Separation Approach Research Articles

Unsupervised separation of nonlinearly mixed event-related potentials using manifold clustering and non-negative matrix factorization

Event-related potentials (ERPs) can quantify brain responses to reveal the neural mechanisms of sensory perception. However, ERPs often reflect nonlinear mixture responses to multiple sources of sensory stimuli, and an accurate separation of the response to each stimulus remains a challenge. This study aimed to separate the ERP into nonlinearly mixed source components specific to individual stimuli. We developed an unsupervised learning method based on clustering of manifold structures of mixture signals combined with channel optimization for signal source reconstruction using non-negative matrix factorization (NMF). Specifically, we first implemented manifold learning based on Local Tangent Space Alignment (LTSA) to extract the spatial manifold structure of multi-resolution sub-signals separated via wavelet packet transform. We then used fuzzy entropy to extract the dynamical process of the manifold structures and performed a k-means clustering to separate different sources. Lastly, we used NMF to obtain the optimal contributions of multiple channels to ensure accurate source reconstructions. We evaluated our developed approach using a simulated ERP dataset with known ground truth of two components of ERP mixture signals. Our results show that the correlation coefficient between the reconstructed source signal and the true source signal was 92.8 % and that the separation accuracy in ERP amplitude was 91.6 %. The results show that our unsupervised separation approach can accurately separate ERP signals from nonlinear mixture source components. The outcomes provide a promising way to isolate brain responses to multiple stimulus sources during multisensory perception.

PH-sensitive spontaneous decay of functionalized carbon dots in solutions.

Carbon quantum dots have become attractive in various applications, such as drug delivery, biological sensing, photocatalysis, and solar cells. Among these, pH sensing via luminescence lifetime measurements of surface-functionalized carbon dots is one application currently investigated for their long lifetime and autonomous operation. In this article, we explore the theoretical connection between excitation lifetimes and the pH value of the surrounding liquid via the protonation and deprotonation of functional groups. Example calculations applied to m-phenylenediamine, phloroglucinol, and tethered disperse blue 1 are shown by applying a separation approach treating the electronic wave function of functional groups separately from the internal electronic structure of the (large) carbon dot. The bulk of the carbon dot is treated as an environment characterized by its optical spectrum that shifts the transition rates of the functional group. A simple relationship between pH, pKa, and mixed fluorescence lifetime is derived from the transition rates of the protonated and deprotonated states. pH sensitivity improves when the difference in the transition rates is greatest between protonated and deprotonated species, with the greatest sensitivity found where the pKa is close to the pH region of interest. The introduced model can directly be extended to consider multicomponent liquids and multiple protonation states.

Recent 10-year development on surface modification of polymeric hollow fiber membranes via surface coating approach for gas separation: a review

Recent 10-year development on surface modification of polymeric hollow fiber membranes via surface coating approach for gas separation: a review

The Effect of the Independent Learning Strategy on the Achievement of Grammar Subject for Students of the College of Islamic Sciences

The present analysis targets to recognize the influence of the separate teaching approach on the accomplishment of grammar for scholars of the College of Islamic Sciences. The target of attaining this target led the investigations developing the subsequent null theories: 1. No statistically substantial variance is happened at the consequence level of 0.05 between the mean scores of the scholars in the investigational category who learnt consistent with the separate learning approach and the mean scores of the scholars in the control category who learnt in the conventional method in the accomplishment test. 2. No statistically substantial variance has been observed at the consequence level of 0.05 in the mean differences between the scores of the pre-tests and post-tests in the accomplishment test among the scholars of the investigational assemblage. The present investigation was restrained to a sample of third-stage scholars in the Arabic Language Department at the College of Islamic Sciences, University of Baghdad, for the academic year 2022-2023 AD. The pre-test (separate learning) was utilized to the first-stage scholars in the department, who numbered (120) male and female scholars. They were allocated between two categories: an investigational category with (60) male and female scholars and a control category with (60) male and female scholars. The investigational category examined benefiting the separate learning approach, while the control category studied benefiting the conventional method. At the finish of the investigation, a t-test was utilized for two separate and interconnected samples. The outcomes presented what follows: 1. A statistically major variance was noticed between the mean scores of the investigational collection scholars who learned grammar based on the separate learning approach and the averages of the control category scholars who learned the same topic in the traditional manner in the post-test and in support of the investigational category. 2. A statistically substantial variance was recorded between the mean scores of the investigational category scholars who learned grammar consistent with the separate learning approach and the scores of the investigational category scholars in the pre-test and their scores in the post-test, and this requires higher accomplishment among the investigational category scholars.

