Step Size Research Articles

Liver Cancer Diagnosis: Enhanced Deep Maxout Model with Improved Feature Set

This work proposed a liver cancer classification scheme that includes Preprocessing, Feature extraction, and classification stages. The source images are pre-processed using Gaussian filtering. For segmentation, this work proposes a LUV transformation-based adaptive thresholding-based segmentation process. After the segmentation, certain features are extracted that include multi-texon based features, Improved Local Ternary Pattern (LTP-based features), and GLCM features during this phase. In the Classification phase, an improved Deep Maxout model is proposed for liver cancer detection. The adopted scheme is evaluated over other schemes based on various metrics. While the learning rate is 60%, an improved deep maxout model achieved a higher F-measure value (0.94) for classifying liver cancer; however, the previous method like Support Vector Machine (SVM), Random Forest (RF), Recurrent Neural Network (RNN), Long Short Term Memory (LSTM), K-Nearest Neighbor (KNN), Deep maxout, Convolutional Neural Network (CNN), and DL model holds less F-measure value. An improved deep maxout model achieved minimal False Positive Rate (FPR), and False Negative Rate (FNR) values with the best outcomes compared to other existing models for liver cancer classification.

Bidirectional locomotion induces unilateral limb adaptations.

Humans can acquire and maintain motor skills throughout their lives through motor learning. Motor learning and skill acquisition are essential for rehabilitation following neurological disease or injury. Adaptation, the initial stage of motor learning, involves short-term changes in motor performance in response to a new demand in the person's environment. Repeated adaptation can improve skill performance and result in long-term skill retention. Locomotor adaptation is extensively studied using split-belt treadmill paradigms. In this study we explored whether bidirectional walking (BDW) on a split-belt treadmill can induce short-term gait adaptations. Twelve healthy volunteers participated in our single session, starting with 2 minutes of normal walking (NW), followed by four 5-minute blocks of BDW with a 1-minute passive rest in between blocks, and ending with another 2-minute of NW. We recorded body kinematics and ground reaction forces throughout the experiment. Participants quickly adapted to BDW with both legs showing decreased step lengths. However, only the backward-walking leg exhibited aftereffects upon returning to NW, indicating short-term adaptation. Notable kinematic changes were observed, particularly in hip extension and pelvis tilt, though these varied among participants. Our findings suggest that BDW induces unilateral adaptations despite bilateral changes in gait, offering new insights into locomotor control and spinal CPG organization.

Forming mechanism of a novel shape-surface integrated incremental sheet forming process for fabricating parts with enhanced surface functionality

Metallic components with surface microfeatures are increasingly used in aerospace, automotive and other applications due to their drag-reducing properties. In this situation, we proposed a novel shape-surface integrated incremental sheet forming (S2-ISF) process to achieve the simultaneous fabrication of both surface microfeatures and desired geometric shape. It was found that an increase in the forming angle was most effective in improving the formation of microgroove array, followed by the step size, the spindle speed and the feed rate. Moreover, when the forming angle is constant, the step size and microgroove pitch increase linearly. Compared to the as-received sheet, the yield strength is increased by more than 20% after the S2-ISF process. Moreover, we observed that the increase of forming angle in the S2-ISF process has promoted grain deformation and grain fragmentation, which is favorable for the formed part to obtain more uniform and finer grains. The grain size distribution along the thickness direction shows the characteristics of ‘coarse in the middle and fine at both ends’. Finally, we found that the formed parts with different microgroove pitch from the S2-ISF process have the significant enhancement on the hydrophobicity and anti-friction property. When microgroove pitch is 155.5 μm, the surface contact angle of formed part improved from 70.65° to 101.39°, and the wear volume of formed part decreases from 4.89 × 10−3 mm3 to 3.65 × 10−3 mm3. This work provides a solution for the efficient and economical fabrication of three-dimensional thin-walled parts with microgroove array to enhance surface functionality.

