Investigation of direct contact membrane distillation (DCMD) performance using CFD and machine learning approaches.

Direct Contact Membrane Distillation (DCMD) is emerging as an effective method for water desalination, known for its efficiency and adaptability. This study delves into the performance of DCMD by integrating two powerful analytical tools: Computational Fluid Dynamics (CFD) and Artificial Neural Networks (ANN). The research thoroughly examines the impact of various factors, such as inlet temperatures, velocities, channel heights, salt concentration, and membrane characteristics, on the process's efficiency, specifically calculating the water vapor flux. A rigorous validation of the CFD model aligns well with established studies, ensuring reliability. Subsequently, over 1000 data points reflecting variations in input factors are utilized to train and validate the ANN. The training phase demonstrated high accuracy, with near-zero mean squared errors and R2 values close to one, indicating a strong predictive capability. Further analysis post-ANN training shed light on key relationships: higher membrane porosity boosts water vapor flux, whereas thicker membranes reduce it. Additionally, it was detailed how salt concentration, channel dimensions, inlet temperatures, and velocities significantly influence the distillation process. Finally, a mathematical model was proposed for water vapor flux as a function of key input factors. The results highlighted that salt mole fraction and hot water inlet temperature have the most effect on the water vapor flux. This comprehensive investigation contributes to the understanding of DCMD and emphasizes the potential of combining CFD and ANN for optimizing and innovating water desalination technology.

Industrial Distillation Fractions of Garlic Essential Oil, Design, Synthesis, and Antifungal Activity Evaluation of Aliphatic Substituted Trisulfide Derivatives.

Garlic oil has a wide range of biological activities, and its broad-spectrum activity against phytopathogenic fungi still has the potential to be explored. In this study, enzymatic treatment of garlic resulted in an increase of approximately 50 % in the yield of essential oil, a feasible GC-MS analytical program for garlic oil was provided. Vacuum fractionation of the volatile oil and determination of its inhibitory activity against 10 fungi demonstrated that garlic oil has good antifungal activity. The antifungal activity levels were ranked as diallyl trisulfide (S-3)>diallyl disulfide (S-2)>diallyl monosulfide (S-1), with an EC50 value of S-3 against Botrytis cinerea reached 8.16 mg/L. Following the structural modification of compound S-3, a series of derivatives, including compounds S-4~7, were synthesized and screened for their antifungal activity. The findings unequivocally demonstrated that the compound dimethyl trisulfide (S-4) exhibited exceptional antifungal activity. The EC50 of S-4 against Sclerotinia sclerotiorum reached 6.83 mg/L. SEM, In vivo experiments, and changes in mycelial nucleic acids, soluble proteins and soluble sugar leakage further confirmed its antifungal activity. The study indicated that the trisulfide bond structure was the key to good antifungal activity, which can be developed into a new type of green plant-derived fungicide for plant protection.

Teacher-student collaborated multiple instance learning for pan-cancer PDL1 expression prediction from histopathology slides.

Programmed cell death ligand 1 (PDL1), as an important biomarker, is quantified by immunohistochemistry (IHC) with few established histopathological patterns. Deep learning aids in histopathological assessment, yet heterogeneity and lacking spatially resolved annotations challenge precise analysis. Here, we present a weakly supervised learning approach using bulk RNA sequencing for PDL1 expression prediction from hematoxylin and eosin (H&E) slides. Our method extends the multiple instance learning paradigm with the teacher-student framework, which assigns dynamic pseudo-labels for intra-slide heterogeneity and retrieves unlabeled instances using temporal ensemble model distillation. The approach, evaluated on 12,299 slides across 20 solid tumor types, achieves a weighted average area under the curve of 0.83 on fresh-frozen and 0.74 on formalin-fixed specimens for 9 tumors with PDL1 as an established biomarker. Our method predicts PDL1 expression patterns, validated by IHC on 20 slides, offering insights into histologies relevant to PDL1. This demonstrates the potential of deep learning in identifying diverse histological patterns for molecular changes from H&E images.

Crosslinking and fluorination reinforced PTFE nanofibrous membrane with excellent amphiphobic performance for low-scaling membrane distillations.

