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Electrolytic oxidation is a promising decomposition method for perfluorooctanoic acid (PFOA) because it is relatively mild compared with incineration and ultraviolet (UV) treatment, does not use chemicals, and has a low environmental impact. However, the electrolytic oxidation method is inefficient when the concentration of the target substance is low. In this study, a new concentration-decomposition technology based on adsorption by the metal-organic framework zeolitic imidazolate framework (ZIF)-8, followed by electrolysis, was developed for PFOA degradation. The concentration of PFOA was increased by performing adsorption with reduced-pressure filtration, resulting in an adsorption efficiency of >90%. Then, considering that the structure of ZIF-8 can be destroyed under strong acid conditions, a H2SO4 aqueous solution was added after adsorption to destroy ZIF-8, followed by neutralisation by adding a NaOH aqueous solution. Subsequent electrolysis of PFOA reached the same level of mineralisation energy efficiency as that obtained via conventional electrolysis. The mineralisation energy efficiency under the five-fold concentrated condition with acid-base treatment was 1.2 mg/Wh, which is higher than that of other degradation methods. Although optimisation of the amounts of ZIF-8 and the acid-base reagents is still required, this study provides a new foundation for developing efficient approaches for PFOA detoxification, including PFOA desorption via acid-base treatment and energy efficiency enhancement through the concentration process.
This study evaluated the feasibility of recovering and concentrating lactose from ultrafiltration (UF) permeate of sweet whey using nanofiltration (NF) and reverse osmosis (RO) under industrial conditions. The objective was to adapt existing whey protein recovery systems to valorize the lactose fraction. UF permeates from different batches were concentrated by NF at 16-25 bar, with 16 bar combined with diafiltration showing the best performance for lactose concentration. The NF concentrate was further concentrated by RO, increasing lactose content and reducing mineral levels. Spray drying produced a powder containing 94.10% lactose, with low moisture (0.98%), low water activity (0.258), and moderate hygroscopicity (5.20%). FTIR and physicochemical analyses confirmed lactose purity, and the overall process yield reached 87.68%. The integration of NF, RO, and drying proved technically efficient for lactose valorization and demonstrated that membrane systems traditionally used for protein recovery can be adapted for industrial lactose production, contributing to the added value of whey. A complete mass balance and environmental assessment would further strengthen the evaluation of its economic and environmental performance. PRACTICAL APPLICATIONS: This research demonstrates the feasibility of converting cheese whey, a commonly discarded dairy byproduct, into high-purity lactose using industrial membrane technologies. The proposed process achieved good yield and operational efficiency by adapting existing systems already used in the dairy industry. This approach supports the valorization of whey, reducing environmental impact while generating a high-value ingredient. The recovered lactose has wide applicability, including use in the food industry, pharmaceuticals, cosmetics, and potential bioenergy applications. Overall, the study contributes to improving sustainability and value generation within the dairy production chain.
Bisphenol A (BPA), a prototypical environmental endocrine disruptor with ubiquitous presence in aquatic systems, has prompted significant environmental and public health concerns. Addressing the urgent need for rapid and sensitive monitoring, we developed an innovative tri-modal lateral flow assay (LFA) platform powered by multifunctional MWCNTs/Fe-N-C nanozymes. The designed nanozyme enables the simultaneous output of three signal modes: intrinsic colorimetry, catalytic colorimetry, and catalytic photothermal colorimetry. The three signal channels act synergistically: intrinsic coloration provides basic visual readout, peroxidase-like activity enhances signal intensity by catalyzing the TMB chromogenic reaction, and the oxTMB-mediated photothermal effect further amplifies the detection signal. These three modes constitute a self-validating system through mutual cross-verification. This cross-checking mechanism effectively reduces background interference and improves detection accuracy and reliability. The platform achieves a detection limit of 0.13 ng mL-1 (intrinsic colorimetric), 0.082 ng mL-1 (catalytic colorimetric) and 0.047 ng mL-1 (catalytic photothermal colorimetric) for BPA, with progressively enhanced sensitivity across modalities. Besides, validated through spiked recovery tests in real lake water showing 92.8-107.3% recovery rates, the method proves robust against environmental interferences. This work establishes an approach for environmental contaminant monitoring by harmonizing nanozyme multifunctionality with multimodal sensing architectures, offering a versatile analytical framework for environmental contaminant monitoring.
