Separate Classes Research Articles

Automated collective variable discovery for MFSD2A transporter from molecular dynamics simulations

Biomolecules often exhibit complex free energy landscapes in which long-lived metastable states are separated by large energy barriers. Overcoming these barriers to robustly sample transitions between the metastable states with classical molecular dynamics (MD) simulations presents a challenge. To circumvent this issue, collective variable (CV)-based enhanced sampling MD approaches are often employed. Traditional CV selection relies on intuition and prior knowledge of the system. This approach introduces bias, which can lead to incomplete mechanistic insights. Thus, automated CV detection is desired to gain a deeper understanding of the system/process. Analysis of MD data with various machine-learning algorithms, such as principal component analysis (PCA), support vector machine, and linear discriminant analysis (LDA) based approaches have been implemented for automated CV detection. However, their performance has not been systematically evaluated on structurally and mechanistically complex biological systems. Here, we applied these methods to MD simulations of the MFSD2A (Major Facilitator Superfamily Domain 2A) lysolipid transporter in multiple functionally relevant metastable states with the goal of identifying optimal CVs that would structurally discriminate these states. Specific emphasis was on the automated detection and interpretive power of LDA-based CVs. We found that LDA methods, which included a novel gradient descent-based multiclass harmonic variant, termed GDHLDA, we developed here, outperform PCA in class separation, exhibiting remarkable consistency in extracting CVs critical for distinguishing metastable states. Furthermore, the identified CVs included features previously associated with conformational transitions in MFSD2A. Specifically, conformational shifts in transmembrane helix 7 and in residue Y294 on this helix emerged as critical features discriminating the metastable states in MFSD2A. This highlights the effectiveness of LDA-based approaches in automatically extracting from MD trajectories CVs of functional relevance that can be used to drive biased MD simulations to efficiently sample conformational transitions in the molecular system.

Challenges Faced by Public School Teachers in Teaching Pupils in the Third District of Bohol

The study presented the challenges faced by public kindergarten teachers in the Third Congressional District of Bohol, Philippines. Employing descriptive survey method, data from 226 kindergarten teachers across 19 municipalities were analyzed to identify challenges based on class types and schedules. The demographic profile of respondents revealed a young workforce aged 20-40. Majority of which are holding a Bachelor of Elementary Education degree specializing in Early Childhood Education. All respondents were LET Board passers and had participated in relevant workshops. The study confirmed compliance to the mandated teacher-pupil ratio of 1:25, with a total student population of 5,868. Findings demonstrated significant differences in challenges faced by teachers based on class type. Teachers managing multi-grade classes reported more difficulties compared to those handling single-grade classes. The findings suggest potential violations of DepEd Order No. 47, s. 2016, which mandates separate kindergarten classes in multi-grade schools. This result heightened the challenges faced by multi-grade teachers. The research provides information for the development of teacher programs, and informing policy adjustments. It will also serve as a reference for future studies on the challenges in teaching kindergarten that aims to enhance the experiences of students in public schools.

A class-aware representation refinement framework for graph classification

Graph Neural Networks (GNNs) are widely used for graph representation learning. Despite its prevalence, GNN suffers from two drawbacks in the graph classification task, the neglect of graph-level relationships, and the generalization issue. Each graph is treated separately in GNN message passing/graph pooling, and existing methods to address overfitting operate on each individual graph. This makes the graph representations learnt less effective in the downstream classification. In this paper, we propose a Class-Aware Representation rEfinement (CARE) framework for the task of graph classification. CARE computes simple yet powerful class representations and injects them to steer the learning of graph representations towards better class separability. CARE is a plug-and-play framework that is highly flexible and able to incorporate arbitrary GNN backbones without significantly increasing the computational cost. We also theoretically prove that CARE has a better generalization upper bound than its GNN backbone through Vapnik-Chervonenkis (VC) dimension analysis. Our extensive experiments with 11 well-known GNN backbones on 9 benchmark datasets validate the superiority and effectiveness of CARE over its GNN counterparts.

