely within the 1100 ppm range recommended by many organizations, while the average CO2 concentrations can be three times high under recirculation mode. This research suggests that low-cost sensors can provide valuable insights into the dynamics of air pollution in the in-vehicle microenvironment, which can better help commuters reduce health risks.

Southwest Ohio has been known for PM2.5 issues due to emissions from multiple local sources, such as industry and multimodal traffic, as well as regional impacts from sulfates, nitrates, and ammonia. To better understand the speciation characteristics of PM2.5 in this area, data from five monitoring sites, Taft, Hook Field, St. Bernard, Lower Price Hill, and Chase, were studied for the time period of 2003 to 2013. The total concentration of PM2.5 has decreased significantly since the last decade, from 13.41 µg m−3 in 2003 to 10.55 µg m−3 in 2013. The overall PM2.5 concentration also exhibited seasonal variations, with four out of five of the highest concentrations occurring in summer and the fifth one occurring in winter. Due to various air pollution control measures (such as the Cross-State Air Pollution Rule, and Mercury and Air Toxics Standards), both the total concentrations and the speciation of PM2.5 have changed over time. The most dominant components of PM2.5 include sulfates, organic matter (OM), and nitrates, which contributed 33.4

Polychlorinated biphenyls (PCBs) is a group of persistent organic pollutants that still requires remediation and reduction long after the production is discontinued. Aroclor 1232 is a commercial PCB mixture that has been much less studied. To address this void, this paper presented catalytic hydro-dechlorination (HDC) of Aroclor 1232 using palladium on activated carbon (Pd/AC) at atmospheric pressure. Experimental variables studied including three mild temperatures, 22.5 °C, 50 °C, and 80 °C, and four different co-catalyst loadings. This batch of Aroclor 1232 constituted of more than 83% of mono-, di-, and tri-, chlorinated biphenyls (CB), in addition to biphenyl, tetra- and penta CBs. HDC efficiency increased with temperature, and reached 99.9% within 4 h of reaction at 80 °C. HDC efficiency also increased with co-catalyst loading. HDC efficiencies followed the trend of para > meta > ortho positions among isomers. The increase of temperature and Et3N dosage are especially effective in dechlorination at ortho and meta positions. The apparent activation energy of Aroclor 1232 HDC was estimated as 25.57 kJ/mol based on pseudo-first order assumption, indicating that the reaction may be diffusion limited. Given the modest reaction conditions used, the HDC of Aroclor 1232 can potentially be a low-cost process.

A substantial portion of the phosphorus utilized in crop and food production is dispersed into soil and water, posing a challenge to the management of eutrophication and sustainable nutrient recovery. This research focuses on the reclamation of phosphate from polluted water through affinitive adsorption on biochar derived from spent coffee grounds (SCG). SCG were subjected to pyrolysis within a N2-purged vertical furnace across a temperature range of 300-550 degrees C, with a 1-h holding time. The adsorption capability of SCG biochar was systematically investigated and experimental data were interpreted using Langmuir and Freundlich isotherm models. Notably, the biochar pyrolyzed at 450 degrees C and activated with a Fe/biochar mass ratio of 2:1 demonstrated the highest adsorption capacity (0.87 mg P/g biochar) when exposed to the highest initial phosphate concentration in the solution (15 mg P/L). Comparative analyses revealed that the removal efficiency of non-activated SCG biochar was considerably lower (5.7%) compared to the corresponding activated biochar (up to 17.3%). This highlights the significant increase in adsorption capacity facilitated by the introduction of ferric chloride. Furthermore, phosphate desorption experiments were conducted to assess the biochar's phosphorus release characteristics and stability. The results demonstrate the positive outcomes of upcycling SCG waste material as a pollutant sorbent and the potential to diminish reliance on chemical fertilizers through the recovery of Fe-phosphate-enriched SCG biochar.

Graph classification has been widely used for knowledge discovery in numerous practical application scenarios, such as social networks and protein-protein interaction networks. Recently, Graph Neural Networks (GNNs), which generalize deep neural networks to graph-structured data, have drawn considerable attention and achieved state-of-the-art performance in graph classification. However, existing GNN models mainly focus on capturing the information of immediate or first-order neighboring nodes within a single layer. The graph substructure and substructure interaction, which plays an important role in learning graph representations, are usually overlooked. In this paper, we propose a Substructure Assembling Graph Attention Network (SA-GAT) to extract graph features and improve the performance of graph classification. SA-GAT is able to fully explore higher-order substructure information hidden in graphs by a core module called Substructure Interaction Attention (SIA), which takes both the information of neighbors’ substructures and the interaction information among them into account during aggregation process. Theoretically, we have also proved that SA-GAT satisfies the graph isomorphism theory of graph neural network design, which is that the network should map isomorphic graphs to the same representation and output the same prediction. Extensive experimental results on multiple real-world graph classification datasets demonstrate that the proposed SA-GAT outperforms the state-of-the-art methods including graph kernels and graph neural networks.

