Comparison of quantity, quality and antibacterial activity of essential oil Mentha longifolia (L.) L. under different traditional and modern extraction methods.

Extraction is the first and most important step in obtaining the effective ingredients of medicinal plants. Mentha longifolia (L.) L. is of considerable economic importance as a natural raw material for the food and pharmaceutical industries. Since the effect of different extraction methods (traditional and modern methods) on the quantity, quality and antimicrobial activity of the essential oil of this plant has not been done simultaneously; the present study was designed for the first time with the aim of identifying the best extraction method in terms of these features. For this purpose, extracting the essential oil of M. longifolia with the methods of hydrodistillation with Clevenger device (HDC), steam distillation with Kaiser device (SDK), simultaneous distillation with a solvent (SDE), hydrodistillation with microwave device (HDM), pretreatment of ultrasonic waves and Clevenger (U+HDC) and supercritical fluid (SF) were performed. Chemical compounds were identified by gas chromatography coupled with mass spectrometer (GC-MS). Antimicrobial activity of essential oils against various clinical microbial strains was evaluated by agar diffusion method and determination of the minimum inhibitory concentration and minimum bactericidal concentration (MIC and MBC). The results showed that the highest and lowest yields of M. longifolia leaf essential oil belonged to HDC (1.6083%) and HDM (0.3416%). The highest number of compounds belonged to SDK essential oil and was equal to 72 compounds (with a relative percentage of 87.13%) and the lowest number of compounds was related to the SF essential oil sample (7 compounds with a relative percentage of 100%). Piperitenone (25.2-41.38%), piperitenone oxide (22.02-0%), pulegone (10.81-0%) and 1,8-cineole (5-35.0%) are the dominant and main components of M. longifolia essential oil were subjected to different extraction methods. Antimicrobial activity results showed that the lowest MIC value belonged to essential oils extracted by HDM, SDK, SDE and U+HDC methods with a value of 1000 µg/mL was observed against Gram-negative bacteria Shigella dysenteriae, which was 5 times weaker than rifampin and 7 times weaker than gentamicin. Therefore, it can be concluded that in terms of efficiency of the HDC method, in terms of the percentage of compounds of the HDM method, and in terms of microbial activity, the SDK, HDM and U+HDC methods performed better.

Performances of PTFE and PVDF membranes in achieving the discharge limit of mixed anodic oxidation coating wastewaters treated by membrane distillation.

The mixed wastewater generated by anodic oxidation coating facilities contains high levels of various contaminants, including iron, aluminum, conductivity, chemical oxygen demand (COD), and sulfate. In this study, the effectiveness of the membrane distillation (MD) process using polytetrafluoroethylene (PTFE) and polyvinylidene fluoride (PVDF) membranes was investigated to treat mixed wastewater from an anodized coating factory. The results indicate that both hydrophobic membranes effectively removed targeted contaminants. However, the PTFE membrane achieved higher removal efficiencies, with over 99% removal of sulfate, conductivity, iron, and aluminum, 85.7% of COD, and 86% of total organic carbon (TOC). In contrast, the PVDF membrane exhibited a significant decline in removal efficiency as the temperature increased and performed well only at lower feed temperatures. The PTFE membranes outperformed the PVDF membranes in treating chemically intensive anodic oxidation wastewaters. This superiority can be attributed to the PTFE membrane's morphology and structure, which are less influenced by feed water temperature and chemicals. Additionally, its slippery surface imparts anti-adhesion properties, effectively preventing membrane fouling, and maintaining the treated water quality and flux for longer operation time.

Progress in membrane distillation processes for dye wastewater treatment: A review.

Textile and cosmetic industries generate large amounts of dye effluents requiring treatment before discharge. This wastewater contains high levels of reactive dyes, low to none-biodegradable materials and chemical residues. Technically, dye wastewater is characterised by high chemical and biological oxygen demand. Biological, physical and pressure-driven membrane processes have been extensively used in textile wastewater treatment plants. However, these technologies are characterised by process complexity and are often costly. Also, process efficiency is not achieved in cost-effective biochemical and physical treatment processes. Membrane distillation (MD) emerged as a promising technology harnessing challenges faced by pressure-driven membrane processes. To ensure high cost-effectiveness, the MD can be operated by solar energy or low-grade waste heat. Herein, the MD purification of dye wastewater is comprehensively and yet concisely discussed. This involved research advancement in MD processes towards removal of dyes from industrial effluents. Also, challenges faced by this process with a specific focus on fouling are reviewed. Current literature mainly tested MD setups in the laboratory scale suggesting a deep need of further optimization of membrane and module designs in near future, especially for textile wastewater treatment. There is a need to deliver customized high-porosity hydrophobic membrane design with the appropriate thickness and module configuration to reduce concentration and temperature polarization (CP and TP). Also, energy loss should be minimized while increasing dye rejection and permeate flux. Although laboratory experiments remain pivotal in optimizing the MD process for treating dye wastewater, the nature of their time intensity poses a challenge. Given the multitude of parameters involved in MD process optimization, artificial intelligence (AI) methodologies present a promising avenue for assistance. Thus, AI-driven algorithms have the potential to enhance overall process efficiency, cutting down on time, fine-tuning parameters, and driving cost reductions. However, achieving an optimal balance between efficiency enhancements and financial outlays is a complex process. Finally, this paper suggests a research direction for the development of effective synthetic and natural dye removal from industrially discharged wastewater.

Contrasting mixed scaling patterns and mechanisms of nanofiltration and membrane distillation.