A method for seismic fault identification based on self-training with high-stability pseudo-labels

The imaging principle of seismic image is different from that of natural image. There are many problems on seismic images, such as limited resolution, complex reflection feature and strong uncertainty, which leads to significant difference in the research emphases of computer vision on seismic image. In response to the above issues, the semi-supervised semantic segmentation method based on self-training with high-stability pseudo-labels is proposed for seismic fault identification. Specifically, the self-training strategy Fault-Seg-ST is proposed to decouple Teacher–Student model predictions by adding random noise to unlabeled images. The selective retraining strategy Fault-Seg-SST is proposed to evaluate the reliability of pseudo-labels based on the stability of model predictions. The contrastive loss function is proposed to learn pixel-level feature representation with intra class affinity and inter class separability. The Fault-Seg-SST has achieved the optimal segmentation performance on both DeepLabV2 (Dice 86%, mIoU 77%) and UNet with ResNet 18 (Dice 83%, mIoU 76%). Experimental results show that the fault identification method based on self-training with high-stability pseudo-labels demonstrates the superiority of selective retraining strategy, and the fault identification model trained with Fault-Seg-SST strategy can achieve the fine-grained fault segmentation.

MDB-71. IDENTIFICATION AND DESCRIPTION OF A NOVEL TYPE OF MEDULLOBLASTOMA

Abstract BACKGROUND Medulloblastoma (MB) is one of the most frequent high-grade brain tumor types of childhood and adolescence. Based on biology, histology, and its clinical course, MB is a heterogeneous disease with four different molecular types (WNT, SHH, Group 3, and Group 4), which are most reliably distinguishable by their global DNA methylation pattern. METHODS We analyzed DNA methylation (n=47), copy number variants, transcriptomic data (n=7) and histology (n=28) of a previously unrecognized type of MB. RESULTS As a result of integrating DNA methylation data of >2,600 MB and screening of >140,000 data sets uploaded to the DKFZ brain tumor classifier (www.molecularneuropathology.org), we identified a small group of MB that displayed a homogeneous DNA methylation pattern, which was clearly distinct from previously known MB types. Tumors within this group have also been recognized as a separate methylation class by latest versions of the classifier, which has provisionally been named medullomyoblastoma (MB_MYO). Transcriptomic data were similarly distinct from other MB types. Comparison of these data to various cell types of the developing hindbrain revealed transcriptional similarities to precursor cells of the rhombic lip with signatures of WNT signaling and myogenic differentiation. In line with the latter findings, 2/11 cases with DNA sequencing data harbored hotspot CTNNB1 mutations and 5/28 cases showed a myogenic differentiation based on histology. MYC amplifications were present in 11/47 cases, corresponding to a higher fraction of MYC-amplified tumors compared to all known MB types. Median age at diagnosis in this cohort was 16 years, and five-year overall survival was ~75 %. CONCLUSIONS In summary, we describe a novel type of MB identified by DNA methylation profiling that likely needs to be addressed separately during the retrospective analyses of MB patient cohorts, for the design of future clinical trials, and when evaluating targeted therapies.

Outlier classification for microbiological open set recognition

Precise detection and effective intervention of pathogenic microorganisms are pivotal to ensure crop yield. Traditional open set recognition methods distinguish unknown classes, i.e., those not involved in training, as outliers or separate classes, which is insufficient for real-world applications like recognizing airborne microorganisms and particles. In this paper, a new setting for the classification of outliers for open set recognition is proposed. The proposed method classifies unknown classes while achieving the classification of known classes, treating each category in the open set equally. The two cores of the method are hierarchical feature representation and feature gallery construction. To enhance the feature representation, this paper proposes a novel architectural module of spatial attention on region hierarchy, namely SPARC. This module extracts features from hierarchical levels and models spatial interaction through the attention mechanism. With the induction of local-to-global description, minute differences with ambiguous appearances between classes can be detected, and the negative effects of image quality degradation can be ameliorated. Furthermore, a feature gallery containing the feature representation from both known and unknown classes is developed for feature retrieval. To demonstrate the effectiveness of the proposed model, the first dataset composed of agricultural microorganisms, named SMAD, is constructed, and experiments are conducted on existing datasets and the newly proposed SMAD dataset. The experimental results outperform the prior art on these datasets under both the outlier rejection and outlier classification settings.