Membrane distillation (MD) has emerged as a promising technology for desalination and concentration of hypersaline brine. However, the efficient preparation of a structurally stable and salinity-resistant membrane remains a significant challenge. In this study, an amphiphobic polytetrafluoroethylene nanofibrous membrane (PTFE NFM) with exceptional resistance to scaling has been developed, using an energy-efficient method. This innovative approach avoids the high-temperature sintering treatment, only involving electrospinning with PTFE/PVA emulsion and subsequent low-temperature crosslinking and fluorination. The impact of the PVA and PTFE contents, as well as the crosslinking and subsequent fluorination on the morphology and MD performance of the NFM, were systematically investigated. The optimized PTFE NFM displayed robust amphiphobicity, boasting a water contact angle of 155.2º and an oil contact angle of 132.7º. Moreover, the PTFE NFM exhibited stable steam flux of 52.1 L·m-2·h-1 and 26.7 L·m-2·h-1 when fed with 3.5 wt % and 25.0 wt % NaCl solutions, respectively, and an excellent salt rejection performance (99.99 %, ΔT = 60 °C) in a continuous operation for 24 h, showing exceptional anti-scaling performance. It also exhibited stable anti-wetting and anti-fouling properties against surfactants (sodium dodecyl sulfate) and hydrophobic contaminants (diesel oil). These results underscore the significant potential of the PTFE nanofibrous membrane for practical applications in desalination, especially in hypersaline or polluted aqueous environments.

Constructing spherical-beads-on-string structure of electrospun membrane to achieve high vapor flux in membrane distillation.

Hydrophobic membranes with a reentrant-like structure have shown high hydrophobicity and high anti-wetting properties in membrane distillation (MD). Here, PVDF spherical-beads-on-string (SBS) fibers were electrospun on nonwoven fabric and used in the MD process. Such a reentrant-like structure was featured with fine fibers, a low ratio of bead length to bead diameter, and high bead frequency. It was revealed that the SBS-structured membranes exhibited an exceptional capability for vapor flux, due to the formation of a network of more interconnected macropores than that of fibers and fusiform-beads-on-string structures, ensuring unimpeded vapor diffusion. In the desalination of formulated seawater (3.5 wt.% NaCl solution), a vapor flux of 61 ± 3 kg m-2 h-1 with a salt rejection of >99.98 % was achieved at a feed temperature of 60 °C. Furthermore, this SBS structured membrane showed satisfactory seawater desalination performance with a stable flux of 40 kg m-2 h-1 over a 27 h MD process. These findings suggest a viable approach for fabricating SBS-structured membranes that significantly enhance vapor flux in MD for desalination applications. Besides, the hydrophobic membranes with SBS structure can be prepared by single-step electrospinning, and it is facile to scale-up manufacture. This strategy holds promise for advancing the development of high-performance MD membranes tailored for efficient seawater desalination processes.

Exploiting the potential of a novel "in-situ latent heat recovery" in hollow-fiber vacuum membrane distillation process for simultaneously improved water production and energy efficiency.

Thermal driven membrane distillation (MD) technology is a promising method for purifying & recovering various salty (especially high salty) or contaminated wastewaters with low-grade heat sources. However, the drawbacks of "high energy consumption" and "high cooling water consumption" pose special challenges for the future development of this technology. In this article, we report an innovative strategy called "in-situ heat transfer", which is based on the jacketed structure composed of hollow fiber membranes and capillary heat exchange tubes, to simplify the migration steps of condensation latent heat in MD heat recovery process. The results indicate that the novel heat recovery strategy exhibits higher growth rates both in the flux and gained output ratio (47.4 % and 173.1 %, respectively), and further reduces the system's dependence on cooling water. In sum, under the control of the "in-situ heat transfer" mechanism, the functional coupling of "vapor condensation (exothermic)" and "feed evaporation (endothermic)" in limited-domain space is an attractive alternative solution, because it eliminates the disadvantages of the imbalance between heat supply and demand in traditional heat recovery methods. Our research may facilitate the development of MD heat recovery modules for industrial applications, which will help to further achieve the goal of energy saving and emission reduction.

Solar-powered single-stage distillation and complex conductivity analysis for sustainable domestic wastewater treatment.