Oriented towards the pressing needs for hypersaline wastewater desalination and zero liquid discharge (ZLD), the contrasting mixed scaling of thermal-driven vacuum membrane distillation (VMD) and pressure-driven nanofiltration (NF) were investigated in this work. Bulk crystallization was the main mechanism in VMD due to the high salinity and temperature, but the time-independent resistance by the adsorption of silicate and organic matter dominated the initial scaling process. Surface crystallization and the consequent pore-blocking were the main scaling mechanisms in NF, with the high permeate drag force, hydraulic pressure, and cross-flow rate resulting in the dense scaling layer mainly composed of magnesium-silica hydrate (MSH). Silicate enhanced NF scaling with a 75% higher initial flux decline rate attributed to the MSH formation and compression, but delayed bulk crystallization in VMD. Organic matter presented an anti-scaling effect by delaying bulk crystallization in both VMD and NF, but specifically promoted CaCO3 scaling in NF. Furthermore, the incipient scaling was intensified as silicate and organic matter coexisted. The scaling mechanism shifted from surface to bulk crystallization due to the membrane concentration in both VMD and NF. This work fills the research gaps on mixed scaling mechanisms in different membrane processes, which offers insights for scaling mitigation and thereby supports the application of ZLD.

Prototype-based sample-weighted distillation unified framework adapted to missing modality sentiment analysis.

Missing modality sentiment analysis is a prevalent and challenging issue in real life. Furthermore, the heterogeneity of multimodality often leads to an imbalance in optimization when attempting to optimize the same objective across all modalities in multimodal networks. Previous works have consistently overlooked the optimization imbalance of the network in cases when modalities are absent. This paper presents a Prototype-Based Sample-Weighted Distillation Unified Framework Adapted to Missing Modality Sentiment Analysis (PSWD). Specifically, it fuses features with a more efficient transformer-based cross-modal hierarchical cyclic fusion module. Subsequently, we propose two strategies, namely sample-weighted distillation and prototype regularization network, to address the issues of missing modality and optimization imbalance. The sample-weighted distillation strategy assigns higher weights to samples that are located closer to class boundaries. This facilitates the obtaining of complete knowledge by the student network from the teacher's network. The prototype regularization network calculates a balanced metric for each modality, which adaptively adjusts the gradient based on the prototype cross-entropy loss. Unlike conventional approaches, PSWD not only connects the sentiment analysis study in the missing modality to the full modality, but the proposed prototype regularization network is not reliant on the network structure and can be expanded to more multimodal studies. Massive experiments conducted on IEMOCAP and MSP-IMPROV show that our method achieves the best results compared to the latest baseline methods, which demonstrates its value for application in sentiment analysis.

Steam distillation process for flavor enhancement of milk coffee: Effects of condensation temperature on volatile compounds and flavor characteristics.

To enhance the flavor characteristics of milk coffee, steam distillation was applied to roasted ground coffee to obtain extracts that were then added to the hot water extract of the residue. The effects of different condensation temperatures for steam distillation on the volatile compounds of condensates and the flavor characteristics of the milk coffees prepared with each condensate were investigated. The volatile compounds were analyzed by gas chromatography/mass spectrometry, and principal component analysis (PCA) was performed on the mean peak areas of the volatiles that showed significant differences between the samples. The five types of milk coffees prepared with/without condensates were evaluated by consumer panelists using the check-all-that-apply question combined with the milk coffee flavor lexicon. The results showed that the concentration of volatile compounds tended to be higher in response to decreasing condensation temperature in steam distillation. The volatile compounds were grouped into four patterns based on their concentration in the condensates, which was affected by the volatility of the compounds and the duration of the condensation process in steam distillation. PCA clarified the characteristic volatile compounds that contribute to differences between the three condensates. The check-all-that-apply results indicated that the samples prepared with the condensates enhanced some specific coffee flavors, although acceptances for them were not enhanced. Implementing a steam distillation step in the milk coffee production process could lead to enhancing the coffee flavor strength of milk coffee products, and changing the condensation temperature for steam distillation was effective for providing different flavor characteristics of milk coffee. PRACTICAL APPLICATION: Changing the condensation temperature for steam distillation is effective in differentiating the flavor characteristics of milk coffee. Increasing the condensation temperature resulted in decreased concentrations of volatile compounds, which enhanced the milk and rich flavor. Decreasing the condensation temperature resulted in increased concentrations of volatile compounds, which provided a stronger coffee flavor to the milk coffee, possibly leading to a reduction in the use of coffee for milk coffee production. The check-all-that-apply question combined with the milk coffee flavor lexicon could effectively evaluate consumers' perceptions of the milk coffee flavor characteristics and their acceptances in a single survey.

Profiling of seized Cannabis sativa L. flowering tops by means of microwave-assisted hydro distillation and gas chromatography analyses.

This research aimed to support police forces in their battle against illicit drug trafficking by means of a multi-technique approach, based on gas chromatography. In detail, this study was focused on the profiling of volatile substances in narcotic Cannabis sativa L. flowering tops. For this purpose, the Scientific Investigation Department, RIS Carabinieri of Messina, provided 25 seized samples of Cannabis sativa L. The content of Δ9-tetrahydrocannabinol (THC), useful to classify cannabis plant as hemp (≤ 0.2 %) or as marijuana (> 0.2 %), was investigated. Essential oils of illicit drug samples were extracted using a microwave-assisted hydro-distillation (MAHD) system; GC-MS and GC-FID analytical techniques were used for the characterization of the terpenes and terpenoids fingerprint. Furthermore, the enantiomeric and carbon isotopic ratios of selected chiral compounds were investigated using a heart-cutting multidimensional GC (MDGC) approach. The latter exploited a combination of an apolar column in the first dimension, and a chiral cyclodextrin-based column in the second one, prior to parallel isotope-ratio mass spectrometry (C-IRMS) and MS detection. Finally, all the data were gathered into a statistical model, to demonstrate the existence of useful parameters to be used for the classification of seized samples.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.