Potential Applicability Research Articles

Brain Tumor Detection: Transfer Learning Approach with VGG-16

Brain tumors have a profound impact on individuals worldwide, necessitating early detection to improve patient outcomes. This study explores the potential of deep learning in facilitating early tumor detection to aid medical professionals in timely intervention and personalized treatment strategies. While current tumor detection methodologies rely on traditional imaging techniques and manual analysis, they may suffer from limitations in accuracy and efficiency. To address these challenges, we propose a novel method using the VGG-16 architecture, a powerful Convolutional Neural Network (CNN) model, with pre-trained weights for feature extraction. By harnessing the capability of VGG-16, our model effectively discerns tumor patterns from brain MRI scans. The dataset utilized for training and evaluation consists of Brain MRI Images for Brain Tumor Detection from Kaggle, featuring a diverse range of annotated scans. During the evaluation, we focus on the primary performance metric of accuracy, which measures the model's ability to precisely classify tumor and non-tumor cases. Through extensive experimentation and analysis, we critically assess the model's performance and its potential clinical applicability

The enhanced connectivity between the frontoparietal, somatomotor network and thalamus as the most significant network changes of chronic low back pain

The prolonged duration of chronic low back pain (cLBP) inevitably leads to changes in the cognitive, attentional, sensory and emotional processing brain regions. Currently, it remains unclear how these alterations are manifested in the interplay between brain functional and structural networks. This study aimed to predict the Oswestry Disability Index (ODI) in cLBP patients using multimodal brain magnetic resonance imaging (MRI) data and identified the most significant features within the multimodal networks to aid in distinguishing patients from healthy controls (HCs). We constructed dynamic functional connectivity (dFC) and structural connectivity (SC) networks for all participants (n = 112) and employed the Connectome-based Predictive Modeling (CPM) approach to predict ODI scores, utilizing various feature selection thresholds to identify the most significant network change features in dFC and SC outcomes. Subsequently, we utilized these significant features for optimal classifier selection and the integration of multimodal features. The results revealed enhanced connectivity among the frontoparietal network (FPN), somatomotor network (SMN) and thalamus in cLBP patients compared to HCs. The thalamus transmits pain-related sensations and emotions to the cortical areas through the dorsolateral prefrontal cortex (dlPFC) and primary somatosensory cortex (SI), leading to alterations in whole-brain network functionality and structure. Regarding the model selection for the classifier, we found that Support Vector Machine (SVM) best fit these significant network features. The combined model based on dFC and SC features significantly improved classification performance between cLBP patients and HCs (AUC=0.9772). Finally, the results from an external validation set support our hypotheses and provide insights into the potential applicability of the model in real-world scenarios. Our discovery of enhanced connectivity between the thalamus and both the dlPFC (FPN) and SI (SMN) provides a valuable supplement to prior research on cLBP.

Eucommia ulmoides extract assisted green nanopalladium for efficient catalysis of diverse azo dyes

The eco-friendly synthesis of palladium nanoparticles (PdNPs) utilizing plant phytochemicals has demonstrated its efficacy as a nano-catalyst for azo dye degradation. However, the catalytic prowess of these nanoparticles has been predominantly explored on specific model compounds. Consequently, a fundamental inquiry arises: Can these nanoparticles uniformly catalyze a spectrum of diverse azo dyes, irrespective of structural intricacies, azo bond quantity, side-chain functional groups, molecular weight (Mw) fluctuations, water solubility gradients, ionic characteristics, and dye classification (reactive or acid)? To address this comprehensively, nine azo compounds, encompassing the aforementioned criteria, were systematically chosen for catalysis utilizing the synthesized PdNPs. The synthesized particles were spherical (∼10.5 nm in diameter) with highly crystalline surfaces (d-spacing of 0.194–0.224 nm) and have a uniform size distribution. Employing green chemistry principles and Eucommia ulmoides bark extract (EUBE), this synthesis exhibits exceptional catalytic efficiency across a varied array of azo dyes, achieving degradation rates of up to 99% within 1–5 minutes. This methodical exploration offers a holistic understanding of the catalytic capabilities of PdNPs, shedding light on their potential applicability across a diverse spectrum of azo compounds, thereby propelling advancements in the field.