Influence of graphene oxide additives on the NF separation of triazine-based H2S scavenging compounds using advanced membrane technology

This work proposes an innovative approach for the membrane separation of spent and unspent H2S scavengers (SUS) derived from the application of MEA-triazine in offshore oil and gas production. Modified nanofiltration membranes were fabricated by incorporating graphene oxide (GO) and polyvinyl alcohol (PVA) into a thin film composite (TFC) to obtain a thin film nanocomposite (TFN) with enhanced permeability. In addition, various immobilization strategies for GO were investigated. The performance of the membranes and the effect of the GO loading were evaluated in terms of permeability, fouling propensity, and rejection of key components of the SUS, i.e., MEA-triazine (unspent scavenger), dithiazine (spent scavenger), and monoethanolamine, operating on a sample of SUS wastewater obtained from an offshore oil and gas platform. Various characterization techniques, such as contact angle, FTIR, XRD, SEM, TGA, and AFM, were employed to evaluate the structure, composition, and hydrophilicity of the membrane. The results show a remarkable increase in permeability (from 0.22 Lm-2 h−1 bar−1 for the TFC to 5.8 Lm-2 h−1 bar−1 for the TFN membranes), due to the enhanced hydrophilicity from GO incorporation. The strong interfacial interaction between GO and PVA within the TFN membrane results in negligible nanofiller leaching. The incorporation of GO moderately increases the rejection of the unspent scavenger (63%–73%, 62%–79%, 62%–80%, and 68%–76%), while drastically increasing the rejection of the spent scavenger, which is approximately null for the TFC membrane without GO and increases up to 58% in the TFN membrane with GO. Therefore, while the proposed membranes cannot be used for the selective separation of the unspent form the spent scavenger, they can achieve substantial recovery of all the key components contained in the SUS to avoid their discharge into the sea.

Fusion and fission phenomena in a (2+1)-dimensional Sawada-Kotera type system

In this study, we extend the generalized multilinear variable separation approach to a fifth-order nonlinear evolution equation. By performing asymptotic analysis on the variable separation solution, which is composed of three lower-dimensional functions, we identify a resonant regime governing dromion-dromion/solitoff interactions. In the case of two-dromion interactions, elastic, inelastic, and completely inelastic collisions are possible, while for the dromion-solitoff interaction only inelastic and completely inelastic collisions are permitted. Furthermore, we derive two types of semi-rational solutions from the quadratic function ansatz. In particular, in the scenario of a completely resonant collision between a lump and a line-soliton pair, the lump separates from one line soliton and exists briefly before merging with the other soliton, forming a localized lump in both time and space dimensions. The fusion or fission phenomena between the dromion-dromion/solitoff interaction and the lump-line soliton interaction are shown graphically.

Spatially resolved air quality index prediction in megacities with a CNN-Bi-LSTM hybrid framework

Monitoring pollution in megacities is challenging due to their vast urban expanses and numerous pollution sources. To address this, cutting-edge deep learning models are proposed for monitoring, managing, and forecasting pollution levels. In this study, data concerning six pollutants (O3, NO2, CO, SO2, PM2.5, PM10) and Air Quality Index (AQI) values were collected over a five-year period from three distinct zones within the Tehran megacity. The gathered data were incorporated into a dataset comprising 37,044 records, encompassing the concentrations of examined pollutants and the related AQI across various study points. The primary aim was to enhance the precision of deep learning models using a regional strategic separation approach. A frequency domain filter method was employed to address data noise. Four models—Long Short-Term Memory (LSTM), Bidirectional LSTM (Bi-LSTM), Convolutional Neural Network with LSTM (CNN-LSTM), and Convolutional Neural Network with Bidirectional LSTM (CNN-Bi-LSTM)—were developed to predict AQI values. Evaluation showed disparities in pollution levels among the studied areas. The CNN-Bi-LSTM model exhibited the highest accuracy at 97 %, but challenges in predicting sudden fluctuations reduced accuracy to 95 % in some cases. However, the model consistently captured the underlying pattern of changes. Segregating megacity regions enhances accuracy in forecasting air pollution conditions.