At this stage, 3D elliptical vibration-assisted cutting(3D-EVAC) systems have proven to be a highly promising method of machining for a wide range of new materials, which can meet the requirements of ultra-precision machining. In practice, however, there are still various problems that affect its ability to achieve the desired accuracy. The functional reliability of the system should therefore be improved. Considering that the failure probability of 3D-EVAC system components cannot be obtained accurately, this paper proposes an improved similarity aggregation method (SAM) based on the butterfly optimization algorithm (BOA), called BOA-SAM. In the experiment, thirty-six events are selected and these events are aggregated by BOA-SAM. The experimental results show that BOA-SAM not only avoids the variability of the aggregation results with the relaxation factor (beta) but also improves the accuracy of the aggregation results compared with SAM. BOA-SAM and FTA are connected by the center-of-mass method to form BOA-SAM-FFTA. Finally, a fuzzy fault tree analysis of the 3D-EVAC system was carried out using the BOA-SAM-FFTA method to calculate the probability of failure for each bottom event and to analyze its importance. It was found that non-uniform heating of the tool was the event that had the greatest impact on the functional reliability of the system, which offered a theoretical basis for improving the functional reliability of the system. The probability of failure of other bottom events during the experiment also can provide some theoretical guidance for the system maintenance plan .

There have been multiple studies of biodiesel particulate matter (BPM) emissions over the years, but few are on non-road diesel engines despite their higher emissions and less regulation. The goal of this paper is to further investigate the impacts of biodiesel fuel on particulate matter emissions. Compositional analysis of BPM was performed on a non-road diesel generator under various loads using different diesel and biodiesel blends. In order to account for organic compositions from both petroleum diesel and biodiesel, two types of analytical columns were used, one for polar compounds such as fatty acid methyl esters (FAME) and another non-polar column for hydrocarbons and PAHs (polycyclic aromatic hydrocarbons). In the BPM emitted, FAME constituted 6% to 11% of the total mass at different loads, which is the highest among the soluble organic fractions. This is an indication that biodiesel fuel might not be completely combusted in this diesel engine. The PAH fraction of the B50 (50% biodiesel) is much less than that found in petroleum diesel PM (B0). The elemental carbon fraction of the B50 particulate matter is less than that from B0. The lower PAH and soot from biodiesel blends may correspond to lower toxicity.

14 15 In this study, ozone and particulate matter variations from four monitoring stations in the 16 Southwest Ohio region were analyzed at different stages of the COVID-19 pandemic in 2020, 17 and compared with those in 2019. These stations include a US EPA NCore site (Taft), an urban- 18 suburban site (Sycamore), an industrial source site (Yankee) and a residential site near the source 19 (Amanda). The air quality data were broken down to the lockdown period (March 23 to May 31) 20 and the re-opening periods from June to December, 2020. Publicly available monitoring data on 21 PM 2.5, ozone and PM 10 were used for analysis. PM 2.5 reductions were non-uniform with strong 22 seasonal variations. PM 2.5 reductions were 4.04%, 15.6%, 11.63% at Sycamore, Taft and Yankee 23 sites respectively during the lockdown, but increased 11.23% at Amanda. Reductions at Taft 24 may be related to traffic restrictions while those at Yankee may be due to both reduced industrial production and source control measures. Ozone reductions were 7.94% and 6.50% at Sycamore 26 and Taft sites during the lockdown with Sycamore having higher ozone concentrations pre, 27 during and post lockdown. Ozone formation is NOx-limited in Southwest Ohio region and the 28 variations are uniform. Lower temperatures during the lockdown and fall of 2020 can also be a 29 contributing factor. The Air Quality Index (AQI) of combined ozone and PM 2.5 improved during 30 the pandemic year. Consistent with a few other studies, COVID-19 restrictions did not bring 31 uniform pollutant reductions to the Southwest Ohio region.