Subsidy policies towards zero-emission bus fleets: A systematic technical-economic analysis

We establish a bus market evolution model to explore the implications of subsidy policies in Hong Kong (HK) for the transition to a zero-emission bus (ZEB) fleet using an unsubsidized case as the benchmark scenario. Using the system dynamics (SD) approach, we capture the intricate feedback mechanisms among market share dynamics, cost considerations, consumer preferences, and competitive interactions within the bus market. These interactions are encapsulated within three pivotal feedback loops: scale economy, public acceptance, and bus market competition. We systematically integrate the HK-specific features of hydrogen buses (H-buses), battery electric buses (E-buses), and diesel buses (D-buses) from technical and economic perspectives and compare them to the benchmark scenario. The results show that only 66% of the bus fleet would be ZEBs by 2050, and this underscores the necessity for governmental financial incentives to expedite the transition to a fully zero-emission fleet. Furthermore, we evaluate various ZEB subsidy policy alternatives in terms of timing, intensity, and recipients. The analysis suggests that policies offering long-term and low-intensity subsidies are most beneficial, taking into account the cumulative effects and inherent delays associated with policy implementation. Importantly, the study highlights the critical balance that policymakers must achieve between short- and long-term objectives, especially given the counter-intuitive outcomes observed in the early stages of policies favoring H-buses exclusively. This study provides valuable policy insights for policymakers and upstream energy companies in HK, with potential applicability in wider real-world contexts.

Innovative deep learning approach for cross-crop plant disease detection: A generalized method for identifying unhealthy leaves

One of the most serious threats to global food security is plant diseases compromising agricultural productivity and threatening the livelihoods of millions. These diseases can decimate crops, disrupt food supply chains, and escalate the risk of food shortages, underscoring the urgency of implementing robust strategies to safeguard the world’s food sources. Deep learning methods have revolutionized the field of plant disease detection, offering advanced and accurate solutions for early identification and management. However, a recurring problem in deep learning models is their susceptibility to a lack of robustness and generalization when facing novel crop and disease types that were not included in the training dataset. In this paper, we address this issue by proposing a novel deep learning-based system capable of recognizing diseased and healthy leaves across different crops, even if the system was not trained on them. The key idea is to focus on recognizing the diseased small leaf regions rather than the overall appearance of the diseased leaf, along with determining the disease’s prevalence rate on the entire leaf. For efficient classification and to leverage the excellence of the Inception model in disease recognition, we employ a small Inception model architecture, which is suitable for processing small regions without compromising performance. To confirm the effectiveness of our method, we trained and tested it using the widely acclaimed PlantVillage dataset, recognized as the most utilized dataset for its comprehensive and diverse coverage. Our method achieved an accuracy rate of 94.04%. Furthermore, when tested on new datasets, it achieved an accuracy rate of 97.13%. This innovative approach not only enhances the accuracy of plant disease detection but also addresses the critical challenge of model generalization to diverse crops and diseases. In addition, it outperformed the existing methods in its ability to identify any disease across any crop type, showcasing its potential for broad applicability and contribution to global food security initiatives.

A comprehensive transformer-based approach for high-accuracy gas adsorption predictions in metal-organic frameworks

Gas separation is crucial for industrial production and environmental protection, with metal-organic frameworks (MOFs) offering a promising solution due to their tunable structural properties and chemical compositions. Traditional simulation approaches, such as molecular dynamics, are complex and computationally demanding. Although feature engineering-based machine learning methods perform better, they are susceptible to overfitting because of limited labeled data. Furthermore, these methods are typically designed for single tasks, such as predicting gas adsorption capacity under specific conditions, which restricts the utilization of comprehensive datasets including all adsorption capacities. To address these challenges, we propose Uni-MOF, an innovative framework for large-scale, three-dimensional MOF representation learning, designed for multi-purpose gas prediction. Specifically, Uni-MOF serves as a versatile gas adsorption estimator for MOF materials, employing pure three-dimensional representations learned from over 631,000 collected MOF and COF structures. Our experimental results show that Uni-MOF can automatically extract structural representations and predict adsorption capacities under various operating conditions using a single model. For simulated data, Uni-MOF exhibits remarkably high predictive accuracy across all datasets. Additionally, the values predicted by Uni-MOF correspond with the outcomes of adsorption experiments. Furthermore, Uni-MOF demonstrates considerable potential for broad applicability in predicting a wide array of other properties.