This study investigates the efficacy of a solar-powered single-stage distillation system for treating domestic wastewater, supplemented with complex conductivity analysis. Domestic wastewater samples were collected from a municipal manhole in El Jadida, Morocco, over a 24-h period. The single-stage distillation system, designed for domestic wastewater treatment, utilizes heat to vaporize the wastewater, followed by condensation to produce pure liquid water. The system demonstrated increased distilled water production with rising temperatures, with domestic wastewater outperforming seawater as a feed water source. Physical and chemical testing of the treated water revealed significant improvements in water quality, meeting, or exceeding Moroccan irrigation water standards. Reductions in parameters such as biological oxygen demand (BOD), chemical oxygen demand (COD), suspended matter, and heavy metals underscored the effectiveness of the distillation process. Complex conductivity analysis provided insights into the electrical properties of untreated wastewater and distilled water. Deconvolution of complex conductivity data using an equivalent electrical circuit model elucidated the electrochemical processes during treatment, highlighting the efficiency of the distillation process. The integration of solar energy addresses water scarcity while promoting environmental sustainability. Complex conductivity analysis enhances process understanding, offering avenues for monitoring and control in wastewater treatment.

Hypercrosslinked Hydrogel Composite Membranes Targeted for Removal of Volatile Organic Compounds via Selective Solution-Diffusion in Membrane Distillation.

Membrane distillation (MD) has attracted considerable interest in hypersaline wastewater treatment. However, its practicability is severely impeded by the ineffective interception of volatile organic compounds (VOCs), which seriously affects the product water quality. Herein, a hypercrosslinked alginate (Alg)/aluminum (Al) hydrogel composite membrane is facilely fabricated via Alg pregel formation and ionic crosslinking for efficient VOC interception. The obtained MD membrane shows a sufficient phenol rejection of 99.52% at the phenol concentration of 100 ppm, which is the highest rejection among the reported MD membranes. Moreover, the hydrogel composite membrane maintains a high phenol interception (>99%), regardless of the feed temperature, initial phenol concentration, and operating time. Diffusion experiments and molecular dynamics simulation verify that the selective diffusion is the dominant mechanism for VOCs-water separation. Phenol experiences a higher energy barrier to pass through the dense hydrogel layer compared to water molecules as the stronger interaction between phenol-Alg compared with water-Alg. Benefited from the dense and hydratable Alg/Al hydrogel layer, the composite membrane also exhibits robust resistance to wetting and fouling during long-term operation. The superior VOCs removal efficiency and excellent durability endow the hydrogel composite membrane with a promising application for treating complex wastewater containing both volatile and nonvolatile contaminants.

TCN-attention-HAR: human activity recognition based on attention mechanism time convolutional network.

Wearable sensors are widely used in medical applications and human-computer interaction because of their portability and powerful privacy. Human activity identification based on sensor data plays a vital role in these fields. Therefore, it is important to improve the recognition performance of different types of actions. Aiming at the problems of insufficient time-varying feature extraction and gradient explosion caused by too many network layers, a time convolution network recognition model with attention mechanism (TCN-Attention-HAR) was proposed. The model effectively recognizes and emphasizes the key feature information. The ability of extracting temporal features from TCN (temporal convolution network) is improved by using the appropriate size of the receiver domain. In addition, attention mechanisms are used to assign higher weights to important information, enabling models to learn and identify human activities more effectively. The performance of the Open Data Set (WISDM, PAMAP2 and USC-HAD) is improved by 1.13%, 1.83% and 0.51%, respectively, compared with other advanced models, these results clearly show that the network model presented in this paper has excellent recognition performance. In the knowledge distillation experiment, the parameters of student model are only about 0.1% of those of teacher model, and the accuracy of the model has been greatly improved, and in the WISDM data set, compared with the teacher's model, the accuracy is 0.14% higher.

Membrane distillation of wastewater: comparison of model and real organics.