Open set transfer learning for bearing defect recognition based on selective momentum contrast and dual adversarial structure

Data-driven rotating machinery fault diagnosis methods gradually enter people’s vision due to their fast and accuracy. However, in practical engineering applications, the unpredictability of machine failure modes and the appearance of new defect types can severely disrupt the diagnostic task with closed set defect recognition data-driven methods. Addressing this concern, this paper delves into the defect transfer diagnosis of rolling bearings in an open-set context and introduces the Momentum Contrastive Dual Adversarial (MCDA) domain adaptive method. This novel approach leverages instance-based difference comparison for unsupervised clustering and implements Negative Samples Selection Optimization (NSSO) via K Nearest Neighbor across all domains. It features intra-module adversarial learning for domain adaptation between source and target domains, and inter-module adversarial learning where encoders and feature extractors across different modules share weights. The methodology prioritizes the separation of private classes during the initial phase of contrastive learning, ensuring the isolation of target private classes. Concurrently, it aligns shared classes in the subsequent phase through semi-supervised training for domain adaptation. Our experiments on the Paderborn Dataset and Railway Freight Wheel-set Bearing (RFWB) Dataset demonstrate the effectiveness of this method.

Few-shot multi-domain text intent classification with Dynamic Balance Domain Adaptation Meta-learning

User intents are ever-changing, which requires deep learning models to have the ability to classify unknown intents. Meta-learning aims to solve this problem by improving the model’s generalization ability to unknown intent. However, learning on a small amount of text can easily lead to overfitting of the model. Domain adaptation can help us train a more robust model. However, most existing methods only focus on global feature alignment and ignore alignment in subdomains. Therefore, in this study, we first consider the case where the model can maintain robustness with a small amount of data and then explore and mine the higher quality transferable features. Based on these ideas, we propose Dynamic Balance Domain Adaptation Meta-learning (DBDAML), which adaptively learns higher quality transferable features in both the global domain and subdomains.11in this paper, each subdomain represents the domain under a separate class. At the same time, we define a dynamic balance factor to enable DBDAML to dynamically focus on the global domain and subdomains. This allows the model to give different attention to different domain adaptations and prevents it from overfitting of a domain feature alignment. The dynamic balance factor is estimated by the contribution of different domain discriminators to the loss, which also makes it easy to calculate and accurate. Finally, we use the meta-learning framework to model the entire theoretical idea. Extensive experiments demonstrate that our approach achieves better performance than state-of-the-art baseline methods.

MEDOIDS AS A PACKING OF ORB IMAGE DESCRIPTORS

The aim of the research. The paper presents the research about the feasibility to use matching medoids obtained from the set of ORB descriptors instead matching the full set of binary descriptors for image classification problem. Research results. Different methods that include direct brute force medoids matching, grouping of medoids for separate classes, and grouping of descriptors followed by calculation of medoids amongst them were proposed. Numerical experiments were performed for all these methods in order to compare the classification accuracy and inference time. It has been shown that using of medoids allowed us to redistribute processing time in order to perform more calculations during preprocessing rather than during classification. According to modelling performed on the Leeds Butterly dataset matching images based on medoids could have the same accuracy as matching of descriptors (0.69–0.88 for different number of features). Medoids require additional time for the calculation during preprocessing stage but classification time becomes faster: in our experiments we have obtained about 9–10 times faster classification and same 9–10 times increasing preprocessing time for the models that have comparable accuracies. Finally, the efficiency of the proposed ideas was compared to the CNN trained and evaluated on the same data. As expected, CNN required much more preprocessing (training) time but the result is worth it: this approach provides the best classification accuracy and inference time. Conclusion. Medoids matching could have the same accuracy as direct descriptors matching, but the usage of medoids allows us to redistribute the overall modeling time with the increasing preprocessing time and making inference faster.

