Extraction Efficiency Research Articles

Multi-scale fusion and efficient feature extraction for enhanced sonar image object detection

Sonar imaging is an underwater detection technology that relies on the transmission and reception of acoustic pulse waves. This technique plays a crucial role in various domains including underwater archaeology, energy exploration, and oceanographic surveying. A primary challenge in sonar imaging is the low signal-to-noise ratio and significant noise interference, issues that are influenced by the constraints of equipment performance and the underwater environment. Traditional object detection techniques have been inadequate in effectively extracting deep features from sonar images possessing targets with complex structures and have also demonstrated shortcomings in the fusion processing of target features at multiple scales, thereby affecting the robustness and accuracy of object detection. To improve the performance of sonar image object detection, we propose an advanced detection framework that integrates efficient feature extraction with multi-scale feature fusion. We employ EfficientNet as the backbone network. EfficientNet exhibits excellent feature extraction capabilities through comprehensive adjustments of depth, width, and resolution. We introduce a dual-channel attention module that blends Squeeze-and-Excitation (SE) and Efficient Channel Attention (ECA) mechanisms to amplify the expression of crucial feature channels and suppress the lesser ones. Additionally, we utilize a modified bidirectional feature pyramid network (BiFPN) to strengthen the integration of features across different layers. Employing these methods, we amalgamate the features into a shared weights classification network and bounding box prediction network for accurate target class discernment and localization. The experimental outcomes provide evidence of the notable superlative nature of the proposed framework in sonar image object detection, effectively ameliorating detection performance amidst noise interference.

Enhanced removal of toxic Disperse Blue 35 dye through cloud point extraction: influence of parameters and solvent regeneration

Abstract The release of the dye Disperse Blue 35 (DB35) into water has serious environmental and health consequences, due to its toxicity and resistance to degradation. This paper investigates the effectiveness of cloud point extraction (CPE) to remove this industrial dye from aqueous solution by Lutensol AO7 and Triton X-114, two environmentally friendly nonionic surfactants. First, the partial phase diagrams of the water–surfactant binary systems are constructed. Then, the effects of pollutants, sodium sulfate and cetyltrimethylammonium bromide on the cloud point temperature (T c) are determined. The experimental results are expressed by four responses: Extraction efficiency (E), residual concentrations of solute and surfactant in the dilute phase (X s,w and X t,w, respectively) and the volume fraction of coacervate (Ф c). An empirical smoothing method was applied. For each parameter, the results obtained were modeled using the response surface methodology (RSM) and represented on three-dimensional diagrams. The results show that the efficiency of dye extraction with Lutensol AO7 and Triton X-114 at a concentration of 6 wt% is about 95 % and 82 %, respectively. The influence of salt and ionic surfactant on the effectiveness of CPE for the removal of DB 35 dye was determined. The regeneration of surfactant was only achieved by pH adjustment.

Class II (three-layer system) phenomenological model based on limiting current density and dynamic chelation chemistry for separation of rare earth elements using electrodialysis

This paper presents an in-depth investigation into the optimization of rare earth element (REE) separation through electrodialysis, leveraging a newly developed Class II phenomenological model. This study explores the pivotal roles of the HEDTA/Nd molar ratio and pH of feed solution on enhancing the separation efficiency of neodymium (Nd) and praseodymium (Pr) from lanthanum (La) and cerium (Ce). By integrating expanded Nernst-Planck equations and the concept of limiting current density, the model offers a sophisticated understanding of ion transport dynamics and the impacts of concentration polarization. Experimental validation confirms the model’s predictive accuracy, demonstrating its practical applicability for industrial-scale operations. The research delineates how operational parameters such as chelating agent concentration and pH critically influence the purity and yield of separated REEs. The dynamic nature of chelation chemistry is also examined, highlighting its evolution during the electrodialysis process and its effect on the system’s overall performance. Key findings illustrate that lower HEDTA/Nd molar ratios significantly enhance the purity of Nd + Pr by minimizing the chelation of La and Ce, thus facilitating their migration to the concentrate compartment. Conversely, higher ratios maximize yield by retaining more Nd + Pr in the feed compartment. This dual approach allows for optimized separation based on specific industrial requirements. The outcomes of this study not only advance the field of REE separation but also provide a framework for further research into more efficient and sustainable extraction methods. The developed model and its validation represent a step forward in the practical application of electrodialysis in REE processing, offering substantial benefits for the critical materials sector.