CME Arrival Modeling with Machine Learning

Space weather phenomena have long captured the attention of the scientific community, and along with recent technological developments, the awareness that such phenomena can interfere with human activities on Earth has grown considerably. Coronal mass ejections (CMEs) are among the main drivers of space weather. Therefore, developing tools to provide information on their arrival at Earth's nearby space has become increasingly important. Liu et al. developed a tool, called CME Arrival Time Prediction Using Machine Learning Algorithms (CAT-PUMA), to obtain fast and accurate predictions of CME transit time. This present work aims at the expansion of the CAT-PUMA concept, employing supervised learning to obtain vital information about the arrival of CMEs at Earth. In this study, we report the results of our work following the implementation of supervised regression and classification models in the CAT-PUMA framework. We conducted a comparison of various machine learning models in the context of predicting the transit time of CMEs and classifying CMEs as either Earth impacting or non-impacting. In this way, we are able to provide information on the possibility of a CME reaching Earth relying on CME features and solar wind parameters measured at take-off. This application thus provides quantitative indications about the geoeffectiveness of these space weather events. While machine-learning models can demonstrate fairly strong performance in regression and classification tasks, it is not always straightforward to extrapolate their practical potential and real-world applicability. To address this challenge, we employed model interpretation techniques, specifically Shap values, to gain quantitative insights into the limitations that affect these models.

New Perspectives on the Role and Therapeutic Potential of Melatonin in Cardiovascular Diseases.

Cardiovascular diseases (CVDs) are the leading cause of death and disability worldwide. It is essential to develop novel interventions to prevent/delay CVDs by targeting their fundamental cellular and molecular processes. Melatonin is a small indole molecule acting both as a hormone of the pineal gland and as a local regulator molecule in various tissues. It has multiple features that may contribute to its cardiovascular protection. Moreover, melatonin enters all cells and subcellular compartments and crosses morphophysiological barriers. Additionally, this indoleamine also serves as a safe exogenous therapeutic agent. Increasing evidence has demonstrated the beneficial effects of melatonin in preventing and improving cardiovascular risk factors. Exogenous administration of melatonin, as a result of its antioxidant and anti-inflammatory properties, has been reported to decrease blood pressure, protect against atherosclerosis, attenuate molecular and cellular damage resulting from cardiac ischemia/reperfusion, and improve the prognosis of myocardial infarction and heart failure. This review aims to summarize the beneficial effects of melatonin against these conditions, the possible protective mechanisms of melatonin, and its potential clinical applicability in CVDs.

Graduate program collaborations to identify and cultivate graduate enrollment best practices

AbstractDetermining best practices for graduate enrollment requires continuous dedication by graduate enrollment personnel to identify innovative and relevant recruitment strategies to attract potential graduate applicants. The dynamic landscape of higher education creates further complexity as these best practices must also align with institutional expectations and the expectations of those overseeing graduate academic programs. To establish a robust enrollment strategy, it is crucial for graduate program officers and graduate enrollment personnel to establish a collaborative relationship that supports timely and ongoing dialogues to attract and retain quality admissions candidates. This manuscript explores how graduate enrollment personnel at a mid‐size regional institution collaborate with graduate program officers to achieve the enrollment goals of the institution and to support continued program growth.Practical Takeaways Establishing collaborative relationships between graduate program officers and graduate enrollment personnel are essential to support program growth and the continuous evaluation of admissions criteria to promote holistic admissions. Recognizing the diverse expectations of applicants and establishing a graduate enrollment framework that allows for individual program needs creates a supportive environment that encourages program growth. The elimination of extraneous admissions requirements can support program growth without negatively impacting the quality of applicants. Supporting graduate program officers throughout the graduate enrollment process is imperative to foster positive experiences for applicants throughout and following the admissions cycle.