In this study, ozone and particulate matter variations from four monitoring stations in the Southwest Ohio region were analyzed at different stages of the COVID-19 pandemic in 2020, and compared with those in 2019. These stations include a US EPA NCore site (Taft), an urban-suburban site (Sycamore), an industrial source site (Yankee) and a residential site near the source (Amanda). The air quality data were broken down to the lockdown period (March 23 to May 31) and the re-opening periods from June to December, 2020. Publicly available monitoring data on PM2.5, ozone and PM10 were used for analysis. PM2.5 reductions were non-uniform with strong seasonal variations. PM2.5 reductions were 4.04%, 15.6%, 11.63% at Sycamore, Taft and Yankee sites respectively during the lockdown, but increased 11.23% at Amanda. Reductions at Taft may be related to traffic restrictions while those at Yankee may be due to both reduced industrial production and source control measures. Ozone reductions were 7.94% and 6.50% at Sycamore and Taft sites during the lockdown with Sycamore having higher ozone concentrations pre, during and post lockdown. Ozone formation is NOx-limited in Southwest Ohio region and the variations are uniform. Lower temperatures during the lockdown and fall of 2020 can also be a contributing factor. The Air Quality Index (AQI) of combined ozone and PM2.5 improved during the pandemic year. Consistent with a few other studies, COVID-19 restrictions did not bring uniform pollutant reductions to the Southwest Ohio region.

In the field of ultraprecision machining, the structured surfaces with various micro/nano characteristics may have different advanced functions, such as wettability modifications, tribological control and hybrid micro-optics. However, the machining of micro/nano structured surfaces is becoming a challenge for present cutting method. Especially for the difficult-to-cut materials, it is impossible to manufacture complex micro/nano features by using traditional cutting methods. The complex features require a cutting tool no longer confined to the traditional motion guide. The cutting tool should have more quick response velocity and flexible modulated ability. This chapter aims to make an introduction for piezoelectric tool actuator used in elliptical vibration cutting, which can be offering tertiary cutting operations with quick response and flexible modulated ability. The content covers the working principle of piezoelectric tool actuator, compliant mechanism design, static modeling, kinematic and dynamic modeling, structure optimization and offline testing.

Graph neural networks can learn graph structure data directly and mine its information, which can be used in drug research and development, financial fraud prevention, and other fields. The existing research shows that the graph neural network is lacking robustness and is vulnerable to attack by adversarial examples. At present, there are two problems in the generation of confrontation examples for graph neural networks. One is that the properties of graph structure are not fully used to describe the antagonistic examples, the other is that the gradient calculation is linked with the loss function and not directly linked with the properties of graph structure, which leads to excessive search space. To solve these two problems, this paper proposes a graph structure data confrontation example generation scheme based on graph theory measurement. In this paper, the average distance and clustering coefficient is used as the basis for each step of disturbance, and the counterexamples are generated under the premise of keeping the data characteristics. Experimental results on small-world networks and random graphs show that, compared with the previous methods, the proposed method makes full use of the nature of graph structure, does not need complex derivation, and takes less time to generate confrontation examples, which can meet the needs of iterative development.

As the main output of industrial waste gas, SO2 is harmful to environment and human health. For reducing the hazards to human from SO2, it is urgent and necessary to control the atmospheric pollution. Atmospheric pollution forecasting can provide effective information for controlling atmospheric pollution, so this research proposes a CEEMD-MR-Hybrid model for SO2 forecasting, which employs CEEMD, machine learning models (CNN, PSOSVR, PSOBP) and mode refactor system (MR). The main feature of proposed model is to reconstruct the decomposed series using MR including random forest and sample entropy for hybrid forecasting. Sample entropy can combine decomposed series with similar characteristics by measuring the complexity of distinct decomposed series. Random forest can obtain decomposed sub-sequences high correlated with the original sequence by ranking characteristics importance. Five geographically diverse cities in China are selected to test the validation of the proposed model. Compared with previous CEEMD-RN-Hybrid models, the results of proposed hybrid models have higher consistency with actual data. Therefore, the innovative model can be utilized as an effective model for SO2 forecasting. Taking Shenzhen as an example, the MAPE values of all CEEMD-MR-Hybrid models are smaller than 10% for SO2 forecasting. Therefore, the innovative model provides a machine learning system which can efficiently refactor the decomposition series to improve the accuracy for hybrid model.

Coffee is the world’s second largest beverage only next to water. After coffee consumption, spent coffee grounds (SCGs) are usually thrown away and eventually end up in landfills. In recent years, technologies and policies are actively under development to change this century old practice, and develop SCGs into value added energy and materials. In this paper, technologies and practices are classified into two categories, those reuses SCGs entirely, and those breakdown SCGs and reuse by components. This article provided a brief review of various ways to reuse SCGs published after 2017, and provided more information on SCG quantity, SCG biochar development for pollutant removal and using SCG upcycle cases for education. SCG upcycle efforts align the best with the UN Sustainable Development Goals (SDG) #12 “ensure sustainable consumption and production patterns,” the resultant fuel products contribute to SDG #7 “affordable and clean energy,” and the resultant biochar products contribute to SDG #6, “clean water and sanitation.”