Enhancing hoof health and locomotion in crossbred dairy cows: impact of recycled manure solids bedding on lameness incidence and gait kinematics.

Recycled manure solids has emerged as a promising alternative for animal bedding, owing to its economic feasibility, ready availability on farms, and soft, non-abrasive nature. This research aimed to assess the impact of recycled manure solids (RMS) bedding, combined with a conditioner containing 7.5% lime and 6% sodium hydrosulphate, on dairy cow welfare and gait kinematics over three months. Hock and knee injury scores, lameness incidence, and gait kinematic parameters were evaluated for animals housed on cement flooring (Control), RMS bedding (Treatment I), and conditioner-added RMS bedding (Treatment II) on days 0, 45, and 90 of the experiment with six crossbred cows in each group. The results revealed a significant reduction (p < 0.05) in lameness scores (5-point scale) for animals in both the RMS and conditioner-added RMS groups, with scores of 1.09 ± 0.05 and 1.04 ± 0.03, respectively, compared to those on cement floors. Moreover, a noteworthy decrease (p < 0.05) in knee and hock injury scores (4-point scale) was observed in the RMS groups, indicating a potentially positive impact on joint health. Gait kinematic analysis demonstrated that animals in the RMS (1.03 ± 0.04m/s) and conditioner-added RMS (1.02 ± 0.06m/s) groups exhibited higher walking speeds and increased step angles (158.59 ± 4.82° and 149.58 ± 3.85°) compared to their cement-floor counterparts. No significant changes (p > 0.05) were observed in stride length, step asymmetry, step length, and step width. The study concluded that the conditioner incorporated recycled manure solids resulting in a substantial decrease in lameness incidence and a reduction in hock and knee injuries among dairy cows. Additionally, the improved gait kinematics observed in non-lame animals suggest that this bedding combination positively influences overall animal well-being. These findings underscore the potential of sustainable bedding practices to enhance both physical health and locomotor behaviour in dairy cattle.

Towards an open pipeline for the detection of critical infrastructure from satellite imagery—a case study on electrical substations in The Netherlands

Abstract Critical infrastructure (CI) are at risk of failure due to the increased frequency and magnitude of climate extremes related to climate change. It is thus essential to include them in a risk management framework to identify risk hotspots, develop risk management policies and support adaptation strategies to enhance their resilience. However, the lack of information on the exposure of CI to natural hazards prevents their incorporation in large-scale risk assessments. This study sets out to improve the representation of CI for risk assessment studies by building a neural network model to detect CI assets from optical remote sensing imagery. We present a pipeline that extracts CI from OpenStreetMap (OSM), processes the imagery and assets’ masks, and trains a Mask R-CNN model that allows for instance segmentation of CI at the asset level. This study provides an overview of the pipeline and tests it with the detection of electrical substations assets in the Netherlands. Several experiments are presented for different under-sampling percentages of the majority class (25 %, 50 % and 100 %) and hyperparameters settings (batch size and learning rate). The highest scoring experiment achieved an Average Precision at an Intersection over Union of 50 % of 30.93 and a tile F-score of 89.88 %. This allows us to confirm the feasibility of the method and invite disaster risk researchers to use this pipeline for other infrastructure types. We conclude by exploring the different avenues to improve the pipeline by addressing the class imbalance, Transfer Learning and Explainable Artificial Intelligence.

Non-polynomial spline method for computational study of reaction diffusion system

This work addresses an efficient and new numerical technique utilizing non-polynomial splines to solve system of reaction diffusion equations (RDS). These system of equations arise in pattern formation of some special biological and chemical reactions. Different types of RDS are in the form of spirals, hexagons, stripes, and dissipative solitons. Chemical concentrations can travel as waves in reaction-diffusion systems, where wave like behaviour can be seen. The purpose of this research is to develop a stable, highly accurate and convergent scheme for the solution of aforementioned model. The method proposed in this paper utilizes forward difference for time discretization whereas for spatial discretization cubic non-polynomial spline is used to get approximate solution of the system under consideration. Furthermore, stability of the scheme is discussed via Von-Neumann criteria. Different orders of convergence is achieved for the scheme during a theoretical convergence test. Suggested method is tested for performance on various well known models such as, Brusselator, Schnakenberg, isothermal as well as linear models. Accuracy and efficiency of the scheme is checked in terms of relative error (E R ) and L ∞ norms for different time and space step sizes. The newly obtained results are analyzed and compared with those available in literature.