Revisiting the Relationship between Polar Lows and Weather Regimes

Abstract The link between weather regimes (WRs) and polar low (PL) activity is examined over the North Atlantic and North Pacific basins. Compared to earlier studies based on limited, regional PL datasets, our study conducts a more complete evaluation of regional WR–PL relationships using an expanded PL climatology. Our findings show that PL activity is increased over the Norwegian and Barents Seas during the Atlantic ridge regime and decreased over the former region during the Scandinavian blocking regime, with negative impacts also stretching to the Irminger Sea. Over the Labrador and Irminger Seas, PL activity is modulated strongest by the North Atlantic Oscillation (NAO), with positive impacts during the positive phase and vice versa. Over the North Pacific, the Arctic low contributes to increased PL activity over most regions, while the opposite is true for the Pacific wave train regime. The variability of PL activity associated with WRs is strongly related to changes in key environmental conditions. In general, regions of enhanced (reduced) PL activity are coincident with anomalous low-level northerly (southerly) flow and reduced (increased) static stability. Further analysis shows that certain persistent WRs can strongly modulate PL activity over some regions, either due to the amplification or propagation of favorable or unfavorable conditions, which cautions the limitation of regarding WRs as stationary patterns. A previously developed PL genesis potential index is shown to represent the observed impacts well, which serves to confirm the robustness of our findings and suggests the potential applicability of WRs to the subseasonal prediction of PLs. Significance Statement Polar lows are intense small-scale (∼300 km) cyclones that form over high-latitude oceanic regions. The hazardous impacts they pose to coastal communities and maritime and air operations in the Arctic motivate their skillful prediction, which remains a major challenge at lead times beyond a few days. In this study, we relate PLs and the key environmental conditions that favor their development to weather regimes, which are recurrent large-scale circulation patterns that can persist for weeks at a time. We find that weather regimes have strong impacts on polar low activity through the modulation of key environmental conditions. These relationships can potentially be utilized in the extended-range prediction of polar lows.

Sources and transformation of nitrate in an agricultural riverine reservoir watershed of the Sichuan Basin

Elucidating the origins and transformations of nitrate in groundwater and surface water within agricultural regions is imperative for effectively managing watershed contamination and environmental improvement. This study conducted a comprehensive geochemical investigation using multiple stable isotopes (δD-H2O and δ18O–H2O, δ15N–NO3- and δ18O–NO3-, and δ15N–NH4+) and hydrogeochemical characteristics to explore the potential sources and transformation of nitrate in groundwater and reservoir water within an agricultural riverine reservoir watershed. The results indicated that nitrate pollution in groundwater exhibited a higher severity compared to the reservoir water within this watershed, thereby posing potential risks to the quality of the reservoir water through hydraulic connection. The integration of δ15N–NH4+ and dual-isotopes of nitrate, combined with hydrogeochemical analyses, revealed that the presence of NO3− in groundwater and reservoir water primarily originated from manure/sewage sources. Moreover, intricate biogeochemical processes containing denitrification, nitrification, and DNRA were identified within this watershed. The findings of the Bayesian stable isotope mixing model (SIAR) further corroborated the conclusion that manure/sewage was the primary source of nitrate. It revealed that manure/sewage accounted for an average of 46 % of groundwater NO3− concentration, followed by atmospheric precipitation (25 %), soil nitrogen (18 %), and chemical fertilizer (11 %). There were no distinct differences in nitrate source contributions between groundwater and reservoir water. For reservoir water, manure or sewage (45 % on average) > atmospheric precipitation (28 %) > soil nitrogen (16 %) > chemical fertilizer (11 %). This study demonstrates the effectiveness of incorporating δ15N–NH4+ with dual isotopes of nitrate and hydrogeochemical analyses, serving as a reliable methodology that yields valuable insights into the origins and transformations of NO3− in an agricultural reservoir watershed, with potential applicability to other regions.

Plant Fiber Reinforced Polymeric Composites for Engineering Applications: A Review