Decomposition solutions and Bäcklund transformations of the B-type and C-type Kadomtsev–Petviashvili equations

This paper introduces a modified formal variable separation approach, showcasing a systematic and notably straightforward methodology for analyzing the B-type Kadomtsev–Petviashvili (BKP) equation. Through the application of this approach, we successfully ascertain decomposition solutions, Bäcklund transformations, the Lax pair, and the linear superposition solution associated with the aforementioned equation. Furthermore, we expand the utilization of this technique to the C-type Kadomtsev–Petviashvili (CKP) equation, leading to the derivation of decomposition solutions, Bäcklund transformations, and the Lax pair specific to this equation. The results obtained not only underscore the efficacy of the proposed approach, but also highlight its potential in offering a profound comprehension of integrable behaviors in nonlinear systems. Moreover, this approach demonstrates an efficient framework for establishing interrelations between diverse systems.

Surgical methods of treatment for tibial diaphyseal fractures

Background. Bone fractures are a public health concern. In recent years, there has been an upward trend in body weight of people of all age groups. Obesity has long been thought to help protect against fractures, but recent studies have shown that for every 5 cm increase in waist circumference, the risk of any fracture is 3 % higher. The purpose: according to the meta-analysis of modern medical literature, to determine the main directions of surgical treatment for long bone fractures, their advantages, and disadvantages, including in overweight patients, the features of diaphyseal fracture fixation in overweight patients. Materials and methods. A meta-analysis of special literature from scientific databases was conducted: Cochrane Library, Scopus, National Library of Medicine — National Institutes of Health, ReLAB-HS Rehabilitation Resources Repository. One hundred and thirty articles were analyzed, from them 31 were selected, which, in our opinion, reflect the purpose of the study. Results. All methods of surgical fixation of fractures have their advantages and disadvantages. The frequency of nonunions caused by intramedullary fixation of the femoral shaft can reach 10 %, and varus/valgus and rotational deformities and shortening can also be observed. But the use of locking screws prevents the occurrence of most complications. When fixing the fractures with plates, the main complications are related to superficial and deep infections, which were more often observed in overweight patients. The analysis demonstrated that in overweight and obese patients, the main complicating factor is not the method for fixing the fracture zone, but factors related to the health of the patient himself. So, despite the fact that the results of treatment of fractures in patients with normal weight and obesity did not have a statistically significant difference, an increase in cardiovascular complications, exacerbation of chronic respiratory diseases was observed. Moreover, it is the presence of concomitant diseases that often makes surgical intervention impossible. Conclusions. There is a large amount of research on surgical methods of fixing tibial diaphyseal fractures, but data on the choice of fixation method in overweight and obese patients as a separate approach were not found. There are data on complicating factors of excess weight in the treatment of fractures and surgical interventions. Systematic studies related specifically to the algorithm for choosing the method of fracture fixation and complications have also not been found.

Spontaneous Particle Ordering, Sorting, and Assembly on Soap Films.

Soap bubbles exhibit abundant fascinating phenomena throughout the entire life of evolution with different fundamental physics governing them. Nevertheless, the complicated dynamics of small objects in soap films are still unrevealed. Here, we report the first observation of spontaneous particle ordering in a complicated galaxy of soap films without any external energy. The balance of interfacial tension at two liquid-gas interfaces is theoretically predicted to govern belted wetted particles (BWPs) traveling along a specified path spontaneously. Such spontaneous particle path-finding is found to depend on the particle size and hydrophilic properties. Spontaneous particle sorting is directly realized via these discrete and distinctive paths for different particles. The deformation of the soap membrane facilitates 1D/2D particle organization along the path. This observation represents the discovery of a new spontaneous order phenomenon in soap film systems and provides a new energy-free approach for particle separation and soft colloidal crystal assembly.