Enhancing colorectal cancer histology diagnosis using modified deep neural networks optimizer

Optimizers are the bottleneck of the training process of any Convolutionolution neural networks (CNN) model. One of the critical steps when work on CNN model is choosing the optimal optimizer to solve a specific problem. Recent challenge in nowadays researches is building new versions of traditional CNN optimizers that can work more efficient than the traditional optimizers. Therefore, this work proposes a novel enhanced version of Adagrad optimizer called SAdagrad that avoids the drawbacks of Adagrad optimizer in dealing with tuning the learning rate value for each step of the training process. In order to evaluate SAdagrad, this paper builds a CNN model that combines a fine- tuning technique and a weight decay technique together. It trains the proposed CNN model on Kather colorectal cancer histology dataset which is one of the most challenging datasets in recent researches of Diagnose of Colorectal Cancer (CRC). In fact, recently, there have been plenty of deep learning models achieving successful results with regard to CRC classification experiments. However, the enhancement of these models remains challenging. To train our proposed model, a learning transfer process, which is adopted from a pre-complicated defined model is applied to the proposed model and combined it with a regularization technique that helps in avoiding overfitting. The experimental results show that SAdagrad reaches a remarkable accuracy (98%), when compared with Adaptive momentum optimizer (Adam) and Adagrad optimizer. The experiments also reveal that the proposed model has a more stable training and testing processes, can reduce the overfitting problem in multiple epochs and can achieve a higher accuracy compared with previous researches on Diagnosis CRC using the same Kather colorectal cancer histology dataset.

Classification of Pneumonia via a Hybrid ZFNet-Quantum Neural Network Using a Chest X-ray Dataset.

Deep neural networks (DNNs) have made significant contributions to diagnosing pneumonia from chest X-ray imaging. However, certain aspects of diagnosis and planning can be further enhanced through the implementation of a quantum deep neural network (QDNN). Therefore, we introduced a technique that integrates neural networks with quantum algorithms named the ZFNet-quantum neural network for detecting pneumonia using 5863 X-ray scans with binary cases. The hybrid model efficiently pre-processes complex and high-dimensional data by extracting significant features from the ZFNet model. These significant features are given to the quantum circuit algorithm and further embedded into a quantum device. The parameterized quantum circuit algorithm using qubits, superposition theorem, and entanglement phenomena generates 4 features from 4098 features extracted from images via a deep transfer learning model. Moreover, to validate the outcome measures of the proposed technique, we used various PennyLane quantum devices to detect pneumonia and normal control images. By using the Adam optimizer, which exploits an adaptive learning rate that is fixed to 10-6 and six layers of a quantum circuit composed of quantum gates, the proposed model achieves an accuracy of 96.5%, corresponding to 25 epochs. The integrated ZFNet-quantum learning network outperforms the deep transfer learning network in terms of testing accuracy, as the accuracy gained by the convolutional neural network (CNN) is 94%. Therefore, we use a hybrid classical-quantum model to detect pneumonia in which a variational quantum algorithm enhances the outcomes of a ZFNet transfer learning method. This approach is an efficient and automated method for detecting pneumonia and could significantly enhance outcome measures related to the speed and accuracy of the network in the clinical and healthcare sectors.