Fouling behaviour in membrane distillation (MD) processes plays a crucial role in determining their widespread acceptability. Most studies have primarily focused on model organic foulants, such as humic acid (HA) and sodium alginate (SA). This study investigates the fouling of a polytetrafluoroethylene membrane in a direct contact MD (DCMD) using model organics (i.e., HA and SA) and real wastewater. The results indicated that the flux decline (5-60%) was only observed during the initial phase of the operation with model organic foulants. In contrast, real wastewater caused a gradual decline in flux throughout the experiment in both the concentrate (40%) and continuous (90%) modes. The study also found significant differences in the fouling layer morphology, composition, and hydrophobicity between the model organic foulants and real wastewater. Fourier transform infrared spectroscopy findings demonstrated that the fouling layer formed by real wastewater varied significantly from model organics, which primarily comprised of protein-like and polysaccharide-like functional groups. Finally, liquid chromatography-organic carbon detection revealed that the fouling layer of the MD membrane with real wastewater was composed of 40.7% hydrophobic and 59.3% hydrophilic organics. This study suggests that model organics may not accurately reflect real wastewater fouling.

Advanced oxidation process/coagulation coupled with membrane distillation (AOP/Coag-MD) for efficient ammonia recovery: Elucidating biofouling control performance and mechanism.

The inadequate understanding of the biofouling formation mechanism and the absence of effective control have inhibited the commercial application of membrane distillation (MD). In this study, an advanced oxidation process (AOP)/coagulation-coupled (Coag) membrane distillation system was proposed and exhibited the potential for MD ammonia recovery (recovery rate: 94.1%). Extracellular polymeric substances (EPS) and soluble microbial products (SMP) components such as humic acid and tryptophan-like proteins were disrupted and degraded in the digestate. The curtailment and sterilizing efficiency of AOP on biofilm growth was also verified by optical coherence tomography (OCT) in situ real-time monitoring and confocal laser scanning microscopy (CLSM). Peroxymonosulfate (PMS) was activated to generate sulfate (SO4•-) and hydroxyl radicals (HO•), which altered the microbial community. After oxidative treatment, 16 S rRNA sequencing indicated that the dominant phylum of the microbial community evolved into Firmicutes. Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis demonstrated that free radicals produced by PMS could disrupt cells' signaling molecules and interactions. In conjunction with these analyses, the mechanisms of response to free radical attack by Gram-negative bacteria, Gram-positive bacteria, and fungi were revealed. This research provided new insights into the field of membrane fouling control for membrane technology resource recovery processes, broadening the impact of AOP applications on microbiological response and fate in the environment.

Molecular insights into the adsorption and penetration of oil droplets on hydrophobic membrane in membrane distillation.

Membrane fouling induced by oily substances significantly constrains membrane distillation performance in treating hypersaline oily wastewater. Overcoming this challenge necessitates a heightened fundamental understanding of the oil fouling phenomenon. Herein, the adsorption and penetration mechanism of oil droplets on hydrophobic membranes in membrane distillation process was investigated at the molecular level. Our results demonstrated that the adsorption and penetration of oil droplets were divided into four stages, including the free stage, contact stage, spreading stage, and equilibrium stage. Due to the extensive non-polar surface distribution of the polytetrafluoroethylene (PTFE) membrane (comprising 95.41 %), the interaction between oil molecules and PTFE was primarily governed by van der Waals interaction. Continuous oil droplet membrane fouling model revealed that the new oil droplet molecules preferred to penetrate into membrane pores where oil droplets already existed. The penetration of resin (a component of medium-quality oil droplets) onto PTFE membrane pores required the "pre-paving" of light crude oil. Finally, the ΔE quantitative structure-activity relationships (QSAR) models were developed to evaluate the penetration mechanism of pollutant molecules on the PTFE membrane. This research provides new insights for improving sustainable membrane distillation technologies in treating saline oily wastewater.

The Flow Rate of the Condenser Cooling Water in the Distillation Process Increases the Quality and Quantity of Patchouli Oil.