Tutorial on MUedit: An open-source software for identifying and analysing the discharge timing of motor units from electromyographic signals

We introduce the open-source software MUedit and we describe its use for identifying the discharge timing of motor units from all types of electromyographic (EMG) signals recorded with multi-channel systems. MUedit performs EMG decomposition using a blind-source separation approach. Following this, users can display the estimated motor unit pulse trains and inspect the accuracy of the automatic detection of discharge times. When necessary, users can correct the automatic detection of discharge times and recalculate the motor unit pulse train with an updated separation vector. Here, we provide an open-source software and a tutorial that guides the user through (i) the parameters and steps of the decomposition algorithm, and (ii) the manual editing of motor unit pulse trains. Further, we provide simulated and experimental EMG signals recorded with grids of surface electrodes and intramuscular electrode arrays to benchmark the performance of MUedit. Finally, we discuss advantages and limitations of the blind-source separation approach for the study of motor unit behaviour during tonic muscle contractions.

Effects of elastic band resistance training on the physical and mental health of elderly individuals: A mixed methods systematic review.

Elastic band resistance training in elderly individuals can improve physical fitness and promote mental health in addition to other benefits. This systematic review aimed to review, summarize, and evaluate quantitative, qualitative, and mixed methodological studies on the use of elastic band resistance training in elderly individuals, and to investigate the influence of elastic band resistance training on the physical and mental health of elderly individuals, as well as their preferences and obstacles in training. A convergent separation approach was used to synthesize and integrate the results, specifically the mixed systematic review approach recommended by the Joanna Briggs Institute. The extensive search strategy included electronic database searches in the Cochrane Library, PubMed, Embase, Web of Science, Google Scholar, MEDLINE, and CINAHL. The researchers rigorously screened the literature, extracted and analyzed the data, and evaluated the quality of the included studies using the Mixed Methods Appraisal Tool (MMAT). Twenty-eight studies were included, of which 25 were quantitative studies, 2 were qualitative studies, and 1 was a mixed-methods study. A total of 1,697 subjects were investigated across all studies. Quantitative evidence supports the notion that elastic band resistance training can improve upper and lower limb flexibility, endurance, upper strength, physical balance, and cardiopulmonary function and enhance the mental health of elderly individuals. Participants in the qualitative study reported some preferences and obstacles with band resistance training, but most participants reported physical benefits. Despite the heterogeneity between studies, this review is the first systematic review to comprehensively evaluate the effectiveness of elastic band resistance training in older adults. It not only shows the influence of elastic band resistance training on the physical and mental health of the elderly, but also emphasizes the preference and obstacles of elderly individuals face.

Controllable concentric electric field line distribution for simultaneous separation of DNA

An approach for the controllable separation and concentration of nucleic acid using a circular nonuniform electric field was proposed and developed. Using six different lengths of DNA molecules as standard samples, the distribution of the gradient electric field was increased from the outer circular electrode to the inner rod-shaped electrode, contributing to the migration of DNA molecules at a velocity gradient towards the region with the strongest inner electric field. The DNA molecules were arranged in a distribution of concentric circles that aligned with the distribution of concentric equipotential lines. The concentration of DNA multiplied with the alternation of radius. As a result, this platform allowed simultaneous DNA separation, achieving a resolution range of 1.17–3.03 through an extended electrophoresis time, resulting in enhanced concentration factors of 1.08–6.27. Moreover, the manipulation of the relative height of the inner and outer electrodes enabled precise control over the distribution and the deflection degree of electric field lines, leading to accurate control over DNA deflection.

Interpretable baseflow segmentation and prediction based on numerical experiments and deep learning

Baseflow is a crucial water source in the inland river basins of high-cold mountainous region, playing a significant role in maintaining runoff stability. It is challenging to select the most suitable baseflow separation method in data-scarce high-cold mountainous region and to evaluate effects of climate factors and underlying surface changes on baseflow variability and seasonal distribution characteristics. Here we attempt to address how meteorological factors and underlying surface changes affect baseflow using the Grey Wolf Optimizer Digital Filter Method (GWO-DFM) for rapid baseflow separation and the Long Short-Term Memory (LSTM) neural network model for baseflow prediction, clarifying interpretability of the LSTM model in baseflow forecasting. The proposed method was successfully implemented using a 63-year time series (1958–2020) of flow data from the Tai Lan River (TLR) basin in the high-cold mountainous region, along with 21 years of ERA5-land meteorological data and MODIS data (2000–2020). The results indicate that: (1) GWO-DFM can rapidly identify the optimal filtering parameters. It employs the arithmetic average of three methods, namely Chapman, Chapman-Maxwell and Eckhardt filter, as the best baseflow separation approach for the TLR basin. Additionally, the baseflow significantly increases after the second mutation of the baseflow rate. (2) Baseflow sources are mainly influenced by precipitation infiltration, glacier frozen soil layers, and seasonal ponding. (3) Solar radiation, temperature, precipitation, and NDVI are the primary factors influencing baseflow changes, with Nash-Sutcliffe efficiency coefficients exceeding 0.78 in both the LSTM model training and prediction periods. (4) Changes in baseflow are most influenced by solar radiation, temperature, and NDVI. This study systematically analyzes the changes in baseflow and response mechanisms in high-cold mountainous region, contributing to the management of water resources in mountainous basins under changing environmental conditions.

