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The pore structure of the regenerated cellulose hydrogel was used as a microreactor for generating magnetic MnFe2O4 nanoparticles via in situ co-precipitation technology. Sodium periodate and sodium chlorite were used as oxidants, and the carboxyl modification of magnetic cellulose aerogels was achieved through a two-step oxidation reaction. The method used was convenient and environmental-friendly, and the raw materials were also green and non-toxic. The addition of magnetic nanoparticles promoted the adsorption and enabled the recycling of materials. The results showed that the adsorption equilibrium time was only 120 min and adsorption capacity could be increased by increasing the adsorption temperature. The maximum adsorption capacity of Cu(II) was 73.70 mg/g at 25 °C when the initial concentration of solution was150 mg/L. The adsorption behaviour towards Cu(II) followed the pseudo-secondary reaction kinetics and the Langmuir isotherm adsorption model. According to the results of XPS, carboxyl and hydroxyl groups were the main active adsorption sites for Cu(II). Moreover, all the composite aerogels exhibited good reusability and could be easily separated from the water after adsorption. The pore structure of the regenerated cellulose hydrogel was used as a microreactor for generating magnetic MnFe2O4 nanoparticles via in situ co-precipitation technology. Sodium periodate and sodium chlorite were used as oxidants, and the carboxyl modification of magnetic cellulose aerogels was achieved through a two-step oxidation reaction. According to the results of XPS, carboxyl and hydroxyl groups were the main adsorption sites for Cu(II). Moreover, all the composite aerogels exhibited good reusability and could be easily separated from the water after adsorption. [ABSTRACT FROM AUTHOR]

Summary: Frequency‐domain detection stands as a critical method in remote geophysical exploration; however, its practical application is constrained by the considerable size of the requisite instrumentation. In this paper, we present a compact frequency domain electromagnetic detection system. The integration of intelligent power devices and a novel hardware architecture substantially diminishes the system's dimensions, thereby enhancing its portability. Furthermore, the system design incorporates sinusoidal pulse width modulation and digital phase‐locked amplification to ensure the efficacy of the proposed system. Both the transmitter and receiver system are constrained to dimensions smaller than 1.5 m. Subsequent experimental validation attests to the exemplary hardware performance of the proposed system, with the transmitting voltage of the transmitter circuit attaining 500 V, and the receiver circuit exhibiting a sensitivity as low as 10−8 V. Significantly, this design paradigm not only facilitates the integration of transmitter and receiver systems for frequency‐domain electromagnetic detection but also introduces novel prospects for the application of frequency‐domain electromagnetic detection methods across diverse fields. [ABSTRACT FROM AUTHOR]

Physical and biological treatment technology are considered a highly feasible and economic way to treat slaughterhouse wastewater. To achieve the desired effluent quality for disposal or reuse, various technological options were reviewed. However, most practical operations are accompanied by several advantages and disadvantages. Nevertheless, due to the presence of biodegradable organic matter in slaughterhouse waste, anaerobic digestion technology is commonly applied for economic gain. In this paper, the common technologies used for slaughterhouse wastewater treatment and their suitability were reviewed. The advantages and disadvantages of the different processes were evaluated. Physical treatments (dissolved air floatation (DAF), coagulation–flocculation and sedimentation, electrocoagulation process and membrane technology) were found to be more effective but required a large space to operate and intensive capital investment. However, some biological treatments such as anaerobic, facultative lagoons, activated sludge process and trickling filters were also effective but required longer start-up periods. This review further explores the various strategies being used in the treatment of other wastewater for the production of valuable by-products through anaerobic digestion. [ABSTRACT FROM AUTHOR]

There is a growing demand for an integrated assessment to identify and select asset management options based on sustainability in the wastewater industry. However, water companies are often not equipped with a rigorous methodology and sufficient resources to perform sustainability assessments. Although many frameworks and tools for sustainability assessment have been developed in academia, practical challenges such as feasibility and usability remain when implementing sustainability assessment methods to support corporate decision-making. This study developed a Multi-Criteria Analysis based framework to evaluate wastewater treatment processes from a sustainability perspective. This study firstly explored the decision and organizational context of a water company with preliminary interviews and then applied the Analytical Hierarchy Process (AHP) with composite scores to evaluate wastewater technologies at a sewage treatment works. The preliminary interviews with stakeholders highlighted that the existing investment decisions were primarily driven by financial cost and compliance whilst calling for a wider consideration of other criteria. A selection of assessment criteria and indicators were then proposed to compare seven treatment technologies at a sewage treatment works. The results of composite scores indicated that the baseline activated sludge process (ASP) was the best option for this study. Experience from the development process highlighted usability, stakeholder engagement and the organizational context should all be considered as part of the design and implementation of the sustainability assessment. The insights from this study provide a valuable practical foundation for applying a multi-criteria approach to perform sustainability assessments and inform asset management decisions in the water company. [ABSTRACT FROM AUTHOR]