Integrated choline chloride/citric acid-microwave pretreatment for efficient nanolignin extraction and bioethanol production from cocoa pod husk waste

The necessity to address environmental issues has driven efforts toward exploring sustainable bio-based materials as a viable alternative to conventional energy sources. The current study explores the utilization of cocoa pod husk (CPH) biomass for lignin extraction and cellulose production, aiming to contribute to the eco-friendly production of lignin nanoparticles and bioethanol. A synergistic green deep eutectic solvent (DES) (choline chloride/citric acid)-microwave method was employed to effectively fractionate CPH biomass, resulting in an impressive 77.58% lignin removal at 600 W microwave power. The extracted lignin (211.56 mg/g biomass) was utilized to synthesize lignin nanoparticles that were subsequently characterized. Enzyme-driven hydrolysis of the residual cellulose yielded a reducing sugar content of 198.34 mg/g biomass, demonstrating a saccharification efficiency of 70.78%. Fermentation of monomeric sugars by Saccharomyces cerevisiae and Scheffersomyces stipitis, respectively, gave a maximum ethanol yield of 130 mg/g biomass with a high fermentation efficiency (67.17%). The alterations in the CPH's surface structure and morphology following sequential pretreatment were assessed through FT-IR, BET, and SEM analyses, facilitating effective enzymatic hydrolysis. The current investigation adds to the increasing recognition of sustainable approaches in leveraging waste biomass resources toward a more environmentally conscious future.

π-electron injection activated dormant ligands in graphitic carbon nitride for efficient and stable uranium extraction

Graphitic carbon nitride (CN) as an adsorbent exhibit promising potential for the removal of uranium in water. However, the lack of active sites seriously restricts its practical application. In contrast to the traditional method of introducing new ligands, we propose a strategy to activate original ligands on CN by injecting π electrons, which can be realized by grafting 4-phenoxyphenol (PP) on CN (PCN). Compared with CN, the maximum adsorption capacity of PCN for uranium increased from 150.9 mg/g to 380.6 mg/g. Furthermore, PCN maintains good adsorption properties over a wide range of uranium concentrations (1 ∼ 60 mg/L) and pH (4 ∼ 8). After 5 consecutive cycles, PCN exhibited sustained uranium removal performance with a little of losses. The experimental and theoretical results show that the enhancement of adsorption performance is mainly due to the ligands activation of CN by delocalization of π electrons from PP. Furthermore, this activation can be enhanced by irradiation, as the CN can be photoexcited to provide additional photoelectrons for PP. As a result, dormant ligands such as N-CN, C-O-C, C-N-H and N-(C)3 can be activated to participate in coordination with uranium. This work provides theoretical guidance for the design and preparation of high efficiency uranium adsorbent.

IEA-Net: Internal and External Dual-Attention Medical Segmentation Network with High-Performance Convolutional Blocks.

Currently, deep learning is developing rapidly in the field of image segmentation, and medical image segmentation is one of the key applications in this field. Conventional CNN has achieved great success in general medical image segmentation tasks, but it has feature loss in the feature extraction part and lacks the ability to explicitly model remote dependencies, which makes it difficult to adapt to the task of human organ segmentation. Although methods containing attention mechanisms have made good progress in the field of semantic segmentation, most of the current attention mechanisms are limited to a single sample, while the number of samples of human organ images is large, ignoring the correlation between the samples is not conducive to image segmentation. In order to solve these problems, an internal and external dual-attention segmentation network (IEA-Net) is proposed in this paper, and the ICSwR (interleaved convolutional system with residual) module and the IEAM module are designed in this network. The ICSwR contains interleaved convolution and hopping connection, which are used for the initial extraction of the features in the encoder part. The IEAM module (internal and external dual-attention module) consists of the LGGW-SA (local-global Gaussian-weighted self-attention) module and the EA module, which are in a tandem structure. The LGGW-SA module focuses on learning local-global feature correlations within individual samples for efficient feature extraction. Meanwhile, the EA module is designed to capture inter-sample connections, addressing multi-sample complexities. Additionally, skip connections will be incorporated into each IEAM module within both the encoder and decoder to reduce feature loss. We tested our method on the Synapse multi-organ segmentation dataset and the ACDC cardiac segmentation dataset, and the experimental results show that the proposed method achieves better performance than other state-of-the-art methods.