Coping with AI errors with provable guarantees

AI errors pose a significant challenge, hindering real-world applications. This work introduces a novel approach to cope with AI errors using weakly supervised error correctors that guarantee a specific level of error reduction. Our correctors have low computational cost and can be used to decide whether to abstain from making an unsafe classification. We provide new upper and lower bounds on the probability of errors in the corrected system. In contrast to existing works, these bounds are distribution agnostic, non-asymptotic, and can be efficiently computed just using the corrector training data. They also can be used in settings with concept drifts when the observed frequencies of separate classes vary. The correctors can easily be updated, removed, or replaced in response to changes in distributions within each class without retraining the underlying classifier. The application of the approach is illustrated with two relevant challenging tasks: (i) an image classification problem with scarce training data, and (ii) moderating responses of large language models without retraining or otherwise fine-tuning.

Thin layer chromatography of methylated derivatives of sodium alkylbenzenesulfonates in water analysis by GC-MS

Sodium alkylbenzene sulfonates or linear alkyl benzene sulfonates (LAS) are the most common synthetic anionic surfactants and water pollutants. They can cause both acute and chronic toxic effects on water organisms. Selective determination of sodium alkylbenzene sulfonates as a separate class of anionic surfactants is possible using gas chromatography coupled with mass spectrometry (GC-MS) in the form of linear alkylbenzenesulfonic acid methyl esters (LABSA ME). In order to purify the extracts and concentrate the analytes, we studied the behaviour of these compounds by ascending high-performance thin-layer chromatography using Kieselgel 60 F254 and Sorbfil plates. A mixture of n-hexane and methanol solvents in a ratio of 23:1 (by volume) was used as the mobile phase. Under these conditions, sodium alkylbenzenesulphonates remain on the start line, while their derivatives (ME ABSC), obtained by methylation with trimethyl orthoformate in the presence of trifluoroacetic acid (with a yield of η=98%), form zones characterised by retention coefficient values of Rf = 0.62 and Rf = 0.71 on Kieselgel 60 F254 and Sorbfil plates, respectively. The repeatability of the Rf values was characterised by a standard deviation of 6.1 and 5.9 %, respectively (n=16). The completeness of extraction (95.0–100.0 %) of analytes from the plates by descending TLC with acetonitrile was noted. The applicability of the TLC method for the concentration of analytes and pretreatment of extracts on the example of real water samples was shown. Using GC-MS with electron impact ionisation, we determined the concentrations of sodium alkylbenzenesulphonates in water sampled in the southern basin of Lake Baikal from a depth of 400 m (0.24±0.02 µg/dm3) and in snowmelt water from the ice of the Krestovka River where it flows into Lake Baikal near the village of Listvyanka (31.1 ± 1.0 µg/dm3).

Prenatal ethanol and cannabis exposure have sex- and region-specific effects on somatostatin and neuropeptide Y interneurons in the rat hippocampus.

Cannabis is increasingly being legalized and socially accepted around the world and is often used with alcohol in social settings. We recently showed that in utero exposure to both substances can alter the density of parvalbumin-expressing interneurons in the hippocampus. Here we investigate the effects of in utero alcohol and cannabis exposure, alone or in combination, on somatostatin- and neuropeptide Y-positive (NPY) interneurons. These are separate classes of interneurons important for network synchrony and inhibition in the hippocampus. A 2 (Ethanol, Air) × 2 (tetrahydrocannabinol [THC], Vehicle) design was used to expose pregnant Sprague-Dawley rats to either ethanol or air, in addition to either THC or the inhalant vehicle solution, during gestational days 5-20. Immunohistochemistry for somatostatin- and NPY-positive interneurons was performed in 50 μm tissue sections obtained at postnatal day 70. Exposure to THC in utero had region-specific and sex-specific effects on the density of somatostatin-positive interneurons in the adult rat hippocampus. A female-specific decrease in NPY interneuron cell density was observed in the CA1 region following THC exposure. Combined exposure to alcohol and THC reduced NPY neurons selectively in the ventral dentate gyrus hippocampal subfield. However, overall, co-exposure to alcohol and cannabis had neither additive nor synergistic effects on interneuron populations in other areas of the hippocampus. These results illustrate how alcohol and cannabis exposure in utero may affect hippocampal function by altering inhibitory processes in a sex-specific manner.