The goal of our work is to generate high-quality novel views from monocular videos of complex and dynamic scenes. Prior methods, such as DynamicNeRF, have shown impressive performance by leveraging time-varying dynamic radiation fields. However, these methods have limitations when it comes to accurately modeling the motion of complex objects, which can lead to inaccurate and blurry renderings of details. To address this limitation, we propose a novel approach that builds upon a recent generalization NeRF, which aggregates nearby views onto new viewpoints. However, such methods are typically only effective for static scenes. To overcome this challenge, we introduce a module that operates in both the time and frequency domains to aggregate the features of object motion. This allows us to learn the relationship between frames and generate higher-quality images. Our experiments demonstrate significant improvements over state-of-the-art methods on dynamic scene datasets. Specifically, our approach outperforms existing methods in terms of both the accuracy and visual quality of the synthesized views. Our code is available on https://github.com/xingy038/CTNeRF. • Presented a novel dynamic neural rendering approach for dynamic monocular videos, leveraging the aggregation of multi-view feature vectors to enhance the quality of rendering novel views. • Combining multi-frame feature vectors can lead to the loss or merging of intricate details, risking the preservation of crucial characteristics from the original data. To address this, we introduce a Ray-based cross-time transformer. • To mitigate potential blurring effects during feature aggregation, we propose the incorporation of a Global Spatio-Temporal Filter. [ABSTRACT FROM AUTHOR]

Zero-shot learning (ZSL) methods currently employed to identify seen or unseen classes rely on semantic attribute prototypes or class information. However, hand-annotated attributes are only for the category rather than for each image belonging to that category. Furthermore, attribute information is inconsistent across different images of the same category due to variant views. Therefore, we propose a dynamic visual-guided selection (DVGS) which helps dynamically focus on different regions and refines class prototype on each image. Instead of directly aligning an image's global feature with its semantic class vector or its local features with all attribute vectors, the proposed method learns a vision-guided soft mask to refine the class prototype for each image. Additionally, it discovers the most task-relevant regions for fine-grained recognition with the refined class prototype. Extensive experiments on three benchmarks verify the effectiveness of our DVGS and achieve the new state-of-the-art. Our DVGS achieved the best results on fine-grained datasets within both the conventional zero-shot learning (CZSL) and generalized zero-shot learning (GZSL) settings. In particular, on the SUN dataset, our DVGS demonstrates a significant superiority of 10.2% in the CZSL setting compared with the second-best approach. Similarly, our method outperforms the second-best method by an average of 4% on CUB in both the CZSL and GZSL settings. Despite securing the second-best result on the AWA2 dataset, DVGS remains closely competitive, trailing the best performance by a mere 3.4% in CZSL and 1.2% in GZSL. [ABSTRACT FROM AUTHOR]

[Display omitted] • The accuracy of species distribution models is influenced by physiological tolerance data. • The red-eared slider, as a typical invasive alien species, is widespread in China. • The embryo temperature tolerance range of the red-eared slider was between 21.8 °C and 33.1 °C. • Red-eared slider's high suitability areas mainly in south and central. The suitability areas predicted by the SDMs reduced 20% when considering embryo temperature tolerance data of red-eared slider. • The high suitability areas for red-eared sliders are mainly concentrated in South China, Central China, and East China. Species distribution models (SDMs) have been widely used to predict potentially suitable habitats for invasive alien species (IAS) and evaluate invasion risk. However, SDMs have been discredited because they ignore the physiological processes by which species respond to their environment. Integrating physiological tolerance into the model is essential to improve the prediction accuracy of SDMs. Currently, this approach has not been applied in the study of the worldwide invasive species, the red-eared slider (Trachemys scripta elegans), which is one of the world's 100 worst invasive species and is widespread in China. In this study, based on hatching experiments, we found that the embryo temperature tolerance range of the red-eared slider was between 21.8 °C and 33.1 °C. Further, we studied the effect of embryo temperature tolerance on the prediction of potential invasion areas for this alien reptile species. The high suitability area (530,214.71 km2) predicted by the SDM incorporating embryo temperature tolerance data were 20.9% smaller than that (641,107.60 km2) predicted by the SDM without considering embryo temperature tolerance. The difference between the two SDMs is primarily concentrated at the edges of the high suitability areas. The incorporation of embryo temperature tolerance data influenced the model's predictions by effectively reducing the extent of edges of the high suitability areas. High suitability areas for red-eared sliders are mainly concentrated in South China, Central China, and East China, with a few in North and Southwest China. There is almost no invasion risk in most of the northeast and northwest provinces of China. This study not only has theoretical significance for optimizing model predictions, but also provides an important scientific basis for prevention and risk assessment of invasion by red-eared sliders in China. [ABSTRACT FROM AUTHOR]