Efficient extraction of Li and Rb from zinnwaldite via thermal activation and acid leaching

Lithium and its associated rubidium are regarded as the strategic metal resource, which have a wide use in many commercial products. Nowadays, the continuing growth in demand for Li resources, propelling the object of industrial exploitation will focus on low-grade, complex minerals composition and unwieldy ores. Zinnwaldite has received increasing attention as a potential lithium-containing resource in recent years. However, there are a lack of effective extraction processes for zinnwaldite. Hence, this paper proposes an effective method to extract metal cleanly and efficiently from zinnwaldite by using CaO and B2O3 as thermal activation additives and H2SO4 as the leaching agent. The results showed that the destruction of Fe-O and Si-O-Si bonds led to increased extraction efficiency. The breathing vibrations of oxygen atoms symmetric in 4- and 3-membered rings and Si-O symmetric stretch could influence the Li separation from silica skeleton. Ultimately, the 91.32 % Li, 93.09 % Rb, 81.14 % Fe, 95.00 % Al and 71.12 % K were recovered under the optimal conditions. Meanwhile, about 10 % F escaped into the air during thermal activation and the concentration of F in the leaching solution was lower than 2 mg/L. The use of B2O3 provides a valuable reference for the efficient and clean treatment of lepidolite using the salt roasting method.

13-Week repeated-dose toxicity study of optimized aqueous extract of Moringa oleifera leaves in mice

Ethnopharmacological relevanceMoringa oleifera (Moringaceae family), commonly known as horseradish or tree of life, is traditionally used for various diseases, such as diabetes, hypercholesterolemia, neurological disorders, among others. Aim of the studyTo evaluate the toxicological profile of the oral use of an aqueous extract of Moringa oleifera leaves for 13 weeks in mice. Materials and methodsInitially, a factorial design (23) was carried out to optimize aqueous extraction using as variables; the extraction method and proportion of drug. The 13-week repeated-dose toxicity trial used female and male mice, with oral administration of aqueous extract of Moringa oleifera leaves at doses of 250, 500, and 1000 mg/kg. The animals were evaluated for body weight, water and feed intake, biochemical and hematological parameters, urinalysis, ophthalmology and histopathology of the liver, spleen and kidneys. ResultsThe extraction efficiency was evidenced by the extraction by maceration at 5%, obtaining the optimized extract of Moringa oleifera (OEMo). The oral administration of OEMo did not promote significant difference (p > 0.05) in the weight gain, food and water consumption of the control animals and those treated with 250 and 500 mg/kg. However, treatment with 1000 mg/kg promoted a reduction (p < 0.05) in food intake and body weight from the 7th week onwards in male and female mice. No alterations were detected in the hematological and histological parameters in the concentrations tested for both sexes. The highest concentration treatment (1000 mg/kg) promoted an increase in transaminases in males and females. All concentrations promoted a significant decrease (p < 0.05) in the serum lipid profile of mice. ConclusionThis study developed an optimized extract of Moringa oleifera leaves, which should be used with caution in preparations above 500 mg/kg for the long term because it leads to significant changes in liver enzymes. On the other hand, the extract proved to be a promising plant preparation for hyperlipidemia in mice.

Sustainable approaches to analyzing phenolic compounds: a green chemistry perspective.

Innovative and eco-friendly methodologies for the determination of phenolic compounds, showing a paradigm shift in analytical chemistry toward sustainability. Phenolic compounds, valued for their diverse health benefits, have historically been analyzed using methods that often involve hazardous solvents and energy-intensive processes. This review focuses on green analytical chemistry principles, emphasizing sustainability, reduced environmental impact, and analytical efficiency. The use of DES, specifically Ch: Chl-based DES, emerges as a prominent green alternative for extracting phenolic compounds from various sources. The integration of UAE with DES enhances extraction efficiency, contributing to a more sustainable analytical approach. Furthermore, the review highlights the significance of DLLME and SPME in reducing solvent consumption and simplifying extraction procedures. These techniques exemplify the commitment to making phenolic compound analysis environmentally friendly. The incorporation of portable measurement tools, such as smartphones, into analytical methodologies is a notable aspect discussed in the review. Techniques like UA-DLLME leverage portable devices, making phenolic compound determination more accessible and versatile. Anticipating the future, the review foresees ongoing advancements in sustainable analytical approaches, driven by collaborative efforts across diverse disciplines. Novel solvents, extraction techniques, and portable measurement methods are expected to play pivotal roles in the continuous evolution of green analytical methodologies for the analysis of phenolic compounds. The review encapsulates a transformative journey toward environmentally responsible and efficient analytical practices, paving the way for further research and application in diverse analytical settings.