Heavy metal contamination remains a critical global environmental issue due to the persistence, bioaccumulation, and toxicity of metal ions such as Pb2+, Cd2+, Hg2+, and As³+. Although conventional analytical techniques provide high sensitivity and accuracy, they often rely on energy-intensive instrumentation, hazardous reagents, and generate considerable chemical waste, raising concerns regarding their environmental sustainability. In this context, molecularly imprinted polymer (MIP)-based electrochemical sensors have emerged as promising alternatives, offering high selectivity, operational simplicity, and compatibility with miniaturized and in situ analysis. This review critically examines the integration of Green Analytical Chemistry (GAC) principles into the design and fabrication of MIP-based electrochemical sensors for heavy metal monitoring. Particular attention is given to material selection, polymerization strategies, template removal approaches, and electrode modification techniques, with emphasis on their environmental implications. The applicability of quantitative greenness assessment tools, including the Analytical Eco-Scale, GAPI, AGREE, and AGREEMIP, is discussed in the context of sensor development workflows, highlighting both their strengths and current limitations in addressing fabrication stages, nanomaterial synthesis, and end-of-life considerations. By identifying methodological bottlenecks, particularly solvent-intensive template removal and limited reusability, this review outlines practical directions for advancing more sustainable sensor platforms. Overall, the work provides a critical framework for aligning analytical performance with environmental responsibility in next-generation MIP-based electrochemical sensing systems.
To save energy and reduce environmental pollution in cotton bleaching, it is urgent to exploit environmentally friendly bleaching methods. Utilizing bleach activators in the H2O2 bleaching system is an efficient approach, and the search for green alternatives is of importance. Glucosamine pentaacetate (GAPA), a carbohydrate derivative characterized by a multi-acetyl structure, theoretically possesses superior environmental compatibility and activating performance, but its application in the bleaching of cotton fabric has rarely been reported. Herein, GAPA was synthesized and applied to a low-temperature H2O2 bleaching system. The effect of GAPA on the H2O2 bleaching system was systematically evaluated using the CIE whiteness index (WI), H2O2 decomposition rate, fabric wettability, and bursting strength as critical performance indicators. These results indicate that with the introduction of GAPA, the bleaching temperature can be reduced to 70 °C under nearly neutral alkaline conditions, while the WI is significantly enhanced. Moreover, the bleaching mechanism of the H2O2/GAPA system was evaluated, with particular attention to the possible presence of different reactive bleaching species (e.g., peracetic acid) and their respective impacts on bleaching performance. Finally, energy consumption, water usage, biotoxicity, and antibacterial properties of the bleached fabric were investigated, and thus this novel bleaching system was verified to exhibit remarkable eco-friendliness and sustainability potential.
The efficient and orderly realisation of the value of grassland ecological products is the key to achieving the strategic goal of transforming 'green mountains' into 'golden mountains'. The behaviour of herders, as the direct promoters of the realisation of the value of grassland ecological products, is crucial to achieving this goal. Therefore, it is of great significance to carry out research on the behaviour of micro-entrepreneurs in order to accelerate the cracking of the problem between the shortage of ecological products and the constraints of natural resources, and to promote balanced development. Based on the eco-ethical perspective, the contradictory factors and research frameworks affecting the behaviours of grassland eco-products value realisation were extracted from five aspects: natural relationship cognition, grassland ecological function cognition, grassland ecological statute awareness, pro-environmental attitudes, and knowledge of grassland ecological husbandry. A mixed method of structural equation modelling (SEM) and fuzzy set qualitative comparative analysis (fsQCA) was used to explore the influencing mechanisms and antecedent configurations of the behaviours of value realization of grassland ecological products in China. The results showed that natural relationship cognition, grassland ecological function cognition, grassland ecological statute awareness, pro-environmental attitude and grassland ecological animal husbandry knowledge all significantly influenced the intention and behaviour of grassland ecological product value realisation. And the intention to realise the value of grassland ecological products played a partial mediating effect between the ecological ethical view and the behaviour of realising the value of grassland ecological products. Secondly, policy support played a positive moderating role in the intention and behaviour of grassland ecological product value realization. Finally, six nested configuration pathways leading to the behaviour of value realisation of grassland ecological products have been identified.