Generating the decision map with accurate boundaries is the key to fusing multi-focus images. In this paper, we introduce edge-preservation (EP) techniques into neural networks to improve the quality of decision maps, supported by an interesting phenomenon we found: the maps generated by traditional EP techniques are similar to the feature maps in the trained network with excellent performance. Based on the manifold theory in the field of edge-preservation, we propose a novel edge-aware layer derived from isometric domain transformation and a recursive filter, which effectively eliminates burrs and pseudo-edges in the decision map by highlighting the edge discrepancy between the focused and defocused regions. This edge-aware layer is incorporated to a Siamese-style encoder and a decoder to form a complete segmentation architecture, termed Y-Net, which can contrastively learn and capture the feature differences of the sourced images with a relatively small number of training data (i.e., 10,000 image pairs). In addition, a new strategy based on randomization is devised to generate masks and simulate multi-focus images with natural images, which alleviates the absence of ground-truth and the lack of training sets in multi-focus image fusion (MFIF) task. The experimental results on four publicly available datasets demonstrate that Y-Net with the edge-aware layers is superior to other state-of-the-art fusion networks in terms of qualitative and quantitative comparison. [ABSTRACT FROM AUTHOR]

Zero-shot learning (ZSL) aims to predict unseen classes without using samples of these classes in model training. The ZSL has been widely used in many knowledge-based models and applications to predict various parameters, including categories, subjects, and anomalies, in different domains. Nonetheless, most existing ZSL methods require the pre-defined semantics or attributes of particular data environments. Therefore, these methods are difficult to be applied to general data environments, such as ImageNet and other real-world datasets and applications. Recent research has tried to use open knowledge to enhance the ZSL methods to adapt it to an open data environment. However, the performance of these methods is relatively low, namely the accuracy is normally below 10%, which is due to the inadequate semantics that can be used from open knowledge. Moreover, the latest methods suffer from a significant "semantic gap" problem between the generated features of unseen classes and the real features of seen classes. To this end, this paper proposes a multi-view graph representation with a similarity diffusion model, applying the ZSL tasks to general data environments. This model applies a multi-view graph to enhance the semantics fully and proposes an innovative diffusion method to augment the graph representation. In addition, a feature diffusion method is proposed to augment the multi-view graph representation and bridge the semantic gap to realize zero-shot predicting. The results of numerous experiments in general data environments and on benchmark datasets show that the proposed method can achieve new state-of-the-art results in the field of general zero-shot learning. Furthermore, seven ablation studies analyze the effects of the settings and different modules of the proposed method on its performance in detail and prove the effectiveness of each module. [ABSTRACT FROM AUTHOR]

• A practical solution for sewer control without chemical dosing was developed. • This new approach is based on urine separation, collection and intermittent dosing. • Sulfide and methane production in sewer sediments were simultaneously reduced. • The proposed approach was cost-effective and greenhouse-gas reductive. Sulfide and methane production are a major concern in sewer management. Many solutions with the use of chemicals have been proposed yet incurring huge costs. Here, this study reports an alternative solution to reduce sulfide and methane production in sewer sediments. This is achieved through integration of urine source separation, rapid storage, and intermittent in situ re-dosing into a sewer. Based on a reasonable capacity of urine collection, an intermittent dosing strategy (i.e. 40 min per day) was designed and then experimentally tested using two laboratory sewer sediment reactors. The long-term operation showed that the proposed urine dosing in the experimental reactor effectively reduced sulfidogenic and methanogenic activities by 54% and 83%, compared to those in the control reactor. In-sediment chemical and microbial analyses revealed that the short-term exposure to urine wastewater was effective in suppressing sulfate-reducing bacteria and methanogenic archaea, particularly within a surface active zone of sediments (0–0.5 cm) likely attributed to the biocidal effect of urine free ammonia. Economic and environmental assessments indicated that the proposed urine approach can save 91% in total costs, 80% in energy consumption and 96% in greenhouse gas emissions compared to the conventional use of chemicals (including ferric salt, nitrate, sodium hydroxide, and magnesium hydroxide). These results collectively demonstrated a practical solution without chemical input to improve sewer management. [Display omitted] [ABSTRACT FROM AUTHOR]

[Display omitted] • The sludge anaerobic fermentation process efficiently reduces Fe3+ to Fe2+. • The acid-elutriation solubilizes Fe and P from the solid phase to the liquid phase. • The acid-elutriation significantly enhances the liquid–solid separation. • High-quality vivianite is harvested via neutralizing sludge eluate. The waste activated sludge (WAS) of wastewater treatment system is often rich in phosphorus (P), which is a basic element of human life and could use up in the near future. This study proposed an integrated approach to efficiently recover P as vivianite from WAS and simultaneously enhance the sludge dewaterability. The raw WAS was first acidified using FeCl 3 , which was then fed to anaerobic fermenter for Fe3+ reduction. After fermentation, a technology named acid-elutriation was introduced to convert Fe and P from solid phase to liquid phase and concomitantly enhance the liquor-solid separation. Finally, vivianite was obtained via sludge eluate neutralization. The enhanced sludge dewaterability not only increases the recovery efficiency of Fe and P but also decreases the cost of sludge disposal. [ABSTRACT FROM AUTHOR]