Development of a GC-MS Method for the Analysis of Selected Opioids in Human Hair Samples

Background: Hair samples are recognized as alternative biological specimens in forensic and clinical toxicology for detecting drug abuse and poisoning. Forensic testing for opioids in hair has become a useful diagnostic measure for assessing chronic drug usage through segmental analysis. However, accurate, sensitive and specific analytical methods are needed. The aim of this study was to introduce a simple, sensitive and specific GC-MS method for the identification and quantitation of selected and commonly abused opioids (tramadol, methadone, morphine, and codeine) in hair samples. Methods: After external decontamination, a 50 mg portion of powdered hair sample was combined with hydrochloric acid (0.1 M) and incubated on a magnetic stirrer at 56°C for 16 hours. Then, 1 mL of sodium hydroxide (0.1 M) and 2 mL of phosphate buffer (1 M, pH=8.4) were added. Chloroform-isopropanol (ratio: 80:20 V/V) was utilized as the extracting solvent and the sample was homogenized and centrifuged for 5 minutes (at 3500 rpm). After centrifugation, the organic phase was dried using dry nitrogen gas. The sample was derivatized with N-methyl-N-(trimethylsilyl) trifluoroacetamide (MSTFA). The blank, standards, and real samples were subsequently analyzed by GC-MS.Results: The limits of detection in the linearity experiments ranged from 0.12 to 0.21 ng/mg. According to the validation results, the method exhibited linearity in the concentration range of 0.1-2.5 ng/mg for all analytes, with calibration curve slopes ranging from R2=0.98 to 0.99. Good inter and intra-day precision relative standard deviations (RSDs) were observed to be &lt;3.5% for all compounds. Extraction efficiency varied from 91.8 to 102.4%. Conclusion: The validation and analysis of actual samples indicate that this method is straightforward, sensitive, and specific for the analysis of opioids in routine hair analysis.

Analysis of Fracturing Expansion Law of Shale Reservoir by Supercritical CO2 Fracturing and Mechanism Revealing

The rapid expansion of reservoir fractures and the enlargement of the area affected by working fluids can be accomplished solely through fracturing operations of oilfield working fluids in geological reservoirs. Supercritical CO2 is regarded as an ideal medium for shale reservoir fracturing owing to the inherent advantages of environmental friendliness, excellent capacity, and high stability. However, CO2 gas channeling and complex propagation of fractures in shale reservoirs hindered the commercialization of Supercritical CO2 fracturing technology. Herein, a simulation method for Supercritical CO2 fracturing based on cohesive force units is proposed to investigate the crack propagation behavior of CO2 fracturing technology under different construction parameters. Furthermore, the shale fracture propagation mechanism of Supercritical CO2 fracturing fluid is elucidated. The results indicated that the propagation ability of reservoir fractures and Mises stress are influenced by the fracturing fluid viscosity, fracturing azimuth angle, and reservoir conditions (temperature and pressure). An azimuth angle of 30° can achieve a maximum Mises stress of 3.213 × 107 Pa and a crack width of 1.669 × 10−2 m. However, an apparent viscosity of 14 × 10−6 Pa·s results in a crack width of only 2.227 × 10−2 m and a maximum Mises stress of 4.459 × 107 Pa. Additionally, a weaker fracture propagation ability and reduced Mises stress are exhibited at the fracturing fluid injection rate. As a straightforward model to synergistically investigate the fracture propagation behavior of shale reservoirs, this work provides new insights and strategies for the efficient extraction of shale reservoirs.