SEMG-based automatic characterization of swallowed materials

Monitoring of ingestive activities is critically important for managing the health and wellness of individuals with various health conditions, including the elderly, diabetics, and individuals seeking better weight control. Monitoring swallowing events can be an ideal surrogate for developing streamlined methods for effective monitoring and quantification of eating or drinking events. Swallowing is an essential process for maintaining life. This seemingly simple process is the result of coordinated actions of several muscles and nerves in a complex fashion. In this study, we introduce automated methods for the detection and quantification of various eating and drinking activities. Wireless surface electromyography (sEMG) was used to detect chewing and swallowing from sEMG signals obtained from the sternocleidomastoid muscle, in addition to signals obtained from a wrist-mounted IMU sensor. A total of 4675 swallows were collected from 55 participants in the study. Multiple methods were employed to estimate bolus volumes in the case of fluid intake, including regression and classification models. Among the tested models, neural networks-based regression achieved an R2 of 0.88 and a root mean squared error of 0.2 (minimum bolus volume was 10 ml). Convolutional neural networks-based classification (when considering each bolus volume as a separate class) achieved an accuracy of over 99% using random cross-validation and around 66% using cross-subject validation. Multiple classification methods were also used for solid bolus type detection, including SVM and decision trees (DT), which achieved an accuracy above 99% with random validation and above 94% in cross-subject validation. Finally, regression models with both random and cross-subject validation were used for estimating the solid bolus volume with an R2 value that approached 1 and root mean squared error values as low as 0.00037 (minimum solid bolus weight was 3 gm). These reported results lay the foundation for a cost-effective and non-invasive method for monitoring swallowing activities which can be extremely beneficial in managing various chronic health conditions, such as diabetes and obesity.

TLCE: Transfer-Learning Based Classifier Ensembles for Few-Shot Class-Incremental Learning

Few-shot class-incremental learning (FSCIL) struggles to incrementally recognize novel classes from few examples without catastrophic forgetting of old classes or overfitting to new classes. We propose TLCE, which ensembles multiple pre-trained models to improve separation of novel and old classes. Specifically, we use episodic training to map images from old classes to quasi-orthogonal prototypes, which minimizes interference between old and new classes. Then, we incorporate the use of ensembling diverse pre-trained models to further tackle the challenge of data imbalance and enhance adaptation to novel classes. Extensive experiments on various datasets demonstrate that our transfer learning ensemble approach outperforms state-of-the-art FSCIL methods.

An online fuzzy fraud detection framework for credit card transactions

Credit card transaction fraud is one of the challenging security concerns for financial firms globally. The continuously changing environment of fraud characteristics and the class imbalance and complete separation issues in fraud data create difficulties in accurately and efficiently predicting fraudulent transactions and implementing fraud detection systems in real-time. The study aims to develop a new real-time fraud detection framework that can efficiently be implemented online and address the issues caused by non-stationary changes in transaction and fraud characteristics, class imbalance, and complete separation. We propose a new approach to handle the impact of non-stationary changes in fraud transaction patterns. It enables efficiency in model training, given the sheer size of datasets. By implementing a robust fuzzy logistic regression model against class imbalance and separation problems, we address the challenge of having a very low rate of fraudulent transactions in the dataset and having separation issues due to specific characteristics of transactions. The analysis of the performance versus efficiency nexus of the proposed methodology reveals that the proposed framework shows strong performance results with specificity and sensitivity greater than 0.90 and Matthew’s correlation coefficient greater than 0.80 even on small sample sizes and produces highly accurate results in identifying fraudulent and non-fraudulent transactions with an accuracy greater than 0.99. Benchmarking with machine learning and other fraud detection approaches reveals that the proposed framework provides better detection performance while maintaining a higher rate of identifying non-fraudulent transactions than the alternative approaches. Improved classification performance leads to detecting fraudulent transactions with higher precision while avoiding misclassifying legitimate transfers, resulting in lower financial losses and improved customer satisfaction.