• Significant loss of total nitrogen is observed in an acidic aerobic sludge digester. • Nitrite is mainly reduced to NO and N 2 O under the acidic aerobic condition. • Rate of aerobic nitrite reduction is comparable to that of aerobic ammonia oxidation. • Controlling the nitrite reduction is critical to development of acidic sludge treatment. The acidic (i.e., pH ∼5) activated sludge process is attracting attention because it enables stable nitrite accumulation and enhances sludge reduction and stabilization, compared to the conventional process at neutral pH. Here, this study examined the production and potential pathways of nitric oxide (NO) and nitrous oxide (N 2 O) during acidic sludge digestion. With continuous operation of a laboratory-scale aerobic digester at high dissolved oxygen concentration (DO>4 mg O 2 L−1) and low pH (4.7±0.6), a significant amount of total nitrogen (TN) loss (i.e., 18.6±1.5% of TN in feed sludge) was detected. Notably, ∼40% of the removed TN was emitted as NO, with ∼8% as N 2 O. A series of batch assays were then designed to explain the observed TN loss under aerobic conditions. All assays were conducted with a low concentration of volatile solids (VS), i.e., VS<4.5 g L−1. This VS concentration is commensurate with the values commonly found in the aeration tanks of full-scale wastewater treatment systems, and thus no significant nitrogen loss should be expected when DO is controlled above 4 mg O 2 L−1. However, nitrite disappeared at a significant rate (with the chemical decomposition of nitrite excluded), leading to NO production in the batch assays at pH 5. The nitrite reduction could be associated with endogenous microbial activities, e.g., nitrite detoxification. The significant NO production illustrates the importance of aerobic nitrite reduction during acidic aerobic sludge digestion, suggesting this process cannot be neglected in developing acidic activated sludge technology. [Display omitted] [ABSTRACT FROM AUTHOR]

Background: Emerging evidence identifies enhancer RNAs (eRNAs) as a class of regulatory ncRNAs that can contribute to the transcription of target genes. In this study, we used an integrated data analysis method to identify the important role of eRNAs in ovarian cancer (OC). Methods: Gene expression profiles and clinical information from The Cancer Genome Atlas (TCGA) database were used for this study. Based on expression analysis using GEPIA2 gene and Kaplan–Meier survival was performed to ensure the significance of the selected enhancer RNA ADCY10P1 in OC. Next, we explored the correlation and clinical significance between ADCY10P1 and target gene NFYA. Furthermore, we evaluated the effects of overexpression of ADCY10P1 on the proliferation, migration, invasion and epithelial-mesenchymal transformation (EMT) of OC cell lines. We also investigated the biological function enrichment score of ADCY10P1 and verified it with OC cell lines. Finally, external validation was conducted, and the prognostic value of the ADCY10P1 in different tumors was demonstrated. Results: We selected the eRNA ADCY10P1 associated with OC prognosis, with NFYA as its predicted target gene. Low ADCY10P1 expression was found to be associated with poor overall survival, high histological grade, and advanced stage of OC. Additionally, overexpression of ADCY10P1 inhibited the proliferation, migration, invasion and EMT phenotype of OC cell lines. Furthermore, ADCY10P1 was observed to inhibit glycolysis and fatty acid metabolism, thereby affecting OC progression. Meanwhile, OC tissue samples were externally validated. In addition, the pan-cancer analysis revealed that ADCY10P1 had prognostic value in other cancers. Conclusions: This study showed that ADCY10P1 plays a key role in OC progression and may facilitate prognosis prediction. [ABSTRACT FROM AUTHOR]

[Display omitted] • A new biotechnology was conceived to produce carboxylates and alcohols from WAS. • Highest MCFAs yield from WAS was achieved by the lowest Lactobacillu s inoculation. • High alcohols production was achieved under higher Lactobacillu s inoculation. • A metabolic route for carboxylates and alcohols production was established. Producing medium chain fatty acids (MCFAs) from waste activated sludge (WAS) is crucial for sustainable chemical industries. This study addressed the electron donor requirement for MCFAs production by inoculating Lactobacillus at varying concentrations (7.94 × 1010, 3.18 × 1011, and 6.35 × 1011 cell/L) to supply lactate internally. Interestingly, the highest MCFAs yield (∼2000 mg COD/L) occurred at the lowest Lactobacillus inoculation. Higher inoculation concentrations redirected more carbon from WAS towards alcohols production rather than MCFAs generation, with up to 2852 mg COD/L alcohols obtained under 6.35 × 1011 cell/L inoculation. Clostridium dominance and increased genes abundance for substrate hydrolysis, lactate conversion, and MCFAs/alcohol production collectively enhanced WAS-derived MCFAs and alcohols synthesis after Lactobacillus inoculation. Overall, the strategy of Lactobacillus inoculation regulated fermentation outcomes and subsequent carbon recovery in WAS, presenting a sustainable technology to achieve liquid bio-energy production from underutilized wet wastes. [ABSTRACT FROM AUTHOR]