Indonesia is an important essential oil-exporting country globally, where 40 types of essential oils have been traded on the international market and are products of Indonesia. However, the quality and quantity of patchouli oil produced in Indonesia are still low. Most essential oil processing units use simple or traditional technology and generally have limited production capacity. This study aimed to obtain the optimum water flow rate in a condenser system for patchouli oil production in Maluku, Indonesia. Patchouli oil extraction from fresh patchouli leaves and twigs was carried out by increasing the condenser water discharge rate. Patchouli oil extraction with a condenser cooling water discharge treatment of 1.74 L/min and drying time for 5 days produced the highest patchouli oil yield of 1.4%. The greater the condenser water discharge rate, the better the yield and accumulation of patchouli oil recovery obtained. In addition, based on the results of the analysis of the composition of patchouli oil compounds with GCMS, it can be seen that 13 compounds can be detected in patchouli oil. The three main components of patchouli oil in all condenser cooling water treatments were alpha-guaiene, delta-guaiene, and patchouli alcohol. Considering the results of all parameters mentioned above, the treatment of the condenser cooling water discharge of 1.74 L/min and drying time for 5 days increases the quality and quantity of patchouli oil.

Distillation of essential oils: An innovative technological approach focused on productivity, quality and sustainability.

Essential oil (EO) distillation units, which are commonly installed on farms, have difficultly accessing knowledge centers. The apparent simplicity of the process hides unwanted losses and deviations that go undetected and, therefore, not corrected. This article proposes improvements to the process that are based on "4.0" technologies in order to detect and correct two important deficiencies, with an impact on the yield, quality and environmental performance. The first deficiency comprises the steam channels that are formed through green mass (channeling), are well known and are considered normal by EO producers. Without detection and correction, this negatively affects the extraction results. The second is the lack of technology that is able to automatically determine the extraction endpoint. Smart sensing, control, self-configuration and the dynamic determination of improved process parameters make up a set of actions undertaken by a smart extraction plant (50-liter capacity). Nineteen experiments using lemongrass (Cymbopogon citratus) exhibited remarkable 24% and 10% improvements in the yield and quality, respectively. Energy consumption and a more than 50% reduction in the processing complete the set of results achieved. In addition to manufacturing costs and the utilization of capacity, better sustainability indicators are positive consequences of this technological updating.

Importance-aware adaptive dataset distillation.

Herein, we propose a novel dataset distillation method for constructing small informative datasets that preserve the information of the large original datasets. The development of deep learning models is enabled by the availability of large-scale datasets. Despite unprecedented success, large-scale datasets considerably increase the storage and transmission costs, resulting in a cumbersome model training process. Moreover, using raw data for training raises privacy and copyright concerns. To address these issues, a new task named dataset distillation has been introduced, aiming to synthesize a compact dataset that retains the essential information from the large original dataset. State-of-the-art (SOTA) dataset distillation methods have been proposed by matching gradients or network parameters obtained during training on real and synthetic datasets. The contribution of different network parameters to the distillation process varies, and uniformly treating them leads to degraded distillation performance. Based on this observation, we propose an importance-aware adaptive dataset distillation (IADD) method that can improve distillation performance by automatically assigning importance weights to different network parameters during distillation, thereby synthesizing more robust distilled datasets. IADD demonstrates superior performance over other SOTA dataset distillation methods based on parameter matching on multiple benchmark datasets and outperforms them in terms of cross-architecture generalization. In addition, the analysis of self-adaptive weights demonstrates the effectiveness of IADD. Furthermore, the effectiveness of IADD is validated in a real-world medical application such as COVID-19 detection.

Engineered eco-friendly composite membranes with superhydrophobic/hydrophilic dual-layer for DCMD system.

Considerable advancements have been made in the development of hydrophobic membranes for membrane distillation (MD). Nonetheless, the environmentally responsible disposal of these membranes poses a critical concern due to their synthetic composition. Herein, an eco-friendly dual-layered biopolymer-based membrane was fabricated for water desalination. The membrane was electrospun from two bio-polymeric layers. The top hydrophobic layer comprises polycaprolactone (PCL) and the bottom hydrophilic layer from cellulose acetate (CA). Additionally, silica nanoparticles (SiO2 NPs) were electrosprayed onto the top layer of the dual-layered PCL/CA membrane to enhance the hydrophobicity. The desalination performance of the modified PCL-SiO2/CA membrane was compared with the unmodified PCL/CA membrane using a direct contact membrane distillation (DCMD) unit. Results revealed that silica remarkably improves membrane hydrophobicity. The modified PCL-SiO2/CA membrane demonstrated a significant increase in water contact angle of 152.4° compared to 119° for the unmodified membrane. In addition, PCL-SiO2/CA membrane has a smaller average pore size of 0.23 ± 0.16 µm and an exceptional liquid entry pressure of water (LEPw), which is 3.8 times higher than that of PCL/CA membrane. Moreover, PCL-SiO2/CA membrane achieved a durable permeate flux of 15.6 kg/m2.h, while PCL/CA membrane showed unstable permeate flux decreasing approximately from 25 to 12 kg/m2.h over the DCMD test time. Furthermore, the modified PCL-SiO2/CA membrane achieved a high salt rejection value of 99.97% compared to a low value of 86.2% for the PCL/CA membrane after 24 h continuous DCMD operation. In conclusion, the proposed modified PCL-SiO2/CA dual-layer biopolymeric-based membrane has considerable potential to be used as an environmentally friendly membrane for the MD process.