Depth linear discrimination-oriented feature selection method based on adaptive sine cosine algorithm for software defect prediction

Software Defect Prediction (SDP) plays a vital role in the software development life cycle as it helps identify and fix software defects. However, predicting software defects with irrelevant features and overlapping classes is challenging and can lead to lengthy training and low model accuracy. To address these challenges, this research introduces a novel Depth Linear Discrimination-Oriented Feature Selection Method based on Adaptive Sine Cosine Algorithm, named Depth Adaptive Sine Cosine Feature Selection (DASC-FS). DASC-FS integrates the Adaptive Sine Cosine Algorithm (ASCA) as a search algorithm to determine the relevant features and adopts Depth Linear Discriminant Analysis (D-LDA) to identify the discriminative features that maximize class separation. The paper proposes ASCA which is a metaheuristic algorithm meticulously designed to enhance the search capabilities of the standard Sine Cosine Algorithm (SCA). Combining the simplicity of the SCA with the efficiency of multiple mutation operators inspired by Genetic Algorithms (GA), ASCA enhances the diversity of the solutions and imparts remarkable adaptability to various situations. Furthermore, this study introduces a novel linear discriminant method, called Depth Linear Discriminant Analysis (D-LDA) to enhance the robustness of the original LDA. D-LDA systematically integrates the matrix depth concept into LDA, offering a systematic approach to address the challenges associated with scatter matrix estimation. As matrix depth measures how central or deep a particular matrix is within a distribution with respect to different directions, it is an efficient tool for computing a robust scatter matrix estimator that can handle outliers and complex data structures. The experimental results showed that DASC-FS consistently obtains the highest accuracy compared to most existing methods by integrating ASCA and D-LDA, thereby considering both accuracy optimization and class separation. The results also show that the use of multiple mutation operators in ASCA improves the search process capabilities. The results also show that the capacity of D-LDA to reduce data dimensionality and increase class separation yields highly competitive results compared to other LDAs. Finally, features related to code size and complexity have emerged as key factors for SDP because they consistently rank as important features across different classifiers and datasets. DASC-FS offers a valuable solution in domain knowledge for enhancing predictive accuracy and understanding factors contributing to software defects through enhanced search capabilities, robust scatter matrix estimation, and the ability to reduce data dimensionality.

Spatio-temporal segregation: commuting time that unite and separate classes and races

Resumo É marcante o intenso crescimento das cidades brasileiras caracterizado pela periferização e pelas desigualdades socioespaciais. Porém, ainda são escassas pesquisas que enfoquem a dimensão temporal quanto à compreensão da segregação espacial. Este artigo visa compreender o papel do tempo de deslocamento na explicação do processo de segregação espacial na metrópole de São Paulo. Para tanto, sua metodologia baseia-se em dados estatísticos das áreas de ponderação da amostra do Censo Demográfico, através da variável tempo de deslocamento habitual para o trabalho, combinada com outras variáveis socioeconômicas, de renda e raça. Busca-se contribuir para o entendimento da segregação espaço-temporal, demonstrando que o tempo de deslocamento une os mais pobres e os negros, separando-os dos mais ricos e dos brancos na referida metrópole.

Enhancing Student Performance Prediction Using a Combined SVM-Radial Basis Function Approach

This research aims to improve student performance predictions using a combined SVM (Support Vector Machine) and radial basis function (RBF) approach. The developed model utilizes a combination of the strengths of SVM in handling class separation and the ability of RBF to capture complex patterns in data. Student assessment data, including math, reading, and writing scores, is used as a feature to predict student performance on tests. Preprocessing steps, including feature normalization and label encoding, are applied to prepare the data for model training. Next, the SVM model with the RBF kernel is initialized and optimized using GridSearchCV to find the best parameters. Model evaluation was carried out using the R2 metric to evaluate how well the model predicts student performance. Experimental results show that the combined SVM-RBF approach can improve student performance predictions with fairly accurate prediction results of 88%. The practical implication of this research is the development of a more accurate model for predicting student performance, which can be used as a tool to improve educational interventions and decision-making in educational institutions.