This review provides a comprehensive overview of the occurrence, fate, treatment and multi-criteria analysis of microplastics (MPs) and organic contaminants (OCs) in biosolids. A meta-analysis was complementarily analysed through the literature to map out the occurrence and fate of MPs and 10 different groups of OCs. The data demonstrate that MPs (54.7% occurrence rate) and linear alkylbenzene sulfonate surfactants (44.2% occurrence rate) account for the highest prevalence of contaminants in biosolids. In turn, dioxin, polychlorinated biphenyls (PCBs) and phosphorus flame retardants (PFRs) have the lowest rates (<0.01%). The occurrence of several OCs (e.g., dioxin, per- and polyfluoroalkyl substances, polycyclic aromatic hydrocarbons, pharmaceutical and personal care products, ultraviolet filters, phosphate flame retardants) in Europe appear at higher rates than in Asia and the Americas. However, MP concentrations in biosolids from Australia are reported to be 10 times higher than in America and Europe, which required more measurement data for in-depth analysis. Amongst the OC groups, brominated flame retardants exhibited exceptional sorption to biosolids with partitioning coefficients (log K d) higher than 4. To remove these contaminants from biosolids, a wide range of technologies have been developed. Our multicriteria analysis shows that anaerobic digestion is the most mature and practical. Thermal treatment is a viable option; however, it still requires additional improvements in infrastructure, legislation, and public acceptance. [Display omitted] • LAS (44.2%) and microplastic (54.7%) account for the highest prevalence in biosolids. • Occurrence of several OCs in Europe is higher than in Asia and America. • Australia has microplastic in biosolids 10 times higher than other continents. • Anaerobic digestion is the most mature and practical for OCs treatment in biosolids. • Thermal treatment is a viable option but still requires additional improvements. [ABSTRACT FROM AUTHOR]

• Low DO can initiate NOB suppression in MABRs. • Low DO cannot suppress active NOB in MABRs once prevail. • The presence of anammox increases the DO threshold to suppress NOB. • NOB suppression is more challenging in counter-diffusion biofilms than in co-diffusion biofilms. Membrane aerated biofilm reactor (MABR) and shortcut nitrogen removal are two types of solutions to reduce energy consumption in wastewater treatment, with the former improving the aeration efficiency and the latter reducing the oxygen demand. However, integrating these two solutions, i.e., achieving shortcut nitrogen removal in MABR, is challenging due to the difficulty in suppressing nitrite-oxidizing bacteria (NOB). In this study, four MABRs were established to demonstrate the feasibility of initiating, maintaining, and restoring NOB suppression using low dissolved oxygen (DO) control, in the presence and absence of anammox bacteria, respectively. Long-term results revealed that the strict low DO (< 0.1 mg/L) in MABR could initiate and maintain stable NOB suppression for more than five months with nitrite accumulation ratio above 90 %, but it was unable to re-suppress NOB once they prevailed. Moreover, the presence of anammox bacteria increased the threshold of DO level to maintain NOB suppression in MABRs, but it was still incapable to restore the deteriorated NOB suppression in conjunction with low DO control. Mathematical modelling confirmed the experimental results and further explored the differences of NOB suppression in conventional biofilms and MABR biofilms. Simulation results showed that it is more challenging to maintain stable NOB suppression in MABRs compared to conventional biofilms, regardless of biofilm thickness or influent nitrogen concentration. Kinetic mechanisms for NOB suppression in different types of biofilms were proposed, suggesting that it is difficult to wash out NOB developed in the innermost layer of MABR biofilms because of the high oxygen level and low sludge wasting rate. In summary, this study systematically demonstrated the challenges of NOB suppression in MABRs through both experiments and mathematical modelling. These findings provide valuable insights into the applications of MABRs and call for more studies in developing effective strategies to achieve stable shortcut nitrogen removal in this energy-efficient configuration. [Display omitted] [ABSTRACT FROM AUTHOR]

Hazardous heavy metals and radionuclides in water and wastewater are of drastic concern owing to their detrimental impacts on the organisms as well as the circumambient ecosystem. To remove them as much as we can, both technique and materials were studied in the past years. The adsorption technique as superior water remediation method with the simplicity of design, environmental friendliness and high efficiency was well established. Consequently, it is practically important to explore advanced and economically feasible absorbents for removing these poisonous pollutants from aqueous solutions. So far, large numbers of experiments proved hydrothermally synthesized titanate nanomaterials (TNMs) could be a prospectively excellent adsorbent extracting heavy metals and radionuclides from water due to the high specific surface area, tunable pore size, abundant surface active sites, favorable hydrophilic properties. The objective of this work is to give an overview of hydrothermal synthesis, adsorption performance of TNMs for heavy metals and radionuclides, as well as the various influencing factors for water purification. It comprehensively reviews the structural changes and regenerability of TNMs after adsorption, and different modification methods adopted for improving removal capacity. Additionally, it uniquely highlights the efficient decontamination of the pollutants through a synergistic effect of adsorption and photocatalysis by TNMs. This review provides detailed information for the development, application, and research challenges faced by hydrothermally synthesized TNMs for the removal of heavy metals and radionuclides from aqueous solutions, which will serve as a reference guide for scientists in related fields. [Display omitted] • Hydrothermally synthesized titanate nanomaterials are superior for removing heavy metals and radionuclides in water. • Their adsorption properties are strongly dependent on the hydrothermal synthesis process and reaction conditions. • They could be modified into many composites for enhanced adsorption capacity. • There exists an interesting variation of structure after adsorption. • Removal performance could be boosted though a synergistic effect of adsorption and photocatalysis. [ABSTRACT FROM AUTHOR]