Some credible third parties collect and share mobile sensor data with users to enhance scientific innovation. However, since the sensor data contains sensitive attributes, it may lead to an unexpected privacy leak. Despite privacy filtering, differential privacy, and inferential privacy techniques that attempt to address this problem, the preservation of greater privacy results in a considerable loss of utility and vice versa. In this research, we tackle the privacy-preserving for sensor-based activity recognition data-sharing issue via balancing between (1) data quality perspective and (2) privacy-preserving perspective. Two novel private generative adversarial approaches, namely PGAN1 and PGAN2, are proposed, whereby we generate filtered data to be shared before revealing the raw data to a data analyst. PGAN1 and PGAN2 differ in the privacy mechanism of raw data filtering. Therefore, PGAN1 relies on a transformation algorithm, while PGAN2 relies on a synthesis algorithm. We theoretically characterize the problem and formulate new objective functions, each one as an analogy of minimax formulation among more than two networks. We evaluate the utility performance by different classifiers while the privacy is checked by overcoming the attacks. Experimental results show that PGAN1 and PGAN2 improve utility and effectively prevent privacy leaks better than previous works.

The advancement of science and technology provides the possibility of personalized intelligent education. Representation learning of students’ behavior data is challenging because whether time sequences and interactive behaviors or the correlation between knowledge points and students carrying important information. Some researchers propose knowledge tracing to provide ideas for solving this dilemma. However, existing knowledge tracing methods are divided into machine learning and deep learning. Machine learning-based methods require manual feature extraction and a large amount of prior knowledge. Although deep learning-based methods can automatically extract features, most methods either only use the time series information of the data, or use the association between knowledge points. All the methods ignore the association between knowledge points and students. To fill this gap, we propose a Gated Heterogeneous Graph Convolutional Network (GHGCN) model. We utilize the encoder-decoder framework to predict student performance using the representations of nodes, which is learned from heterogeneous convolutional networks and gate recurrent unit. To validate the effectiveness of the proposed GHGCN model, we conduct the experiments on three public datasets: Simulated Data, Assistments 2009, and Assistments 2015. The results indicate that our method can achieve better performance compared with state-of-the-art algorithms.

This study investigated the physical and chemical properties of a single or combination of permeable materials which can be used as fillers in the Sponge City program in China. Four types of fillers, perlite, coral sand, vermiculite and ceramsite, were selected from six alternative fillers by an analytic hierarchy process. The optimal city sponge, which consists of vermiculite (10 cm), ceramsite (15 cm), perlite (15 cm), coral sand (20 cm) and Canna indica L, was found by the orthogonal experiment (L16(45)). The results of the simulated rainwater experiment of the optimal sponge showed that the permeability coefficient K10, NH3-N, total phosphorus (TP) and chemical oxygen demand (COD) removal rate were 1.20 ± 0.23 mm/s, 96.6 ± 0.2%, 36.8 ± 0.07% and 9.6 ± 0.07% respectively. The results suggested that the optimal sponge had an excellent treatment effect on NH3-N in rainwater while ensuring rapid infiltration. It provided a simple, economical and effective method for rainwater treatment and the Sponge City program in the future.

In this report, the surface topography of crescent array in the Nepenthes slippery zone is measured and bionic fabricated by two‐photon polymerization (TPP) technology. A subregion outside‐in scanning method (SOSM) is proposed to solve the photoresist converge and large area surface positioning problem existing in bionic crescent array surface (BCAS) fabrication. The geometric parameters of BCAS, including area fraction, height, offset, and outer–inner radius ratio, are systematically investigated to reveal their effects on the hydrophobic performance. Perfluorinated polyether oil and fluorocarbon solvent were used to increase the hydrophobicity and homogeneity of bionic surface. The contact angles of BCASs coated with and without the solution are measured. The maximum contact angle can reach to 152.1°. What's more, considering the anisotropy of BCAS, anisotropic wetting research is conducted. Offset and outer–inner radius ratio are found to have obvious effects on the contact angle differences between convexity and concavity directions. The maximum contact angle difference can reach to 5.1°. Spreading and pinning phenomenon caused by droplet anisotropic wetting are observed and explained. This work is expected to provide references for the BCAS to be applied in super‐hydrophobic and droplets motion fields.