Azoxystrobin is a widely used strobilurin fungicide. Its environmental persistence and potential toxicity to aquatic organisms demand accurate trace-level quantification in biological tissues. However, its sensitive and specific determination in small tissue samples using conventional methods remains challenging. In this study, a novel high-sensitivity analytical method based on ultra-performance liquid chromatography coupled with triple-stage mass spectrometry (UPLC-MS3) was established for the trace determination of azoxystrobin in zebrafish liver tissues. Following protein precipitation extraction, chromatographic separation was performed on a C18 column using a gradient of 0.1% aqueous formic acid and acetonitrile. Detection relied on an optimized MS3 transition (m/z 404.0 → 371.9 → 344.2). The method exhibited excellent linearity (r > 0.9984) from 0.1 to 20 ng mL-1, with accuracy between -3.33% and 3.67% and precision (CV) between 5.57 and 10.19%. Consistent recoveries (94.93-106.64%) and minimal matrix effects (99.45-104.79%) were achieved across all tissue matrices. Compared to conventional MRM, MS3 scanning significantly enhanced specificity by reducing endogenous interference. The validated approach was successfully applied to tissue distribution studies in zebrafish, confirming its reliability for environmental toxicology research and providing a robust platform for investigating fungicide biodistribution in aquatic organisms.
Biofouling caused by the rapid formation of microbial biofilms on submerged surfaces poses severe economic and environmental challenges for the maritime industry. Conventional antifouling coatings, such as biocidal or silicon-based, face environmental and durability limitations. To overcome these challenges, enzyme-mimicking nanomaterials (nanozymes) have emerged as promising alternatives due to their exceptional stability, cost-effectiveness, and tunable catalytic properties. In this study, we report the ability of vanadium pentoxide nanosheets (V2O5 NS) to mimic the activity of natural vanadium bromoperoxidase, catalysing the in-situ generation of reactive halogen species. This activity resulted in pronounced antibacterial effects against Escherichia coli and Bacillus subtilis. The haloperoxidase activity was monitored using the phenol red assay. Furthermore, incorporating V2O5 NS into commercial paint significantly reduced bacterial colonisation of stainless-steel substrates, demonstrating their practical application as an eco-friendly antifouling coating. These findings highlight V2O5 NS as a potent antifouling agent.
Paper-based sensors have emerged as a groundbreaking class of analytical devices, offering affordable, biodegradable, and flexible platforms for a broad range of applications, including environmental monitoring (e.g., heavy metals, PFAS, and microplastics detection), medical diagnostics (such as procalcitonin and glucose monitoring), food safety (like ammonia detection), and wearable electronics (for strain, pressure, and humidity sensing). This review looks at how using cellulosic-based materials with advanced nanomaterials-especially graphene and its variations-can improve the sensitivity, conductivity, and durability of sensors. The deploy of graphene-based electrodes, such as reduced graphene oxide (rGO), laser-induced graphene (LIG), and graphene-molybdenum disulphide (Gr/MoS2) composites, has developed devices with high responsiveness (up to 1.38 × 10-7 µA L-1 µg-1), low detection inhibits (as low as 1.36 pM), excellent mechanical flexibility, and strong thermal equilibrium (up to 700 °C). These developments highlight the immense potential of graphene-paper hybrid systems for constructing the next generation of environmentally friendly and multipurpose sensors that can tackle new worldwide issues in smart packaging, public health, the Internet of Things (IoT), and sustainable electronics.