The rapid growth of the Internet of Things (IoT) has led to the widespread adoption of the IoT networks in numerous digital applications. To counter physical threats in these systems, automatic modulation classification (AMC) has emerged as an effective approach for identifying the modulation format of signals in noisy environments. However, identifying those threats can be particularly challenging due to the scarcity of labeled data, which is a common issue in various IoT applications, such as anomaly detection for unmanned aerial vehicles (UAVs) and intrusion detection in the IoT networks. Few-shot learning (FSL) offers a promising solution by enabling models to grasp the concepts of new classes using only a limited number of labeled samples. However, prevalent FSL techniques are primarily tailored for tasks in the computer vision domain and are not suitable for the wireless signal domain. Instead of designing a new FSL model, this work suggests a novel approach that enhances wireless signals to be more efficiently processed by the existing state-of-the-art (SOTA) FSL models. We present the semantic-consistent signal pretransformation (ScSP), a parameterized transformation architecture that ensures signals with identical semantics exhibit similar representations. ScSP is designed to integrate seamlessly with various SOTA FSL models for signal modulation recognition and supports commonly used deep learning backbones. Our evaluation indicates that ScSP boosts the performance of numerous SOTA FSL models, while preserving flexibility.

Background: CAR-T therapy has emerged as a potentially effective treatment for individuals diagnosed with hematologic malignancies. Understanding patients' unique experiences with this therapeutic approach is essential. This knowledge will enable the development of tailored nursing interventions that align with the increasing importance of patient-centered care., Objective: To examine and synthesize qualitative data on patients and their family caregivers' experiences during the treatment journey., Design: We conducted a systematic review and qualitative meta-synthesis. Eligible studies contained adult patient or family caregiver quotes about experiences of CAR-T therapy, published in English or Chinese in a peer-reviewed journal since 2015. Data sources included MEDLINE, CINAHL, Embase, PsycINFO, Web of Science, Scopus, Cochrane Library, CNKI, and WanFang., Methods: Systematic search yielded 6373 identified articles. Of these, 12 reports were included in the analysis, which covered 11 separate studies. Two reviewers independently extracted data into NVIVO 12.0. Qualitative meta-synthesis was performed through line-by-line coding of full text, organization of codes into descriptive themes, and development themes., Results: The qualitative meta-synthesis yielded eight primary themes. Noteworthy revelations from patients and their family caregivers regarding the CAR-T therapy journey encompassed various aspects. Prior to CAR-T therapy, patients experienced a lack of actual choice, struggled with expectations for treatment outcomes, and encountered intricate emotional experiences. During or immediately after CAR-T therapy, patients reported both comfortable and uncomfortable experiences. Additionally, patients emphasized that concerns regarding treatment efficacy and adverse reactions intensified treatment-related distress. After CAR-T therapy, significant changes were observed, and the burden of home-based rehabilitation. Additionally, we found factors contributed to the high CAR-T therapy cost., Conclusions: To ensure the safety and sustainability of CAR-T therapy, it is crucial to address the physical and psychological aspects of the patient's experience. Effective communication and comprehensive management are highly valued by patients and their caregivers. Further research should investigate ways to reduce burdens and develop self-management education programs for patients and their families., (© 2024. The Author(s).)

Mathematical modeling plays a crucial role in understanding and managing urban water systems (UWS), with mechanistic models often serving as the foundation for their design and operations. Despite the wide adoptions, mechanistic models are challenged by the complexity of dynamic processes and high computational demands. Data-driven models bring opportunities to capture system complexities and reduce computational cost, by leveraging the abundant data made available by recent advance in sensor technologies. However, the interpretability and data availability hinder their wider adoption. This paper advocates for a paradigm shift in the application of data-driven models within the context of UWS. Integrating existing mechanistic knowledge into data-driven modeling offers a unique solution that reduces data requirements and enhances model interpretability. The knowledge-informed approach balances model complexity with dataset size, enabling more efficient and interpretable modeling in UWS. Furthermore, the integration of mechanistic and data-driven models offers a more accurate representation of UWS dynamics, addressing lingering uncertainties and advancing modelling capabilities. This paper presents perspectives and conceptual framework on developing and implementing knowledge-informed data-driven modeling, highlighting their potential to improve UWS management in the digital era., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2024 The Authors.)