EPANet-KD: Efficient progressive attention network for fine-grained provincial village classification via knowledge distillation.

OBJECTIVE: Fine-grained classification of historical traditional villages plays a crucial role in guiding the future development and construction of urban and rural areas. This study aims to propose a new dataset for fine-grained classification of traditional villages and to propose an efficient progressive attention network for the problem of low accuracy and efficiency of fine-grained traditional historical village classification. METHODS AND RESULTS: Firstly, in order to further study the long-standing problem of fine-grained classification of traditional villages, a new fine-grained classification dataset of traditional villages containing 4,400 images, referred to as PVCD, is proposed by crawling and hand-arranging. Secondly, a new Progressive Attention Module, abbreviated as PAM, is also proposed. PAM engages in attentional modeling of prominent spatial features within the spatial dimension, subsequently applying attentional modeling to channel features beneath the identified salient spatial features. This process involves salient spatial feature attention modeling of prominent channel features within the dimension to extract discriminative information for fine-grained classification, thereby enhancing the performance of classifying traditional villages with precision. Finally, a new knowledge distillation strategy of softened alignment distillation, or SAD for short, is proposed, which simply and efficiently transfers the knowledge of softened category probability distributions through. Notably, based on the above proposed PAM, the lightweight EPANet-Student and the heavyweight EPANet-Teacher are proposed. In addition, the heavyweight EPANet-Teacher transfers the knowledge of fine-grained categorization of traditional villages to the lightweight EPANet-Student through the proposed SAD, abbreviated as EPANet-KD. The experimental results show that the proposed EPANet-Teacher achieves state-of-the-art performance with an accuracy of 67.27%, and the proposed EPANet-KD achieves comparable performance to the proposed EPANet-Teacher with 3.32M parameters and 0.42G computation. CONCLUSION: The proposed EPANet-KD maintains a good balance of accuracy and efficiency in the fine-grained classification of traditional villages, considerably promoting the research on the fine-grained classification of traditional villages. In addition, it facilitates the digital preservation and development of traditional villages. All datasets, codes and benchmarking results are publicly available for the promotion of this research area. https://github.com/Jack13026212687/EPANet-KD.

Variation of the essential oil components of Citrus aurantium leaves upon using different distillation techniques and evaluation of their antioxidant, antidiabetic, and neuroprotective effect against Alzheimer's disease.

Citrus fruit essential oil is considered one of the widely studied essential oils while its leaves attract less attention although being rich in nearly the same composition as the peel and flowers. The leaves of bitter orange or sour orange (Citrus aurantium L.) were extracted using three different techniques namely; hydrodistillation (HD), steam distillation (SD), and microwave-assisted distillation (MV) to compare their chemical composition. The three essential oil samples were analyzed through GC/FID and GC/MS analyses. The samples were tested in vitro using different antioxidant techniques (DPPH, ABTS, CUPRAC, FRAP, PBD, and MCA), neuroprotective enzyme inhibitory activities (acetylcholine and butyl choline enzymes), and antidiabetic activities (α-amylase and α-glucosidase). The results showed that thirty-five volatile ingredients were detected and quantified. Monoterpenes represented the most abundant class in the three essential oils followed by sesquiterpenes. C. aurantium essential oil carried potential antioxidant activity where SD exhibited the highest antioxidant activity, with values arranged in the following order: FRAP (200.43 mg TE/g), CUPRAC (138.69 mg TE/g), ABTS (129.49 mg TE/g), and DPPH (51.67 mg TE/g). SD essential oil also presented the most potent α-amylase (0.32) inhibition while the MV essential oil showed the highest α-glucosidase inhibition (2.73 mmol ACAE/g), followed by HD (2.53 mmol ACAE/g), and SD (2.46 mmol ACAE/g). The SD essential oil exhibited the highest BChE and AChE inhibitory activities (3.73 and 2.06 mg GALAE/g), respectively). Thus, bitter orange essential oil can act as a potential source of potent antioxidant, antidiabetic, and neuroprotective activities for future drug leads.