Tomato leaf miner, Tuta absoluta, is a major pest responsible for significant yield losses in tomato cultivation worldwide. Increasing resistance to chemical insecticides and growing environmental concerns have underscored the need for eco-friendly pest management alternatives aligned with sustainable development Goals (SDGs) such as zero hunger and responsible consumption and production. This study evaluated the insecticidal potential of Annona squamosa seed extracts prepared using five different solvents: acetone, ethyl acetate, ethanol, methanol, and hexane. Their toxicity was tested against T. absoluta eggs and larvae. Bioassays revealed that the hexane extract, at the highest concentration (250 ppm), completely suppressed egg hatchability (0%) up to 120 h. The same concentration exhibited larvicidal activity, causing 46.66% at 24 h and 73.33% mortality at 48 h post-treatment. The lowest LC₅₀ and LC₉₀ values were observed in the hexane extract: 155.04 and 344.06 ppm at 24 h, and 85.49 and 169.43 ppm at 48 h, respectively. Enzymatic assays indicated a decrease in catalase (CAT) activity and an increase in glutathione S-transferase (GST) activity in T. absoluta larvae 24 h after treatment with the hexane extract. A non-target assay on Eudrilus eugeniae showed 5.33% minimal mortality at the highest concentration, compared to 96.66% mortality in Imidacloprid treatment and 0% in control. GC-MS analysis identified two major compounds, E-11-hexadecenoic acid ethyl ester (25.18%) and 9,12-octadecadienoyl chloride (Z, Z) (15.23%), which may be involved in the insecticidal activity. Molecular docking studies demonstrated strong binding affinities of E-11-hexadecenoic acid ethyl ester and 9,12-octadecadienoyl chloride (Z, Z) with the target insect enzyme acetylcholinesterase (AChE), suggesting their potential mode of action. These findings indicate that the hexane extract of A. squamosa seeds contains promising bioactive molecules with significant insecticidal potential. This research highlights the potential of plant-derived insecticides for environmentally safe and sustainable management of T. absoluta, reducing dependence on synthetic chemicals and promoting sustainable tomato production, good health and well-being and responsible consumption and production.
Phytoremediation is a cost-effective and environmentally friendly remediation technology that uses plants to treat contaminated sites, thereby restoring ecosystems and reclaiming degraded land. Plant remediation efficiency is influenced by multiple factors, often leading to inconsistent outcomes. This study was conducted on Artemisia lavandulaefolia, a pioneer plant with remediation potential. A space-for-time substitution approach was employed to investigate its adaptive strategies to contaminated environments, heavy metal remediation potential, and the underlying driving mechanisms during natural succession in an abandoned Pb-Zn mining area. It was observed that A. lavandulaefolia transitioned from a stress-tolerant pioneer to an active rhizosphere regulator as succession progressed. Its cadmium (Cd) enrichment coefficient was found to peak at 20.86 under intense interspecific competition during the late successional stage. Partial least squares path modeling (GoF = 0.60) revealed that soil properties significantly influenced the niche parameters (path coefficient = 0.84, R2 = 0.71), functional traits, and tissue elemental composition of A. lavandulaefolia (path coefficient = 0.98, R2 = 0.95), thereby shaping the rhizosphere soil environment (path coefficients = 0.46 and 0.57, R2 = 0.96). The resulting rhizosphere effects were found to largely determine the heavy metal enrichment and transfer coefficients (path coefficient = 1.13, R2 = 0.96). At the 25-year site, the rhizosphere effects of β-glucosidase (802.98%), available manganese (613.29%), available phosphorus (1243.66%), and available cadmium (1123.63%) were observed to peak simultaneously. Through combined random forest and Spearman correlation analyses, it was demonstrated that rhizosphere β-glucosidase activity, available manganese, and available phosphorus were key factors driving the Cd enrichment coefficient of A. lavandulaefolia. Our findings identify key rhizosphere regulators (phosphorus, manganese, and β-glucosidase activity) for maximizing cadmium enrichment in A. lavandulaefolia, offering a targeted strategy for precision ecological restoration in mining areas.