The detection head constitutes a pivotal component within object detectors, tasked with executing both classification and localization functions. Regrettably, the commonly used parallel head often lacks omni perceptual capabilities, such as deformation perception (DP), global perception (GP), and cross-task perception (CTP). Despite numerous methods attempting to enhance these abilities from a single aspect, achieving a comprehensive and unified solution remains a significant challenge. In response to this challenge, we develop an innovative detection head, termed UniHead, to unify three perceptual abilities simultaneously. More precisely, our approach: 1) introduces DP, enabling the model to adaptively sample object features; 2) proposes a dual-axial aggregation transformer (DAT) to adeptly model long-range dependencies, thereby achieving GP; and 3) devises a cross-task interaction transformer (CIT) that facilitates interaction between the classification and localization branches, thus aligning the two tasks. As a plug-and-play method, the proposed UniHead can be conveniently integrated with existing detectors. Extensive experiments on the COCO dataset demonstrate that our UniHead can bring significant improvements to many detectors. For instance, the UniHead can obtain + 2.7 AP gains in RetinaNet, + 2.9 AP gains in FreeAnchor, and + 2.1 AP gains in GFL. The code is available at https://github.com/zht8506/UniHead.

Few-shot medical image segmentation has achieved great progress in improving accuracy and efficiency of medical analysis in the biomedical imaging field. However, most existing methods cannot explore inter-class relations among base and novel medical classes to reason unseen novel classes. Moreover, the same kind of medical class has large intra-class variations brought by diverse appearances, shapes and scales, thus causing ambiguous visual characterization to degrade generalization performance of these existing methods on unseen novel classes. To address the above challenges, in this paper, we propose a Prototype correlation Matching and Class-relation Reasoning (i.e., PMCR) model. The proposed model can effectively mitigate false pixel correlation matches caused by large intra-class variations while reasoning inter-class relations among different medical classes. Specifically, in order to address false pixel correlation match brought by large intra-class variations, we propose a prototype correlation matching module to mine representative prototypes that can characterize diverse visual information of different appearances well. We aim to explore prototypelevel rather than pixel-level correlation matching between support and query features via optimal transport algorithm to tackle false matches caused by intra-class variations. Meanwhile, in order to explore inter-class relations, we design a class-relation reasoning module to segment unseen novel medical objects via reasoning inter-class relations between base and novel classes. Such inter-class relations can be well propagated to semantic encoding of local query features to improve few-shot segmentation performance. Quantitative comparisons illustrates the large performance improvement of our model over other baseline methods.

Aim: The purpose of this study was to understand the caregiving experiences of breast cancer family caregivers and explore the profound impacts of those experiences on their quality of life., Design: A qualitative research method was used., Methods: We extended invitations to 23 family caregivers of outpatients and inpatients receiving breast surgery and oncology treatments in Taiyuan, China, to participate in semi-structured interviews. The interviews were audio-recorded and transcribed verbatim. Thematic analysis was employed to analyse the interview data., Results: Four themes and associated categories were identified: (1) changes in family dynamics, (2) the socio-medical context, (3) interactions between family and society, (4) self-efficacy and nine subthemes and their related categories, where virtually all participants expressed future uncertainty, emotional contagion, and personal challenges, and self-efficacy had a moderating influence on the first three themes., Patient or Public Contribution: This study did not involve direct participation of patients or the public. However, their experiences and perspectives on caregiving were indirectly reflected through the insights provided by the family caregivers who participated in the interviews. Their valuable input contributed to a deeper understanding of the caregiving experience and its impact on the quality of life for Chinese breast cancer family caregivers., (© 2024 The Authors. Nursing Open published by John Wiley & Sons Ltd.)

Magnesium hydroxide [Mg(OH) 2 ] is a non-hazardous chemical widely applied in sewer systems for managing odour and corrosion. Despite its proven effectiveness in mitigating these issues, the impacts of dosing Mg(OH) 2 in sewers on downstream wastewater treatment plants have not been comprehensively investigated. Through a one-year operation of laboratory-scale urban wastewater systems, including sewer reactors, sequencing batch reactors, and anaerobic sludge digesters, the findings indicated that Mg(OH) 2 dosing in sewer systems had multifaceted benefits on downstream treatment processes. Compared to the control, the Mg(OH) 2 -dosed experimental system displayed elevated sewage pH (8.8±0.1vs 7.1±0.1), reduced sulfide concentration by 35.1%±4.9% (6.7±0.9mgSL -1 ), and lower methane concentration by 58.0%±4.9% (19.1±3.6mgCODL -1 ). Additionally, it increased alkalinity by 16.3%±2.2% (51.9±5.4mgCaCO 3 L -1 ), and volatile fatty acids concentration by 207.4%±22.2% (56.6±9.0mgCODL -1 ) in sewer effluent. While these changes offered limited advantages for downstream nitrogen removal in systems with sufficient alkalinity and carbon sources, significant improvements in ammonium oxidation rate and NOx reduction rate were observed in cases with limited alkalinity and carbon sources availability. Moreover, Mg(OH) 2 dosing in upstream did not have any detrimental effects on anaerobic sludge digesters. Magnesium-phosphate precipitation led to a 31.7%±4.1% reduction in phosphate concertation in anaerobic digester sludge supernatant (56.1±10.4mgPL -1 ). The retention of magnesium in sludge increased settleability by 13.9%±1.6% and improved digested sludge dewaterability by 10.7%±5.3%. Consequently, the use of Mg(OH) 2 dosing in sewers could potentially reduce downstream chemical demand and costs for carbon sources (e.g., acetate), pH adjustment and sludge dewatering., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 The Author(s). Published by Elsevier Ltd.. All rights reserved.)