Neural correlates of proactive avoidance deficits and alcohol use motives in problem drinking

Physical pain and negative emotions represent two distinct drinking motives that contribute to harmful alcohol use. Proactive avoidance, in contrast, can reduce consumption in response to these motives but appears to be impaired in those with problem drinking. Despite such evidence, proactive avoidance and its underlying neural deficits have not been assessed experimentally. How these deficits inter-relate with drinking motives to influence alcohol use also remains unclear. The current study leveraged neuroimaging data in forty-one problem and forty-one social drinkers who performed a probabilistic learning go/nogo task featuring proactive avoidance of painful outcomes. We identified the brain responses to proactive avoidance and contrasted the neural correlates of drinking to avoid negative emotions vs. physical pain. Behavioral results confirmed proactive avoidance deficits in problem drinking individuals’ learning rate and performance accuracy, both which were associated with greater alcohol use. Imaging findings in the problem drinking group showed that negative emotions as a drinking motive predicted attenuated right anterior insula activation during proactive avoidance. In contrast, physical pain motive predicted reduced right putamen response. These regions’ activations as well as functional connectivity with the somatomotor cortex also demonstrated a negative relationship with drinking severity and positive relationship with proactive avoidance performance. Path modeling further delineated the pathways through which physical pain and negative emotions influenced the neural and behavioral measures of proactive avoidance. Taken together, the current findings provide experimental evidence for proactive avoidance deficits in alcohol misuse and establish the link between their neural underpinnings and drinking behavior.

Validation of a micro-doppler radar for measuring gait modifications during multidirectional visual perturbations

BackgroundChanges in spatio-temporal gait parameters and their variability during balance-challenging tasks are markers of motor performance linked to fall risk. Radio frequency (RF) sensors hold great promise towards achieving continuous remote monitoring of these parameters. Research questionsTo establish the concurrent validity of RF-based gait metrics extracted using micro-Doppler (µD) signatures and to determine whether these metrics are sensitive to gait modifications created by multidirectional visual perturbations. MethodsFifteen participants walked overground in a virtual environment (VE) and VE with medio-lateral (ML) and antero-posterior (AP) perturbations. An optoelectronic motion capture system and one RF sensor were used to extract the linear velocity of the trunk and estimate step time (ST), step velocity (SV), step length (SL), and their variability (STV, SVV, and SLV). Intra-class coefficient for consistency (ICC), mean and standard deviation of the differences (MD), 95 % limits of agreement, and Pearson correlation coefficients (r) were used to determine concurrent validity. One-way repeated-measures analysis of variance was used to analyze the main and interaction effects of visual conditions. ResultsAll outcomes showed good to excellent reliability (r>0.795, ICC>0.886). Average gait parameters showed good to excellent agreement, with values obtained with the RF sensor systematically smaller than the values obtained with the markers (MD of 0.001 s, 0.09 m/s, and 0.06 m). Gait variability parameters showed poor to moderate agreement, with values obtained with the RF sensor systematically larger than those obtained with the markers (MD of 1.9 %-3.9 %). Both measurement systems reported decreased SL and SV during ML perturbations, but the gait variability parameters extracted with the radar were not able to detect the higher STV and SLV during this condition. SignificanceThe radar µD signature is a valid and reliable method for the assessment of average spatio-temporal gait parameters but gait variability measures need to be viewed with caution because of the lower levels of agreement and sensitivity to ML visual perturbations. This work represents an initial investigation for the development of a low-cost system that will facilitate aging-in-place by providing remote monitoring of gait in natural settings.

An obstacle avoidance safety detection algorithm for power lines combining binocular vision technology and improved object detection

In this paper, a framework of obstacle avoidance algorithm applied to power line damage safety distance detection is constructed, and its overall architecture and key processes are described in detail. The system design covers three core modules: visual data acquisition and preliminary processing, accurate target recognition and distance measurement, and system error analysis and correction. In the visual data processing chain, we deeply analyze every step from image acquisition to preprocessing to feature extraction, aiming to enhance the adaptability of applications to complex scenes. The target recognition and distance estimation part integrates advanced technology of deep learning to improve the reliability of recognition accuracy and distance estimation. In addition, many common error sources, such as system bias, parallax discontinuity, fluctuation of illumination conditions, etc., are discussed in depth, and corresponding correction strategies are proposed to ensure the accuracy and stability of the system, which provides powerful technical support for achieving efficient and accurate safety monitoring. Specifically, by carefully adjusting the learning rate, convolution kernel size, batch size, pooling layer type, and number of hidden layer nodes, we succeeded in improving the overall accuracy from the initial average of 92.4–95%, and the error rate decreased accordingly.