Clean-In-Place (CIP) wastewater management using nanofiltration (NF)-forward osmosis (FO)-direct contact membrane distillation (DCMD): Effects of draw salt.

A substantial amount of water is being used during Clean-in-Place (CIP) operation, and is transformed into wastewater that can cause eutrophication to the nearby ecosystem. The present study proposed the Nanofiltration (NF) - Forward Osmosis (FO) - Direct Contact Membrane Distillation (DCMD) to recover the cleaning agents and reclaim freshwater from the model CIP wastewater. NF steps were suggested as prefiltration steps to remove organic compounds from the CIP wastewater. NF steps reduced the lactose and protein contents by 100 % and 95.6 %, respectively. The permeates from NF steps were further managed by the integrated FO-DCMD system. Several draw salts such as NaCl, KCl, MgCl2, and CaCl2 were compared to investigate the influence on FO and DCMD performance. It was found that monovalent salts (NaCl and KCl) outperformed the divalent salts (MgCl2 and CaCl2) in terms of water flux for both FO and DCMD. This can be attributed to the lower viscosity and higher mass transfer coefficient. In addition, the replenishment costs of each salt were evaluated since salts loss occurred during FO and DCMD operation. The cost evaluation revealed that NaCl is most the cheapest salts per reclaimed water. All of this observation indicates that NaCl is preferred in terms of water flux and replenishment cost. The NF permeate kept concentrated using the integrated FO-DCMD or single FO with 2 M of NaCl. Compared to a single FO that showed a consistent decline in draw solution concentration, FO-DCMD could maintain the concentration of the draw solution. Despite the constant concentration, flux decline of FO was observed due to fouling formation caused by the high-temperature operation. However, the FO-DCMD could accomplish the recovery of pure water. Finally, the cleaning agents recovered by the NF-FO-DCMD showed the cleaning efficacy comparable to the fresh NaOH. These results suggest the potential of the proposed system to manage the CIP wastewater.

A novel Multi-Level Refined (MLR) knowledge graph design and chatbot system for healthcare applications.

Imagine having a knowledge graph that can extract medical health knowledge related to patient diagnosis solutions and treatments from thousands of research papers, distilled using machine learning techniques in healthcare applications. Medical doctors can quickly determine treatments and medications for urgent patients, while researchers can discover innovative treatments for existing and unknown diseases. This would be incredible! Our approach serves as an all-in-one solution, enabling users to employ a unified design methodology for creating their own knowledge graphs. Our rigorous validation process involves multiple stages of refinement, ensuring that the resulting answers are of the utmost professionalism and solidity, surpassing the capabilities of other solutions. However, building a high-quality knowledge graph from scratch, with complete triplets consisting of subject entities, relations, and object entities, is a complex and important task that requires a systematic approach. To address this, we have developed a comprehensive design flow for knowledge graph development and a high-quality entities database. We also developed knowledge distillation schemes that allow you to input a keyword (entity) and display all related entities and relations. Our proprietary methodology, multiple levels refinement (MLR), is a novel approach to constructing knowledge graphs and refining entities level-by-level. This ensures the generation of high-quality triplets and a readable knowledge graph through keyword searching. We have generated multiple knowledge graphs and developed a scheme to find the corresponding inputs and outputs of entity linking. Entities with multiple inputs and outputs are referred to as joints, and we have created a joint-version knowledge graph based on this. Additionally, we developed an interactive knowledge graph, providing a user-friendly environment for medical professionals to explore entities related to existing or unknown treatments/diseases. Finally, we have advanced knowledge distillation techniques.