Supramolecular Cucurbit[5]uril Modulates the Buried SnO2/Perovskite Interface for Efficient and Stable Perovskite Solar Cells

AbstractReducing non‐radiative recombination caused by defects at buried interfaces is crucial to the development of efficient and stable perovskite solar cells (PSCs). Herein, supramolecular cucurbit[5]uril (CB[5]) is introduced into the SnO2 layer, where it engages in host–guest interactions to suppress oxygen vacancies in SnO2, prevent particle aggregation, and enhance the electron mobility of SnO2. By serving as a bridging agent at the buried interface between SnO2 and the perovskite layer, CB[5] reduces the defect density and improves the carrier extraction efficiency. It also reduces the surface energy of the SnO2 substrate, facilitates the formation of large grains in the perovskite film, alleviates residual lattice stresses, and enhances the film quality. Consequently, the PSC with CB[5] shows a champion power conversion efficiency of 24.83%. Moreover, an unencapsulated device incorporating CB[5] retains more than 87% of its initial PCE under continuous illumination at the maximum power point tracking for 1000 h. This study pioneers the utilization of cucurbiturils in PSCs and provides insights into how supramolecular compounds can regulate buried interfaces.

Cross-domain fault diagnosis network based on attributes and features transfer with dual classifier under limited and unbalanced datasets

Abstract Deep learning (DL)-based methods have demonstrated significant success in fault diagnosis owing to their robust feature extraction and non-linear fitting capabilities. Meanwhile, their remarkable performance is accompanied by constant operating conditions and sufficient monitoring data. However, in real engineering environments, variable working conditions or limited and unbalanced data are common, which can widen the gap between fault diagnosis methods and real industrial applications. In this paper, we proposed a cross-domain fault diagnosis network based on a dual classifier (CFDNet) with input being limited and unbalanced data to learn attributes and features for unsupervised domain adaptation. We found that the diagnostic performance is commonly bounded by the underlying knowledge, especially feature extraction from original data. Therefore, we designed a new feature encoder with features and relationships, i.e. using a convolutional neural network and graph convolutional network, which improves extraction efficiency while retaining valuable information. Then, we discovered that enforced feature transfer can lead to negative transfer. To mitigate this, we present a feature and attribute transfer framework, which not only achieves features transfer but also enables attributes transfer. Furthermore, it was noted that limited and unbalanced datasets can introduce label bias and lead to biased model training. Hence, we designed dual classifiers to improve the probability of high-confidence final prediction by synthesizing diagnostic results. Comprehensive experiments conducted on three case studies demonstrate the effectiveness and superiority of our method for cross-domain fault diagnosis under limited and unbalanced datasets, which outperforms state-of-the-art methods in this study.

Analysis of Inter-Layer Interference in Multi-Layer Reservoir Commingled Production Wells

During the operation of commingled production wells, inter-layer interference is a key factor affecting the recovery rate and flooding extraction efficiency. This study proposes a well deliverability equation that is characterized by a multi-parameter coupled inter-layer interference coefficient, which is applied to quantify the commingled production wells in a multi-layer reservoir. This study used principal component analysis (PCA) to study the combined effects of correlation parameters for inter-layer interference. The results show that the starting pressure gradient and the crude oil viscosity contrast have the most significant impact on inter-layer interference. Changes in these two parameters directly enhance the heterogeneity between different oil layers, thereby intensifying inter-layer interference. Additionally, the positive correlation between permeability and permeability contrast also highlights the contribution of physical property differences in the oil layers to the interference. The sensitivity analysis shows the main influencing factors of inter-layer interference in commingled production wells. providing references for subsequent improvements and optimization of the exploitation schemes and adjustments in reservoir management measures. The results not only enhance the understanding of the mechanisms of inter-layer interference in the exploitation of multi-layer oil reservoirs but also provide scientific evidence and technical support for oilfield development.

Effect of Wettability and Permeability on Pore-Scale of CH4–Water Two-Phase Displacement Behavior in the Phase Field Model

Hydraulic measures such as hydraulic slotting and hydraulic fracturing are commonly used in coal seam pressure relief and permeability enhancement. Two-phase flow patterns of CH4–water in pore-sized coal seams after hydraulic measures are critical to improve gas extraction efficiency. The phase field module in COMSOL Multiphysics™ 5.4 and the classical ordered porous media model were used in this paper. The characteristics of CH4–water two-phase immiscible displacement in coal seams under different capillary numbers (Ca) and viscosity ratios (M) were simulated and quantitatively analyzed. By changing the contact angle of the porous media, the flow patterns of CH4–water two-phase in coal with different wettability were simulated. Results show that wettability significantly affects the displacement efficiency of CH4. Additionally, by constructing a dual-permeability model to simulate the varying local permeability of the coal, the flow patterns of different Ca and M in dual-permeability media were further investigated. It is found that CH4 preferentially invades high-permeability regions, and the displacement efficiency in low-permeability regions increases with higher Ca and M, providing a reference for gas extraction from coal seams after hydraulic measures.