Accurate prediction of water-quality indicators remains challenging in small tabular environmental datasets because physicochemical variables can exhibit nonlinear interdependencies and modern deep-learning models are sensitive to hyperparameter configuration. In this study, the supervised regression task is defined as predicting dissolved oxygen (mg/L) from the remaining measured physicochemical variables using a modest public dataset of 200 samples. To address this task, the Automatic Feature Interaction Network (AutoInt) is coupled with the Ninja Optimization Algorithm (NiOA) for wrapper-based hyperparameter optimization under a controlled and reproducible experimental protocol. All evaluations use a fixed train/validation/test split (70%/15%/15%) with leakage-safe preprocessing based only on training-set statistics, and results are aggregated over [Formula: see text] independent runs with controlled initialization seeds and mean ± standard deviation reporting. Baseline AutoInt without metaheuristic optimization achieves a mean squared error of [Formula: see text], whereas the NiOA-optimized AutoInt configuration reaches [Formula: see text] under the matched optimization budget of 1500 function evaluations ([Formula: see text], [Formula: see text]). These findings indicate that NiOA-guided hyperparameter tuning can substantially improve AutoInt performance within this fixed benchmark setting. However, because the dataset is small, cross-sectional, and lacks explicit spatial or temporal structure, the results should be interpreted as benchmark-specific evidence rather than broad operational validation. Further evaluation on larger, independent, and spatiotemporally diverse water-quality datasets is required before generalizing the fitted model to wider environmental monitoring or decision-support applications.
The microbiota-gut-brain axis (MGBA) is a critical bidirectional communication system governing cognitive function and intestinal homeostasis. Despite growing evidence linking environmental chemicals to neurological disorders, the mechanisms underlying bisphenol A (BPA)-induced cognitive deficits remain poorly understood. Here, we demonstrate that chronic BPA exposure may induce cognitive impairment in male offspring through disruption of the MGBA, specifically via upregulation of the NLRP3 inflammasome/pyroptosis-related markers. Gravid Kunming mice received BPA (0, 2, 20, or 200µg/kg body weight/day) in drinking water until weaning; their male offspring were then orally administered identical doses for nine weeks. Behavioral tests revealed significant deficits in short- and long-term memory following high-dose (200µg/kg) BPA exposure. Mechanistically, high-dose BPA reduced hippocampal neuron density, compromised ileal barrier integrity, and induced dysbiosis characterized by decreased α-diversity (Chao1, ACE, Shannon; P < 0.05) and an elevated Firmicutes/Bacteroidota ratio. LEfSe analysis identified increased abundance of potentially pro-inflammatory genera at 200µg/kg. Crucially, high-dose BPA upregulated the expression of NLRP3, ASC, Caspase-1, GSDMD, and IL-18 in both the hippocampus and ileum, alongside elevated serum TNF-α and IL-18, indicating systemic inflammation. Correlation analyses further linked specific microbial shifts to pyroptosis markers and cognitive decline. Collectively, our findings establish that chronic BPA exposure may triffer gut dysbiosis and barrier dysfunction, leading to NLRP3 inflammasome activation and pyroptotic cell death in both the gut and brain, ultimately impairing cognition. These results underscore the neurotoxic risk posed by BPA and provide a mechanistic rationale for stricter regulatory controls on its use in food-contact materials.
Residues from industrial hemp seed oil production are increasingly explored for agricultural and biorefinery applications, yet their ecological compatibility with soil organisms remains poorly understood. This study investigated the effects of non-fermented and fermented hemp pomace (3 and 10 days, Thermomyces lanuginosus) on biochemical and behavioural responses in the earthworm Eisenia andrei following 48 h exposure to soils amended with different proportions of the substrates. Neurotoxicity (acetylcholinesterase [AChE]), detoxification pathways (carboxylesterase [CES], glutathione S-transferase [GST], multixenobiotic resistance [MXR]), oxidative stress responses (catalase [CAT], reduced glutathione [GSH], reactive oxygen species [ROS]), and avoidance behaviour were evaluated. Exposure to both non-fermented and fermented hemp pomaces showed alterations in the neurotoxicity- and detoxification-related biomarker responses, which were treatment-dependent, with the strongest effects observed in the non-fermented treatment. In contrast, there was lack of consistent oxidative stress responses. In parallel, earthworms strongly avoided soils amended with both non-fermented and fermented hemp pomace, indicating clear substrate incompatibility. These findings show that both non-fermented and fermented hemp pomace are chemically complex and biologically active organic materials rather than inert soil amendments. Behavioural responses, together with selected neurotoxicity and detoxification biomarkers, proved more sensitive than oxidative stress endpoints. Overall, the study highlights the importance of using integrated bioassays when assessing the environmental compatibility of agro-industrial residues intended for soil application.