In the pursuit of energy and carbon neutrality, nitrogen removal technologies have been developed featuring nitrite (NO 2 - ) accumulation. However, high NO 2 - accumulations are often associated with stimulated greenhouse gas (i.e., nitrous oxide, N 2 O) emissions. Furthermore, the coexistence of free nitrous acid (FNA) formed by NO 2 - and proton (pH) makes the consequence of NO 2 - accumulation on N 2 O emissions complicated. The concurrent three factors, NO 2 - , pH and FNA may play different roles on N 2 O and nitric oxide (NO) emissions simultaneously, which has not been systematically studied. This study aims to decouple the effects of NO 2 - (0-200 mg N/L), pH (6.5-8) and FNA (0-0.15 mg N/L) on the N 2 O and NO production rates and the production pathways by ammonia oxidizing bacteria (AOB), with the use of a series of precisely executed batch tests and isotope site-preference analysis. Results suggested the dominant factors affecting the N 2 O production rate were NO 2 - and FNA concentrations, while pH alone played a relatively insignificant role. The most influential factor shifted from NO 2 - to FNA as FNA concentrations increased from 0 to 0.15 mg N/L. At concentrations below 0.0045 mg HNO 2 -N/L, nitrite rather than FNA played a significant role stimulating N 2 O production at elevated nitrite concentrations. The inhibition effect of FNA emerged with further increase of FNA between 0.0045-0.015 mg HNO 2 -N/L, weakening the promoting effect of increased nitrite. While at concentrations above 0.015 mg HNO 2 -N/L, FNA inhibited N 2 O production especially from nitrifier denitrification pathway with the level of inhibition linearly correlated with the FNA concentration. pH and the nitrite concentration regulated the production pathways, with elevated pH promoting the nitrifier nitrification pathway, while elevated NO 2 - concentrations promoting the nitrifier denitrification pathway. In contrast to N 2 O, NO emission was less susceptible to FNA at concentrations up to 0.015 mg N/L but was stimulated by increasing NO 2 - concentrations. This study, for the first time, distinguished the effects of pH, NO 2 - and FNA on N 2 O and NO production, thereby providing support to the design and operation of novel nitrogen removal systems with NO 2 - accumulation., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 The Author(s). Published by Elsevier Ltd.. All rights reserved.)

Graph representation is a critical technology in the field of knowledge engineering and knowledge-based applications since most knowledge bases are represented in the graph structure. Nowadays, contrastive learning has become a prominent way for graph representation by contrasting positive-positive and positive-negative node pairs between two augmentation graphs. It has achieved new state-of-the-art in the field of self-supervised graph representation. However, existing contrastive graph representation methods mainly focus on modifying (normally removing some edges/nodes) the original graph structure to generate the augmentation graph for the contrastive. It inevitably changes the original graph structures, meaning the generated augmentation graph is no longer equivalent to the original graph. This harms the performance of the representation in many structure-sensitive graphs such as protein graphs, chemical graphs, molecular graphs, etc. Moreover, there is only one positive-positive node pair but relatively massive positive-negative node pairs in the self-supervised graph contrastive learning. This can lead to the same class, or very similar samples are considered negative samples. To this end, in this work, we propose a Virtual Masking Augmentation (VMA) to generate an augmentation graph without changing any structures from the original graph. Meanwhile, a node augmentation method is proposed to augment the positive node pairs by discovering the most similar nodes in the same graph. Then, two different augmentation graphs are generated and put into a contrastive learning model to learn the graph representation. Extensive experiments on massive datasets demonstrate that our method achieves new state-of-the-art results on self-supervised graph representation. The source code of the proposed method is available at https://github.com/DuanhaoranCC/CGRA., Competing Interests: Declaration of competing interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Cheng Xie reports financial support was provided by National Natural Science Foundation of China. Cheng Xie reports a relationship with Yunnan University that includes: employment., (Copyright © 2023 The Author(s). Published by Elsevier Ltd.. All rights reserved.)