A novel deep learning rainfall–runoff model based on Transformer combined with base flow separation

ABSTRACT Precise long-term runoff prediction holds crucial significance in water resource management. Although the long short-term memory (LSTM) model is widely adopted for long-term runoff prediction, it encounters challenges such as error accumulation and low computational efficiency. To address these challenges, we utilized a novel method to predict runoff based on a Transformer and the base flow separation approach (BS-Former) in the Ningxia section of the Yellow River Basin. To evaluate the effectiveness of the Transformer model and its responsiveness to the base flow separation technique, we constructed LSTM and artificial neural network (ANN) models as benchmarks for comparison. The results show that Transformer outperforms the other models in terms of predictive performance and that base flow separation significantly improves the performance of the Transformer model. Specifically, the performance of BS-Former in predicting runoff 7 days in advance is comparable to that of the BS-LSTM and BS-ANN models with lead times of 4 and 2 days, respectively. In general, the BS-Former model is a promising tool for long-term runoff prediction.

Reconstruction of occluded pelvis markers during marker-based motion capture with industrial exoskeletons

Industrial back support exoskeletons are a promising solution to alleviate lumbar musculoskeletal strain. Due to the complexity of spinal loading, evaluation of EMG data alone has been considered insufficient to assess their support effects, and complementary kinematic and dynamic data are required. However, the acquisition of marker-based kinematics is challenging with exoskeletons, as anatomical reference points, particularly on the pelvis, are occluded by exoskeleton structures. The aim of this study was therefore to develop and validate a method to reliably reconstruct the occluded pelvic markers. The movement data of six subjects, for whom pelvic markers could be placed while wearing an exoskeleton, were used to test the reconstructions and compare them to anatomical landmarks during lifting, holding and walking. Two separate approaches were used for the reconstruction. One used a reference coordinate system based on only exoskeleton markers (EXO), as has been suggested in the literature, while our proposed method adds a technical marker in the lumbar region (LUMB) to compensate for any shifting between exoskeleton and pelvis. Reconstruction with EXO yielded on average an absolute linear deviation of 54 mm ± 16 mm (mean ± 1SD) compared to anatomical markers. The additional marker in LUMB reduced mean deviations to 14 mm ± 7 mm (mean ± 1SD). Both methods were compared to reference values from the literature for expected variances due to marker placement and soft tissue artifacts. For LUMB 99% of reconstructions were within the defined threshold of 24 mm ±9 mm while for EXO 91% were outside.

GP-HTNLoc: A graph prototype head-tail network-based model for multi-label subcellular localization prediction of ncRNAs

Numerous research results demonstrated that understanding the subcellular localization of non-coding RNAs (ncRNAs) is pivotal in elucidating their roles and regulatory mechanisms in cells. Despite the existence of over ten computational models dedicated to predicting the subcellular localization of ncRNAs, a majority of these models are designed solely for single-label prediction. In reality, ncRNAs often exhibit localization across multiple subcellular compartments. Furthermore, the existing multi-label localization prediction models are insufficient in addressing the challenges posed by the scarcity of training samples and class imbalance in ncRNA dataset. To address these limitations, this study proposes a novel multi-label localization prediction model for ncRNAs, named GP-HTNLoc. To mitigate class imbalance, GP-HTNLoc adopts separate training approaches for head and tail location labels. Additionally, GP-HTNLoc introduces a pioneering graph prototype module to enhance its performance in small-sample, multi-label scenarios. The experimental results based on 10-fold cross-validation on benchmark datasets demonstrate that GP-HTNLoc achieves competitive predictive performance. The average results from 10 rounds of testing on an independent dataset show that GP-HTNLoc outperforms the best existing models on the human lncRNA, human snoRNA, and human miRNA subsets, with average precision improvements of 31.5%, 14.2%, and 5.6%, respectively, reaching 0.685, 0.632, and 0.704. A user-friendly online GP-HTNLoc server is accessible at https://56s8y85390.goho.co.