Purpose: Scientists are looking for unconventional materials to achieve sustainable goals and reduce the consumption of non-renewable resources. This review focuses on polymeric composite materials with improved properties and low cost, synthesized from plant fibres and their potential fields of application. Theoretical Framework: Natural fibers, which are globally accessible, play a crucial role in improving thermal and mechanical properties when incorporated into polymers. With remarkable stiffness-density, strength and lightness, fibers stand out as promising, low-cost composite materials for applications in various areas of engineering. The plant diversity of the Amazon makes Brazil one of the largest global producers of natural fibers, providing an opportunity to develop new sustainable technologies. Method/Design/approach: Systematic literature review addressing potential applications of polymeric materials with plant fibers, fiber extraction methods, influence of fiber surface treatments and composite processing, and mechanical properties of polymeric composites with fibers. Results: Polymer composites reinforced with natural fibers are emerging as sustainable alternatives to synthetic materials, driven by their ecological nature, easy availability, low cost and biodegradability. A systematic review of the literature revealed that the mechanical properties of these composites are intrinsically linked to various factors, such as fibre orientation and length, which require careful optimization. In addition, it was observed that surface treatments, such as chemical acetylation and alkaline treatments, play a crucial role in improving the properties of plant fiber reinforced materials. These processes are responsible for removing impurities present in the cell walls of the fibers, such as lignins, waxes and hemicellulose, among others. Given Brazil's significant potential in the production of natural fibers, their application in industrial contexts is promising, offering an environmentally friendly solution for waste management. Thus, it is believed that, in the future, plant fibers will gain even more prominence, possibly effectively replacing the synthetic fibers widely used in the market. Research implications: This work contributes to the development of innovative, low-cost, sustainable materials, capable of minimizing the consumption of non-renewable resources and with potential applicability. Originality/value: The results are promising for the development of environmentally sustainable polymer composites with plant fibers, with wide applications in engineering.

Advanced Drilling Optimization through Predictive Data Analytics and Machine Learning Pipeline

The paper provides a pattern and methodology to optimize drilling in the oil and gas industry through integration of data analytics and machine learning. Drilling operations have unpredictable subsurface conditions, which leads to operational inefficiencies. My methodology ingests historical drilling data, real-time sensor data, and geological data to train machine learning models. I discuss the implementation and analysis of a data analytics framework that collects and preprocesses data streams, and send the data for machine learning. The paper offers keywords aimed at enhancing discoverability, indexing, clarity, and visibility. The introduction section details context, relevance, objectives, scope and structure of the paper. The Problem Statement section details business problem. The solution section provides methodology to address the identified problem. The architecture diagram section illustrates the system architecture and design. The architecture review details the selected architecture. The implementation section provides steps to implement solution, detailing the specific tools, technologies, and methodologies utilized in its development. The implementation for Proof of Concept (PoC) section provides a strategy as how to plan for the implementation of the solution at the organization level. The use cases section provides details about what a business can derive information to make decision. The impact section details the business value. The extended use case section so how the proposed solution can be implemented across diverse industries and domains, highlighting the potential for widespread applicability.

Extensive Synergy Between Recombinant Methioninase and Eribulin Against Fibrosarcoma Cells But Not Normal Fibroblasts.

The aim of the present study was to determine the synergy of recombinant methioninase (rMETase) and the anti-tubulin agent eribulin on fibrosarcoma cells, in comparison to normal fibroblasts, in vitro. HT1080 human fibrosarcoma cells and HS27 human fibroblasts were used for in vitro experiments. Four groups were analyzed in vitro: No-treatment control; eribulin; rMETase; eribulin plus rMETase. Dual-color HT1080 cells which express red fluorescent protein (RFP) in the cytoplasm and green fluorescent protein (GFP) in the nuclei were used to visualize cytoplasmic and nuclear dynamics during treatment. Eribulin combined with rMETase greatly decreased the viability of HT 1080 cells. In contrast, eribulin combined with rMETase did not show synergy on Hs27 normal fibroblasts. Eribulin combined with rMETase also caused more fragmentation of the nucleus than all other treatments. The combination treatment of eribulin plus rMETase demonstrated efficacy on fibrosarcoma cells in vitro. In contrast, normal fibroblasts were resistant to this combination, indicating the potential clinical applicability of the treatment.

Asphalt Concrete Production Technology Using Oil Sludge from Zhaik Munay LLP

Oil sludge exhibits a compositional similarity to bitumen, a pivotal constituent in asphalt concrete mixtures. This similarity underscores the potential applicability of oil waste in the production of asphalt concrete, serving not only as an organic binder to fortify indigenous soils but also as a binding agent for the fabrication of organomineral mixtures. The incorporation of oil sludge in road construction endeavors holds promise for the conservation of natural resources, the amelioration of the environmental landscape, and a concurrent reduction in the cost of construction materials. The focus of this study encompasses a comprehensive examination of the physical and mechanical properties pertaining to asphalt concrete of Grade I, Type B. To enhance the performance attributes of asphalt concrete, an additive in the form of oil sludge sourced from ZhaikMunay LLP (Uralsk) was introduced. Various proportions of oil sludge, namely 5%, 10%, and 15%, were incorporated into the asphalt concrete mixture. The utilization of 5% oil sludge elicited negligible alterations in the properties of the asphalt concrete. However, with a 15% addition of oil sludge, discernible reductions were observed in maximum compressive efficiency (0.03% by volume) and shear resistance, indicated by the internal friction coefficient efficiency (0.01% by volume).