Online prediction of joint mechanical properties of FSW thick AA2219-T8 based on multi-source information fusion using 1DCNN

High strength AA2219-T8, Al-Cu alloy of 18 mm plate is welding by friction stir welding (FSW) technology and used for heavy-lift launch vehicles tank. During FSW thick AA2219-T8 plate, joint tensile strength and microhardness are affected by multiple interconnected variables including welding force, temperature, and geometric quantity of the butt face. The joint mechanical properties are difficult to predict and ensure. This paper proposed a hybrid predicting approach based on multi-source information fusion, called Down-Sampling and One-Dimensional Convolutional Neural Network (DS-1DCNN). Initially, FSW experiments were conducted to collect the axial force, welding surface temperature at advancing and retreating side, gap, and mismatch signals, which were fused through DS technology. To extract temporal information and features from signals, a 1DCNN prediction model was established, with the fused multi-source signals as input and the tensile strength and Vickers hardness as output. Subsequently, the optimal model architecture and hyper-parameters of 1DCNN were determined through experiments and grid search combined with K-fold cross-validation, and an online prediction system was developed. Finally, effective validation experiments were conducted and the experimental results agreed with the DS-1DCNN prediction results with an acceptable error of approximately 5%. Parameter sensitivity analysis revealed that the number of convolutional layers and learning rate were identified as critical parameters affecting the model performance. Additionally, time analysis showed that the calculation time of the prediction system was less than 200 ms which can meet the time requirements of real-time thick-plate FSW application. The research lays a foundation on the real-time quality monitoring in the process of FSW thick-plate butt joint.

Crested Porcupine Optimizer-Optimized CNN-BiLSTM-Attention Model for Predicting Main Girder Temperature in Bridges

Stage-built long-span bridges deform with temperature, affecting alignment to design needs. In this paper, a model for predicting temperature time series is proposed, which can predict temperatures in engineering practice and utilize the predicted results to adjust the elevation of stage construction. The model employs convolutional neural networks (CNNs) for initial feature extraction, followed by bidirectional long short-term memory (BiLSTM) layers to capture temporal dependencies. An attention mechanism is applied to the LSTM output, enhancing the model’s ability to focus on the most relevant parts of the sequence. The Crested Porcupine Optimizer (CPO) is used to fine-tune parameters like the number of LSTM units, dropout rate, and learning rate. The experiments on the measured temperature data of an under-construction cable-stayed bridge are conducted to validate our model. The results indicate that our model outperforms the other five models in comparison, with all the R2 values exceeding 0.97. The average of the mean absolute error (MAE) on the 30 measure points is 0.19095, and the average of the root mean square error (RMSE) is 0.28283. Furthermore, the model’s low sensitivity to data makes it adaptable and effective for predicting temperatures and adjusting the elevation in large-span bridge construction.

Enhancing the Transferability of Adversarial Patch via Alternating Minimization

Adversarial patches, a type of adversarial example, pose serious security threats to deep neural networks (DNNs) by inducing erroneous outputs. Existing gradient stabilization methods aim to stabilize the optimization direction of adversarial examples through accumulating gradient momentum to enhance attack transferability on black-box models. However, they are not fully effective for adversarial patches. The accumulated momentum during optimization often misaligns with the optimization direction, reducing their efficacy in black-box scenarios. We introduce an optimization method called Alternating Minimization for Adversarial Patch (AMAP). This method decomposes the original AP into multiple sub-patches and utilizes their update direction to stabilize the optimization of the original AP. Additionally, we propose an adaptive step size optimization method that accelerates convergence and boosts the attack performance. In face recognition tasks, AMAP outperforms baseline methods by a remarkable 5.21% and exceeds the second-best method by 1.4%. Furthermore, AMAP demonstrates practical feasibility in the physical domain, highlighting its potential for robust computer security testing applications.