Class imbalance is a common challenge in real-world health science applications, including medical diagnosis, rare disease detection, and ICU mortality prediction, where one or more classes are underrepresented. Although several methods address imbalance in binary classification, multiclass imbalance remains particularly challenging due to multiple minority classes, often leading to biased performance and reduced predictive accuracy. Despite several advancements, most classification models struggle to identify patterns in imbalanced data, limiting their effectiveness in real-world applications. A structured literature search was conducted to identify methodological studies on imbalanced multiclass classification, including algorithmic strategies and advances in performance evaluation. Articles published up to 2024 were retrieved from Scopus and Web of Science using predefined keywords. Studies were screened through titles and abstracts based on predefined inclusion and exclusion criteria, with additional backward citation searching for methodologically relevant studies. In total, 75 studies were included in the final methodological review to synthesize key challenges and recent advances. Despite the introduction of several metrics for assessing multiclass imbalance, the Imbalance Ratio (IR) remains the most commonly used measure for quantifying imbalance severity. Existing balancing techniques mainly rely on distance-based, cluster-based, and distribution-based approaches, reflecting methodological diversity. In multiclass settings, various decomposition strategies, classification algorithms, and performance metrics have been proposed to address imbalance; however, repeated use of imbalance-handling mechanisms, such as class weight adjustments across decomposition, training, and evaluation stages, may introduce bias. The effectiveness of these strategies depends on data characteristics including dimensionality, sample size, distribution, number of classes, and imbalance severity. Notably, insufficient reporting of these characteristics in many studies limits the assessment of feasibility and generalizability across diverse data settings. This review synthesizes the strengths and limitations of existing methods for handling imbalanced multiclass classification, offering practical insights for improving model robustness and predictive performance. Effective management of class imbalance supports several Sustainable Development Goals by promoting equitable decision-making and enhancing reliable analysis across diverse health, societal, and environmental challenges, making it essential for developing robust and generalizable models across diverse domains.
This study proposes an integrated multi-model coupling framework for ex-ante carbon assessment in new urban district planning. The framework combines investment allocation, land-use carbon estimation, and traffic emission models within a unified analytical environment to simulate spatial and economic feedbacks that drive urban carbon emissions. Compared with conventional carbon assessment approaches, which often evaluate land use and transport as independent modules, the proposed framework introduces a rule-based coupling mechanism that dynamically links investment intensity, spatial configuration, and carbon output through iterative feedback. Each sub-model exchanges parameters via a shared data layer, enabling recursive interactions among economic input, land-use change, and mobility patterns. The framework advances urban carbon modeling through three key innovations. First, it establishes an investment-carbon elasticity mechanism that quantifies how capital concentration influences spatial emission patterns. Second, it formulates a rule-based symbolic coupling algorithm that enables cross-model parameter updating during iteration. Third, it develops a feedback-controlled carbon evaluation process that transforms traditional PSS from descriptive visualization tools into predictive decision-support frameworks. Applied to a representative new urban district, the framework demonstrates effectiveness in identifying low-carbon planning strategies under varying investment-intensity scenarios. By integrating economic, spatial, and environmental dimensions into a unified analytical logic, this research provides a scalable foundation for quantitative decision-making in sustainable urban transitions and contributes a theoretical model applicable to broader regional and national carbon-neutral planning frameworks.
Bisphenol A (BPA) is implicated in impairing ovarian function by disrupting the normal function of granulosa cells (GCs); however, the underlying molecular mechanisms remain unclear. Based on our previous results, this study used KGN cells as a model and combined functional assays with mechanistic analyses to investigate the role of the YY1/HIF-1α/NDUFA4L2 signaling axis in BPA-induced granulosa cell injury. The results showed that BPA treatment upregulated YY1, HIF-1α, and NDUFA4L2 expression, accompanied by decreased ATP levels, suppressed mitochondrial complex I activity, accumulation of mitochondrial reactive oxygen species, and increased apoptosis. Further analyses demonstrated that YY1 knockdown markedly attenuated BPA-induced mitochondrial dysfunction, oxidative stress, and apoptosis, while also suppressing HIF-1α and NDUFA4L2 expression. Mechanistically, YY1 promoted NDUFA4L2 expression mainly by enhancing HIF-1α protein stability. YY1 knockdown alleviated BPA-induced cellular injury, whereas restoration of HIF-1α substantially weakened this protective effect, and further silencing of NDUFA4L2 mitigated the injury phenotype again. Taken together, these findings indicate that BPA induces mitochondrial bioenergetic impairment, oxidative stress, and apoptosis in granulosa cells through activation of the YY1/HIF-1α/NDUFA4L2 signaling axis, and further suggest that YY1 serves as an important upstream regulatory node linking environmental exposure to mitochondrial injury.