Acidic deep eutectic solvent as a greener medium for highly efficient extraction of anthocyanins from blackberry fruit: Optimization, stability and purification with two-aqueous phase method

Natural deep eutectic solvents (NADESs) are a sustainable and green solution for the recovery of food ingredients such as pigments. In this work, the extraction conditions to obtain the maximum anthocyanins from blackberry fruit were investigated using NADES composed of choline chloride-acetic acid (CHAC) as the green solvent. The central composite design was used conseidering molar ratio, water content, solvent ratio and extraction time as independent variables and total anthocyanin content (TAC), cyanidin-3-glucoside, cyanidin-3-rutinoside, pelargonidin-3-glucoside and cyanidin chloride as responses. The results showed that the independent variables significantly affected the responses. The optimum conditions for CHAC were 1:3.5 M, 40 %, 30 g and 15 min for molar ratio, water content, solvent ratio and extraction time, respectively. Under these conditions, the experimental responses were 131.54 ± 7.89 mg CGE/100 g, 1208.08 ± 13.76 mg/kg, 163.43 ± 1.72 mg/kg, 53.78 ± 1.37 mg/kg and 91.98 ± 0.62 mg/kg for TAC, cyanidin-3-glucoside, cyanidin-3-rutinoside, pelargonidin-3-glucoside and cyanidin chloride, respectively. The pH, viscosity and electric conductivity of CHAC were determined as 1.24 ± 0.03, 1.21 ± 0.17 and 7.50 ± 0.55 mPa and 4675.33 ± 57.74 µS.cm−1, respectively. After storing the NADES extract for 28 days in the dark, cyanidin-3-glucoside, cyanidin-3-rutinoside, pelargonidin-3-glucoside and cyanidin chloride remained 71.57 ± 0.64 %, 75.00 ± 0.26 %, 33.42 ± 0.41 % and 71.45 ± 0.21 %, respectively. Over the 28-day exposure of the NADES extract to light, the cyanidin-3-glucoside, cyanidin-3-rutinoside, pelargonidin-3-glucoside and cyanidin chloride remained 19.09 ± 0.10 %, 34.89 ± 2.28, 13.28 ± 0.25 and 30.97 ± 1.10, respectively. The application of the aqueous two-phase system resulted in the preliminary purification of the NADES extract, providing the highest purity in the top phase for cyanidin-3-glucoside (68.85 ± 1.05 %). The findings revealed that CHAC is a promising green medium for the industrial extraction of anthocyanins, and the aqueous two-phase is suitable for the purification of the anthocyanin-rich extract obtained with NADES.

Medical Image Classification with a Hybrid SSM Model Based on CNN and Transformer

Medical image classification, a pivotal task for diagnostic accuracy, poses unique challenges due to the intricate and variable nature of medical images compared to their natural counterparts. While Convolutional Neural Networks (CNNs) and Transformers are prevalent in this domain, each architecture has its drawbacks. CNNs, despite their strength in local feature extraction, fall short in capturing global context, whereas Transformers excel at global information but can overlook fine-grained details. The integration of CNNs and Transformers in a hybrid model aims to bridge this gap by enabling simultaneous local and global feature extraction. However, this approach remains constrained in its capacity to model long-range dependencies, thereby hindering the efficient extraction of distant features. To address these issues, we introduce the MambaConvT model, which employs a state-space approach. It begins by locally processing input features through multi-core convolution, enhancing the extraction of deep, discriminative local details. Next, depth-separable convolution with a 2D selective scanning module (SS2D) is employed to maintain a global receptive field and establish long-distance connections, capturing the fine-grained features. The model then combines hybrid features for comprehensive feature extraction, followed by global feature modeling to emphasize on global detail information and optimize feature representation. This paper conducts thorough performance experiments on different algorithms across four publicly available datasets and two private datasets. The results demonstrate that MambaConvT outperforms the latest classification algorithms in terms of accuracy, precision, recall, F1 score, and AUC value ratings, achieving superior performance in the precise classification of medical images.

A proteome-wide quantitative platform for nanoscale spatially resolved extraction of membrane proteins into native nanodiscs.