Highly efficient suppression of zincate ion crossover in zinc–air batteries using selective membrane PVA-KOH/ZIF-8 gel polymer electrolytes

Rechargeable zinc–air batteries have attracted considerable attention in the industries due to their high energy density, affordability, and safety. However, these batteries still have limitations in their lifetime because of the issue of zincate ion (Zn(OH)42−) crossover. To suppress this issue, a new PVA-KOH-based gel polymer electrolyte membrane incorporating zeolitic imidazolate framework-8 (ZIF-8) nanoparticles was developed as a selective membrane. The influence of different-sized ZIF-8 nanoparticles synthesized with three different zinc salts on thermal, physical, and electrochemical properties was investigated. The PVA-KOH/ZIF-8 membrane significantly reduced the diffusion coefficient of zincate ions to 3.27 × 10−6, 2.01 × 10−6, and 1.99 × 10−6 cm2 min−1 by the incorporation of ZIF-8, synthesized with Zn(NO3)2, Zn(OAc)2, and ZnSO4, respectively. Because of the pore apertures of ZIF-8 that are equivalent to Zn(OH)42−, the PVA-KOH/ZIF-8 selective membrane could block Zn(OH)42−and allowed OH− to pass through the membrane. Moreover, a large external hydrophilic surface was achieved by the influence of smaller-sized particles, which improved the retention of electrolytes. As a result, the ionic conductivity of the PVA-KOH/ZIF-8 membrane increased up to 164.27 mS cm−1 when added 10 wt% ZIF-8, synthesized by ZnSO4. Furthermore, this membrane showed the highest value of battery life cycle of 89 cycles at a current density 10 mA cm−2 and the discharge capacity of 396 mAh g−1. These results indicated that the development of a PVA-KOH/ZIF-8 membrane has a high potential and suitability for use as a new class of separators in rechargeable zinc–air batteries.

A comparison of the effects of two protocols of concurrent resistance and aerobic training on physical fitness in middle school students.

This study aimed to compare the effects of two concurrent training (CT) protocols on the physical fitness of middle school students. A 12-week quasi-experimental pre-test/post-test study was conducted with 157 middle school students (age = 12.48 ± 0.34, n = 90 females) divided into three groups: CT group A (CT-0h) received combined resistance training (RT) and aerobic training (AT) in each physical education session, CT group B (CT-48h) received RT and AT across two separate physical education classes 48 h apart, and a control group (Con) received no training. Training occurred twice a week. Test indicators included cardiorespiratory fitness (CRF) measured by estimated VO2max and 20 m shuttle run (laps), as well as muscle strength assessed through long jump, vertical jump, and handgrip strength. The intervention groups exhibited significant increases in estimated VO2max and muscle strength compared to their baseline values (p < 0.05). Both CT-0h and CT-48h groups demonstrated significant improvements in 20 m shuttle run (laps) (mean difference: 8.88 laps, p < 0.01; mean difference: 4.81 laps, p < 0.01, respectively), standing long jump (mean difference: 6.20 cm, p < 0.01; mean difference: 3.68 cm, p < 0.01, respectively), vertical jump (mean difference: 4.95 cm, p < 0.01; mean difference: 4.04 cm, p < 0.01, respectively), and handgrip strength (mean difference: 11.17 kg, p < 0.01; mean difference: 6.99 kg, p < 0.01, respectively). CT-0h group exhibited significantly increased estimated VO2max (mean difference: 1.47 ml/kg/min, p < 0.01) compared to the CT-48h group. Both CT programs effectively improved adolescents' physical fitness indicators. However, the program that integrated RT and AT within the same physical education class demonstrated superior enhancement in adolescents' CRF.