This scoping review aimed to synthesise empirical studies on communication access during face-to-face service encounters in the service sector for people who have communication support needs in low-and middle-income countries. The methodological framework by Arksey and O'Malley guided this review. Studies were identified from nine electronic databases and independently screened for inclusion. Data were extracted aligned to the research questions and the environmental categories highlighted in the International Classification of Functioning, Disability and Health. Sixty studies met the inclusion criteria. Most studies focused on communication access for people who are hard of hearing or d/Deaf and service encounters in health care services. Various barriers and facilitators were identified, including factors related to the service system, policies, assistive technology, and the physical environment. The attitudes, knowledge, and skill of service providers were frequently mentioned as barriers. A variety of factors influencing communication access were identified in the review. Many of these are amenable to change and may be productively addressed to increase communication access for people who have communication support needs. Studies are needed to document the development, implementation, and effects of integrated and comprehensive intervention efforts in close collaboration with people who have communication support needs.
Based on the complementary and enhanced fusion of 3D point clouds and 2D RGB images, this paper designs an end-to-end learning framework-Point Cloud Enhanced Depth Pixel Fusion Network (PEPF-Net), aimed at enabling robots to achieve accurate 3D perception of unstructured environments. In the process, we address four key problems in 3D perception tasks: enhancing RGB representation using the reflection intensity and depth information of point clouds to generate Depth-RGB Pixel (D-Pixel); proposing Point-by-Point Vector Attention (PVA-Net) to model the vector relationships of point clouds,  to obtain deep-level point cloud features, and to achieve direct and effective fusion of heterogeneous data; designing a Layered-Transformer (L-TsfmNet) feature extractor to hierarchically extract D-Pixel features; proposing Variable Window Self-attention (VS-a) to focus on the relationships between local "window tokens" and avoid the complexity of global computation. Extensive experiments on the KITTI dataset demonstrate that PEPF-Net outperforms the currently common advanced environmental 3D perception algorithms.
In this work, α-glucosidase was successfully immobilized on commercially available cellulose filter paper and applied to screen inhibitors from traditional Chinese medicines combined with capillary electrophoresis analysis. For the immobilization of α-glucosidase, cellulose filter paper was employed as the carrier. Which was first coated with a chitosan-polyvinyl alcohol blend solution and subsequently activated with polyethyleneimine to introduce a positively charged surface. Finally, α-glucosidase was immobilized onto the modified cellulose filter paper via electrostatic adsorption. The carrier was characterized, and several parameters affecting the catalytic activity of immobilized enzymes were optimized. The results showed that the tensile strength of chitosan-polyvinyl alcohol-modified cellulose filter paper was 1.8 times that of the original cellulose filter paper, indicating that the mechanical properties of the carrier were enhanced. Moreover, the immobilized α-glucosidase showed good storage stability (retained 51.7 ± 0.43% of its initial activity after storing at 4℃ for 30 days), excellent reusability (retained 79.5 ± 1.18% of its initial activity after reusing 10 times), improved environmental tolerance (70°C and pH 8.0). In addition, the reliability of the immobilized enzyme for enzyme inhibitor screening was verified using acarbose as a model inhibitor, and its half-maximal inhibitory concentration was 0.85 ± 0.02 μM. Eventually, the immobilized α-glucosidase was used to screen inhibitors from 12 traditional Chinese medicine extracts combined with capillary electrophoresis analysis, and Sanguisorbae Radix exhibited the strongest inhibitory effect. This established platform provides a rapid and reliable strategy for screening α-glucosidase inhibitors from traditional Chinese medicine.