The intricate molecular environment of the native membrane profoundly influences every aspect of membrane protein (MP) biology. Despite this, the most prevalent method of studying MPs uses detergent-like molecules that disrupt and remove this vital local membrane context. This severely impedes our ability to quantitatively decipher the local molecular context and comprehend its regulatory role in the structure, function, and biogenesis of MPs. Using a library of membrane-active polymers we have developed a platform for the high-throughput analysis of the membrane proteome. The platform enables near-complete spatially resolved extraction of target MPs directly from their endogenous membranes into native nanodiscs that maintain the local membrane context. We accompany this advancement with an open-access database that quantifies the polymer-specific extraction variability for 2065 unique mammalian MPs and provides the most optimized condition for each of them. Our method enables rapid and near-complete extraction and purification of target MPs directly from their endogenous organellar membranes at physiological expression levels while maintaining the nanoscale local membrane environment. Going beyond the plasma membrane proteome, our platform enables extraction from any target organellar membrane including the endoplasmic reticulum, mitochondria, lysosome, Golgi, and even transient organelles such as the autophagosome. To further validate this platform, we took several independent MPs and demonstrated how our resource can enable rapid extraction and purification of target MPs from different organellar membranes with high efficiency and purity. Further, taking two synaptic vesicle MPs, we show how the database can be extended to capture multiprotein complexes between overexpressed MPs. We expect these publicly available resources to empower researchers across disciplines to efficiently capture membrane 'nano-scoops' containing a target MP and interface with structural, functional, and other bioanalytical approaches. We demonstrate an example of this by combining our extraction platform with single-molecule TIRF imaging to demonstrate how it can enable rapid determination of homo-oligomeric states of target MPs in native cell membranes.

Numerical Analysis of Low-Enthalpy Deep Geothermal Energy Extraction Using a Novel Gravity Heat Pipe Design

Geothermal energy, derived from the Earth’s internal heat, can be harnessed due to the geothermal gradient between the Earth’s interior and its surface. This heat, sustained by radiogenic decay, varies across regions, and is highest near volcanic areas. In 2020, 108 countries utilised geothermal energy, with an installed capacity of 15,950 MWe for electricity and 107,727 MWt for direct use in 2019. Low-enthalpy sources require binary systems for power production. Open-loop systems face issues like scaling, difficult water treatment, and potential seismicity, while closed-loop systems, using abandoned petroleum or gas wells, reduce costs and environmental impacts greatly. The novel geothermal gravity heat pipe (GGHP) design eliminates parasitic power consumption by using hydrostatic pressure for fluid circulation. Implemented in an abandoned well in north-east (NE) Slovenia, the GGHP uses a numerical finite difference method to model heat flow. The system vaporises the working fluid in the borehole, condenses it at the surface, and uses gravitational flow for circulation, maintaining efficient heat extraction. The model predicts that continuous maximum capacity extraction depletes usable heat rapidly. Future work will explore sustainable heat extraction and potential discontinuous operation for improved efficiency.

Screening diluents to optimize cesium contaminant separation using t-BAMBP extractant

The widespread use of nuclear energy has raised concerns about nuclear safety and radioactive waste management, particularly due to the release of radioactive cesium. This study investigates the use of t-BAMBP (4-tert-butyl-2-(α-methylbenzyl) phenol) for the extraction and separation of cesium from simulate high concentration cesium containing wastewater, focusing on the selection of suitable diluents to enhance the efficiency of the process. We performed a systematic study using density functional theory (DFT) calculations to evaluate the intrinsic properties and interactions of various common diluents with t-BAMBP. The diluents studied include aromatic hydrocarbons (benzene, toluene, xylene), alkanes (cyclohexane, hexane, heptane), and alcohols (hexanol, octanol). Our computational results revealed that cyclohexane is the most suitable diluent due to its moderate solvation-free energy, high nonpolarity, and optimal balance between solubility and reactivity. Experimental validation confirmed the computational findings. The cyclohexane-diluted t-BAMBP system achieved the highest cesium extraction efficiency of over 94 %, with a separation factor (βCs/K) of 767.67. Cyclohexane demonstrated the lowest toxicity and cost among the diluents evaluated, making it a safer and more economical choice for practical applications. The results of this study provide a comprehensive theoretical and experimental basis for the selection of diluents in the t-BAMBP extraction system, offering insights for the sustainable utilization of cesium resources and effective management of radioactive waste.