Influence of ethanol–gasoline–hydrogen and methanol–gasoline–hydrogen blends on the performance and hydrogen knock limit of a lean-burn spark ignition engine

This study investigates the effects of ethanol–gasoline and methanol–gasoline blends on the performance of a lean-burn spark ignition engine and the hydrogen knock limit of the engine with hydrogen-enriched fuel. The experimental setup includes liquid in-cylinder fuel injection and hydrogen port induction. Ethanol and methanol are blended with pure gasoline up to 50% by volume (20%, 35% and 50%) and all fuels are drip-fed with hydrogen until combustion knock is detected in step size of 2LPM. The hydrogen knock limit extension is achieved with ethanol–gasoline and methanol–gasoline blends and is further extended by spark-timing retardation. Retarded spark timing and ethanol–methanol blended fuels reduce the brake thermal efficiency, brake mean effective pressure, and peak in-cylinder pressure. The cyclic variation increases with ethanol and methanol addition and decreases with spark-timing retardation and hydrogen enrichment. Unlike hydrogen enrichment, ethanol and methanol addition reduce the CO2 and NOx emissions but increase the CO emission.

A Kaczmarz-inspired approach to accelerate the optimization of neural network wavefunctions

Neural network wavefunctions optimized using the variational Monte Carlo method have been shown to produce highly accurate results for the electronic structure of atoms and small molecules, but the high cost of optimizing such wavefunctions prevents their application to larger systems. We propose the Subsampled Projected-Increment Natural Gradient Descent (SPRING) optimizer to reduce this bottleneck. SPRING combines ideas from the recently introduced minimum-step stochastic reconfiguration optimizer (MinSR) and the classical randomized Kaczmarz method for solving linear least-squares problems. We demonstrate that SPRING outperforms both MinSR and the popular Kronecker-Factored Approximate Curvature method (KFAC) across a number of small atoms and molecules, given that the learning rates of all methods are optimally tuned. For example, on the oxygen atom, SPRING attains chemical accuracy after forty thousand training iterations, whereas both MinSR and KFAC fail to do so even after one hundred thousand iterations.

Dopamine transients encode reward prediction errors independent of learning rates.

Biological accounts of reinforcement learning posit that dopamine encodes reward prediction errors (RPEs), which are multiplied by a learning rate to update state or action values. These values are thought to be represented in synaptic weights in the striatum, and updated by dopamine-dependent plasticity, suggesting that dopamine release might reflect the product of the learning rate and RPE. Here, we leveraged the fact that animals learn faster in volatile environments to characterize dopamine encoding of learning rates in the nucleus accumbens core (NAcc). We trained rats on a task with semi-observable states offering different rewards, and rats adjusted how quickly they initiated trials across states using RPEs. Computational modeling and behavioral analyses showed that learning rates were higher following state transitions, and scaled with trial-by-trial changes in beliefs about hidden states, approximating normative Bayesian strategies. Notably, dopamine release in the NAcc encoded RPEs independent of learning rates, suggesting that dopamine-independent mechanisms instantiate dynamic learning rates.

Dynamic contact and stiffness characteristic analysis of angular contact ball bearings under combined external loads

A nonlinear dynamic model of angular contact ball bearing under combined external loads is established based on the nonlinear elastic Hertz contact theory and raceless control theory. The established dynamic model is solved by employing variable step size Newton Raphson iteration method and the validation of model is verified by comparing the proposed results with the corresponding results come from the existing literatures. The solution of the presented model has high accuracy compared with the existing experimental results. In addition, the computational efficiency of the proposed model is improved significantly by introducing iteration step size adjustment factor. According to the proposed model, the dynamic contact and stiffness characteristics of angular contact ball are studied systematically by investigating the effects of combined external load working conditions on the contact parameters including contact angle and contact force and the stiffness parameters including diagonal stiffness and off-diagonal stiffness. This research can provide the theoretical basis and technique guidance for the designation and manufacture of angular contact ball bearing under combined external loads.