Accurate calculation of losses in amorphous alloy cores based on a dynamic Jiles-Atheron model under wide frequency and variable temperature operating conditions

The stator core of the high-speed amorphous alloy permanent magnet synchronous motor(HSAAPMSM）experiences significant temperature fluctuations due to exposure to a high-frequency alternating magnetic field. We introduce the dynamic Jiles-Atheron(J-A) model under wide frequency and variable temperature operating conditions to achieve accurate modeling for stator core loss. In contrast to previous modeling techniques, this approach does not rely on amount of experience data and laborious parameter identifications. The adaptive chaotic dung beetle optimization algorithm is applied to improve the effectiveness of parameter identification. The temperature-dependent static J-A model is modified into a dynamic J-A model by combining the field separation approach with the Riemann-Liouville (R-L) fractional derivative theory to enable the model's application under high-frequency operating circumstances. Finally, the model described in this study is verified through experimentation and can be used to precisely calculate the loss of amorphous alloy cores.

Impact of conditioning runs on hydrophilic interaction chromatography repeatability and its application as a second dimension in online comprehensive two-dimensional liquid chromatography.

A common separation approach for polar compounds involves coupling reversed-phase liquid chromatography (RPLC) with hydrophilic interaction chromatography (HILIC) in two-dimensional chromatography. The higher proportion of acetonitrile used in the HILIC mobile phase, which enhances mass spectrometry detection, encourages its use in the second dimension. Previous studies demonstrated that the HILIC column can be partially equilibrated within very short timeframes without compromising retention time stability, rendering it suitable in online comprehensive two-dimensional liquid chromatography (LC×LC) setups. In addition, a specific number of conditioning cycles seems necessary to establish stable retention times. Here, the repeatability of HILIC when employed as second dimension in LC×LC was investigated, with a focus on determining the required number of conditioning cycles to achieve repeatable retention times. Various parameters influenced by the LC×LC online modulation system were studied, such as steep gradient slopes up to 8%, and very short equilibration times, less than or equal to dead time, as well as injection volume and solvent, which depend on the first dimension. Finally, the use of HILIC as a second dimension with tailored conditioning runs was applied to the analysis of hyaluronic acid hydrogel digests. The application of an RPLC×HILIC method using five conditioning runs yielded exceptional stability in second-dimension retention times.

Comparing two magnetic separation pretreatment methods for molecular detection using (q)PCR assays targeting Staphylococcus aureus in milk as models

Magnetic separation presents significant potential for culture-independent detection of foodborne pathogens in food samples. In this study, we compared two magnetic separation pretreatment strategies for molecular detection using (q)PCR assays targeting Staphylococcus aureus in milk as models. The first approach employed amino-modified silica magnetic particles (ASMPs) for DNA separation, while the second utilized pig IgG-labeled magnetic beads (IgG-MBs) for cell separation. In the ASMPs-based DNA separation strategy, a sensitivity of 36 CFU/mL was achieved when ASMPs-DNA complexes were employed as templates for PCR analysis. To mitigate noise signals originating from ASMPs during qPCR, DNA on the surface of ASMPs was replaced with dNTP, resulting in a sensitivity of 1.6 × 103 CFU/mL. The IgG-MBs-based cell separation approach yielded sensitivities of 3.6 × 104 CFU/mL for PCR and 1.6 × 103 CFU/mL for qPCR following DNA isolation from the bacteria-IgG-MBs complexes. Both methods exhibited exceptional specificity and robustness against background bacteria interference. However, neither approach effectively discriminated between live and dead bacteria. In comparison, the ASMPs-based DNA separation strategy exhibited superior potential especially when ASMPs do not influence the detection system. This research contributes to the optimization of magnetic separation pretreatment strategies for the molecular detection of foodborne pathogens, highlighting the potential of culture-independent detection methods.