Identification of Cellulose Producing Bacterial Strains: An Eco-friendly and Cost-effective Approach

Bacterial cellulose (BC) stands out as a prominent biopolymer of global importance, distinguished by its unique advantages over plant-derived cellulose. Strains such as Acetobacter xylinum, renowned for their proficient BC production, draw considerable attention in both commercial and biomedical areas. This research aimed to selectively isolate cellulose-producing bacteria with enhanced efficiency from a variety of fruit samples utilizing a cost-effective methodology. A total of 60 fruit samples were selected, and the assessment focused on 17 strains derived from rotten banana, red apple, green apple, and pineapple samples. The evaluation encompassed an examination of bacteriological traits and cellulose synthesis, with subsequent identification of strains achieved through DNA extraction and 16S rRNA PCR analysis. The experimental findings reveal cellulose-producing strains, including model A. xylinum (KCCM 40407) obtained from the Pharmacy lab of COMSATS University Islamabad Abbottabad Campus, Pakistan, designated as number 2, serving as a control. Notably, strains isolated from deteriorated fruits (samples 1, 4, 8, 11, 12, and 15) demonstrated the capacity to produce soluble cellulose. A. xylinum (model strain 2) was cultured under static conditions in HS media, demonstrating remarkable efficacy for cellulose sheet production. Subsequent characterization employing scanning electron microscopy (SEM), Fourier-transform infrared spectroscopy (FT-IR), and X-ray diffraction (XRD) unveiled a nano-fiber mat featuring multi-layered fibers. This eco-friendly approach has the potential for large-scale, high-quality cellulose production, applicable in biomedical and industrial fields. The research highlights an environmentally sustainable and economically viable method for cellulose production, presenting potential applicability across biomedical and industrial arenas on a significant scale.

Development of a natural language chatbot interface for website users

Today, there are a variety of technology solutions for creating chatbots, including universal platforms such as Google's Dialogflow, Microsoft's Azure Bot Service, and IBM's Watson Assistant, as well as chatbot builders such as ActiveChat, SendPulse, and BotPress. The disadvantage of the considered platforms and designers is the limitation of the free plan. This study considered the development and integration of an informative chatbot into the department's website in order to create a virtual assistant for potential applicants and applicants. The relevance of the study is due to the modern conditions of admission, limited opportunities to conduct career guidance events in connection with the consequences of the COVID 19 pandemic and the war on the territory of Ukraine. The object of research is a system of informing website users using natural language. A software product has been developed, which is an information system for applicants. The dynamic JavaScript programming language combined with the Node.js code execution environment and the Telegraf library was chosen to create the chatbot. The Heroku cloud platform with an integrated Heroku PostgreSQL database became the server for the operation of the chatbot. The system is implemented using the @BotFather Telegram service, which makes it possible to integrate it into any web resource. The stages of chatbot development are highlighted with examples of information, linguistic, and technical support. An analysis of the compliance of the developed chatbot with regard to the criteria of informativeness, multimodality, emotionality, productivity, and interactivity was carried out. The structure of the information content of the chatbot, which includes a set of modules, has been developed. A test version of the chatbot has been designed, tested, and integrated into the website of the Department of Applied Linguistics, which will continue to be tested and improved in 2023–2024. Practical significance: the structure of information content and the method of developing a chatbot could be used to develop authentic systems for informing applicants in institutions of higher education

PPS/TA-PEI/β-FeOOH membranes with powerful photo-Fenton self-cleaning capability for efficient oil–water emulsion separation