Gradient Boosting Prediction of Overlapping Genes From Weighted Co-expression and Differential Gene Expression Analysis of Wnt Pathway: An Artificial Intelligence-Based Bioinformatics Study.

Introduction The Wnt (wingless-related integration site) signalling pathway is crucial for bone formation and remodelling, regulating the commitment of mesenchymal stem cells (MSCs) to the osteoblastic lineage. It triggers the transcriptional activation of Wnt target genes and promotes osteoblast proliferation and survival. Weighted co-expression network analysis (WGCNA) and differential gene expression analysis help researchers understand gene roles. Gradient boosting, a machine learning technique, enhances understanding of genetic and molecular mechanisms contributing to overlap genes, improving gene regulation and functional genomics. The aim is to predict overlapping genes in the Wnt signalling pathway. Methods Differential gene expression analysis was performed using the National Center for Biotechnology Information (NCBI) geo dataset-GSE251951, focusing on the effect of Wnt signaling on treatment. The WGCNA module was analyzed using the iDEP tool to identify interconnected gene clusters. Hub genes were identified by calculating module eigengenes, correlated with external traits, and ranked based on module membership values. The study utilized gradient boosting, an ensemble learning method, to predict models, evaluate their performance using metrics like accuracy, precision, recall, and F1 score, and adjust predictions based on gradient and learning rate. Results The dendrogram uses the "Dynamic TreeCut" algorithm to analyze gene clusters, aiding researchers in understanding gene modules and biological processes, identifying co-expressed genes, and discovering new pathways. The confusion matrix displays 88 actual and predicted cases. The gradient boosting model achieves 78.9% accuracy in predicting Wnt pathway overlapping genes, with a respectable area under the curve (AUC) and classification accuracy values. It accurately predicts 73.9% of samples, with a high precision ratio and low recall. Conclusion Future research should enhance differential expression analysis and WGCNA to identify key Wnt pathway genes, improve sensitivity, specificity, hyperparameter tuning, and validation experiments, and use larger datasets.

Exploring the causes, consequences, and solutions of Chinese EFL teachers' psychological ill‐being: A qualitative investigation

AbstractTeachers' psycho‐affective states are considerably reported to play a vital role in English as a foreign language (EFL) education. However, the overall mechanism, realization, and representation of teachers' psychological ill‐being (PIB) have been widely kept under the shadow of well‐being. To fill this gap, this qualitative investigation examined 46 Chinese EFL teachers' perceptions about the causes, outcomes and solutions of PIB. The results of thematic analysis revealed that PIB was caused by different personal, interpersonal, professional, task, student and contextual factors among Chinese EFL teachers. More specifically, ill‐being emerged out of “heavy workload pressure”, “mental and physical exhaustion”, “unrelated tasks to teaching”, “students' classroom behavior and performance”, “unfriendly school climate”, “poor interpersonal relationships with colleagues” and “social and cultural environment”. The findings also indicated that teachers' ill‐being led to different outcomes such as “reducing teaching quality”, “creating negative emotions”, “causing physical and mental health problems”, “leading to burnout” and “hurting students' feeling and learning rate”. To solve this aversive state, the participants suggested different techniques such as “reducing workload”, “learning emotion regulation strategies”, “establishing a positive classroom/school climate”, “offering teacher training courses”, “providing support and facilities for teachers”, “increasing salary” and “reading books”. A discussion of the findings and implications for EFL teachers, teacher educators and policy‐makers is provided to enhance their emotional literacy and competency regarding negative teacher‐related emotions in L2 teaching.