Polyphenylene sulfide (PPS) microporous membranes have garnered extensive research enthusiasm concerning their outstanding permeation performance and separation accuracy in treating oily wastewater. Nevertheless, the inherent hydrophobic chemical structure, along with the noticeable concentration polarization effect, predisposes them to severe membrane contamination during the separation process. Accordingly, it is desirable to design a PPS self-cleaning membrane with superior anti-fouling capability, fast transport, and remarkable flux recovery. This study marks the first combination of PPS membrane materials with the photo-Fenton process. In detail, the polyphenol (tannic acid, TA)-polycation (polyethyleneimine, PEI)-metal ion (Fe3+) double cross-linking coatings were first assembled on the PPS substrate surface. Subsequently, β-FeOOH photocatalysts were synthesized through in-situ mineralization to further strengthen the hydrophilicity of the membranes. The obtained PPS/TA-PEI/β-FeOOH composite membrane achieved exceptional separation of diverse oil-in-water (O/W) emulsions. The PPS/TA-PEI/β-FeOOH membranes exhibited in-air superhydrophilic and under-water superoleophobicity, exhibiting under-water oil contact angles greater than 151° for diverse oil agents. The fabricated composite membranes displayed high permeability (a flux of the petroleum ether-in-water (P/W) emulsion reached 3400.5 L m−2h−1 bar−1 driven by a pressure of 0.05 MPa) along with ideal oil rejection (98.6 %). Significantly, the PPS/TA-PEI/β-FeOOH membrane possessed powerful photo-Fenton self-cleaning capability, and its flux recoveries of the contaminated membrane exceeded 90.3 % for varied O/W emulsions after being cleaned by visible light for 10 min. We believe that our research presents a novel approach for the exploitation of PPS-based photo-Fenton self-cleaning membranes, which also exhibit great applicative potential in oily wastewater treatment.

Characterization and photodegradation of methylene blue dye using bio-synthesized cerium oxide nanoparticles with Spirulinaplatensis extract

Increasing concern about environmental pollution attracts researchers to develop eco-friendly, low-cost, and sustainable approaches for green biosynthesis of nanoparticles to overcome pollutants. This study focuses on the green synthesis of ceria NPs using Spirulinaplatensis extract as a stabilizing and reducing agent. Characterization measurements, such as optical properties, X-ray diffraction, SEM, TEM, and FT-IR spectroscopy, confirmed the successful synthesis of crystalline and stable ceria NPs with well-defined morphological features. The calculated bandgaps energy of pure ceria, green CeO2@Sp 2:1, and CeO2@Sp 1:1 were 3.3, 3.15, and 2.94 eV, respectively. The as-synthesized and green ceria NPs showed an excellent degradation efficacy of MB dye under UV irradiation. Furthermore, the green ceria NPs showed high photodegradation efficiency of MB dye (R% = 86.2 and 88.8%) than pure ceria (R% = 76.4%) at certain specific conditions (pH = 11, contact time = 90 min, catalyst dose = 0.3 g/L and MB dye initial concentration = 100 mg/L). The isothermal constants confirmed that the degradation of MB dye is well-fitted with the Freundlich isotherm model (R2 > 0.99) better than the Langmuir model (R2 < 0.8). The kinetics models revealed a rapid degradation rate of MB dye, which follows pseudo-second-order models with Ce values ranging from 83.33 to 89.29 mg/g, with R2 > 0.99. These results indicated the potential applicability and promising avenue for developing advanced ceria NPs for wastewater treatment applications.

Subjective and objective image quality of low-dose CT images processed using a self-supervised denoising algorithm.

This study aimed to assess the subjective and objective image quality of low-dose computed tomography (CT) images processed using a self-supervised denoising algorithm with deep learning. We trained the self-supervised denoising model using low-dose CT images of 40 patients and applied this model to CT images of another 30 patients. Image quality, in terms of noise and edge sharpness, was rated on a 5-point scale by two radiologists. The coefficient of variation, contrast-to-noise ratio (CNR), and signal-to-noise ratio (SNR) were calculated. The values for the self-supervised denoising model were compared with those for the original low-dose CT images and CT images processed using other conventional denoising algorithms (non-local means, block-matching and 3D filtering, and total variation minimization-based algorithms). The mean (standard deviation) scores of local and overall noise levels for the self-supervised denoising algorithm were 3.90 (0.40) and 3.93 (0.51), respectively, outperforming the original image and other algorithms. Similarly, the mean scores of local and overall edge sharpness for the self-supervised denoising algorithm were 3.90 (0.40) and 3.75 (0.47), respectively, surpassing the scores of the original image and other algorithms. The CNR and SNR for the self-supervised denoising algorithm were higher than those for the original images but slightly lower than those for the other algorithms. Our findings indicate the potential clinical applicability of the self-supervised denoising algorithm for low-dose CT images in clinical settings.